Amy Sheide Clinical Informaticist 3M Health Information Systems USA Achieving Data Standardization in Health Information Exchange and Quality Measurement

Abstract This presentation reviews the benefits and challenges of achieving and maintaining interoperability. Specifically, it showcases successful implementation of a centralized terminology server in health information exchange, biosurveillance and quality measurement.

Exchang e Interpret Share Understand Background “Interoperability describes the extent to which systems and devices can exchange data, and interpret that shared data. For two systems to be interoperable, they must be able to exchange data and subsequently present that data such that it can be understood by a user.”

Benefits of interoperability Delivery of High Quality Cost Effective Care Process Improvement Coordination Across Care Settings Medical Error Reduction Providing care within clinical guidelines A complete health history Accurate medication Lists Examining variation in physician practice Allergy and adverse reaction lists Vaccine history

“ The complexity of patient data in electronic medial records, coupled with expectations that these data facilitate clinical decision making, healthcare cost effectiveness, medical error reduction, and evidence based medicine, makes obvious the role of standardized terminologies as a foundation for comparable and consistent representation of patient information.” - Pathak and Chute, Division of Biomedical Statistics and Informatics, Mayo Clinic Pathak, J., & Chute, C. G. (2010). Analyzing categorical information in two publicly available drug terminologies: RxNorm and NDF-RT. Journal of the American Medical Informatics Association, 17(4),

Drivers for interoperability in the US The 2009 Health Information Technology for Economic and Clinical Health (HITECH) Act has the goal of using certified electronic health record technology (CEHRT) to promote patient safety and interoperability between and within health care systems. The initiative in HITECH Act are also known as Meaningful Use (MU).

How do you obtain and implement the standards? Are the current standards robust to function in current clinical workflows? Standard terminology is free but how much does it cost to implement and understand? How is your organization going to share data elements that don’t have a standard code? Reaching the interoperability target Governing bodies have defined structured data requirements with standard terminology Limitations and challenges exist in adopting these standards Current State

Challenges in interoperability Standardization Multiple Standard Terminologies Variable Versions Gaps in Standard Terminologies Integration Variable Release Formats Synonymous concepts with different Identifiers Unidirectional Data Translation Organization Flat Lists of CodesVariation in Granularity Significant effort to maintain mappings

Centralized Terminology Server (CTS) solution Metadata repository which enables the translation and integration of healthcare data Standardized terminology vocabulary compliance Knowledge Base to understand how data is represented and structured across the organization Addresses the simple questions that are hardest to manage,“ What does it mean, where is it from, and how does it relate to everything else!”

CTS components Terminology consulting Integration of local codes Interoperability Translation between source and target system Browsing and Runtime Services Terminology and mapping container Search and browse content Mapping tools Delivery of standard terminologies in a consistent consumable format Update to the standards content Local content Content Software Mapping Services Web Service APIs

Health Information Exchange (HIE) use case Goal: Make patient lab results available to any provider regardless of performing laboratory Standardization: Map labs to Standard Terminology Integration: Translate Standard Terminology back to Source Lab Organization: Location to Store the Mappings

HIE without a CTS HGB HEME 12HB HPLAS Plasma Hemoglobin Requires mapping from each source system. Each change at one site require a remap across systems. Updates require a remap across all systems The amount of variability results in difficulty maintaining translation and consumption to source systems

Facilitating HIE with a CTS HGB 12HB HEME HPLAS Mapping storage and retrieval via the CTS Bidirectional Data Exchange Economies of scale in Centralized Mapping Plasma Hemoglobin Updates applied once and automated across systems

Biosurvalence use case Goal: Automate the identification and tracking of reportable diseases Standardization: Identify LOINC, ICD or SNOMED CT codes for reportable diseases Integration: Group standard terminology codes by disease group Organization: Store the requirements from the county, state and federal level

Biosurvalence without a CTS Intensive data mining effort to find diagnosis and lab information that meet the reportable criteria (due to the use of multiple code systems required) Resources to manage updates from the reporting agencies as well as updates to the code system Maintaining the lists at each level of reporting (county, state, federal) ICD-9-CM Coding: Mumps SNOMED CT ICD-10-CM Coding: B26.9 Campyloba cter coli SNOMED CT ICD-10-CM Coding: A04.5 Mumps Virus Antibodies, Serum, Semi-Quantitative LOINC Local Code: A Campylobacter Species Identified, Stool Culture LOINC $ ICD-9-CM Coding: Campylobacteriosis SNOMED CT Campylobacter Species SNOMED CT Campylobac ter jejuni SNOMED CT

Facilitating biosurvalence with a CTS Local Code: B Local Code: A Utah Reportable Conditions US Nationally Reportable Conditions Has Associated Disease County Reportable Conditions ICD-9-CM Coding: Problems ICD-9-CM Coding: Mumps SNOMED CT ICD-10-CM Coding: B26.9 Has Analyte NCID Campylobacter coli SNOMED CT Labs Campylobacteriosis SNOMED CT Campylobacter Species Identified, Stool Culture LOINC Campylobacter jejuni SNOMED CT Campylobacter Species SNOMED CT ICD-10-CM Coding: A04.5 Mumps Virus Antibodies, Serum, Semi-Quantitative LOINC Centralized location to manage code sets Add groupings across terminologies Allows instantiation of reports to different agencies Enterprise wide structured data integration

Clinical Quality Measure (CQM) use case Goal: Identify groups of patients receiving or eligible for treatment Standardization: Over 20 different code systems required to calculate CQMs Integration: Manage multiple versions of value sets and code systems Organization: Link local codes to CQM data elements and measures

Clinical Quality Measure without a CTS Simple CQMs require multiple data elements Each CQM data element can have multiple value sets Value set and code set versioning cause a high level of variability

Facilitating CQM with a CTS Cost and process benefits in managing the complexity of data value sets and values Technical benefit in accessing CQM content with APIs and runtime services Versioning reduces variability of content

Achieving enterprise intelligence with a scalable CTS Enterprise Intelligence Accelerates implementation of electronic health records Longitudinal patient care record Personal health records Enables structured clinical data capture, queries and analytics Data mining Complex secondary data use Semantically interoperable data for exchange, analytics, decision support, alerts and reminders Lower total cost of ownership Maximized consistency, quality and efficiency of mapping

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