Uses of the NIH Collaboratory Distributed Research Network Jeffrey Brown, PhD for the DRN Team Harvard Pilgrim Health Care Institute and Harvard Medical.

Slides:



Advertisements
Similar presentations
Meaningful Use and Health Information Exchange
Advertisements

An Essential Component of Health Systems Strengthening Presented on: May 23, 2011 Akiko Maeda Health, Nutrition & Population Network The World Bank.
A Plan for a Sustainable Community Behavioral Health Information Network Western States Health-e Connection Summit & Trade Show September 10, 2013.
For the Healthcare Provider
Clinical Trials What Are They and When Are They Right For You? Maura N. Dickler Assistant Attending Physician Breast Cancer Medicine Service Memorial Sloan-Kettering.
Copyright ©2011 Freedman Healthcare, LLC All Payer Claims Datasets: Big Data is Coming to Public Health Officials, Providers and Patients Near You StrataRx.
HealthNet connect Telehealth
2.11 Conduct Medication Management University Medical Center Health System Lubbock, TX Jason Mills, PharmD, RPh Assistant Director of Pharmacy.
Challenges in Conducting Multi-Center Clinical Studies: Results from the Rapid Empiric Treatment with Oseltamivir Study (RETOS) Kendra Thompson, Kelly.
Components of EHR Farrokh Alemi, Ph.D.. Definitions Electronic Medical Record Electronic Medical Record Electronic Health Record Electronic Health Record.
1.01 E LECTRONIC M EDICAL R ECORD S YSTEMS AND D ISEASE R EGISTRIES : S ELECTION A LONG THE S PECTRUM Wayne T. Pan, MD Medical Director Choosing a Chronic.
AHCCCS/ASU Clinical Data Project March 17 th, 2009 Arizona Health Care Cost Containment Health System Medicaid Transformation Grant Program.
The NIH Biomedical Translational Research Information System (BTRIS) Town Hall Meeting - Information Session February 26, 2008 Lipsett Auditorium.
Lifelines2: Hypothesis Generation in Multiple EHRs Taowei David Wang Catherine Plaisant Ben Shneiderman Shawn Murphy Mark Smith Human-Computer Interaction.
A First Look at Meaningful Use Stage 2 John D. Halamka MD.
Meaningful Use Stage 2 Esthee Van Staden September 2014.
Software Integration with PopMedNet PopMedNet Users Group Meeting Boston, MA July 27, 2015 Jeffrey S. Brown, PhD Associate Professor Department of Population.
Proposed Cross-center Project Survey of Federally Qualified Health Centers Vicky Taylor & Vicki Young.
Developing a Metric Set for Measuring and Reporting Ambulatory Quality of Care in the Setting of Health IT with HIE Lisa M. Kern, MD, MPH Rina V. Dhopeshwarkar,
IDR Snapshot: Quantitative Assessment Methodology Evaluating Size and Comprehensiveness of an Integrated Data Repository Vojtech Huser, MD, PhD a James.
New Opportunity for Network Value: Using Health IT to Improve Transitions of Care 600 East Superior Street, Suite 404 I Duluth, MN I Ph
Decision Support for Quality Improvement
Memorial Hermann Healthcare System Clinical Integration & Disease Management Dan Wolterman April 15, 2010.
1 The Three Phases of Collaboration: Chronic Disease Management, Cancer Prevention, and Capacity Kim Salamone, Ph.D. Vice President, Health Information.
Darren A. DeWalt, MD, MPH Division of General Internal Medicine Maihan B. Vu, Dr.PH, MPH Center for Health Promotion and Disease Prevention University.
Developing Interoperable EHR: Maximizing Quality of Care Gregory J Downing, DO, PhD Office of the Secretary Department of Health and Human Services July.
A First Look at Meaningful Use Stage 2 John D. Halamka MD.
The Effect of Quality Improvement on Racial Disparities in Diabetes Care Thomas D. Sequist, MD MPH Alyce S. Adams, PhD Fang Zhang, MS Dennis Ross-Degnan,
Rural Health Network Development Grantee Meeting August 2, 2010 Diane M. Hughes, MBA Executive Director.
Basma Y. Kentab MSc.. 1. Define ambulatory care 2. Describe the value of ambulatory care practices 3. Explore pharmacy services in some ambulatory care.
Physician Performance and Reporting Commentary David W. Bates, MD, MSc Medical Director of Clinical and Quality Analysis, Partners Healthcare Chief, Division.
Universal Adoption of the EHR What is Meaningful Use and why should it be important to me?
How to Achieve Cost Savings and Patient Satisfaction Through Clinical Best Practices James Cox-Chapman, MD February 18,
July 31, 2009Prepared by the Maine Health Information Center Overview of All Payer Claims Data Suanne Singer, Senior Consultant Maine Health Information.
PopMedNet in Mini-Sentinel Tiffany Siu Woodworth PopMedNet User Group Conference July 27, 2015.
INTRODUCTION TO THE ELECTRONIC HEALTH RECORD CHAPTER 1.
© Copyright IBM Corporation 2008 Health Analytics: An Overview HealthTech Net November 20, 2008 Richard Singerman, Ph.D.
Chapter 6 – Data Handling and EPR. Electronic Health Record Systems: Government Initiatives and Public/Private Partnerships EHR is systematic collection.
Are EHR’s enough for population health? Lisa Dolan-Branton Indian Health Service AHRQ Annual Conference Sept 15, 2007.
California Stroke Registry Right Care Initiative Meeting August 13, 2012.
Universal Screening for Lynch Syndrome with Cascade Screening for Relatives September 7, 2012 Deb Duquette, MS, CGC Michigan Department of Community Health.
Building Clinical Infrastructure and Expert Support Michael Steinberg, MD, FACR ULAAC Disparity Project Centinela/Freeman Health System.
Community Connectivity The MA Experience John D. Halamka MD CIO, Harvard Medical School CIO, CareGroup Chairman, NEHEN.
Vaccine Safety Datalink (CDC, 1990) Cancer Research Network (NCI, 1999) Integrated Delivery System Research Networks (AHRQ, 2000) Epidemiological Studies.
June 18, 2010 Marty Larson.  Health Information Exchange  Meaningful Use Objectives  Conclusion.
HMORN Member Organizations Most sites include insurance companies and multi-specialty practices Representing 10 million covered lives Reimbursement models.
Information Technology and Data Collection: February 28, 2008 Optimizing Lab Results and Pharmacy Data Collection Under P4P Concurrent Session 1.07 Horace.
Realizing the Benefits of Health IT For CHCs November 8, 2005 Ralph Silber, MPH, CEO Community Health Center Network 1320 Harbor Bay Parkway, Suite 250.
Collaborative Networks for Conducting Comparative Effectiveness Research Tuesday September 9, :00 – 9:30 am.
Danielle M. MacFee, MPH New York State Department of Health Healthy Heart Program 1 State of New York Department of Health.
1 Patient Safety In China Gao Xinqiang 23 June 2014.
Enrollment and Monitoring Procedures for NCI Supported Clinical Trials Barry Anderson, MD, PhD Cancer Therapy Evaluation Program National Cancer Institute.
This material was developed by Duke University, funded by the Department of Health and Human Services, Office of the National Coordinator for Health Information.
This material was developed by Oregon Health & Science University, funded by the Department of Health and Human Services, Office of the National Coordinator.
 The patient has better access to their healthcare information allowing decisions based on better knowledge.  Tools available to assist the patient.
The TJU Human Research Protection Program (HRPP): Part I – Which Entities/Offices are Involved ? J. Bruce Smith, MD, CIP.
S ecure A rchitecture F or E xchanging Health Information in Central Massachusetts Larry Garber, M.D. Peggy Preusse, R.N. June 9 th, 2005.
Overview: Common Formats Overview: Common Formats Event Reporting vs. Surveillance Future of Automation Prepared for the HL-7 CQI Meeting CDR A. Gretchen.
Building Capacity for EMR Adoption and Data Utilization Among Safety Net Organizations Presented by Chatrian Reynolds, MPH, Evaluator, LPHI Shelina Foderingham,
Moving Toward HITECH Healthcare EHR Adoption at the Dawn of a New Era
Using CDC Edits Metafile in the Registry to Support Clinical Trials Recruitment Alan R. Houser, MA, MPH C/NET Solutions Dennis Deapen, DrPH Los Angeles.
Public Health February 2017
MUSC i2b2 Jean Craig, Biomedical Informatics
The CQUIN Learning Network: Partnering to Advance Differentiated Care
pSCANNER’s Value: Beckstrom’s Law at Work
National Cancer Center
FDA Sentinel Initiative
Community Connectivity The MA Experience
Megan Eguchi, MPh Sana karam, md, phd
REACHnet: Research Action for Health Network
Presentation transcript:

Uses of the NIH Collaboratory Distributed Research Network Jeffrey Brown, PhD for the DRN Team Harvard Pilgrim Health Care Institute and Harvard Medical School March 11, 2016

The Goal The NIH Collaboratory DRN facilitates research partnerships with organizations (Data Partners) that possess electronic health data that have been quality checked and formatted to support multi-site biomedical research 2

Advantages of a distributed research network (DRN) Ability to work with analysis-ready datasets covering many millions Standardized data using a common data model Data stewards keep and analyze their own data Provide results, not data, to the requestor All activities audited and secure Collaboratory DRN Objective Goal: Facilitate multisite research collaborations between investigators and data stewards by creating secure networking capabilities and analysis tools.

Research planning Assess background rates and population impact of conditions / treatments Prioritize research domains Identify sites for participation in interventional or observational studies Answer research questions! Requestors do not have to be experts in use of healthcare data Coordinating Center helps requestors understand and use the network Assess fit between requests and DRN capabilities Suggest ways to maximize usefulness of DRN data and resources Uses of the Distributed Network

NIH Collaboratory Distributed Research Network Partners Millions of people. Strong collaborations. Privacy first. Data Partners All participate in FDA’s Sentinel System

Critical Partners in a National Infrastructure Health Plan 2 Health Plan 1 Health Plan 5 Health Plan 4 Health Plan 7 Hospital 1 Health Plan 3 Health Plan 6 Health Plan 8 Hospital 3 Health Plan 9 Hospital 2 Hospital 4 Hospital 6 Hospital 5 Outpatient clinic 1 Outpatient clinic 3 Patient network 1 Patient network 3 Patient network 2 Outpatient clinic 2

Critical Partners in a National Infrastructure Health Plan 2 Health Plan 1 Health Plan 5 Health Plan 4 Health Plan 7 Hospital 1 Health Plan 3 Health Plan 6 Health Plan 8 Hospital 3 Health Plan 9 Hospital 2 Hospital 4 Hospital 6 Hospital 5 Outpatient clinic 1 Outpatient clinic 3 Patient network 1 Patient network 3 Patient network 2 Outpatient clinic 2 Each organization can participate in multiple networks

Critical Partners in a National Infrastructure Health Plan 2 Health Plan 1 Health Plan 5 Health Plan 4 Health Plan 7 Hospital 1 Health Plan 3 Health Plan 6 Health Plan 8 Hospital 3 Health Plan 9 Hospital 2 Hospital 4 Hospital 6 Hospital 5 Outpatient clinic 1 Outpatient clinic 3 Patient network 1 Patient network 3 Patient network 2 Outpatient clinic 2 Each organization can participate in multiple networks Each network controls its governance and coordination

Critical Partners in a National Infrastructure Health Plan 2 Health Plan 1 Health Plan 5 Health Plan 4 Health Plan 7 Hospital 1 Health Plan 3 Health Plan 6 Health Plan 8 Hospital 3 Health Plan 9 Hospital 2 Hospital 4 Hospital 6 Hospital 5 Outpatient clinic 1 Outpatient clinic 3 Patient network 1 Patient network 3 Patient network 2 Outpatient clinic 2 Each organization can participate in multiple networks Each network controls its governance and coordination Networks share infrastructure, data curation, analytics, lessons, security, software development

Critical Partners in a National Infrastructure Health Plan 2 Health Plan 1 Health Plan 5 Health Plan 4 Health Plan 7 Hospital 1 Health Plan 3 Health Plan 6 Health Plan 8 Hospital 3 Health Plan 9 Hospital 2 Hospital 4 Hospital 6 Hospital 5 Outpatient clinic 1 Outpatient clinic 3 Patient network 1 Patient network 3 Patient network 2 Outpatient clinic 2 Each organization can participate in multiple networks Each network controls its governance and coordination Networks share infrastructure, data curation, analytics, lessons, security, software development

Coordinating Center(s) Quality of Care Sponsor(s) Public Health Surveillance Sponsor(s) Coordinating Center(s) Medical Product Safety Surveillance FDA Sentinel Coordinating Center Sponsor(s) Medical Product Safety Coordinating Center(s) Comparative Effectiveness Research Sponsor(s) Coordinating Center(s) Queries Results Queries Results Queries Results Queries Providers Hospitals Physicians Integrated Systems Payers Public Private Registries Disease-specific Product-specific Common Data Model Data Standards Sponsor(s) Clinical Research Coordinating Center(s) Queries Results Randomized Clinical Trials Results Execution National Evidence Generation Concept Patients Populations Consumers

Coordinating Center(s) Quality of Care Sponsor(s) Public Health Surveillance Sponsor(s) Coordinating Center(s) Medical Product Safety Surveillance FDA Sentinel Coordinating Center Sponsor(s) Medical Product Safety Coordinating Center(s) Comparative Effectiveness Research Sponsor(s) Coordinating Center(s) Queries Results Queries Results Queries Results Queries Providers Hospitals Physicians Integrated Systems Payers Public Private Registries Disease-specific Product-specific Common Data Model Data Standards Sponsor(s) Clinical Research Coordinating Center(s) Queries Results Randomized Clinical Trials Results Execution National Evidence Generation Concept Patients Populations Consumers

Rapid-response distributed querying available across data partners with over 90 million lives >300 million person-years of observation time Detailed information for billions of medical encounters and outpatient pharmacy dispensings Analysis-ready datasets (i.e., quality checked and formatted) representing >90% of the FDA Sentinel program Available Data

Data Elements Available Ambulatory care diagnoses and procedures Outpatient pharmacy dispensing Laboratory testing and selected test results Inpatient diagnoses, treatments, and procedures itemized in hospital bill Not available Out-of-hospital death OTC medication Community-based immunizations

Special NIH supplement in 2014 for pilot test Three pilot test queries developed by 3 NIH Institutes Pilot used publically-available Sentinel querying tools DRN Team and NIH staff (led by NHLBI & NCI) used queries as test cases for developing processes, and refining strategies to format queries Assess recruitment feasibility of replicating the Trial to Assess Chelation Therapy (TACT) Characterize statin users >75 years of age Assess rates of abnormal cancer screening test results and rates of follow up testing Pilot Test of the Collaboratory DRN

Rationale: Assess recruitment feasibility of replicating the Trial to Assess Chelation Therapy (TACT) Goal: Characterize individuals with prevalent diabetes and prior AMI but no prior heart failure or chelation therapy Simple counts: Counts and prevalence of chelation therapy and diabetes Complex counts: First diagnosis of diabetes in people over 50 years of age in 2007 through 2014 with evidence of a prior AMI but no evidence of heart failure or chelation therapy Any care setting 365 day “look-back” window Diabetes and Chelation Therapy

Rationale: Characterize statin users over the age of 75 with regard to CVD and diabetes status Complex counts: All and long-term (>=180 days) prevalent and incident statin users With no evidence of CVD With and without a evidence of a diabetes diagnosis the day of or in the 90 days before first statin dispensing Statin Users >75 years old and Cardiovascular Disease (CVD)

Rationale: Characterize frequency of abnormal breast, colorectal, and cervical cancer screening test results and follow-up care Background rates: Incidence of cancer screenings and abnormal cancer screening results 270 day “look-back” window to define new screen and new result Abnormal screening results and follow-up: For each cancer, count patients with a new abnormal finding, and among them, count how many had a follow-up test within 90 days 183 day “look-back” window to define new abnormal result Abnormal Cancer Screening Results and Follow-up

Breast cancer Screening ( ) 6,719,382 eligible members (female, ages 40+, meets enrollment/incidence requirements, etc) 3,750,337 new patients with a breast cancer screening 8,809,583 new breast cancer screenings Abnormal Results ( ) 6,898,880 eligible members (female, ages 40+, meets enrollment/incidence requirements, etc) 1,075,964 patients with a new abnormal result 1,418,562 new abnormal results Results: Cancer Screening and Abnormal Cancer Screen Result

Breast cancer continued… Follow-up after Abnormal Result (2013 only) 220,735 patients with a new abnormal result 216,179 patients with a follow-up procedure/diagnosis 97.9% follow-up within 90 days 3.1 mean time to follow-up (days) Results: Abnormal Cancer Screen Result and Follow-up

Colorectal cancer Screening ( ) 8,735,964 eligible members (ages 50+, meets enrollment/incidence requirements, etc) 2,630,125 new patients with a colorectal cancer screening 3,966,484 new colorectal cancer screenings Abnormal Results ( ) 8,856,555 eligible members (ages 50+, meets enrollment/incidence requirements, etc) 69,531 patients with a new abnormal result 72,616 new abnormal results Results: Cancer Screening and Abnormal Cancer Screen Result

Colorectal cancer continued… Follow-up after Abnormal Result (2013 only) 12,121 patients with a new abnormal result 8,545 patients with a follow-up procedure/diagnosis 70.5% follow-up within 90 days 32.0 mean time to follow-up (days) Results: Abnormal Cancer Screen Result and Follow-up

Cervical cancer Screening ( ) 10,808,847 eligible members (female, ages 21+, meets enrollment/incidence requirements, etc) 5,322,691 new patients with a cervical cancer screening 10,703,839 new cervical cancer screenings Abnormal Results ( ) 11,216,026 eligible members (female, ages 21+, meets enrollment/incidence requirements, etc) 768,962 patients with a new abnormal result 927,948 new abnormal results Results: Cancer Screening and Abnormal Cancer Screen Result

Cervical cancer continued… Follow-up after Abnormal Result (2013 only) 126,620 patients with a new abnormal result 93,430 patients with a follow-up procedure/diagnosis 73.8% follow-up within 90 days 25.0 mean time to follow-up (days) Results: Abnormal Cancer Screen Result and Follow-up

Test cases assessed in three data organizations, representing ~1/3 of the total data Test cases informative of the necessary iterative process needed to refine queries Pilot informative of types of queries that are readily addressed vs. those that require a more iterative process over time to address Manual updated based on experience of the team with the test cases Revised processes and timelines for future test cases Summary of NIH Pilot Test

Collaboratory DRN is based on administrative claims and outpatient pharmacy dispensing data Complete data for most reimbursed care → if no evidence of an event, it very likely didn’t occur Limited access to medical record information PCORnet is based on EHR data Detailed information care provided by clinical organization, including vital signs, lab test results Limited information about care provided by other organizations or drug dispensing Comparing Collaboratory DRN and PCORnet

27

Exploring possibilities for testing additional NIH queries to refine process Considering pilot testing of external queries from the research community For additional information, please go to: network.aspx#HowSubmit network.aspx#HowSubmit Next Steps