The document discusses how predictive analytics can help achieve the Triple Aim of improving patient experience of care, improving population health, and reducing per capita costs. It describes different types of predictive models that can identify high-risk patients for proactive intervention, such as models predicting disease progression, hospitalization, death, and healthcare expenditures. Examples of successful predictive models and interventions for conditions like heart failure, COPD, diabetes, and CKD are provided. The document concludes that predictive modeling can aid in reducing spending, increasing patient satisfaction, and improving overall population health as part of the Triple Aim.

1. Predictive Analytics Help
Achieve the Triple Aim
Holli White | BMHI MPS Student
INLS 770 | University of North Carolina at Chapel Hill

2. Agenda
 What is the Triple Aim?
 Financial/utilization trends
 Types of predictive analytics
Disease progression
Hospitalization and/or death prediction
Expenditure
 Tying it all together

3. What is the Triple Aim?
2. Triple Aim Goals

4. A very small percentage of the population is
responsible for half of the dollars spent.
1. Expenses for Costly
Medical Conditions

5. 3. NIHCM Foundation Data Brief July 2012

6. Predictive models can identify patients with
whom to take a proactive approach.
 Types of predictive analytics
Disease progression
Hospitalization and/or death prediction
Expenditure
 Progression of CKD to Kidney Failure
 Many biometric/laboratory variables
 High accuracy rate
8. Predictive for Progression of CKD

7. Predictive models can identify patients with
whom to take a proactive approach.
 Types of predictive analytics
Disease progression
Hospitalization and/or death prediction
Expenditure
 HF predictive models - uses risk markers and CHA2DS2-VASc scores,
current treatment therapies and response, EF values
 Models incorporating clinical data are more accurate than claims
4. Factors influencing HF predictive models

8. Predictive models can identify patients with
whom to take a proactive approach.
 Types of predictive analytics
Disease progression
Hospitalization and/or death prediction
Expenditure
 COPD study
 Successful risk model put together based on scoring of various
measurements
7. Pred. Model of Hosp/Death in COPD

9.  Types of predictive analytics
Disease progression
Hospitalization and/or death prediction
Expenditure
 H
7. Pred. Model of Hosp/Death in COPD

10. Predictive models can identify patients with
whom to take a proactive approach.
 Types of predictive analytics
Disease progression
Hospitalization and/or death prediction
Expenditure
 Diabetes predictive model estimates cost of complications over 1-,
3-, and 5-year timeframes
 Allows organizations to identify opportunities around network
leakage or overutilization of services
9. Estimating the Cost of Incident
Diabetes Complications

11. Predictive models can identify patients with
whom to take a proactive approach.
 Types of predictive analytics
Disease progression
Hospitalization and/or death prediction
Expenditure
Most Costly Diseases Annual Expected Cost 5-Year Expected Cost
CHF $7,320,287 $50,697,865
ESRD $4,225,384 $13,211,204
Gangrene $2,844,381 $17,200,417
Most costly complications in a sample of 10,000 diabetic adults
9. Estimating the Cost of Incident
Diabetes Complications

12. How do we tie this together with patient care?
 Community based outreach for children with asthma had positive
results
Homes based education
Quality of life increased
Reduction in hospitalizations
93% of patients had management plan vs. 31% at baseline
6. Eval. of Community Based Outreach

13. How do we tie this together with patient care?
 Aurora Healthcare in Milwaukee
Utilized Optum One’s CHF predictive models
Reduced all cause readmissions by 30%
60% reduction in HF admissions
Reduced ER utilization
5. Predictive Analytics for Population Health Management

14. In summary, predictive modeling can identify high risk
patients for intervention, aiding in achieving the Triple Aim.
 Reduce spend
 Increase patient satisfaction
 Improve the health of the population

15. References
1. Cohen, S. (October 2014). The Concentration of Health Care Expenditures and Related Expenses for Costly Medical Conditions,
2012. Medical Expenditure Panel Survey, Statistical Brief #455.
2. Maine Health Management Coalition. (2016). Triple Aim Goals. Retrieved from: http://www.mehmc.org/about-us/what-we
-do/triple-aim-goals/
3. NIHCM Foundation. (July 2012). The Concentration of Health Care Spending. NIHCM Foundation Data Brief July 2012.
4. Ouwerkerk, W., Voors, A., & Zwinderman, A. (2014). Factors Influencing the Predictive Power of Models for Predicting Mortality
and/or Heart Failure Hospitalization in Patients With Heart Failure. The American College of Cardiology Foundation, Vol 2,
429-436. http://dx.doi.org/10.1016/j.jchf.2014.4.006
5. Paulus, J. & Salfity, K. 2016 SOA Health Meeting. Session 131 PD, Predictive Analytics for Population Health Management. June
15-17, 2016. Philadelphia, PA.
6. Primomo, J., Johnston, S., DiBiase, F., Nodolf, J., & Noren, L. (2006). Evaluation of a Community-Based Outreach Worker
Program for Children With Asthma. Public Health Nursing, Vol 23, 234-241. doi: 10.1111/j.1525-1446.2006.230306.x
7. Schembri, S., Anderson, W., Morant, S., Winter, P., Pettitt, D., MacDonald, T., & Winter, J. (November 23, 2008). A predictive
model of hospitalisation and death from chronic obstructive pulmonary disease. Respiratory Medicine, 103, 1461-1467.
8. Tangri, N., Stevens, L., Griffith, J., Tighiouart, H., Djurdjev, O., Naimark, D., Levin, A., & Levey, A. (April 20, 2011). A Predictive
Model for Progression of Chronic Kidney Disease to Kidney Failure. JAMA, Vol 305, 1553-1559.
9. Zhu, J., Kahn, P., Knudsen, J., Mehta, S., & Gabbay, R. (2016). Predictive Model for Estimating the Cost of Incident Diabetes
Complications. Diabetes Technology & Therapeutics, Vol 18, 625-634. doi: 10.1089/dia.2016.0132