PSCI Case Study

1. Leading Physician Network lowersPer Member Per Month (PMPM) costs by reducing acute care admissions for chronic disease conditions through effective care management

2. Patient centric medical home (PCMO ) leverages unique “state-of-health” population risk stratification approach from PSCI. PCMO uses Population Predictive Risk Analytics from PSCI.

3. PCMO SITUATION • The successful patient centric medical home (PCMO) is a leading provider of Primary Care management services and is known for its network of outstanding physicians in the local market. • The innovative, growth-oriented management team made the decision to proactively acquire the capabilities required to prosper in the emerging climate of pay-for-performance.

4. OPPORTUNITY • The ACO, bundled payment and pay-for-performance models require transformational process improvements in the primary care setting to avoid unnecessary hospitalizations and ER visits. • The PCMO’s growth strategy was to offer the local leading self-insured employers a compelling value proposition with their focus on preventive care and chronic care management, to minimize the total cost of care to their membership across the continuum-of-care.

5. OPPORTUNITY… • The value proposition needed to be credible and measurable in order to negotiate higher rates for physician services and also increase market share in the local market.

6. CALL TO ACTION • After careful analysis of their patient population healthcare costs, it was clear that the highest cost population category was chronic disease care and unnecessary ER visits.

7. CALL TO ACTION… • The management team, together with its physician “think tank” came to the conclusion that the key driver to manage chronic care costs was to minimize hospitalizations and ER visits with proactive, targeted care and case management programs

8. CALL TO ACTION… • To accomplish this, they needed analysis tools to continuously identify and monitor “high risk” patients proactivelyby major chronic condition along with the risk drivers. • They also wanted decision support tools to measure patient risk based on current “state of health” using clinical data from their existing EMR systems on a monthly basis.

9. CALL TO ACTION… • High risk chronic patients were defined as those with a high probability for admission to acute care facilities within the next 12-18 months due to complications. • Furthermore, the team wanted physicians to have the ability to analyze which processes were needed to fill any gaps in care management that may lead to hospitalizations.

10. CALL TO ACTION… • The required tools had to be comprehensive yet provide easy-to-absorb information with a clinical perspective. • The client insisted that physicians be able to quickly and easily identify the key risk drivers and prescribe appropriate care and case management programs at patient and population levels. • However, the client were adamant that these tools not be used for physician profiling or as clinical outcome predictors.

11. THE CHALLENGE… • The team searched the market for a vendor to provide decision support tools. They reviewed risk adjustor applications, and determined the tool did not adequately meet their requirements. • Furthermore, the evaluation team learned that most risk adjustment tools were primarily built to address payer needs. • They reported that claims-based risk predictor tools did not serve their objectives for the following reasons:

12. THE CHALLENGE… • Acute care cost centric Risk adjustor models are extremely complex and heavily skewed to acute care costs and past resource utilization. Models incorporate many variables that are cost-focused and not under primary care management control.

13. THE CHALLENGE… • Claims-based Models are heavily based on claims data with a payer-centric perspective, whereas the physicians wanted clinical-centric models. • These models are very controversial and have a negative connotation with clinical teams because they are commonly used for physician profiling.

14. THE CHALLENGE… • Cost-prohibitive These tools are very expensive and it is difficult to interpret results from a care management perspective. Near “real-time” analysis with weekly/monthly frequency is prohibitively expensive.

15. THE CHALLENGE… • Population-based models. Baseline models are built at a population level and require a large population mix for credible results – they are not appropriate for smaller populations.

16. THE CHALLENGE… • These models perform regression analysis at a population level, then attempt to take scores to a patient level. • Risk scores at patient levels were based on relative scores aligned with the population, therefore individual patient scores would vary with population changes, with no change in the individual state of health. • It was difficult to interpret the clinical drivers and their impact on the risk scores

17. THE DECISION… The evaluation committee realized that risk adjustor tools were not built to address primary care provider-driven care management programs. The team decided to build an application in partnership with an innovative healthcare decision support provider.

18. THE DECISION PSCI was selected to build a Population Predictive Risk tool with the following capabilities:

19. THE APPROACH • PSCI, with the help of clinical teams, conducted extensive research and identified nationally accepted “state-of-health” models for each major chronic condition to start with. • PSCI developers worked with physician teams to make the models more pragmatic in context of available data, with standardized assumptions, and simplification in agreement with larger expert teams. • The solution collected clinical data from existing ambulatory EMR, lab, pharmacy, and claims systems on a regular basis to refresh patient “state-of-health” risk scores.

20. PSCI’s EMR-based Population Risk Predictive Model PSCI uses a patent pending, transformational approach for predicting risk of hospitalization that takes into account 6 dimensions. No one in the industry has put all of them together to predict risk of hospitalization/re-admission.

21. THE APPROACH… • Calculate patient “state-of-health” scores by chronic disease condition for the most common chronic conditions for the target population mix using latest patient records from EMR • The score would indicate the probability of hospital admission for any given patient due to complications within 12-18 months.

22. THE SOLUTION…

23. THE SOLUTION… • Identify evidence-based best practices based on data analysis and physician input for each chronic condition. • Provide insight and data for optimal care-management programs for patient risk groups.

24. THE SOLUTION… • Help physicians maximize pay-for-performance and Shared Savings Model (ACOs) and help physicians proactively manage patient population risk. • Not a point-of-care solution. • Not an outcome prediction tool.

25. THE SOLUTION… • Provides easy-to-understand risk score drivers, and pinpoint which variable (demographic, clinical, etc.) is contributing to an adverse state-of-health at any given time. • Physicians and clinical teams then determine what diagnosis, treatments, and care management strategies to focus on to improve the specific patient risk scores.

26. RESULTS

27. RESULTS… PSCI delivered Population Risk Analyzer, a care management decision support tool that: • Helped in reduction of hospitalizations &ER visits with an increase in case manager and care manager productivity.

28. RESULTS… • Provides a state of health risk score for each chronic condition for a patient or a population based on current clinical information. The risk scores are calculated at the patient level and then rolled up to the population level. • The solution enables physicians and administrators in their local setting – ACOs, clinics in an integrated health care system, etc. to look at the information and identify clinically high-risk patients ER visits/hospitalization/readmissions.

29. Population Risk Stratification

30. RESULTS…Customized Care Management Programs • Target right patients (High Risk Patients) at right time • Strong individualized care management programs • Intensive, multi-level, multi-dimensional, high contact programs • Provider-driven programs • Broad programs have no impact • Data-driven care management analytics 16

31. OUTCOMES “BlueCross BlueShield has been running medical home pilots since 2010 with Village Health Partners in Plano and the 42 offices of the Medical Clinic of North Texas. The pilots improved care and saved an average of $10.50 a month for 25,000 patients, said Scott Albosta, a division vice president with the insurance company.” - (Dallas Morning News June 23, 2012).

32. TESTIMONIALS By using near real-time patient health records from EMRs along with financial claims and demographics data, PSCI presents clinical teams information that allows them to understand the risk drivers associated with patient care across the patient population. By understanding the clinical cost, quality and risk drivers, physicians make interventions to have a dramatic impact to lower the healthcare cost curve.” – Karen Kennedy, CEO – Medical Clinic of North Texas

33. ABOUT PSCI • PSCI is an innovative provider of predictive population risk analytics for care management and contract optimization leveraging EMR, Claims & Demographics data for medical homes, physician groups, ACOs, hospital systems, IDNs, and shared savings programs.

34. ABOUT PSCI • PSCI delivers predictive chronic disease models for population state-of-health risk stratification, quality-cost-risk visibility, "what-if" modeling and ACO demand planning for improving overall healthcare provider and payer performance. • PSCI is critical to managing “At-Risk” populations and pay-for-performance objectives. For more information, please visit http://www.PSCIsolutions.com