Aims: Chronic diseases impose a substantial healthcare burden. This study sought to evaluate the clinical and economic impact of new disease management (DM) programs, targeting four major chronic disease groups: diabetes, coronary heart disease (CHD)/hypertension (HTN), asthma/chronic obstructive pulmonary disease (COPD), and congestive heart failure (CHF)/chronic kidney disease (CKD).
Aims: To evaluate the risk-of-hospitalization (ROH) models developed at Blue Cross Blue Shield of Louisiana (BCBSLA) and compare this approach to the DxCG risk-score algorithms utilized by many health plans. Materials and Methods: Time zero for this study was December 31, 2016. BCBSLA members were eligible for study inclusion if they were fully insured; aged 80 years or younger; and had continuous enrollment starting on or before June 1, 2016, through time zero. Up to 2 years of historical claims data from time zero per patient was included for model development. Members were excluded if they had cancer, renal failure, or were admitted for hospice. The Blue Cross ROH models were developed using (1) regularized logistic regression and (2) random decision forests (a tree ensemble learning classification method). All models were generated using Scikit-learn: Machine Learning in Python. Prognostic capabilities of DxCG risk-score algorithms were compared to those of the Blue Cross models. Results: When stratifying by the top 0.1% of members with the highest ROH, the Blue Cross logistic regression model had the highest area under the receiving operator characteristics curve (0.862) based on the result of 10-fold cross-validation. The Blue Cross random decision forests model had the highest positive predictive value (49.0%) and positive likelihood ratio (61.4), but sensitivity, specificity, negative predictive values, and negative likelihood ratios were similar across all four models. Limitations: The Blue Cross ROH models were developed and evaluated using BCBSLA data, and predictive power may fluctuate if applied to other databases. Conclusions: The predictability of the Blue Cross models show how member-specific, regional data can be used to accurately identify patients with a high ROH, which may allow healthcare workers to intervene earlier and subsequently reduce the healthcare burden for patients and providers.
discharged for any of the 280 PACT policy defined DRGs with an LOS shorter than the geometric mean for the DRG. To investigate the potential benefits of this policy, we assessed differences in re-admissions and healthcare costs between PACT eligible patients discharged to HH and those discharged to home with no home healthcare. Methods: Patients enrolled in Medicare Advantage with a PACT eligible discharge in 2018 were evaluated for this retrospective, claims analysis of a large national health plan. Index was the date of discharge. Patients with a claim for HH services within 7 days post-index comprised the HH group. Patients discharged to home with no claim for HH in the 90-day post-index period were the comparison group. Cox proportional-hazards models with an instrumental variable (hospital-level probability of HH referral) and adjustment for case-mix were used to assess all-cause readmissions post-index. Post-index total healthcare costs were modeled using generalized estimating equations fit to a gamma distribution. Results: There were 3,753 HH patients and 13,342 home only patients in the study cohort. HH patients were older [mean age 71.7 (+/-7) vs 70.8 (+/-9)], more frequently female (58.5%), with mostly surgical DRGs (85.4%). For HH patients, the risk of 30-day readmission was reduced by 60%, 60-day by 45% and 90-day by 37%. Healthcare costs were 11% lower for the HH group. Conclusions: For PACT eligible patients, the provision of additional healthcare services in the home setting was associated with fewer hospital re-admissions and reduced total healthcare costs, including costs of home health.
This article introduces a process-oriented approach for improving present moment conceptualization in psychotherapy that is in alignment with neuroscience: the Temporospatial movements of mind (TSMM) model. We elaborate on seven temporal movements that describe the moment-to-moment morphogenesis of emotional feelings and thoughts from inception to maturity. Temporal refers to the passage of time through which feelings and thoughts develop, and electromagnetic activity, that among other responsibilities, bind information across time. Spatial dynamics extend from an undifferentiated to three dimensional experiences of emotional and cognitive processes. Neurophysiologically, spatial refers to structures within the brain and their varying interactions with one another. This article culminates in the development of an atheoretical temporospatial grid that may help clinicians conceptualize where patients are in their cognitive and emotional development to further guide technique.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.