Intermediate care to intensive care triage: A quality improvement project to reduce mortality

Creation and Expansion of a Mixed Patient Intermediate Care Unit to Improve ICU Capacity

OBJECTIVES

ICU capacity strain is associated with worsened outcomes. Intermediate care units (IMCs) comprise one potential option to offload ICUs while providing appropriate care for intermediate acuity patients, but their impact on ICU capacity has not been thoroughly characterized. The aims of this study are to describe the creation of a medical-surgical IMC and assess how the IMC affected ICU capacity.

DESIGN

Descriptive report with retrospective cohort review.

SETTING

Six hundred seventy-three-bed tertiary care academic medical center with 77 ICU beds.

PATIENTS

Adult inpatients who were admitted to the IMC.

INTERVENTIONS

An interdisciplinary working group created an IMC which was located on a general ward. The IMC was staffed by hospitalists and surgeons and supported by critical care consultants. The initial maximum census was three, but this number increased to six in response to heightened critical care demand. IMC admission criteria also expanded to include advanced noninvasive respiratory support defined as patients requiring high-flow nasal cannula, noninvasive positive pressure ventilation, or mechanical ventilation in patients with tracheostomies.

MEASUREMENTS AND MAIN RESULTS

The primary outcome entailed the number of ICU bed-days saved. Adverse outcomes, including ICU transfer, intubation, and death, were also recorded. From August 2021 to July 2022, 230 patients were admitted to the IMC. The most frequent IMC indications were respiratory support for medical patients and post-operative care for surgical patients. A total of 1023 ICU bed-days were made available. Most patients were discharged from the IMC to a general ward, while 8% of all patients required transfer to an ICU within 48 hours of admission. Intubation (2%) and death (1%) occurred infrequently within 48 hours of admission. Respiratory support was the indication associated with the most ICU transfers.

CONCLUSIONS

Despite a modest daily census, an IMC generated substantial ICU bed capacity during a time of peak critical care demand.

The Admission Checklist: The key steps and responsibilities for the admitting resident

The process of admitting patients from the emergency department to the general medicine floor is foundational to the medical training process and medical practice more generally. Yet this process is rife with potential error if not approached systematically, and residents rarely receive explicit teaching in this area. The creation of an “Admission Checklist” proposed by the authors could serve the function of reducing error and enhancing inter-provider communication throughout this process. Such a checklist could improve trainee experience and education, and ultimately allow for improved outcomes for patients during transitions of care.

Outcome Differences Between Direct Admissions to the PICU From ED and Escalations From Floor

OBJECTIVES

To compare the outcomes (mortality and ICU length of stay) of patients with direct admissions to the PICU from the emergency department [ED]) versus as an escalation of care from the floor.

METHODS

A retrospective cohort study with a secondary analysis of registry data. Patient demographics and outcome variables collected from January 1, 2015, to December 31, 2019, were obtained from the Virtual Pediatric Systems database. Patients with a source of admission other than the hospital's ED or pediatric floor were excluded. Multivariable regression analysis controlling for age groups, sex, race, diagnostic categories, and severity of illness (Pediatric Index of Mortality III), with clustering for sites, was performed.

RESULTS

A total of 209 695 patients from 121 sites were included in the analysis. A total of 154 716 (73.7%) were admitted directly from the ED, and 54 979 were admitted (26.2%) as an escalation of care from the floor. Two groups differed in age and race distribution, medical complexity, diagnostic categories, and severity of illness. After controlling for measured confounders, patients with floor escalations had higher mortality (2.78% vs 1.95%; P < .001), with an odds ratio of 1.71 (95% CI 1.5 to 1.9) and longer PICU length of stay (4.9 vs 3.6 days; P < .001). The rates of most of the common ICU procedures and their durations were also significantly higher in patients with an escalation of care.

CONCLUSIONS

Compared with direct admissions to the PICU from the ED, patients who were initially triaged to the pediatric floor and then require escalation to the PICU have worse outcomes. Further research is needed to explore the potential causes of this difference.

Reduction of early inpatient transfers and rapid response team calls after implementation of an emergency department intake huddle process

Patients admitted to the hospital and requiring a subsequent transfer to a higher level of care have increased morbidity, mortality and length of stay compared with patients who do not require a transfer during their hospital stay. We identified that a high number of patients admitted to our intermediate care (IMC) unit required a rapid response team (RRT) call and an early (<24 hours) transfer to the intensive care unit (ICU). A quality improvement project was initiated with the goal to reduce subsequent early transfers to the ICU and RRT calls. We started by focusing on IMC patients, implementing acuity-based nursing assignments and standardised daily nursing rounds in the IMC aiming to reduce early patient transfers to the ICU. Then, we expanded to all patients admitted to a hospital medical unit from the emergency department (ED), targeting patients with gastrointestinal (GI) bleed and sepsis who were at a higher risk for early transfer to the ICU. We then created an ED intake huddle process that over time was refined to target patients with SIRS criteria with an elevated serum lactic acid level greater than 2.0 mmol/L or a GI bleed with a haematocrit value less than 24%. These interventions resulted in an 10.8 percentage points (31.7% (225/710) to 20.9% (369/1764)) decrease in the early transfers to the ICU for all hospital medicine patients admitted to the hospital from the ED. Mean RRT calls/day decreased by 17%, from 3.0 mean calls/day preintervention to 2.5 mean calls/day postintervention. These quality improvement initiatives have sustained successful outcomes for over 6 years due to integrating enhanced team communication as organisational cultural norm that has become the standard.

Performance of Critical Care Outcome Prediction Models in an Intermediate Care Unit

BACKGROUND

Intermediate care units (IMCUs) are heterogeneous in design and operation, which makes comparative effectiveness studies challenging. A generalizable outcome prediction model could improve such comparisons. However, little is known about the performance of critical care outcome prediction models in the intermediate care setting. The purpose of this study is to evaluate the performance of the Acute Physiology and Chronic Health Evaluation version II (APACHE II), Simplified Acute Physiology Score version II (SAPS II) and version 3 (SAPS 3), and Mortality Probability Model version III (MPM₀III) in patients admitted to a well-characterized IMCU.

MATERIALS AND METHODS

In the IMCU of an academic medical center (July to December 2012), the discrimination and calibration of each outcome prediction model were evaluated using the area under the receiver-operating characteristic and Hosmer-Lemeshow goodness-of-fit test, respectively. Standardized mortality ratios (SMRs) were also calculated.

RESULTS

The cohort included data from 628 unique IMCU admissions with an inpatient mortality rate of 8.3%. All models exhibited good discrimination, but only the SAPS II and MPM₀III were well calibrated. While the APACHE II and SAPS 3 both markedly overestimated mortality, the SMR for the SAPS II and MPM₀III were 0.91 and 0.91, respectively.

CONCLUSIONS

The SAPS II and MPM₀III exhibited good discrimination and calibration, with slight overestimation of mortality. Each model should be further evaluated in multicenter studies of patients in the intermediate care setting.

Impact of Telemedicine on Mortality, Length of Stay, and Cost Among Patients in Progressive Care Units: Experience From a Large Healthcare System

Objectives:

To determine whether Telemedicine intervention can affect hospital mortality, length of stay, and direct costs for progressive care unit patients.

Design:

Retrospective observational.

Setting:

Large healthcare system in Florida.

Patients:

Adult patients admitted to progressive care unit (PCU) as their primary admission between December 2011 and August 2016 (n = 16,091).

Interventions:

Progressive care unit patients with telemedicine intervention (telemedicine PCU [TPCU]; n = 8091) and without telemedicine control (nontelemedicine PCU [NTPCU]; n = 8000) were compared concurrently during study period.

Measurements and Main Results:

Primary outcome was progressive care unit and hospital mortality. Secondary outcomes were hospital length of stay, progressive care unit length of stay, and mean direct costs. The mean age NTPCU and TPCU patients were 63.4 years (95% CI, 62.9–63.8 yr) and 71.1 years (95% CI, 70.7–71.4 yr), respectively. All Patient Refined-Diagnosis Related Group Disease Severity (p < 0.0001) and All Patient Refined-Diagnosis Related Group patient Risk of Mortality (p < 0.0001) scores were significantly higher among TPCU versus NTPCU. After adjusting for age, sex, race, disease severity, risk of mortality, hospital entity, and organ systems, TPCU survival benefit was 20%. Mean progressive care unit length of stay was lower among TPCU compared with NTPCU (2.6 vs 3.2 d; p < 0.0001). Postprogressive care unit hospital length of stay was longer for TPCU patients, compared with NTPCU (7.3 vs 6.8 d; p < 0.0001). The overall mean direct cost was higher for TPCU ($13,180), compared with NTPCU ($12,301; p < 0.0001).

Conclusions:

Although there are many studies about the effects of telemedicine in ICU, currently there are no studies on the effects of telemedicine in progressive care unit settings. Our study showed that TPCU intervention significantly decreased mortality in progressive care unit and hospital and progressive care unit length of stay despite the fact patients in TPCU were older and had higher disease severity, and risk of mortality. Increased postprogressive care unit hospital length of stay and total mean direct costs inclusive of telemedicine costs coincided with improved survival rates. Telemedicine intervention decreased overall mortality and length of stay within progressive care units without substantial cost incurrences.

Use of Systems Engineering to Design a Hospital Command Center

BACKGROUND

In hospitals and health systems across the country, patient flow bottlenecks delay care delivery-emergency department boarding and operating room exit holds are familiar examples. In other industries, such as oil, gas, and air traffic control, command centers proactively manage flow through complex systems.

METHODS

A systems engineering approach was used to analyze and maximize existing capacity in one health system, which led to the creation of the Judy Reitz Capacity Command Center. This article describes the key elements of this novel health system command center, which include strategic colocation of teams, automated visual displays of real-time data providing a global view, predictive analytics, standard work and rules-based protocols, and a clear chain of command and guiding tenets. Preliminary data are also shared.

RESULTS

With proactive capacity management, subcycle times decreased and allowed the health system's flagship hospital to increase occupancy from 85% to 92% while decreasing patient delays.

CONCLUSION

The command center was built with three primary goals-reducing emergency department boarding, eliminating operating room holds, and facilitating transfers in from outside facilities-but the command center infrastructure has the potential to improve hospital operations in many other areas.

An Electronic Dashboard to Monitor Patient Flow at the Johns Hopkins Hospital: Communication of Key Performance Indicators Using the Donabedian Model

Efforts to monitoring and managing hospital capacity depend on the ability to extract relevant time-stamped data from electronic medical records and other information technologies. However, the various characterizations of patient flow, cohort decisions, sub-processes, and the diverse stakeholders requiring data visibility create further overlying complexity. We use the Donabedian model to prioritize patient flow metrics and build an electronic dashboard for enabling communication. Ten metrics were identified as key indicators including outcome (length of stay, 30-day readmission, operating room exit delays, capacity-related diversions), process (timely inpatient unit discharge, emergency department disposition), and structural metrics (occupancy, discharge volume, boarding, bed assignation duration). Dashboard users provided real-life examples of how the tool is assisting capacity improvement efforts, and user traffic data revealed an uptrend in dashboard utilization from May to October 2017 (26 to 148 views per month, respectively). Our main contributions are twofold. The former being the results and methods for selecting key performance indicators for a unit, department, and across the entire hospital (i.e., separating signal from noise). The latter being an electronic dashboard deployed and used at The Johns Hopkins Hospital to visualize these ten metrics and communicate systematically to hospital stakeholders. Integration of diverse information technology may create further opportunities for improved hospital capacity.