Uncover Hidden Population Using Predictive Modeling Tool
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GlobalHealth, an HMO in Oklahoma, implemented a predictive modeling tool to identify at-risk patients who may require high-cost healthcare services, aiming to reduce per member per month (PMPM) costs and increase member satisfaction. By analyzing various data points, the tool identified a 'hidden' cohort of low-utilizing members who were at high risk of acute events, allowing for targeted outreach and preventative care measures. Preliminary results indicated a 20% reduction in emergency department encounters and a 5% decrease in hospital admissions, leading to significant cost savings and improved member satisfaction.
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Uncover Hidden Population Using Predictive Modeling Tool
- 1. Uncover Hidden Opportunity to Prevent Unnecessary Admissions
- 2. Predictive Modeling Tool for Sustainable Care Management Using Predictive Modeling Tool to Identify at Risk Patients who has a chance of becoming users of High-Cost Healthcare service and subsequently Reducing PMPM (Per Member Per Month) Costs While Increasing Member Satisfaction.
- 3. Challenge • Over the years, Health Maintenance Organizations (HMOs) have been using traditional care management programs to reduce healthcare cost and improve quality of care for their plan members. • GlobalHealth, an Oklahoma based HMO has been doing well at managing care after a disease or condition is identified. • But they didn’t have necessary data to identify clinically high risk members who may not be high utilizers of Care Management programs. • This resulted increase in ED encounters and readmissions of members thus increase PMPM.
- 4. Scenario at a Glance • GlobalHealth identified group of members who are low utilizer of services. • Some of these members experience a sudden acute event that requires a hospitalization and, consequently, become high users. • GlobalHealth’s clinical and administrative leaders found clear indicators of these acute events after reviewing statistics on daily inpatient admissions (e.g. reasons for admissions, diagnosis, health and claims history)
- 5. • GlobalHealth realized the need of Predictive Modeling Tool. • Predictive modeling tool could prevent large percentage hospitalizations by evaluating the member data and identifying at-risk patients. • Overall the challenge was to prevent acute incidents by identifying at-risk patients. • Indentifying at-risk patients was important from a cost as well as quality of care and service standpoint.
- 6. Solution • GlobalHealth used VitreosHealths predictive modeling tool to retrospective review of health plan member data. • Care Managers found approximately 4,000 health plan members who were low-cost healthcare user at the end of 2011, had a high risk of facing an acute event. • These members became ‘high utilizers’ of services between 2012 and 2013 compared to 2011. • Healthcare costs associated with them tripled in 2012 and again in 2013.
- 7. • Surprisingly every year about 12 to 15 percent members from ‘hidden’ category moved to the ‘high-utilizer’ of the service category. • Predictive risk modeling tool can help the HMOs to identify hidden population—who are not high users of healthcare services but have high risk for an acute event in future. • At present this ‘hidden’ cohort members do not take routine care (in the form of primary care visits, health screenings, and diagnostic tests). • Routine care could prevent more acute and expensive emergency admissions.
- 8. • VitreosHealth’s predictive modeling tool creates a profile for each plan members after analyzing clinical and nonclinical data. • Predictive modeling tool use various data like HMO’s Electronic Health Record (EHR), Utilization data from claims, Medication data from pharmacy systems, Scheduling data from practice management systems and Demographic data to create member’s profile • Then the tool calculates a member’s risk (State of Health score) for clinical chronic conditions, including congestive heart disease, diabetes, asthma, and hypertension.
- 9. Importantly, it also assesses five nonclinical factors that can affect a member’s risk of experiencing an acute event: • Utilization: Utilization score is derived from the claims data and calculated based on member’s resource utilization patterns. E.g. Number of hospitalizations, ER visits and medication. • Compliance: Compliance score is calculated by measuring member’s adherence to evidence-based care protocols such as appointments, lab test and medications. • Access to care: Ease of access to appropriate care services have an effect on availing care management services.
- 10. • Socioeconomic: Demographic data (e.g. education levels, household incomes, family size, and native languages) may also affect health status. • Perceived well-being: This measures how patient feels about his/ her health condition. Studies have shown that patients’ perceptions about their health may affect their actual health condition.
- 11. Vitreos uses a patent pending transformational approach for predicting risk of hospitalization / ER visits / High Cost Intervention that takes into account 6 dimensions.
- 12. Based on clinical and non-clinical factors, plan-members are categorized into four cohorts: • High risk, high cost • Low risk, high cost • Low risk, low cost • High risk, low cost Opportunity for cost savings lies in the last cohort – “Hidden”. Healthcare leaders need to identify hidden members and put them into effective care management to maximize PMPM savings.
- 13. Outcomes • GlobalHealth care managers receive a daily, weekly and monthly report from the Vitreos predictive modeling tool. • The report indicates of high-risk members, including the hidden cohort. • It also identifies problem areas based on the member’s clinical and nonclinical scores, prioritizes the members who should receive outreach first, and recommends care management actions.
- 14. Sample report are sent to the care managers daily, weekly or monthly
- 15. Key observations The preliminary result shows GlobalHealth has been successful in reducing ED encounters and readmissions by 20% in among all members between January 2014 and May 2014. • Hospital admissions have declined by 5 % – 20% reduction of ED visits resulted cost savings of $2 to $3 in PMPM ( one ED visit = 10 % of PMPM costs ) – Saving of $3 to $4 PMPM due to 5 % reduction in inpatient admission ( one inpatient admission represent about one-third of PMPM costs ) • Member satisfaction has increased by 3.5% among all health plan members
- 16. Based on the preliminary results of this program the improvement in both member satisfaction and cost savings exceeded the cost of adding staff. As a result, GlobalHealth is planning to double its care management staff to accommodate the additional member outreach in one year. The results of the program will become more apparent in August or September 2014, but the overall trend is promising. “Right now it is indicating very positively that we’re putting our resources in the right spot,” says J. David Thompson, GlobalHealth’s vice president of health plan operations.