The Effect of Predictive Analytics-Driven Interventions on Healthcare Utilization

Among high-risk Medicare Advantage members with congestive heart failure, a proactive outreach program driven by a claims-based predictive algorithm reduced the likelihood of an emergency department (ED) or specialist visit in one year by 20% and 21%, respectively. The average number of visits dropped as well, with a 40% reduction in the volume of ED visits and a 27% reduction in the volume of cardiology visits after the first year

Closing the Gap in High-Risk Pregnancy Care Using Machine Learning and Human-AI Collaboration

Health insurers often use algorithms to identify members who would benefit from care and condition management programs, which provide personalized, high-touch clinical support. Timely, accurate, and seamless integration between algorithmic identification and clinical intervention depends on effective collaboration between the system designers and nurse care managers. We focus on a high-risk pregnancy (HRP) program designed to reduce the likelihood of adverse prenatal, perinatal, and postnatal events and describe how we overcome three challenges of HRP programs as articulated by nurse care managers; (1) early detection of pregnancy, (2) accurate identification of impactable high-risk members, and (3) provision of explainable indicators to supplement predictions. We propose a novel algorithm for pregnancy identification that identifies pregnancies 57 days earlier than previous code-based models in a retrospective study. We then build a model to predict impactable pregnancy complications that achieves an AUROC of 0.760. Models for pregnancy identification and complications are then integrated into a proposed user interface. In a set of user studies, we collected quantitative and qualitative feedback from nurses on the utility of the predictions combined with clinical information driving the predictions on triaging members for the HRP program

The Impact of Outpatient Supportive Oncology on Cancer Care Cost and Utilization

Research Objective In patients with advanced cancer, interprofessional, non hospital-based care models of palliative care or Supportive Oncology (SO) have been shown in some studies to reduce symptom severity, hospital admissions, and healthcare costs. However, there is little consistency in the composition of SO programs or the degree of integration of social work, nutrition counseling, patient navigation, and nursing care services. There is limited research on quality of care and cost outcomes and current fee-for-service models do not cover the high costs of these non-billable services. We examine the impact of Interprofessional SO care on utilization and medical costs in patients with advanced cancer.https://jdc.jefferson.edu/medoncposters/1018/thumbnail.jp

Adjuvant Chemotherapy Use and Health Care Costs After Introduction of Genomic Testing in Breast Cancer

Genomic testing in patients with early-stage breast cancer is associated with decreased use of chemotherapy and lower costs in younger patients, and slightly increased use of chemotherapy and higher costs in older patients. Genomic testing in actual practice may “rule out” chemotherapy in younger women, and “rule in” chemotherapy in older women

Is it true? (When) does it matter? The roles of likelihood and desirability in argument judgments and attitudes

Several theoretical perspectives either directly or indirectly specify roles for likelihood and desirability information in argument judgments and attitude formation. Some perspectives assume that argument judgments and attitudes are a function of the likelihood of the consequences or conclusions, others contend that the desirability of the consequences or conclusions underlie judgments and attitudes, and expectancy-value perspectives, (e.g., Fishbein, 1963) propose that judgments and attitudes should depend on the likelihood × desirability interaction. Construal level theory (CLT; Trope & Liberman, 2003) also suggests that both likelihood and desirability information impact argument judgments and attitudes, but the roles of each are moderated by when the outcomes are to occur. Three studies examined the sometimes-competing predictions regarding the roles of these variables by orthogonally manipulating levels of likelihood and desirability. Although likelihood and desirability both emerged as components of argument strength, and contributed to attitudes, all 3 studies showed that desirability information was more closely associated with argument strength and attitudes than was likelihood information. In Study 1a, argument strength was shown to mediate the desirability-attitude relation. The likelihood × desirability interaction did not predict attitudes in a manner consistent with expectancy-value predictions, though in some instances likelihood and desirability judgments interacted to predict attitudes and attitude change in the predicted expectancy-value pattern. Studies 1b and 2 showed that the desirability-attitude relation was best described as a cubic trend consistent with prospect theory. CLT predictions examined in Study 2 were largely unsupported. Theoretical and methodological implications are discussed.