Defining measures of strain on hospital wards and their influence on survivors of acute respiratory failure

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Project Summary/Abstract Patients with acute respiratory failure (ARF) commonly endure prolonged intensive care unit (ICU) and hospital lengths of stay (LOS) and high short-term mortality. They also suffer long-term complications including profound deconditioning, weakness, neurocognitive insults, and physical and psychological sequelae, the risks of which increase with longer LOS. Thus, efforts to hasten recoveries to hospital discharge may improve short- and long-term outcomes and reduce costs. An important but largely unexamined contributor to recovery may be the timing and quality of transitions from ICUs to general hospital wards. Approximately 90% of ARF survivors undergo this transition prior to hospital discharge. It represents a vulnerable time due to fragmentation of care, communication gaps, lack of standardization, and a reduction in intensive monitoring, and care lapses during this time may lead to ICU readmission or death. High workload or “capacity strain” in hospital units adversely affects patient outcomes. For example, in the Emergency Department (ED), high patient volume is associated with increased wait times and adverse patient outcomes. Further, our research group has demonstrated that ICU patient volume, turnover, and severity of illness define the construct of ICU capacity strain, and that these variables are associated with ICU patient flow, adverse outcomes, and physician workflow. Capacity strain on general hospital wards could complicate the transitions of ARF ICU survivors as these patients may be the most complex and tenuous patients transferred to the wards and may be particularly vulnerable to the effects of capacity strain. The goals of this study are to define the construct of “ward capacity strain” and to evaluate its role in ARF patient flow. First, I aim to define the construct of ward capacity strain through factor analysis of candidate ward strain variables (daily patient flow and staff workload variables), and by using multivariable predictive modeling to assess the factors and individual candidate ward strain variables' abilities to predict two key processes of care that I hypothesize will be altered by ward strain: ICU discharge wait time and ED boarding time. Second, I aim to quantify the importance of ward capacity strain by evaluating the degree to which it augments the accuracy of predicting ICU LOS among patients with ARF through predictive modeling using multivariable linear regression and model comparison techniques. This project will provide essential preliminary data for a planned NIH K-series Career Development Award that will (1) examine the impact of ward strain on short- and long-term outcomes of patients with ARF, (2) define the relative impacts of ward, ED, and ICU strain on these outcomes among diverse hospitals, and (3) develop an intervention to mitigate adverse patient-centered outcomes associated with ward, ED, and/or ICU strain.