Predictive modeling in practice: improving the participant identification process for care management programs using condition-specific cut points.

The objective of this study was to optimize predictive modeling in the participant selection process for care management (CM) programs by determining the ideal cut point selection method. Comparisons included: (a) an evidence-based "optimal" cut point versus an "arbitrary" threshold, and (b) condition-specific cut points versus a uniform screening method. Participants comprised adult Medicaid health plan members enrolled during the entire study period (January 2007-December 2008) who had at least 1 of the chronic conditions targeted by the CM programs (n = 6459). Adjusted Clinical Groups Predictive Modeling (ACG-PM) system risk scores in 2007 were used to predict those with the top 5% highest health care expenditures in 2008. Comparisons of model performance (ie, c statistic, sensitivity, specificity, positive predictive value) and identified population size were used to assess differences among 3 cut point selection approaches: (a) single arbitrary cut point, (b) single optimal cut point, and (c) condition-specific optimal cut points. The "optimal" cut points (ie, single and condition-specific) both outperformed the "arbitrary" selection process, yielding higher probabilities of correct prediction and sensitivities. The condition-specific optimal cut point approach also exhibited better performance than applying a single optimal cut point uniformly across the entire population regardless of condition (ie, a higher c statistic, specificity, and positive predictive value, although sensitivity was lower), while identifying a more manageable number of members for CM program outreach. CM programs can optimize targeting algorithms by utilizing evidence-based cut points that incorporate condition-specific variations in risk. By efficiently targeting and intervening with future high-cost members, health care costs can be reduced.

Acceptance of home telemanagement is high in patients with multiple sclerosis.

We assessed acceptance of Home Automated Telemanagement (HAT) in patients with Multiple Sclerosis. The patients were asked to complete their prescribed exercises and track the results with the assistance of the HAT system on a daily basis for 12 weeks. The HAT system generated alerts in cases of patient non-compliance. The attitudinal survey indicated high acceptance of the system. The Six-Minute Walk Test and Berg Balance scale showed statistically significant improvement in patient functional status.

Acceptance and expectations of information technology to support hypertension self-care in African Americans: a qualitative inquiry.

The aim of this study was to explore African American patients' experiences managing their hypertension and to investigate their perspective on how technology might be used to improve hypertension self-management. We conducted and analyzed four focus groups with 32 African American participants diagnosed with hypertension to develop a culturally tailored content for a home-based telecare program aimed at improving hypertension care in African Americans. The discussion about the use of technology was well accepted, demonstrated culturally and gender specific barriers in hypertension care, and generated a comprehensive list of concepts and features to be included to a home-based computerized hypertension telemanagement system.

Using a Home Automated Telemanagement (HAT) system: experiences and perceptions of patients with inflammatory bowel disease.

The aim of this study was to evaluate the experiences and perceptions of patients with inflammatory bowel disease (IBD) who participated in an interactive, patient-centered computerized disease management program using the Home Automated Telemanagement (HAT) system. We conducted and analyzed qualitative exit interviews with 23 participants who used the system for six months. The HAT system was well accepted, increased patients' awareness about the disease and facilitated greater control of their IBD symptoms.