Health Analytics

Structured Data: Essential for Healthcare Analytics & Interoperability

Discrete Diagnostic Information is the New Currency of Healthcare

Kim Futrell, MT(ASCP) October 2013

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013

Discrete Diagnostic Information: The New Currency in Healthcare ................................................................. 2

Structured Data Defined ............................................................................................................................................. 2

Going Forward: Analytics Required .......................................................................................................................... 3

The Need for Interoperability .................................................................................................................................... 4

Standards Needed to Convey Structured Data ........................................................................................................ 5

Electronic Laboratory Reporting (ELR) for Public Health Reporting ................................................................ 6

Laboratory Results Interface Initiative (LRI) ........................................................................................................... 6

HL7 2.5.1 has Interoperability as its Goal ................................................................................................................ 7

MU Requirements ........................................................................................................................................................ 9

Standardized Vocabularies like LOINC .................................................................................................................. 10

LOINC Conjoined with SNOMED........................................................................................................................ 12

Structured Data for Pathology Reporting ............................................................................................................... 13

Synoptic Reporting: Structured or Not? ................................................................................................................. 14

Summary ...................................................................................................................................................................... 16

Notes ............................................................................................................................................................................ 17

Page 1

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013 Discrete Diagnostic Information: The New Currency in Healthcare Structured diagnostic data, discrete and codified, is essential to the future of healthcare. As the Affordable Care Act pushes healthcare organizations towards value-based reimbursement models based on measurable outcomes, the very essence of demonstrating improved outcomes and decreased costs will rely on the use of discrete, codified, structured data.

The future of healthcare will be built on data—data to support population health management and analytics to improve outcomes. Healthcare organizations need the data and analytics necessary to understand the health of their patient population and use that data to better understand how to keep people healthy. The use of structured clinical data and the standards that support it is necessary to enable the development and utilization of population health management, translational research, artificial intelligence, and personalized medicine. Meaningful Use (MU), ICD-10, LOINC® (Logical Observation Identifiers Names and Codes), and SNOMED CT® (Systematized Nomenclature of Medicine—Clinical Terms) exist to structure and codify healthcare data—data used to monitor and measure outcomes, and to develop future treatment protocols.

Structured Data Defined The definition of structured data is very simply a literal translation; it refers to data or information that is organized in a structured manner, making it computer “processable” and identifiable for data- mining and analytic purposes. Structured data that resides in fixed or discrete fields within a record or file can also be classified as discrete. Commonly structured data is captured by the use of standard vocabularies, templates, drop-down lists, radio buttons, and check boxes to capture discrete data; whereas free text, on the other hand, is not structured.1

A key element in maximizing the value of structured data is to use codified, standardized vocabularies, such as LOINC or SNOMED CT. Structured, discrete data encoded with a standardized vocabulary allows disparate systems to communicate by ensuring data is structured in the same way, categorizing and adding meaning to terms that will facilitate quick, clear understanding and retrievable data. Using HL7 (Health Level 7) as the standard for HIT (Healthcare Information Technology) interoperability, to format the flow of data between systems, is another facet that aids in achieving precise electronic communication.

Information flowing in and out of EHR and HIE systems needs structure to provide the most value. Clinical data produced by the laboratory makes up a large percentage of data in the patient electronic medical record. The laboratory needs the ability to send codified, structured data wherever it needs to go. Codified, structured data from the laboratory is essential for MU Stages 2 and 3 to definitively identify specific lab tests as they are electronically communicated across systems.

Page 2

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013 Going Forward: Analytics Required Along with clinical integration and a strong HIT infrastructure, it’s important to understand that imperative to a value-based healthcare facility are analytics that measure and demonstrate outcomes and “value”. The foundation for those analytics is structured data—data that can be mined and shared with other systems. Structured data is available in a controlled format or vocabulary, rather than in free text. The consistency of structuring data allows for statistical research, business intelligence reporting, and data interoperability that cannot be obtained with unstructured or narrative data.

Figure 1: Structured Data  Analytics

Traditionally, there has been a lack of focus on the importance of structured data in the healthcare industry. It is a known issue that facilities struggle with systems that lack interoperability, posing a significant barrier to the exchange of data among healthcare providers. However, in order to achieve success in a value-based environment, structured data will become essential. Already many healthcare initiatives are requiring structured data (e.g. MU, Accountable Care Organizations, Patient-Centered Medical Homes, HIEs, etc.). These programs require specific and thorough methods of reporting to demonstrate the quality of clinical data and to provide clinical decision support to providers.

Sharing the laboratory’s clinical data in a structured format can assist in coordinating clinical care, as well as providing valuable data for population health management, clinical decision making, and research efforts—making structured data crucial for facilities to succeed in a value-based reimbursement model.2 Data mining will become vital as healthcare puts more emphasis on outcomes. In order to collect the data necessary to achieve the analytics required to report quality measures, it is essential that the data be formatted in a structured way.

Page 3

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013 The Need for Interoperability Because the scope of healthcare interoperability touches on many areas (e.g., between departments within a facility, between ancillary systems, from EHRs to research databases, etc.), agreeing on a definition for healthcare interoperability has brought on much debate.3 The basic idea, however, is to be able to exchange information electronically, securely, accurately, and verifiably, when and where needed, using standardized, coded languages.

This study of the term “interoperability” in healthcare has identified three major types of interoperability—technical, semantic, and process (see Figure 2). The level of interoperability needed to support healthcare is not limited to technical interoperability, which is simply exchanging the data; both semantic and process interoperability are also needed.

Semantic interoperability is exchange of information that is meaningful, along with the context of that information. The HL7 Interoperability Workgroup in Coming to Terms: Scoping Interoperability for Health Care, explains semantic interoperability: “It takes a standardized vocabulary and goes beyond structured data into communicating the intent or meaning of the data to the end user. This is essential for clinical care due to the complexity of the domains involved and the nuanced implications of information for the overall care of a patient.”3

Figure 2: Levels of Interoperability: Source- www.hln.com/assets/pdf/Coming-to-Terms-February-2007.pdf

Levels/Types of Interoperability

• is concerned with connectivity—ensures that the message was exchanged completely and in correct format.

Technical

• is the ability of information shared by systems to be understood , typically by using codes and identifiers.

Semantic • enables shared human understanding that is

needed to coordinate work processes and enable business systems to interoperate; essential to provide benefits within the organization.

Process

Page 4

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013 Semantic interoperability is most easily achieved when all applications are using consistent coding standards that accurately describe the clinical terms, similar to two people speaking the same language, resulting in a conversation that both parties clearly understand.4 Furthermore, as we continue to work through the stages of MU and other outcomes-based healthcare models, we will move into the use of process interoperability to develop improved work processes.

Interoperability specific to the lab is explained in CAP’s white paper Laboratory Interoperability Best Practices: “In order for systems to be interoperable, there must be a shared understanding of what certain concepts mean—in order to transmit test results successfully, we need to have a system for ensuring that both the sending and receiving system know how to interpret and file a given result.”5

In order to properly exchange the laboratory’s discrete, structured, codified data, interoperability will be crucial. Lab results are a fundamental part of the patient record. Without lab interoperability, many aspects of healthcare can be hindered, such as decision support, transitions of care, and quality reporting. Unclear, non-standardized or missing lab data can result in increased costs or potential for harm.6 Benefits of lab interoperability include timely result delivery, error reduction, improved tracking, and improved data analytics.

Standards Needed to Convey Structured Data The vast amount of infrastructure, interoperability, and standards necessary to convey structured data in a meaningful way involves multiple pilot projects and teamwork among experts from many areas of healthcare. Figure 3 is an introduction to some of the standards involved.

Abbrev. Standard Name Standard for:

ELR Electronic Laboratory Reporting electronic transmission from laboratories to public health for reportable conditions

LRI Laboratory Reporting Interface Initiative

electronic reporting of ambulatory care laboratory test results into an EHR

HL7 Health Level 7 electronic message format for the exchange of electronic health information

LOINC Logical Observation Identifiers Names and Codes

universal identifiers for laboratory and other clinical observations to facilitate exchange and storage of clinical results; selected as the preferred standard of HL7 for identifying laboratory tests.

SNOMED CT

Systematized Nomenclature of Medicine—Clinical Terms

comprehensive nomenclature of clinical medicine for the purpose of accurately storing and retrieving clinical records; coding system is hierarchal with relationships based on description logic.

Figure 3: Standards List

Page 5

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013 Electronic Laboratory Reporting (ELR) for Public Health Reporting The standard applicable to electronically submit laboratory results specifically to public health laboratories is Electronic Laboratory Reporting (ELR). Defined in 1997 by the Centers for Disease Control and Prevention (CDC) and other public health agencies, ELR is used to transmit reportable conditions—specified diseases or conditions that state regulations mandate healthcare providers and laboratories report to health departments. Laboratory reports are critical to public health surveillance because they initiate investigations of cases of reportable diseases, outbreaks of infections, or potential terrorist activity.7 When using ELR, laboratories export data from their information systems in a standard file format and send it to their state health department electronically through a secure interface. Using ELR standards makes reporting to public health agencies more streamlined and accurate.

Laboratory Results Interface Initiative (LRI) The ONC (Office of the National Coordinator) has focused on the challenge of structured data capture (SDC) through a Standards & Interoperability (S&I) Framework initiative. One of the current S&I initiatives is the Laboratory Results Interface Initiative (LRI). The mission of the LRI is to enable ambulatory primary care physicians to receive and meaningfully use standardized structured electronic lab results, building on existing HL7 2.5.1-based lab reporting guides, starting with ambulatory care and adding other uses down the road.8 Clinical laboratory test results will be formatted as standardized structured data so that they can be incorporated that way into a certified EHR. The LRI focuses on identifying requirements, specifications and standards, and providing implementation guidance for electronically reporting laboratory test results. The requirements are directed at laboratory test result reporting between a laboratory information system and an ambulatory EHR system in different organizational entities. However, the resulting implementation guide may also be useful within organizations and in non-ambulatory care settings.

For both ELR and LRI transmission guides, the consensus is the use of LOINC and SNOMED CT as the standardized vocabularies, as well as UCUM (Unified Code for Units of Measure) vocabulary for analyte reporting units. Additionally, the use of unique global object identifiers or OIDs to link to key information is mandated in HL7 2.5.1. These information objects include patients, orders, providers and organizations. This unique code ensures that each identifier can be broadly shared among independent healthcare organizations and still point to its originally associated object.9

Page 6

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013 HL7 2.5.1 has Interoperability as its Goal HL7 (Health Level Seven International), the global authority on standards for interoperability of HIT, is an organization that provides the comprehensive framework and related standards for the exchange, integration, sharing, and retrieval of electronic health information to support clinical practice, management, delivery, and evaluation of health services.10 HL7 is the standard for exchanging information between medical applications, within a specific format, for the transmission of health- related information. Information sent using the HL7 standard is sent as a collection of one or more messages, each of which transmits one record or item of health-related information.11

A sample HL7 formatted message looks like this:

MSH|^~\&|IA PHIMS Stage^2.16.840.1.114222.4.3.3.5.1.2^ISO|IA Public Health Lab^2.16.840.1.114222.4.1.10411^ISO|IA.DOH.IDSS^2.16.840.1.114222.4.3.3.19^ISO|IA DOH^2.16.840.1.114222.4.1.3650^ISO|201203142359||ORU^R01^ORU_R01|2.16.840.1.114222 .4.3.3.5.1.2-20120314235954.325|T|2.5.1|||AL|NE|USA||||PHLabReport- Ack^^2.16.840.1.113883.9.10^ISO SFT|Orion Health^L|2.4.3.52854|Rhapsody|2.4.3.52854||20070725111624 PID|1||14^^^IA PHIMS Stage&2.16.840.1.114222.4.3.3.5.1.2&ISO^PI^IA Public Health Lab&2.16.840.1.114222.4.1.10411&ISO||Joe^Patient^^^^^L||19630815|M||2106- 3^White^CDCREC^^^^04/24/2007~1002-5^American Indian or Alaska Native^CDCREC^^^^04/24/2007|721 SPRING STREET^^GRINNELL^IA^50112^USA^H|||||M^Married^HL70002^^^^2.5.1||||||H^Hispanic or Latino^HL70189^^^^2.5.1 ORC|RE||986^IA PHIMS Stage^2.16.840.1.114222.4.3.3.5.1.2^ISO||A|||||||^SAWYER TOM MD^^^^^^^^L|||||||||MISSOURI DEPARTMENT OF HEALTH LABORATORY - MISSOURI DEPARTMENT OF HEALTH LABORATORY^L|307 W MCCARTY ST^^JEFFERSON CITY^MO^65101^USA^B|^WPN^PH^^1^^5555555 OBR|1||986^IA PHIMS Stage^2.16.840.1.114222.4.3.3.5.1.2^ISO|625-4^Bacteria identified in Stool by Culture^LN^^^^2.33^^Enteric Culture|||20120301|||||||||^SAWYER TOM MD^^^^^^^^L||||||201203140957|||P NTE|1|L|Enteric culture includes testing for Salmonella, Shigella, Campylobacter, Yersinia, E.coli O157:H7 \T\ other STECs, and Aeromonas|RE^Remark^HL70364^^^^2.5.1 OBX|1|CWE|625-4^Bacteria identified in Stool by Culture^LN^^^^2.33^^result1|1|27268008^ Salmonella^SCT^^^^20090731^^Salmonella species|||A^A^HL70078^^^^2.5|||P|||20120301|||^^^^^^^^ Bacterial Culture||201203140957||||State Hygienic Laboratory^L^^^^IA Public Health Lab&2.16.840.1.114222.4.1.10411&ISO^FI^^^16D0648109|State Hygienic Laboratory^UI Research Park - Coralville^Iowa City^IA^52242- 5002^USA^B^^19103|^Atchison^Christopher^^^^^^^L SPM|1|^2012999999&IA PHIMS Stage&2.16.840.1.114222.4.3.3.5.1.2&ISO||119339001^Stool specimen (specimen)^SCT^SL^Stool^L^20090731^v unknown|||||||P^Patient^HL70369^^^^2.5.1||||||20120301| 201203061451

Page 7

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013

What’s in each HL7 Segment? Abbrev. Segment Information sent in segment MSH Message Header Provides sending system and facility information,

receiving system and agency information and contains the message control ID used to uniquely identify each message sent from the facility.

SFT Software Provides information about the sending application or the integration engine used to manipulate the message before the receiving application processes the message.

PID Patient Identification Provides basic patient demographics. ORC Common Order Information

Provides basic information about the order; includes identifiers for the order, who placed the order, when it was placed, what action to take regarding the order, etc.

OBR Observation Request Used to capture information about one test being performed on the specimen; identifies the type of testing requested and ties that information to the healthcare provider ordering the test.

NTE Notes and Comments Used to convey additional comments regarding the associated segment.

OBX Observation Result Contains information regarding a single observation related to a single test (OBR segment) or specimen (SPM segment); includes identification of the specific type of observation, the result for the observation, when the observation was made, etc.

SPM Specimen Information Describes the characteristics of a single sample, e.g., type of specimen, where and how it was collected, who collected it, and basic characteristics.

Figure 4: HL7 Message: Source: www.idph.state.ia.us/adper/common/pdf/idss/handout_080212.pdf

HL7 Standards are foundational to MU. With passage of the 2009 American Recovery and Reinvestment Act’s Health Information Technology for Economic and Clinical Health Act (ARRA- HITECH), HL7 versions 2.3.1 and 2.5.1 were specifically selected as the healthcare standards to be used to attain Meaningful Use criteria (see Figure 5).

Page 8

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013 Among other differences, HL7 version 2.5.1 is distinguished from the previous version 2.3.1, by the additional requirement of reporting laboratory results containing additional fields to specify the performing laboratory. This was added to version 2.5.1 to comply with CLIA requirements and affords substantial value for public health purposes.12

Figure 5: MU and HL7 2.5.1: Source: http://www.corepointhealth.com/geni/meaningful-use-stage-2-how-final-rule-impacts-integration

MU Requirements In order to comply with Meaningful Use rules, providers have to capture much of their clinical data as "structured data," namely discrete, searchable data that appears in identifiable fields. In order to meet Stage 2 MU requirements, laboratories must incorporate more than 55% of all clinical laboratory test results into Certified EHR Technology (CEHRT) as structured data, and this percentage is expected to increase to 80 in Stage 3 (see Figure 6).13

Stage 2 MU criteria that requires HL7 2.5.1

• Clinical quality measure data import, export, and electronic submission

• Transmission to public health agencies for surveillance

• Transmission to immunization registries • Transmission of reportable laboratory tests and

values/results

Page 9

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013

MU Applicable to Laboratories

Stage 2 Expected for Stage 3

EP (Eligible Professionals) AND EH (Eligible Hospitals) Core Objectives Computerized Physician Order Entry (CPOE)

Use CPOE for more than 60% of medication, 30% of laboratory, and 30% of radiology.

Expected to increase to 55%

Lab Results Incorporate lab results as structured data into certified EMR for more than 55%.

Expected to increase to 80%

Additional EH Core Objective Lab Results to Public Health

Successful ongoing transmission of lab results to public health agencies

No change expected

Menu option for EH Lab Results Provide structured electronic lab results

to EPs for more than 20%. Expected to increase to 80%

Figure 6: MU for Labs

Standardized Vocabularies like LOINC In order to identify textual data, each specific data element has to be linked to a specific, unique code (typically alpha-numeric) that translates it. These standardized vocabularies are used to distinguish diagnostic terms and are developed by recognized groups of experts. Coding languages ensure that results are managed to remain specific, contextually consistent, organized, and appropriately granular in the system database, data views, and reports. 14

LOINC is one of several standards used to exchange clinical health information, and has been selected as the preferred standard of HL7 International for identifying laboratory tests.15 LOINC codes embedded in HL7 messages allow healthcare facilities who receive messages from multiple sources to automatically electronically file those results into the right slots in the patient EHR, research, or public health systems.16 It allows broad distribution of healthcare information without the need for individual institutions to exchange master files for data such as test codes, result codes, etc. Each institution maps its own local vocabularies to the standard code, allowing information to be shared broadly, rather than remaining isolated as a single island of information. Standard vocabularies, particularly coded laboratory results, enable more automated decision support for patient healthcare, as well as more automated public health surveillance of populations.9

Page 10

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013 LOINC was created in 1994 by the Regenstrief Institute Inc., in response to demand for electronic transmission of clinical data. LOINC assigns identifying codes to more than 70,000 medical terms to enable transmission into an EHR.17 Because of the extensive number of codes, there is a program called Regenstrief LOINC Mapping Assistant (RELMA®) available to assist with LOINC mapping. Both LOINC and RELMA are downloadable from the LOINC website: http://loinc.org/.

LOINC codes contain six parts in a specific order, each with a discrete meaning that together describe in intricate detail how a particular lab test is performed (see Figure 7).

Figure 7: LOINC Breakdown: Source - loinc.org/

The purpose of the LOINC database is to provide universal identifiers for observations in HL7 messages. In the setup of HL7, the OBX-3 field holds the observation identifier and the OBX-5 field holds the observation value. Specifically, LOINC provides a code system to fill the observation identifier field (OBX-3) of the HL7 observation reporting message.18 Historically, laboratories have sent their own internal identifying codes in the OBX-3 field of the HL7 interface message. Each laboratory could have unique codes for tests, creating difficulty in interface communication. Large healthcare institutions have taken advantage of LOINC to standardize the information coming from many different sources.

Component • The substance or entity being measured or observed, such as glucose, sodium or lipids.

Property Measured

• The characteristic of the analyte, such as mass concentration or enzyme activity.

Timing • The interval when the test was taken, such as a

one-point-in-time result or a result compiled over the course of 24 hours.

System • The organic origin of the specimen, such as

from whole blood, urine or spinal fluid, or from a substance such as the liver or tissue.

Scale

• Quantitative (a true measurement); ordinal (ranked set of options); nominal (responses that do not have a natural ordering, such as names of bacteria); and narrative, (description, such as dictation results from X-rays).

Method • Where relevant, a LOINC code includes the method used to produce the result.

Page 11

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013 The use of LOINC codes to identify laboratory tests can provide major benefits to organizations that receive such messages because it allows them to organize and analyze results from many HL7 sources without manual labor. Healthcare organizations can import test results into their EHR in a codified manner when their source laboratories include LOINC codes in their HL7 messages.18

Thus, LOINC aids in data interoperability. The granularity inherent in LOINC ensures accurate comparison of data from multiple disparate sources. This is imperative in the move to improve patient care and reduce the costs associated with redundant and duplicative testing.16 Going forward, it will be the commonly accepted terminology for lab and other test results, as providers begin to exchange larger volumes of information across organizational boundaries. Using structured data coded by LOINC will help expand the interoperability of healthcare data, thus presents a form of structured data that can be used to improve quality analysis and patient care.

Additionally, greater interoperability of lab results means organizations can aggregate larger volumes of data for individual patients and to support population health management. The ability to aggregate this data in the same language allows providers to more easily and accurately analyze and report their patient care performance. By fostering this kind of interoperability, LOINC can help healthcare organizations achieve better patient care and improved revenue in the years to come.

LOINC Conjoined with SNOMED So how do LOINC and SNOMED work together and why do we need both? An easy way to think about it is that LOINC provides codes that ask the question and, where needed, SNOMED provides codes for the answer.19 LOINC includes codes that identify the test observation per se, (e.g., serum glucose or blood culture), not the codes that might be reported in the values of some test observations. Simply put, LOINC is used to code the testing method, whereas SNOMED CT portrays non-numeric answers. In HL7 messages, LOINC provides codes for the question in fields OBR-4 and OBX-3, while SNOMED provides codes for the answers in OBX-5.20

But for more complex test results, SNOMED CT is needed to properly define testing.21 SNOMED is a clinical terminology which provides clinical content and expressivity for medical documentation with greater granularity than LOINC, and must be used when describing more complicated, process driven tests, such as anatomic pathology and microbiology.21

Page 12

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013 A common interaction between LOINC and SNOMED CT occurs in microbiology. For example, when a culture is ordered, the order will be coded in LOINC, however the most sophisticated communication LOINC can offer for the results would be the general microbe physical description, e.g. Gram negative Cocci. Therefore, Meaningful Use requires that the result be coded in SNOMED CT.16 SNOMED CT can take the result of the complex biogram, the organism's name, and create the binary code to communicate it across an interface. The Gram negative Cocci can then be fully identified. Then, LOINC can be used to communicate the antimicrobial susceptibility panel results.

Because of this connection between the two coding systems, there is an initiative by the National Library of Medicine and Regenstrief Institute currently under way to map LOINC to SNOMED CT for a closer integration between the two terminologies.16 Figure 8 lists SNOMED CT benefits suggested by the LRI.

Figure 8: SNOMED Benefits Per LRI

Structured Data for Pathology Reporting In the realm of anatomic pathology, the pathology report is steadily developing into more than a one- way communication from the pathologist to the physician. It is becoming more data-rich and vital to individual patient treatment decisions.22 Cancer diagnoses account for many of the specimens reviewed in pathology labs. Traditional surgical pathology reports provide basic information such as tumor type, grade and stage (tumor size/extent, margin involvement, and lymphatic involvement) in order to provide a morphologic perspective on general tumor makeup. The modern surgical pathology report has evolved to become complex and to include detailed information on tumor biology that incorporates morphology data, as well as new molecular technologies, often mandated for synoptic reporting.

SNOMED: Potential Benefits per Laboratory Results Interface Initiative (LRI)

Adopted by public health, especially

for organism names

Used in combination with

generic LOINC terms (for

cultures), it will help automate

detection of reportable lab

results

Used in reporting absence and

presence findings (detected, not

detected, etc.), it helps to enable automation and aggregation of

results

Increased use across the lab domain may potentially

increase interoperability while preserving

the local language

Page 13

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013 Traditional narrative pathology reports are reflective of a given pathologist’s style; therefore they include significant variability in format, context, and content. With the increasing complexity demanded of the modern surgical pathology report, necessary elements are occasionally overlooked. Having the pathology report organized in a discrete, codified, and structured format ensures that no key elements are missed. Overlooking vital results in a pathology report can lead to delay in treatment and increased costs.22 Most pathologists understand that free text information cannot be parsed effectively, and that only well-defined vocabularies and fully contextually defined data will provide efficient and unequivocal data streams between systems. 14

Synoptic Reporting: Structured or Not? In an attempt to standardize and improve pathology reporting, the American College of Surgeons Commission on Cancer (ACS-CoC), along with the College of American Pathologists (CAP) created checklists that trigger synoptic reporting and mandated that pathology reports at accredited cancer hospitals contain all of the scientifically validated data elements that need to be reported for cancer specimens. Synoptic reporting for pathology specimens provides uniform standardized data elements in the form of checklists to ensure that pathologists make note of all pertinent findings. These synoptic checklists make reporting efficient, uniform, and complete, especially for major tumors. In addition, synoptic reporting increases efficiency, reduces transcription errors, and improves specimen turnaround time.23

The difference between synoptic reporting and structured data is a source of constant confusion. Synoptic simply means to provide a summary of the pertinent findings—a general view of the whole. However, a synoptic report is capable of providing structured data sets that correspond to the synoptic elements. If it does, then it is structured data in addition to being a synoptic report.25 Synoptic reporting goes hand-in-hand with structured data. As long as the data is formatted into individual data elements, it is possible for a computer to intelligently analyze those individual elements for clinical and research purposes.

However, not all synoptic reports contain structured data. Many are simply word processing documents with a structured appearance. Thus, synoptic reports structure and clarify findings for clinicians, whereas structured data clarifies findings for computers. Upfront capture of consistently structured data clearly provides much better results.25 Mineable, retrievable, accessible data has far more value than narrative text in providing relevant, searchable information. Structured data reporting creates the advantage of data capture for future reference.25

Page 14

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013 The ideal form of synoptic reporting involves entering information as discrete data elements. These discrete data elements are passed to a relational database where they are organized and can be efficiently searched and retrieved. In contrast, are databases that are based on natural language processes, where there is no logical organization of the words within the report, causing search and retrieval of natural word elements to be cumbersome. As opposed to synoptic reporting, data stored as free-text words lacks relational structure, making retrieval inefficient and slow.

Synoptic reporting, using structured data enables quick, complete and concise documentation while increasing clinician satisfaction with reports. With synoptic reporting, the pathologist completes prearranged data entry templates, often choosing from finite drop-down options to promote consistency. The data-mining capabilities supported by structured, synoptic reporting allows for the retrieval and comparison of pathology data that can directly impact patient care and treatment protocols.25

However, a number of pathologists remain reluctant to embrace the front-end data input required for structured, synoptic reporting. Some pathologists feel that synoptic reports are somewhat cumbersome and time-consuming because they may require additional steps to enter and edit worksheets as compared to usual free text reports. Despite the benefits of structured reporting, a large portion of historical data and free text practice still exists.26

With synoptic reporting, clinical data as well as research data can be captured, creating a uniformity of data capture that allows for subsequent ease of data extraction and review. With more powerful capture of information, key data elements stored in the LIS database can be quickly accessed to provide the desired information for business intelligence, research or personalized treatments. Because of this feature, synoptics are being recognized as the future of pathology reports.23

Figure 9: Structured Data Benefits: Source: Essential of Anatomic Pathology, 3rd Ed.

• Easily define synopsis of diagnostic findings • Creates a standard framework for each disease category • Promotes rapid comprehension of data • Forces standardization within a group • Ensures completeness while encouraging brevity • Data in one field can be used to generate data in another

field—reduces errors • Can create clinically significant auto comments, graphs, or

list of suggested follow-up • Logic rules can be used to provide data based on input to

guide prognostic information or follow-up

Structured Data Reporting Benefits

Page 15

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013 Summary Structured data is essential for precise data-mining needed to capture and organize the analytics needed to monitor the health of a patient population. The historic lack of structured data and standardization in the healthcare industry causes problems when sharing patient data between providers. Currently, the healthcare industry is far from the desired state where patients have one complete, accurate EHR from which the quality of their individual health care can be monitored and maintained.

Cultural barriers to adopting standards and using structured data do exist. While structured data is required to aggregate, report and transmit data at the point of care, it is often perceived by physicians to inhibit their ability to practice medicine and document in a fashion they feel is most effective.27

Many healthcare initiative programs (Meaningful use, Accountable Care Organizations, HIEs, etc.) require structured data. Healthcare facilities need a robust level of reporting to measure the quality of clinical data and provide clinical decision support tools to physicians and hospitals. The goal is to exchange structured clinical data with accuracy and precision to improve patient care. Ideally this would include data that is codified in a standard terminology that supports semantic interoperability and is structured in a way that enables data retrieval and data exchange via a standardized messaging format that supports data integrity.

Dr. Michael Glant, pathologist and Medical Director at Orchard Software summarizes, “I am committed to structured data and standardized vocabularies because these two components are powerful tools in defining healthcare outcomes. It is clear that various healthcare IT systems will need to aggregate data to determine outcomes. These same datasets will usher in new ways to manage patient care with increased safety and efficiency. All of this demands granular, standardized, and structured data.”

Page 16

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013 Notes

1. American Medical Association. Meaningful Use Glossary and Requirements Table. 2011-2012. Accessed at: www.ama-assn.org/resources/doc/hit/meaningful-use-table.pdf.iwnew

2. Fridsma, MD. Doug. EHR Interoperability: The Structured Data Capture Initiative. HealthITBuzz. Jan. 2013. Accessed at: www.healthit.gov/buzz-blog/electronic-health-and-medical-records/ehr- interoperability-structured-data-capture-initiative/

3. Health Level Seven EHR Interoperability Work Group. Coming to Terms: Scoping Interoperability for Health Care. Feb 2007. Accessed at: www.hln.com/assets/pdf/Coming-to- Terms-February-2007.pdf

4. Brull, Rob. Vocabulary Recommendation, A Step Towards Semantic Interoperability. HL7 Standards. Sept 2011. Accessed at: www.hl7standards.com/blog/2011/09/29/vocabulary- recommendation-a-step-towards-semantic-interoperability/

5. Beckwith, Bruce A. MD, et al. Laboratory Interoperability Best Practices. CAP White Paper. March 2013. Accessed at: www.cap.org/apps/docs/committees/informatics/cap_dihit_lab_interop_final_march_2013.pdf

6. Alaska eHealth Network. Identifying Opportunities for Innovation with HIT Lab Results Information Exchange. 2012 Lab Stakeholder Meeting. Accessed at: dhss.alaska.gov/HIT/Documents/2012_Lab_Stakeholder_Meeting_v.05.pdf

7. Wooster, Ginger, MBA, MLS. Debugging Microbiology LOINC. Accessed at: www.orchardsoft.com/blog/?cat=39

8. The Office of the National Coordinator for Health Information Technology. S&I Framework Laboratory Results Interface (LRI) Initiative Update. Accessed at: wwwn.cdc.gov/cliac/pdf/Addenda/cliac0811/F_addendum_Asnaani_S_and_I_LRI.pdf

9. S&I Framework. LRI Public Health Reportable Lab Results. Accessed at: wiki.siframework.org/LRI+Public+Health+Reportable+Lab+Results+-+Abbreviated+Use+Case

10. Health Level Seven International. Accessed at: www.hl7.org/ 11. Interfaceware HL7 Overview. Accessed at: www.interfaceware.com/hl7.html 12. HIT Standards Committee’s Surveillance Implementation Guide Power Team Report. Accessed at:

www.healthit.gov/sites/default/files/4-chute_sig_powerteam_report_8-17-11-final.pdf 13. Meaningful Use Workgroup Stage 3 Recommendations. Accessed at:

http://www.healthit.gov/sites/default/files/080102_muwg_stage3_recommendations.pdf 14. Glant, Michael D. MD. Challenges for Anatomic Pathology in an Integrated World. Accessed at:

www.orchardsoft.com/customer_area/main/download/download_center_misc.asp#symposium 15. SearchHealthIT. LOINC (Logical Observation Identifiers Names and Codes). Accessed at:

searchhealthit.techtarget.com/definition/LOINC 16. Levy, MD. Brian. LOINC: Five Things Provider Organizations Need to Know. ADVANCE for

Health Information Professionals. May 2013. Accessed at: health- information.advanceweb.com/Features/Articles/LOINC-Five-Things-Provider-Organizations- Need-to-Know.aspx

Page 17

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013

17. LOINC from Regenstrief. Accessed at : loinc.org/ 18. McDonald, CJ, et.al. LOINC, a Universal Standard for Identifying Laboratory Observations: A 5-

Year Update. Clin Chem. Apr 2003. Accessed at: www.ncbi.nlm.nih.gov/pubmed/12651816 19. Vreeman, Daniel, et al. Meeting terminology requirements for order entry and result reporting

Regenstrief Institute/ International Health Terminology, Standards Development Organisation. Accessed at: loinc.org/collaboration/ihtsdo/2012%2010%2025%20- %20Ihtsdo_Showcase2012_OrderEntryResultReporting.pdf

20. Jo Anna. LOINC and other standards. Dec 2010. Accessed at: loinc.org/faq/getting-started/loinc- and-other-standards/

21. Baker, Ginger. A Trip Through a New Landscape, LOINC, SNOMED and ICD-10, Oh, My! ADVANCE for Administrators of the Laboratory. Dec 2012. Accessed at: laboratory- manager.advanceweb.com/Features/Articles/A-Trip-Through-a-New-Landscape-Part-2.aspx

22. Mammen JJ, Tuthill MJ. Structuring data in anatomic pathology reports. Indian J Pathol Microbiol 2009

23. Utility and applications of synoptic reporting in pathology. Accessed at: wiki.ihe.net/index.php?title=Anatomic_Pathology_Structured_Reports

24. Baskovich, Brett W and Robert W Allan. Web-based synoptic reporting for cancer checklists. JPath Inf. Feb 2011. Accessed at: www.jpathinformatics.org/article.asp?issn=2153- 3539;year=2011;volume=2;issue=1;spage=16;epage=16;aulast=Baskovich

25. mTuitive white paper. Business Case for Synoptic Reporting and Beyond. Accessed at: ssr- anapath.googlecode.com/files/SynopticReporting.pdf

26. Nguyen, Anthony, et al. Structured Pathology Reporting for Cancer from Free Text: Lung Cancer Case Study. eJHI. 2012

27. Murray, Tom and Laura Berberian. The Importance of Structured Data Elements in EHRs. Computerworld guest blog. March 2011. Accessed at: blogs.computerworld.com/18057/the_importance_of_structured_data_elements_in_ehrs

Page 18

Structured Data: Essential for Healthcare Analytics & Interoperability October, 2013

For more information, visit www.orchardsoft.com or call us at (800) 856-1948.

Write the author at [email protected]

Page 19