ObjectiveThe objective is to develop and validate a predictive model for 15-month mortality using a random sample of community-dwelling Medicare beneficiaries.Data sourceThe Centres for Medicare & Medicaid Services’ Limited Data Set files containing the five per cent samples for 2014 and 2015.ParticipantsThe data analysed contains de-identified administrative claims information at the beneficiary level, including diagnoses, procedures and demographics for 2.7 million beneficiaries.SettingUS national sample of Medicare beneficiaries.Study designEleven different models were used to predict 15-month mortality risk: logistic regression (using both stepwise and least absolute shrinkage and selection operator (LASSO) selection of variables as well as models using an age gender baseline, Charlson scores, Charlson conditions, Elixhauser conditions and all variables), naïve Bayes, decision tree with adaptive boosting, neural network and support vector machines (SVMs) validated by simple cross validation. Updated Charlson score weights were generated from the predictive model using only Charlson conditions.Primary outcome measureC-statistic.ResultsThe c-statistics was 0.696 for the naïve Bayes model and 0.762 for the decision tree model. For models that used the Charlson score or the Charlson variables the c-statistic was 0.713 and 0.726, respectively, similar to the model using Elixhauser conditions of 0.734. The c-statistic for the SVM model was 0.788 while the four models that performed the best were the logistic regression using all variables, logistic regression after selection of variables by the LASSO method, the logistic regression using a stepwise selection of variables and the neural network with c-statistics of 0.798, 0.798, 0.797 and 0.795, respectively.ConclusionsImproved means for identifying individuals in the last 15 months of life is needed to improve the patient experience of care and reducing the per capita cost of healthcare. This study developed and validated a predictive model for 15-month mortality with higher generalisability than previous administrative claims-based studies.

The study demonstrates that a commercially delivered heart failure disease-management program significantly reduced hospitalizations, emergency department visits, and SNF days. The intervention group had 17% lower costs than the control group; when intervention costs were included, the intervention group had 10% lower costs.

Estimating the economic and clinical impact of asthma disease management programs traditionally has relied on non-experimental designs and employed matching or stratification methods with limited success. Selecting similar comparison subjects is problematic since subjects must be compared across numerous pretreatment factors. In cases where treatment and comparison subjects differ greatly on observed characteristics, conclusions may be particularly sensitive to an incorrectly specified model used for matching. A propensity score method constructs matched samples of treated-control pairs, addresses program selection bias, and reduces bias in estimates of treatment effects. To investigate the program effects of an asthma care support program delivered to high-risk asthmatics (persons with a previous inpatient admission, emergency department [ED] visit, or observation visit), we conducted a matched-cohort study on 196 participants. Using administrative claims data and selected clinical indicators, we analyzed hospitalization, ED, and physician office visit rates to estimate effects of program enrollment. Total hospitalizations, asthma-related hospitalizations, bed days, and ED visits for participants were lower and statistically different from that of the matched-cohort group during the program period, suggesting the beneficial effects of monitoring, education, and counseling activities for participants. Where controlled randomized clinical trials cannot be performed because of ethical, cost, or feasibility issues, the use of propensity scores provides an alternative for estimating treatment effects using observational data. This study employs a propensity score-matching methodology to select a subset of comparison units most comparable to treatment units, and documents the beneficial outcomes of participation in an asthma care support program.

Our objective was to investigate the utilization, drug, and clinical outcomes of a telephonic nursing disease management (DM) program for elderly patients with diabetes. We employed a 24-month, matched-cohort study employing propensity score matching. The setting involved Medicare + Choice recipients residing in Ohio, Kentucky, and Indiana. There were 610 intervention group members over the age of 65 matched to a control group of members over the age of 65. The DM diabetes program employed a structured, evidence-based, telephonic nursing intervention designed to provide patient education, counseling, and monitoring services. Measurements consisted of Medical service utilization, including hospitalizations, emergency department visits, physician evaluation and management visits, skilled nursing facility days, drug utilization, and selected clinical indicators. Among the results, the intervention group had considerably and significantly lower rates of acute service utilization compared to the control group, including a 17.5% reduction in hospitalizations, 22.4% reduction in bed days, 12.3% increase in physician evaluation and management visits, 23.7% increase in angiotensin-converting enzyme (ACE) inhibitor use, 13.3% increase in blood glucose regulator use, 11.8% increase in hemoglobin A1c (HbA1c) tests, 10.3% increase in lipid panels, 26.0% increase in eye exams, and 35.5% increase in microalbumin tests. In conclusion, the study demonstrates that a commercially delivered diabetes DM program significantly reduces hospitalizations and bed-days while increasing the use of ACE inhibitors and blood glucose regulators along with selected clinical procedures such as HbA1c tests, lipid panels, eye exams, and microalbumin tests.