Ward Capacity Strain: A Novel Predictor of Delays in Intensive Care Unit Survivor Throughput

The Capacity-to-Serve Model as a Data-Driven Process for Provider Capacity Management in Outpatient Community Mental Health

Despite increasing mental health provider supply shortages, research on capacity planning and management in the field of outpatient community mental healthcare is limited. There is an immediate need for strategies to plan and manage the capacity of existing mental healthcare providers to ensure a balance between demand and resources. To address this need, research on capacity planning and management in healthcare and mental healthcare settings is reviewed. Next, the Capacity-to-Serve Model is introduced and defined as a data-driven process for quantifying and reporting real-time standardized estimates of mental health provider availability based on qualifications, monitoring of outcome targets, and use of the Capacity-to-Serve Ratio and Realizing Capacity Measure. Finally, implications for using the model as an innovative solution for capacity management to meet demand in mental health are addressed. A case example is provided to demonstrate the application of the model. Ultimately, the Capacity-to-Serve Model can standardize capacity reporting of existing provider organizations and networks, both small and large, to support increased access to and supply of mental health services.

A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure

Supply-demand mismatch of ward resources ("ward capacity strain") alters care and outcomes. Narrow strain definitions and heterogeneous populations limit strain literature. Evaluate the predictive utility of a large set of candidate strain variables for in-hospital mortality and discharge destination among acute respiratory failure (ARF) survivors. In a retrospective cohort of ARF survivors transferred from intensive care units (ICUs) to wards in five hospitals from 4/2017-12/2019, we applied 11 machine learning (ML) models to identify ward strain measures during the first 24 hours after transfer most predictive of outcomes. Measures spanned patient volume (census, admissions, discharges), staff workload (medications administered, off-ward transports, transfusions, isolation precautions, patients per respiratory therapist and nurse), and average patient acuity (Laboratory Acute Physiology Score version 2, ICU transfers) domains. The cohort included 5,052 visits in 43 wards. Median age was 65 years (IQR 56-73); 2,865 (57%) were male; and 2,865 (57%) were white. 770 (15%) patients died in the hospital or had hospice discharges, and 2,628 (61%) were discharged home and 964 (23%) to skilled nursing facilities (SNFs). Ward admissions, isolation precautions, and hospital admissions most consistently predicted in-hospital mortality across ML models. Patients per nurse most consistently predicted discharge to home and SNF, and medications administered predicted SNF discharge. In this hypothesis-generating analysis of candidate ward strain variables' prediction of outcomes among ARF survivors, several variables emerged as consistently predictive of key outcomes across ML models. These findings suggest targets for future inferential studies to elucidate mechanisms of ward strain's adverse effects.

Association between Intraoperative Blood Transfusion, Regional Anesthesia and Outcome after Pediatric Tumor Surgery for Nephroblastoma

BACKGROUND

Recent data suggest that anesthesiologic interventions-e.g., the choice of the anesthetic regimen or the administration of blood products-might play a major role in determining outcome after tumor surgery. In contrast to adult patients, only limited data are available regarding the potential association of anesthesia and outcome in pediatric cancer patients.

METHODS

A retrospective multicenter study assessing data from pediatric patients (0-18 years of age) undergoing surgery for nephroblastoma between 2004 and 2018 was conducted at three academic centers in Europe. Overall and recurrence-free survival were the primary outcomes of the study and were evaluated for a potential impact of intraoperative administration of erythrocyte concentrates, the use of regional anesthesia and the choice of the anesthetic regimen. The length of stay on the intensive care unit, the time to hospital discharge after surgery and blood neutrophil-to-lymphocyte ratio were defined as secondary outcomes.

RESULTS

In total, data from 65 patients were analyzed. Intraoperative administration of erythrocyte concentrates was associated with a reduction in recurrence-free survival (hazard ratio (HR) 7.59, 95% confidence interval (CI) 1.36-42.2, p = 0.004), whereas overall survival (HR 5.37, 95% CI 0.42-68.4, p = 0.124) was not affected. The use of regional anesthesia and the choice of anesthetic used for maintenance of anesthesia did not demonstrate an effect on the primary outcomes. It was, however, associated with fewer ICU transfers, a shortened time to discharge and a decreased postoperative neutrophil-to-lymphocyte ratio.

CONCLUSIONS

The current study provides the first evidence for a possible association between blood transfusion as well as anesthesiologic interventions and outcome after pediatric cancer surgery.

Interventions to Reduce Hospital Length of Stay in High-risk Populations: A Systematic Review

IMPORTANCE

Many strategies to reduce hospital length of stay (LOS) have been implemented, but few studies have evaluated hospital-led interventions focused on high-risk populations. The Agency for Healthcare Research and Quality (AHRQ) Learning Health System panel commissioned this study to further evaluate system-level interventions for LOS reduction.

OBJECTIVE

To identify and synthesize evidence regarding potential systems-level strategies to reduce LOS for patients at high risk for prolonged LOS.

EVIDENCE REVIEW

Multiple databases, including MEDLINE and Embase, were searched for English-language systematic reviews from January 1, 2010, through September 30, 2020, with updated searches through January 19, 2021. The scope of the protocol was determined with input from AHRQ Key Informants. Systematic reviews were included if they reported on hospital-led interventions intended to decrease LOS for high-risk populations, defined as those with high-risk medical conditions or socioeconomically vulnerable populations (eg, patients with high levels of socioeconomic risk, who are medically uninsured or underinsured, with limited English proficiency, or who are hospitalized at a safety-net, tertiary, or quaternary care institution). Exclusion criteria included interventions that were conducted outside of the hospital setting, including community health programs. Data extraction was conducted independently, with extraction of strength of evidence (SOE) ratings provided by systematic reviews; if unavailable, SOE was assessed using the AHRQ Evidence-Based Practice Center methods guide.

FINDINGS

Our searches yielded 4432 potential studies. We included 19 systematic reviews reported in 20 articles. The reviews described 8 strategies for reducing LOS in high-risk populations: discharge planning, geriatric assessment, medication management, clinical pathways, interdisciplinary or multidisciplinary care, case management, hospitalist services, and telehealth. Interventions were most frequently designed for older patients, often those who were frail (9 studies), or patients with heart failure. There were notable evidence gaps, as there were no systematic reviews studying interventions for patients with socioeconomic risk. For patients with medically complex conditions, discharge planning, medication management, and interdisciplinary care teams were associated with inconsistent outcomes (LOS, readmissions, mortality) across populations. For patients with heart failure, clinical pathways and case management were associated with reduced length of stay (clinical pathways: mean difference reduction, 1.89 [95% CI, 1.33 to 2.44] days; case management: mean difference reduction, 1.28 [95% CI, 0.52 to 2.04] days).

CONCLUSIONS AND RELEVANCE

This systematic review found inconsistent results across all high-risk populations on the effectiveness associated with interventions, such as discharge planning, that are often widely used by health systems. This systematic review highlights important evidence gaps, such as the lack of existing systematic reviews focused on patients with socioeconomic risk factors, and the need for further research.

Association of a Novel Index of Hospital Capacity Strain with Admission to Intensive Care Units

Rationale: Prior approaches to measuring healthcare capacity strain have been constrained by using individual care units, limited metrics of strain, or general, rather than disease-specific, populations.Objectives: We sought to develop a novel composite strain index and measure its association with intensive care unit (ICU) admission decisions and hospital outcomes.Methods: Using more than 9.2 million acute care encounters from 27 Kaiser Permanente Northern California and Penn Medicine hospitals from 2013 to 2018, we deployed multivariable ridge logistic regression to develop a composite strain index based on hourly measurements of 22 capacity-strain metrics across emergency departments, wards, step-down units, and ICUs. We measured the association of this strain index with ICU admission and clinical outcomes using multivariable logistic and quantile regression.Results: Among high-acuity patients with sepsis (n = 90,150) and acute respiratory failure (ARF; n = 45,339) not requiring mechanical ventilation or vasopressors, strain at the time of emergency department disposition decision was inversely associated with the probability of ICU admission (sepsis: adjusted probability ranging from 29.0% [95% confidence interval, 28.0-30.0%] at the lowest strain index decile to 9.3% [8.7-9.9%] at the highest strain index decile; ARF: adjusted probability ranging from 47.2% [45.6-48.9%] at the lowest strain index decile to 12.1% [11.0-13.2%] at the highest strain index decile; P < 0.001 at all deciles). Among subgroups of patients who almost always or never went to the ICU, strain was not associated with hospital length of stay, mortality, or discharge disposition (all P ≥ 0.13). Strain was also not meaningfully associated with patient characteristics.Conclusions: Hospital strain, measured by a novel composite strain index, is strongly associated with ICU admission among patients with sepsis and/or ARF. This strain index fulfills the assumptions of a strong within-hospital instrumental variable for quantifying the net benefit of admission to the ICU for patients with sepsis and/or ARF.

Locally Informed Simulation to Predict Hospital Capacity Needs During the COVID-19 Pandemic

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic challenges hospital leaders to make time-sensitive, critical decisions about clinical operations and resource allocations.

OBJECTIVE

To estimate the timing of surges in clinical demand and the best- and worst-case scenarios of local COVID-19-induced strain on hospital capacity, and thus inform clinical operations and staffing demands and identify when hospital capacity would be saturated.

DESIGN

Monte Carlo simulation instantiation of a susceptible, infected, removed (SIR) model with a 1-day cycle.

SETTING

3 hospitals in an academic health system.

PATIENTS

All people living in the greater Philadelphia region.

MEASUREMENTS

The COVID-19 Hospital Impact Model (CHIME) (http://penn-chime.phl.io) SIR model was used to estimate the time from 23 March 2020 until hospital capacity would probably be exceeded, and the intensity of the surge, including for intensive care unit (ICU) beds and ventilators.

RESULTS

Using patients with COVID-19 alone, CHIME estimated that it would be 31 to 53 days before demand exceeds existing hospital capacity. In best- and worst-case scenarios of surges in the number of patients with COVID-19, the needed total capacity for hospital beds would reach 3131 to 12 650 across the 3 hospitals, including 338 to 1608 ICU beds and 118 to 599 ventilators.

LIMITATIONS

Model parameters were taken directly or derived from published data across heterogeneous populations and practice environments and from the health system's historical data. CHIME does not incorporate more transition states to model infection severity, social networks to model transmission dynamics, or geographic information to account for spatial patterns of human interaction.

CONCLUSION

Publicly available and designed for hospital operations leaders, this modeling tool can inform preparations for capacity strain during the early days of a pandemic.

PRIMARY FUNDING SOURCE

University of Pennsylvania Health System and the Palliative and Advanced Illness Research Center.

A Conceptual and Adaptable Approach to Hospital Preparedness for Acute Surge Events Due to Emerging Infectious Diseases

At the time this article was written, the World Health Organization had declared a global pandemic due to the novel coronavirus disease 2019, the first pandemic since 2009 H1N1 influenza A. Emerging respiratory pathogens are a common trigger of acute surge events—the extreme end of the healthcare capacity strain spectrum in which there is a dramatic increase in care demands and/or decreases in care resources that trigger deviations from normal care delivery processes, reliance on contingencies and external resources, and, in the most extreme cases, nonroutine decisions about resource allocation. This article provides as follows: 1) a conceptual introduction and approach to healthcare capacity strain including the etiologies of patient volume, patient acuity, special patient care demands, and resource reduction; 2) a framework for considering key resources during an acute surge event—the “four Ss” of preparedness: space (beds), staff (clinicians and operations), stuff (physical equipment), and system (coordination); and 3) an adaptable approach to and discussion of the most common domains that should be addressed during preparation for and response to acute surge events, with an eye toward combating novel respiratory viral pathogens.