Practical Development and Implementation of EHR Phenotypes. NIH Collaboratory Grand Rounds Friday, November 15, 2013
|
|
|
- Johnathan Austin
- 10 years ago
- Views:
Transcription
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: / [email protected]
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
Adventures in EHR Computable Phenotypes: Lessons Learned from the Southeastern Diabetes Initiative (SEDI)
Adventures in EHR Computable Phenotypes: Lessons Learned from the Southeastern Diabetes Initiative (SEDI) PCORnet Best Practices Sharing Session Wednesday, August 5, 2015 Introductions to the Round Table
Using EHRs to extract information, query clinicians, and insert reports
Using EHRs to extract information, query clinicians, and insert reports Meghan Baker, MD, ScD NIH HCS Collaboratory EHR working group webinar March 26, 2013 1 E S P V A E R S Electronic Support for Public
Environmental Health Science. Brian S. Schwartz, MD, MS
Environmental Health Science Data Streams Health Data Brian S. Schwartz, MD, MS January 10, 2013 When is a data stream not a data stream? When it is health data. EHR data = PHI of health system Data stream
Research Opportunities using the PaTH Network
Research Opportunities using the PaTH Network DBMI Colloquium Chuck Borromeo Oct. 30, 2015 PaTH is funded through the Patient Centered Outcomes Research Institute (PCORI) PaTH Network Goals Build network
What you need to know about Health Reform, Accountable Care, and Collaborative Care
ACO and Collaborative Care - The Basics What you need to know about Health Reform, Accountable Care, and Collaborative Care Healthcare is changing Costs vs. volume ACO Benefits How to Achieve ACO Health
Clinical Decision Support (CDS) to improve colorectal cancer screening
Clinical Decision Support (CDS) to improve colorectal cancer screening NIH Collaboratory Grand Rounds Sept 26, 2014 Presented by: Tim Burdick MD MSc OCHIN Chief Medical Informatics Officer Adjunct Associate
Design and Implementation of an Automated Geocoding Infrastructure for the Duke Medicine Enterprise Data Warehouse
Design and Implementation of an Automated Geocoding Infrastructure for the Duke Medicine Enterprise Data Warehouse Shelley A. Rusincovitch, Sohayla Pruitt, MS, Rebecca Gray, DPhil, Kevin Li, PhD, Monique
Diabetes The Basics. What is Diabetes? How does sugar get into your cells? Type 1 diabetes. Type 2 diabetes. Why control Diabetes?
Diabetes The Basics What is Diabetes? Diabetes is when there is too much sugar in your blood. Sugar from food you eat and drink stays in your blood instead of going into the cells in your body. The cells
Interoperability and Analytics February 29, 2016
Interoperability and Analytics February 29, 2016 Matthew Hoffman MD, CMIO Utah Health Information Network Conflict of Interest Matthew Hoffman, MD Has no real or apparent conflicts of interest to report.
The ICD-10 Transition Implications for Pragmatic Trials
The ICD-10 Transition Implications for Pragmatic Trials NIH Health Care Systems Research Collaboratory Grand Rounds August 14, 2015 Kin Wah Fung, MD, MS, MA National Library of Medicine Rachel Richesson,
Genomics and Health Data Standards: Lessons from the Past and Present for a Genome-enabled Future
Genomics and Health Data Standards: Lessons from the Past and Present for a Genome-enabled Future Daniel Masys, MD Professor and Chair Department of Biomedical Informatics Professor of Medicine Vanderbilt
The FDA s Mini- Sen*nel Program and the Learning Health System
info@mini- sen*nel.org 1 The FDA s Mini- Sen*nel Program and the Learning Health System Richard PlaB, MD, MS Harvard Pilgrim Health Care Ins*tute Harvard Medical School October 1, 2014 Vision We seek the
Tertiary Use of Electronic Health Record Data. Maggie Lohnes, RN, CPHIMS, FHIMSS VP Provider Relations Anolinx, LLC October 26, 2015
Tertiary Use of Electronic Health Record Data Maggie Lohnes, RN, CPHIMS, FHIMSS VP Provider Relations Anolinx, LLC October 26, 2015 Disclosure of Conflicts of Interest No relevant conflicts of interest
ADVANCING MEASUREMENT OF PATIENT- CENTERED OUTCOMES AND QUALITY METRICS WITH ELECTRONIC HEALTH RECORDS
ADVANCING MEASUREMENT OF PATIENT- CENTERED OUTCOMES AND QUALITY METRICS WITH ELECTRONIC HEALTH RECORDS Tina Hernandez-Boussard, PhD, MPH, MS Director, Surgical Health Services Research Unit Assistant Professor
Assessing Data Quality for Healthcare Systems Data Used in Clinical Research (Version 1.0)
Assessing Data Quality for Healthcare Systems Data Used in Clinical Research (Version 1.0) An NIH Health Care Systems Research Collaboratory Phenotypes, Data Standards, and Data Quality Core White Paper
Pediatric Research Database
Pediatric Research Database February 2014 R.G.Wade, Ph.D. CFRI Children s Foundation Research Institute An institution formed as a partnership among: Le Bonheur Children s Hospital University of Tennessee
Using EHRs, HIE, & Data Analytics to Support Accountable Care. Jonathan Shoemaker June 2014
Using EHRs, HIE, & Data Analytics to Support Accountable Care Jonathan Shoemaker June 2014 Agenda Allina Health overview ACO framework- setting the stage Health Information Technology and ACOs Role of
Analytics and Business Intelligence
Analytics and Business Intelligence CE-IT Virtual Town Hall series December 12, 2012 JD Whitlock, MPH, MBA, CPHIMS James E. Gaston, FHIMSS HIMSS Clinical & Business Intelligence Overview HIMSS Resources
Ryan Jeffrey Shaw CURRICULUM VITAE MARCH 5, 2012 D UMC 3322 307 TRENT DR., 3088 DURHAM, NC 27710 PHONE: 919-410- 6372 RYAN.SHAW@DUKE.
Ryan Jeffrey Shaw CURRICULUM VITAE MARCH 5, 2012 D UMC 3322 307 TRENT DR., 3088 DURHAM, NC 27710 PHONE: 919-410- 6372 [email protected] EDUCATION Duke University Durham, NC Expected August 2012 PhD, Nursing
Toward Meaningful Use of HIT
Toward Meaningful Use of HIT Fred D Rachman, MD Health and Medicine Policy Research Group HIE Forum March 24, 2010 Why are we talking about technology? To improve the quality of the care we provide and
Research Skills for Non-Researchers: Using Electronic Health Data and Other Existing Data Resources
Research Skills for Non-Researchers: Using Electronic Health Data and Other Existing Data Resources James Floyd, MD, MS Sep 17, 2015 UW Hospital Medicine Faculty Development Program Objectives Become more
Dori M. Steinberg, PhD, MS, RD 310 Trent Drive Room 134, Trent Hall Box 90519 Durham, NC 27708. dori.steinberg@duke.
CURRENT POSITION Dori M. Steinberg, PhD, MS, RD 310 Trent Drive Room 134, Trent Hall Box 90519 Durham, NC 27708 [email protected] 919-613-5453 Research Scholar, Duke Global Digital Health Science
Bench to Bedside Clinical Decision Support:
Bench to Bedside Clinical Decision Support: The Role of Semantic Web Technologies in Clinical and Translational Medicine Tonya Hongsermeier, MD, MBA Corporate Manager, Clinical Knowledge Management and
Meaningfully Incorporating Social Determinants into EHRs. Paul Tang, MD Palo Alto Medical Foundation
Meaningfully Incorporating Social Determinants into EHRs Paul Tang, MD Palo Alto Medical Foundation Agenda The Meaningful Use context Not all data are the same The makings of good EHR data Example social
Use and Characteristics of Electronic Health Record Systems Among Office-based Physician Practices: United States, 2001 2013
Use and Characteristics of Electronic Health Record Systems Among Office-based Physician Practices: United States, 2001 2013 Chun-Ju Hsiao, Ph.D., and Esther Hing, M.P.H. Key findings In 2013, 78% of office-based
Data Analytics for Population Health
Data Analytics for Population Health Sponsored by 1915 N. Fine Ave #104 Fresno CA 93720-1565 Phone: (559) 251-5038 Fax: (559) 251-5836 www.californiahia.org Program Handouts Monday, June 8 Track Two 2:10
REVISION, PROCEDURE CODING SYSTEM (ICD-10-PCS) VERSION 2.Ø
NCPDP IMPLEMENTATION TIMELINES AND RECOMMENDATIONS FOR INTERNATIONAL CLASSIFICATION OF DISEASES, 10TH REVISION, CLINICAL MODIFICATION (ICD-10- CM) AND THE INTERNATIONAL CLASSIFICATION OF DISEASES, 10TH
A Multitier Fraud Analytics and Detection Approach
A Multitier Fraud Analytics and Detection Approach Jay Schindler, PhD MPH DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not necessarily represent official
Understanding Meaningful Use Stage 2
Understanding Meaningful Use Stage 2 Miranda Ladue, Manager, Product Management Adam Plotts, Manager, Product Management Copyright 2011 Allscripts Healthcare Solutions, Inc. Agenda Review MU Timeline MU
Secondary Uses of Data for Comparative Effectiveness Research
Secondary Uses of Data for Comparative Effectiveness Research Paul Wallace MD Director, Center for Comparative Effectiveness Research The Lewin Group [email protected] Disclosure/Perspectives Training:
The Diabetes Registry and
The Diabetes Registry and Future Panel Management Tool Jianji Yang, PhD Judy McConnachie, MPH Roger Renfro Steve Schreiner Stephanie Tallett, BA Lisa Winterbottom, MD MPH In collaboration with clinical
Robert Okwemba, BSPHS, Pharm.D. 2015 Philadelphia College of Pharmacy
Robert Okwemba, BSPHS, Pharm.D. 2015 Philadelphia College of Pharmacy Judith Long, MD,RWJCS Perelman School of Medicine Philadelphia Veteran Affairs Medical Center Background Objective Overview Methods
REVISION, PROCEDURE CODING SYSTEM (ICD-10-PCS) VERSION 2.2
NCPDP IMPLEMENTATION TIMELINES AND RECOMMENDATIONS FOR INTERNATIONAL CLASSIFICATION OF DISEASES, 10TH REVISION, CLINICAL MODIFICATION (ICD-10- CM) AND THE INTERNATIONAL CLASSIFICATION OF DISEASES, 10TH
Using Public Health- Focused EHR Decision Support in Primary Care Se>ings
Using Public Health- Focused EHR Decision Support in Se>ings ISDS Webinar Series: Usage of Surveillance Information to Assist Clinical Decision- Making Winfred Y. Wu, MD, MPH Primary Care Information Project
Visual Analytics to Enhance Personalized Healthcare Delivery
Visual Analytics to Enhance Personalized Healthcare Delivery A RENCI WHITE PAPER A data-driven approach to augment clinical decision making CONTACT INFORMATION Ketan Mane, PhD kmane@renci,org 919.445.9703
Applied Health Informatics Through the Lens of an Integrated Care Delivery System
Applied Health Informatics Through the Lens of an Integrated Care Delivery System Andrew Masica, MD, MSCI VP, Chief Clinical Effectiveness Officer Baylor Scott & White Health [email protected]
Problem-Centered Care Delivery
HOW INTERFACE TERMINOLOGY MAKES STANDARDIZED HEALTH INFORMATION POSSIBLE Terminologies ensure that the languages of medicine can be understood by both humans and machines. by June Bronnert, RHIA, CCS,
Physician Adoption of Electronic Health Record Systems: United States, 2011
This report was revised on January, 203, after a problem was found with the weighting of the 20 survey data. NCHS Data Brief No. 98 July 202 Physician Adoption of Electronic Health Record Systems: United
FHIM Model Content Overview
FHIM Model Content Overview Federal Health Information Model (FHIM) and Associated Terminology Models Goal Produce a logical, health information model that supports semantic interoperability and that is
Integrating EHR and Predictive Modeling:
The Seventh Annual Predictive Modeling Summit Integrating EHR and Predictive Modeling: Opportunities and Challenges Dave Bodycombe, Ph.D., Director of R&D for the ACG Team Copyright Notice 2 This presentation
Collaborative Intelligence: Unlocking the Power of Narrative Documentation
M*Modal White Paper WP CI Collaborative Intelligence: Unlocking the Power of Narrative Documentation See us at HIMSS booth 5725 WP CI Page 2 Current Situation The healthcare industry is currently undergoing
Predictive Analytics and Risk Models to Prevent Sepsis, Patient Falls, and Readmissions
Predictive Analytics and Risk Models to Prevent Sepsis, Patient Falls, and Readmissions Today s Agenda: Provide an overview of Mount Sinai s end-to-end informatics and clinical data analytics approach
Center for. Clinical Informatics. Clinical Informatics. Mission Statement
Clinical Informatics Center for Clinical Informatics Clinical Informatics transforms health care by analyzing, designing, implementing, and evaluating information and communication systems that enhance
Data Standards and the National Cardiovascular Research Infrastructure (NCRI)
Data Standards and the National Cardiovascular Research Infrastructure (NCRI) A partnership with Duke Clinical Research Institute (DCRI) and the American College of Cardiology Foundation (ACCF) November
Extracting Value from Health Care Big Data with Predictive Analytics
Extracting Value from Health Care Big Data with Predictive Analytics Gregory Veltri, CIO, Denver Health Mical DeBrow, PhD RN, Siemens Clinical Strategic Consulting DISCLAIMER: The views and opinions expressed
Big Data Infrastructure and Analytics for Healthcare Applications Arjun Shankar, Ph.D. [email protected]
Big Data Infrastructure and Analytics for Healthcare Applications Arjun Shankar, Ph.D. [email protected] Computational Sciences and Engineering Division Outline Current big data context in healthcare Data
Implementation of the ABCDE Bundle: Results from a Real-World, Pragmatic Study Design. Andrew Masica, MD, MSCI Chief Clinical Effectiveness Officer
Implementation of the ABCDE Bundle: Results from a Real-World, Pragmatic Study Design Andrew Masica, MD, MSCI Chief Clinical Effectiveness Officer 0 Gap Between Knowledge and Delivery Translational Roadmap
Getting Ready for ICD-10. Part 1: The Basics
Getting Ready for ICD-10 Part 1: The Basics Introduction In the United States, on October 1, 2015 the ICD 9 code set used to report medical diagnoses and inpatient procedures will be replaced by the International
Quality Improvement in Primary Care Settings
Quality Improvement in Primary Care Settings Eboni Price Haywood, MD, MPH Chief Medical Officer, Tulane Community Health Medical Director, Tulane Community Health @ Covenant House Team Approach to Quality
Modeling Healthcare Information Technology (HIT) Adoption using Systems Dynamics
Modeling Healthcare Information Technology (HIT) Adoption using Systems Dynamics Nima A. Behkami, Doctoral Student (1)(2) David A. Dorr, MD, Professor (1) Tugrul U. Daim, PhD, Professor (2) (1) OHSU, Dept
Using EHRs for Heart Failure Therapy Recommendation Using Multidimensional Patient Similarity Analytics
Digital Healthcare Empowering Europeans R. Cornet et al. (Eds.) 2015 European Federation for Medical Informatics (EFMI). This article is published online with Open Access by IOS Press and distributed under
Using Health Information Technology to Improve Quality of Care: Clinical Decision Support
Using Health Information Technology to Improve Quality of Care: Clinical Decision Support Vince Fonseca, MD, MPH Director of Medical Informatics Intellica Corporation Objectives Describe the 5 health priorities
By: Latarsha Chisholm, MSW, Ph.D. Department of Health Management & Informatics University of Central Florida
By: Latarsha Chisholm, MSW, Ph.D. Department of Health Management & Informatics University of Central Florida Health Disparities Health disparities refers to population-specific differences in the presence
Michael Friedman, MPT, MBA CURRICULUM VITAE
Michael Friedman, MPT, MBA CURRICULUM VITAE January 14, 2014 Michael Friedman DEMOGRAPHIC AND PERSONAL INFORMATION Current Appointments Director Rehabilitation Therapy Services, Physical Medicine and Rehabilitation,
Care Transformation and the Journey to Population Health Management
Care Transformation and the Journey to Population Health Management Richard A Bankowitz, MD MBA FACP Chief Medical Officer, Premier healthcare alliance AMGA Institute for Quality September 26, 2013 Premier:
HL7 & Meaningful Use. Charles Jaffe, MD, PhD CEO Health Level Seven International. HIMSS 11 Orlando February 23, 2011
HL7 & Meaningful Use Charles Jaffe, MD, PhD CEO Health Level Seven International HIMSS 11 Orlando February 23, 2011 Overview Overview of Meaningful Use HIT Standards and Meaningful Use Overview HL7 Standards
National Cancer Institute
National Cancer Institute Information Systems, Technology, and Dissemination in the SEER Program U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES National Institutes of Health Information Systems, Technology,
Tjeerd van Staa Professor of Health eresearch Centre for Health Informatics, University of Manchester
Tjeerd van Staa Professor of Health eresearch Centre for Health Informatics, University of Manchester Disclosure statement I have participated in expert meetings with GSK and Boehringer and provided methodological
Standardizing the measurement of drug exposure
Standardizing the measurement of drug exposure The ability to determine drug exposure in real-world clinical practice enables important insights for the optimal use of medicines and healthcare resources.
Going beyond Meaningful Use with EMR solutions from the Centricity portfolio
Going beyond Meaningful Use with EMR solutions from the Centricity portfolio The IT tools and services you need now. The support you need for the future. GE Healthcare is focused on providing customers
Demonstration Study of Healthcare Utilization by Obese Patients. Joseph Vasey PhD Director, Epidemiology Quintiles Outcome May 22, 2013
Demonstration Study of Healthcare Utilization by Patients Joseph Vasey PhD Director, Epidemiology Quintiles Outcome May 22, 2013 Copyright 2013 Quintiles Revised April 2013 Introduction Obesity in the
Clinical Decision Support: Marrying Technology and Policy
Clinical Decision Support: Marrying Technology and Policy Douglas S. Bell, MD, PhD Associate Professor In Residence, UCLA Department of Medicine Research Scientist, RAND Corporation AXIS Research Seminar
FAQs on Billing for Health and Behavior Services
FAQs on Billing for Health and Behavior Services by Government Relations Staff January 29, 2009 Practicing psychologists are eligible to bill for applicable services and receive reimbursement from Medicare
Nurse Practitioners, Certified Nurse Midwives, and Physician Assistants in Physician Offices
Nurse Practitioners, Certified Nurse Midwives, and Physician Assistants in Physician Offices Melissa Park, M.P.H.; Donald Cherry, M.S.; and Sandra L. Decker, Ph.D. Key findings Data from the National Ambulatory
EVALUATION OF A BASAL-BOLUS INSULIN PROTOCOL FROM CONTINUING DOSING EFFICACY AND SAFETY OPTIMIZATION IN NON-CRITICALLY ILL HOSPITALIZED PATIENTS
EVALUATION OF A BASAL-BOLUS INSULIN PROTOCOL FROM CONTINUING DOSING EFFICACY AND SAFETY OPTIMIZATION IN NON-CRITICALLY ILL HOSPITALIZED PATIENTS Joanne Archer, MSN, APRN, BC-ADM, Maureen T. Greene, PhD,
Analytic-Driven Quality Keys Success in Risk-Based Contracts. Ross Gustafson, Vice President Allina Performance Resources, Health Catalyst
Analytic-Driven Quality Keys Success in Risk-Based Contracts March 2 nd, 2016 Ross Gustafson, Vice President Allina Performance Resources, Health Catalyst Brian Rice, Vice President Network/ACO Integration,
The Six A s. for Population Health Management. Suzanne Cogan, VP North American Sales, Orion Health
The Six A s for Population Health Management Suzanne Cogan, VP North American Sales, Summary Healthcare organisations globally are investing significant resources in re-architecting their care delivery
Meaningful Use Reporting Quick Reference
Allscripts Professional EHR 9.2 none Copyright 2010 Allscripts Healthcare Solutions, Inc. www.allscripts.com Copyright 2010 Allscripts Healthcare Solutions, Inc. This document is the confidential property