The increasing impact of length of stay "outliers" on length of stay at an urban academic hospital

Effects of a medical admission unit on in-hospital patient flow and clinical outcomes

BACKGROUND

the burden of acute complex patients, increasingly older and poli-pathological, accessing to Emergency Departments (ED) leads up hospital overcrowding and the outlying phenomenon. These issues highlight the need for new adequate patients' management strategies. The aim of this study is to analyse the effects on in-hospital patient flow and clinical outcomes of a high-technology and time-limited Medical Admission Unit (MAU) run by internists.

METHODS

all consecutive patients admitted to MAU from Dec-2017 to Nov-2019 were included in the study. The admissions number from ED and hospitalization rate, the overall in-hospital mortality rate in medical department, the total days of hospitalization and the overall outliers bed days were compared to those from the previous two years.

RESULTS

2162 patients were admitted in MAU, 2085(95.6%) from ED, 476(22.0%) were directly discharged, 88(4.1%) died and 1598(73.9%) were transferred to other wards, with a median in-MAU time of stay of 64.5 [0.2-344.2] hours. Comparing the 24 months before, despite the increase in admissions/year from ED in medical department (3842 ± 106 in Dec2015-Nov2017 vs 4062 ± 100 in Dec2017-Nov2019, p<0.001), the number of the outlier bed days has been reduced, especially in surgical department (11.46 ± 6.25% in Dec2015-Nov2017 vs 6.39 ± 3.08% in Dec2017-Nov2019, p=0.001), and mortality in medical area has dropped from 8.74 ± 0.37% to 7.29 ± 0.57%, p<0.001.

CONCLUSIONS

over two years, a patient-centred and problem-oriented approach in a medical admission buffer unit run by internists has ensured a constant flow of acute patients with positive effects on clinical risk and quality of care reducing medical outliers and in-hospital mortality.

Addressing the Knowledge Deficit in Hospital Bed Planning and Defining an Optimum Region for the Number of Different Types of Hospital Beds in an Effective Health Care System

Based upon 30-years of research by the author, a new approach to hospital bed planning and international benchmarking is proposed. The number of hospital beds per 1000 people is commonly used to compare international bed numbers. This method is flawed because it does not consider population age structure or the effect of nearness-to-death on hospital utilization. Deaths are also serving as a proxy for wider bed demand arising from undetected outbreaks of 3000 species of human pathogens. To remedy this problem, a new approach to bed modeling has been developed that plots beds per 1000 deaths against deaths per 1000 population. Lines of equivalence can be drawn on the plot to delineate countries with a higher or lower bed supply. This method is extended to attempt to define the optimum region for bed supply in an effective health care system. England is used as an example of a health system descending into operational chaos due to too few beds and manpower. The former Soviet bloc countries represent a health system overly dependent on hospital beds. Several countries also show evidence of overutilization of hospital beds. The new method is used to define a potential range for bed supply and manpower where the most effective health systems currently reside. The method is applied to total curative beds, medical beds, psychiatric beds, critical care, geriatric care, etc., and can also be used to compare different types of healthcare staff, i.e., nurses, physicians, and surgeons. Issues surrounding the optimum hospital size and the optimum average occupancy will also be discussed. The role of poor policy in the English NHS is used to show how the NHS has been led into a bed crisis. The method is also extended beyond international benchmarking to illustrate how it can be applied at a local or regional level in the process of long-term bed planning. Issues regarding the volatility in hospital admissions are also addressed to explain the need for surge capacity and why an adequate average bed occupancy margin is required for an optimally functioning hospital.

Medically ready for discharge: A multisite "point-in-time" assessment of hospitalized patients

BACKGROUND

Time spent awaiting discharge after the acute need for hospitalization has resolved is an important potential contributor to hospital length of stay (LOS).

OBJECTIVE

To measure the prevalence, impact, and context of patients who remain hospitalized for prolonged periods after completion of acute care needs.

DESIGN, SETTING, AND PARTICIPANTS

We conducted a cross-sectional "point-in-time" survey at each of 15 academic US hospitals using a structured data collection tool with on-service acute care medicine attending physicians in fall 2022.

MAIN OUTCOMES AND MEASURES

Primary outcomes were number and percentage of patients considered "medically ready for discharge" with emphasis on those who had experienced a "major barrier to discharge" (medically ready for discharge for ≥1 week). Estimated LOS attributable to major discharge barriers, contributory discharge needs, and associated hospital characteristics were measured.

RESULTS

Of 1928 patients sampled, 35.0% (n = 674) were medically ready for discharge including 9.8% (n = 189) with major discharge barriers. Many patients with major discharge barriers (44.4%; 84/189) had spent a month or longer medically ready for discharge and commonly (84.1%; 159/189) required some form of skilled therapy or daily living support services for discharge. Higher proportions of patients experiencing major discharge barriers were found in public versus private, nonprofit hospitals (12.0% vs. 7.2%; p = .001) and county versus noncounty hospitals (14.5% vs. 8.8%; p = .002).

CONCLUSIONS

Patients experience major discharge barriers in many US hospitals and spend prolonged time awaiting discharge, often for support needs that may be outside of clinician control.

Hospital length of stay: A cross-specialty analysis and Beta-geometric model

BACKGROUND

The typical hospital Length of Stay (LOS) distribution is known to be right-skewed, to vary considerably across Diagnosis Related Groups (DRGs), and to contain markedly high values, in significant proportions. These very long stays are often considered outliers, and thin-tailed statistical distributions are assumed. However, resource consumption and planning occur at the level of medical specialty departments covering multiple DRGs, and when considered at this decision-making scale, extreme LOS values represent a significant component of the distribution of LOS (the right tail) that determines many of its statistical properties.

OBJECTIVE

To build actionable statistical models of LOS for resource planning at the level of healthcare units.

METHODS

Through a study of 46, 364 electronic health records over four medical specialty departments (Pediatrics, Obstetrics/Gynecology, Surgery, and Rehabilitation Medicine) in the largest hospital in Thailand (Siriraj Hospital in Bangkok), we show that the distribution of LOS exhibits a tail behavior that is consistent with a subexponential distribution. We analyze some empirical properties of such a distribution that are of relevance to cost and resource planning, notably the concentration of resource consumption among a minority of admissions/patients, an increasing residual LOS, where the longer a patient has been admitted, the longer they would be expected to remain admitted, and a slow convergence of the Law of Large Numbers, making empirical estimates of moments (e.g. mean, variance) unreliable.

RESULTS

We propose a novel Beta-Geometric model that shows a good fit with observed data and reproduces these empirical properties of LOS. Finally, we use our findings to make practical recommendations regarding the pricing and management of LOS.

Development and validation of a multivariable preoperative prediction model for postoperative length of stay in a broad inpatient surgical population

BACKGROUND

Postoperative length of stay is a meaningful patient-centered outcome and an important determinant of healthcare costs. The Surgical Risk Preoperative Assessment System preoperatively predicts 12 postoperative adverse events using 8 preoperative variables, but its ability to predict postoperative length of stay has not been assessed. We aimed to determine whether the Surgical Risk Preoperative Assessment System variables could accurately predict postoperative length of stay up to 30 days in a broad inpatient surgical population.

METHODS

This was a retrospective analysis of the American College of Surgeons' National Surgical Quality Improvement Program adult database from 2012 to 2018. A model using the Surgical Risk Preoperative Assessment System variables and a 28-variable "full" model, incorporating all available American College of Surgeons' National Surgical Quality Improvement Program preoperative nonlaboratory variables, were fit to the analytical cohort (2012-2018) using multiple linear regression and compared using model performance metrics. Internal chronological validation of the Surgical Risk Preoperative Assessment System model was conducted using training (2012-2017) and test (2018) datasets.

RESULTS

We analyzed 3,295,028 procedures. The adjusted R2 for the Surgical Risk Preoperative Assessment System model fit to this cohort was 93.3% of that for the full model (0.347 vs 0.372). In the internal chronological validation of the Surgical Risk Preoperative Assessment System model, the adjusted R2 for the test dataset was 97.1% of that for the training dataset (0.3389 vs 0.3489).

CONCLUSION

The parsimonious Surgical Risk Preoperative Assessment System model can preoperatively predict postoperative length of stay up to 30 days for inpatient surgical procedures almost as accurately as a model using all 28 American College of Surgeons' National Surgical Quality Improvement Program preoperative nonlaboratory variables and has shown acceptable internal chronological validation.

Hospital Capacity Command Centers: A Benchmarking Survey on an Emerging Mechanism to Manage Patient Flow

BACKGROUND

Delayed hospital and emergency department (ED) patient throughput, which occurs when demand for inpatient care exceeds hospital capacity, is a critical threat to safety, quality, and hospital financial performance. In response, many hospitals are deploying capacity command centers (CCCs), which co-locate key work groups and aggregate real-time data to proactively manage patient flow. Only a narrow body of peer-reviewed articles have characterized CCCs to date. To equip health system leaders with initial insights into this emerging intervention, the authors sought to survey US health systems to benchmark CCC motivations, design, and key performance indicators.

METHODS

An online survey on CCC design and performance was administered to members of a hospital capacity management consortium, which included a convenience sample of capacity leaders at US health systems (N = 38). Responses were solicited through a targeted e-mail campaign. Results were summarized using descriptive statistics.

RESULTS

The response rate was 81.6% (31/38). Twenty-five respondents were operating CCCs, varying in scope (hospital, region of a health system, or entire health system) and number of beds managed. The most frequent motivation for CCC implementation was reducing ED boarding (n = 24). The most common functions embedded in CCCs were bed management (n = 25) and interhospital transfers (n = 25). Eighteen CCCs (72.0%) tracked financial return on investment (ROI); all reported positive ROI.

CONCLUSION

This survey addresses a gap in the literature by providing initial aggregate data for health system leaders to consider, plan, and benchmark CCCs. The researchers identify motivations for, functions in, and key performance indicators used to assess CCCs. Future research priorities are also proposed.

Inappropriate Hospital Admission According to Patient Intrinsic Risk Factors: an Epidemiological Approach

BACKGROUND

Inappropriate hospital admissions compromise the efficiency of the health care system. This work analyzes, for the first time, the prevalence of inappropriate admission and its association with clinical and epidemiological patient characteristics.

OBJECTIVES

To estimate the prevalence, associated risk factors, and economic impact of inappropriate hospital admissions.

DESIGN AND PARTICIPANTS

This was a cross-sectional observational study of all hospitalized patients in a high complexity hospital of over 901 beds capacity in Spain. The prevalence of inappropriate admission and its causes, the association of inappropriateness with patients' intrinsic risk factors (IRFs), and associated financial costs were analyzed with the Appropriateness Evaluation Protocol in a multivariate model.

MAIN MEASURES AND KEY RESULTS

A total of 593 patients were analyzed, and a prevalence of inappropriate admissions of 11.9% (95% CI: 9.5 to 14.9) was found. The highest number of IRFs for developing health care-related complications was associated with inappropriateness, which was more common among patients with 1 IRF (OR [95% CI]: 9.68 [3.6 to 26.2.] versus absence of IRFs) and among those with surgical admissions (OR [95% CI]: 1.89 [1.1 to 3.3] versus medical admissions). The prognosis of terminal disease reduced the risk (OR [95% CI]: 0.28 [0.1 to 0.9] versus a prognosis of full recovery based on baseline condition). Inappropriate admissions were responsible for 559 days of avoidable hospitalization, equivalent to €17,604.6 daily and €139,076.4 in total, mostly attributable to inappropriate emergency admissions (€96,805.3).

CONCLUSIONS

The prevalence of inappropriate admissions is similar to the incidence found in previous studies and is a useful indicator in monitoring this kind of overuse. Patients with a moderate number of comorbidities were subject to a higher level of inappropriateness. Inappropriate admission had a substantial and avoidable financial impact.

Analyzing Hospital High Length of Stay Outliers in Turkey

Purpose: The aim of this study is to examine length of stay (LOS) outliers by analyzing hospital administrative database. Material and Methods: The Turkish Ministry of Health DRG grouper database was utilized to obtain hospital administrative data on discharges for 15 training and research hospitals in 2012. For each diagnosis-related group (DRG), the geometric mean plus two standard deviations were calculated to identify outliers. Analyses were conducted using descriptive statistics and logistic regression using generalized estimating equations (GEE). Results: High LOS outliers found to be 4.4 % of the cases, they were responsible for 24.50 percent of all discharge days. Alcohol, drug use disorders, burns, and diseases of the ear, nose, mouth, and throat were the factors that had the greatest impact on high LOS outliers, according to the multivariate model. Conclusion: A quarter of all inpatient days are made up of LOS outliers. Burns, neonate cases, and alcohol/drug use issues should all be carefully evaluated. In order to improve clinical quality and effectively manage hospital resources, hospital administrators and health policy makers should take length of stay outliers into consideration.