Evaluation of a High-Risk Case Management Pilot Program for Medicare Beneficiaries with Medigap Coverage

The objective was to evaluate the 3-year experience of a high-risk case management (HRCM) pilot program for adults with an AARP Medicare Supplement (Medigap) Insurance Plan. Participants were provided in-person visits as well as telephonic and mailed services to improve care coordination from December 1, 2008, to December 31, 2011. Included were adults who had an AARP Medigap Insurance Plan, resided in 1 of 5 pilot states, and had a Hierarchical Condition Category score>3.74, or were referred into the program. Propensity score weighting was used to adjust for case-mix differences among 2015 participants and 7626 qualified but nonparticipating individuals. Participants were in the program an average of 15.4 months. After weighting, multiple regression analyses were used to estimate differences in quality of care and health care expenditures between participants and nonparticipants. Increased duration in the program was associated with fewer hospital readmissions. Additionally, participants were significantly more likely to have recurring office visits and recommended laboratory tests. The program demonstrated $7.7 million in savings over the 3 years, resulting in a return on investment of$1.40 saved for every dollar spent on the program. Savings increased each year from 2009 to 2011 and with longer length of engagement. The majority of savings were realized by the federal Medicare program. This study focused on quality of care and savings for an HRCM program designed solely for Medicare members with Medicare Supplement coverage. This program had a favorable impact on quality of care and demonstrated savings over a 3-year period. (Population Health Management 2015;18:93?103)Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140185/1/pop.2014.0035.pd

Propensity to Succeed: Prioritizing Individuals Most Likely to Benefit from Care Coordination

The objective was to develop a propensity to succeed (PTS) process for prioritizing outreach to individuals with Medicare Supplement (ie, Medigap) plans who qualified for a high-risk case management (HRCM) program. Demographic, socioeconomic, health status, and local health care supply data from previous HRCM program participants and nonparticipants were obtained from Medigap membership and health care claims data and public data sources. Three logistic regression models were estimated to find members with higher probabilities of engaging in the HRCM program, receiving high quality of care once engaged, and incurring enough monetary savings related to program participation to more than offset program costs. The logistic regression model intercepts and coefficients yielded the information required to build predictive models that were then applied to generate predicted probabilities of program engagement, high quality of care, and cost savings a priori for different members who later qualified for the HRCM program. Predicted probabilities from the engagement and cost models were then standardized and combined to obtain an overall PTS score, which was sorted from highest to lowest and used to prioritize outreach efforts to those newly eligible for the HRCM program. The validity of the predictive models also was estimated. The PTS models for engagement and financial savings were statistically valid. The combined PTS score based on those 2 components helped prioritize outreach to individuals who qualified for the HRCM program. Using PTS models may help increase program engagement and financial success of care coordination programs. (Population Health Management 2015;18:402?411)Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140189/1/pop.2014.0121.pd

Propensity to Succeed: Prioritizing Individuals Most Likely to Benefit from Care Coordination

The objective was to develop a propensity to succeed (PTS) process for prioritizing outreach to individuals with Medicare Supplement (ie, Medigap) plans who qualified for a high-risk case management (HRCM) program. Demographic, socioeconomic, health status, and local health care supply data from previous HRCM program participants and nonparticipants were obtained from Medigap membership and health care claims data and public data sources. Three logistic regression models were estimated to find members with higher probabilities of engaging in the HRCM program, receiving high quality of care once engaged, and incurring enough monetary savings related to program participation to more than offset program costs. The logistic regression model intercepts and coefficients yielded the information required to build predictive models that were then applied to generate predicted probabilities of program engagement, high quality of care, and cost savings a priori for different members who later qualified for the HRCM program. Predicted probabilities from the engagement and cost models were then standardized and combined to obtain an overall PTS score, which was sorted from highest to lowest and used to prioritize outreach efforts to those newly eligible for the HRCM program. The validity of the predictive models also was estimated. The PTS models for engagement and financial savings were statistically valid. The combined PTS score based on those 2 components helped prioritize outreach to individuals who qualified for the HRCM program. Using PTS models may help increase program engagement and financial success of care coordination programs. (Population Health Management 2015;18:402?411)Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140189/1/pop.2014.0121.pd