Data, Metrics, and Evaluation Dora Barilla, DrPH, MPH Michael Knecht, MDiv
Imagine Increased flexibility and being proactive
Imagine Holistic in understanding the community s problems and capacity
Imagine Approaching information as an asset not a cost
Imagine Increase our communication and interaction with our communities
Community Health Management System Vision: Create a real- )me community health management system to provide geographically enabled health u)liza)on informa)on to influence strategic resource, pa)ent care, and popula)on health decisions.
Health Data View Census Demographics Health Status Indicators Service Utilization Provider Network Design Variability Market Potential
Moving the Community Needle Low High Under Over Health Status Minimal Optimum Provider Supply Community Resources
Community Health Management System Welcome Dora Barilla Today is Thursday, July 26, 2012 Health Status Admissions Re-Admissions Health Indicators Page 1 Page 2 Page 3 Print Help Log Out Mortality Rates Admissions Type % of Tot Status Medicare 23% Medicaid 12% BlueCross 5% Uninsured 1% Re-Admissions Chronic Disease Management Re-admissions ED Treat & Street? 9??????? 230,00 7,587 1,945,112 89,778 1,200,110 234,000 223,000 145,112 3,346,782 75% 65% 65% 65% 71% 70% 75% 69% Stroke Suicide Heart Disease Cancer Respiratory Disease Injury Diabetes Hypertension ED Visits by Cause Asthma Diabetes Expected ER Caseloads Hypertension Mental Health At-a-Glance (last 24 hrs)-ed only Substance Abuse/Violence Frequent Flyers 4 Priority Areas Health Indicators M&M Billed Charges $1,223,600 Loma Linda University and Loma Linda University Health System has identified three priority areas in No-Pay Charges $707,250 the Inland Empire for 2013-2015 to help improve the health of the communities most in need of health ED Visits Sent Home 69 % improvement. $19,500 Together we are much greater than the sum of our parts and thank you for helping us Discharges w 6+ Meds 85% move $33,000 towards greater collaboration. Homeless ED Patient 2 1. San Bernardino Low Birth Weight Life Expectancy Infant Mortality Rate Religion RFEI Obesity/BMI
Healthy Living Map
Model Specification AAD it = + 1 AA it + 2 AT it + 3H GR it + 4 FA it + 5 HI it + 6 P it + 7 GY it + 8 I it + 9 PC it + 10 OB it + 11 E it + 12 S it + 13 AP it + it Variable Description (By County 2005-2009) AA AP AT AAD FA S HI P GY I GR PC OB E Alcohol Consumption by Adults Air Pollution Rate Alcohol Consumption by Teens Average Age at Death (in years) Rate of Felony Arrests Smoking Percent with Health Insurance Percent of Poverty Graduation Year Per Capita Personal Income Percent of High School Graduates Prenatal Care Rate Obesity Rates Exercise Rates Jason Gurtovoy
Regression Results County Level AAD = -.006FA +.016E +. 007P -.015PC +.028GR +. 016AA +.003I -.025AT +. 016HI -.027OB +.866GY -. 15AP - 1668.956 R 2 =.5529 χ 2 = 58.22 p-value < 0.000
Ranking (Top 8) R Policy Variables (Effect on Average Age at Death) Coeffi cient P-Value 1 Graduation Rate.028.001 2 Obesity -.027.002 3 Graduation Year.866.004 4 Alcohol Consumption by Adults 5 Alcohol Consumption by Teens.16.006 -.25.018 6 Income.003.043 7 Health Insurance.016.056 8 Air Pollution -.015.126
Dashboard of Health (County of San Bernardino) The model is the engine of the dashboard which guides the arrow. The model is flexible and can be easily updated, modified, and changed. Intuitive way to internalize the health of the county The arrow will move when new information becomes available. Low Moderate High Severe Runs on continuous interval. We can look at the effects of shocks in the region. We can see how different policy effects will move the dashboard.
Health System Service Area Census Tract 7301 $1,173,626 in charges (2nd largest dollar amount in a single census tract) 687 Patients 31% No Religious Affiliation 21% Roman Catholic 20% Non-Denominational 18% Protestant 9% Other religion 1% Unknown 34% Ages 18-34 33% Ages 34-84 32% Ages 0-17 36% White/Caucasian 32% Native Am/Ind 27% Afro-American
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System-wide CHMS Infrastructure Leadership support GIS software on internal server GIS competency POS address validation Internal cloud-based data and report storage
System-wide CHMS Infrastructure De-identified community health data published on web (CB) Identify key metrics for tracking population health improvement System-wide culture of decision making based on real-time GIS enabled data, and displayed in a usable format
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Reversing Obesity Trends* Among U.S. Adults BRFSS, Now (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20% 24% 25% 29% 30%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2013 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20% 24% 25% 29% 30%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2014 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20% 24% 25% 29% 30%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2015 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20% 24% 25% 29% 30%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2016 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20% 24% 25% 29% 30%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2017 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20% 24% 25% 29% 30%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2018 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20% 24% 25%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2019 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20% 24% 25%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2020 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20% 24% 25%
Obesity Trends* Among U.S. Adults BRFSS, 2021 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20% 24% 25%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2022 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2023 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2024 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2025 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19% 20%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2026 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2027 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2028 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2029 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2030 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2031 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14% 15% 19%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2032 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2033 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2034 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2035 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2036 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14%
Reversing Obesity Trends* Among U.S. Adults BRFSS, 2037 (*BMI 30, or ~ 30 lbs. overweight for 5 4 person) No Data <10% 10% 14%
Dora Barilla, DrPH, MPH, CHES Michael Knecht, MDiv Jason Gurtovoy (909) 558-3842