Dataset Information

Can differences in length of stay between Dutch university hospitals and other hospitals be explained by patient characteristics? A cross-sectional study.

ABSTRACT:

Objectives

The indicator unexpectedly long length of stay (UL-LOS) is used to gain insight into quality and safety of care in hospitals. The calculation of UL-LOS takes patients' age, main diagnosis and main procedure into account. University hospitals have relatively more patients with a UL-LOS than other hospitals. Our main research question is whether the high number of patients with a UL-LOS in university hospitals is caused by differences in additional patient characteristics between university hospitals and other hospitals.

Design

We performed a cross-sectional study and used administrative data from 1 510 627 clinical admissions in 87 Dutch hospitals. Patients who died in hospital, stayed in hospital for 100 days or longer or whose country of residence was not the Netherlands were excluded from the UL-LOS indicator. We identified which patient groups were treated only in university hospitals or only in other hospitals and which were treated in both hospital types. For these last patient groups, we added supplementary patient characteristics to the current model to determine the effect on the UL-LOS model.

Results

Patient groups treated in both hospital types differed in terms of detailed primary diagnosis, socioeconomic status, source of admission, type of admission and amount of Charlson comorbidities. Nevertheless, when adding these characteristics to the current model, university hospitals still have a significantly higher mean UL-LOS score compared with other hospitals (p<0.001).

Conclusions

The difference in UL-LOS scores between both hospital types remains after adding patient characteristics in which both hospital types differ. We conclude that the high UL-LOS scores in university hospitals are not caused by the investigated additional patient characteristics that differ between university and other hospitals. Patients might stay relatively longer in university hospitals due to differences in work processes because of their education and research tasks or financing differences of both hospital types.

SUBMITTER: Ghielen J

PROVIDER: S-EPMC6398690 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Can differences in length of stay between Dutch university hospitals and other hospitals be explained by patient characteristics? A cross-sectional study.

Ghielen Janine J Cihangir Sezgin S Hekkert Karin K Borghans Ine I Kool Rudolf Bertijn RB

BMJ open 20190215 2

<h4>Objectives</h4>The indicator unexpectedly long length of stay (UL-LOS) is used to gain insight into quality and safety of care in hospitals. The calculation of UL-LOS takes patients' age, main diagnosis and main procedure into account. University hospitals have relatively more patients with a UL-LOS than other hospitals. Our main research question is whether the high number of patients with a UL-LOS in university hospitals is caused by differences in additional patient characteristics betwee ...[more]

PMID: 30772843

Similar Datasets

Project description:Retrospective cohort.To identify factors that were independently associated with increased postoperative length of stay (LOS) and readmission in patients who underwent elective laminectomy for lumbar spinal stenosis.Lumbar spinal stenosis is a common pathology that is traditionally treated with decompressive laminectomy. Risk factors associated with increased LOS and readmission have not been fully characterized for laminectomy.Patients who underwent laminectomy for lumbar spinal stenosis during 2011 and 2012 were identified from the American College of Surgeons National Surgical Quality Improvement Program database. Patient characteristics were tested for association with LOS and readmission using bivariate and multivariate analyses. Patients with LOS more than 10 days were excluded from the readmission analysis as the American College of Surgeons National Surgical Quality Improvement Program only captures readmissions within 30 postoperative days, and the window for potential readmission was deemed too short for patients staying longer than 10 days.A total of 2358 patients who underwent laminectomy met inclusion criteria. The average age was 66.4 ± 11.7 years (mean ± standard deviation). Average postoperative LOS was 2.1 ± 2.6 days. Of those meeting criteria for readmission analysis, 3.7% of patients (86 of 2339) were readmitted within 30 days postoperatively.Independent risk factors for prolonged LOS were increased age (P < 0.001), increased body mass index (P = 0.004), American Society of Anesthesiologists class 3-4 (P = 0.005), and preoperative hematocrit less than 36.0 (P = 0.001). Independent risk factors for readmission were increased age (P = 0.013), increased body mass index (P = 0.040), American Society of Anesthesiologists class 3-4 (P < 0.001), and steroid use (P = 0.001). The most common reason for readmission was surgical site-related infections (25.0% of patients readmitted in 2012).The identified factors associated with LOS and readmission after lumbar laminectomy may be useful for optimizing patient care.

Project description:BackgroundHealthcare management faces complex challenges in allocating hospital resources, and predicting patients' length-of-stay (LOS) is critical in effectively managing those resources. This work aims to map approaches used to forecast the LOS of Pediatric Patients in Hospitals (LOS-P) and patients' populations and environments used to develop the models.MethodsUsing the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology, we performed a scoping review that identified 28 studies and analyzed them. The search was conducted on four databases (Science Direct, Scopus, Web of Science, and Medline). The identification of relevant studies was structured around three axes related to the research questions: (i) forecast models, (ii) hospital length-of-stay, and (iii) pediatric patients. Two authors carried out all stages to ensure the reliability of the review process. Articles that passed the initial screening had their data charted on a spreadsheet. Methods reported in the literature were classified according to the stage in which they are used in the modeling process: (i) pre-processing of data, (ii) variable selection, and (iii) cross-validation.ResultsForecasting models are most often applied to newborn patients and, consequently, in neonatal intensive care units. Regression analysis is the most widely used modeling approach; techniques associated with Machine Learning are still incipient and primarily used in emergency departments to model patients in specific situations.ConclusionsThe studies' main benefits include informing family members about the patient's expected discharge date and enabling hospital resources' allocation and planning. Main research gaps are associated with the lack of generalization of forecasting models and limited reported applicability in hospital management. This study also provides a practical guide to LOS-P forecasting methods and a future research agenda.

Project description:BackgroundPredicting bed occupancy for hospitalised patients with COVID-19 requires understanding of length of stay (LoS) in particular bed types. LoS can vary depending on the patient's "bed pathway" - the sequence of transfers of individual patients between bed types during a hospital stay. In this study, we characterise these pathways, and their impact on predicted hospital bed occupancy.MethodsWe obtained data from University College Hospital (UCH) and the ISARIC4C COVID-19 Clinical Information Network (CO-CIN) on hospitalised patients with COVID-19 who required care in general ward or critical care (CC) beds to determine possible bed pathways and LoS. We developed a discrete-time model to examine the implications of using either bed pathways or only average LoS by bed type to forecast bed occupancy. We compared model-predicted bed occupancy to publicly available bed occupancy data on COVID-19 in England between March and August 2020.ResultsIn both the UCH and CO-CIN datasets, 82% of hospitalised patients with COVID-19 only received care in general ward beds. We identified four other bed pathways, present in both datasets: "Ward, CC, Ward", "Ward, CC", "CC" and "CC, Ward". Mean LoS varied by bed type, pathway, and dataset, between 1.78 and 13.53 days. For UCH, we found that using bed pathways improved the accuracy of bed occupancy predictions, while only using an average LoS for each bed type underestimated true bed occupancy. However, using the CO-CIN LoS dataset we were not able to replicate past data on bed occupancy in England, suggesting regional LoS heterogeneities.ConclusionsWe identified five bed pathways, with substantial variation in LoS by bed type, pathway, and geography. This might be caused by local differences in patient characteristics, clinical care strategies, or resource availability, and suggests that national LoS averages may not be appropriate for local forecasts of bed occupancy for COVID-19.Trial registrationThe ISARIC WHO CCP-UK study ISRCTN66726260 was retrospectively registered on 21/04/2020 and designated an Urgent Public Health Research Study by NIHR.

Project description:BackgroundResearch showed that promising approaches such as benchmarking, operations research, lean management and six sigma, could be adopted to improve patient logistics in healthcare. To our knowledge, little research has been conducted to obtain an overview on the use, combination and effects of approaches to improve patient logistics in hospitals. We therefore examined the approaches and tools used to improve patient logistics in Dutch hospitals, the reported effects of these approaches on performance, the applied support structure and the methods used to evaluate the effects.MethodsA survey among experts on patient logistics in 94 Dutch hospitals. The survey data were analysed using cross tables.ResultsForty-eight percent of all hospitals participated. Ninety-eight percent reported to have used multiple approaches, 39% of them used five or more approaches. Care pathways were the preferred approach by 43% of the hospitals, followed by business process re-engineering and lean six sigma (both 13%). Flowcharts were the most commonly used tool, they were used on a regular basis by 94% of the hospitals. Less than 10% of the hospitals used data envelopment analysis and critical path analysis on a regular basis. Most hospitals (68%) relied on external support for process analyses and education on patient logistics, only 24% had permanent internal training programs on patient logistics. Approximately 50% of the hospitals that evaluated the effects of approaches on efficiency, throughput times and financial results, reported that they had accomplished their goals. Goal accomplishment in general hospitals ranged from 63% to 67%, in academic teaching hospitals from 0% to 50%, and in teaching hospitals from 25% to 44%. More than 86% performed an evaluation, 53% performed a post-intervention measurement.ConclusionsPatient logistics appeared to be a rather new subject as most hospitals had not selected a single approach, they relied on external support and they did not have permanent training programs. Hospitals used a combination of approaches and tools, about half of the hospitals reported goal accomplishment and no approach seemed to outperform the others. To make improvement efforts more successful, research should be conducted into the selection and application of approaches, their contingency factors, and goal-setting procedures.

Dataset Information

Can differences in length of stay between Dutch university hospitals and other hospitals be explained by patient characteristics? A cross-sectional study.

Objectives

Design

Results

Conclusions

Publications

Can differences in length of stay between Dutch university hospitals and other hospitals be explained by patient characteristics? A cross-sectional study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets