Dataset Information

Drivers of hospital expenditure and length of stay in an academic medical centre: a retrospective cross-sectional study.

ABSTRACT: BACKGROUND:As healthcare expenditure and utilization continue to rise, understanding key drivers of hospital expenditure and utilization is crucial in policy development and service planning. This study aims to investigate micro drivers of hospital expenditure and length of stay (LOS) in an Academic Medical Centre. METHODS:Data corresponding to 285,767 patients and 207,426 inpatient visits was extracted from electronic medical records of the National University of Hospital in Singapore between 2005 to 2013. Generalized linear models and generalized estimating equations were employed to build patient and inpatient visit models respectively. The patient models provide insight on the factors affecting overall expenditure and LOS, whereas the inpatient visit models provide insight on how expenditure and LOS accumulate longitudinally. RESULTS:Although adjusted expenditure and LOS per inpatient visit were largely similar across socio-economic status (SES) groups, patients of lower SES groups accumulated greater expenditure and LOS over time due to more frequent visits. Admission to a ward class with greater government subsidies was associated with higher expenditure and LOS per inpatient visit. Inpatient death was also associated with higher expenditure per inpatient visit. Conditions that drove patient expenditure and LOS were largely similar, with mental illnesses affecting LOS to a larger extent. These observations on condition drivers largely held true at visit-level. CONCLUSIONS:The findings highlight the importance of distinguishing the drivers of patient expenditure and inpatient utilization at the patient-level from those at the visit-level. This allows better understanding of the drivers of healthcare utilization and how utilization accumulates longitudinally, important for health policy and service planning.

SUBMITTER: Rahman N

PROVIDER: S-EPMC6604431 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Drivers of hospital expenditure and length of stay in an academic medical centre: a retrospective cross-sectional study.

Rahman Nabilah N Ng Sheryl Hui-Xian SH Ramachandran Sravan S Wang Debby D DD Sridharan Srinath S Tan Chuen Seng CS Khoo Astrid A Tan Xin Quan XQ

BMC health services research 20190702 1

<h4>Background</h4>As healthcare expenditure and utilization continue to rise, understanding key drivers of hospital expenditure and utilization is crucial in policy development and service planning. This study aims to investigate micro drivers of hospital expenditure and length of stay (LOS) in an Academic Medical Centre.<h4>Methods</h4>Data corresponding to 285,767 patients and 207,426 inpatient visits was extracted from electronic medical records of the National University of Hospital in Sing ...[more]

PMID: 31266515

Similar Datasets

Project description:ObjectivesStudies in western healthcare settings suggest that preoperative anaemia is associated with poor outcomes after elective orthopaedic surgery. We investigated the prevalence of preoperative anaemia among patients with primary unilateral total knee arthroplasty (TKA) in Singapore and its association with length of hospital stay (LOS), perioperative blood transfusion and hospital readmission rates.MethodsRetrospective cohort study performed in a tertiary academic medical centre in Singapore, involving patients who underwent primary unilateral TKA between January 2013 and June 2014. Demographics, comorbidities, preoperative haemoglobin (Hb) level, LOS and 30-day readmission data were collected. Anaemia was classified according to WHO definition. Prolonged LOS was defined as more than 6 days, which corresponds to >75th centile LOS of the data.ResultsWe analysed 2394 patients. The prevalence of anaemia was 23.7%. 403 patients (16.8%) had mild anaemia and 164 patients (6.8%) had moderate to severe anaemia. Overall mean LOS was 5.4±4.8 days. Based on multivariate logistic regression, preoperative anaemia significantly increased LOS (mild anaemia, adjusted OR (aOR) 1.71, p<0.001; moderate/severe anaemia, aOR 2.29, p<0.001). Similar effects were seen when preoperative anaemia was defined by Hb level below 13 g/dL, regardless of gender. Transfusion proportionately increased prolonged LOS (1 unit: aOR 2.12, p=0.006; 2 or more units: aOR 6.71, p<0.001). Repeat operation during hospital stay, previous cerebrovascular accidents, general anaesthesia and age >70 years were associated with prolonged LOS. Our 30-day related readmission rate was 1.7% (42) cases.ConclusionAnaemia is common among patients undergoing elective TKA in Singapore and is independently associated with prolonged LOS and increased perioperative blood transfusion. We suggest measures to correct anaemia prior to surgery, including the use of non-gender-based Hb cut-off for establishing diagnosis.

Project description:BackgroundHospitalized patients are designated alternate level of care (ALC) when they no longer require hospitalization but discharge is delayed while they await alternate disposition or living arrangements. We assessed hospital costs and complications for general internal medicine (GIM) inpatients who had delayed discharge. In addition, we developed a clinical prediction rule to identify patients at risk for delayed discharge.MethodsWe conducted a retrospective cohort study of consecutive GIM patients admitted between 1 January 2015 and 1 January 2016 at a large tertiary care hospital in Canada. We compared hospital costs and complications between ALC and non-ALC patients. We derived a clinical prediction rule for ALC designation using a logistic regression model and validated its diagnostic properties.ResultsOf 4311 GIM admissions, 255 (6%) patients were designated ALC. Compared to non-ALC patients, ALC patients had longer median length of stay (30.85 vs. 3.95 days p < 0.0001), higher median hospital costs ($22,459 vs. $5003 p < 0.0001) and more complications in hospital (25.5% vs. 5.3% p < 0.0001) especially nosocomial infections (14.1% vs. 1.9% p < 0.0001). Sensitivity analyses using propensity score and pair matching yielded similar results. In a derivation cohort, seven significant risk factors for ALC were identified including age > =80 years, female sex, dementia, diabetes with complications as well as referrals to physiotherapy, occupational therapy and speech language pathology. A clinical prediction rule that assigned each of these predictors 1 point had likelihood ratios for ALC designation of 0.07, 0.25, 0.66, 1.48, 6.07, 17.13 and 21.85 for patients with 0, 1, 2, 3, 4, 5, and 6 points respectively in the validation cohort.ConclusionsDelayed discharge is associated with higher hospital costs and complication rates especially nosocomial infections. A clinical prediction rule can identify patients at risk for delayed discharge.

Project description:BackgroundPostoperative length of stay is a key indicator in the management of medical resources and an indirect predictor of the incidence of surgical complications and the degree of recovery of the patient after cancer surgery. Recently, machine learning has been used to predict complex medical outcomes, such as prolonged length of hospital stay, using extensive medical information.ObjectiveThe objective of this study was to develop a prediction model for prolonged length of stay after cancer surgery using a machine learning approach.MethodsIn our retrospective study, electronic health records (EHRs) from 42,751 patients who underwent primary surgery for 17 types of cancer between January 1, 2000, and December 31, 2017, were sourced from a single cancer center. The EHRs included numerous variables such as surgical factors, cancer factors, underlying diseases, functional laboratory assessments, general assessments, medications, and social factors. To predict prolonged length of stay after cancer surgery, we employed extreme gradient boosting classifier, multilayer perceptron, and logistic regression models. Prolonged postoperative length of stay for cancer was defined as bed-days of the group of patients who accounted for the top 50% of the distribution of bed-days by cancer type.ResultsIn the prediction of prolonged length of stay after cancer surgery, extreme gradient boosting classifier models demonstrated excellent performance for kidney and bladder cancer surgeries (area under the receiver operating characteristic curve [AUC] >0.85). A moderate performance (AUC 0.70-0.85) was observed for stomach, breast, colon, thyroid, prostate, cervix uteri, corpus uteri, and oral cancers. For stomach, breast, colon, thyroid, and lung cancers, with more than 4000 cases each, the extreme gradient boosting classifier model showed slightly better performance than the logistic regression model, although the logistic regression model also performed adequately. We identified risk variables for the prediction of prolonged postoperative length of stay for each type of cancer, and the importance of the variables differed depending on the cancer type. After we added operative time to the models trained on preoperative factors, the models generally outperformed the corresponding models using only preoperative variables.ConclusionsA machine learning approach using EHRs may improve the prediction of prolonged length of hospital stay after primary cancer surgery. This algorithm may help to provide a more effective allocation of medical resources in cancer surgery.

Project description:BACKGROUND:Failure in the timely follow-up of test results has been widely documented, contributing to delayed medical care. Yet, the impact of delay in reviewing test results on hospital length of stay (LOS) has not been studied. We examine the relationship between laboratory tests review time and hospital LOS. METHODS:A retrospective cohort study of inpatients admitted to a metropolitan teaching hospital in Sydney, Australia, between 2011 and 2012 (n = 5804). Generalized linear models were developed to examine the relationship between hospital LOS and cumulative clinician read time (CRT), defined as the time taken by clinicians to review laboratory test results performed during an inpatient stay after they were reported in the computerized test reporting system. The models were adjusted for patients' age, sex, and disease severity (measured by the Charlson Comorbidity index), the number of test panels performed, the number of unreviewed tests pre-discharge, and the cumulative laboratory turnaround time (LTAT) of tests performed during an inpatient stay. RESULTS:Cumulative CRT is significantly associated with prolonged LOS, with each day of delay in reviewing test results increasing the likelihood of prolonged LOS by 13.2% (p < 0.0001). Restricting the analysis to tests with abnormal results strengthened the relationship between cumulative CRT and prolonged LOS, with each day of delay in reviewing test results increasing the likelihood of delayed discharge by 33.6% (p < 0.0001). Increasing age, disease severity and total number of tests were also significantly associated with prolonged LOS. Increasing number of unreviewed tests was negatively associated with prolonged LOS. CONCLUSIONS:Reducing unnecessary hospital LOS has become a critical health policy goal as healthcare costs escalate. Preventing delay in reviewing test results represents an important opportunity to address potentially avoidable hospital stays and unnecessary resource utilization.

Project description:BackgroundOutcomes following proximal humeral fracture (PHF) may be impacted by a range of clinical, fracture and premorbid factors. The aim of this study was to examine factors impacting hospital admission; length of stay (LOS) and new discharge destination for patients presenting to hospital with PHF.MethodsRetrospective audit conducted at a tertiary health service. Data was collected from adult patients presenting to hospital with a PHF over a 54-month period. Fractures that were pathological or sustained during admission were excluded. Univariable and multivariable logistic and negative binomial regression were used to explore factors associated with hospital admission, LOS and new discharge destination.ResultsData were analyzed from 701 participants (age 70 years (IQR 60, 81); 72.8% female); 276 (39.4%) participants required a hospital admission. New discharge destination was required for 109 (15.5%) participants, of whom 49 (45%) changed from home alone to home with family/friend(s). Greater comorbidities, as indicated by the Charlson Comorbidity Index score, were associated with hospital admission, longer LOS and new discharge destination. Premorbid living situations of home with family/friend(s) or from an external care facility were associated with a decreased likelihood of hospital admission, shorter LOS and reduced risk of a new discharge destination. Surgical treatment was associated with shorter LOS. Older age and dementia diagnosis were associated with a new discharge destination.ConclusionsMany factors potentially impact on the likelihood or risk of hospitalization, LOS and new discharge destination post PHF. Patients with greater comorbidities are more likely to have negative outcomes, while patients who had premorbid living situations of home with family/friend(s) or from an external care facility are more likely to have positive outcomes. Early identification of factors that may impact patient outcomes may assist timely decision making in hospital settings. Further research should focus on developing tools to predict hospital outcomes in the PHF population.

Project description:ObjectivesPatient satisfaction impacts healthcare quality and outcomes. Residents play an important role in patient satisfaction at academic institutions. This study aims to assess residents' patient satisfaction knowledge and determine which learning experiences contributed to their knowledge acquisition.SettingsThis study was conducted at a health science university in a large, urban, tertiary-care academic medical centre in the USA.ParticipantsAll residents from internal medicine (n=185) and paediatrics (n=156) were asked to participate.DesignResidents completed a survey from April 2013 to December 2013 that assessed (1) knowledge of factors that impact patient satisfaction and (2) learning experiences that may have contributed to their understanding of the drivers of patient satisfaction (eg, experiential (personal or clinical) or didactics). Trainees identified the importance of factors in determining patient satisfaction on a five-point Likert scale; answers were compiled into a knowledge score. The score was correlated with prior personal/clinical experience and didactics.ResultsOf the 341 residents, 247 (72%) completed the survey. No difference was found in knowledge among training levels or residency programme. More than 50% incorrectly thought physician board certification, patient's education, patient's income and physician's age impacted satisfaction. Personal experience, through hospitalisation of a relative or friend, was correlated with higher knowledge (67% vs 71%, p=0.03). Ninety-nine per cent (n=238) stated peer observation, and all stated faculty feedback impacted their patient satisfaction knowledge. Seventy-seven per cent (n=185) had attended didactics on satisfaction, but attendance did not correlate with higher scores.ConclusionsOur study showed trainees have a few gaps in their patient satisfaction knowledge, and attending past educational sessions on patient satisfaction did not correlate with higher knowledge scores. Our data suggest that academic centres should leverage residents' personal experiences, their observations of peers and faculty feedback to enhance patient satisfaction knowledge.

Project description:BackgroundThe remarkable advancements in surgical techniques over recent years have shifted the clinical focus from merely reducing mortality to enhancing the quality of postoperative recovery. The duration of a patient's hospital stay serves as a crucial indicator in evaluating postoperative recovery and surgical outcomes. This study aims to identify predictors of the length of hospital stay for children who have undergone corrective surgery for Ebstein Anomaly (EA).MethodsWe conducted a retrospective cohort study on children (under 18 years of age) diagnosed with EA who were admitted for corrective surgery between January 2009 and November 2021 at Fuwai Hospital. The primary outcome was the Time to Hospital Discharge (THD). Cox proportional hazard models were utilized to identify predictors of THD. In the context of time-to-event analysis, discharge was considered an event. In cases where death occurred before discharge, it was defined as an extended THD, input as 100 days (exceeding the longest observed THD), and considered as a non-event.ResultsA total of 270 children were included in this study, out of which three died in the hospital. Following the Cox proportional hazard analysis, six predictors of THD were identified. The hazard ratios and corresponding 95% confidence intervals were as follows: age, 1.030(1.005,1.055); C/R > 0.65, 0.507(0.364,0.707); Carpentier type C or D, 0.578(0.429,0.779); CPB time, 0.995(0.991,0.998); dexamethasone, 1.373(1.051,1.795); and transfusion, 0.680(0.529,0.875). The children were categorized into three groups based on the quartile of THD. Compared to children in the ≤ 6 days group, those in the ≥ 11 days group were associated with a higher incidence of adverse outcomes. Additionally, the duration of mechanical ventilation and ICU stay, as well as hospital costs, were significantly higher in this group.ConclusionWe identified six predictors of THD for children undergoing corrective surgery for EA. Clinicians can utilize these variables to optimize perioperative management strategies, reduce adverse complications, improve postoperative recovery, and reduce unnecessary medical expenses.

Dataset Information

Drivers of hospital expenditure and length of stay in an academic medical centre: a retrospective cross-sectional study.

Publications

Drivers of hospital expenditure and length of stay in an academic medical centre: a retrospective cross-sectional study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets