Info System / Health Care

Content

Week 3, Monday, November 4, 2019 - Sunday, November 10, 2019

IFSM 305 7980 Information Systems in Health Care …

The following should be completed in Week 3:

Read:

Read/View all Week 3 Content

Do:

Participate in Discussion(s), as assigned

0 % 0 of 3 topics complete

Last week we focused on processes in health care organizations. All processes

use data – as input, as output, and/or to perform the function required. This

week we will concentrate on data. You will learn about data, how health care

organizations collect, store and use data, and how data is shared. You have

probably heard the term "garbage in – garbage out." This refers to the fact that,

in order to create useful information, the data used must be correct. But, what

does that mean? To answer this question, we will explore the characteristics of

quality data.

Electronic Health Records (EHR) are becoming more and more prevalent in the

health care sector. This week, you will learn about EHR systems, how they are

selected and implemented and how data quality can impact overall health care

quality and patient safety. We will also discuss the Health Information Exchange

and other methods of sharing health data between organizations.

Activities

Week 3 Learning Resources Link

Discussion #1 for Week 3 Discussion Topic

Before you begin this week's readings, you should review the Case Study and

read the Stage 2 assignment. They will provide a roadmap for you as you read

the course materials, and you will be able to take notes as you do your readings.

The following table lists the Week 3 outcomes, mapped to the corresponding

course outcome. The course outcome gives you "the big picture," and the weekly

outcomes provide more detailed information that will help you achieve the

course outcome.

Course Outcome Met in Week 3 Week 3 Outcomes

Analyze the flow of data and

information among disparate

health information systems

to support internal and

external business processes

explain the purpose of a database and

what a relational database is

apply the characteristics of data qualit

describe the use of electronic health

records (EHR)

explain methods of sharing health care

data between organizations

describe data quality improvement

Discussion #2 for Week 3 Discussion Topic

Working with Health IT Systems is available under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported license.

© Johns Hopkins University.

Welcome to Introduction to Information and Computer Science: Databases and SQL. This is Lecture (a).

The component, Introduction to Information and Computer Science, is a basic overview of computer architecture; data organization, representation and structure; structure of programming languages; networking and data communication. It also includes basic terminology of computing.

1

The Objectives for Databases and SQL are to:

• Define and describe the purpose of databases • Define a relational database • Describe data modeling and normalization • Describe the structured query language (SQL) • Define the basic data operations for relational databases and how to implement them in SQL • Design a simple relational database and create corresponding SQL commands • Examine the structure of a healthcare database component

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It is important to review some computer science basics in order to understand the details of information storage. Remember that for a computer, data consists of ones and zeros. In other words, every data value is represented by a combination of binary ones and zeroes, or simply values of on and off, for example, the number 01000001 [zero-one-zero-zero-zero-zero-zero-one] on this slide. When a computer system examines this value it does not know what it represents. It depends on the system application’s knowledge of the underlying data. If the application is a text editor, it knows that this value represents a capital ‘A’ as defined by the American Standard Code for Information Interchange, better known as ASCII [as key]. On the other hand, in the context of data in a spreadsheet, the cell formatting may indicate that the stored binary number actually represents the number 65. That binary data may also be used in many other ways, including as a central processing unit instruction or as part of an audio or video file.

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One very large component of computer systems is the management of data. Consider the information maintained on a personal computer – this might include programs, photos, music, videos, tax returns and class papers, just to name a few. Some files may remain unchanged; others might be modified over time, such as revisions to a class paper.

Now consider an electronic healthcare record (EHR) system that may contain information for thousands or tens of thousands of patients. With this volume of information, it is important that information be stored efficiently, is quickly accessible, and has the capacity to be updated.

4

Data can be stored electronically in different ways. The first way is to store it in a simple text file. Another is to store it a spreadsheet, which is more powerful than a simple text file. Finally, data can be stored in databases, which is the topic of this unit. Before discussing databases, this lecture will provide information about the other options for data storage and when they are appropriate to use.

5

A file is a collection of information, or data, stored in a single electronic location. How that information is stored in files is also important. Files can contain text or data that is not readable by humans. If data is to be accessed by a person, then it needs to be human-readable; however, a computer system may use a different format – it just needs to know how to interpret the data. For example, an audio file and a text file contain information that is stored in different formats. A text editor cannot edit an audio file, and a music player cannot play a text file. An audio file is not readable by humans, but its data can be interpreted by a music player and converted to the music that humans listen to.

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Files are stored on file systems, which every computer has. Because of that, every computer has the ability to create, use and store files. Files can be easily shared; email and shared drives are some options for sharing. They're also used by many applications; for example, a PowerPoint presentation is stored in a file. Also, many scientific applications and instruments use input data files and/or generate output data files. For example, genomic data is often stored in large data files that are searched and parsed by special programs.

On the other hand, files have limitations. The security of files is limited to that of the file system. Also, by default, multiple users cannot use the same file at the same time. Usually, one user can open the file for editing; any additional users open a read-only copy of the file. Finally, using files to store structured data with relationships can result in redundancy and inconsistency as shown in the following example.

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This slide shows a file that contains contact information for individuals and their organizations. It contains names, such as Bill Robeson, Walter Schmidt and Mary Small and the corresponding organization names and addresses – there are only two different organizations in this small sample.

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After review of the previous slide, answer the following questions:

• Do Bill and Albert work for the same company? • What is the difference between Catherine and Walter’s information? • If a computer application was looking at the data would it be able to tell that there was an issue with the addresses for Catherine and Walter? • Can you sort this list by last name? • Could you sort a list of 10,000 contacts?

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While it is easy to see that Bill and Albert work for the same company, note that in the file, Bill’s company name is Community Hospital Inc. and Albert’s is Community Hospital Incorporated. There is a similar issue with Catherine and Walter’s information. Catherine’s address is 14 12th Street with street spelled out. Walter has the same address, but notice in this case that 14 12th St. uses the abbreviation for street.

Humans can easily handle these variations in data and determine that they are the same. However, a computer system, even one with an artificial intelligence system, would have significantly greater challenges in determining that the companies and addresses are the same.

And while sorting these few entries may be feasible just looking at the list, sorting a file with 10,000 contacts would be extremely time- consuming without the use of technology.

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A bigger challenge might be if “Community Hospital, Inc.” becomes “Community General”. If this change were done manually, or with an automated system, every single instance of “Community Hospital” would have to be identified in the data. Additionally, every different representation of “Community Hospital”, for example, “Community Hospital, Inc.” and “Community Hospital Incorporated”, would also need to be located; and there is no guarantee that the word “Community” was spelled correctly in every instance.

Once all of the entries are identified, each one needs to be modified to correctly read “Community General”, If done manually, this still has the potential for human data-entry error and in a large system would be very time-consuming. If it is done using a simple search and replace automated function, it may not take as long, but it may or may not result in partial changes to other existing records, for example, Portland Community Hospital being changed to Portland Community General.

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Spreadsheet applications were first developed for businesses to automate accounting tasks. Today, spreadsheets are widely used for storing, manipulating and presenting data. Today's spreadsheet applications perform calculations using predefined or user-created formulas. They provide features for easily sorting and filtering data and can even perform data analysis. Advanced spreadsheet users can create very complex calculations and relationships between data.

Spreadsheets have become very powerful tools for data analysis and manipulation. However, they still have the same limitations as plain text files as shown on the following slide.

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Here is an example of an OpenOffice Calc spreadsheet. (Other spreadsheet applications include Microsoft Excel, IBM Lotus Symphony, Corel QuatroPro, Apple Numbers and Google documents spreadsheets). The data is organized into numbered rows and lettered columns; column names can be provided in the first row. The data itself does not look very different from the data in the simple text file; however, there are vast options for manipulating and presenting this data on the menus above the data. We can sort the data very easily and quickly, unlike plain text files. Regardless, spreadsheets have the same problems as the text file--there is data defined multiple times (company name and address) which is inefficient and error prone.

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Since spreadsheets are just a special type of file, they have similar advantages and disadvantages. While spreadsheets require a special application such as Microsoft Excel, these applications are widely available. Spreadsheet applications provide powerful calculations and basic sorting and filtering. But like files, they have limited security, multiple user access and a potential for redundant and inconsistent data. Spreadsheets are good for doing calculations on static snapshots of datasets, but they aren't the best solution for long term storage and access of data.

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So what exactly is a database? A database is a structured data collection which is accessed electronically. in other words, it is information stored on a computer for access through a computer program. The text file used in this lecture that contained the contact information can be considered to be a very simple database – it contains organized, though not necessarily consistent, information, that might be accessed through a text editor. A relational database is a database that maintains relationships between data elements and are the focus of this unit.

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The concept of a relational database was first published by E.F. Codd in “Communications of the ACM” in June 1970. Codd held the view that users should not have to keep track of how the information is stored in a computer in order to use it. To quote Codd, “Future users of large data banks must be protected from having to know how the data is organized in the machine (the internal representation).”

In other words, users should not have to know whether the binary bits discussed previously in this lecture represent a capital A or the number 65, or even what they are related to – rather, the system should keep track of that information once it is provided by the user.

So a relational database is an organized collection of data accessible by electronic means where the information type and information relationships are maintained internally by the system itself.

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A relational database consists of one or more tables defined by the database designer in a meaningful fashion. A table is a collection of information organized into rows and columns. Each table contains one or more rows of data. The data in a row is ordered by columns, and each column is of a known and specified type where the data and type are independent. The order of rows in the table is irrelevant, but the order of the columns in the row is significant.

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A relational database has quite a number of advantages over files and spreadsheets. Database systems are designed to be highly secure; control to data can be precisely defined. In addition, databases are designed to be accessed and modified by multiple users at the same time. Relationships between tables support organized data that prevents data redundancy and inconsistency. The highly optimized underlying data structures used by the relational database result in highly efficient and fast access. Because a database system is designed for the specific purpose of data organization, the basic operations of retrieving, adding, modifying and deleting data are more efficient than general-purpose applications and storage such as spreadsheets and files. Furthermore, relationships and efficient access allow for complex queries and searches of data.

On the other hand, databases are complex systems that require expertise to install, maintain and use. There are free, open-source databases, but the commercially available databases are very expensive. In comparison, files and spreadsheets are more widely available and easy to use. Also, data in databases is not as easily analyzed using complex data calculations. Instead, data is usually exported from databases into a spreadsheet or data file for statistical software.

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This concludes Lecture (a) of Databases and SQL. There are several options for data storage including files, spreadsheets or databases. Files and spreadsheets are widely available and are good for data computations. Databases are very secure and optimized systems for storing, accessing and modifying data over the long term. Multiple users can access and modify data at the same time. Furthermore, relationships are stored in a database along with the data which allows for less data redundancy and inconsistency as well as for complex queries.

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References slide. No audio.

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Characteristics of Quality Data

People depend on the systems they use to contain high quality data. If they find the data to be wrong,

outdated or incomplete, they begin to distrust the system and will likely stop using it. If the data in the

system is personally important to the individual, such as the data in payroll or medical systems, then

there is a strong need to have it corrected as quickly as possible.

What are the characteristics of good quality data? There are a variety of characteristics, but we will

focus on the six listed here. Let's look at the data that may be in a health care system and how each of

the characteristics of quality data are important, and consider an example of each.

 Accuracy – Is the information correct? For example, is the diagnosis correct?

 Completeness – Is all the information there? For example, are all the laboratory test results

recorded?

 Currency – Is the data in the system up to date? For example, old, outdated data could result in

the wrong medication being prescribed.

 Uniqueness – Does the medical record apply to a specific individual? Each patient's medical

record must be able to be uniquely identified for that patient; duplicates in names or medical

record numbers must not exist.

 Validity – Is the data in the medical record based on the accepted ranges? For example, is the

code for laboratory test within the accepted list of codes?

 Consistency – Is the data in the medical record correctly aligned with other data in the system?

For example, if a prescription is generated to be sent to a pharmacy, is the pharmacy in the

system or is it a new one?

It is important that the data in information systems be of high quality. As systems are developed, the

testing should include ensuring that the quality of data is maintained throughout the system, from its

source to the final output. Therefore, data needs to have the characteristics listed above when it is

entered into the system. The data entry process should include validation that the data entered meets

these quality attributes, and then the data needs to be protected as it resides in and flows through the

system, and even as it is shared with other systems. If any of these characteristics are missing, the

system must be analyzed to discover where the problem lies. The correction may be as simple as fixing

an individual record, or, if it is not clear where the problem lies, the system may be considered

unreliable overall and may need to be taken offline until the problem is identified and corrections are

applied.

Working with Health IT Systems is available under a Creative Commons Attribution-NonCommercial- ShareAlike 3.0 Unported license. © Johns Hopkins University. UMUC has modified this work and it is available under the original license.

Welcome to Health Management Information Systems, Electronic Health Records. This is Lecture a.

The component, Health Management Information Systems, is a “theory” component that provides an introduction to health

care applications and the systems that use them, health information technology standards, health-related data structures, and

enterprise architecture in health care organizations.

Lecture a will define an electronic medical record (EMR) and electronic health record (EHR) and explain their similarities and

differences, identify attributes and functions of an EHR, discuss the issues surrounding EHR adoption and implementation, and

describe the impact of EHRs on patient care.

1

The Objectives for this unit, Electronic Health Records are to:

• State the similarities and differences between an electronic medical record (EMR) and electronic health record (EHR);

• Identify attributes and functions of an EHR;

• Describe the perspectives of health care providers and the public regarding acceptance of or issues with an EHR, which can

serve as facilitators of or major barriers to its adoption; and

• Explain how the use of an EHR can affect patient care safety, efficiency of care practices, and patient outcomes;

2

Additional Objectives for this unit, Electronic Health Records are to:

• Discuss how Health Information Exchange (HIE) and Nationwide Health Information Network (NHIN) impact health care

delivery and the practice of health care providers;

• Outline issues regarding governmental regulation of EHR systems, such as meaningful use of interoperable health information

technology and a qualified EHR;

• Summarize how the Institute of Medicine’s Vision for 21st Century Health Care and Wellness may impact health management

information systems;

• and Identify how ongoing developments in biomedical informatics can affect future uses and challenges related to health

information systems.

3

As a way of introduction to electronic health records, let’s identify why a patient or medical record exists in the first place.

According to Dr. Reiser, the purpose of a patient record is “to recall observations, to inform others, to instruct students, to gain

knowledge, to monitor performance, and to justify interventions” (Reiser, 1991, p. 902).

The medical record is a way of communicating between staff managing patient care. It also allows for an integrated view of

patient data.

The patient medical record is also the legal business record for a health care provider, as the American Health Information

Management Association (AHIMA) e-HIM Work Group on Maintaining the Legal EHR, pointed out in the article Maintaining a

Legally Sound Health Record—Paper and Electronic. In this same article, the Work Group states “As such, it must be

maintained in a manner that follows applicable regulations, accreditation standards, professional practice standards, and legal

standards” (AHIMA, 2005, para. 1).

4

Historically, patient records have been paper-based. However, more and more health care providers are moving away from

paper-based to adoption of an electronic form. There are two terms associated with the electronic form. They are electronic

medical record (or EMR) and electronic health record (or EHR).

The report, Defining Key Health Information Technology Terms defines an EMR as “an electronic record of health-related

information on an individual that can be created, gathered, managed, and consulted by authorized clinicians and staff within one

health care organization” (NAHIT, 2008, p. 6). This same report stated “health-related information encompasses health,

wellness, administrative data, and information derived from public health and scientific research. It includes past and present

observations and facts documented in the provision of health care that may be related to preventing illness and promoting

wellness or that may be used in the process of informing consent” (NAHIT, 2008, p. 10).

An electronic medical record is a record of medical care created, managed, and maintained by one health care organization.

This does not mean a single physical location. There may be instances when information is shared among multiple facilities and

still be within one EMR. For example, an electronic record used in a physician practice with several offices is still an EMR when

all sites are using the same proprietary data structure and architecture and the information is not moving outside the confines of

the organization.

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EMRs are the electronic equivalent of an individual’s legal medical record for use by providers and staff within one health care

organization.

5

The purpose of an EMR is to provide an electronic equivalent of an individual’s legal medical record for use by providers and

staff within one health care organization.

The EMR is understood to meet specific business needs for care, reimbursement, and disclosure, follow regulation and rules

promulgated by Federal, State, or accrediting entities, and contain information as defined by the provider organization.

The electronic medical record encapsulates a record of medical care provided in a single health care organization, i.e., an intra-

organizational medical record.

6

The other term associated with electronic records is electronic health record, or EHR.

The report Defining Key Health Information Technology Terms also provided a definition for electronic health record. An EHR is

“An electronic record of health-related information on an individual that conforms to nationally recognized interoperability

standards and that can be created, managed, and consulted by authorized clinicians and staff across more than one health

care organization” (NAHIT, 2008, p. 6).

Being a repository of individual health records that reside in numerous information systems and locations, EHRs are intended to

support efficient, high-quality integrated health care, independent of the place and time of health care delivery. Consequently,

EHRs are part of a health information technology infrastructure.

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The purpose of an EHR is to provide an electronic equivalent of an individual’s health record for use by providers and staff

across more than one health care organization. An EHR is inter-organizational, that is, two or more unrelated health care

organizations contribute to the record which becomes an aggregation of one record focused around a person’s comprehensive

health history rather than being one provider’s record. However, to arrive at this level of information aggregation, all contributors

must be able to send and receive information using standards that facilitate the interoperable exchange of health-related

information.

An EHR is intended to support efficient, high-quality integrated health care, independent of the place and time of health care

delivery.

It encapsulate an electronic equivalent of an individual’s health record for use by providers and staff in multiple unrelated

facilities.

As the National Alliance for Health Information Technology’s report Defining Key Health Information Technology Terms

explained, “The principal difference between an EMR and an EHR is the ability to exchange information interoperably. An EMR

aligns with the prevailing state of electronic records today (whether the record is branded an EMR or an EHR). However, the

movement of the industry is toward electronic records that are capable of using nationally recognized interoperability standards,

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which is a key defining component of an EHR” (NAHIT, 2008, p. 5).

8

Adding to NAHIT’s principle difference, other comparisons illustrating similarities and differences between an EMR and EHR

are shown in Table 3.1.

The first row in Table 3.1 states an EMR is a record of medical care created, managed, and maintained by one health care

organization (intra-organizational) while an EHR is a repository of individual health records that reside in numerous information

systems and locations (inter-organizational).

The second row explains an EMR is an integration of health care data from a participating collection of systems from one health

care organization in contrast to an EHR which is an aggregation of health-related information into one record focused around a

person’s health history, i.e., a comprehensive, longitudinal record.

The third row points out an EMR is consulted by authorized clinicians and staff within one health care organization while an

EHR is consulted by authorized clinicians and staff across more than one health care organization.

The fourth and final row reiterates NAHIT’s principle difference, that is, in an EMR, data continuity exists throughout one health

care organization but in the case of an EHR, data interoperability across different organizations occurs.

9

While these distinctions can be made between an EMR and EHR, many regard the two terms as synonymous.

9

According to a Centers for Medicare and Medicaid Services Fact Sheet, Electronic Health Records at a Glance, electronic

health records improve care by enabling functions that paper records cannot deliver.

These include:

• “EHRs can make a patient’s health information available when and where it is needed – it is not locked away in one office or

another.

• EHRs can bring a patient’s total health information together in one place, and always be current – clinicians need not worry

about not knowing the drugs or treatments prescribed by another provider, so care is better coordinated.

• EHRs can support better follow-up information for patients – for example, after a clinical visit or hospital stay, instructions

and information for the patient can be effortlessly provided; and reminders for other follow-up care can be sent easily or even

automatically to the patient.

• EHRs can improve patient and provider convenience – patients can have their prescriptions ordered and ready even before

they leave the provider’s office, and insurance claims can be filed immediately from the provider’s office” (CMS, 2010, para.

5)

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Additionally,

• “EHRs can link information with patient computers to point to additional resources – patients can be more informed and

involved as EHRs are used to help identify additional web resources.

• EHRs don’t just “contain” or transmit information, they also compute with it – for example, a qualified EHR will not merely

contain a record of a patient’s medications or allergies, it will also automatically check for problems whenever a new

medication is prescribed and alert the clinician to potential conflicts.

• EHRs can improve safety through their capacity to bring all of a patient’s information together and automatically identify

potential safety issues -- providing “decision support” capability to assist clinicians” (CMS, 2010, para. 5).

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The final group of ways in which EHRs can improve care according to CMS are:

• “EHRs can deliver more information in more directions, while reducing “paperwork” time for providers – for example, EHRs

can be programmed for easy or automatic delivery of information that needs to be shared with public health agencies or quality

measurement, saving clinician time.

• EHRs can improve privacy and security – with proper training and effective policies, electronic records can be more secure

than paper.

• EHRs can reduce costs through reduced paperwork, improved safety, reduced duplication of testing, and most of all improved

health through the delivery of more effective health care” (CMS, 2010, para 5).

With regards to improving privacy and security, EHRs can be encrypted and stored on password-protected systems thereby

restricting their access to only those authorized. In addition, systems can track who accessed a record, when it occurred and for

what purpose. Firewalls and other physical security measures can be put in place to prevent unauthorized users from gaining

access to patient records.

Overall EHRs have the potential for improvements in patient safety and quality. However, improvements are not an automatic

result of implementing an EHR.

12

Thus, an electronic health record is not an electronic version of the paper record. An electronic health record has additional

attributes or properties that a paper record does not.

The Healthcare Information and Management Systems Society or HIMSS described eight attributes of an electronic health

record in their report HIMSS Electronic Health Record Definitional Model. The first two attributes are that the EHR:

• “Provides secure, reliable, real-time access to patient health record information, where and when it is needed to support care

• Captures and manages episodic and longitudinal electronic health record information” (Handler, et al., 2003, p. 3).

13

The next three attributes as described in the HIMSS report are the EHR:

• “Functions as clinicians’ primary information resource during the provision of patient care

• Assists with the work of planning and delivering evidence-based care to individual and groups of patients and

• Supports continuous quality improvement, utilization review, risk management, and performance monitoring ” (Handler, et

al., 2003, pp. 4-5).

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The final three attributes listed in the HIMSS report are the EHR

• “Captures the patient health-related information needed for reimbursement

• Provides longitudinal, appropriately masked information to support clinical research, public health reporting, and population

health initiatives

• Supports clinical trials” (Handler, et al., 2003, pp. 6-7).

In addition to those identified in the HIMSS report, two additional attributes are the EHR supports timely access to patient

information and by more than one person at a time and provides the ability to generate reports that can help measure activity

and determine levels of compliance with policies and evidence-based medicine protocols.

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In addition to the HIMSS report, Health Level Seven International, or HL7, published an EHR System Functional Model.

According to HL7’s web site, HL7 is “an ANSI-accredited standards developing organization dedicated to providing a

comprehensive framework and related standards for the exchange, integration, sharing, and retrieval of electronic health

information that supports clinical practice and the management, delivery and evaluation of health services” (HL7, 2011, para. 1).

The HL7 EHR System Functional Model establishes EHR systems (EHR-S) standards that will enable the development of

EHRs based on one set of functional requirements. The model contains three sections. They are Direct Care functions,

Supportive functions, and Information Infrastructure functions.

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According to the HL7 EHR-S Model (2007), direct care functions are functions employed in the provision of care to individual

patients. Direct care functions are the set of functions that enable delivery of healthcare or offer clinical decision support.

Subsets of direct care functions include care management, clinical decision support, and operations management and

communication.

Some examples of the Care Management subset are the capability to identify and maintain a patient record, manage patient

demographics, and manage problem lists.

For the Clinical Decision Support subset, examples of direct care functionality include support for standard care plans,

guidelines, protocols; support for medication and immunization administration; and orders, referrals, results and care

management.

Examples for the Operations Management and Communication subset are clinical workflow tasking, support clinical

communication, and support for provider-pharmacy communication.

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The HL7 EHR-S Model (2007) describes supportive functions as functions that support the delivery and optimization of care,

but generally do not impact the direct care of an individual patient. These functions assist with the administrative and financial

requirements associated with the delivery of healthcare, provide support for medical research and public health, and improve

the global quality of healthcare.

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The final section, Information Infrastructure Functions, define the heuristics of a system necessary for reliable, secure and

interoperable computing (HL7 EHR-S Model, 2007). These functions are not involved in the provision of healthcare, but are

necessary to ensure that the information system provides safeguards for patient safety, privacy and information security, as well

as operational efficiencies and minimum standards for interoperability.

The functions for this section include security, health record information and management, registry and directory services,

standard terminologies and terminology services, standards-based interoperability, business rules management, and workflow

management.

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In addition to HL7’s EHR systems (EHR-S) standards, the Office of the National Coordinator for Health Information Technology published The Health Information Technology: Initial Set of Standards, Implementation Specifications, and Certification Criteria for Electronic Health Record Technology Final Rule (2010) which includes the following standards for the certification of EHR technology:

• Content exchange standards for exchanging electronic health information. For example, the National Council for the Prescription Drug Programs (NCPDP) Prescriber/Pharmacist Interface SCRIPT standard or the HL7 Clinical Document Architecture (CDA) Release 2, Continuity of Care Document (CCD)

• Vocabulary standards for representing electronic health information. Two examples of vocabulary standards are the Systematized Nomenclature of Medicine Clinical Terms and Logical Observation Identifiers Names and Codes.

• Standards for health information technology to protect electronic health information created, maintained, and exchanged. For example one standard is any encryption algorithm identified by the National Institute of Standards and Technology (NIST) as an approved security function in Annex A of the Federal Information Processing Standards (FIPS) Publication 140–2. Another example is a hashing algorithm with a security strength equal to or greater than SHA–1 (Secure Hash Algorithm (SHA–1) as specified by the NIST in FIPS PUB 180–3. (p. 44650)

20

With more and more health care providers moving away from paper-based to adoption of an electronic medical record, with the

ultimate goal of implementing an electronic health record, it stands to reason a question one might ask is “why aren’t we there

yet?” To answer that question, the perspectives of health care providers and the public regarding acceptance of, or issues with,

an EHR will be explored.

First from the standpoint of the provider, EHR acceptance is on the rise throughout the health care community as more and

more research supports the benefits far outweigh the costs.

Regarding costs to implement, monetary incentives have been put in place by the Federal Government to stimulate EHR

adoption. Momentum for widespread adoption and implementation has picked up since the American Recovery and

Reinvestment Act, or ARRA, was signed into law February 2009. ARRA provides many different stimulus opportunities, one of

which is $19.2 billion for health IT. The Health Information Technology for Economic and Clinical Health, often referred to as

HITECH, is a provision of the American Recovery and Reinvestment Act. The funding is expected to assist providers and states

in adopting and utilizing health IT in order to achieve widespread adoption of health IT and enable electronic exchange of health

information.

Providers have also begun to accept EHRs since the establishment of the Certification Commission For Health Information

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Technology or CCHIT. With certification, a certain comfort level exists with regards that the EHR purchased and implemented will have

longevity and meet specific requirements. In addition to CCHIT, ONC-Authorized Testing and Certification Bodies are Drummond

Group, InfoGard Laboratories, SLI Global Solutions, ICSA Labs, and Surescripts. The American National Standards Institute (ANSI) has

been approved as the ONC-Approved Accreditor (AA) for the Permanent Certification Program.

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As cited in IHE Moves EHR Goals Forward, “The public has mixed feelings about EHRs. A national Harris Interactive survey

found that 45 percent of adults believe that tools to track and maintain their own personal medical information with an EHR

system are very important, but they worry that computerization could increase rather than decrease medical errors and that

federal health privacy rules will be reduced in the name of efficiency” (RSNA, 2005, para. 9).

22

A more recent poll conducted by Harris Interactive (2010) online from June 8-10, 2010, among 2,035 U.S. adults, showed little

change from 2009 to 2010 with regards to adults attitudes of electronic medical records.

Seventy-eight percent in both 2009 and 2010 answered "Strongly/Somewhat Agree“ that all physicians treating me should have

access to information contained in my EMR.

Seventy-two and seventy-one percent in 2009 and 2010 respectively answered "Strongly/Somewhat Agree“ that an EMR would

be a valuable tool to track the progress of my health.

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Even with acceptance on the rise, barriers still exist. An editorial Stimulating the Adoption of Health Information Technology

describes barriers to adoption as “their substantial cost, the perceived lack of financial return from investing in them, the

technical and logistical challenges involved in installing, maintaining, and updating them, and consumers’ and physicians’

concerns about the privacy and security of electronic health information” (Blumenthal, 2009).

Each one of these has its own complexities. For example, logistical challenges would include resources issues, training and re-

training, resistance by potential users, and development of new workflow processes. The possibility of poor clinical system

performance would impact provider productivity and also become a significant barrier to adoption. Privacy and security

concerns include identity theft and widespread exposure of personal health information with the risk of it being seen by

unauthorized personnel if it is sent electronically. Breeches through stolen laptops or hacking is also a concern.

Another barrier to adoption is the perceived lack of return on investment to the practitioner.

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Even though perceived or bona fide barriers do exist, potential benefits to adopting and implementing EHRs are surfacing. With

respect to having an effect on patient care safety they include:

• Reducing the need to repeat tests,

• Reducing the number of lost reports, and

• Supporting provider decision making

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EHRs also have an effect on efficiency by

• Improving accessibility of patient information, e.g., being able to access reports anytime/anywhere,

• Integrating data from multiple internal and external sources, e.g., improving charge capture, and

• Facilitating coordination of health care delivery, e.g., no need to retrieve and copy paper charts.

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The final effect of EHR adoption and implementation is on patient outcomes. An EHR has the potential to improve the quality of

patient care and help providers practice better medicine. Being a repository of individual health records that reside in numerous

information systems and locations, EHRs are intended to support efficient, high-quality integrated health care, independent of

the place and time of health care delivery. An EHR also has the potential to provide seamless exchange of information among

providers.

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This concludes Lecture a of Electronic Health Records. This lecture defined an electronic medical record (EMR) and an

electronic health record (EHR) and explained their similarities and differences, identified EHR attributes and functions,

discussed the issues surrounding EHR adoption and implementation, and described the impact of EHRs on patient care.

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Working with Health IT Systems is available under a Creative Commons Attribution-NonCommercial- ShareAlike 3.0 Unported license. © Johns Hopkins University. UMUC has modified this work and it is available under the original license.

Welcome to Health Management Information Systems, Electronic Health Records. This is Lecture b.

The component, Health Management Information Systems, is a “theory” component that provides an introduction to health

care applications and the systems that use them, health information technology standards, health-related data structures, and

enterprise architecture in health care organizations.

Lecture b will link EHRs to the Health Information Exchange (HIE) and the Nationwide Health Information Network (NHIN)

initiatives, discuss how HIE and NHIN impact health care delivery and the practice of health care providers, summarize the

governmental efforts related to EHR systems including meaningful use of interoperable health information technology and a

qualified EHR, describe the Institute of Medicine’s vision of a health care system and its possible impact on health management

information systems, and list examples of the effects of developments in bioinformatics on health information systems.

1

The Objectives for this unit, Electronic Health Records are to:

• State the similarities and differences between an electronic medical record (EMR) and electronic health record (EHR);

• Identify attributes and functions of an EHR;

• Describe the perspectives of health care providers and the public regarding acceptance of or issues with an EHR, which can

serve as facilitators of or major barriers to its adoption;

• Explain how the use of an EHR can affect patient care safety, efficiency of care practices, and patient outcomes;

2

Additional Objectives for this unit, Electronic Health Records are to:

• Discuss how Health Information Exchange (HIE) and Nationwide Health Information Network (NHIN) impact health care

delivery and the practice of health care providers;

• Outline issues regarding governmental regulation of EHR systems, such as meaningful use of interoperable health information

technology and a qualified EHR;

• Summarize how the Institute of Medicine’s Vision for 21st Century Health Care and Wellness may impact health management

information systems;

• and Identify how ongoing developments in biomedical informatics can affect future uses and challenges related to health

information systems.

3

A definition of health information exchange begins our discussion. The report, Defining Key Health Information Technology

Terms defines health information exchange as the electronic movement of health-related information among organizations

according to nationally recognized standards” (NAHIT, 2008, p. 6).

According to the report, “the process of health information exchange enables the sharing of health-related information among

health care organizations and with individuals on a local, regional, and national basis” (NAHIT, 2008, p. 23).

The EHR is a central component of HIE.

4

The report Defining Key Health Information Technology Terms goes on to state “HIE supports the sharing of health-related

information to facilitate coordinated care through the utilization of EHRs…. This interplay of electronic records and health

information exchange is an important component in establishing the basics of an infrastructure that will become the Nationwide

Health Information Network (NHIN)” (NAHIT, 2008, p. 23).

The paper, Health Information Exchanges: Similarities and Differences, identifies three models of HIE.

“A federated model allows the data source organization to maintain custodianship and control over the patient’s medical record

and indices. When requested, data is queried from the data source organization.

A centralized model has organizations sending patient demographic and clinical information to a shared repository. This

centralized repository is queried to obtain a patient’s clinical results and other information.

A hybrid model is a mixture of the federated and centralized models” (HIMSS, 2009, p. 15).

Some HIE requirements include policies and procedures for exchanging health information, security utilities, matching

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algorithm, and record locator service.

5

Given what is known about HIEs, what potential impact does health information exchange have on health care delivery and the

practice of the health care provider?

From a health care delivery viewpoint, HIEs may have both a clinical and financial impact. Health care quality is affected by the

ability to exchange electronic heath records across multiple payers and providers. HIEs, enabled by technology, are expected

to improve the quality of care and patient safety and reduce health care costs of health care delivery.

The practice of health care providers may also be impacted by having real-time patient care data at the point-of-care and

access to patients’ longitudinal test results may facilitate coordination of care and improve clinical decision making, such as the

prevention of errors of omission by enabling automated reminders when follow-up studies are indicated. Streamlined

information flows may allow for productivity gains by providers who have access to the electronic HIE network.

6

External influences, specifically the Federal government, are having a major influence on the adoption and implementation of

electronic health records and health information exchange. The national agenda for HIT is twofold: increase adoption of

Electronic Health Records (EHRs) and build a framework that enables these EHRs to be sharable and interoperable. The

Nationwide Health Information Network or NHIN, is part of this national agenda.

According to the Office of the National Coordinator for Health Information Technology, “The Nationwide Health Information

Network is the set of standards, services and policies that enable the secure exchange of health information over the Internet”

(ONC, 2011, para. 1)

Think of the Nationwide Health Information Network as a collection of standards, protocols, legal agreements, specifications

and services overseen by the Office of the National Coordinator for Health Information Technology to support the secure

exchange of health information over the Internet. The NHIN has been referred to as a "Health Internet," which is intended to

involve consumers, providers, government organizations, and others in its fabric.

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The image is entitled “Nationwide Health Information Network (NHIN)” and consists of a map of the United States with two

rings. The red outer ring is labeled “The Internet.” The blue dotted inner ring is labeled “Standards, Specifications, and

Agreements for Secure Connections.” Outside the rings starting in the upper left corner and going counterclockwise are the

labels, Community #1, Integrated Delivery System, Community #2, various Federal agencies, Community Health Centers, and

Health Bank or Personal Health Record or PHR Support Organization.

What is the reason behind the development of the Nationwide Health Information Network? It is “…to provide a secure,

nationwide, interoperable health information infrastructure that will connect providers, consumers, and others involved in

supporting health and healthcare. This critical part of the national health IT agenda will enable health information to follow the

consumer, be available for clinical decision making, and support appropriate use of healthcare information beyond direct patient

care so as to improve health” (DHHS, 2008. para. 1).

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The NHIN is a key component of the nationwide health IT strategy and is expected to provide a common platform for health

information exchange across diverse entities, within communities and across the country, helping to achieve the goals of the

Health Information Technology for Economic and Clinical Health (HITECH) Act. The HITECH Act (Section 3001(b)) calls for the

Office of the National Coordinator for Health Information Technology (ONC) to develop “a nationwide health information

technology infrastructure that allows for the electronic use and exchange of information and that…ensures that each patient’s

health information is secure and protected, in accordance with applicable improvements in health care quality, reduces medical

errors, reduces health disparities, and advances the delivery of patient centered medical care” among other goals.

NHIN is a critical part of the national health IT agenda. The goal is to enable health information to follow the consumer, be

available for clinical decision making, and support appropriate use of health care information beyond direct patient care and, as

a result, improve population health.

The role of the NHIN is to provide means by which health and health care entities are able to securely exchange interoperable

health information.

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The Office of the National Coordinator for Health IT (ONC) believes that with broad implementation, the secure exchange of

health information using NHIN standards, services and policies will help improve the quality and efficiency of healthcare for all

Americans. Driven by emerging technology, users, uses, and policies, the NHIN is evolving to meet emerging needs for

exchanging electronic health information securely over the Internet.

One example is the initiative, the NHIN Direct Project. The NHIN Direct Project is being launched to explore the NHIN

standards and services required to enable secure health information exchange at a more local and less complex level, such as

a primary care provider sending a referral or care summary to a local specialist electronically.

The report The Direct Project Overview states “The Direct Project specifies a simple, secure, scalable, standards-based way for

participants to send authenticated, encrypted health information directly to known, trusted recipients over the Internet. The

Direct Project focuses on the technical standards and services necessary to securely push content from a sender to a receiver

and not the actual content exchanged. However, when these services are used by providers and organizations to transport and

share qualifying clinical content, the combination of content and Direct-Project-specified transport standards may satisfy some

Stage 1 Meaningful Use requirements” (The Direct Project, 2010, p. 4).

This may include for example communication of summary care records in support of continuity of care.

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NHIN Direct will also provide an easy "on-ramp" for a wide set of providers and organizations.

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At its most fundamental level the NHIN is a network. Networks for exchanging health related information are essential to

aggregating patient-focused information into EHRs.

Health care delivery may be impacted by the NHIN by establishing a standards-based infrastructure which will increase the

ability to collect and store aggregated data. The practice of health care providers may be impacted by the NHIN by providing a

care coordination platform.

Other NHIN architecture requirements may impact health care delivery and the practice of health care providers including the

ability to

• Discover and exchange healthcare information among participant entities,

• Match patients to their data without a universal or national patient identifier,

• Support patient preferences regarding their data exchange,

• Support secure data exchange,

• Support harmonized standards,

• Support diverse sets of organizations, technologies, and approaches, and

• Support a common trust agreement.

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In addition to the Nationwide Health Information Network, there are State-Level Health Initiatives. These are initiatives designed

to ensure that states and regional efforts to achieve health information exchange (HIE) are aligned with the national agenda.

The Office of the National Coordinator for Health Information Technology (ONC, 2010) initiatives describes these initiatives in

the following manner:

State Health Policy Consortium. The SHPC is responsible for working with groups of states to address policy issues to enable

the electronic exchange of health information across state lines.

State-Level Health Information Exchange Consensus Project - The purpose of this initiative is to provide a forum for ONC to

work with and disseminate information to states and for the states based efforts to inform the federal government to ensure all

health information exchange activities throughout the Unites States align.

State Alliance for e-Health is also a forum consisting of an executive-level body of state elected and appointed officials with the

responsibility of working together to facilitate the adoption of interoperable electronic HIE, to identify new inter- and intrastate-

based policies and best practices, and explore solutions to programmatic and legal issues related to the exchange of health

information.

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Health Information Security and Privacy Collaboration (HISPC) are multi-state collaboratives that are addressing privacy and security

challenges related to the electronic exchange of health information with the intended outcome to develop common, replicable multi-state

solutions that have the potential to reduce variation in and harmonize privacy and security practices, policies, and laws.

12

Additional key federal initiatives related to the adoption and implementation of electronic health records tied to HITECH

programs include the meaningful use of interoperable health information technology and qualified EHRs and the HIT Advisory

Committees.

The Health Information Technology for Economic and Clinical Health Act, or the "HITECH Act" established programs under

Medicare and Medicaid to provide incentive payments for the "meaningful use" of certified EHR technology. According to the

Centers for Medicare and Medicaid Services (CMS), “The Medicare and Medicaid EHR Incentive Programs will provide

incentive payments to eligible professionals, eligible hospitals and critical access hospitals (CAHs) as they adopt, implement,

upgrade or demonstrate meaningful use of certified EHR technology” (CMS, 2011, para. 1)

On July 13, 2010, the Secretary of HHS published in the Federal Register a final rule that adopted standards, implementation

specifications, and certification criteria for HIT. The final rule was released in conjunction with the Medicare and Medicaid EHR

Incentive Programs final rule. The CMS regulations specify the objectives that providers must achieve in payment years 2011

and 2012 to qualify for incentive payments. The ONC regulations specify the technical capabilities that EHR technology must

have to be certified and to support providers in achieving the “meaningful use” objectives.

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The Department of Health and Human Services (HHS) issued a final rule on June 18, 2010 establishing a temporary

certification program for EHR technology and included information on how organizations can become ONC-Authorized Testing

and Certification Bodies (ONC-ATCBs). According to the Office of the National Coordinator for Health Information Technology,

ONC-ATCBs “…test and certify that certain types of EHR technology (Complete EHRs and EHR Modules) are compliant with

the standards, implementation specifications, and certification criteria adopted by the HHS Secretary and meet the definition of

“certified EHR Technology” (ONC, 2010).

The Temporary Certification Program Final Rule specifically establishes a temporary certification program to assure the

availability of Certified EHR Technology prior to the date on which health care providers seeking the incentive payments would

begin to report demonstrable meaningful use of Certified EHR Technology.

The final rule to establish the Permanent Certification Program for Health Information Technology was issued in January 2011.

The American National Standards Institute (ANSI) was approved as the ONC-Approved Accreditor (AA) for the Permanent

Certification Program which instills the responsibility of accrediting organizations who will certify electronic health record

technology. Implementation of the permanent certification program is expected to occur in mid 2012.

The certification program provides a way for developers of EHR technology to have their EHR technology tested and certified

so that it can be subsequently adopted by health care providers who seek to achieve meaningful use. Eligible professionals and

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eligible hospitals who seek to qualify for incentive payments under the Medicare and Medicaid EHR Incentive Programs are required by

statute to use Certified EHR technology.

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The Health Information Technology: Initial Set of Standards, Implementation Specifications, and Certification Criteria for

Electronic Health Record Technology Final Rule (2010) states certified EHR technology means:

(1) A Complete EHR that meets the requirements included in the definition of a Qualified EHR and has been tested and

certified in accordance with the certification program established by the National Coordinator as having met all applicable

certification criteria adopted by the Secretary; or

(2) A combination of EHR Modules in which each constituent EHR Module of the combination has been tested and certified

in accordance with the certification program established by the National Coordinator as having met all applicable

certification criteria adopted by the Secretary, and the resultant combination also meets the requirements included in the

definition of a Qualified EHR.

Complete EHR means EHR technology that has been developed to meet, at a minimum, all applicable certification criteria

adopted by the Secretary. (p. 44649)

15

The HITECH Act (Section 3001(13)) defines a qualified EHR as:

“An electronic record of health-related information on an individual that:

(A) Includes patient demographic and clinical health information, such as medical history and problem lists; and

(B) has the capacity:

(i) To provide clinical decision support;

(ii) to support physician order entry;

(iii) to capture and query information relevant to health care quality; and

(iv) to exchange electronic health information with, and integrate such information from other sources” (p. 229).

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The American Recovery and Reinvestment Act of 2009 (ARRA) also provided for the creation of two Federal advisory

committees under the auspices of the Federal Advisory Committee Act (FACA). These committees are the Health IT Policy

Committee and the Health IT Standards Committee.

Per the ONC, the Health IT Policy Committee makes “recommendations to the National Coordinator for Health IT on a policy

framework for the development and adoption of a nationwide health information infrastructure, including standards for the

exchange of patient medical information. The American Recovery and Reinvestment Act of 2009 (ARRA) provides that the

Health IT Policy Committee shall at least make recommendations on standards, implementation specifications, and

certifications criteria in eight specific areas” (ONC, 2011, para. 1)

The other Federal Advisory Committee is the Health IT Standards Committee. This group is “charged with making

recommendations to the National Coordinator for Health IT on standards, implementation specifications, and certification

criteria for the electronic exchange and use of health information. Initially, the Health IT Standards Committee is focusing on the

policies developed by the Health IT Policy Committee’s initial eight areas…. In developing, harmonizing, or recognizing

standards and implementation specifications, the Health IT Standards Committee also provides for the testing of the same by

the National Institute for Standards and Technology (NIST)” (ONC, 2011, para. 1)

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Another external influence on the future of health management information systems, electronic health records, and the process

of building a framework that enables these EHRs to be sharable and interoperable, is the Vision for 21st Century Health Care

and Wellness. As cited in chapter two of Computational Technology for Effective Health Care: Immediate Steps and Strategic

Directions, “The IOM vision calls for a health care system that is systematically organized and acculturated in ways that make it

easy and rewarding for providers and patients to do the right thing, at the right time, in the right place, and in the right way. This

vision entails many different factors (e.g., systemic changes in paying for health care, an emphasis on disease prevention

rather than disease treatment)” (Stead & Lin, 2009, p. 20)

The principal factor in the Institute of Medicine’s vision of a health care system is the effective use of information.

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The report identified information-intensive aspects of the IOM’s vision for 21st century health care. These important health care

IT capabilities include:

• “Comprehensive data on patients’ conditions, treatments, and outcomes;

• Cognitive support for health care professionals and patients to help integrate patient-specific data where possible and

account for any uncertainties that remain;

• Cognitive support for health care professionals to help integrate evidence-based practice guidelines and research results

into daily practice;

• Instruments and tools that allow providers to manage a portfolio of patients and to highlight problems as they arise both

within individual patients and within populations;” (Stead & Lin, 2009, pp. 4-5)

As the Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions report explains

“cognitive support” refers to IT-based tools and systems that provide users (clinicians and patients) with the information,

abstractions, and models needed to achieve the IOM’s vision of health care quality” (Stead & Lin, 2009, p. 21)

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Three additional important health care IT capabilities identified in the IOM’s vision for 21st century health care are:

• “Rapid integration of new instrumentation, biological knowledge, treatment modalities, and so on, into a “learning” health

care system that encourages early adoption of promising methods but also analyzes all patient experience as experimental

data;

• Accommodation of the growing heterogeneity of locales for the provision of care, including home instrumentation for

monitoring and treatment, lifestyle integration, and remote assistance; and

• Empowerment of patients and their families in effective management of health care decisions and execution, including

personal health records (as contrasted to medical records held by care providers), education about the individual’s

conditions and options, and support of timely and focused communication with professional health care providers” (Stead &

Lin, 2009, p. 5)

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Ongoing developments in biomedical informatics can affect future uses and challenges related to health information systems

and electronic health records. The report listed five principles related to evolutionary change and four principles related to

radical change as guidance towards successful use of health care IT to support a 21st century vision of health care.

The five principles related to evolutionary change are:

• “Focus on improvements in care—technology is secondary.

• Seek incremental gain from incremental effort.

• Record available data so that today’s biomedical knowledge can be used to interpret the data to drive care, process

improvement, and research.

• Design for human and organizational factors so that social and institutional processes will not pose barriers to appropriately

taking advantage of technology.

• Support the cognitive functions of all caregivers, including health professionals, patients, and their families” (Stead & Lin,

2009, p. 6)

Accordingly, those in the field of biomedical informatics can affect future developments related to health information systems by,

for example, creating technology that address organizational factors, supporting the cognitive functions of caregivers, and

designing software for human factors.

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The four principles related to radical change are:

• “Architect information and workflow systems to accommodate disruptive change.

• Archive data for subsequent re-interpretation, that is, in anticipation of future advances in biomedical knowledge that may

change today’s interpretation of data and advances in computer science that may provide new ways of extracting

meaningful and useful knowledge from existing data stores.

• Seek and develop technologies that identify and eliminate ineffective work processes.

• Seek and develop technologies that clarify the context of data” (Stead & Lin, 2009, p. 6).

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This concludes Electronic Health Records.

Lecture a defined an electronic medical record (EMR) and an electronic health record (EHR) and explained their similarities and

differences, identified EHR attributes and functions, discussed the issues surrounding EHR adoption and implementation, and

described the impact of EHRs on patient care.

Lecture b linked EHRs to the Health Information Exchange (HIE) and the Nationwide Health Information Network (NHIN)

initiatives, discussed how HIE and NHIN impact health care delivery and the practice of health care providers, summarized the

governmental efforts related to EHR systems including meaningful use of interoperable health information technology and a

qualified EHR, described the Institute of Medicine’s vision of a health care system and its possible impact on health

management information systems, and listed examples of the effects of developments in bioinformatics on health information

systems.

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Working with Health IT Systems is available under a Creative Commons Attribution-NonCommercial- ShareAlike 3.0 Unported license. © Johns Hopkins University. UMUC has modified this work and it is available under the original license.

Welcome to Installation and Maintenance of Health IT Systems, Elements of a Typical EHR system, This is lecture a.

This component covers fundamentals of selection, installation and maintenance of typical Electronic Health Records (EHR)

systems.

1

Today’s first lecture, Health IT System Elements, is designed to give you a brief overview of a typical Electronic Health Record

(or EHR) system. We will discuss the Institute of Medicine’s six aims for improving healthcare, what an EHR is, and how it has

evolved. Additionally, we will outline the types of network elements an EHR system needs to function, as well as its typical

hardware and software components.

The objectives for this unit are to:

• Identify the core elements that comprise an EHR system

• Describe the use of client and server hardware for access to and storage of EHRs

• Describe network needs for access to and storage of EHRs

• Identify the application software and back-end data storage software needed for a comprehensive, effective health IT system

2

The Institute of Medicine, also referred to as the “IOM,” is an independent organization which works to improve healthcare at the national level by delivering unbiased, evidence-based healthcare advice.

According to a report completed by IOM In 2001, the United States ranked 37th worldwide for quality of healthcare. That same year, the Institute of Medicine compiled a report listing 13 recommendations designed to revamp the nation’s healthcare system. One of these core recommendations was a call for a renewed effort on the part of the government and private sectors to build an information infrastructure to support healthcare delivery, community health, quality measurement and improvement, public accountability, and improving research and clinical education.

The committee further noted that “information technology … holds enormous potential for transforming the healthcare delivery system” and challenged the healthcare arena to virtually eliminate handwritten clinical data by the end of the decade.

The Electronic Health Record, or EHR, system, though not born from this effort, certainly has seen renewed life and product evolution as the healthcare arena struggles to meet the IOM’s challenge to improve healthcare delivery on a national scale. In 2007-08, surveys of ambulatory practices indicated that only 4% had a “fully functional” electronic records system and another 13% had a “basic” system.

However, these numbers are increasing. In the last two years alone, EHR adoption rates have doubled. A recent survey conducted in August 2011, showed that 51% of physicians' offices with three to five providers and 31% of the solo-provider

3

practices now currently use EHRs, many of which were added in the past few months, thanks in part to 2009’s HITECH Act. Through the HITECH Act, the U.S. federal government has committed billions of dollars to promote both adoption and “meaningful use” of EHRs.

3

The IOM listed six aims in improving health care quality:

1.To make healthcare environments safer for their patients

2.To provide more effective healthcare

3.To make healthcare more patient-centered - that is, to ensure that the patient is more involved in the decision-making process

and has a better understanding of the healthcare choices available

4.To improve the timeliness of healthcare service

5.To make the process of providing healthcare, as a whole, more efficient

6.To work toward the elimination of healthcare disparities among diverse populations … ensuring that all patients have equal

access to healthcare

Throughout the remainder of our course, think about each of the EHR systems you will be evaluating and ask yourself how each

of them adequately addresses these six aims.

4

So, what is an electronic health record anyway?

According to the Computerized Patient Record , published in 1991 by the Institute of Medicine, an electronic health record

system is defined as “The set of components that form the mechanism by which patient records are created, used, stored, and

retrieved.” A patient record system is usually located within a health care provider setting. It includes people, data, rules and

procedures, processing and storage devices (e.g., paper and pen, hardware and software), and communication and support

facilities.”

5

The federal government has defined a complete EHR system as containing four basic functions: computerized orders for

prescriptions and other therapies, computerized orders for tests, reporting of test results, and physician notes. To date, however,

no federally enforced single standard based on this definition has been reached and which of these records are stored

electronically is determined largely by each individual healthcare practice.

6

Likewise, the Institute of Medicine has also listed the key capabilities any EHR system should address as “(1) a longitudinal

collection of electronic health information for and about persons, where health information is defined as information pertaining to

the health of an individual or the health care provided to an individual; (2) immediate electronic access to person and population-

level information by authorized, and only authorized, users; (3) provision of knowledge and decision-support that enhance the

quality, safety, and efficiency of patient care; and (4) support of efficient processes for health care delivery.”

It is important to note these definitions while evaluating your present and/or prospective EHR systems … since an effective EHR

system will ultimately be judged by how well it can adequately address and satisfy all these objectives.

I will also note that when we talk about longitudinal data, collection involves repeated observations of the same items over long

periods of time — often many decades.

7

The Electronic Health Record, or EHR, is just the latest chapter in an evolutionary process which can be traced as far back as

the early 1960s.

The development of the electronic patient record began when El Camino Hospital, teamed up with Lockheed Corporation to

create a new way and more effective method of tracking patient data. Together, they spent over two years analyzing their

operational data flow in order to develop a way of electronically storing and updating patient information. This system began to

go live in 1973 and the first computerized Patient Record (or CPR) system was born. These earlier versions of electronic health

records required hospitals to invest in expensive computer hardware running UNIX. El Camino’s CPR ran on an IBM mainframe

computer. Typically, these systems were powerful, but somewhat limited, and training costs associated with running these

systems were prohibitively high.

Several institutions attempted their own versions of CPRs during the 70s and 80s, but overall, successful implementation was

quite difficult and rarely achieved.

8

In the 1990s, however, computer technology was experiencing significant advances and processing power became more

abundant. Additionally, the IT industry began moving toward large scale communication networks and distributed computing

models, lifting many of the limitations seen in earlier CPR technology. Ambulatory clinics began utilizing Electronic Medical

Records (or EMRs) during this time as well; however, despite software improvements, adoption rates were comparatively low.

This was partly due to the rapid technological advancements in computer and software technologies which frequently made EMR

systems obsolete right out of the box. Additionally, usability issues often made adoption very difficult, particularly for smaller

institutions with limited resources.

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By about 2000, however, PCs low cost and ubiquity lowered barriers for adopting electronic systems with a sharp decline of

startup costs. Today, an abundance of inexpensive and extremely powerful computer systems are available, making EHR

adoption much cheaper. What's more, electronic health record systems are simply better thanks to the adoption & evolution of

graphical user interfaces, most commonly on the Windows platform. Such interfaces make learning electronic health record

systems extremely easy.

With training costs being dramatically cut, along with networking of computers making updating and fixes a breeze to install in

comparison to the older models, the need for additional on-site IT staff has dramatically decreased.

Today the installation of an EHR system makes more sense than ever before.

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So what exactly is the difference between a CPR, an EMR, and an EHR, anyway?

Well, generally when we refer to a Computerized Patient Record, or CPR, we are referring to a record system designed for use

in an acute care setting such as a hospital, while we generally think of Electronic Medical Records, or EMRs, as simplified

versions of CPRs designed for use in ambulatory care and physicians’ practices. EMRs are designed to provide patient data

recording and tracking and quality assurance functions within a practice. Both CPRs and EMRs are typically designed to provide

interoperability only within the host enterprise, offering limited, if any real interoperability beyond the institution.

Electronic Health Records, or EHRs, on the other hand, are geared to provide a comprehensive healthcare record capable of

moving with the patient. EHRs are designed for interoperability on a more regional or even global level. Since the industry shift is

toward the implementation of EHR models, our component focuses on EHR implementation though many of the underlying

principles apply regardless of how the system is categorized.

11

It is true that many “legacy” EHR systems have been slow to be adopted. A research study has indicated a strong need for a more refined strategy by the industry to build more EHRs which don’t force medical staff to change the way they practice medicine but instead provides the flexibility physicians need to solve real world issues. With that said, EHR systems still offer many potential advantages to the typical clinical environment.

As an example, it’s well known in the industry that doctors tend to have illegible handwriting, which can often lead to inaccurate and costly data entry errors. With an EHR system, the physician enters the data directly into the system interface itself, thereby dramatically reducing handwriting errors.

With EHRs, massive amounts of data can now be stored digitally in a substantially smaller space, eliminating storage problems and virtually eliminating record search time. With an EHR system, healthcare staff can have critical patient information at their fingertips.

These realized efficiencies, combined with value-added software designed to minimize procedural and prescription errors, should, over time, improve overall patient safety in the healthcare environment. The increased ease of updating records while with the patient should equate to more time devoted to physician-patient interaction.

The introduction of EHR systems shows a huge potential for cost savings and decreasing workplace inefficiencies. It’s expected that these cost reductions, combined with a reduction in patient care errors, should eventually result in lower malpractice

12

premiums and litigation fees.

12

Before we move on, let’s take a moment to define two terms you will hear throughout the component: “hardware” and “software”.

Hardware consists of all the physical computing devices or components that make up a computer system. Examples of hardware

include desktop computers, laptops, servers, switches and routers…along with any internal components which make the devices

function such as a hard drive, a keyboard, or a NIC Card.

Software consists of computer programs and any of their accompanying data which tell the computer what to do and how to

behave. Programmers develop software by writing lines of programming code which is eventually compiled and stored on some

sort of media in an electronic format.

13

This illustration depicts that, prior to centralized EHR system management software, each organization or department maintain

its own system and software designed to capture the data required for each specialty area. This meant that multiple databases

and patient records existed and the healthcare provider was required to open a different client application for each department

and compile the data using a manual process. Additionally, data may or may not have been in conformance with a standard.

Image courtesy of National Center for Research Resources. Printed with permission. All rights reserved.

14

This illustration depicts that EHR systems are designed to receive data from each of these organizational silos and compile them

within a centralized database. EHR software is designed to compile the data in a more efficient manner, allowing the healthcare

provider to access and cross-reference data from all available sources from one convenient client interface. This should allow the

provider to more effectively manage patient care.

Image courtesy of National Center for Research Resources. Printed with permission. All rights reserved.

15

Most of today’s in-house EHR systems are based on the client-server model. The client–server model in the computing world is a structure that separates tasks or workloads between service providers, called “servers”, and service requesters, called “clients”. Usually, a client computer and a server computer are two separate devices, each customized for their designed purpose and communicating over a computer network. For example, a Web client works best with a large screen display, while a Web server does not need any display at all to parse out requested web pages, and it can be located anywhere. In some rare instances, however, both client software and server software reside in the same system.

As we stated earlier, software can best be defined as the collection of computer programs and related data that provide the instructions for what a computer should do. EHRs use several different types of application software.

A server machine is a host that is running one or more server programs which share its resources with clients. Server software is usually installed and operated from dedicated “server” hardware designed to reliably and efficiently handle large numbers of client requests.

A client machine does not share any of its resources, but requests one or more server's content or service function. Client software therefore initiates communication sessions with servers which await (listen to) incoming requests.

Many business applications being written today use the client–server model.

16

As this picture demonstrates, using a client-server architecture enables the roles and responsibilities of a computing system to

be distributed throughout the network using several independent computers. All data is stored on the servers, which generally

have far greater security controls than most clients. Additionally, because the data is stored centrally, it is easier to manage and

update. Using a client-server model also spreads the workload among multiple systems, generally easing the burden on client

systems which would otherwise have to expend more resources for processing and storage of data.

17

Let’s summarize what we have learned so far:

Despite early set backs in implementation, EHRs hold great promise for improving safety and efficiency in the healthcare setting

over the long term.

EHRs require both hardware - that is the devices, computers, and network infrastructure and software – which includes

databases, applications, and device drivers, to name a few – in order to function…and that in today’s market, the “Client-Server”

model is predominant in terms of EHR software strategies. The client-server model is a structure that separates tasks or

workloads between service providers, called “servers”, and service requesters, called “clients”. Usually, a client computer and a

server computer are two separate devices, each customized for their designed purpose and communicating over a computer

network.

In part b of our lecture we will discuss in more detail these software and hardware elements commonly found in EHR systems.

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Working with Health IT Systems is available under a Creative Commons Attribution-NonCommercial- ShareAlike 3.0 Unported license. © Johns Hopkins University. UMUC has modified this work and it is available under the original license.

Welcome to Quality Improvement: Data Quality Improvement. This is Lecture a.

We have discussed the impact of poor data quality on quality measurement. We have defined

ten different attributes that are used to define data quality; we reviewed an example of each

attribute in various clinical settings and highlighted key process recommendations to improve

data quality. Let’s summarize what we’ve learned by reviewing common causes of insufficient

data quality and review best practices that you can implement in your role to assure or improve

the quality of health information.

1

The Objectives for Data Quality Improvement are to:

Understand the different purposes of data.

Discuss the impact of poor data quality on quality measurement.

Identify ten attributes of data quality and key process recommendations.

2

In today’s information age, data are increasingly driving healthcare decision making.

Healthcare databases are filled with data that reflect clinical and clinically-related information.

The data are usually collected through the routine processes and activities of patient care;

however, its usefulness goes beyond the operational applications that generate the data. The

documentation within the electronic health record is often used for quality improvement,

payment, legal, research, and accreditation and licensing purposes.

Quality aims such as eliminating duplicate or unnecessary tests, or screening and

implementing preventive strategies for identified safety risks can be achieved by ensuring

complete data collection and an effective health information exchange. Reimbursement can be

enhanced by providing the appropriate prompts to substantiate medical necessity. Malpractice

cases can often be more successfully defended if the content and quality of the record provides

an accurate depiction of the events and jogs the memory of the provider. Research through

public health and bio-surveillance agencies can be facilitated if the data collected meet

identified definitions and standards for data quality. Last but not least, accreditation and

licensing decisions often rest on whether or not the organization’s documentation substantiates

the standards set forth by the agency. Data are more precious than ever as their use and

application expand.

3

It is important that a clear distinction is made regarding the use of data. When data are

collected to improve care the collection characteristics are different than when it is collected to

advance research and expand our knowledge.

When the aim of the data collection is to improve care, you use observable data. Accept that

there is a consistent bias and collect just enough data to make a decision regarding the

outcome of the test in a small and sequential manner.

When the aim is to acquire new knowledge, data are better collected blinded (when neither the

researcher nor the subject are aware of the test being performed), attempt to eliminate biases

when they exist and collect a wide variety of data points for a sample size determined through

a power calculation.

4

National Quality Forum (NQF) joined with HL7, AHIMA and consulting firm Alschuler

Associates to develop a draft standard called Health Quality Measure Format (HQMF). HQMF

was developed in part to support, “Meaningful Use,” as described by the American Recovery

and Reinvestment Act (ARRA).

The main goal of this format is to standardize data to have consistency across vendors and

regions and thus support the, “Meaningful Use,” of HIT technology. This standardization is

accomplished through a number of domains such as document structure (e.g. sections),

metadata (e.g. author, verifier), and definitions (e.g. “numerator”, “initial patient population”).

Although this standardization is an important first step, it does not enable full machine

processing at this time.

5

As the previous scenario illustrates, many healthcare errors and adverse events occur as a result of poor data and information quality.

Quality and safety issues can often be linked back to poor documentation, inaccurate data or insufficient communication between providers

Operationally, poor-data quality leads to low satisfaction and increased cost. Even simple errors such as inaccurate/incorrect names, addresses, and insurance or benefit information can have a negative impact on the satisfaction of patients and staff alike. Patients have a right to expect that the details of their care are documented completely and correctly and that the quality and safety of their care is not compromised by inaccurate or ambiguous data. Operational costs can be increased because of unnecessary duplication of tests or procedures, inefficient care processes, or additional time and resources that must be directed toward detecting and correcting data problems.

Strategically, poor data compromise individual and organizational decision making. Good decisions require an effective synthesis of multiple bits of datum that can be converted into meaningful information. Gaps in the data due to missing or incomplete detail or suspicious accuracy complicate the process of effective decision making. Teams can also get diverted by deliberating the quality of the data and never even get around to deliberating the decision at hand. Mistrust of data can spill over into mistrust of other team members and their motives. This can lead to duplicate-data collection resulting in further delays in analyses and ineffective decision-making. If decision making is hindered so is strategic planning, since it is a process that requires decision making. It requires thoughtful assessment of strengths, weaknesses, opportunities and threats and depends on high-quality internal and external data. Once developed and implemented, the plan must be evaluated to determine its effectiveness. If the reported results are of poor quality, knowing how to modify the strategic plan is made even more difficult.

Data-quality problems have always persisted, sometimes to a larger degree in some data elements or processes than in others. In the past, the important data were “edited” or processes of data collection were “corrected” to ensure accuracy whenever the data were specifically identified as required for quality improvement or regulatory monitoring purposes. The other data elements were not deemed important for correction as these were assumed to be unnecessary. As we move into a new era of health information technology, we are finding new uses for the data and the accuracy and quality are increasingly important. For example, data within EHRs are increasingly used to detect errors. By applying queries, algorithms and decision rules, we can identify cases that represent potential adverse events. In order for these tools to be effective, the data contained in the EHR has to be completely and consistently entered by health care providers. Data-quality management must involve more than fixing problems after the data are entered; it involves preventing the issues from occurring. A fundamental shift to design with the end in mind requires the knowledge and skills of insightful clinical and IT professionals. The first step requires you to begin with quality data.

6

Technology is a critical tool in achieving high-quality data in an electronic health record and

realizing the benefits of health information exchange. However, technology alone is not

sufficient. It is imperative for organizations to hard-wire patient safety and quality of care

measures into their electronic processes and systems. Because data quality will positively

impact the efficiency, quality and safety of care – it follows then that a robust and high quality

EHR is required. Such an EHR then becomes an important adjunct to quality, and takes its

place as an evidence-based decision-making tool. In 1998, the American Health Information

Management Association’s e-HIM® workgroup developed the Data Quality Management (or

DQM) Model for implementing an EHR documentation improvement process. The model was

reviewed and adopted again in 2006 and includes continuous quality improvement in the

domains of data application, collection, analysis and warehousing.

In this model, the application is the purpose for which the data are collected. The collection

includes the processes by which data elements are accumulated. Translating the data into a

form that can be used for the designated purpose is part of the analysis phase. You may hear

this referred to as “transforming data into information”. And, warehousing describes the

processes and systems used to archive data and data journals.

The model includes a number of data quality attributes that can be applied to each domain.

The model is generic. It can be adapted to any care setting, used with any application, and can

be used in any role that you, as an HIT professional, choose.

7

Data quality is a complex topic and it is affected by more than just the accuracy of the data. A

review of the literature yields a number of terms that can be used to describe data quality

attributes. The DQM model attributes include definition, accuracy, accessibility,

comprehensiveness, consistency, currency, timeliness, granularity, precision, and relevancy.

Each of these attributes will be described, and an example will be provided as well as key

process issues that HIT professionals should consider for effective health information

exchange.

8

We know and understand that data are often used for purposes other than that for which they

were originally collected. It has been said that one man’s junk is another man’s treasure!

Therefore, to support the multi-use of data that is collected, clear definitions for each data

element should be provided so current and future users will know what the data mean. For

example, does the word “football” mean the same thing to people all around the world? No –

and without a clear definition that lets the user know what type of “football” we are referring to –

errors in interpretation are likely. In addition, standard definitions are necessary in order to

compare data with data stored in other databases, for example external registries or quality

databases, or to compare data over time, such as trend data for quality purposes. Each

element should have clear meaning and acceptable values. For example, in addition to clear

definitions, a data dictionary should provide data type, length restrictions, and other rules

including uniqueness, consecutiveness or calculated data and acceptable ranges. For

example, should an EHR allow a temperature with 4 digits (without a decimal point) to be

entered? Should numeric data be allowed where only text is expected? Inaccurate data can

result from mistakes that are made when data are extracted, transformed or transferred to

secondary data sources. Well documented data definitions and rules that govern accepted

values can protect against inappropriate use of data.

The federal Health IT legislation described earlier requires that, “Meaningful Use,” of health

information technology includes electronic reporting of data on the quality of care. When the

Centers for Medicare and Medicaid Services were preparing to announce their final rules for

the meaningful use standards and finalizing the list of core quality measures, one hospital had

examined their opportunities for outcome reporting through a nationally recognized database

for quality reporting. They decided to participate in the National Health Safety Network

(NHSN), a voluntary, secure internet-based surveillance system that is managed by the

Division of Healthcare Quality Promotion at the Centers for Disease Control and Prevention.

Exchange of data through NHSN requires adherence to the guidelines and procedures for data

9

collection that includes specific definitions for all variables collected and reported.

9

One of the most important functions you, as an HIT professional, can perform is to assist the

team in the development of a thorough data dictionary. Let’s discuss how the attribute of data

definitions can be applied to the Data Quality Management Model domains of application,

collection, analysis, and warehousing. Appropriate use of the data requires an understanding of

the purpose and data definitions. The data collection process should guide the user to enter

only acceptable values and minimize or eliminate any ambiguity.

Meaningful analysis relies on clear understanding of the data and making appropriate

relationships among the variables. Warehousing requires assigning responsibility for the

ownership and maintenance of the data and documentation over time along with corresponding

policies and procedures for data and information management.

10

Accuracy is a term used to refer to the extent that the data properly represent the “real-life”

objects they are intended to represent. Accuracy implies that the value is valid and correct, and

the person who the value is related to is properly assigned. Inaccuracy can result from

deficiencies in other attributes that we will be discussing later on. A lack of precision or

completeness can also influence the accuracy of the data and the answers to the questions

you intend to find through the use of that data. Remember, inaccuracy can lead to an

understanding of a valid real-world state, but NOT the one intended.

Payment for care rendered is critical to the survival of any provider in all care settings. During

the registration process, insurance information must be gathered and validated. You, as an HIT

professional, can assist in improving the data quality in this process. By working with business

office personnel, you can establish a process to develop and maintain a reference table with

codes for all approved insurance providers. An automated process can be instituted to verify

the accuracy of the insurance information by limiting entry to only those codes that are

available in a drop down menu that is linked to the reference table. Entry of any other data

would require a process of verification and pre-approval for any manual entries for this field of

data.

11

A key process recommendation for you to enhance accuracy in the application of the Data

Quality Management Model is to collaborate with users of the data to establish a policy or

process to identify how data used in EHRs are generated and how data content will be

determined and standardized. In an effort to improve data accuracy, you can prompt the users

to think about purpose and application of the data and how choices in the data entry may be

limited to improve the accuracy, maintain integrity and improve the reliability and validity of the

data.

Accuracy in data collection can be improved by educating and communicating data definitions

to those who collect the data. For example, if the time of initiation and discontinuation is critical

for payment, the staff need to know exact, not approximate, times that must be collected.

You can also assist in accurate analysis of the data by ensuring that the algorithms, formulas

and translation software are correct. For example, one of the alternate quality measures to

demonstrate meaningful use of electronic health records is childhood immunization status.

You would meet with the pediatric providers to discuss the purpose of this metric, the data

elements that are collected, who will collect them, how the data will be applied to decision-

making, and how the data will be transmitted to the immunization registry without losing data

accuracy.

Appropriate edits must be made to ensure accuracy prior to warehousing the data for future

use. Exception and errors reports should be developed so that corrections to the data can be

made. For example, some diagnoses or patient locations may be incorrect for the age or

gender of the patient. Screening for these types of problems and making corrections will

improve the accuracy.

12

Accessibility is the extent to which data is available or easily obtainable for use. But easily

obtainable does not mean that unauthorized individuals should be able to gain entry into

protected personal health information. Accessibility incorporates ease of gaining entry with the

safeguards that are absolutely required to assure confidentiality and privacy of patient data.

These safeguards should be built into the process and automatically deploy without any special

effort by the user. The Health Insurance Portability and Accountability Act (HIPAA) includes

rules, standards and guidelines to guide you in establishing the appropriate procedures for

health data access.

The burden of data collection can often derail safety, quality improvement, and research

efforts. Often the data that are needed already exist someplace within the scope of the

electronic health record. A typical example of data that are often needed, but shouldn’t have to

be collected again by clinicians, are the demographics of a selected population of patients,

such as home care patients with known congestive heart failure, who will be included in a

quality improvement or research study. However, the detail and the use of this data must be

evaluated for the patient’s protection under HIPAA and other regulations. You can guide the

team to select the best, least-costly, and legally-appropriate way to access and collect the data

that are needed. The amount and accessibility of the necessary data can be increased through

system interfaces.

13

Inaccessibility of data can be a frustration to clinicians who need data to generate information

about ways to improve care. However, a lack of data stewardship also has serious risks and

consequences associated with unauthorized access or inappropriate use of health information.

Proper observation of the domains in the Data Quality Management Model requires you to

work with clinicians to define and agree on the types of data and the minimum amount of data

that needs to be available to support the team in achieving its mission and objectives. The

intended application or use of data, and the legal, regulatory and financial boundaries often

determine which data should be accessible.

Collection of accessible data should be assigned based on the expertise and scope of practice

of team members; with registration staff collecting demographics, clinicians documenting

physiologic findings such as symptoms or scale ratings, and coders assigning medical record

coding.

Data analyses should be supported by timely access to the required data. For example, if there

is a recall on a lot of vaccines, care providers in a primary-care office need rapid access to

vaccine administration data to be aware of potential patients to be alerted to the recall. Policies

should define the process, restrictions and rights for retrieval of data from database systems

and warehouses. The accountability and chain of trust within HIPAA should be delineated.

Organizations should be specific in their internal policies and business associate contracts

about what identifiable health data may be used and for what purpose, by both the business

associate and its agents; also what HIPAA de-identified data may be used and to whom they

are applied; the requirement that business associates have contracts with their agents that are

equivalent to business associate contracts; and the use of HIPAA definitions for any de-

identification of protected health information. Methods to regularly monitor and audit access to

data should be in place.

14

Comprehensiveness is the ability of an information system to reflect every possible state in the

real world. Intentional limitations of the data should be documented, and every effort to include

all of the data elements that are required is made. It is understood, of course, that not every

piece of data can be captured, but many projects have suffered from lack of deep forethought

about what and how to measure. A high level of missing data will reduce the reliability and

validity of your analysis. In order to minimize missing data, rules can be assigned to a data set

to define mandatory elements that require a value, optional elements that may have a value

assigned based on some set of conditions, or inapplicable attributes that may not have a value.

Comprehensiveness is illustrated in the following scenario. In October 2008, the Center for

Medicare and Medicaid Services began requiring hospitals that receive federal funding from

Medicare and Medicaid to begin disclosing, “never events.” Never events are conditions that

CMS defines as preventable, and serious in their consequences for patients, and that indicate

a real problem in the safety and credibility of a healthcare facility. Included in this list of

conditions are pressure ulcers or what is often referred to in layman terms as bed sores. CMS

has stated that they will no longer reimburse hospitals for any costs associated with never

events, and hospitals are prohibited from passing the costs onto patients.

The ability to differentiate conditions that were present when the patient was admitted, versus

those that were acquired during the hospital stay, requires a comprehensive assessment and

documentation as a means to avoid potential penalty and quality concerns. Increasingly,

clinicians are turning to HIT professionals to assist them in defining data elements and rules for

their completion for clinical, financial and risk management needs.

15

Key process recommendations to apply the attribute of comprehensiveness within the context

of the Data Quality Management Model is for you to seek clarity from the team about how the

data will be used and how end-users can assist to ensure that complete data will be collected.

Opportunities to create interfaces with other automated systems should be pursued when

doing so can enhance the comprehensiveness and quality of the data collection. An example

of this might be to link the skin assessment completed in the emergency department’s

electronic health record from one vendor to the skin assessment completed in inpatient

electronic health records made by a different vendor. The goal is to make the collection of the

necessary data elements as comprehensive and as seamless as possible across care-settings.

Be alert to the multiple places that the same data element might be recorded and attempt to

reduce the variation in data completeness. Whenever possible, provide structured response

choices and reduce the number of free-text entries to facilitate complete data entry and

extraction. You should recommend that all relevant data are collected and analyzed in concert.

For example, in addition to assessing whether or not pressure ulcers were present on

admission, the team may also want to know about risk factors to aid in a comprehensive

assessment of the quality problem if a number of patients later develop ulcers. In warehousing

data, be aware of and educate all data stakeholders of the data that are available to prevent

redundancy and conflicting data collection.

16

This concludes Lecture a of Data Quality Improvement. In summary, data use for research

purposes is collected under different conditions than that used for QI.

Poor data quality contributes to error.

The ten attributes of data quality are:

Definition

Accuracy

Accessibility

Comprehensiveness

Consistency

Currency

Timeliness

Granularity

Precision

Relevancy

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End.

18

Working with Health IT Systems is available under a Creative Commons Attribution-NonCommercial- ShareAlike 3.0 Unported license. © Johns Hopkins University. UMUC has modified this work and it is available under the original license.

Welcome to Quality Improvement: Data Quality Improvement. This is Lecture b.

1

The Objective for Data Quality Improvement is to:

Explore the attributes of data quality and key process recommendations for maintaining data

integrity.

2

The term “gold-standard” often comes to mind in the discussion of the attribute of consistency. The

value of the data or the representation of what the data mean should be reliable and mean the same

thing across applications. The same data element when drawn from two different places should not

conflict. Often in the subjective measures or complex concepts, a preferred definition or set of

conditions must be provided in order to guide the user in how best to judge the “real-world” state and

record the correct data value from among the possible choices. It is important to note, however, that

data can be consistent and still be incorrect. For example, a primary-care provider’s patient roster

and billing data may note a patient as a correct, current patient when they have, in fact, transferred to

another provider. Consistency does not always equate to accuracy.

Allergy status is an example of an issue related to consistency that crosses all types of care settings.

Consistent information is not only important from a data-quality perspective, it is important to assure

patient safety. The issue of allergies can be complex and can involve several levels of questioning to

arrive at a conclusion. Within a particular care setting and across interfaced systems, the question

can be standardized and users can be educated on the guidelines so that the data are consistent

across all areas and across time.

3

A recommendation for you to improve data quality is to assist the team in identifying if a gold

standard exists to improve consistency in the use of the data. This process also involves determining

if there is a true owner of the data who has the final say in determining the phrasing for the data

element or who has the final editing rights. Auditing for inconsistencies across interfacing systems or

across the stays, or encounters, for an individual patient can aid the team in their assessment of data

quality for an important safety concern. Consistency in the collection of data may require user training

and standardized data collection rules. Analysis requires monitoring consistency of the data elements

to ensure that a valid comparison of data elements can be applied. The data warehousing process

may require edits and conversion tables to facilitate consistent interpretation and use of the data for

any element whose definition may be changed.

4

System currency is judged based on how well it reflects the current world. Currency means the data

are, “up-to-date,” and the information is correct despite time-related changes. The data are up-to-

date if they are current for a particular point in time and outdated if the situation changes and the data

are no longer correct. The term “decay” can be used to describe the temporal aspect of data currency

quality. Data values can be accurate when entered but become inaccurate over time. For example, a

blood glucose drawn in the past is current only for the date and time the sample was taken. It should

not be assumed that the value is current for any other point in time.

Here’s an example of data currency. The International Classification of Disease, Clinical Modification,

version 9 (ICD-9-CM) is the official system of assigning codes to diagnoses and procedures

associated with hospital utilization in the United States. In addition to their use for billing and

reimbursement, these readily accessible coded data are often used to evaluate co-morbidities and

potential hospital-acquired conditions for quality studies and research. The ICD classification schema

has been revised periodically to incorporate changes in the medical field. The next update, to version

10, will become effective Oct. 1, 2013. All tables used to map the codes and crosswalks will need to

be in place for this transition. Teams need to be aware that the use of the codes before, after, or

during the transition must be properly mapped to the correct definitions in order to assure currency of

the data and the analyses.

5

As the real-world state changes over time, the values and the purpose for which the data are

collected will also change. As an HIT professional who is astute in preserving the quality and

accuracy for reliable use of data, you will work with the team to establish requirements and provide

definitions for the currency of each type of data. Documentation of the rules and logic that were used

for the data collection should reflect any changes or modifications that have been made over time so

users of the analyses have a clear understanding of what the values and results of any analyses

mean. Warehousing will involve continually updating tables and maintaining look-up and cross-walks

with clear documentation of when data changes occur.

6

Currency means that the data represent the world at the time they are captured. Timeliness is more

closely related to the accessibility and availability of the data for use when needed. Data are timely if

the data are processed for use in time to conduct business and facilitate decision-making.

An electronic bed board is an automated method to track and communicate patient locations. They

are replacing older paper and telephonic tracking systems. When it was originally implemented, the

only interface that was built was with the patient registration system to provide a daily midnight

census download. Over time, interfaces with other systems such as pharmacy, laboratory, dietary,

and other ancillary services systems have been built. The increased demand for current data about

patient location has required more timely updates to the system to conduct daily business and inform

the decisions that are made related to patient care and census management.

7

Application of the Data Quality Management Model includes working with the team to establish

standards or policies for the timeliness of the data collected and stored in relation to key events.

Timeliness is defined by the use of the data. For example, hourly or daily census data may be

needed to support decisions such as daily staffing, where monthly or annual census data would be

used for longer range planning such as budgeting or strategic planning. The use of the data will also

drive the standards for the frequency and process of data collection and warehousing. Timely

analyses allow for the opportunity to monitor progress and to make course corrections to avoid

negative outcomes.

8

Data granularity is the degree of detail that is represented by the data; where the greater the detail,

the finer the granularity. Depending on the requirements, multiple levels of detail may be present.

Granularity in measurement refers to the intervals in either space (such as inches or feet) or time

(such as seconds or hours). If data refer to entities sorted into categories, granularity refers to the

choice between a larger number of narrow categories (such as the ICD-9 category) or a smaller

number of broad categories (such as Diagnoses Related Groupings, or DRG codes). Quality in this

attribute means striking the proper balance in the level of detail needed. The granularity of the data

should not become so detailed that it is neither useful, nor compatible with other systems to which

the data may be transmitted or stored.

The Institute for Safe Medication Practices (known as ISMP) is the only nonprofit organization in the

United States committed totally to medication error prevention and the safe use of medications. It

regularly publishes alerts with “lessons learned” to assist the healthcare community in identifying

potential pitfalls to avoid and safe practices to implement. One of the very first alerts issued by the

ISMP was to caution providers about the expression of a decimal without a leading zero. An example

of this type of error can be found on their website; it describes the death of a 9-month-old infant

whose order for .5 milligrams of morphine, without the leading zero, was interpreted as 5 milligrams,

resulting in an overdose and death. Misinterpretation of decimal points and other dangerous dose

expressions and abbreviations continue as a source of medication errors, causing tragic results for

patients, their families, and unsuspecting health-care providers who have made these mistakes.

Proper programming and identification of the appropriate levels of granularity in HIT are intended as

safeguards to prevent these types of errors. Care should be taken to avoid these dangerous

abbreviations and dose expressions in other communications such as computer-generated labels,

medication administration records, labels for drug storage bins and shelves, preprinted orders and

protocols, and pharmacy and prescriber computer order entry screens.

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Hierarchy is a logical structure that uses ordered levels as a means of organizing data. Aggregation

of data can be defined in terms of a hierarchy.

For example, if we think about time or sequence as a dimension, then a hierarchy works to aggregate

data on a yearly, quarterly, monthly, or daily level. Within a hierarchy, each level is connected to the

levels above and below it. Data values at lower levels aggregate into the data values at higher

levels. The purpose and use of the data may require different levels of detail.

The process of data collection requires thoughtful determination of the appropriate level of granularity

for the task at hand and the outcome analyses that will be completed. For example, granularity of age

may be defined as days for neonates, as months for infants and years for those one year old or

greater. These determinations will follow-through to the level of granularity that is appropriate for the

data that are warehoused.

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Precision refers to the degree to which repeated measures yield the same result under unchanged

conditions. Precision, or reproducibility, is essential in order to allow for valid comparisons. Quality in

precision means that the data values should be just large enough or contain just enough detail to

serve the intended purposes. Note that precision is not intended to be applied only to quantitative

data, it can also refer to the descriptive terms used to represent qualitative data.

The “Meaningful Use” core, clinical-quality measures for primary-care providers includes preventive-

care screening for over- and under- weight conditions in adults. In order to meet the quality measure

and qualify for possible incentives for patients age 18 years old or older, there must be a calculated

body-mass index (or BMI) documented in the patient’s medical record in the past six months or

during the current visit documented in the medical record. In addition, if the BMI is outside of the

identified parameters, a follow-up plan must be documented. The BMI requires information about the

patient’s current weight and height. Knowing that neither of these are measures that people self-

report with a high degree of accuracy, it would be more precise to obtain these measures and record

them rather than rely on self-reported measurements. Another strategy to increase the precision of

this measure is to reduce the factor of human-error in the calculation and to program automatic

calculations of the data with automatic alerts when follow-up is required based on the established

action parameters. Care should also be taken to complete these measurements with devices that are

reliable, as evidenced by documented periodic calibration checks.

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Your knowledge can be used to guide the team to clarify the purpose and intended uses of the data

and the questions that need to be answered. As discussed in several of the other data attributes, you

will need to identify what level of detail is required, what the appropriate ranges for each element

should be and what categories of data elements will be needed for data collection. It is important to

remember that accuracy is the degree of truth and precision is the degree of reproducibility. There

should be validation or quality checks to determine if data analyses elicit the same or similar results

and contain the needed level of detail when compared to past reports using the same data. Precision

of the data must be demonstrated prior to storage in or extraction from the data warehouse.

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Relevance means that the data are meaningful or applicable to the performance or purpose for which

they are collected. Every piece of information collected and stored should be important and provide

information and value to the business at hand. If it is unclear how the data are to be used, the highest

level of detail should be provided. Giving careful scrutiny to the relevance of data can build trust in

the data, enable key processes, increase the accuracy of analyses using the data, foster support for

decisions made from the data and optimize performance of the database.

Take this example of the importance of data relevancy. A multi-specialty medical practice group

implements an EHR with a number of product features. In addition to registration to support billing

and coding, it includes standardized history forms and progress notes, clinical reminders, and

pharmacy and laboratory interfaces and reports. Members prioritize the critical decisions related to

customization pre-implementation and determine that they will go with the “out-of-the box” standard

version of the clinical summary. However, two months after implementation, when each member has

had the opportunity to determine the usefulness of the summary, they realize the ever-growing

amount of clinical data that is accumulating for each individual patient. Knowing that one of the most

critical tasks they must perform is to sort through large amounts of data to find pertinent information,

they seek the opportunity to optimize the usefulness and relevance of the clinical summary. They

determine the information that is relevant to all clinicians and supports the business needs of the

practice for routine inclusion in the summary, and allow each provider to customize the report to

include data pertinent to their specialty, which allows each user to answer clinical questions and

support his or her clinical decision-making.

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This concludes Lecture b of Data Quality Improvement.

In summary, data used for research purposes are collected under different conditions than those

used for QI; poor-data quality contributes to error; and the 10 attributes of data quality are:

Definition,

Accuracy,

Accessibility,

Comprehensiveness,

Consistency,

Currency,

Timeliness,

Granularity,

Precision and

Relevancy.

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Working with Health IT Systems is available under a Creative Commons Attribution-NonCommercial- ShareAlike 3.0 Unported license. © Johns Hopkins University. UMUC has modified this work and it is available under the original license.

Welcome to Quality Improvement: Data Quality Improvement. This is Lecture c.

1

The Objectives for Data Quality Improvement are to:

•Discuss common causes of data insufficiency.

•Describe how Health Information Technology (HIT) design can enhance quality.

2

A case study of data quality in medical registries published by Arts, De Keizer, and Scheffer, in 2002,

offers a good summary of many of the data quality issues previously presented in this module.

In this article, they discuss common causes of insufficient data quality as either systematic or

random.

The systematic causes (or what is statistically referred to as Type I errors) are those that can be

attributed to some bias or flaw in the measurement process that is not due to chance. Systematic

causes, if not corrected, will cause repeated flaws or errors with a predictable pattern or a high

degree of uncertainty.

Some frequent systematic causes of insufficient data quality are:

•Unclear or ambiguous definitions

•Incomplete or unsuitable format

•Violations in the collection, processing or analysis

•Poor design in the tools or forms for data entry

•And a lack of quality auditing or control processes.

3

Random causes occur with less predictability and can include

•Inaccurate transcription or typing (as in free-text entries),

•Sheer data overload and the possibility of ambiguity or selection of irrelevant data,

•Inattention or poor understanding on the part of the individual completing the entry, analysis,

or data warehousing procedures.

4

The team examined planned and systematic procedures that take place before, during and after the

data collection to identify causes of insufficient data quality. As an HIT professional, you will be in the

best position to consider what can be done to prevent, detect, and facilitate improvement efforts. You

will seek to create the best possible quality through the design of the application, the data collection

process and subsequent reports or analyses. You will implement activities to detect potential or real

flaws that can pose threats to data quality and you will take corrective actions to improve data quality.

5

Let’s recap what some of those activities include under each of the three areas.

Identify the required data elements for the task. A data dictionary with standard definitions and data

formats will be essential to promoting data quality. Seek terminology harmonization with accepted

standards and avoid “local” naming conventions or glossaries. Data capture and completeness will

improve when the required data location is limited to fewer locations within the EHR. Optimizing the

use of structured data fields over free-text will reduce the likelihood of missing data and improve data

retrieval. While data extraction programs, such as natural language processing programs can be

used to extract data, these programs work best when the variables are narrowly and consistently

defined. Standard guidelines for data collection, analysis, and storage should also be documented

and should note clear inclusion and exclusion criteria.

6

Privacy and security policies should govern the controlled access of the data, and responsibility and

accountability for data management must be delineated and observed. Attention must be paid to how

clinical workflow and system design can affect data quality. The placement and design of structured

data should facilitate the work of the clinician. Excessive navigation and requiring multiple "clicks" will

decrease documentation compliance and promote the unintended use of free-text, or lead to missing

or inaccurate data. The clinical specificity and number of option choices for structured data, if

designed correctly, can facilitate data quality. Synonym and acronym recognition should be used

wisely. It can speed data entry, but can also lead to inappropriate entries if the wrong choice is

selected. Recognition and correction of data flaws requires a thoughtful monitoring plan. Priority data

elements, such as those used for quality measurement, should be identified and targeted for

monitoring and improvement as indicated. These data should be reviewed for data granularity,

precision, currency, timeliness, completion, and accuracy. Data documentation quality can be

improved through staff training and education. Content can include system use, screen navigation,

use of references such as the data dictionary and collection guidelines and placement and

completion of priority data elements. Standard content and delivery methods should be identified to

minimize localized work-arounds. A plan for ongoing education of new users and new content or

upgrades should be identified to prevent deterioration in the data quality due to lack of knowledge.

7

Automatic domain or consistency checks, such as out-of-range data or inconsistencies between two

data fields, at data entry, extraction or transfer can detect potential flaws or errors. Data errors, such

as incorrect patient location, which can be undetectable through programming checks, can still occur.

Manual processes for auditing or data checking should be developed. Regular review of data

collection protocols and report logic should be conducted with care to correct sources of ambiguity or

a lack of currency with other changes in data definitions or catalog updates. Review of priority data

items, such as frequency analysis or cross tabulations, can be conducted to detect flaws or

unacceptable deviations in the data.

8

Users will continue to assume that quality is present in the absence of data to inform them otherwise.

Regular reports about data quality should be made available to them. Once inaccuracies or flaws in

the data are known, corrections and edits to the data must be made. Documentation and correction

of the flaws or errors is essential and may provide justification for future design modification. As

discussed in the beginning of this module, data are often shared across multiple interfaces and often

used for applications other than the originally intended purpose. Systematic study of the source of

error and inadequacies should be conducted so current and future corrections can be made across

all applicable systems.

9

HIT solutions offer a number of opportunities to improve the quality of data.

Standardization of terminology has multiple effects in the improvement of data quality.

In their study, Thede et al, described some of the benefits of standardization in the nursing field as including:

•better communication among healthcare providers,

•improved patient care

•enhanced data collection to evaluate outcomes

•greater adherence to standards of care

•and facilitation of assessment of competency.

Some advantages can be assigned to the use of standardized terminology by other professionals.

Another significant aspect of the improvement of data quality relies on the use of structured data fields. Structured data fields are fields that contain information that has a pre-defined data model. This allows this data to fit into relational tables thus permitting the easy extraction into reports. The use of structured data fields is somewhat challenging, particularly for clinicians. During their training and their previous experience with paper charts clinicians have used a narrative form to document the ailments of their patients. This ‘story telling’ format is difficult to translate into the more dry structured data format and requires some retraining for all clinicians. However, in the current EMRs there are areas where clinicians can document in free text or ‘story telling’ format and yet preserve the integrity of the structured data fields establishing a balance that preserves data quality and the needs for narrative documentation. Structured data fields can be accomplished through pick lists, check buttons or radio buttons.

Finally, there is another format of data capture based on voice recognition that will enable the user to capture more comprehensive data. However the majority of this data capture albeit comprehensive happens in a free text format.

10

Finally, there are options that although not currently in wide use can enhance the quality of data

collected through an HIT solution. The use of natural-language processing machines capable of

learning can eventually prove a valuable tool to enhance quality of data. The use of biometric tools

that enter the measurements directly into the EHR can be an additional tool for data quality

improvement.

11

This concludes Lecture c of Data Quality Improvement. Clinical data are increasingly used to drive

healthcare decisions. The data that are captured to document patient care are also used for billing,

risk management, accreditation, quality, and health care research.

Poor data quality can threaten patient safety and quality; decrease satisfaction; increase cost; and

compromise strategic planning. Data quality is a complex, multi-dimensional concept and a number

of attributes should be considered at all phases of HIT development and use. An HIT professional

who is aware of the common systematic and/or random causes of data insufficiency can skillfully

employ best practices in the areas of prevention, detection, and quality improvement to enhance the

overall quality and usefulness of healthcare data.

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