This book includes a plain text version that is designed for high accessibility. To use this version please follow this link.
average health risk by providing higher rates to those plans managing sicker populations. The methodology also protected the needs of Massachusetts residents by deterring plans from attempting to “cherry pick” only the healthiest patients.


The other side of the coin: Lessons from managed care


Matt Siegel is VP of market strategy at Verisk Health, a subsidiary of Verisk Analytics. For more on Verisk Health: www.rsleads.com/206ht-204


MCOs and other health plans across the country will soon be bidding for the healthcare business of previously uninsured individuals and newly eligible public program benefi ciaries. These new members come in all shapes and sizes – from the young and healthy to the elderly and sick. Changes to


Medicaid eligibility and the subsidized coverage available through the exchanges will drive a greater number of low- income individuals with pent-up demand for healthcare services to seek subsidized coverage.


How will private payers manage the health and wellness of such diverse and potentially high-risk populations? Risk adjustment and predictive modeling technology is not only designed to drive pricing but it can also be used to optimize reimbursement, contain costs, measure performance and – most importantly – implement targeted population health- management initiatives.


Similarly to the process taken by the Massachusetts Health Connector, private payers can transform disparate data sources – including diagnosis, eligibility and utilization – and assess the risk, likelihood of adverse events and future cost of individuals, cohorts and populations. Beyond risk assessment and cost prediction, payers can also utilize this valuable infor- mation to improve the quality of care provided to members and drive down spending and unnecessary utilization. Event-based predictive models and other risk-assessment methodologies designed for medical management enable payers to strategically design, implement and target clinical interventions based on near-term predicted costs, disease factors impacting the population, as well as potentially avoid- able utilization such as hospitalizations, emergency depart- ment visits and expensive imaging. Payers can stratify large amounts of diffi cult-to-measure data to investigate discreet populations with specifi c conditions in order to distinguish patient needs and implement the most impactful clinical interventions.


Another factor to consider in the health management of these new benefi ciaries is that a sub-set of this group could periodically move between private coverage and public pro- gram eligibility. Since Medicaid and exchange eligibility will be based on income, changes in those guidelines or changes in the fi nancial status of an individual could spark a shift. MCOs can utilize advanced risk adjustment and predictive modeling to assess the risk of newly eligible members using less than one year of historical data.


www.healthmgttech.com


What’s up, doc? Engaging providers with predictive modeling technologies Managing the health and wellness of Medicaid and ex- change patients in this new healthcare landscape will require collaboration between MCOs and service providers. Support- ing medical groups, hospitals and primary care physicians with predictive analytics on their patient populations can enable proactive care management initiatives at the point of care. Understanding which patients are likely to be hospitalized, end up in the emergency room or be high cost in the near future will enable providers to intervene early and potentially prevent avoidable utilization and reduce associated costs. Besides near-term cost savings, early interventions at the point of care can also improve the long-term health status of a population and strengthen relationships between doctors and patients.


But predictive models are not just designed for cost pre- diction and medical management. MCOs and other private payers can use advanced methodologies to measure and moni- tor network performance and incent providers to collaborate in the cost-effective care delivery process. Risk-adjustment models can be used to assess the risk of patient populations (based on conditions, demographics and utilization factors) managed by individual doctors in order to level the playing fi eld and objectively compare and benchmark quality and effi ciency of care delivered across a network of providers. Risk-adjusted measurement of network performance can be used by health plans to incent doctors and medical groups with bonus dollars and pay-for-performance programs. Sharing of capitation dollars with point-of-care providers can also go a long way at reducing costs and improving network relations.


Next steps: Risk sharing and collaboration The future of our health system will be dependent on many things, but the use of risk adjustment and predictive modeling has proved to be essential for states and private payers to establish risk-sharing partnerships that benefi t both organizations and patients.


Many things can be learned from the Massachusetts ex- perience. The details of the state’s healthcare reform efforts were recently published by the state’s Blue Cross Blue Shield Foundation (BCBS MA) as part of the organization’s Health Reform Tool Kit Series. To keep up with the changing times, BCBS MA implemented its own risk-sharing contract initia- tive in 2009 using the same risk-adjustment technology as the Health Connector. The private plan’s Alternative Quality Contract enables local providers to share in the risk, as well as the rewards, of managing the health and wellness of Mas- sachusetts residents.


Navigating the new healthcare landscape will require col- laboration between public payers, private health plans and providers. Risk adjustment and predictive models will play a signifi cant role in fostering those partnerships and engaging consumers in the health management process.


HMT HEALTH MANAGEMENT TECHNOLOGY June 2012 15


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36