Population Health Management: Using Geospatial Analytics to Enable Data-Driven Decisions
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1 Population Health Management: Using Geospatial Analytics to Enable Data-Driven Decisions Brian Jacobs, MD VP, CMIO & CIO Children s National Health System Washington, DC
2 Volume vs Value Based Care Delivery Affordable Care Act of 2010 IHI Triple Aim of 2012
3 Hospital Admissions & ED Visits 5,686 hospitals in US 125 Hospital admissions/1000 population One or More ED Visits % % % ACA 10% 0% ACA Year Year
4 IHI Triple Aim Promote Health Primary Care Medical Home Keep patients out of the hospital Avoid ED Visits
5 Healthcare Cycle Health System: Small Influence on Patient
6 Healthcare Cycle Healthcare encounters impact a small fraction of health factors, others include: Diet Race Exercise Gender Med compliance Stress Pollution Relationships Climate Substance Abuse Genetics Etc Socioeconomics
7 Why Geospatial Analytics? Healthcare conditions have geographic & environmental variation Many co-variables effect the expression of health and disease Understanding geographic & co-variable distribution can impact targeting of epidemiology, prevention, treatment & research efforts
8 Geospatial Analytics & EHR (Crime Reports) 8
9 Geospatial Analytics & EHR (Military Applications since 2009) 9
10 Geospatial Analytics & EHR (Geology/Geography)
11 Geospatial Analytics & EHR (Healthcare) EHR represents rich source of essential health data to power similar work
12 EHR-Rich Granular Data Blood Pressure Weight Hemoglobin Chest x-ray Age Gender Cost Triage to Doc Time Blood Culture BMI Heart Rate Medications Allergies Immunizations Procedures Address Insurance Race
13
14 Students Restaurants Groceries Education Health Services Demographics Dining Take out Fast Food Delis Convenience Stores Supermarkets Elementary Schools Middle Schools High Schools Clinics Hospitals Population Data Economic Data
15
16 GIS Methodology Applied to EHR Identify EHR Data Fields Write Data Query Run Data Query Filter/Clean Data De-identify & Geocode Data Upload Data to GIS Map Data Against Covariables in GIS
17 Representative Conditions Burns in Infants Childhood Obesity Sickle Cell Disease
18 Epidemiology: Burn Injuries in Infants Lorch M, et al. Pediatric Emerg Care 2011;27: Studied factors determining ED disposition of infants sustaining burn injuries 344 patients treated in the ED Analyzed EHR data, environmental & socioeconomic variables Scalds (53.2%) & contact burns (39.8%) were most common Race played significant role in mechanism & severity of burn Focal geographic distribution
19 Burn Data DC Neighborhoods
20 Childhood Obesity Patients presenting to Children s National Inpatient, Inner City Clinics & Suburban Clinics Weight, Height, Gender, Race, Age & Address extracted from 3-regional EHRs Primary extract cleaned, de-identified & analyzed CDC BMI percentile equations applied
21 Percent of Regional Population 3.2 million Children in VA, MD, DC 400,000 Immediate Population (12.5%) 49,713 unique patients (12.4%) EHR Data Extraction
22 Results - Total Population Children Ages 2-20 CP&A, Goldberg, and Inpatients October 1, October 1, % % 49,713 Unique Patients Healthy Morbidly Obese % % % Obese Overweight Underweight
23 Obese by Zipcode
24 Obesity Cases Obesity Rates
25 Fast Food Overlay
26 Sickle Cell Disease Readmission Retrospective analysis using EHR-derived data on children with SCD-related pain crises Readmissions described, geospatial analysis conducted Models constructed to obtain readmission risk factors 373 subjects, 125 with at least one 30-day readmission compared to no readmission group. Readmission risk factors: Older, decreased LOS, increased pain scores, >3 hospitalizations in 1-year McMillan, JE, et al. Hosp Pediatr 2015;5:423-31
27 Readmitted Patients with Sickle Cell Disease
28 Conclusions Improved health & achieving the Triple Aim cannot rely solely on encounters with healthcare organizations. Care model redesign must harness other factors beyond the healthcare system in addition to EHR data. Geospatial analytics is an important tool to bring qualitative & quantitative information forward to augment traditional approaches to healthcare analytics.
29 ? Questions?
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