Cross the Finish Line First

1. Cross the Finish Line First BY LISTER ROBINSON

2. Why Chronic Conditions First? According to the Centers for Disease Control and Prevention (CDC), "Chronic diseases are the leading cause of death and disability in the U.S.," and Healthcare Cost Monitor underscores this fact, revealing that "Seventy-six percent of Medicare spending is on patients with five or more chronic diseases”. The Agency of Healthcare Research and Quality (AHRQ) also emphasizes the high cost of chronic conditions.

3. Yesterday:Claims-Based Predictive Models For years, healthcare insurance companies (payers) have mined claims data for chronic patients and have built predictive models to identify high-risk patients. Data used by payers to flag high risk patients are historical claims data—primarily costs, admissions, and diagnoses. Because this view is retrospective and heavily biased toward cost, patients with past high acute care costs are flagged as "risky" regardless of their current State-of-Health (SOH).

4. These models lack a correlation to clinical information. The score becomes even more credible when there is evidence of ER admissions or acute care inpatient (IP) admissions. Unfortunately, an individual’s current SOH has no bearing on his or her claims-based risk score. Claims based risk scores are created with regression analysis at a population level to predict scores at the patient level.

5. Claims-based risk scores provide no insight for care at the provider level. Not only are today’s calculations unsuitable for determining a patient’s true risk, they provide no insight into how an individual’s score improves or deteriorates after each clinical visit. Information lags far behind; Claims-based risk scores are not actionable–they provide no insight for care at the provider level.

6. A New Approach The best approach is to monitor all patients, healthy and chronic, for risk of hospitalization. There is inherent conflict between physicians and payers. To realize bonuses, they must choose cost of care over effective care. Progressive medical groups do not use claims-based patient risk reports created by payers to develop care management programs.

7. Vital Progress The new generation of primary care management solutions delivers real-time, meaningful use clinical data from EHR records. These systems use patient medical records to measure SOH and evaluate the effectiveness of care programs and evidence-based medicine. Real-time clinical data from EHR records is also being used to create sustainable, repeatable programs to reduce the number of high-risk patients and design individualized care management programs.

8. Closed-Loop CMP Using real-time clinical data from EHR records, healthcare providers now have the capacity to design a closed-loop population care management program (Figure 1). A well-designed program delivers primary care to drive higher quality, reduce costs, and deliver greater value in health care.

9. Population SOH Stratification The very foundation of the well-designed program is population SOH stratification, the ability to categorize patients into high (red), moderate (yellow), and low (green) risk groups by chronic condition (see Figure 2)

10. SOH stratification provides actionable and measurable information about actual health status at the population and patient levels, with visibility of controllable and non controllable factors. An SOH model takes into consideration every patient’s age, gender, ethnicity, family history, all clinical factors (e.g. BMI, lipid panel, blood HM, HcA1C). A low score indicates excellent health.

11. Origins of SOH Models Nationally accepted clinical models are the basis for SOH models. SOH scores are calculated at the patient level and rolled up to a population level. This approach allows healthcare providers to design meaningful preventive care programs for the exact population and create individualized programs for specific patients.

12. Chronic Disease Management Patients who comply with prescribed care programs are typically more successful in managing chronic conditions. This is where care coordinators play an important role. Leveraging SOH scores, care coordinators pinpoint high-risk patients by chronic condition.

14. Incentive Management Effective incentive programs clearly drive high quality and reduce costs for greater value in health care by: • Aligning team incentives with population quality and cost performance targets (physicians and care coordinators) • Establishing and sharing best evidence practices by chronic condition • Encouraging teamwork to lower healthcare costs • Illustrating accurate physician and clinical coordinator population performance and the impact on incentives

15. Incentive programs reward care teams for reducing population risk scores, improving patient satisfaction scores, and reducing overall patient costs. Continuum of care dashboards (ambulatory and acute) are useful in designing incentive programs and illustrate risk-cost quality details for each patient (Figure 4).

16. In this case, the quality metric captures population SOH, ACO quality measures and patient satisfaction scores. The intersection of the crosshairs is the target for quality and cost for the specific patient population. Each bubble corresponds to a specific physician/care coordinator team, and the size of the bubble illustrates the size of the population they manage. The distance of each bubble from the crosshair indicates the positive or negative variance from the target and is proportional to each team’s bonus or penalty.

18. Results:Validating the SOH Model Approach To validate the new SOH model, researchers (authors of this article) compared it with a leading claims-based risk model (payer model). The SOH model calculated a risk score between 0–100 for four chronic conditions—type 2 adult diabetes, coronary artery disease (CAD), CHF, and asthma. Next, researchers calculated an SOH score for each patient using historical data over two years (2008– 2009) and stratified the population based on SOH scores.

19. Relationship: IP/ER Admissions and SOH Score Figure 6 shows the relationship between SOH scores and IP/ER admissions. The X axis shows SOH ranges. Y Axis shows the percentage of patients in the SOH range with IP/ER admissions. As the score increases, the admission within that band also increases. Thus, Figure 6 validates the accuracy and predictive power of the SOH score.

20. Proven Track Records Healthcare providers can enable continuous improvement using SOH models together with care management programs. This approach already has proven track records in a number of leading PCMHs such as the Medical Clinic of North Texas (MCNT). Within these organizations, a wide variety of individuals actively use these models in their daily work, including: • Administrators and management • Physicians • Care coordinators

21. MCNT has pioneered the SOH-based population management approach. Their managed population of 2.4 percent better-than-market performance was the culmination of various quality-of-care drivers. Overall performance index improved in Facility Outpatient (-5%), Other Medical Services (-6%), and Professional (-1%) categories, relative to the market.

22. Chronic Diseases CDC on Chronic Diseases Seven out of 10 deaths among Americans each year are from chronic diseases. Heart disease, cancer, and stroke account for more than 50% of all deaths each year. Obesity has become a major health concern. One in every 3 adults is obese, and almost 1 in 5 youth between the ages of 6–19 is obese. Source: www.cdc.gov/chronicdisease/overview / index.htm

23. AHrQ on Cost of Chronic Conditions The 15 most expensive health conditions account for 44% of total healthcare expenses. Patients with multiple chronic conditions cost up to seven times as much as patients with only one chronic condition. Source: www.ahrq.gov/research/ria19/expendria.htm

24. Cost Monitor:Chronic Disease Spending 76% of Medicare spending is on patients with 5 or more chronic diseases. Currently, 10% of healthcare dollars are spent on overall direct costs related to diabetes, amounting to $92 billion a year (1.5X the amount spent on stroke or heart disease). CDC predicts spending on diabetes care will reach $192 billion in 2020. Source: http://healthcarecostmonitor.thehastingscenter. org/ kimberlyswartz/projected-costs-of-chronic-diseases

25. Summary To lower health costs, physician networks and medical homes must employ a closed loop population management program that focuses on patient SOH (risk) stratification, chronic disease management, care coordination, and incentive management. To become masters in their population management programs, they need decision support systems such as population SOH stratification and predictive models.