Association of decision support for hospital discharge disposition with outcomes.

OBJECTIVES: To assess the association of a clinical decision support (CDS) algorithm for hospital discharge disposition with spending, readmissions, and postdischarge emergency department (ED) use. STUDY DESIGN: A retrospective study in a cohort of fee-for-service Medicare patients 65 years or older linked to a database of patients receiving CDS. METHODS: We evaluated (1) patients whose discharge disposition was concordant with the CDS recommendation versus those whose disposition was not and (2) patients receiving CDS for discharge disposition versus those not receiving CDS, regardless of concordance. Outcomes were spending over a 90-day episode, 90-day readmissions, and postdischarge ED utilization not associated with a readmission. RESULTS: Analysis of concordant versus discordant cases showed decreased spending for concordant cases ($860 savings; 95% CI, $162-$1558; P = .016), a decrease in readmissions (adjusted odds ratio [OR], 0.920; 95% CI, 0.850-0.995; P = .038), and no change in rate of postdischarge ED use (adjusted OR, 0.990; 95% CI, 0.882-1.110; P = .858). Analysis of patients receiving CDS versus not receiving CDS showed no significant difference in spending ($221 savings; 95% CI, -$115 to $557; P = .198), ED use (adjusted OR, 0.959; 95% CI, 0.908-1.012; P = .128), or readmission rate (adjusted OR, 1.004; 95% CI, 0.966-1.043; P = .840). CONCLUSIONS: Following the recommendation of a CDS algorithm for hospital discharge disposition was associated with lower spending, fewer readmissions, and no change in ED use over a 90-day episode of care.

Association of State Medicaid Expansion With Quality of Care and Outcomes for Low-Income Patients Hospitalized With Acute Myocardial Infarction.

Importance: Lack of insurance is associated with worse care and outcomes among adults hospitalized for acute myocardial infarction (AMI). It is unclear whether states' decision to expand Medicaid eligibility under the Patient Protection and Affordable Care Act in 2014 were associated with improved quality of care and outcomes among low-income patients hospitalized with AMI. Objective: To investigate whether rates of uninsurance, quality of care, and outcomes changed among patients hospitalized for AMI 3 years after states elected to expand Medicaid compared with nonexpansion states. Design, Setting, and Participants: Retrospective cohort study completed at hospitals participating in National Cardiovascular Data Registry Acute Coronary Treatment and Intervention Outcomes Network Registry. Participants were patients younger than 65 years hospitalized for AMI from January 1, 2012, to December 31, 2016. Exposures: State Medicaid expansion in 2014. Main Outcomes and Measures: Rates of uninsured and Medicaid-insured hospitalizations for AMI in states that expanded Medicaid vs those that did not. Comparison of in-hospital care quality, procedure use, and mortality between expansion and nonexpansion states for the years prior to and after Medicaid expansion. Hierarchical logistic regressions models were used to assess the association between Medicaid expansion and outcomes. Results: The initial cohort included 325 343 patients. Uninsured AMI hospitalizations declined in expansion states (18.0% [4395 of 24 358 hospitalizations] to 8.4% [2638 of 31 382 hospitalizations]) and more modestly in nonexpansion states (25.6% [7963 of 31 137 hospitalizations] to 21.1% [8668 of 41 120 hospitalizations]) from 2012 to 2016 (P < .001 difference in trend expansion vs nonexpansion). Medicaid coverage increased from 7.5% (1818 of 24 358 hospitalizations) to 14.4% (4502 of 31 382 hospitalizations) in expansion states and 6.2% (1924 of 31 137 hospitalizations) to 6.6% (2717 of 41 120 hospitalizations) in nonexpansion states (P < .001). The low-income cohort included 55 737 patients across 765 sites. In expansion states, low-income adults' odds of receipt of defect-free care increased (76.3% to 75.9%, adjusted odds ratio 1.11; 95% CI, 1.02-1.21) but to a lesser degree than in nonexpansion states (72.8% to 74.5%, adjusted odds ratio, 1.38; 95% CI, 1.30-1.47; P for interaction < .001). There was no change in use of most procedures (ie, percutaneous coronary intervention for non-ST-segment elevation myocardial infarction) in expansion compared with nonexpansion states. Improvement in in-hospital mortality was similar between expansion and nonexpansion states (3.2% to 2.8%, adjusted odds ratio, 0.93; 95% CI, 0.77-1.12 vs 3.3% to 3.0%, adjusted odds ratio, 0.85; 95% CI, 0.73-0.99; P for interaction = .48). Conclusions and Relevance: Medicaid expansion was associated with a significant reduction in rates of uninsurance among patients hospitalized with AMI. Quality of care and outcomes did not improve among low-income adults in expansion compared with nonexpansion states. Hospital care for AMI may be less sensitive to insurance than has been recognized in the past.

Preliminary Outcomes from an Integrated Pediatric Mental Health Outpatient Clinic.

An estimated 1 in 5 children in the United States meet criteria for a diagnosable mental disorder, yet fewer than 20% receive mental health services. Unmet need for psychiatric treatment may contribute to patterns of increasing use of the emergency department. This article describes an integrated pediatric evaluation center designed to prevent the need for treatment in emergency settings by increasing access to timely and appropriate care for emergent and critical mental health needs. Preliminary results showed that the center provided rapid access to assessment and treatment services for children and adolescents presenting with a wide range of psychiatric concerns.

A comparison of models for predicting early hospital readmissions.

Risk sharing arrangements between hospitals and payers together with penalties imposed by the Centers for Medicare and Medicaid (CMS) are driving an interest in decreasing early readmissions. There are a number of published risk models predicting 30day readmissions for particular patient populations, however they often exhibit poor predictive performance and would be unsuitable for use in a clinical setting. In this work we describe and compare several predictive models, some of which have never been applied to this task and which outperform the regression methods that are typically applied in the healthcare literature. In addition, we apply methods from deep learning to the five conditions CMS is using to penalize hospitals, and offer a simple framework for determining which conditions are most cost effective to target.