Key Points Question Can machine-learning approaches predict opioid overdose risk among fee-for-service Medicare beneficiaries? Findings In this prognostic study of the administrative claims data of 560 057 Medicare beneficiaries, the deep neural network and gradient boosting machine models outperformed other methods for identifying risk, although positive predictive values were low given the low prevalence of overdose episodes. Meaning Machine-learning algorithms using administrative data appear to be a valuable and feasible tool for more accurate identification of opioid overdose risk.
Rationale: Telemedicine is an increasingly common care delivery strategy in the ICU. However, ICU telemedicine programs vary widely in their clinical effectiveness, with some studies showing a large mortality benefit and others showing no benefit or even harm.Objectives: To identify the organizational factors associated with ICU telemedicine effectiveness.Methods: We performed a focused ethnographic evaluation of 10 ICU telemedicine programs using site visits, interviews, and focus groups in both facilities providing remote care and the target ICUs. Programs were selected based on their change in risk-adjusted mortality after adoption (decreased mortality, no change in mortality, and increased mortality). We used a constant comparative approach to guide data collection and analysis.Measurements and Main Results: We conducted 460 hours of direct observation, 222 interviews, and 18 focus groups across six telemedicine facilities and 10 target ICUs. Data analysis revealed three domains that influence ICU telemedicine effectiveness: 1) leadership (i.e., the decisions related to the role of the telemedicine, conflict resolution, and relationship building), 2) perceived value (i.e., expectations of availability and impact, staff satisfaction, and understanding of operations), and 3) organizational characteristics (i.e., staffing models, allowed involvement of the telemedicine unit, and new hire orientation). In the most effective telemedicine programs these factors led to services that are viewed as appropriate, integrated, responsive, and consistent.Conclusions: The effectiveness of ICU telemedicine programs may be influenced by several potentially modifiable factors within the domains of leadership, perceived value, and organizational structure.
Background Intensive care unit (ICU) telemedicine is an increasingly common strategy for improving the outcome of critical care, but its overall impact is uncertain. Objectives To determine the effectiveness of ICU telemedicine in a national sample of hospitals and quantify variation in effectiveness across hospitals. Research design We performed a multi-center retrospective case-control study using 2001–2010 Medicare claims data linked to a national survey identifying United States hospitals adopting ICU telemedicine. We matched each adopting hospital (cases) to up to 3 non-adopting hospitals (controls) based on size, case-mix and geographic proximity during the year of adoption. Using ICU admissions from 2 years before and after the adoption date, we compared outcomes between case and control hospitals using a difference-in-differences approach. Results 132 adopting case hospitals were matched to 389 similar non-adopting control hospitals. The pre- and post-adoption unadjusted 90-day mortality was similar in both case hospitals (24.0% vs. 24.3%, p=0.07) and control hospitals (23.5% vs. 23.7%, p<0.01). In the difference-in-differences analysis, ICU telemedicine adoption was associated with a small relative reduction in 90-day mortality (ratio of odds ratios: 0.96, 95% CI = 0.95–0.98, p<0.001). However, there was wide variation in the ICU telemedicine effect across individual hospitals (median ratio of odds ratios: 1.01; interquartile range 0.85–1.12; range 0.45–2.54). Only 16 case hospitals (12.2%) experienced statistically significant mortality reductions post-adoption. Hospitals with a significant mortality reduction were more likely to have large annual admission volumes (p<0.001) and be located in urban areas (p=0.04) compared to other hospitals. Conclusions Although ICU telemedicine adoption resulted in a small relative overall mortality reduction, there was heterogeneity in effect across adopting hospitals, with large-volume urban hospitals experiencing the greatest mortality reductions.
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