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A Modular Approach to Disease Management

by Scott MacStravic

It continues to appear that disease management (DM) has trouble meeting strict government expectations – for both engaging targeted participants and achieving positive return on investment (ROI).  It is possible that both these challenges can be addressed through adaptation of technology already in widespread use: modular education.

The modular approach to education is similar to modular construction, modular workstations, modular closets and storage systems.  It involves separate modules (units, pieces, elements) that can be put together in a variety of ways to meet different situations and needs.  It is most widely used in “distance learning”, where students interact with an educational program online and perhaps by phone and mail as supplements to their online communications.

The particular set of modules that any individual student or DM participant interacts with can be customized to each, depending on how many different modules there are and how different individual participants’ needs and preferences are.  The set can be based on an “expert system” assessment of individual needs and preferences, as well as personality, attitude, and readiness to change dimensions of each.

In DM, the set of modules appropriate for each individual would logically reflect the combination of the risk/reward potential estimated for each by the expert system’s analysis of assessment inputs – compared to the costs involved for different mixes of modules.  Higher-intensity modules that involve face visits to clinicians could be reserved for the highest risk/reward potential targets, while lower-intensity modules involving online communications could be used for lower risk/reward targets.

The intensity and costs for each individual participant could be almost infinitely varied by selecting different numbers and mixes of face visits, phone coaching sessions, online or mailed outbound communications, and inbound website visits by participants, for example.   All participants could be offered a personal web page on DM providers’ websites for their posting of behavior changes, health indicator changes, personal evaluation of programs and results.

Customization of DM programs for individuals has been shown to have significantly greater positive impact on their health and sickness care expenditures than one-size-fits all approaches.  Enabling individuals to participate in the customization process, along with mentors, guides, etc. who are professionals, can help in achieving a higher level of participation among targets.  And customization based on risk/reward potential can increase the likelihood that a positive ROI will be achieved, since the costs of interventions can be matched to the benefits achievable for each participant.

The Duke University Health system, for example, uses a modular approach in its “Duke Prospective Health” program, aimed at employees and dependents enrolled in its self-insured health plans. All participants can choose among a number of basic support programs, such as a health risk assessment module, a personal health status tracker, group classes and a rewards program.  Higher risk/reward potential participants can qualify for individual case management, personal coaching, and referral to programs aimed at specific goals, such as smoking cessation and weight management – which may be offered in person, by phone or online.

The use of predictive modeling technology in assessing the risk/reward potential of individuals, together with customized modules that reflect individual differences as well as this potential, can yield far greater impact on participants, as well as far greater savings for sponsors.  The technologies, in both analysis and communications, already exist and are in use for education and training for example.  Why not use them for DM, to make the costs more closely fit the potential benefit to sponsors, as well as more closely fit the preferences of participants?


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