The role of stepdown beds in hospital care

A Conceptual Framework for Improving Critical Care Patient Flow and Bed Use

RATIONALE

High demand for intensive care unit (ICU) services and limited bed availability have prompted hospitals to address capacity planning challenges. Simulation modeling can examine ICU bed assignment policies, accounting for patient acuity, to reduce ICU admission delays.

OBJECTIVES

To provide a framework for data-driven modeling of ICU patient flow, identify key measurable outcomes, and present illustrative analysis demonstrating the impact of various bed allocation scenarios on outcomes.

METHODS

A description of key inputs for constructing a queuing model was outlined, and an illustrative simulation model was developed to reflect current triage protocol within the medical ICU and step-down unit (SDU) at a single tertiary-care hospital. Patient acuity, arrival rate, and unit length of stay, consisting of a "service time" and "time to transfer," were estimated from 12 months of retrospective data (n = 2,710 adult patients) for 36 ICU and 15 SDU staffed beds. Patient priority was based on acuity and whether the patient originated in the emergency department. The model simulated the following hypothetical scenarios: (1) varied ICU/SDU sizes, (2) reserved ICU beds as a triage strategy, (3) lower targets for time to transfer out of the ICU, and (4) ICU expansion by up to four beds. Outcomes included ICU admission wait times and unit occupancy.

MEASUREMENTS AND MAIN RESULTS

With current bed allocation, simulated wait time averaged 1.13 (SD, 1.39) hours. Reallocating all SDU beds as ICU decreased overall wait times by 7.2% to 1.06 (SD, 1.39) hours and increased bed occupancy from 80 to 84%. Reserving the last available bed for acute patients reduced wait times for acute patients from 0.84 (SD, 1.12) to 0.31 (SD, 0.30) hours, but tripled subacute patients' wait times from 1.39 (SD, 1.81) to 4.27 (SD, 5.44) hours. Setting transfer times to wards for all ICU/SDU patients to 1 hour decreased wait times for incoming ICU patients, comparable to building one to two additional ICU beds.

CONCLUSIONS

Hospital queuing and simulation modeling with empiric data inputs can evaluate how changes in ICU bed assignment could impact unit occupancy levels and patient wait times. Trade-offs associated with dedicating resources for acute patients versus expanding capacity for all patients can be examined.

Critical Care Medicine Beds, Use, Occupancy, and Costs in the United States: A Methodological Review

This article is a methodological review to help the intensivist gain insights into the classic and sometimes arcane maze of national databases and methodologies used to determine and analyze the ICU bed supply, use, occupancy, and costs in the United States. Data for total ICU beds, use, and occupancy can be derived from two large national healthcare databases: the Healthcare Cost Report Information System maintained by the federal Centers for Medicare and Medicaid Services and the proprietary Hospital Statistics of the American Hospital Association. Two costing methodologies can be used to calculate U.S. ICU costs: the Russell equation and national projections. Both methods are based on cost and use data from the national hospital datasets or from defined groups of hospitals or patients. At the national level, an understanding of U.S. ICU bed supply, use, occupancy, and costs helps provide clarity to the width and scope of the critical care medicine enterprise within the U.S. healthcare system. This review will also help the intensivist better understand published studies on administrative topics related to critical care medicine and be better prepared to participate in their own local hospital organizations or regional critical care medicine programs.

Rising Billing for Intermediate Intensive Care among Hospitalized Medicare Beneficiaries between 1996 and 2010

RATIONALE

Intermediate care (i.e., step-down or progressive care) is an alternative to the intensive care unit (ICU) for patients with moderate severity of illness. The adoption and current use of intermediate care is unknown.

OBJECTIVES

To characterize trends in intermediate care use among U.S. hospitals.

METHODS

We examined 135 million acute care hospitalizations among elderly individuals (≥65 yr) enrolled in fee-for-service Medicare (U.S. federal health insurance program) from 1996 to 2010. We identified patients receiving intermediate care as those with intensive care or coronary care room and board charges labeled intermediate ICU.

MEASUREMENTS AND MAIN RESULTS

In 1996, a total of 960 of the 3,425 hospitals providing critical care billed for intermediate care (28%), and this increased to 1,643 of 2,783 hospitals (59%) in 2010 (P < 0.01). Only 8.2% of Medicare hospitalizations in 1996 were billed for intermediate care, but billing steadily increased to 22.8% by 2010 (P < 0.01), whereas the percentage billed for ICU care and ward-only care declined. Patients billed for intermediate care had more acute organ failures diagnoses codes compared with general ward patients (22.4% vs. 15.8%). When compared with patients billed for ICU care, those billed for intermediate care had fewer organ failures (22.4% vs. 43.4%), less mechanical ventilation (0.9% vs. 16.7%), lower mean Medicare spending ($8,514 vs. $18,150), and lower 30-day mortality (5.6% vs. 16.5%) (P < 0.01 for all comparisons).

CONCLUSIONS

Intermediate care billing increased markedly between 1996 and 2010. These findings highlight the need to better define the value, specific practices, and effective use of intermediate care for patients and hospitals.

The effect of physician staffing model on patient outcomes in a medical progressive care unit

PURPOSE

Although evidence supports the impact of intensivist physician staffing in improving intensive care unit (ICU) outcomes, the optimal coverage for progressive care units (PCU) is unknown. We sought to determine how physician staffing models influence outcomes for intermediate care patients.

MATERIALS AND METHODS

We conducted a retrospective observational comparison of patients admitted to the medical PCU of an academic hospital during 12-month periods of high-intensity and low-intensity staffing.

RESULTS

A total of 318 PCU patients were eligible for inclusion (143 high-intensity and 175 low-intensity). We found that low-intensity patients were more often stepped up from the emergency department and floor, whereas high-intensity patients were ICU transfers (61% vs 42%, P = .001). However, Mortality Probability Model scoring was similar between the 2 groups. In adjusted analysis, there was no association between intensity of staffing and hospital mortality (odds ratio, 0.84; 95% confidence interval, 0.36-1.99; P = .69) or PCU mortality (odds ratio, 0.96; 95% confidence interval, 0.38-2.45; P = .69). There was also no difference in subsequent ICU admission rates or in PCU length of stay.

CONCLUSIONS

We found no evidence that high-intensity intensivist physician staffing improves outcomes for intermediate care patients. In a strained critical care system, our study raises questions about the role of the intensivist in the graded care options between intensive and conventional ward care.

Epidemiology of admissions to 11 stand-alone high-dependency care units in the UK

PURPOSE

High-dependency care units (HDUs) are a focus of research to optimize critical care resource allocation. HDUs provide a level of care between the general ward and the intensive care unit (ICU). However, few data report on the case mix and outcomes of patients in these units.

METHODS

Retrospective observational cohort study of patients admitted to 11 stand-alone HDUs in the UK from 2008 to 2011. We stratified patients by location prior to HDU admission and location on discharge from HDU, and we summarized the case mix, transitions of care, and mortality.

RESULTS

Of 9008 patients admitted to 11 stand-alone HDUs, 56.5% were male and the mean age was 62.7 ± 17.9 years. The majority of patients admitted to HDUs were non-surgical (59.3%), with 22.4 and 20.1% admitted from the ICU and general ward, respectively; 41.3% were admitted from the operating room or recovery suite. The median length of stay in HDU was 1.8 days (IQR 0.9-3.5) and in-HDU mortality was 5.1%. Among HDU survivors (n = 8551), 8.5% were discharged to an ICU, 80.9% to a general ward, and 10.6% to other care areas. For patients admitted to HDU from an ICU, only 5.8% were readmitted to ICU. Hospital mortality for the HDU population was 14.8%; for patients discharged to an ICU, hospital mortality was 43.6%.

CONCLUSIONS

In a sample of 11 stand-alone HDUs in the UK, patients are from many different hospital locations. Hospital mortality for patients requiring HDU care is high, particularly for patients who require transfer to an ICU.

Critical care bed growth in the United States. A comparison of regional and national trends

RATIONALE

Although the number of intensive care unit (ICU) beds in the United States is increasing, it is unknown whether this trend is consistent across all regions.

OBJECTIVES

We sought to better characterize regional variation in ICU bed changes over time and identify regional characteristics associated with these changes.

METHODS

We used data from the Centers for Medicare and Medicaid Services and the U.S. Census to summarize the numbers of hospitals, hospital beds, ICU beds, and ICU occupancy at the level of Dartmouth Atlas hospital referral region from 2000 to 2009. We categorized regions into quartiles of bed change over the study interval and examined the relationship between change categories, regional characteristics, and population characteristics over time.

MEASUREMENTS AND MAIN RESULTS

From 2000 to 2009 the national number of ICU beds increased 15%, from 67,579 to 77,809, mirroring population. However, there was substantial regional variation in absolute changes (median, +16 ICU beds; interquartile range, -3 to +51) and population-adjusted changes (median, +0.9 ICU beds per 100,000; interquartile range, -3.8 to +5.9), with 25.0% of regions accounting for 74.8% of overall growth. At baseline, regions with increasing numbers of ICU beds had larger populations, lower ICU beds per 100,000 capita, higher average ICU occupancy, and greater market competition as measured by the Herfindahl-Hirschman Index (P < 0.001 for all comparisons).

CONCLUSIONS

National trends in ICU bed growth are not uniformly reflected at the regional level, with most growth occurring in a small number of highly populated regions.

The impact of the organization of high-dependency care on acute hospital mortality and patient flow for critically ill patients

RATIONALE

Little is known about the utility of provision of high-dependency care (HDC) that is in a geographically separate location from a primary intensive care unit (ICU).

OBJECTIVES

To determine whether the availability of HDC in a geographically separate unit affects patient flow or mortality for critically ill patients.

METHODS

Admissions to ICUs in the United Kingdom, from 2009 to 2011, who received Level 3 intensive care in the first 24 hours after admission and subsequently Level 2 HDC. We compared differences in patient flow and outcomes for patients treated in hospitals providing some HDC in a geographically separate unit (dual HDC) with patients treated in hospitals providing all HDC in the same unit as intensive care (integrated HDC) using multilevel mixed effects models.

MEASUREMENTS AND MAIN RESULTS

In 192 adult general ICUs, 21.4% provided dual HDC. Acute hospital mortality was no different for patients cared for in ICUs with dual HDC versus those with integrated HDC (adjusted odds ratio, 0.94 [0.86-1.03]; P = 0.16). Dual HDC was associated with a decreased likelihood of a delayed discharge from the primary unit. However, total duration of critical care and the likelihood of discharge from the primary unit at night were increased with dual HDC.

CONCLUSIONS

Availability of HDC in a geographically separate unit does not impact acute hospital mortality. The potential benefit of decreasing delays in discharge should be weighed against the increased total duration of critical care and greater likelihood of a transfer out of the primary unit at night.

Does intermediate care improve patient outcomes or reduce costs?

ICUs are an essential but expensive part of all modern hospitals. With increasingly limited healthcare funding, methods to reduce expenditure without negatively influencing patient outcomes are, therefore, of interest. One possible solution has been the development of ‘intermediate care units’, which provide more intensive monitoring and patient management with higher nurse:patient ratios than the general ward but less than is offered in the ICU. However, although such units have been introduced in many hospitals, there is relatively little published, especially prospective, evidence to support the benefits of this approach on costs or patient outcomes. We review the available data and suggest that, where possible, a larger unit with combined intermediate care and intensive care beds in one location may be preferable in terms of greater flexibility and efficiency.