Nicohwilliam

Assignment : answer real world case 6.1 and 6.2 questions; at least one

Page per case ; cite textbook

Please see chapter readings from textbook below

Real World Case 6.1

A large urban children’s hospital in Dallas, Texas, is leading in the delivery of care provided to children from birth through age 18. After implementing an electronic health record, the hospital identified operations in need of improvement. It found that individual business units were working in their own silos with little interdepartmental communication occurring, and the ­individual business units had different policies, procedures, and processes for information governance and data management. The hospital quickly realized the need to standardize processes and ­create an effective information governance program to help streamline and manage the vast amount of data being ­collected across the organization.

Using tools that are available through AHIMA’s Information Governance Adoption Model (IGAM), the hospital evaluated the current state of information governance at the organization. This was done through the evaluation and review of information-related policies and procedures throughout the system. It also created the foundation necessary to implement a process to review, edit, and update all those information policies and procedures to create a consistent and standardized process across all business units of the organization. Most important, it showed the need to educate workforce members on the importance of having a consistent format for data collection across the entire organization.

The outcome of implementing an information governance program at the children’s hospital produced many benefits. The hospital was able to create a consistent process for training and educating all workforce members to support the transparency of data management to use the information to its competitive advantage. It created a platform to have open and transparent conversations throughout the healthcare organization, supporting the mission of the organization. By streamlining all the policies and procedures across the organization, the hospital was able to break down department silos that existed within the organization and implement an organization-wide culture supporting the information governance program. (Fahy and Hermann 2017.)

Real World Case 6.1 questions

1. As new clinics came onto the health system, they had issues with documentation identification because the same documents were often called different names. What principle of information governance can be applied when documenting the decision to standardize the naming of documents across the healthcare system? Why?                 

   

2. Why would an interdisciplinary team be selected?  

3. What skills does an eHIM manager need?  

Real-World Case 6.2

A medium-sized hospital had been using an electronic health record (EHR) for 12 months. It was having great success in getting the providers to document within a timely fashion; however, many of the notes did not provide enough information to code the record or key components to adequately code diagnoses and procedures were missing. The hospital had a process for physician query, as follows:

Electronically flag the record for physician query

Create a paper query form for the provider

Send the electronic query to the HIM operations department to put in a physician completion folder

HIM operations adds a deficiency to the patient health record to flag the provider that a coding query needs to be completed

The provider comes to the HIM department to complete the query

The deficiency is removed, and the query is scanned into the health record

HIM operations notifies the coder via e-mail that the query was answered

The health record is coded and the codes are sent to billing

While it was a strong process and the providers did answer the questions, it caused a spike in the amount of time it took to get the health record coded and billed, as providers usually came into the department once every 20 to 25 days. In some cases, providers would leave the coding queries unanswered for up to 60 days. The average turnaround time for a coding query was 28 days. The hospital needed to accelerate the query process and reduce the physicians’ frustrations with ­having to come to the HIM department.

New functionality within the EHR was used to send an electronic query that automatically assigned the deficiency and sent a note to the ­provider’s inbox alerting them that there was a coding query. The new process had fewer steps and involved fewer people; however, the physicians were concerned that the additional time required to learn the new process and system was impacting time spent with their patients. With careful training and education, the new process was implemented and reduced the steps, which made the physician query process easier for coding, HIM operations, and the providers. The following are the new process steps:

Electronically flag the record for physician query

Create the electronic physician query through predesigned templates and assign the correct physician (this would automatically assign the deficiency and send the coding query to the inbox)

The physician electronically completes the coding query through the EHR

The electronic deficiency is automatically removed, and the coding query is electronically submitted to the physician and retained and the health record then automatically flagged to complete coding

The health record is coded and sent to billing

With the change in the process, the HIM operations department has little involvement unless it is supporting the physician in completing the query. The turnaround time for completion of coding queries was reduced from 28 days to 15 days within the first 60 days of completion. The process was a success and the hospital has significantly reduced the time it takes to code and bill all patient encounters.

Real-World Case 6.2 questions

 

         1. Examine the use of the electronic-based query and identify positive impacts that it made on the healthcare organization.  

                   

2. Critique their strategy for addressing documentation issues.   

   

   

3. Recommend something else that the healthcare organization could do to improve the query process.

HITT 1301 CHAPTER 6

Health Information Management Technology,

An Applied Approach

Nanette Sayles, Leslie Gordon

Copyright ©2020 by the American Health Information Management Association. All rights reserved.

Except as permitted under the Copyright Act of 1976, no part of this publication may be reproduced,

stored in a retrieval system, or transmitted, in any form or by any means, electronic, photocopying,

recording, or otherwise, without the prior written permission of AHIMA, 233 North Michigan Avenue,

21st Floor, Chicago, Illinois 60601-5809 (http://www.ahima.org/reprint).

ISBN: 978-1-58426-720-1

AHIMA Product No.: AB103118

With the advancement of technology in the US healthcare system, most healthcare organizations are inundated with data from multiple sources, which are stored and maintained in a variety of locations. Data are representations of basic facts and observations about people, processes, measurements, and conditions. An example of data is 50 patients were discharged yesterday. Healthcare-­specific data focus on patients and include demographic, financial, and clinical data. Data management is the combined practices of HIM, IT, and HI that affect how data and documentation combine to create a single business record for an organization. Effective oversight and management of the data is an essential part of the day-to-day operations of a healthcare organization. Knowing and understanding how data are produced, why certain types and formats of data are produced, how data are stored and managed, and how data integrity is maintained become foundational steps to ensuring the data within healthcare organizations are properly managed.

Data Sources

A foundational step to the management of data within a healthcare organization is to understand the basic sources of data generated and stored by the healthcare organization. Data includes both clinical and administrative elements. The data elements stored in the electronic health record are an example of clinical data. Administrative data includes the data elements required for billing and quality improvement. The common data sources in healthcare: are the following:

Electronic health records (EHR) (discussed in chapter 11, Health Information Systems)

Practice management systems (discussed in chapter 11, Health Information Systems)

Lab information systems (discussed in chapter 11, Health Information Systems)

Radiology information systems (discussed in chapter 11, Health Information Systems)

Picture archival and communications (PACs) (discussed in chapter 11, Health Information Systems)

Other clinical documentation systems (home health, therapy, long-term care) (discussed in chapter 11, Health Information Systems)

Master patient index (discussed in chapter 3, Health Information Functions, Purpose, and Users)

Other patient index (indices) (discussed in chapter 7, Secondary Data Sources)

Databases (discussed later in this chapter)

Registries (discussed in chapter 7, Secondary Data Sources)

To manage the different aspects of data effectively, the healthcare organization should conduct ­system characterization. System characterization is the process of creating an inventory of all systems that contain data, including documenting where the data are stored, what types of data are created or stored, how they are managed, with what hardware and software they interact, and providing ­basic security measures for the systems (Walsh 2013). This process helps identify all sources of data that exist within a healthcare organization, which supports effective oversight over all the data created and maintained by an organization.

Data Management

Managing the data that healthcare organizations create and produce is challenging. Data can exist in an information system, on a file on an employee’s computer or file server, in an email, and in many other formats and locations. Healthcare organizations are challenged with how to properly manage all the data that exist and how to effectively use and preserve that data.

The process of data collection has evolved over the years as healthcare organizations migrate from paper-based recordkeeping systems to electronic health records (EHR). For additional information on EHRs, refer to chapter 11, Health Information Systems. Additionally, healthcare organizations are collecting more patient data and using the data to support patient care and healthcare operations. The ability to properly collect, analyze, and utilize patient data is more important now than ever before. Third-party payers, government agencies, accreditation organizations, and others also use data to support the healthcare delivery system and improve patient care. One of the challenges for healthcare systems that are managing data in an electronic environment is the vast differences in the collection of healthcare data throughout the organization’s electronic information systems, such as EHRs, lab information systems, radiology information systems, and billing systems (discussed in chapter 11, Health Information Systems). Many methods, formats, and processes are used for the collection and storage of patient information, such as direct entry into an electronic system, scanning of documents, and uploading of transcribed documentation. Data management is further complicated because data are collected and stored in many locations in the healthcare organization. Given the various methodologies that exist for data collection, understanding data and data collection is important. Data management focuses on understanding data elements, data sets, databases, indices, data mapping, and data warehousing.

Data Elements

The term data is actually the plural format of datum; however, it is more common to hear the term data element to describe one fact or measurement. A data element can be a single or individual fact that represents the smallest unique subset of a larger database. Data elements are sometimes referred to as the raw facts and figures. Examples of data elements include age, gender, blood pressure, temperature, test results, and date of birth. Data elements create a measure for progress to be determined and the future to be calculated and planned for. Data elements are entered into different formats through the EHR and other supporting patient systems. Information is different from data in that it refers to data elements that have been combined and then manipulated into something meaningful regarding a patient or a group of patients. For example, a healthcare organization can create a report on the data element’s most recent A1C test result and diagnosis of a heart attack and analyze and determine if there is a relationship between the A1C test score and the heart attack diagnosis. By taking the specific data elements of the heart attack diagnosis and the most current A1C result, the healthcare organization can create best practices to enhance patient care based on the findings (Davoudi et al. 2015). For more on data and information, see chapter 3, Health Information Functions, Purpose, and Users.

To help support and manage data elements within an EHR, the use of a data dictionary is implemented to support standardized input and understanding of all data elements. A data dictionary is a listing of all the data elements within a specific information system that defines each individual data element, standard input of the data element, and specific data length. The following are the common data elements within a data dictionary:

Data field (such as date of birth)

Definition

Data type (date, text, number, and so forth)

Format (such as MM-DD-YYYY)

Field size (such as 10 digits for phone number)

Data values (such as M and F for gender)

Data source (where data are collected)

Data first entered (when the data element is first used)

Why item is included (justification for collection of the data element) (AHIMA 2017)

Defining a data dictionary can help with accuracy of patient data and create support for data comparison and data sharing (AHIMA 2016a). (Additional information on the sharing of data is found in chapter 12, Healthcare Information.) Table 6.1 provides a sample of a data dictionary defining the data elements in an EHR.

Defining a data dictionary is a fundamental step to understanding data elements and their meaning and usage. It also supports the creation of well-structured and defined data sets by creating standardized definitions of data elements to help ensure consistency of collection and use of the data. For example, the time of discharge could be the time the discharge order was written, the time the order was entered into the information system, or the time the patient actually left the unit. These times could vary widely so it is important that the data dictionary defines which time should be used.

Data Sets

The concept of comparing data and the need for standardization became a common theme for healthcare organizations in the 1960s as a result of the work of the National Center for Health Statistics (NCHS) and the National Committee on Vital and Health Statistics (NCVHS). It became evident that common structure and collection of data elements was needed to collect consistent data to allow for comparison across all healthcare organizations. As a result, the concept of data sets was created. Data sets are a recommended list of data elements that have defined and uniform definitions that are relevant for a particular use or are specific to a type of healthcare industry. One of the first defined and used data sets across the US healthcare industry was the Uniform Hospital Discharge Data Set (UHDDS), implemented in the mid-1970s. Created by NCHS, the National Center for Health Services Research and Development, and Johns Hopkins University, UHDDS collects uniform data elements from the health records of every hospital inpatient (Brinda 2016). The main data elements defined in the UHDDS data set are listed in figure 6.1.

Figure 6.1 UHDDS data elements

Source: Brinda 2016.

Each of the data elements defined within the UHDDS has specific criteria for data collection. For example, the data of birth are defined as the month, day, and year of birth, with a recommendation to collect all four digits of the birth year. Another example is the definition of type of admission. There are two choices—unscheduled or scheduled admission. Each of the types of admission is defined in the data set for use of the UHDDS (Brinda 2016).

Shortly after the UHDDS was created and implemented, the need to expand uniform data sets across other healthcare settings became evident with the continuing movement from an inpatient, acute setting to outpatient care including surgical centers and emergency care settings. A standardized data set for the ambulatory setting, known as the Uniform Ambulatory Care Data Set (UACDS), was created. With fewer data elements than the UHDDS, the UACDS collects data specific to ambulatory care settings with an intent to improve data comparison across different settings of healthcare (see figure 6.2). After the success of the standardization of data elements with the UHDDS and UACDS, the standardization of data sets across healthcare settings commenced. Another key data set is Data Elements for Emergency Department Systems (DEEDS), which collects data for hospital-­based emergency departments. The following are other data sets that are defined within healthcare settings:

Figure 6.2 UACDS data elements

Source: Brinda 2016.

· Minimum Data Set (MDS)—Long-term care setting

· Outcomes and Assessment Information Set (OASIS)—Home healthcare setting

· Essential Medical Data Set (EMDS)—Emergency care setting

With the success of these data sets and the shift toward the ability to share data that are consistent across the healthcare spectrum, the need for additional standards to support standardized data sets continues to be a focus in the healthcare industry.

Databases

Databases are commonly used throughout the healthcare industry to support and store patient information entered into an EHR or maintained on a paper record. A database is a collection of data organized in such a way that its contents can be easily accessed, managed, reported, and updated. For proper management of data within a healthcare organization it is important to ­understand what databases exist, the purposes of the databases, the storage and backup of the databases, and who accesses and uses the ­databases. Common databases found in healthcare include Medicare Provider Analysis and Review File, National Practitioner Data Bank, and National Health Care Survey (Sharp 2016). Refer to chapter 7, Secondary Data Sources, for specifics on these databases.

The database design and structure impacts how it can be used. A poorly designed database will result in redundant collection of data and data information errors. Understanding the database life cycle (DBLC) is an important step in the proper execution, implementation, and management of databases within healthcare. The following are the six basic steps in the database life cycle:

1. Initial study (determining need for database)

2. Design (identifying data fields, structure, and so forth)

3. Implementation (developing database)

4. Testing and evaluation (ensuring system works as expected)

5. Operation (using database)

6. Database maintenance and evaluation (updating and backing up database and ensuring that it still meets needs)

Health information management (HIM) professionals should be involved in all stages of the database life cycle as they have the knowledge and skills needed to understand the essential steps of data collection privacy and security, and data integrity (Coronel and Morris 2015).

The two most common types of databases used in healthcare are relational databases and object-­oriented databases. A relational database stores data in tables that are predefined and contain rows and columns of information. Typically, a relational database is two-dimensional as it contains rows and columns. Relational databases are used frequently in the healthcare industry because they are easy to build, use, and query within the application. For example, a healthcare organization might choose to use a relational database to document the number of health record deficiencies a physician has at the time of evaluation for reporting to the organization’s board (Sharp 2016). Table 6.2 provides a sample of a relational database for physician deficiency status.

An object-oriented database (OODB) is designed to store different types of data including images, audio files, documents, videos, and data elements. OODBs are useful for storing fetal monitoring strips, electrocardiograms, PACs, and more. The OODB is dynamic because it provides the data as well as the object (image and document). Table 6.3 provides an example of an OODB. Using an OODB for the storage of fetal heart monitors allows a healthcare organization to query the ­database to retrieve an image for a specific person. Another potential use is to produce a report based on the date of the fetal heart monitor for retention and destruction of the images. When this type of database is used, the data are provided with the additional ability to retrieve the file when the link to the image is selected.

Indices

An index is a report or list from a database that provides guidance, indication, or other references to the data contained in the database. An index serves as a guide or indicator to locate something within a database or in other systems storing data. For example, an index of a book will provide key terms and where to find each term within a book; the reader is able to find more information and detail regarding a specific topic. The indices used in healthcare identify where the desired information can be found, making it easier to aggregate and analyze data. There are many types of indices used within the healthcare industry. The following are the most common indices:

· Master patient index. A guide to locating ­specific demographic information about a patient such as the patient name, health ­record number, date of birth, gender, and dates of service. For more details, refer to chapter 3, Health Information Functions, ­Purpose, and Users.

· Disease index. A listing of specific codes such as International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes that link a disease or diagnosis to a patient. (ICD-10-CM is explained later in this chapter.) Common data in a disease index would include diagnosis codes, health record number, gender, age, race, attending physician, hospital service, patient outcomes, and dates of encounter. A disease index can be used to query a specific diagnosis to determine other attributes of patients with the disease. For example, if a healthcare organization wanted to know the age range and gender of all patients diagnosed with a myocardial infarction, the disease index could be queried to get a listing of patients with that specific diagnosis code(s).

· Operation or procedure index. A listing of specific codes such as Current Procedural Terminology (CPT) for procedures or operations performed within the healthcare organization. (CPT is explained later in this chapter.) An operation or procedure code would include information similar to the disease index but would also include the specific code numbers as well as the operating physician. An operation or procedure index can be used to query specific information regarding procedures or operations done within the facility. For example, if a healthcare organization wanted to know the age range of patients who had an appendectomy in the past year, the operation or procedure index could be queried to generate a listing of patients based on the procedure code.

· Physician index. A listing of all physicians within a healthcare organization with all the diagnosis and procedure codes linked to each provider within the index. The data collected in this index include physician’s identification (code or name), health ­record ­number, ­diagnosis, operations, dates of ­service, ­patient gender, patient age, and patient ­outcome from encounter. A provider index can be used to produce information regarding the work of the provider within a healthcare organization, which can be useful for certification and credentialing purposes. For ­example, a healthcare organization may need to produce a report for administration ­detailing the treatment of patients and diagnoses and procedures performed in the past year by a specific provider (Sharp 2016). More ­information on indexes is found in chapter 7 Secondary Data Sources.

Indices support daily operations for healthcare organizations and are tools used to gather specific information quickly.

Data Mapping

Data mapping is a process that allows for connections between two systems. For example, mapping two different coding systems to show the equivalent codes allows for data initially captured for one purpose to be translated and used for another purpose. For example, the ICD-10 code of E10.11, type 1 diabetes mellitus with ketoacidosis can be mapped to SNOMED-CT Code 371055001, type 1 diabetes mellitus with ketoacidosis. This allows for comparison between two different coding systems based on one code. One system in a map is identified as the source while the other is the target. Source data is the location from which the data originate, such as a database or a data set; whereas target data is the location from which the data are mapped or to where the data are sent. A data map creates a process to evaluate the disparity between the two systems and links the data being collected together. Data mapping is conducted to ensure the data exchange from one database to another is done in a meaningful way and maintains the integrity of the data (Maimone 2016).

During the process of data mapping, each data map should have a defined purpose that specifies why the data map is created and what purpose it serves. The purpose should describe why the data map is needed, what it represents, and how it will be used within the healthcare organization. For example, a healthcare organization may create a data map to show the relationship of the types of ambulatory services such as emergency department or ambulatory surgery and map them directly to the ambulatory services.

Data mapping should be completed carefully to evaluate where the data come from and the relationships of the source data to data in other systems. The process helps to ensure the integrity of the data in all systems. When conducting data mapping within a healthcare organization, evaluating the relationship of the data is fundamental to understanding the equivalence between the data. Equivalence of data is the relationship between the source data and target data in regard to how close or distant the data from the two systems are linked. The three common types of relationships are no match, approximate match, and exact match (Maimone 2016). Table 6.4 shows the differences between the three types of relationships.

When creating data maps, healthcare organizations should create a common format for the output of the map to create consistency and ease the end user’s ability to interpret the data map. Table 6.5 is an example of a data map that shows the ­relationship between ICD-10-CM codes and SNOMED CT codes, both explained later in this chapter.

ICD-10-CM code A00.0

ICD-10-CM name Cholera, unspecified

Equivalence Equal

SNOMED CT code 63650001

SNOMED CT name Cholera

Data mapping can be a long and tedious task for a healthcare organization; however, it is important from a data management perspective. To properly manage the data and ensure data integrity between systems, data maps serve as the tool to define the meaning and history of data elements within systems. Inaccurate data mapping can ­result in misinterpretation of data and inaccuracy of information stored and maintained in systems. For example, if the ICD-10-CM code was mapped to the incorrect SNOMED CT code, data used and reported from the SNOMED system could show incorrect information regarding patients diagnosed with ­cholera, unspecified. Data map creators need to understand the data to be mapped between systems and the reasons for the data mapping. One way of doing this is to create a use case. A use case describes how the users will interact with the data map in a specific scenario. Some general data mapping steps are found in table 6.6.

Table 6.6 AHIMA practice brief data mapping best practices: general data mapping steps

Develop a business case first. Questions to ask include:

• What is the reason for the project?

• What is the expected business benefit?

• What are the expected costs of the project?

• What are the expected risks?

Define a use case for how the content will be used within applications. Questions to ask include:

• Who will use the maps?

• Is the mapping between standard terminologies or between proprietary (local) terminologies?

• Are there delivery constraints or licensing issues?

• What systems will rely on the map as a data source?

Develop rules (heuristics) to be implemented within the project. Questions to ask when developing the rules include:

• What is the version of source and target schema to be used?

• What is included or excluded?

• How will the relationship between source and target be defined (such as are maps equivalent or related)?

• What mapping methodologies will be utilized?

• What procedures will be used for ensuring intercoder or interrater reliability (reproducibility) in the map development phase?

• What parameters will be used to ensure usefulness? (For example, a map from the SNOMED CT concept “procedure on head” could be mapped to hundreds of CPT codes, making the map virtually useless.)

• What tools will be used to develop and maintain the map?

Plan a pilot phase to test the rules. Maps must be tested and deemed “fit for purpose,” meaning they are performing as desired. This may be done using random samples of statistically significant size. Additional pilot phases may be needed until variance from the expected result are resolved. Reproducibility is a fundamental best practice when mapping.

Develop full content with periodic testing throughout the process. Organizations should perform a final quality assurance test for the maps and review those data items unable to be mapped to complete the mapping phase. Any modifications from the review process should be retested to assure accuracy.

Organizations should release the map results to software configuration management where software and content are integrated. They should then perform quality assurance testing on the content within the software application (done in a development environment). They can then deploy the content to the production environment, or go-live.

Communicate with source and target system owners when issues are identified with the systems that require attention or additional documentation for clarity.

Source: Maimone 2016.

Data Warehousing

Data warehousing is the process of collecting the data from different data sources within a healthcare organization and storing it in a single database that can be used for decision-making. A data warehouse is a database that makes it possible to access data that exist in multiple databases through a single query and reporting interface.

Data warehouses allow healthcare organizations to obtain information needed to streamline processes and simplify access to the information that is stored among different databases within a healthcare system. If a user had to query each information system, the amount of time needed to combine the data manually and then analyze the data would serve as a barrier to properly reporting and analyzing the data. With the use of a data warehouse, the data can be consolidated by pulling the data from multiple information systems into a single database that allows for ease in reporting and analysis of the information.

Data mining is the processing of extracting from a database or data warehouse information stored in discrete, structured data format—that is, data that have a specific value. Examples of discrete data are a lab value or a diagnosis code. The following are the advantages to the use of data warehouses:

· One consistent data storage area for ­reporting, forecasting, and analysis

· Easier and timely access to data

· Improved end-user productivity

· Improved information services productivity

· Reduced costs

· Scalability

· Flexibility

· Reliability (HIMSS 2009)

Since large amounts of data are being captured electronically within healthcare organizations, data warehousing will become a foundational aspect of healthcare operations due to its ability to gather data from multiple databases, incorporate the data, and then produce meaningful information.

Information Governance

Information assets are becoming an essential strategic and operational part of a healthcare organization, requiring a rigorous process that will protect information from unauthorized access, use, disclosure, modification, and destruction. For example, prevent hackers from accessing health information from outside the healthcare organization. Information assets refer to the information collected during the day-to-day operations of a healthcare organization that has value within the healthcare organization. An example is patient data collected to support patient care for the healthcare organization. Without patient data, the healthcare organization would not be able to support the continuity of patient care or the billing of services provided to the patient.

Information governance (IG) is an “organization-­wide framework for managing information throughout its lifecycle and supporting the organization’s strategy, operations, regulatory, legal, risk, and environmental requirements” (Dickey 2018, p. 38). One of the main goals of IG is to provide trustworthiness of a healthcare organization’s information. Having trustworthy information is essential to improving patient care and safety, reducing or mitigating risks to the information, improving operational efficiency, and achieving and maintaining a competitive advantage in healthcare (Fahy et al. 2018). The implementation of an IG framework in a healthcare organization assists in the establishment of policies and procedures that govern the oversight, aligning it to the strategic ­vision of the organization. In addition, IG helps to prioritize a healthcare organization’s investments, establishes the value of information assets, establishes a process to protect information assets, and creates accountability for managing information over the entire healthcare spectrum (Dickey 2018).

Valued Strategic Asset

Information should be treated as a valued strategic asset. A valued strategic asset is a resource that is used in a way that will improve the healthcare organization today and into the future. For example, a healthcare organization needs information (financial projections, cost of services, and so forth) that can assist in the negotiation of contracts that can run for years. A successful IG initiative must have support from the healthcare organization’s executive leadership and align ­directly to the healthcare organization’s strategic plan. To ensure the success of an IG initiative, it needs to be “driven from the board of directors and C-Suite level down to the rest of the organization while simultaneously being driven up from the grassroots and recognizing the needs of the end-users of data and information” (Fahey et al. 2018, p 4). One of the initial steps in the IG initiative is to secure an executive sponsor at the C-Suite level of the organization. Some common sponsors of an IG initiative are a Chief Financial Officer, Chief Data or Health Information Officer, Chief Financial Officer, Chief Innovation Officer, Chief Strategy Officer, Chief Medical and Information Officer, and Chief Executive Officer. The executive sponsor will ensure the IG initiative has the appropriate resources such as budget, personnel, and tools; that the goals of the IG initiative align with the healthcare organization’s strategy; that the importance of the IG initiative is communicated to the executive team as well as the workforce; and that the appropriate controls and accountability are established to meet the intended goals of the IG initiative (Fahey et al. 2018).

An IG framework can help a company with competing strategic priorities. One of the main ways that an IG initiative can support the strategy of the healthcare organization is by aligning the needs of the leadership with the organizational business strategy and goals. It helps to create the valuation of information and assign resources to the proper areas within an organization. This helps to avoid unnecessary costs with inappropriate assignment of resources to support the organization’s information assets (Fahey et al. 2018). Aligning the IG initiative with the healthcare ­organization’s strategic priorities with the support of an executive sponsor is the foundation of a successful IG initiative.

Business Intelligence

An effective IG initiative will support the information that the healthcare organization needs to make good decisions for the organization as well as the population it serves. One of the benefits of IG is the ability to support business intelligence. Business intelligence (BI)is the end product or goal of knowledge management. In other words, it is what you can do with what you know about your healthcare organization, your community, and so forth. With data being produced at a rapid rate, IG helps the healthcare organization manage and use the information. Using the information to create and support business intelligence is an essential component of IG. With an effective IG initiative, reliable information will be available to support the compliance efforts, benchmarks, and comparisons of an organization in areas such as population health, quality of care, public reporting, financial performance, and regulatory compliance (Warner 2013a). For example, the ability to analyze the top trends in diagnoses in a healthcare organization will enable the organization to expand service lines or enhance patient outcomes in a specific area.

Situation, Background, Assessment, Recommendation (SBAR)

As a healthcare organization begins to implement IG, the reason and intent of the process must be effectively communicated within the organization. The situation, background, assessment, and recommendation (SBAR) tool is an easy to use and understand tool that can help define the intent of the IG program and clearly articulate the entire process to gain organizational and executive support. SBAR uses four distinct components to describe the issues, provide background information, conduct a current state analysis, and define the recommended steps to fix the issue (Glondys 2016; Kadlec 2015). Figure 6.3 describes each component of the SBAR tool.

Figure 6.3 Situation, Background, Assessment, and Recommendation

The SBAR Elements

S = Situation (a concise statement of the problem)

B = Background (pertinent and brief information related to the situation)

A = Assessment (analysis and consideration of options—what you found/think)

R = Recommendation (action requested/recommended—what you want)

Situation

This section of the SBAR process helps determine what is going on and why. In this section, the relevant parties identify the problem and why it is a concern for the organization and then provide a brief description of it.

Background

The goal of the background section is to be able to identify and provide the reason for the problem.

Assessment

At this stage, the situation is analyzed to determine the most appropriate course of action. Include any data that have been gathered and spell out the pros and cons of each option being considered.

Recommendation

Possible solutions that could correct the situation at hand are considered. In this section, a recommendation is provided based on the data presented in the assessment section.

Source: Glondys, 2016, 35.

When using SBAR to support an IG initiative, it is important to be specific about the issue and directly link it to the specific IG principle. If necessary, include information such as accreditation requirements and federal and state regulations to help support the background information. In addition, special considerations should be documented linking the specific issue to the organization’s strategic plan (Glondys 2016). Figure 6.4 demonstrates SBAR linked to the IG principle of availability.

Figure 6.4 Example: the principle of availability

Situation: New fields are being added to EHRs but are not communicated throughout the organization. Output (for release of information) does not include these data, resulting in incomplete information being released.

Background: No control mechanism exists for altering new fields in the EHR. There is no documented standardized process for changing and adding fields. It follows, then, that there is no education for this practice. There has been no audit of input-to-output flow.

Assessment: Survey IT and clinical areas that frequently request template and data field changes. Audit critical content (that is, core measures) that is not part of standard output. Identify examples of adverse impacts of incomplete data on clinical care (resulting in legal action), coding (resulting in a loss of revenue), and reporting (resulting in low performance). List pros and cons for each approach and identify any associated costs.

Recommendation: Formalize the process and approval procedures for changes to the EHR. Educate the workforce about the approved process for EHR changes.

Special Considerations for IG

This example clearly shows the importance of organization-wide communication, collaboration, and commitment to govern the quality of information. People, processes, and technology in every department should be involved in this effort. Everyone is a stakeholder in information quality.

Source: Glondys, 2016.

Enterprise Information Management

Enterprise information management (EIM) is the set of functions created by a healthcare organization to plan, organize, and coordinate the people, processes, technology, and content needed to manage information for the purposes of data quality, patient safety, and ease of use (Johns 2015). As part of the creation of an IG strategy, healthcare organizations should establish EIM policies and procedures to address the collaboration and integrative efforts used across the system to protect the healthcare organization’s enterprise information assets (Warner 2013b).

Information Governance Principles for Healthcare

In 2014, AHIMA established Information Governance Principles of Healthcare (IGPHC), which were aligned with ARMA’s Generally Accepted Recordkeeping (GARP) Principles. The IGPHC were intended to be comprehensive and broad to allow for scalability based on the healthcare organization’s type, size, role, mission, sophistication, legal environment, and resources. AHIMA intended to offer a framework to help organizations leverage information as an asset, while ensuring compliance with legal requirements and other duties and responsibilities. Whether used in whole or in part, the IGPHC were developed to inform an organization’s information governance strategy (Datskovsky et. al, 2015a).

The eight principles included:

· Principle of accountability. This principle recommended that one person, preferably someone in senior leadership, oversee and implement an IG program within an organization. This individual could help approve policies and procedures to guide implementation of an IG program and remediate identified issues (Datskovsky et. al, 2015a).

· Principle of transparency. This stipulates that documentation related to an organization’s IG initiatives be available to its workforce and other appropriate interested parties, according to the principle. Records demonstrating transparency of the information governance program should: Document the principles and processes that govern the program; accurately and completely record the activities undertaken to implement the program; and be available to interested parties (Datskovsky et. al, 2015a).

· Principle of integrity. According to the principles an (IG) program should be arranged such that “the organization has a reasonable and suitable guarantee of authenticity and reliability.” This includes elements such as appropriate workforce training and adherence to the organization’s policies and procedures, as well as acceptable audit trails and admissibility of records for litigation purposes.

· Principle of protection. An IG program must ensure the appropriate levels of protection from breach, corruption, and loss are provided for information that is private, confidential, secret, classified, and essential to business continuity. This facilitates the protection of sensitive healthcare information.

· Principle of compliance. Achieving compliance through IG ensures healthcare entities comply with applicable laws, regulations, standards, and organizational policies, and maintains its information in the manner and for the time prescribed by law or organizational policy (Datskovsky et. al, 2015b). (Compliance is defined in more detail in chapters 9 Data Privacy and Confidentiality and 16 Fraud and Abuse Compliance.)

· Principle of availability. The principle states an organization should maintain information in a manner that ensures timely, accurate, and efficient retrieval. This applies to healthcare teams (patients, caregivers) as well as legal and compliance authorities (Datskovsky et. al, 2015b).

· Principle of retention. This helps organizations create processes for proper retention of information based on requirements from regulations, accrediting organizations, and company policy. According to the principle, “[t]he ability to properly and consistently retain all relevant information is especially important, as organizations create and store large quantities—most of it in electronic form.” (Datskovsky et. al, 2015c). (Chapter 8, Health Law, contains more information regarding retention of information within an organization.)

· Principle of disposition. This principle applies to all information in the custody of an organization and encourages them to “secure and appropriate disposition for information no longer required to be maintained by applicable laws and the organization’s policies.” (Datskovsky et. al, 2015c). See chapter 8 for information regarding disposition of ­information.

AHIMA’s Information Governance Adoption Model Competencies

AHIMA’s Information Governance Adoption Model (IGAM) consists of 10 competencies that were intended to assist a healthcare organization in applying appropriate IG concepts. The adoption model permitted an organization to focus on those areas of IG that it deemed important. This type of scalability promotes a natural progression of IG improvement in terms of expectations and, more importantly, resources, according to AHIMA’s Information Governance Toolkit 3.0 (AHIMA n.d.).

The 10 key organization competencies promoted by the IGAM included:

1. IG Structure

2. Strategic Alignment

3. Enterprise Information Management (EIM)

4. Data Governance

5. IT Governance

6. Analytics

7. Privacy and Security

8. Regulatory and Legal

9. Awareness and Adherence

10. IG Performance

Figure 6.5 AHIMA’s 10 IGAM Competencies

Source: Iron Mountain Advisory Services.

Data Governance

Data governance is “enterprise authority that ensures control and accountability for enterprise data through the establishment of decision rights and data policies and standards that are implemented and monitored through a formal structure of assigned roles, responsibilities, and accountabilities” (Johns 2015, 81). Data governance focuses on how healthcare organizations create processes, policies, and procedures for keeping information that is relevant to patient care and healthcare operations. The goal of data governance is maintaining data accuracy and removing unnecessary data from the health record. Commonly, data governance is confused with the term information governance, even though there is a clear distinction between the two terms. Data governance focuses on managing the data as they are created within an information system. Simply stated, data governance manages the data put into the different information systems used in healthcare and information governance manages the information output from those systems. The core processes of data governance within a healthcare organization are to establish policies and procedures on how data will be connected, who is ­responsible for the data, how the data will be stored, and how the data will be distributed within the healthcare ­organization.

HIM professionals play a key role in the success of implementing information and data governance programs in healthcare organizations. Their training provides them with an understanding of healthcare’s clinical, financial, regulatory, and technology environments, which allows them to lead the information governance within an organization and be the liaison between executive leadership and clinical leadership (AHIMA 2011; AHIMA 2014a).

Data Stewardship

Data stewardship is an important component of the data governance process. Data stewardship creates responsibility for data through principles and practices to “ensure the knowledgeable and appropriate use of data derived from individuals’ personal health information” (Kanaan and Carr 2009, 1). Data stewardship is important due to the increase in availability of health data, the use of the health data within the healthcare industry, the use of health information for population management, and the legal and financial risks associated with health data. Data stewardship is created to establish common and essential practices and principles for the management of health data. Benefits of data stewardship are the following:

· Improved patient safety

· Increased efficiencies

· Decreased cost of care provided

· Improved patient care and outcomes

· Facilitated coordination of care

· Structured data collection

· Comprehensive data collection (Noreen 2017)

Oversight and data stewardship are essential for proper management of information and data. This helps ensure the data and information meet the needs of the healthcare organization. One of the emerging roles in healthcare is the data steward. Data stewards are the people within an organization who are responsible for either a specific system or a specific set of data within the organization (Downing 2016). The data steward serves as the subject matter expert for data governance for the healthcare organization, business unit, or data set that they represent. For example, a heart clinic may have a data steward that oversees imaging and EKGs in the clinic, leads data quality initiatives to evaluate potential issues and risks, and leads the remediation process to ensure the accuracy and integrity of the data. The data steward’s roles are to establish policies, procedures, and processing for the system, data set, or business unit they support as they relate to data management. In an IG framework, the data steward acts as the liaison between the workforce (users of the systems or data) and the information governance committee to help both sides create priorities, identify specific issues, and create plans for resolving the identified issues. Other common job titles for the data steward in healthcare are business analyst or data analyst (Downing 2016).

The National Committee on Vital and Health Statistics (NCVHS) recommends that the creation of principles for data stewardship fall into four categories: (1) individual’s rights, (2) responsibilities of the data steward, (3) needed security safeguards and controls, and (4) accountability, enforcement, and remedies for data stewardship. Individual rights should be analyzed to ensure the following:

· The individual has proper access to their protected health information

· The individual has a right to review and amend their health information

· The individual is provided transparency of information allowing them to understand what information will exist and how it will be used

· The individual must provide consent and authorization for use and disclosure of health information

· Adequate information and education are provided regarding the rights and responsibilities of health information (Kanaan and Carr 2009)

The data steward’s responsibilities should be clearly defined to support adherence to the privacy and security of health information including requirements for uses and disclosures and guarantees for gaining access to what is needed to perform job responsibilities. Data security safeguards and controls should be established to define what technical and nontechnical mechanisms are being used to protect the confidentiality, integrity, and accessibility of protected health information. Data stewardship should express accountability of appropriate use as well as sanctions in the event of noncompliance to the requirements (Downing 2016). The goal is to create and build trust and transparency through the entire healthcare organization as it pertains to the use of health data.

Data Integrity

Many tools and functionalities of information systems are available to assist with the quality, completeness, and timeliness of clinical documentation. While these tools and functionalities of information systems create efficiencies in a healthcare organization, they have also been shown to create data integrity issues when not properly implemented and managed (AHIMA 2013; Vimalachandran et al. 2016). Data integrity is the assurance that the data entered into an information system or maintained on paper are only accessed and amended by individuals with the authority to do so. Integrity of the documentation within the patient’s records includes information governance, data governance, patient identification, authorship validation, amendments and record corrections, and audits of documentation validity for reimbursement (AHIMA 2013; Vimalachandran et al. 2016). A healthcare organization needs to establish proper safeguards with the use of technology, including policies and procedures to help manage the integrity of the data in the health record. The Health Insurance Portability and Accountability Act (HIPAA) requires covered entities (defined in chapter 9, Data Privacy and Confidentiality) to implement policies and procedures to protect electronic protected health information from improper alteration or destruction and to establish security measures to ensure electronically transmitted protected health information is not improperly modified (chapter 10, Data Security, covers this topic in more detail). AHIMA recommends healthcare organizations institute policies and procedures for the management of data integrity. Some key topics to be included in data integrity policies are the documentation requirements, identification of who can document and the scope of that documentation, timeliness of documentation, and safeguards regarding changing and deleting documentation. Guidelines a healthcare organization establishes to reduce the likelihood of issues or damages to the patient data include the following:

Committing to comply with laws and ­regulations in an ethical manner

Requiring accurate data

Holding individuals accountable for errors as per medical staff bylaws or rules and regulations

Identifying penalties for the falsification of information

Requiring periodic training

Defining management responsibility (AHIMA 2013)

Specific HIM department policies and procedures should also be established to address the administrative documentation requirements, clinical documentation requirements, entering information into the EHR, correcting and amending the health record, and time frames for correcting the health record (Maimone 2016). HIM professionals need to be a part of establishing proper integrity throughout the health record as they are the custodians of the health record. It is common practice that the HIM department and HIM professionals ensure the health record is complete and accurate, so it is available for the purposes of patient care and healthcare operations.

Data Sharing

Electronic health information systems were implemented to create a foundation for data sharing across healthcare organizations regardless of the information system(s) used. Data sharing allows information to be exchanged via electronic formats to help support and deliver quality healthcare. Also known as health information exchange, data sharing is the electronic exchange of information between providers’ electronic systems. Data sharing, or health information exchange, has two basic components: (1) the ability of two or more information systems to communicate and exchange patient information and (2) the ability of two or more information systems to effectively collect and use the information that has been exchanged (Dean 2018). When implemented correctly, a proper data sharing process can assist with coordinating patient information, analyzing patient information across multiple healthcare organizations, and reducing unnecessary repeated tests to support improvement in patient outcomes and patient satisfaction. For example, if a CT scan is performed on a patient prior to referral to another healthcare organization, the results of the CT scan can be shared electronically to prevent the patient from having the same test replicated (Dean 2018). See chapter 12, Healthcare Information, for more information regarding health information exchange.

Data Interchange Standards

To help support and drive interoperability and data sharing between healthcare organizations, standards development organizations have created standards for the sharing of information in electronic formats. Interoperability is the capability of two or more information systems and software applications to communicate and exchange information. Standards development organizations (SDOs) are private or government agencies that are involved in the creation and implementation of healthcare standards. In this case, SDOs define standards to support the process of electronic exchange of data. Data interchange standards are developed in order to support and create structure with data exchanges to sustain interoperability. The goal of the data interchange standards is to facilitate consistent, accurate, and reproducible capture of clinical data. Data interchange standards help support data integrity and safeguard data quality when sharing between organizations. Using data interchange standards for interoperability helps to do the following:

Create a basis to enable the electronic ­exchange of data between two or more ­information systems or applications by ­creating consistent formats and sequences of data that are applied during data transmission

Reflect the existing clinical and administrative data contained in both paper and electronic information systems to maintain patient data consistency in growing EHRs

Transfer health data using appropriate business processes and necessary ethical and regulatory demands and guidance

Foster electronic transmission as a business strategy to support patient care and better patient outcomes

Promote efficient information sharing among individual computer systems and institutions (AHIMA 2006; Murphy and Brandt 2001)

In the US, SDOs are managed by the American National Standards Institute (ANSI). ANSI is the organization that oversees the creation of data standards from a variety of business sectors, including healthcare. The following are some common SDOs:

Health Level 7 (HL7). An ANSI-accredited SDO, HL7 established the creation of standards to support the exchange of clinical information in multiple formats

Institute of Electrical and Electronics Engineers (IEEE). A national organization that creates and develops different standards for hospital information systems that need communication between bedside instruments and clinical information systems (for example, cardiac monitoring performed in the intensive care unit being integrated with the EHR); IEEE currently has standards that allow providers and hospitals to achieve interoperability between medical instrumentation and a computer healthcare information system, and though used in multiple types of systems, it is most often used within acute-care settings

National Council for Prescription Drug Programs (NCPDP). A committee within the Designated and Standard Maintenance Organization focused on the development of standards regarding exchanging prescription information and payment information; NCPDP created multiple standards including a standardized data dictionary for pharmacy data, standards for transactions of file submissions between pharmacies and processors, standards for common billing unit language for submission of prescription claims, and standards to communicate formulary and benefit information to prescribers (Orlova et al. 2016).

Information and Data Strategy Methods and Techniques

With the implementation of data and information governance, healthcare organizations must evaluate and implement strategies focused on the oversight and management of the information and data in their healthcare organization. Information and data strategies are the steps taken to manage the data and information. The information and data strategies should align to the healthcare organization’s overall strategy and support the broader business goals of the organization. The information and data strategies help to promote the collection of quality healthcare data, support decision-making, define proper use of information, and manage the compliance risk of an organization (Washington 2015). Figure 6.6 describes the characteristics of a successful information governance strategy.

Figure 6.6 Characteristics of a successful information governance strategy

A successful IG strategy incorporates the following characteristics:

• Business-led and Business-driven: Accountability and responsibility for data and information rests with the business owners who lead the departments or business units that create or generate the data and information, as opposed to IT

• Measurable: Clear goals and objectives and related metrics are established for performance improvements, reduction of risk, and optimization of data and information

• Achievable: A realistic level of resources (funding, staffing, and so on) is provided to develop and sustain IG efforts

• Avoids complexity: Initial goals and objectives should be focused and targeted to specific issues or problem areas

• Communicable: Communications explain and educate employees and clinicians about their information management ­responsibilities

• Copes with uncertainty: Standardization of processes leads to a more consistent approach and response to threats that can help the organization cope with ambiguity or uncertainties

• Flexible: Information management functions provide adequate controls but are designed to allow for flexibility where required to carry out job duties

Source: Washington 2015.

Data strategy should be a clear, concise method created to support proper collection and use of healthcare data within an organization that is approved by executive leadership. A data strategy will clearly define the healthcare organization’s data policies and procedures, roles and responsibilities for data governance, business rules for data governance, process for controlling data redundancy, management of key master data, use of structured and unstructured data, storage for all healthcare data, and safeguards and protections of the data. The strategy must define the following:

Data standardization and integration. Focus on how the data is being entered into the information system, where the same data might exist in multiple areas, and how it is being integrated into other information systems. For example, review where data of birth is being entered into the information system and if it is always in the same format such as MM/DD/YYYY. Also, evaluate if the information system can pull data from one field to another to avoid re-entering information that has already been entered. The intent of data standardization is to ­document the location of data collection and ensure standardized formats and meaning of the data.

Data quality. Data quality focuses on entering data that is true, accurate, and relevant to patient care and business operation into the information system. Data quality is discussed in detail later in this chapter.

Metadata management. Refers to managing and defining the metadata within the information system. Metadata refers to the data that characterizes other data such as creation date of data, date sent, date received, last accessed date, and last modification date. It is important to clearly define what metadata will be collected and why it will be collected.

Data modeling. Refers to the creation of documentation to justify business decisions made based on the different data collection and storage systems that are used within an organization. Creating data models and defining the use of data in relation to business mission and vision allow for the support of data standardization across the organization.

Data ownership. Refers to the creation of business leaders, or owners, of specific areas of the data. For example, the Director of Radiology can be assigned as the business leader, or owner, of the radiology information system. Based on the business need, the business owners are responsible for creating business rules and definitions when collecting specific data to support patient care and their business operations.

Data stewardship. Data stewardship is the evaluation of the data collection based on business need and strategy to ensure the data meets the requirements of patient care and organizational needs (AHIMA 2011; Downing 2016).

A clearly defined data strategy approved by executive leadership is necessary to manage the most important healthcare assets—patient data.

Data Visualization and Presentation

It is important to properly organize and visualize data used for business purposes. Data visualization creates a visual context for data to help people better understand the data and the significance of the data. Data visualization can take a large volume of data and provide key aspects and insights to the data in a visual format (Meyer 2017). Many tools such as graphs, charts, and tables exist to present data. It is easy to create different charts and graphs that provide information and detail regarding data, but it is important to present the data in ways that are appropriate to the healthcare organization and the data being analyzed. For example, to present the frequency of a specific diagnosis by gender, a pie chart—meant to show the percentages of a total—would not be a good selection. A table could provide that information in a better format. Chapter 13, Research and Data Analysis, provides specific information regarding data visualization and presentation methods.

Another important aspect for the management and presentation of data is that the data and information need to be meaningful and useful to the organization. Presenting data that do not support an initiative of the organization can be an unproductive use of company resources and time. It is important to define a clear strategy regarding the type of information to be reported to support business strategy. In other words, the focus should be on presenting data that will help the healthcare organization reach its goals. Additionally, the data may provide information and detail that elicit negative feedback. For example, if a healthcare organization is trying to determine if it wants to add an additional cardiac catheterization room, it may choose to evaluate and create data presentations for individuals within a specific geographic area who have been diagnosed with cardiac conditions. Graphs with data pertaining to emergency department visits would not be useful in evaluating the expansion of a cardiac catheterization room. Understanding the data and properly managing it becomes an essential part of handling data assets appropriately.

Critical Thinking Skills

Another key aspect in the management of data assets in the organization is critical thinking skills. Critical thinking refers to the process of analyzing, assessing, and reconstructing a situation to provide enhanced solutions and outcomes to a problem (The Critical Thinking Community n.d.). It is estimated that in the coming years, new technology advancement, including the use of technology in healthcare, will occur every 30 seconds (Humbert 2018). The skill of critical thinking is essential in healthcare. Humbert (2018) stated: “A critical thinker is able to deduce consequences from what he knows, and he knows how to make use of information to solve problems, and to seek relevant sources of information to inform himself….­Critical thinking can help us acquire knowledge, improve our theories, and strengthen arguments. We can use critical thinking to enhance work processes and improve social institutions” (p. 56).

The issues and challenges that face healthcare organizations and the healthcare industry continue to become more complex and require the effective evaluation of data to help support the change in the healthcare environment (Meyer 2017; Sharp et al. 2013). Many individuals can effectively utilize critical thinking to analyze a situation and generate solutions to an issue. During the critical thinking process, it is common for data to be analyzed to effectively evaluate the current state and future solution in addition to generating a solution to the current issue. For example, a healthcare organization is currently evaluating a new line of service to add to an outpatient clinic being built in a small community where they currently only have family practice providers. Without critical thinking, an individual may evaluate only common types of care associated with family practice and add that new line of service to the outpatient clinic. From a critical thinking perspective, a healthcare organization may evaluate common referrals to other clinics for the patients seen and treated at the clinic. In addition, they may profile the community in which the clinic exists to understand the population and the care potentially needed. Based on this gathering and analysis of data, an enhanced decision on the nature of care can be made to support the community and its care needs.

With the implementation of EHRs, new roles such as data analysts or EHR analysts are being established for the oversight and management of data collection within a healthcare organization as well as how information is used. While principles of information governance have been established by AHIMA and data governance is an essential component of daily operations, the ability to understand, evaluate, and apply the different principles becomes an essential part of a successful information and data governance program.

Data Quality

Data quality is the reliability and effectiveness of data for its intended uses in healthcare operations, decision-making, planning, and patient care. Data quality management is “business processes that ensure the integrity of an organization’s data during collection, application (including aggregation), warehousing, and analysis” (Davoudi et al. 2015, 8). Data quality has always been a focus for HIM professionals; with the implementation of the EHR, the need for more complete and accurate information is critical to support proper patient care and corresponding reimbursement.

Data quality serves as one of the most important elements of healthcare operations and patient care. “All data must be accurate, timely, relevant, valid, and complete to ensure the reliability of the information” (Davoudi et al. 2015, 8). This is because the data support patient care and patient safety, provide evidence for reimbursement and accreditation, and afford documentation needed for quality initiatives and research (AHIMA 2015). Without complete and accurate data in a health record, a healthcare organization is at risk for patient safety issues. For example, if a provider does not document what medications were administered to a patient and the ­exact dosages, the patient may be prescribed ­another medication that could have adverse effects when combined with the first medication. In addition, there are risks such as having to return payment if the documentation does not support the healthcare organization’s billing and reimbursement request. For example, if a physician billed that they performed a procedure, but the documentation does not support the procedure, the physician may have to return the money and ­rebill the services.

Clinical documentation is “any manual or electronic notation (or recording) made by a physician or other healthcare clinician related to a patient’s medical condition or treatment” (Hess 2015). Clinical documentation is the foundation of every health record in that it supports the care the patient received and the reimbursement that should be received for the care. Inaccurate information and poor documentation negatively impact patient care and reimbursement, which can drive up the cost of healthcare (AHIMA 2015), creating a need for data quality and data quality management over healthcare data.

Many accrediting organizations such as the Joint Commission require evidence of clinical care based on the data that is documented in the health record. If the accrediting organization has basic requirements for documentation in the health record, and the healthcare organization does not meet those requirements, it runs the risk of losing accreditation. Data quality is critical to both clinical care and administrative processes. AHIMA’s Data Quality Model is an important tool in ensuring the quality of the data collected.

AHIMA’s Data Quality Management Model

AHIMA created the data quality management model to support the need for true and accurate data. Data quality management is “the business process that ensures integrity of an organization’s data during collection, application, warehousing, and analysis” (Davoudi 2015). Many areas such as patient care, patient outcomes, reimbursement, process improvement, and daily healthcare operations depend on detailed quality of information. Core functions of enterprise information management must be established to create the ability to collect high-quality data from the health record. The goal of EIM is to make sure that information being used for business decisions and patient care is reliable and trustworthy. The data quality management model can help set up policies, processes, and expectations to support EIM within an organization.

The data quality management model defines four domains that link and support data quality. The first domain is application, which is focused on understanding the purpose of data collection. Since the amount of patient data collected through a patient encounter is immense, it is important to evaluate and understand why the data is being gathered and the purpose it serves for the healthcare organization. The second domain is collection, which concentrates on how the data elements are being collected throughout the encounter. Understanding where data is being entered and how it is being entered is an essential part of basic data quality management. This focus allows a healthcare organization to understand if duplicate or redundant information is being collected. The third domain is warehousing, which describes the processes as well as systems a healthcare organization uses to archive data; it also includes understanding where the data is being stored, and how it is being archived and managed. The last domain is analysis, which centers on how the data collected throughout the patient encounter is transformed into meaningful data for use throughout the entire spectrum of the healthcare setting (Davoudi 2015).

Ten characteristics of quality data defined within the AHIMA data quality model are accuracy, accessibility, comprehensiveness, consistency, currency, definition, granularity, precision, relevancy, and timeliness. Understanding and applying each of these characteristics to the data quality management domains is an essential part of effective oversight and management of data quality (Davoudi 2015).

Accuracy

Accuracy focuses on the data being free of errors. It is important that the data within the health record are accurate across the entire health record (that is, the data are valid with the appropriate test results and placed into the proper health record). An example of monitoring the health record for data accuracy is the analysis of patient notes in the health record to ensure they support the diagnosis throughout the entire health record (Davoudi 2015). For instance, the information in an operative report should be compared to information in the discharge summary to confirm the operation performed and findings are accurate (the same) in both documents.

Accessibility

Proper safeguards must be established and employed to ensure the data are available when needed while implementing proper precautions and safeguards to protect the information. An example of data accessibility is ensuring that nurses have ­access to the health records of patients that they are treating (Davoudi 2015). (See chapters 9, Data Privacy and Confidentiality, and 10, Data Security, for additional information on access to health records.)

Comprehensiveness

Data comprehensiveness certifies that all required data elements that should be collected throughout the health record are documented. One way to ensure this is happening in the EHR is to use required fields. Required fields allow for the information system to force a response in a specific data element within an information system. For example, the EHR will require at a minimum the user’s name, date of birth, address, telephone number, and gender on a specific screen collecting patient information. The information system can require information to be entered into all these fields before the user is able to move to the next screen, which supports comprehensiveness. Training and education should be conducted across the healthcare organization to ensure the staff members collect all the required data elements in the health record (Davoudi 2015).

Consistency

Consistency means ensuring the patient data are reliable and the same across the entire patient encounter. In other words, patient data within the health record should be the same and should not contradict other data also in the health record; for example, a test result and diagnosis should be the same throughout the health record (Davoudi 2015).

Currency

The data within the health record need to be current and up to date. EHRs present information across a broad spectrum of care, including data that may be outdated. Specific procedures should be established for updating data elements used for each patient encounter, including the discontinuation of collected data elements that are no longer current. An example of data currency is reviewing and updating patient medications at each patient encounter to remove medications that are no longer being taken and add any new medications (Davoudi 2015).

Definition

All data elements should be clearly defined to guarantee that all individuals using and collecting the data will understand the meaning of that data element. An example of data definition is defining date of birth as the date the individual was born by month, day, and four-digit year (Davoudi 2015).

Granularity

The data collected for patient care must be at the appropriate level of detail. An example of data granularity is documenting the results of a lab test with the appropriate number of characters after a decimal point in the lab value (Davoudi 2015).

Precision

Data should be precise and collected in their exact form within the course of patient care; for example, documenting the exact measurement, such as the height or temperature of the patient. When information is entered precisely, there should be little to no variability of the data (Davoudi 2015).

Relevancy

Data relevancy is the extent to which the data elements being collected are useful for the purposes for which they are collected. If a healthcare organization collects data that are not relevant in supporting patient care and administration, it adds additional, unnecessary information in the health record. For example, if a patient presents with pain during urination, data collection should be focused around the symptoms, testing, and treatment. Collecting additional data or data not relevant to support treatment, payment, and healthcare operations adds superfluous data to the record. An example of relevancy is the creation of templates to collect the correct information during an emergency department visit for a patient, as this can help assemble accurate and relevant data to support the visit and help prevent additional, nonrelevant information from being collected (Davoudi 2015).

Timeliness

Patient documentation should be entered promptly, ensuring up-to-date information is available within specified and required time frames. Timeliness may vary throughout the health record depending on what the data are being used for and how the data are supporting patient care. An example of timeliness is specifying when notes such as discharge summaries or operative reports should be entered in the information system. Healthcare organizations frequently require specific forms such as orders or admitting evaluations to be entered within a defined period.

HIM professionals work in a variety of roles to support and manage the quality of data, especially as the implementation of the EHR continues. Some common HIM roles include clinical data manager, health data analysis, terminology asset manager, clinical documentation integrity specialist, data collection specialist, and EHR documentation specialist (Davoudi 2015). The HIM professional understands the need for quality data and can bring that knowledge and expertise into many different areas of the healthcare delivery system.

Data Collection Tools

The management of data quality depends on how the data are collected during the patient encounter. With data created, stored, and maintained on paper as well as in electronic format, it is important to ensure the data collection tools—such as forms and computer screens—used throughout a healthcare organization are effective and efficient. HIM professionals should be involved in the creation of data collection tools for both electronic and paper-based tools.

In addition to data collection tools, standardization of the collection of patient data is essential to collect the proper information and reach data quality levels needed to support the enhancement of patient care and the healthcare industry. The two major ways to collect data elements are through the use of electronic templates and paper-based forms. Not all paper forms will convert easily to an electronic format, so it is important to evaluate each of the different screens to ensure the data capture is correct. There are standards for both screen design and paper forms to facilitate data collection.

Screen Design

Most EHRs come with prebuilt forms and templates for use within the information system. For example, a template would contain all the information required for inclusion in the discharge summary such as discharge diagnosis, discharge medications, and follow-up. Usually, prebuilt forms and templates do not match the healthcare organization’s specific needs. One reason for this is that a screen typically holds approximately a third of what is contained on a form. In addition, the prebuilt forms may not be constructed to collect the information needed to support the healthcare organization’s patient care and payment processes. The Department of Health and Human Services Office of National Coordinator for Health Information Technology (ONC) discusses the need to evaluate workflow and customize patient data collection functions. They recommend the following for patient data collection functions:

Create templates for common types of notes, visits, and procedures

Configure patient data lists with multiple choices for diagnoses, medications, and orders

Develop flow sheets for common vital signs and blood tests, allowing for trending across a period of time

Confirm that the EHR being used meets basic standards for interoperability and data sharing across systems (HHS 2015).

In addition, analysts who are assisting with building the EHR within the healthcare organization should meet with each department that enters data into the health record to evaluate current data collection processes within the EHR and evaluate additional forms or tools necessary to support current workflow. Some key elements when evaluating forms design is deciding what should be in structured data format and what should be in unstructured data format. Structured data are data that can be read and interpreted by an information system. An example of structured data is a diagnosis code entered in the proper format into the information system, such as an ICD-10-CM code format of XXX.XXXX. If the data element is in the proper format and in the proper location, the information system will read and perform analysis on that data. Other methods to collect structured data include check boxes, drop-down boxes, and radio buttons. Check boxes allow the user to select multiple values. For example, murmurs, gallops, and rubs can be chosen under the cardiac section of the physical exam. Drop-down boxes list the appropriate options, such as states, from which the user can choose. Radio buttons are used when there are few options, such as only male or female, from which the user should choose. Unstructured data, also known as free text, are data entered into the information system with no format specified. An example of unstructured data is a narrative discharge summary that does not follow a specific format or use a template. Unstructured data cannot be interpreted by an information system and usually are not used in structured reports. When choosing how to collect data within an EHR, it is important to evaluate and make decisions based on how data are reported.

Since many healthcare organizations are unsure of what data they need, especially as they transition to the EHR, it is important to have a standardizing committee or process to evaluate data collection within information systems. This individual or group is responsible for assuring that quality data are entered into the information system and proper data reporting is obtained from the information system. The documentation should be used to support decisions for future evaluation.

Forms Design

Forms design is a major part of assuring data quality within a healthcare organization. Forms design is the oversight process in which paper forms are created to make sure that they are easily understood and to collect the correct amount of information necessary. Forms design helps to make sure there is a consistent process to determine if a form is necessary and how it will be developed (Pyramid Solutions 2017). With any new creation of a form, the following questions should be asked:

What is the purpose of this form?

Can the data be collected in electronic format versus paper format?

When will this form be used during the patient encounter and in which type of patient encounters?

Who will use this form within the healthcare organization?

What will be done with the paper once it is created (scanned into system, stored in a paper health record)?

Answering these questions during the assessment of a new form will help the form be appropriate and direct whether it is created in paper or electronic format. The disadvantage of paper forms used to support patient care is that they have to be entered into an EHR manually or by scanning the paper documents, which does not allow for reporting. In some cases, paper forms that do not collect patient information, such as productivity forms or staff vacation requests, will not be entered into the EHR, but may be entered into a human resources management system. It is important to evaluate each form to determine if it will remain on paper or be entered into an electronic system.

The following are the recommended steps for controlling, tracking, and managing paper forms:

Establishing data collection standards within the healthcare organization

Establishing testing and evaluation process

Evaluating the quality of new paper and electronic forms

Systemizing storage, inventory, and distribution of forms

Numbering, tracking, and using bar codes to manage paper forms

Establishing a documentation system that supports decisions that are made by the forms committee

Proper and effective management of data collection requires quality data regardless of the media type. HIM professionals play a vital role in this process to verify that proper data is being collected in the best format and to confirm that forms are designed properly to ensure efficient processing of the information.

There is typically a clinical forms committee that manages both paper and electronic forms design. The clinical forms committee should be comprised of a multidisciplinary team and led by the HIM department. Some common recommended committee members are medical staff, nursing staff, purchasing, information services, performance improvement, support and ancillary departments, EHR support, and forms vendor liaison. In addition, anyone directly affected by the new form or computer view should be invited to attend the forms committee meeting. For example, when a form is being redesigned for use in the intensive care unit, nurses and physicians from that clinical area should be invited to give their input.

Forms control, tracking, and management are important issues. At a minimum, an effective forms control program includes the following activities:

Establishing standards. Written standards and guidelines are essential to ensure the appropriate design and production practices are followed. Standards are fixed rules that must be followed for every form (for example, where the form title should be located). A guideline, on the other hand, provides general direction about the design of a form (for example, usual size of the font used).

Establishing a numbering and tracking system. A unique numbering system should be developed to identify all organizational forms. A master form index should be established, and copies of all forms should be maintained for easy retrieval. At a minimum, information in the master form index should include form title, form number, origination date, revision dates, form purpose, and legal requirements. Ideally, the tracking system should be automated.

Establishing a testing and evaluation plan. No new or revised form should be put into production or use without a field test and evaluation. Mechanisms should be in place to ensure appropriate testing of any new or revised form.

Checking the quality of new forms. A mechanism should be in place to check all newly printed forms prior to distribution. This should be a quality check to confirm that the new form conforms to the original procurement order.

Systematizing storage, inventory, and distribution. Processes should be in place to ensure the forms are stored appropriately. Paper forms should be stored in safe and environmentally appropriate environments. Inventory should be maintained at a cost-effective level, and distribution should be timely.

Establishing a forms database. In an electronic system that supports document imaging, a forms database may be used to store and facilitate updating of forms. Such a database can provide information on utilization rates, obsolescence, and replacement of individual forms or documentation templates (Barnett 1996; Pyramid Solutions 2017).

Clinical Documentation Integrity

Clinical documentation relates to the quality and integrity of patient data while supporting other functions such as timely coding and reimbursement. Clinical documentation integrity (CDI) is defined as the process of reviewing medical information to verify that documentation is clinical specific, is appropriate, and supports the medical codes assigned (AHIMA 2016c). Historically, CDI programs were created to support reimbursement; however, with the implementation of EHRs and the expanded uses of clinically coded data, CDI programs have shifted to facilitate an accurate representation of healthcare services through complete and accurate patient documentation within the record. The following are some basic goals of a CDI program:

Obtain specific documentation that can be used to identify the patient’s severity of illness

Identify and clarify missing, conflicting, or nonspecific provider documentation related to diagnoses and procedures

Support accurate diagnostic and procedural coding, and Medicare Severity Diagnosis Related Group (MS-DRG) assignment, leading to appropriate reimbursement

Promote health record completion during the patient’s course of care, which promotes patient safety

Improve communication between physicians and other members of the healthcare team

Provide awareness and education

Improve documentation to reflect quality and outcome scores

Improve coding professionals’ clinical knowledge (AHIMA 2016c)

CDI programs can help healthcare organizations enhance patient documentation, reduce errors within the health record, and improve the quality of the patient data entered in the system while supporting patient care and reimbursement for the organization. Figure 6.7 provides an example of how CDI impacts the patient.

Figure 6.7 How does CDI impact the patient?

Example Scenario:

This is a 48 y/o male that has hypertensive end-stage renal disease (ESRD) and is on home peritoneal dialysis. He recently had knee replacement surgery. Two days after being discharged home, he went into respiratory failure and was rushed back to the hospital. It was determined he was in fluid overload secondary to a blockage in the peritoneal dialysis catheter from fibrin. This was treated with heparin and returned to normal function. He was discharged home with home health nursing and physical therapy.

After being discharged home, the wife and home health nurse noticed the patient’s oxygen level would continue to drop to the low 80s to upper 70s every time he fell asleep. The primary care physician was called and home oxygen was ordered for a decrease in oxygen saturations. A sleep study was also scheduled. The patient then received a call from the home oxygen vendor and was told his insurance would not cover home oxygen for his condition. He was told the only way it could be delivered was if he paid for it out of pocket. It is now 5:00 pm on a Friday evening and the physician’s office is closed.

This would be of great concern to a patient in this situation. He knows he needs the oxygen but does he have the money to pay for it? The patient is sick enough to need the oxygen but unfortunately the clinical documentation doesn’t have the specificity needed to reflect the true condition of the patient. Home oxygen has a specific set of requirements under the National Coverage Determinations (NCDs) for Medicare that must be met before the treatment will be approved. Some other payers also use these criteria to support medical necessity of certain treatments.

It is important for providers to be aware of National Coverage Determinations and Local Coverage Determinations (LCDs). The Centers for Medicare and Medicaid Services has a website where providers can look at the NCD and LCD requirements (https://www.cms.gov/medicare-coverage-database/indexes/national-and-local-indexes.aspx). In the hospital, patients have case managers who ensure these requirements are met before discharge. But this is not the case in many outpatient settings.

Source: Combs 2016a.

CDI programs should be established with the initial review of the health record to verify all necessary components of the health record. The following areas should be evaluated during the initial ­CDI review process:

Legibility – documentation should be easy to decipher and understand

Reliability – documentation should be trustworthy

Precision – documentation should follow strict medical terminology and be as accurate and exact as possible

Completeness – documentation should contain all details that are necessary to support continuity of care between caregivers and support billing and reimbursement

Consistency – documentation should be consistent throughout the entire record

Clarity – documentation should describe all details regarding the patient’s medical care to the highest level of specificity

Timeliness – documentation must be ­completed in a timely manner (Barnette et al. 2017; Combs 2016b)

Another impact on CDI is the evaluation of present on admission (POA) reporting requirements. POA refers to the conditions that are present in a patient at the time of ordering the inpatient admission. The goal of the POA reporting is to document which conditions are present in a patient at the time of admission into an acute-care facility versus the conditions that may develop during the patient’s stay in the facility (AHIMA 2009; Garrett 2009). A condition acquired during a hospital stay is referred to as a hospital-acquired condition (HAC). If a patient acquires a HAC that increases the cost of the patient care, it may not be paid under Medicare if it is considered preventable. It is important for a CDI program to evaluate these two requirements and make sure proper documentation is in place at the time of the inpatient admission order to prevent loss of reimbursement (AHIMA 2009; Garrett 2009). Chapter 15, Revenue Management and Reimbursement, offers more information regarding POA and HACs.

A CDI program usually has dedicated staff that may include HIM professionals, physicians, nurses, and other healthcare professionals. CDI programs impact quality of care and finances within a healthcare organization along with other key stakeholders such as case management, utilization review, medical staff, physician leadership, executive leadership, patient financial services, revenue cycle management, quality and risk management, nursing, and compliance (AHIMA 2018). The CDI program must have clear goals and strategies that align with the healthcare organization’s requirement for clear and precise clinical documentation. There are several CDI tools that can be used to enhance the quality of documentation. Clinical documentation specialists use these tools.

One of the successes of a CDI program is to have a physician advisor who will not only participate with the CDI program but also has the clinical respect of his or her peers. The CDI physician advisor serves as a liaison between the CDI specialists, the coding professionals, the quality department, and the providers at the organization, supporting the needs of the CDI program (AHIMA 2016c). The primary responsibilities of the CDI physician advisor are to educate physicians on clinical language and coding guidelines, help providers document and reflect the true severity of the patient’s illness, properly capture all the services and treatments performed by the healthcare organization, ensure the documentation supports the code assignments, understand the coded data in quality measures and reporting, and know how documentation impacts payment methodologies. The physician also works closely with the HIM coding department and CDI specialists to review health record documentation, discuss clinical issues that may have been identified during the health record reviews, discuss clinical criteria for disease processes, assist in the development of appropriate, compliant, and ­ethical provider queries, and review HACs and treatment complications (AHIMA 2016c).

CDI Tools

There are different ways to conduct the CDI review within a healthcare organization. CDI tools help manage and document the work of CDI professionals. A variety of tools can be used to help support CDI processes within an organization. One tool is computer-assisted coding (CAC). CAC is software that can search and evaluate clinical documentation to produce information regarding potential areas for documentation integrity. Electronic documentation is passed through the CAC software application, which analyzes the information and produces a report of procedure and diagnosis codes based on the electronic documentation evaluated. The codes are then manually evaluated for accuracy and completeness. The use of CAC software can speed up the coding process as it allows for evaluation of electronic assigned codes rather than having an individual analyze the entire electronic record and manually assign codes. While CAC is mainly used for the coding of the health record for reimbursement purposes, it can be used to automate part of the CDI process as well as provide an electronic evaluation of documentation (AHIMA 2018). Other CDI tools include audits, tip sheets, educational materials, and queries.

Audits Audits are an essential part of a CDI program. For more on audits refer to chapter 16, Fraud and Abuse Compliance. Audits can help an organization determine where there are areas that are missing proper documentation. Audits can also help an organization create a plan on the type of health records and services to focus on for CDI efforts (AHIMA 2018). A healthcare organization can select a specific number of health records from the healthcare organization and perform an audit to determine if the documentation in the health records meets the expectation of the codes being billed to the insurance company. The findings from the audit can provide the healthcare organization with specific details on what areas of the healthcare organization may be at risk due to missing or incomplete documentation. A successful CDI audit program will evaluate all areas of the healthcare organization to determine the areas that are most out of compliance. Additionally, a healthcare organization may decide to increase the number of audits in high-risk billing areas, such as the focus of any federal government billing audits (AHIMA 2018). For example, if the federal government’s Medicare program is focusing on recovery audits for inpatient psychiatry, a healthcare organization may want to increase the audits in that area to uncover any areas of concern. After audits are completed, the CDI department can start working with departments and physicians to make sure proper documentation exists to support the billing.

Queries The most common tool used for CDI is a query. A query is a communication tool for CDI staff to communicate with providers to obtain clinical clarification, provide a documentation alert, clarify documentation, or ask additional questions regarding documentation. Traditionally, queries have been used to support coding and reimbursement; however, queries are expanding to the process of CDI outside the coding department. Queries may be used to help clarify a complex diagnosis within a health record that does not have proper documentation or clarify procedures that may not be specific enough to support patient care or add a valid code. Queries are used to obtain appropriate reimbursement for the care and services provided to the patient, request more detail regarding the documentation that ­exists, or clarify contradictory documentation. Contradictory information exists when two parts of a ­patient’s health record provide conflicting information. For example, if an operative report states that the patient had surgery on the right leg, but in the progress notes there is information regarding the surgical wound on the left leg, a query may be requested to confirm which leg was operated on. There are two formats of queries for CDI: electronic query and paper query. Both queries contain the same demographic information such as the patient name, ­admission date or date of service, health record number, account number, date query initiated, name and contact of the person who created the query, and a statement of issues to be resolved (AHIMA 2018). For additional information on demographic information, refer to chapter 3, Health Information Functions, Purpose, and Users.

An electronic query is conducted through an EHR and allows the healthcare provider to offer more clarification or specific information regarding the patient’s treatment and diagnosis. The typical process for an electronic query is usually the same format as for a written query, however, the information will be sent electronically and will allow the provider to respond electronically or add an additional clarification note in the health record. A paper query uses a standardized physical document to request clarification or further specify a diagnosis. With the use of paper queries, the health record must be made available to the healthcare provider to review and document the clarification in it. Additionally, the response of the query will be documented on paper and the coder or CDI professional will need access to the entire paper health record after the query is completed. The paper query is retained by the healthcare organization and can be stored within the paper health record or scanned into the EHR. Since the query will support patient care and reimbursement, the healthcare organization must create policies and procedures to manage how the query response will be incorporated into the health record and if it will become part of the legal health record or designated record set (AHIMA 2018b). For additional information on the legal health record, refer to chapter 8, Health Law. See chapter 9, Data Privacy and Confidentiality, for information on the designated record set.

Rules for Writing Queries When writing queries, regardless of the medium, healthcare organizations must ensure they are not leading physicians to document a particular response, but rather requesting clarification or additional specification. Policies and procedures should delineate who to query, when to query, when not to query, the query format, and the management of the query response. In general, a query should be created when health record documentation meets one of the following criteria: “[it] is conflicting, imprecise, incomplete, illegible, ambiguous, or inconsistent; describes or is associated with clinical indicators without a definitive relationship to an underlying diagnosis; includes clinical indicators, diagnostic evaluation, and/or treatment not related to a specific condition or procedure; provides a diagnosis without underlying clinical validation; or is unclear for present on admission indicator assignment” (AHIMA 2016c).

There are multiple types of data queries: further specificity of a diagnosis, inconsistency in documentation, and missing clinical indicators. Figure 6.8 provides examples of two different types of queries with leading and nonleading questions.

Figure 6.8 Examples of queries with leading and nonleading queries

Example Open-Ended Query

A patient is admitted with pneumonia. The admitting H&P examination reveals white blood count of 14,000; a ­respiratory rate of 24; a temperature of 102 degrees; heart rate of 120; hypotension; and altered mental status. The patient is ­administered an IV antibiotic and IV fluid resuscitation.

Leading: The patient has elevated WBCs, tachycardia, and is given an IV antibiotic for Pseudomonas cultured from the blood. Are you treating for sepsis?

Nonleading: Based on your clinical judgment, can you provide a diagnosis that represents the below-listed clinical ­indicators? In this patient admitted with pneumonia, the admitting H&P examination reveals the following:

• WBC 14,000

• Respiratory rate 24

• Temperature 102°F

• Heart rate 120

• Hypotension

• Altered mental status

• IV antibiotic administration

• IV fluid resuscitation

Please document the condition and the causative organism (if known) in the health record.

Example Multiple-Choice Query

A patient is admitted for a right hip fracture. The H&P notes that the patient has a history of chronic congestive heart ­failure. A recent echocardiogram showed left ventricular ejection fraction (EF) of 25 percent. The patient’s home medications ­include metoprolol XL, lisinopril, and furosemide.

Leading: Please document if you agree the patient has chronic diastolic heart failure.

Nonleading: It is noted in the impression of the H&P that this patient has chronic congestive heart failure and a recent ­echocardiogram noted under the cardiac review of systems reveals an EF of 25 percent. Can the chronic heart failure be further specified as:

• Chronic systolic heart failure _______________________

• Chronic diastolic heart failure

• Chronic systolic and diastolic heart failure

• Some other type of heart failure

• Undetermined __________________

Source: AHIMA 2016b, 2.

The CDI process needs professional, objective communication. CDI specialists must have strong written and oral communication skills and have basic knowledge of clinical coding guidelines as well as clinical knowledge and knowledge of documentation requirements. All communication, verbal or written, between the CDI professional and the provider needs to be conducted in a professional manner. Most of the information and detail that will be discussed and concluded based on the findings from the CDI process or query process will need to be documented in the health record and may become part of the health record. Both providers and CDI professionals must ensure communication is professional and appropriate to support patient care and reimbursement (AHIMA 2016c).

Reporting To help support the need for and successes of the CDI program, it is important to establish reporting tools with key performance indicators (KPIs) to provide to leadership and providers. Key performance indicators are measures that can be used over time to determine if a structure, process, or outcome supports high-quality performance measures against best practices. KPIs must align with an organization’s strategy and must be measurable (Malmgren and Solberg 2016). A best practice is to establish a dashboard that is updated on a consistent basis and reviewed for opportunities to expand on areas of concern. Some common reporting areas for a CDI program may include the following:

Discharges available/Discharges reviewed for CDI

Number of queries by provider and impact on diagnosis related group

Number of queries resulting in severity of illness changes

Provider response to queries and turnaround time by provider

Outcomes of CDI queries by physician (agree or disagree with CDI specialist)

Case mix index (CMI) impact by services line

Reimbursement impact by queries (AHIMA 2016c)

The most important part of leading a CDI program is to establish the reporting dashboard and process to make sure that leadership within the healthcare organization understands the need and impact of the program. It allows providers to see and understand the impact of appropriateness of documentation on reimbursement and case mix index (AHIMA 2016c). Figure 6.9 provides an ­example of a monthly query repost rate report.

Figure 6.9 Example of monthly query repost rate report

Source: AHIMA 2016c, p. 37.

Education One of the major goals of a CDI program is to provide education based on the findings throughout the CDI process. CDI education programs should bring knowledge and information back to the healthcare provider to enhance the quality and completeness of documentation to support the severity of illness. In addition, a CDI education program can be brought back to the HIM coders to help support the accurate ­assignment of codes based on the documentation. A CDI education component provides ­usable, efficient, compliant, and meaningful documentation findings to help enhance the patient care workflow, collect complete and accurate data in a timelier fashion, and improve healthcare ­reimbursement (AHIMA 2016c).

Data Management and Bylaws

To help with the facilitation of the collection and assurance of quality data within a healthcare organization, bylaws should be created. Bylaws are written documents that provide details and information regarding the rules and regulations established by a healthcare organization to help support healthcare operations. Part of the bylaws set the expectations of the medical staff for documentation and timeliness of documentation, which directly impacts an organization’s data and information governance. Among the concerns with healthcare operations is ensuring that the information documented in the health record supports patient care as well as quality improvement initiatives and accreditation activities. Additionally, the bylaws should define the processes that align with the organization’s data and information governance strategy regarding the completeness and accuracy of health information within a health record, including expectations of timeliness. Data quality is a common area to analyze for the purposes of healthcare operations and creates a need for healthcare organizations to define minimum standards of clinical documentation. The minimum clinical documentation requirements are most often defined in the bylaws of the healthcare organization. By defining the expectations for documentation and data management in the bylaws, the healthcare organization can hold providers accountable if they are not meeting the expectations and impacting the information governance and data governance processes. Additionally, the establishment of data collection and data quality requirements in bylaws can help support and ensure proper documentation as required by the healthcare organization to support the data management processes. Another area commonly addressed in bylaws is ensuring compliance with federal and state laws and regulations through provider contracts and hospital bylaws.

Provider Contracts with Healthcare Organizations

In the ambulatory care setting, healthcare providers enter into a contract with a healthcare organization to provide patient care. The contracts delineate all expectations of the provider as they care for patients in a specific ambulatory care setting. When creating a provider contract, requirements for data quality should be established. These requirements should include documentation and timeliness of documentation within the health record. For example, a provider contract will state that all labs must be reviewed and signed within 24 hours of completion of the lab test. The contract will also include consequences if minimal requirements are not met, such as the cancellation of the contract in the event of a breach.

Hospital Bylaws

Hospital bylaws are written documents that govern the staff members, both medical providers and non-physician providers, who create data within the health record for additional support of patient care and reimbursement. Since medical providers are not the sole authors of clinical documentation, it is important for hospitals to define who can document within the record, the type of documentation that can occur, and the timeliness and completeness of that documentation. Common healthcare professionals who enter information are nurses, ancillary support, therapists, social work, health unit coordinators, and other support staff given rights to document within the record. As with the medical staff bylaws, clear and concise expectations of data entry and documentation should be established, and training provided for all healthcare employees. The hospital bylaws support data governance and data quality across the spectrum of care.

Data Management and Technology

Currently, most of the work supporting data management within healthcare happens in an electronic manner. This is due to the implementation of EHRs and other electronic information systems used to support patient care. Because of the amount of data available in healthcare organizations, data have become valuable assets. While this causes some concern with the amount of data collected by the healthcare organization, it also allows healthcare organizations to use the data to make decisions based on information derived from the data. One of the powerful aspects of having data in electronic format is the ability to use technology to assist in the management of the data.

There are many benefits to using technology in a healthcare organization. Technology can be used to support data management and the implementation of information governance and data governance. Technology can also be used to facilitate working across teams. With advances in technology and the increase in data created, the need for new forms of data management through technology will continue to be a priority in healthcare to ensure standardization in the collection and management of data.

HIM Roles

Many different roles exist for HIM professionals in data management. These roles may exist within a healthcare system, a physician clinic, an insurance company, or a vendor that supports a healthcare organization. HIM professionals can lead an organization’s IG initiative as an Information Governance Program Director, support the IG initiative as a data steward or business analyst, support the information systems and data collection as a database administrator, or take on the role of data analyst. HIM roles require the ability to gather information, analyze the information, and transform data into powerful information the healthcare organization can use for strategic, regulatory, quality, and reimbursement purposes.

HIM professionals have always advocated for clear, accurate, and complete documentation in the health record. HIM professionals fit perfectly in the CDI role as they understand medical coding including guidelines, documentation requirements, the need for complete and accurate information, and billing and reimbursement requirements. The clinical documentation specialist is a new role established to improve work processes related to documentation by communicating with providers, improving clinical documentation design, and ensuring accurate documentation to support code assignment. The clinical documentation specialist must have a strong working relationship with the providers and feel comfortable requesting ­additional information via query processes.