The NYC Macroscope: Harnessing Data from Electronic Health Records for Population Health Surveillance in NYC
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1 The NYC Macroscope: Harnessing Data from Electronic Health Records for Population Health Surveillance in NYC Remle Newton-Dame, MPH Senior Epidemiologist, Primary Care Information Project Tiffany G. Harris, PhD, MS Assistant Commissioner, Bureau of Epidemiology Services New York City Department of Health and Mental Hygiene June 20, 2014
2 Oct-09 Dec-09 Feb-10 Apr-10 Jun-10 Aug-10 Oct-10 Dec-10 Feb-11 Apr-11 Jun-11 Aug-11 Oct-11 EHRs Transform Medical Records into Actionable Information
3 Actionable Information in NYC: Primary Care Information Project (PCIP) A bureau of NYC Department of Health, PCIP was founded by Mayor Bloomberg in 2005 Mission Improve the quality of care in medically underserved areas through health information technology Success On multiple vendor platforms, over 9,000 providers joined PCIP s Regional Health Extension Center for Health IT (NYC REACH) 1095 independent practices 63 community health centers 54 hospitals & outpatient clinics 3
4 PCIP Overview EHR Adoption & Meaningful Use Regional Extension Center Regional Extension Behavioral Center Health Medicaid Specialists Quality Improvement PCMH Pay for Performance Pay for Quality Patient engagement Community Projects Interoperability Health Information Exchange Interfaces Accountable Care Public Health Monitoring Disease Surveillance and Management Diabetes Registry Query Health Data Hub
5 Providers offered many services Contemplation Implementation Go Post go live live Population Health Provider outreach & education Vendor selection Group purchasing discounts Readiness assessments IT consultation Partners for financing & workforce development Contract accountability Project management Workflow redesign (large practices) Social networking Communication outreach 16 CME credits for training Revenue cycle optimization EMR consulting QI consulting PCMH preparation Privacy & security consulting Work flow redesign (small practices) Patient portal training Interfaces (e.g.,labs, registries) Pilots Quality measures Interoperability Patient engagement Biosurveillance Pay-for-Quality programs Primary Care Information Project 5
6 Feedback to providers gets buy-in Quality-ofcare measurement Flu-like Illness Monthly dashboards sent to PCIP providers 10 EHR Use Measures eprescribing Reviewing current meds 10 Quality-of-care measures Diabetes control Blood pressure control Smoking status Mammography Recommendations Flu-like illness Primary Care Information Project
7 Infrastructure to Retrieve EHR Data: The Hub Population Health Network The Hub allows secure exchange of aggregate data with PCIP practices through a distributed model Send out queries Receive patient counts overnight The Hub currently covers: 700 practices 1.9 M patients in M patients since
8 Mechanics of the Hub Using a secured HTTPS connection, SQL queries are pushed from the vendor server, run as a scheduled job at each practice, and returned to an internal data warehouse. Distributed model - no patient-level data shared 700 Practices Queries Results
9 Hub Strengths: Sample Size, Coverage 3,000+ Patients Per Neighborhood in
10 Hub Limitations: Documentation Changes Over Time Per Practice, percent of patients that received >=1 lab result electronically Providers are getting better at using their EHRs. EHRs are getting better at connecting to electronic resources like labs. Incentives are changing provider behavior. 10
11 Critical question: How can EHR data be meaningfully harnessed for population surveillance, taking into account representativeness, bias and data quality issues, in New York City and across the country? 11
12 THE NYC MACROSCOPE
13 Goals of the NYC Macroscope To develop and validate a system to use data from primary care electronic health records (EHRs) for population health surveillance To disseminate the knowledge generated from our work to other jurisdictions Develop a network of people working on this topic Community of practice
14 Challenges Reliability/data quality Construct and diagnostic validity Generalizability From practices to NYC overall From NYC to other jurisdictions Questions about how to analyze trends Qualitative changes in denominators over time Case mix within practices Practices within the Health Information Exchange Limitations of confidence intervals Very large sample sizes create very small confidence intervals Need to develop ways to incorporate uncertainty
15 Strategic Plan Governance Issues Methodological Considerations Plan to Operationalize the NYC Macroscope Indicators to be Evaluated Evaluation Studies
16 NYC Macroscope Indicators Outcomes Prevalence, Treatment and Control Diabetes Hypertension Cholesterol Prevalence Obesity Smoking Depression Use of Preventive Services Vaccination against Influenza Population Subgroups Sex Male Female Age Group Neighborhood Poverty Rate* <10% 10-19% 20-29% 30% *Derived from American Community Survey data on the percent of individuals in a neighborhood living below 100% of the Federal Poverty Line
17 Age Distribution of Hub Population by Sex American Community Survey Hub 85 and over % 6.8% 6.9% 6.8% 5.6% 6.0% 8.1% 9.0% 7.6% 5.8% 5.4% 5.3% 6.0% 4.0% 3.2% 2.3% 2.1% 2.4% 1.5% 1.7% 2.3% 3.1% 3.8% 5.3% 5.1% 6.1% 5.4% 6.7% 6.6% 6.1% 6.8% 7.0% 7.6% 7.1% 8.9% 8.8% 10.0% 5.0%.0% 5.0% 10.0% 85 and over 1.1% 1.0% % 1.4% % 2.1% % 3.0% % 3.8% % 5.2% % 6.0% % 6.9% % 7.2% % 6.6% % 5.8% % 6.0% % 6.1% % 5.7% % 6.8% % 7.8% % 8.7% % 9.8% 10.0% 5.0%.0% 5.0% 10.0% Female, 2011 Male, 2011
18 Reference Surveys NYC Health and Nutrition Examination Survey (NYC HANES 2013) Gold-standard examination survey with household-based sampling Includes clinical and laboratory measures Estimated N = 1,550 Carried out by CUNY School of Public Health in collaboration with NYC Department of Health Previous NYC HANES conducted in 2004 NYC Community Health Survey (CHS) Annual random-digit-dialed telephone survey N = 8,698 (6,905 in care) Carried out by NYC Department of Health
19 Evaluation Studies Population Level Estimate Comparison Convergent validity: NYC Macroscope vs. CHS, NYC HANES Discriminant validity: NYC Macroscope vs. data expected to correlate poorly (e.g. ownership of outdoor cats) Individual Level Outcome Comparison Reliability (NYC Macroscope algorithm vs. entire EHR) Diagnostic validity (NYC Macroscope algorithm vs. NYC HANES 2013) Generalizability across EHR platforms (reliability and validity stratified by EHR platform) Temporal Comparison Cohort definitions/sampling Methods to incorporate uncertainty Bootstrapping Adjustment based on sensitivity/specificity
20 Population-Level Evaluation of Obesity and Hypertension Indicators PILOT STUDY 2012 DATA
21 Research Question How do NYC Macroscope 2012 estimates compare to estimates from CHS 2012 and NYC HANES 2004 (most recent NYC HANES)? Obesity (BMI 30) Prevalence Hypertension Prevalence (diagnosed) Treatment among diagnosed
22 Methods NYC Macroscope estimates were stratified by sex, age group and neighborhood poverty rate (n=24 strata) and were compared to reference survey estimates Restricted to sub-population who had seen a doctor in the past year Metrics of goodness-of-fit Correlation (rho) Mean standardized deviation = avg( NYC Macroscope estimate reference estimate standard error for reference estimate 1.96 means significantly different Mean prevalence ratio = avg( NYC Macroscope estimate reference estimate ) )
23 Comparison of Prevalence Estimates among the Population in Care 2012 NYC Macroscope 2012 CHS 2004 NYC HANES N=640,860 N=7,004* N=1,261* Obesity** Hypertension Diagnosis Hypertension Treatment * Subpopulation that has seen a doctor in the past year. **CHS obesity is based on self-reported height and weight. NYC Macroscope and NYC HANES obesity is based on measured height and weight.
24 Comparison of NYC Macroscope 2012 Estimates of Obesity Prevalence with Reference Estimates for 24 Strata Defined by Sex, Age Group and Neighborhood Poverty Rate CHS 2012 NYC HANES 2004 Correlation 0.84 Mean Standardized Deviation 1.37 Mean Prevalence Ratio 1.21 Correlation 0.72 Mean Standardized Deviation 0.84 Mean Prevalence Ratio 1.15
25 Comparison of NYC Macroscope 2012, CHS 2012 and NYC HANES 2004 Estimates of Obesity Prevalence by Sex and Age Group Among the Population of New Yorkers in Care
26 Comparison of NYC Macroscope 2012 Estimates of Hypertension Diagnosis Prevalence with Reference Estimates for 24 Strata Defined by Sex, Age Group and Neighborhood Poverty Rate CHS 2012 NYC HANES 2004 Correlation (Rho) 0.98 Mean Standardized Deviation: 1.3 Mean Prevalence Ratio: 0.97 Correlation (Rho) 0.92 Mean Standardized Deviation: 1.4 Mean Prevalence Ratio: 0.86
27 Percent Comparison of NYC Macroscope 2012, CHS 2012 and NYC HANES 2004 Estimates of Hypertension Diagnosis Prevalence, by Sex and Age Group * Significantly different from NYC Macroscope estimate * * * * * * 20 to to FEMALE MALE NYC Macroscope 2012 NYC HANES 2004 CHS 2012 Among the Population of New Yorkers in Care
28 Percent Comparison of NYC Macroscope 2012, CHS 2012 and NYC HANES 2004 Estimates of Hypertension Treatment Rates among Adults Diagnosed with Hypertension * * * * * *Significantly different from the NYC Macroscope estimate (p<.05) * * * * 20 to Male Female Age Group Sex (age-adjusted) NYC Macroscope 2012 NYC Hanes 2004 CHS 2012 Among the Population of New Yorkers in Care
29 Pilot Study Conclusions: Prevalence NYC Macroscope estimates of obesity and hypertension diagnosis prevalence tracked very well with CHS 2012 and NYC HANES 2004 estimates, however NYC Macroscope estimates of obesity were higher than reference survey estimates for men ages NYC Macroscope estimates of hypertension diagnosis prevalence were lower than reference survey estimates for the age group, especially among women
30 Pilot Study Conclusions: Hypertension Treatment NYC Macroscope estimates of hypertension treatment are much higher than survey estimates for the age group and somewhat lower for people ages 40 and older However, population estimates of the number of treated New Yorkers is similar between NYC Macroscope and the reference surveys
31 NYC Macroscope Work Plan Generate and compare 2013 estimates from NYC Macroscope, CHS and NYC HANES to assess construct validity Obtain funding and carry out a chart review validation study to assess reliability, diagnostic validity and generalizability across EHR platforms Develop and evaluate methods to incorporate uncertainty and assess trends Support the development of a community of practice
32 Acknowledgements DOHMH Division of Epidemiology Katharine McVeigh, PhD, MPH Sharon E. Perlman, MPH Kevin Konty, MS Sophia Day DOHMH Primary Care Information Project Jesse Singer, DO, MPH Remle Newton-Dame, MPH Carolyn M. Greene, MD Elizabeth F. Snell, MPH Stephen Immerwahr, MA Lauren Schreibstein, MA City University of New York School of Public Health Lorna E. Thorpe, PhD Financial Support de Beaumont Foundation Robin Hood Robert Wood Johnson Foundation NY State Health Foundation
33 Thank You! To join our community of practice, please contact us at: Google NYC Macroscope
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