Practical Development and Implementation of EHR Phenotypes. NIH Collaboratory Grand Rounds Friday, November 15, 2013
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1 Practical Development and Implementation of EHR Phenotypes NIH Collaboratory Grand Rounds Friday, November 15, 2013
2 The Southeastern Diabetes Initiative (SEDI)
3 Setting the Context
4 Risk Prediction and Intervention: The Need for Clinical Risk Factors and Outcomes
5 Why Computable Phenotypes? Correct disease identification Several downstream implications Estimation of incidence Study design: include and exclude Identification of risk factors Effect estimation Who (how) to treat and who to spare Bias due to incorrect disease specification Attribution: Paramita Saha Chaudhuri, PhD
6 Phenotyping and discovery. Hripcsak G, and Albers D J J Am Med Inform Assoc 2013;20: Copyright by the American Medical Informatics Association. All rights reserved.
7 SEDI Medical Social Risk Algorithm Drives Intervention Different intensities of intervention High intensity clinical teams vs. lower intensity community based teams Different modes of intervention Patient basis, neighborhood basis, community basis Targeted intervention Stratifying patients based on risk, both at patient and neighborhood levels
8 The Diabetes Phenotype Comparison
9 Problem Statement EHR-driven computable phenotypes exist and are an important source of knowledge Electronic Medical Records and Genomics (emerge) Network and Phenotype Knowledge Base Entities such as the Center for Medicare & Medicaid Services Many others How should we recognize, document, implement, and validate authoritative source phenotypes? How should we evaluate the best fit and utility of phenotypes, especially applied to population health management?
10 Initial ^ Problem Statement Which patients in a 5-year EHR dataset have diabetes?
11 Richesson RL, Rusincovitch SA, Wixted D, Batch BC, Feinglos MN, Miranda ML, Hammond WE, Califf RM, Spratt SE. A Comparison of Phenotype Definitions for Diabetes Mellitus. J Am Med Inf Assoc 2013 (epub ahead of print).
12 Individual Cohort Yields
13 Challenge: Representing and Comparing Phenotype Criteria
14 Simple Phenotype Criteria Example: ICD 9 CM Diagnosis Category 250.xx
15 Complex Phenotype Criteria Example: SUPREME DM Phenotype
16 Very Complex Phenotype Criteria Example: emerge (NW) Phenotype
17 Phenotypes Development: A Pragmatic Approach
18 Phenotype development and validation. Newton K M et al. J Am Med Inform Assoc 2013;20:e147- e154 Copyright by the American Medical Informatics Association. All rights reserved.
19 Process Diagram Attribution: Center for Predictive Medicine
20 Recognizing Authoritative Sources Attribution: Center for Predictive Medicine
21 Evaluating Existing Definitions Attribution: Maria Grau Sepulveda, MD, MPH
22 SEDI Core Data Domains 1. Patient Demographics 2. Encounters 3. Diagnoses 4. Procedures 5. Lab Results 6. Vital Signs 7. Medications 8. Social History Kahl M, Dunston FG, Morris LM, Rusincovitch SA. Traceability in Healthcare Data Sharing Projects Through the Use of Data Warehousing Artifacts: Methods from the Southeastern Diabetes Initiative (SEDI). HDWA (Healthcare Data Warehouse Association) 2013 Annual Conference. October 1-3, 2013, Scottsdale, Arizona. Abstract: Poster presentation.
23 Healthcare Workflows and EHR Data A work in progress with Greg Simon, Michelle Smerek, and Rachel Richesson
24 Contact Information Shelley A. Rusincovitch, Senior Informatics Analyst Health Intelligence and Research Services Duke Health Technology Solutions (DHTS) Phone: / shelley.rusincovitch@duke.edu
25 Supplemental Slides
26 Selected SEDI References Califf RM, Sanderson I, Miranda ML. The future of cardiovascular clinical research: informatics, clinical investigators, and community engagement. JAMA ;308(17): Miranda ML, Ferranti J, Strauss B, Neelon B, Califf RM. Geographic health information systems: a platform to support the 'triple aim'. Health Aff (Millwood) 2013;32(9): Granger BB, Rusincovitch SA, Avery S, Batch BC, Dunham AA, Feinglos MN, Kelly K, Pierre Louis M, Spratt SE, Califf RM. Missing signposts on the roadmap to quality: a call to improve medication adherence indicators in data collection for population research. Frontier Pharmacol 2013;4(13):
27 Center for Predictive Medicine Paramita Saha Chaudhuri, PhD Maria Grau-Sepulveda, MD, MPH Eric Laber, PhD Nick Meyer Ben Neely, MS Charlotte Nelson, MS Michael Pencina, PhD Shelley Rusincovitch
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