Software innovations enable health plans to structure results-driven disease-management programs and demonstrate true ROI.

With the passage of the American Recovery and Reinvestment Act (ARRA) and its focus on systemic healthcare reform, every segment of the market is investigating strategies that will increase patient access and enhance clinical outcomes, while controlling costs more effectively.

Software innovations enable health plans to structure results-driven disease-management programs and demonstrate true ROI.

With the passage of the American Recovery and Reinvestment Act (ARRA) and its focus on systemic healthcare reform, every segment of the market is investigating strategies that will increase patient access and enhance clinical outcomes, while controlling costs more effectively.

Health plans certainly are not playing a passive role. While providers and patients have, for the most part, been able to focus on a single goal in the past — treating and curing illness and injury — insurers have been attempting to reconcile seemingly contradictory objectives. Their charge has been to ensure that beneficiaries and members receive the best care possible, while maintaining a close eye on the costs.

Over the past few decades, the insurance industry has adopted various approaches to resolve this dilemma, such as designing health plans that encourage patients to take greater financial responsibility for their care by expecting them to pay a portion of the expenses. In addition, protocols for selecting high-quality medical providers also encourage best practices in care delivery.

In the last several years, health plans have developed advanced disease-management (DM) initiatives that address the progression of chronic illnesses and conditions, with the goal of cutting overall medical costs, decreasing frequency of episodes of care and reducing lengths of stay.

Transforming Good Ideas into Good Business

These programs are designed to encourage patients to make recommended lifestyle changes, comply with care plans and physician recommendations, and remain alert to any symptoms that indicate early-stage complications. The difficulty has come with the insurers’ inability to prove the inherent value of DM programs by demonstrating return on investment (ROI).

Corporations that have added DM or population health initiatives to their benefits programs are demanding some accountability. Likewise, C-level executives at health plans are asking program directors to justify the expense and demonstrate quantifiable ROI for these population health offerings.

Health plan executives and their corporate clients are asking which patient populations will be most responsive to DM programs, what types of interventions are most effective, and what level of investment is adequate to generate return in terms of reduced reimbursements.

Identifying Prospects and Designing Interventions

Traditionally, in a pen-and-paper world, this has been virtually difficult to calculate precisely. Emerging data- and technology-enabled models, however, can be applied to the challenge, enabling health plans to prospectively analyze patient populations via intensive risk profiling, predictive modeling and stratification.

Consider this difference between “presumptive” DM design and one founded on analytics:

Health Plan A implements a traditional high-cost, high-risk DM program for patients with congestive heart failure. This patient population typically is identified because of clinical triggers and a history of costly medical events. While outreach helps ensure patients comply with diet, exercise and drug therapy, these efforts are largely palliative because the harm has already been done.

Health Plan B deploys a DM initiative based on risk profiling, prediction and economic modeling. The program is able to identify high-risk members earlier, prompting intervention with those whose behavior can be changed before the patient has required costly medical treatment. Risk profiling, for example, can identify employees who are at high risk for back injury. A care management program can be designed to provide safety education, diet and exercise recommendations, and tips to recognize problems before they progress.

Developing Intervention Models

The ideal for healthcare executives is to not only identify patient populations that will benefit most from DM efforts, but also to determine what level of investment will produce the greatest results, so that neither financial nor human resources are misapplied. Using currently available predictive modeling software, healthcare executives can assign these resources in accordance with likelihood of cost avoidance (e.g., fewer emergency room visits, shorter lengths of stay).

In other words, a program of health-risk assessment using customer service representatives could be launched to stratify the population and identify only those high-risk individuals who warrant attention from more costly clinical resources. A 28-year-old male who has just been diagnosed with diabetes, for instance, may benefit greatly from being enrolled in a nurse-based intervention program.

Most health plans have begun to aggregate data from medical and prescription claims to identify individuals and population groups most likely to benefit from their individualized care-management outreach programs. Program architects have adopted this approach to customize plans for specific groups of patients, and approach them in ways that achieve their buy-in and therefore increase the likelihood of their voluntary participation.

While these tools provide the front-end analysis that enables insurers to design and execute viable DM programs, insurers should also think about the back end — how to assess performance and determine the magnitude of cost savings being achieved. By utilizing technology, plans can report back to employers and other clients with an objective assessment about whether a specific care-management program works, and how much was saved.

Strategies for Adopting New Technology

Health plans should evaluate each tool and determine how to integrate it with existing workflow processes and staffing resources. In some cases, it will make sense to purchase and host these analytic platforms in-house. Other circumstances may indicate that an outsourcing or cosourcing arrangement will be more beneficial.

As DM programs have grown in size and scope, so too have demands for accountability also grown.

Depending on economic conditions or the popularity of any given business management theory, health plans may lean toward building infrastructure themselves to maintain process and data control, or control expenses. At other times, executives focus on core competencies and turn to external resources for functions falling outside of this realm.

Currently, many health plans are looking closely at the benefits of outsourcing the data analysis and reporting inherent to building highly focused DM programs and calculating ROI. They regard outsourcing as a lower-risk approach and are able to limit their exposure to risk, while nevertheless adopting an innovative strategy to prove value. Likewise, they are able to avoid the necessity of recruiting and compensating specialized staff.

As DM programs have grown in size and scope, so too have demands for accountability also grown. Health plan executives and corporate clients alike are asking for proof that these initiatives improve the health and wellness of covered members, and that the resources invested generate positive ROI in a reasonable amount of time.

Christian Birkmeyer is senior VP, medical data management and analytics for SCIOinspire, and Ian Duncan is president and founder of Solucia, a SCIOinspire company.

June 2009

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