Medical claims, pharmacy claims, lab values, HRAs, genetic markers, biometrics – the abundance of data is having an immediate impact on how analytics shape healthcare. Next generation analytics are bringing attention to health and wellness rather than disease-specific guidelines, and generating novel approaches to value-based medicine and care management.
Traditionally, analytics, such as predictive modeling, have been used to identify individuals for chronic care management and to set rates. New predictive models, however, include financial and clinical algorithms, which allow healthcare organizations to implement advanced ways to identify, manage and measure risk across and within a population.
A few examples of these pioneering applications of advanced analytics are outlined below.
Genetic testing More than 1500 genes can now be analyzed to find specific variations and mutations. Approximately 10% are of clinical significance, with a growing number of gene tests being developed in clinical practice settings. Genetic testing early on can lead to improved medical outcomes by optimizing therapeutic interventions and reducing morbidity, and lowering the cost of treatment. Applying clinical algorithms specific to individuals who would benefit from genetic testing promotes better medical outcomes by reducing or eliminating complications, and ensuring correct application of medications. An example is identification of individuals with a predisposition to Factor V Leiden. Early identification avoids the potential complication of venous thromboembolic disease.
Healthcare analytics applied to identify members for genetic testing offer the following benefits:
• Pharmacogenomics (‘Right Therapy’) • Ensures proper therapy and dosing • Disease Management (‘Right CarePath’) • Ensures proper care for individuals presenting disease symptoms • Pure Prevention (‘Right Future’) • Ensure proper preventive care for pre-symptomatic or at-risk individuals • Best Price (‘Right Price’) • Ensure diagnostic cost containment and best price for diagnosisUnderstanding uninsured populations
To better understand the healthcare needs of uninsured populations, and be better informed in contracting with health plans, the Commonwealth Health Insurance Connector Authority in Massachusetts is using a risk–adjusted capitation payment methodology for the FY 2010.
Using available data and applying predictive models, individuals with the most costly manifestations of distinct diseases are identified. A clinical profile of the expected costs expressed as a relative risk score is then generated. Payment to the health plan is based on the risk and expected cost of caring for the individual. This allows the Connector Authority to better align payment with risk by moving dollars from plans with better than average risk to those with worse than average risk.
This is the first step in how the Connector Authority sees applicability of analytics across its program. Future plans call for evaluating members for care management participation, and tracking utilization, quality and financial metrics across the plans.Realignment of Primary Care Physicians payment
As the number of primary care physicians (PCPs) decreases nationwide, a new approach to reimbursement is needed to encourage them to stay in practice. Data analytics are used to promote a risk adjusted comprehensive payment plan to Primary Care practices to replace current encounter- based payments.
Using predictive models, the ‘advanced medical home’ reimburses PCPs with a comprehensive monthly payment based on the relative risk of each patient that covers care costs and the cost for electronic health records. In addition to risk adjusted payment calculations, analytics further support PCP practices by providing normative benchmarks and individual performance for financial, clinical and utilization outcomes.
Analytics provide transparency of data to allow PCPs to review their practice patterns relative to other PCPs, aggregate lists of their patients with gaps in care, and comply with evidence-based guidelines. The PCP is provided with the tools that focus on accountability and achieving health outcomes rather than focusing on the number of patient encounters.
Analytics provide more than predictive modeling. Using financial and clinical algorithms, identification of new online health programs can lower the percentage of the medical dollar contribution by an individual. Individual identification and stratification for engagement in condition and wellness programs allows employers to develop new benefit packages that offer incentives for case/disease management participation, and payment for participation in wellness activities as well as compliance with preventive health measures.
Using analytics, employers can hone in on which intervention programs are needed, which members have care gaps, then adjust health coverage accordingly. Let’s say the data shows 30% of employee medical claim costs are attributed to back pain treatment. To respond, the employer may offer coverage for massage therapy or a reduced co-payment for enrolling in a back pain exercise program. Individuals who ‘opt out’ of the program, or refuse to comply with recommended treatment, are assessed with larger out-of-pocket costs.
The future of analytics is now, as both employers and health plans combine disparate data sources to present one ‘truth’, and use that truth to improve overall health status by initiating health-focused programs, providing incentives, and tracking effectiveness of interventions. The doors opened by healthcare analytics including predictive modeling, clinical algorithms and benchmarks are creating new opportunities to improve care.Deb Bradley is Vice President, Client Solutions at Verisk Health in Waltham, Massachusetts