Research Opportunities using the PaTH Network
|
|
- Cecily Stokes
- 8 years ago
- Views:
Transcription
1 Research Opportunities using the PaTH Network DBMI Colloquium Chuck Borromeo Oct. 30, 2015 PaTH is funded through the Patient Centered Outcomes Research Institute (PCORI)
2 PaTH Network Goals Build network connecting EMR data from 6 sites Develop Computable Phenotypes Improve clinical outcomes through patient centered research Extend an investigator s analytic power
3 Analytic Power Analytic Power: the amount of data one * can access to answer a research question * Access means research ready data at minimal additional cost.
4 Analytic Power Katie is researcher. She has new research grant analyzing EMR data. She does not have the expertise or contacts to collect the necessary EMR data at her local institution. Katie EMR data negatively affect budgets. You must spend money for data acquisition, data storage, data harmonization, etc. Can you analyze a large dataset without sacrificing your grant budget?
5 Analytic Power Utah Geisinger UPMC PSU Temple Katie JHU She PaTHfinds allows the her EMR to data compare at her her institution. data with similar Analytic data Power= found institutional at 5 other institutions. (max n=2m) Analytic Power= network (max n=11m)
6 Analytic Power PaTH PaTH Katie PaTH part of a larger ecosystem called PCORI. PCORI is a nationwide research organization. PCORI allows her to conduct her research using data from across the US. Analytic Power = national (max n=90m)
7 Achievements Develop a new research network: PaTH Create regional research network (JHU, Temple, UPMC, PSU, Utah*, Geisinger*) Connect regional network to national network (PCORnet) Establish a data exchange methodology through Common Data Elements Computable phenotypes to assist with patient accrual Deployed Patient Reported Outcome (PRO) surveys using Epic and REDCap * Phase II sites
8 Aggressive Timeline Start: Mar 2014 End: Sep 2015 Phase 1: 18 months Research Areas Cross Institutional Research Idiopathic pulmonary fibrosis (IPF) Deploy a regional network Atrial Fibrillation Supply data to a national network Weight Deploy PROs Transform EMR data Meaningful Use 2 Common Data Elements
9 Current Timeline Start: Sept 2015 End: Sept 2018 Phase 2: 36 months Answer Research Questions Answer queries from PCORI Maintain data for use by national network Develop Self-sufficient Network Recruit new studies Create funding model Data Quality Improve data quality
10 Tale of Two Networks CDRN #11 Geisinger JHU PSU Temple UPMC Utah Regional Research Network Facilitate grant applications Govern access to PaTH Network Network infrastructure: i2b2/shrine CDRN CDRN CDRN #8#1 #1 10 CDRN 10 #1 CDRN #4 CDRN #6 10 CDRN #12 CDRN #13 CDRN #10 CDRN #7 13 Nationwide Research Networks Conduct large scale studies Fund Studies and Sustainability Network infrastructure: PopMedNet
11 Computable Phenotypes Computable Phenotypes allow researchers to target patients for research projects Partial example: Nichols GA, Desai J, Elston Lafata J, et al. Construction of a Multisite DataLink Using Electronic Health Records for the Identification, Surveillance, Prevention, and Management of Diabetes Mellitus: The SUPREME DM Project. Preventing Chronic Disease. 2012;9:E110. doi: /pcd
12 Problems = Research Opportunities Computable Phenotypes Data Loss Data Interpretation Data Quality Organizational Challenges Mapping Issues
13 Issues with Computable Phenotypes Clinician: I want all the diabetes patients. Here is a computable phenotype with 3 criteria: diagnoses, medications, and lab results. Data Analyst: I extracted the data. There are between 9,441 and 49,613 patients. Clinician: What do you mean by between? Richesson RL, Rusincovitch SA, Wixted D, et al. A comparison of phenotype definitions for diabetes mellitus. Journal of the American Medical Informatics Association : JAMIA. 2013;20(e2):e319 e326. doi: /amiajnl
14 Issues with Computable Phenotypes Clinicians do not understand data representation limitations in EMRs Clinicians do not understand the data quality issues in the EMR Different contexts: retrospective (deceased/alive) vs. prospective (alive only) Ethical Issues False positives send clinical trial notices to people who do not have a disease False negative fail to recruit everyone who has a disease
15 Data Loss Visit Date: 4/5/2006 HISTORY OF PRESENT ILLNESS: Mr. Smith is a 66-year-old gentleman with hypertension ICD9:401.9 and hypercholesterolemia. ICD9:272.0 He reports he is overall doing well. He is not having any trouble with his medications. His blood LOINC: pressures generally have been running around 140/90 at home. He has been having low back pain for the past 6 months. It is more on the right side and occurs intermittently throughout the day. No known initial precipitating event. He denies any radiation down his legs, any fevers, night sweats, weight loss, or bowel or bladder problems. Of note, he CPT:77401 did have significant radiation in this area many years ago for treatment ICD9: for skin cancer. He is not getting much exercise. ICD9:724.2 Source: notes/sample chronic issues note 2
16 Data Loss Not Bidirectional Clinical Narrative Visit Date: 4/5/2006 HISTORY OF PRESENT ILLNESS: Mr. Smith is a 66 year old gentleman with hypertension and hypercholesterolemia. He reports he is overall doing well. He is not having any trouble with his medications. His blood pressures generally have been running around 140/90 at home. He has been having low back pain for the past 6 months. It is more on the right side and occurs intermittently throughout the day. No known initial precipitating event. He denies any radiation down his legs, any fevers, night sweats, weight loss, or bowel or bladder problems. Of note, he did have significant radiation in this area many years ago for treatment for skin cancer. He is not getting much exercise Delta EMR Representation ICD9:401.9 ICD9:272.0 LOINC: ICD9:724.2 CPT:77401 ICD9: Source: notes/sample chronic issues note 2
17 Data Interpretation Found within Patient 123 s EMR: Found within Patient 456 s EMR: ICD ICD Problem List Scratchpad for clinician Suspected Diagnoses Not always cleaned up Context Matters Is ICD = ICD ? ICD is Acute Myocardial Infarction Billing Code Payment to the hospital Upcoding Unbundling
18 Data Quality PaTH Operates at large scale Difficult to manage data quality without specific disease question Only apply generalizable rules independent of specific disease (ex: blood pressure between 0 and 300) Phase II budget includes chart reviews Overall problem: Clinicians not required to correct erroneous EMR data
19 Michael Kahn s Data Quality Framework 1. Attribute domain constraints: Data value anomalies for individual variables, including distributions, units, and missingness. These checks identify values and distributions inconsistent with expectations (e.g., a high proportion of individuals over 120 y old). 2. Relational integrity rules: Compare elements from one data table to related elements in another data table (e.g., every person identifier in the visit table must have a record in the demographic table). 3. Historical data rules: Temporal relationships and trend visualizations to identify data gaps, unusual patterns, and dependencies across multiple data values and variables (e.g., utilization trends can identify shifts in data capture). Slide Courtesy of Shyam Visweswaran
20 Michael Kahn s Data Quality Framework 4. State-dependent objects rules: Extends temporal data assessment to include logical consistency (e.g., a series of prenatal ultrasounds should precede a pregnancy outcome). 5. Attribute dependency rules: Examine conditional dependencies based on knowledge of a clinical scenario (e.g., women should not have a diagnosis of prostate cancer). Slide Courtesy of Shyam Visweswaran
21 Mapping Issues Granularity differences Incomplete coverage Different authors Clinicians Informaticians Different Purposes Billing vs Research Temporal Issues (ex: NDC recycles codes over time)
22 Organizational Challenges Competing priorities At each site PCORI priorities Develop artifacts (documents/software) Arrive at decisions
23 Current Large Scale PCORI Research Projects Aspirin Dosing: A Patient-Centric Trial Assessing Benefits and Long-term Effectiveness (ADAPTABLE)* Duke PCORnet Bariatric Study* Group Health PCORnet Obesity Observational Study: Short- and Long-term Effects of Antibiotics on Childhood Growth Harvard Pilgrim * PaTH is participating in these studies
24 Summary Goals of Research Networks: Access data they need in a cost effective manner (analytic power) Recruit patients for clinical trials Challenges of Research Networks: Relying on questionable data quality Converting data into usable formats
25 Team
26 Research Areas Clinical Technical Clinical Research Questions Patient Reported Outcomes NLP Data Quality Terminology Mapping Clinical Decision Support* Collaborative Science* Data Visualiz ation* Software * Not directly researched in PaTH
From Fishing to Attracting Chicks
The Greater Plains Collaborative: a PCORNet Clinical Data Research Network s Strategies for Creating an Interoperable Architecture From Fishing to Attracting Chicks Russ Waitman, PhD Associate Professor,
More informationEnvironmental 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
More informationSNOMED CT in the EHR Present and Future. with the patient at the heart
SNOMED CT in the EHR Present and Future ` SNOMED CT in the EHR Present and Future Christopher Alban, MD Epic Systems Corporation Objectives Describe the use of SNOMED CT to enable decision support, analytics,
More informationStandardized Representation for Electronic Health Record-Driven Phenotypes
Standardized Representation for Electronic Health Record-Driven Phenotypes April 8, 2014 AMIA Joint Summits for Translational Research Rachel L. Richesson, PhD Shelley A. Rusincovitch Michelle M. Smerek
More informationThe 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,
More informationUsing 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
More informationPractical Development and Implementation of EHR Phenotypes. NIH Collaboratory Grand Rounds Friday, November 15, 2013
Practical Development and Implementation of EHR Phenotypes NIH Collaboratory Grand Rounds Friday, November 15, 2013 The Southeastern Diabetes Initiative (SEDI) Setting the Context Risk Prediction and Intervention:
More informationEmploying SNOMED CT and LOINC to make EHR data sensible and interoperable for clinical research
Employing SNOMED CT and LOINC to make EHR data sensible and interoperable for clinical research James R. Campbell MD W. Scott Campbell PhD Hubert Hickman MS James McClay MD Implementation Showcase October
More informationHealthcare Professional. Driving to the Future 11 March 7, 2011
Clinical Analytics for the Practicing Healthcare Professional Driving to the Future 11 March 7, 2011 Michael O. Bice Agenda Clinical informatics as context for clinical analytics Uniqueness of medical
More informationHow To Maintain Data Discipline in P&P
How To Maintain Data Discipline in P&P Maintaining Data Discipline in P&P The discussion below provides information on how to develop data standards and how to clean and maintain data in your EMR so you
More informationAdventures 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
More informationNatalia Olchanski, MS, Paige Lin, PhD, Aaron Winn, MPP. Center for Evaluation of Value and Risk in Health, Tufts Medical Center.
ISPOR 2013, New Orleans, LA Using EMR data for conducting retrospective studies: Opportunities and Pitfalls Natalia Olchanski, MS, Paige Lin, PhD, Aaron Winn, MPP Center for Evaluation of Value and Risk
More informationUnderstanding Diagnosis Assignment from Billing Systems Relative to Electronic Health Records for Clinical Research Cohort Identification
Understanding Diagnosis Assignment from Billing Systems Relative to Electronic Health Records for Clinical Research Cohort Identification Russ Waitman Kelly Gerard Daniel W. Connolly Gregory A. Ator Division
More informationElectronic Health Record Systems and Secondary Data Use
Electronic Health Record Systems and Secondary Data Use HCQI Expert Group Meeting 10 May 2012 Jillian Oderkirk OECD/HD Background and Needs The 2010 Health Ministerial Communiqué noted that health care
More informationThe What, When, Where and How of Natural Language Processing
The What, When, Where and How of Natural Language Processing There s a mystique that surrounds natural language processing (NLP) technology, regarding how it works, and what it can and cannot do. Although
More informationSession 11 PD, Provider Perspectives of Values Based Payment Programs. Moderator: William T. O'Brien, FSA, FCA
Session 11 PD, Provider Perspectives of Values Based Payment Programs Moderator: William T. O'Brien, FSA, FCA Presenters: Donald Fry, M.D. Lillian Louise Dittrick, FSA, MAAA Colleen Audrey Norris, ASA,
More informationMedical Research from Medical Records Mikel Aickin, PhD Family & Community Medicine University of Arizona, USA The Problem Medical researchers believe that therapeutic knowledge comes from randomized clinical
More informationFind the signal in the noise
Find the signal in the noise Electronic Health Records: The challenge The adoption of Electronic Health Records (EHRs) in the USA is rapidly increasing, due to the Health Information Technology and Clinical
More informationTHE VIRTUAL DATA WAREHOUSE (VDW) AND HOW TO USE IT
THE VIRTUAL DATA WAREHOUSE (VDW) AND HOW TO USE IT Table of Contents Overview o Figure 1. The HCSRN VDW and how it works Data Areas o Figure 2: HCSRN VDW data structures Steps for Using the VDW Multicenter
More informationPreparing for ICD-10 WellStar Medical Group Toolkit
Preparing for ICD-10 WellStar Medical Group Toolkit Preparing for ICD-10 On Oct. 1, 2015, WellStar will transition from ICD-9 to ICD-10 coding for all medical diagnoses and hospital procedures Systemwide.
More informationUsing Big Data to Advance Healthcare Gregory J. Moore MD, PhD February 4, 2014
Using Big Data to Advance Healthcare Gregory J. Moore MD, PhD February 4, 2014 Sequencing Technology - Hype Cycle (Gartner) Gartner - Hype Cycle for Healthcare Provider Applications, Analytics and Systems,
More informationClinical Mapping (CMAP) Draft for Public Comment
Integrating the Healthcare Enterprise 5 IHE Patient Care Coordination Technical Framework Supplement 10 Clinical Mapping (CMAP) 15 Draft for Public Comment 20 Date: June 1, 2015 Author: PCC Technical Committee
More informationICD 10 ESSENTIALS. Debbie Sarason Manager, Practice Enhancement and Quality Reporting
ICD 10 ESSENTIALS Debbie Sarason Manager, Practice Enhancement and Quality Reporting October 29, 2015 CHANGING FROM 1CD 9 TO ICD 10 IN 2015 Rest of world has been using ICD 10 for decades World Health
More informationBench 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
More informationADVANCING 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
More informationAn Easily Accessed Clinical Research Database from your Epic EMR
Loyola University Chicago Health Sciences Division Stritch School of Medicine (SSOM) An Easily Accessed Clinical Research Database from your Epic EMR February 13, 2014 Speakers: Richard H. Kennedy, Ph.D.
More informationAcute Hepatitis C Surveillance using Electronic Health Record Data
Acute Hepatitis C Surveillance using Electronic Health Record Data Hepatitis C National Summit Centers for Disease Control and Prevention June 18, 2014 Michael Klompas MD, MPH, FRCPC, FIDSA CDC Center
More informationDemonstrating Meaningful Use Stage 1 Requirements for Eligible Providers Using Certified EMR Technology
Demonstrating Meaningful Use Stage 1 Requirements for Eligible Providers Using Certified EMR Technology The chart below lists the measures (and specialty exclusions) that eligible providers must demonstrate
More informationTechnical Issues in Aggregating and Analyzing Data from Heterogeneous EHR Systems
Technical Issues in Aggregating and Analyzing Data from Heterogeneous EHR Systems Josh Denny, MD, MS josh.denny@vanderbilt.edu Vanderbilt University, Nashville, Tennessee, USA 2/12/2015 EHR data are dense
More informationThe 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
More informationQUALITY CLINICAL PRACTICE DATA ANALYST SERIES
QUALITY CLINICAL PRACTICE DATA ANALYST SERIES Code No. Class Title Area Area Period Date Action 4966 Clinical Practice Data Analyst 03 441 6 mo. 11/15/13 New 4967 Clinical Practice Data Analyst Specialist
More informationBeacon User Stories Version 1.0
Table of Contents 1. Introduction... 2 2. User Stories... 2 2.1 Update Clinical Data Repository and Disease Registry... 2 2.1.1 Beacon Context... 2 2.1.2 Actors... 2 2.1.3 Preconditions... 3 2.1.4 Story
More informationWelcome! E-Health and Data Analytics: Behavioral Health
Welcome! E-Health and Data Analytics: Behavioral Health For the duration of this presentation, please have your cell phones available. You will be asked to use them. 2014 Poll Everywhere Polling application
More informationRisk Adjustment Factor (RAF) RADV June 1 st 2016
Risk Adjustment Factor (RAF) RADV June 1 st 2016 Disclaimer The information presented herein is for information purposes only. HIMS BMG Coding and Compliance Education has prepared this education using
More informationNEURO-OPHTHALMIC QUESTIONNAIRE NAME: AGE: DATE OF EXAM: CHART #: (Office Use Only)
PAGE 1 NEURO-OPHTHALMIC QUESTIONNAIRE NAME: AGE: DATE OF EXAM: CHART #: (Office Use Only) 1. What is the main problem that you are having? (If additional space is required, please use the back of this
More informationEHR Recruitment Process and Methods Workgroup. John Buse, UNC-CH On behalf of Marie Rape, Chunhua Weng and Peter Embi
EHR Recruitment Process and Methods Workgroup John Buse, UNC-CH On behalf of Marie Rape, Chunhua Weng and Peter Embi REDCap Survey INTRODUCTION The Methods and Process Domain Task Force has established
More informationDashboard Review End of FY 2014
Dashboard Review End of FY 214 Joe Selby, MD, MPH Executive Director * As presented during the PCORI Board of Governors meeting on December 8, 214 Board of Governors Meeting, December 8, 214 1 Percent
More informationUsing 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
More informationOpportunities and challenges for public health surveillance: a new world of interoperability with electronic health records
Opportunities and challenges for public health surveillance: a new world of interoperability with electronic health records James Daniel, MPH The Office of the National Coordinator for Health IT CMS Rule
More informationIs Natural Language Processing the key to data-driven health care? White Paper
Is Natural Language Processing the key to data-driven health care? White Paper Why the need for data are rising The need for clinical data has never been greater in health care. Data are needed to improve
More informationElectronic Health Record (EHR) Data Analysis Capabilities
Electronic Health Record (EHR) Data Analysis Capabilities January 2014 Boston Strategic Partners, Inc. 4 Wellington St. Suite 3 Boston, MA 02118 www.bostonsp.com Boston Strategic Partners is uniquely positioned
More informationLeveraging Existing (Electronic) Systems for Dissemination & Implementation Research
Leveraging Existing (Electronic) Systems for Dissemination & Implementation Research Elsie M. Taveras, M.D., M.P.H Division Chief, General Academic Pediatrics; Director of Pediatric Population Health Management,
More informationRadiology Business Management Association Technology Task Force. Sample Request for Proposal
Technology Task Force Sample Request for Proposal This document has been created by the RBMA s Technology Task Force as a guideline for use by RBMA members working with potential suppliers of Electronic
More information3M Health Information Systems
3M Health Information Systems 1 Data Governance Disparate Systems Interoperability Information Exchange Reporting Public Health Quality Metrics Research Data Warehousing Data Standards What is the 3M Healthcare
More informationDocumentation Proliferation Effect in Electronic Medical Records. Adele Towers, MD and Mark Morsch, MS
Documentation Proliferation Effect in Electronic Medical Records Adele Towers, MD and Mark Morsch, MS DISCLAIMER: The views and opinions expressed in this presentation are those of the author and do not
More informationData Standards, Data Cleaning and Data Discipline. Insight November 24, 2008
Data Standards, Data Cleaning and Data Discipline Designing the Next Generation of EMRs Insight November 24, 2008 InfoClin Inc 2006. All Rights Reserved. Agenda Why is data quality important? Why data
More informationElectronic medical records. Purposes Structures Related nomenclatures Implementations References
Electronic medical records Purposes Structures Related nomenclatures Implementations References Purposes Collecting relevant data Reporting Management of medical data Administrative management Attestation
More informationHealthcare Data: Secondary Use through Interoperability
Healthcare Data: Secondary Use through Interoperability Floyd Eisenberg MD MPH July 18, 2007 NCVHS Agenda Policies, Enablers, Restrictions Date Re-Use Landscape Sources of Data for Quality Measurement,
More informationLeveraging Social Networks to Conduct Observational Research: A Paradigm Shift in Methodology. Presented by: Elisa Cascade, MediGuard/Quintiles
Leveraging Social Networks to Conduct Observational Research: A Paradigm Shift in Methodology Presented by: Elisa Cascade, MediGuard/Quintiles About Elisa Cascade, VP MediGuard/Quintiles Assisted in site
More informationDeveloping VA GDx: An Informatics Platform to Capture and Integrate Genetic Diagnostic Testing Data into the VA Electronic Medical Record
Developing VA GDx: An Informatics Platform to Capture and Integrate Genetic Diagnostic Testing Data into the VA Electronic Medical Record Scott L. DuVall Jun 27, 2014 1 Julie Lynch Vickie Venne Dawn Provenzale
More informationI n t e r S y S t e m S W h I t e P a P e r F O R H E A L T H C A R E IT E X E C U T I V E S. In accountable care
I n t e r S y S t e m S W h I t e P a P e r F O R H E A L T H C A R E IT E X E C U T I V E S The Role of healthcare InfoRmaTIcs In accountable care I n t e r S y S t e m S W h I t e P a P e r F OR H E
More informationAn Essential Ingredient for a Successful ACO: The Clinical Knowledge Exchange
An Essential Ingredient for a Successful ACO: The Clinical Knowledge Exchange Jonathan Everett Director, Health Information Technology Chinese Community Health Care Association Darren Schulte, MD, MPP
More informationChapter 3: Data Mining Driven Learning Apprentice System for Medical Billing Compliance
Chapter 3: Data Mining Driven Learning Apprentice System for Medical Billing Compliance 3.1 Introduction This research has been conducted at back office of a medical billing company situated in a custom
More informationICD-10 Readiness for Public Health
ICD-10 Readiness for Public Health Debbie Widener Sr. Implementation & Training specialist Sonali Luniya, PhD VP, Customer Experience ICD-10 Webinar: Goals ICD-10 Overview and Impacts ICD-9 vs. ICD-10
More informationCarolina s Journey: Turning Big Data Into Better Care. Michael Dulin, MD, PhD
Carolina s Journey: Turning Big Data Into Better Care Michael Dulin, MD, PhD Current State: Massive investments in EMR systems Rapidly Increase Amount of Data (Velocity, Volume, Veracity) The Data has
More informationINFORMATION TECHNOLOGY FOR UNIVERSAL HEALTH COVERAGE (IT4UHC) 25-27 September 2013 Manila, Philippines
INFORMATION TECHNOLOGY FOR UNIVERSAL HEALTH COVERAGE (IT4UHC) 25-27 September 2013 Manila, Philippines Standardizing Terminologies through Health Data Dictionary Khadzir Sheikh Ahmad Ministry of Health,
More informationRecords and Clinical Trials
Integrating Electronic Health Records and Clinical Trials The Children s Hospital An Examination of Pragmatic Issues Affiliated with University of Colorado Health Sciences Center Denver, Colorado Michael
More informationDistributed Networking
Distributed Networking Millions of people. Strong collaborations. Privacy first. Jeffrey Brown, Lesley Curtis, Richard Platt Harvard Pilgrim Health Care Institute and Harvard Medical School Duke Medical
More informationSecondary Use of EMR Data View from SHARPn AMIA Health Policy, 12 Dec 2012
Secondary Use of EMR Data View from SHARPn AMIA Health Policy, 12 Dec 2012 Christopher G. Chute, MD DrPH, Professor, Biomedical Informatics, Mayo Clinic Chair, ISO TC215 on Health Informatics Chair, International
More informationEXPANDING THE EVIDENCE BASE IN OUTCOMES RESEARCH: USING LINKED ELECTRONIC MEDICAL RECORDS (EMR) AND CLAIMS DATA
EXPANDING THE EVIDENCE BASE IN OUTCOMES RESEARCH: USING LINKED ELECTRONIC MEDICAL RECORDS (EMR) AND CLAIMS DATA A CASE STUDY EXAMINING RISK FACTORS AND COSTS OF UNCONTROLLED HYPERTENSION ISPOR 2013 WORKSHOP
More informationTreating Depression to Remission in the Primary Care Setting. James M. Slayton, M.D., M.B.A. Medical Director United Behavioral Health
Treating Depression to Remission in the Primary Care Setting James M. Slayton, M.D., M.B.A. Medical Director United Behavioral Health 2007 United Behavioral Health 1 2007 United Behavioral Health Goals
More informationCARDIA 288 MONTH FOLLOW-UP SUPPLEMENTAL FORM (FORM B) HOSPITALIZATION CASE #: INTERVIEWER ID FY288BIVID2. Page 1 of 6 FY288BH4CN
HOSPITALIZATION CASE #: 2 8 8 0 H FY288BH4CN Has the participant indicated any of the following reasons for being admitted overnight for this case? 1. Suspected or confirmed problems with the heart, circulation,
More informationAGENDA WHAT IS COMPUTER-ASSISTED CODING, REALLY? J03.0 F43.0 I10 A78 R52
R06.2 F43.0 I10 06BY3ZC J03.0 A78 03HK0MZ R52 0SG1430 COMPUTER-ASSISTED CODING AGENDA Evaluating and Understanding the Technology Review of Lessons Learned from Early Adopters Workflow and Analytics with
More informationDelivering Real World Evidence. Canada Let s Get Real!
Delivering Real World Evidence from Electronic Medical Records in Canada Let s Get Real! Neil Corner Director, Real World Evidence, IMS Brogan Alison Dziarmaga Director, Real World Evidence, AstraZeneca
More informationHow To Manage Information Management In An Emr
Information Management in EMR Data Standards, Data Cleaning & Data Discipline CEM Rounds, April 15, 2008 Karim Keshavjee MD, MBA, CCFP InfoClin Inc 2006. All Rights Reserved. Learning Objectives What are
More informationOutcome Data, Links to Electronic Medical Records. Dan Roden Vanderbilt University
Outcome Data, Links to Electronic Medical Records Dan Roden Vanderbilt University Coordinating Center Type II Diabetes Case Algorithm * Abnormal lab= Random glucose > 200mg/dl, Fasting glucose > 125 mg/dl,
More informationDRAFT. To Whom It May Concern:
DRAFT Centers for Medicare & Medicaid Services, Department of Health and Human Services, Attention: CMS-1345-P, P.O. Box 8013, Baltimore, MD 21244-8013 To Whom It May Concern: As a nonprofit, nonpartisan
More informationMeaningful Use Stage 2 Update: Deploying SNOMED CT to provide decision support in the EHR
Meaningful Use Stage 2 Update: Deploying SNOMED CT to provide decision support in the EHR James R. Campbell MD University of Nebraska Medical Center Implementation Showcase October 31, 2014 Disclosure
More informationOECD Study of Electronic Health Record Systems
OECD Study of Electronic Health Record Systems Ministry of Health of the Czech Republic E health Expert Group Meeting 19 June 2012 Jillian Oderkirk OECD/HD Background and Needs The 2010 Health Ministerial
More informationReal- time Performance Improvement for Patient Safety
Real- time Performance Improvement for Patient Safety one two Introduction Real- time Value Proposition three Patient Safety Indicators four five six Point- of- care Alerts & Advice Documentation Improvement
More informationSession Name: e-health (collaborative)
Session Name: e-health (collaborative) In accordance with the policy of The Royal Australian and New Zealand College of Radiologists, the Australian Institute of Radiography and the Australasian College
More informationPhysician and other health professional services
O n l i n e A p p e n d i x e s 4 Physician and other health professional services 4-A O n l i n e A p p e n d i x Access to physician and other health professional services 4 a1 Access to physician care
More information206-478-8227 www.healthdataconsulting.com. Getting Specific. New ICD-10 codes. Will they make a difference?
206-478-8227 www.healthdataconsulting.com Getting Specific New ICD-10 codes. Will they make a difference? [First in a series on getting to specific documentation and coding] Joseph C Nichols MD Principal
More informationClinical Decision Support Consortium Knowledge Management Overview
Clinical Decision Support Consortium Knowledge Management Overview Overview of key steps in creating, maintaining and publishing CDS content The Clinical Decision Support (CDS) Consortium (CDSC) Knowledge
More informationOverview of Vital Records and Public Health Informatics in CDPH
Overview of Vital Records and Public Health Informatics in CDPH Este Geraghty, MD, MS, MPH/CPH, FACP, GISP Deputy Director, Center for Health Statistics and Informatics California Department of Public
More informationSan Luis Dermatology & Laser Clinic, Inc.
San Luis Dermatology & Laser Clinic, Inc. Patient Name: Pharmacy Name: LOCATION Health History Intake Form The federal government has defined a complete electronic medical record (EMR) or electronic health
More informationTHE CHALLENGE OF COORDINATING EMR
THE CHALLENGE OF COORDINATING EMR CLINICAL CONNECT: TWO YEARS OF REGIONAL ELECTRONIC HEALTH INFORMATION EXCHANGE NANCY A. LANDMAN, CIO INTERNATIONAL & COMMERCIAL SERVICES, UPMC 10/8/2014 2 UPMC Today:
More informationSecondary 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 Paul.Wallace@lewin.com Disclosure/Perspectives Training:
More informationDr. Rob Donald - Curriculum Vitae. Email: rob@statsresearch.co.uk, Web: http://www.statsresearch.co.uk Mob: 07780 650 910
Dr. Rob Donald - Curriculum Vitae Email: rob@statsresearch.co.uk, Web: http://www.statsresearch.co.uk Mob: 07780 650 910 Profile Data Scientist, Systems and Data Analyst In my current role I am a senior
More informationQuality 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
More informationIntroduction to Medical Coding For Lawyers
American Health Lawyers Association Institute on Medicare and Medicaid Payment Issues March 20-22, 2013 Introduction to Medical Coding for Payment Lawyers Robert A. Pelaia Senior University Counsel for
More informationUnderstanding how to use the Quality Measure Step by Step Documents
Understanding how to use the Quality Measure Step by Step Documents Step 1: Quality Measure Selection o The first step for Quality Measures is to ensure you know what the measures are looking for from
More informationAN ANALYSIS OF ELECTRONIC HEALTH RECORD-RELATED PATIENT SAFETY CONCERNS
AN ANALYSIS OF ELECTRONIC HEALTH RECORD-RELATED PATIENT SAFETY CONCERNS 1 HARDEEP SINGH, MD, MPH MICHAEL E. DEBAKEY VA MEDICAL CENTER BAYLOR COLLEGE OF MEDICINE DEAN SITTIG, PHD UNIVERSITY OF TEXAS HEALTH
More informationAutomated Problem List Generation from Electronic Medical Records in IBM Watson
Proceedings of the Twenty-Seventh Conference on Innovative Applications of Artificial Intelligence Automated Problem List Generation from Electronic Medical Records in IBM Watson Murthy Devarakonda, Ching-Huei
More informationWelcome UDS Review for Intergy CHC Jeff Urkevich Director of Health Solutions Health Choice Network
Welcome UDS Review for Intergy CHC Jeff Urkevich Director of Health Solutions Health Choice Network 1 Agenda Welcome Where is the UDS data Intergy POMIS Intergy EHR Practice Analytics cross walk Basic
More informationBig Data Analytics Driving Healthcare Transformation
Big Data Analytics Driving Healthcare Transformation Greg Caressi SVP Healthcare & Life Sciences November, 2014 Six Big Themes for the New Healthcare Economy Themes Modernizing Care Delivery Clinical practice
More informationRisk Adjustment Medicare and Commercial
Risk Adjustment Medicare and Commercial Transform your thinking about documentation and coding 900-1169-0715 Introduction In a time of continual regulatory reform and the evolution of payer/provider reimbursement
More informationMedicare- Medicaid Enrollee State Profile
Medicare- Medicaid Enrollee State Profile Montana Centers for Medicare & Medicaid Services Introduction... 1 At a Glance... 1 Eligibility... 2 Demographics... 3 Chronic Conditions... 4 Utilization... 6
More informationTransforming Community Health Through Public Health Data & Surveillance
Transforming Community Health Through Public Health Data & Surveillance Monica Bharel, MD Commissioner, MA Department of Public Health Charles Deutsch, ScD Program Director, Harvard Catalyst Michael Klompas,
More informationPrevention and Wellness Advisory Board August 19, 2013. Cheryl Bartlett, RN Commissioner Massachusetts Department of Public Health
Prevention and Wellness Advisory Board August 19, 2013 Cheryl Bartlett, RN Commissioner Massachusetts Department of Public Health Today s goals: Review RFR Outline focusing on key areas Weigh in on final
More informationReal World Data: How It s Used at a Medical Device Company
MD MEDICAL SAFETY Real World Data: How It s Used at a Medical Device Company Myoung Kim, Ph.D., MBA. Andrew Yoo, M.D., M.S. Epidemiology and Health Informatics Medical Devices Johnson & Johnson May 29,
More informationButler Memorial Hospital Community Health Needs Assessment 2013
Butler Memorial Hospital Community Health Needs Assessment 2013 Butler County best represents the community that Butler Memorial Hospital serves. Butler Memorial Hospital (BMH) has conducted community
More informationCanada ehealth Data Integration Snapshots
Canada ehealth Data Integration Snapshots Collection, Integration, Analysis of Electronic Health Records for Monitoring Patient Outcomes Liam Peyton Agenda Electronic Health Records Why are they so hard
More informationChronic Disease Management Who Cares?
Chronic Disease Management Who Cares? By Stephen Kalyniuk BSc(Health Management), MACS, PCP Member HISA The Information Group - Useful software for people in Healthcare 21 years experience in Health IT
More informationMedicare & Medicaid EHR Incentive Program Meaningful Use Stage 1 Requirements Summary. http://www.cms.gov/ehrincentiveprograms/
Medicare & Medicaid EHR Incentive Program Meaningful Use Stage 1 Requirements Summary 2010 What are the Requirements of Stage 1 Meaningful Use? Basic Overview of Stage 1 Meaningful Use: Reporting period
More informationPredictive analytics: Poised to drive population health. White Paper
Predictive analytics: Poised to drive population health As health care moves toward value-based payments and accountable care, providers need better tools for population health and risk management. The
More informationInterface Terminology to Facilitate the Problem List Using SNOMED CT and other Terminology Standards
Interface Terminology to Facilitate the Problem List Using SNOMED CT and other Terminology Standards Kshitij Saxena MD, MHSA Regional Medical Director, Adventist Health System Agenda Introduction Problem
More informationMore Information, Less Work: EHRs and Public Health Surveillance
More Information, Less Work: EHRs and Public Health Surveillance CSTE 2013 Richard Platt, MD, MS Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA For the ESPnet team, led by
More informationAchieving meaningful use of healthcare information technology
IBM Software Information Management Achieving meaningful use of healthcare information technology A patient registry is key to adoption of EHR 2 Achieving meaningful use of healthcare information technology
More informationEvaluation of Logical Observation Identifiers Names and Codes (LOINC) Mapping and Transmission of Data Processes: Barriers and Lessons Learned
Adding Clinical Data Elements to Administrative Data Evaluation of Logical Observation Identifiers Names and Codes (LOINC) Mapping and Transmission of Data Processes: Barriers and Lessons Learned Bahia
More information