Achieving Data Standardization in Health Information Exchange and Quality Measurement

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

2. Abstract Specifically, it showcases successful implementation of a centralized terminology server inhealth information exchange, biosurveillanceandquality measurement. This presentation reviews the benefits and challenges of achieving and maintaining interoperability.

3. 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.” http://www.himss.org/library/interoperability-standards/what-is Share Exchange Understand Interpret

4. Benefits of interoperability

5. “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), 432-439.

6. 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). Drivers for interoperability in the US

7. Reaching the interoperability target • 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?

8. Challenges in interoperability

9. 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!”

10. CTS components

11. Health Information Exchange (HIE) use case

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

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

14. Biosurvalence use case

15. Biosurvalence without a CTS Mumps Virus Antibodies, Serum, Semi-Quantitative LOINC 31503-6 • Local Code: A 008.43 • 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) Campylobacter Species Identified, Stool Culture LOINC 6331-3 • Campylobacter Species • SNOMED CT 116457002 $ • Campylobacter coli • SNOMED CT 40614002 • Campylobacter jejuni • SNOMED CT 66543000 • ICD-9-CM Coding: 008.43 • Campylobacteriosis SNOMED CT 86500004 • ICD-10-CM Coding: • A04.5 • Mumps SNOMED CT 36989005 • ICD-9-CM Coding: 072.9 • ICD-10-CM Coding: • B26.9

16. US Nationally Reportable Conditions County Reportable Conditions Utah Reportable Conditions • Mumps SNOMED CT 36989005 • Campylobacteriosis SNOMED CT 86500004 Problems Has Associated Disease • ICD-9-CM Coding: 072.9 • ICD-9-CM Coding: 008.43 • ICD-10-CM Coding: • B26.9 • ICD-10-CM Coding: • A04.5 Campylobacter Species Identified, Stool Culture LOINC 6331-3 Mumps Virus Antibodies, Serum, Semi-Quantitative LOINC 31503-6 Labs Has Analyte NCID 76770 • Local Code: A 008.43 • Local Code: B 008.43 • Campylobacter jejuni • SNOMED CT 66543000 • Campylobacter Species • SNOMED CT 116457002 • Campylobacter coli • SNOMED CT 40614002 Facilitating biosurvalence with a CTS • Centralized location to manage code sets • Add groupings across terminologies • Allows instantiation of reports to different agencies • Enterprise wide structured data integration

17. Clinical Quality Measure (CQM) use case

18. 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

19. 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

20. Achieving enterprise intelligence with a scalable CTS Enterprise Intelligence

21. Questions