High-Throughput Phenotyping from Electronic Health Records for Clinical and Translational Research

Transcription

1 High-Throughput Phenotyping from Electronic Health Records for Clinical and Translational Research Jyotishman Pathak, PhD Associate Professor of Biomedical Informatics Department of Health Sciences Research August 15 th, 2013

2 2013 MFMER slide-2 Outline Clinical phenotyping from electronic health records (EHRs) emerge SHARPn Standards-based approaches to EHR phenotyping NQF Quality Data Model JBoss Drools business rules environment PhenotypePortal.org On-going and future work

3 [Green et al. Nature ; MFMER 470: ] slide-3

4 [ MFMER slide-4

5 Phenotyping is still a bottleneck [Image from Wikipedia] 2012 MFMER slide-5

6 EHR systems: United States % 26.6% 25% Adoption Rate 20% 15% 10% 5% 0% 8.8% 7.2% 1.5% % 9.2% 2.7% % 11.5% 3.6% % 8.7% 2011 Any EHR Basic EHR Comprehensive EHR [DesRoches et al., Health Aff (Millwood), 2012; 31(5)] High-Throughput Phenotyping from EHRs 2012 MFMER slide-6

7 2012 MFMER slide-7 Electronic health records (EHRs) driven phenotyping EHRs are becoming more and more prevalent within the U.S. healthcare system Meaningful Use is one of the major drivers Overarching goal To develop high-throughput semi-automated techniques and algorithms that operate on normalized EHR data to identify cohorts of potentially eligible subjects on the basis of disease, symptoms, or related findings both retrospectively and prospectively

8 2012 MFMER slide-8 [emerge Network]

9 EHR-driven Phenotyping Algorithms - I Typical components Billing and diagnoses codes Procedure codes Labs Medications Phenotype-specific co-variates (e.g., Demographics, Vitals, Smoking Status, CASI scores) Pathology Radiology Organized into inclusion and exclusion criteria [emerge Network] High-Throughput Phenotyping from EHRs 2012 MFMER slide-9

10 2012 MFMER slide-10 EHR-driven Phenotyping Algorithms - II Rules Phenotype Algorithm Evaluation Visualization Transform Transform Data Mappings NLP, SQL [emerge Network]

11 2012 MFMER slide-11 Example: Hypothyroidism Algorithm No thyroid-altering medications (e.g., Phenytoin, Lithium) 2+ non-acute visits in 3 yrs ICD- 9s for Hypothyroidism Abnormal TSH/FT4 Thyroid replace. meds An&bodies for TTG or TPO (anti-thyroglobulin, anti-thyroperidase) No ICD-9s for Hypothyroidism No Abnormal TSH/FT4 No thyroid replace. meds No secondary causes (e.g., pregnancy, ablation) Case 1 Case 2 No An&bodies for TTG/TPO No Hx of myasthenia gravis Control [Denny et al., ASHG, 2012; 89: ]

12 Example: Hypothyroidism Algorithm Drugs Demographics Diagnosis Labs Procedures NLP Vitals [Conway et al. AMIA 2011: ] High-Throughput Phenotyping from EHRs 2012 MFMER slide-12

13 Phenotyping Algorithms Alzheimer s Dementia Cataracts Peripheral Arterial Disease Type 2 Diabetes Data Categories used to define EHR-driven Phenotyping Algorithms Clinical gold standard EHR-derived phenotype Validation (PPV/NPV) Sensitivity (Case/Cntrl) Demographics, clinical examination of mental status, histopathologic examination Clinical exam finding (Ophthalmologic examination) Clinical exam finding (ankle-brachial index or arteriography) Laboratory Tests Diagnoses, medications Diagnoses, procedure codes Diagnoses, procedure codes, medications, radiology test results Diagnoses, laboratory tests, medications 73%/55% 37.1%/99% 98%/98% 99.1%/93.6% 94%/99% 85.5%/81.6% 98%/100% 100%/100% Cardiac Conduction ECG measurements ECG report results 97% (case only algorithm) 96.9% (case only algorithm) [emerge Network]

14 2012 MFMER slide-14 EHR Depth plays an important role [Wei et al. IJIM 2012 (Epub ahead of print)]

15 2012 MFMER slide-15 Genotype-Phenotype Association Results gene / disease marker region Atrial fibrillation Crohn's disease Multiple sclerosis Rheumatoid arthritis Type 2 diabetes rs rs rs Chr. 4q25 Chr. 4q25 IL23R rs Chr. 5 rs Chr. 5 rs rs rs rs rs NOD2 PTPN22 DRB1*1501 IL2RA IL7RA rs Chr. 6 rs rs rs rs rs rs rs rs5219 rs5215 rs RSBN1 PTPN22 TCF7L2 TCF7L2 TCF7L2 CDKN2B FTO KCNJ11 KCNJ11 IGF2BP2 published observed Odds Ratio [Ritchie et al. AJHG 2010; 86(4):560-72]

16 Key lessons learned from emerge Algorithm design and transportability Non-trivial; requires significant expert involvement Highly iterative process Time-consuming manual chart reviews Representation of phenotype logic is critical Standardized data access and representation Importance of unified vocabularies, data elements, and value sets Questionable reliability of ICD & CPT codes (e.g., billing the wrong code since it is easier to find) Natural Language Processing (NLP) is critical [Kho et al. Sc. Trans. Med 2011; 3(79): 1-7] High-Throughput Phenotyping from EHRs 2012 MFMER slide-16

17 SHARPn: Secondary Use of EHR Data Journal of Biomedical Informatics xxx (2012) xxx xxx Contents lists available at SciVerse ScienceDirect Journal of Biomedical Informatics journal homepage: Building a robust, scalable and standards-driven infrastructure for secondary use of EHR data: The SHARPn project Susan Rea a,, Jyotishman Pathak b, Guergana Savova c, Thomas A. Oniki d, Les Westberg e, Calvin E. Beebe b, Cui Tao b, Craig G. Parker a, Peter J. Haug a,f, Stanley M. Huff d,f, Christopher G. Chute b a Homer Warner Center for Informatics Research, Intermountain Healthcare, Murray, UT, USA Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA c Childrens Hospital Boston and Harvard Medical School, Boston, MA, USA d Intermountain Medical Center, Intermountain Healthcare, Murray, UT, USA e Agilex Technologies, Inc., Chantilly, VA, USA f Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA b a r t i c l e i n f o Article history: Received 2 September 2011 Accepted 25 January 2012 Available online xxxx Keywords: Electronic health records Meaningful use Health information exchange High-throughput phenotyping Natural language processing a b s t r a c t The Strategic Health IT Advanced Research Projects (SHARP) Program, established by the Office of the National Coordinator for Health Information Technology in 2010 supports research findings that remove barriers for increased adoption of health IT. The improvements envisioned by the SHARP Area 4 Consortium (SHARPn) will enable the use of the electronic health record (EHR) for secondary purposes, such as care process and outcomes improvement, biomedical research and epidemiologic monitoring of the nation s health. One of the primary informatics problem areas in this endeavor is the standardization of disparate health data from the nation s many health care organizations and providers. The SHARPn team is developing open source services and components to support the ubiquitous exchange, sharing and reuse or liquidity of operational clinical data stored in electronic health records. One year into the design and development of the SHARPn framework, we demonstrated end to end data flow and a prototype SHARPn platform, using thousands of patient electronic records sourced from two large healthcare organizations: Mayo Clinic and Intermountain Healthcare. The platform was deployed to (1) receive source EHR data in several formats, (2) generate structured data from EHR narrative text, and (3) normalize the EHR data using common detailed from clinicalehrs models and Consolidated Health Informatics standard High-Throughput Phenotyping 2012 MFMER slide-17

18 2012 MFMER slide-18 Algorithm Development Process - Modified Phenotype Algorithm Standardized and structured representation of phenotype definition criteria Use the NQF Quality Data Model (QDM) Rules Conversion of structured Semi-Automatic Execution phenotype criteria into executable queries Evaluation Use JBoss Drools (DRLs) Visualization Standardized representation of clinical data Create new and re-use existing clinical element models (CEMs) Transform Transform Data Mappings NLP, SQL [Welch et al., JBI 2012; 45(4):763-71]

19 2012 MFMER slide-19 The SHARPn phenotyping funnel Mayo Clinic EHR data QDMs CEMs Intermountain EHR data DRLs Phenotype specific patient cohorts [Welch et al., JBI 2012; 45(4):763-71]

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21 SHARPn data normalization architecture - II CEM CouchDB database with standardized and normalized patient data [Welch et al., JBI 2012; 45(4):763-71] 2012 MFMER slide-21

22 2012 MFMER slide-22 Algorithm Development Process - Modified Semi-Automatic Execution Standardized and structured representation of phenotype definition criteria Use the NQF Quality Data Model (QDM) Rules Phenotype Algorithm Transform Transform Visualization Standardized representation of clinical data Create new and re-use existing clinical element models (CEMs) Data Evaluation Mappings NLP, SQL [Welch et al., JBI 2012; 45(4):763-71]

23 Example algorithm: Hypothyroidism High-Throughput Phenotyping from EHRs 2012 MFMER slide-23

24 NQF Quality Data Model (QDM) Standard of the National Quality Forum (NQF) A structure and grammar to represent quality measures and phenotype definitions in a standardized format Groups of codes in a code set (ICD-9, etc.) "Diagnosis, Active: steroid induced diabetes" using "steroid induced diabetes Value Set GROUPING ( ) Supports temporality & sequences AND: "Procedure, Performed: eye exam" > 1 year(s) starts before or during "Measurement end date" Implemented as a set of XML schemas Links to standardized terminologies (ICD-9, ICD-10, SNOMED-CT, CPT-4, LOINC, RxNorm etc.) High-Throughput Phenotyping from EHRs 2012 MFMER slide-24

25 2012 MFMER slide-25 Example: Diabetes & Lipid Mgmt. - I

26 2012 MFMER slide-26 Example: Diabetes & Lipid Mgmt. - II Human readable HTML

27 2012 MFMER slide-27 Example: Diabetes & Lipid Mgmt. - III

28 2012 MFMER slide-28 Example: Diabetes & Lipid Mgmt. - IV

29 2012 MFMER slide-29 Example: Diabetes & Lipid Mgmt. - V Computer readable XML (based on HL7 RIM semantics)

30 An Evaluation of the NQF Quality Data Model for Representing Electronic Health Record Driven Phenotyping Algorithms William K. Thompson, Ph.D. 1, Luke V. Rasmussen 1, Jennifer A. Pacheco 1, Peggy L. Peissig, M.B.A. 2, Joshua C. Denny, M.D. 3, Abel N. Kho, M.D. 1, Aaron Miller, Ph.D. 2, Jyotishman Pathak, Ph.D. 4, 1 Northwestern University, Chicago, IL; 2 Marshfield Clinic, Marshfield, WI; 3 Vanderbilt University, Nashville, TN; 4 Mayo Clinic, Rochester, MN High-Throughput Phenotyping from EHRs 2012 MFMER slide-30 Algorithm Diabetic retinopathy Cases Controls Boolean Operators Max Depth Temporal Relationships Height Serum lipid level Low HDL cholesterol level Peripheral arterial disease QRS duration Resistant hypertension Cases Controls Type 2 diabetes Cataract Cases Controls [Thompson et al., AMIA 2012; (Epub ahead of print)]

31 An Evaluation of the NQF Quality Data Model for Representing Electronic Health Record Driven Phenotyping Algorithms William K. Thompson, Ph.D. 1, Luke V. Rasmussen 1, Jennifer A. Pacheco 1, Peggy L. Peissig, M.B.A. 2, Joshua C. Denny, M.D. 3, Abel N. Kho, M.D. 1, Aaron Miller, Ph.D. 2, Jyotishman Pathak, Ph.D. 4, 1 Northwestern University, Chicago, IL; 2 Marshfield Clinic, Marshfield, WI; 3 Vanderbilt University, Nashville, TN; 4 Mayo Clinic, Rochester, MN High-Throughput Phenotyping from EHRs 2012 MFMER slide-31 Term Document Section Algorithm Uses NLP No Evidence Of Negation Restriction Diabetic retinopathy Yes Yes No Yes Height Yes No No No Serum lipid level No Low HDL cholesterol level Yes No Yes No Peripheral arterial disease Yes No No No QRS duration Yes Yes Yes Yes Resistant hypertension Yes No No No Type 2 diabetes No Cataract Yes No No No [Thompson et al., AMIA 2012; (Epub ahead of print)]

32 2012 MFMER slide-32 Algorithm Development Process - Modified Phenotype Algorithm Standardized and structured representation of phenotype definition criteria Use the NQF Quality Data Model (QDM) Rules Conversion of structured Semi-Automatic Execution phenotype criteria into executable queries Evaluation Use JBoss Drools (DRLs) Visualization Standardized representation of clinical data Create new and re-use existing clinical element models (CEMs) Transform Transform Data Mappings NLP, SQL [Welch et al., JBI 2012; 45(4):763-71]

33 2012 MFMER slide-33 JBoss Drools rules management system Represents knowledge with declarative production rules Origins in artificial intelligence expert systems Simple when <pattern> then <action> rules specified in text files Separation of data and logic into separate components Forward chaining inference model (Rete algorithm) Domain specific languages (DSL)

34 2012 MFMER slide-34 Example Drools rule {Rule Name} rule Glucose <= 40, Insulin On when then {binding} {Java Class} {Class Getter Method} $msg : GlucoseMsg(glucoseFinding <= 40, currentinsulindrip > 0 ) {Class Setter Method} glucoseprotocolresult.setinstruction(glucoseinstructions.gluc OSE_LESS_THAN_40_INSULIN_ON_MSG); end Parameter {Java Class}

35 2012 MFMER slide-35 Modeling and Executing Electronic Health Records Driven Phenotyping Algorithms using the NQF Quality Data Model and JBoss Drools Engine Dingcheng Li, PhD 1, Gopu Shrestha, MS 1, Sahana Murthy, MS 1 Davide Sottara, PhD 2 Stanley M. Huff, MD 3 Christopher G. Chute, MD, DrPH 1 Jyotishman Pathak, PhD 1 1 Mayo Clinic, Rochester, MN 2 University of Bologna, Italy 3 Intermountain Healthcare, Salt Lake City, UT [Li et al., AMIA 2012; (Epub ahead of print)]

36 2012 MFMER slide-36 The executable Drools workflow [Li et al., AMIA 2012; (Epub ahead of print)]

37 [Endle et al., AMIA 2012; (Epub ahead of print)] High-Throughput Phenotyping from EHRs

38 PhenotypePortal Architecture High-Throughput Phenotyping from EHRs 2013 MFMER slide-38

39 2013 MFMER slide-39 PhenotypePortal Demo

40 On-going/Future planned activities: Measure Authoring Toolkit (MAT) integration Seamless authoring capabilities API decoupling/additional REST interfaces Invocation of services w/o UI client Improve scalability and performance No need to wait for long batch processing as the execution can be done in real time. As a result, multiple translators can be distributed to handle large patient sets. Batch processing can be done and code has been written to integrate with Hadoop to parallelize execution across large patient sets. Additional optimizations are still possible, such as caching, etc. High-Throughput Phenotyping from EHRs 2013 MFMER slide-40

41 Sustainability plan NIH funded R01 (July 2013 June 2017) National Infrastructure for EHR-driven phenotyping algorithm modeling and execution (PI: Pathak) Expand HTP infrastructure to additional workflow engines (KNIME), repositories (i2b2), and analytical platforms (R) Mayo Clinical Informatics Program (Jan 2013-) Library of Cohort Definitions for use in Mayo Clinic practice and research Real-time phenotype/cohort dashboard High-Throughput Phenotyping from EHRs 2013 MFMER slide-41

42 Concluding remarks EHRs contain a wealth of phenotypes for clinical and translational research EHRs represent real-world data, and hence has challenges with interpretation, wrong diagnoses, and compliance with medications Handling referral patients even more so Standardization and normalization of clinical data and phenotype definitions is critical Phenotyping algorithms are often transportable between multiple EHR settings Validation is an important component High-Throughput Phenotyping from EHRs 2012 MFMER slide-42

43 2013 MFMER slide-43 Acknowledgment Mayo HTP team Intermountain HTP team Erin Martin

44 2012 MFMER slide-44 Thank You!

45 2013 MFMER slide-45 Back-Up

46 1. Converts QDM to Drools 2. Rule execution by querying the CEM database 3. Generate summary reports

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50 Translator Distinguishing Features Operates on real-time patient set and/or batch processing Drools rules could be reused and combined Rules can give immediate feedback as a new information is known as new lab results or patient visits are added to the data. Easier integration into clinical setting because the translator can work on observed changes in data instead of only scheduling batch processes. Clients would not have to trigger algorithm execution as this execution would be triggered when new data is encountered. This would minimize points of integration. Open source license, built on established projects such as PopHealth and Cypress.