Potentially Preventable Intensive Care Unit Admissions in the United States, 2006–2015
Year: 2019
Type: article
Study design: Observational Study
Abstract: Rationale Increasing intensive care unit (ICU) beds and the critical care workforce are often advocated to address an aging and increasingly medically complex population. However, reducing potentially preventable ICU stays may be an alternative to ensure adequate capacity. Objectives To determine the proportions of ICU admissions meeting two definitions of being potentially preventable using nationally representative U.S. claims databases. Methods We analyzed claims from 2006 to 2015 from all Medicare Fee-for-Service (FFS) beneficiaries and from a large national payer offering a private insurance (PI) plan and a Medicare Advantage (MA) plan. Potentially preventable hospitalizations were identified using existing definitions for ambulatory care sensitive conditions (ACSCs) and life-limiting malignancies (LLMs). Results We analyzed 420,369,434 person-years of insurance coverage, during which there were 99,793,416 acute inpatient hospitalizations, of which 16,646,977 (16.7%) were associated with an ICU admission. Of these, the proportions with an ACSC were 12.9%, 12.7%, and 15.8%, and with an LLM were 5.2%, 5.4%, and 6.4%, among those with PI, MA, and FFS, respectively. Over 10 years, the absolute percentages of ACSC-associated ICU stays declined (PI = −1.1%, MA −6.4%, FFS −6.4%; all P < 0.001 for all trends). Smaller changes were noted among LLM-associated ICU stays, declining in the MA cohort (−0.8%) and increasing in the FFS (+0.3%) and PI (+0.2%) populations (P < 0.001 for all trends). Conclusions An appreciable proportion of U.S. ICU admissions may be preventable with community-based interventions. Investment in the outpatient infrastructure required to prevent these ICU admissions should be considered as a complementary, if not alternative, strategy to expanding ICU capacity to meet future demand.
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: green
Awards: Using natural language processing and machine learning to identify potentially preventable hospital admissions among outpatients with chronic lung diseases, G2268991709, G3241524, G3404626013, G9000002687, G9000202258, The influence of ward capacity strain on outcomes among survivors of acute respiratory failure
OpenAlex ID: W2978528408
