Related Articles

Aim of the study

To use the hospital standardised mortality ratio (HSMR), as a tool for Dutch hospitals to analyse their death rates by comparing their risk-adjusted mortality with the national average.

Method

The method uses routine administrative databases that are available nationally in The Netherlands—the National Medical Registration dataset for the years 2005–2007. Diagnostic groups that led to 80% of hospital deaths were included in the analysis. The method adjusts for a number of case-mix factors per diagnostic group determined through a logistic regression modelling process.

Results

In The Netherlands, the case-mix factors are primary diagnosis, age, sex, urgency of admission, length of stay, comorbidity (Charlson Index), social deprivation, source of referral and month of admission. The Dutch HSMR model performs well at predicting a patient's risk of death as measured by a c statistic of the receiver operating characteristic curve of 0.91. The ratio of the HSMR of the Dutch hospital with the highest value in 2005–2007 is 2.3 times the HSMR of the hospital with the lowest value.

Discussion

Overall hospital HSMRs and mortality at individual diagnostic group level can be monitored using statistical process control charts to give an early warning of possible problems with quality of care. The use of routine data in a standardised and robust model can be of value as a starting point for improvement of Dutch hospital outcomes. HSMRs have been calculated for several other countries.

Background

Comprehensive evaluations of the nutritional parameters associated with length of hospital stay are lacking. We investigated the association between malnutrition and length of hospital stay in a cohort of ambulatory adult patients.

Methods

From September 2006 to June 2009, we systematically evaluated 1274 ambulatory adult patients admitted to hospital for medical or surgical treatment. We evaluated the associations between malnutrition and prolonged hospital stay (> 17 days [> 75th percentile of distribution]) using multivariable log-linear models adjusted for several potential nutritional and clinical confounders recorded at admission and collected during and at the end of the hospital stay.

Results

Nutritional factors associated with a prolonged hospital stay were a Nutritional Risk Index score of less than 97.5 (relative risk [RR] 1.64, 95% confidence interval [CI] 1.31–2.06) and an in-hospital weight loss of 5% or greater (RR 1.60, 95% CI 1.30–1.97). Sensitivity analysis of data for patients discharged alive and who had a length of stay of at least three days (n = 1073) produced similar findings (adjusted RR 1.51, 95% CI 1.20–1.89, for Nutritional Risk Index score < 97.5). A significant association was also found with in-hospital starvation of three or more days (RR 1.14, 95% CI 1.01–1.28).

Interpretation

Nutritional risk at admission was strongly associated with a prolonged hospital stay among ambulatory adult patients. Another factor associated with length of stay was worsening nutritional status during the hospital stay, whose cause–effect relationship with length of stay should be clarified in intervention trials. Clinicians need to be aware of the impact of malnutrition and of the potential role of worsening nutritional status in prolonging hospital stay.

OBJECTIVE: To examine the efficiency of Manitoba hospitals by analysing variations in length of stay for patients with similar characteristics. DESIGN: Retrospective study. Multiple regression analyses were used to adjust for patient (case-mix) characteristics and to identify differences in length of stay attributable to the hospital of admission for 14 specific, frequently encountered diagnostic categories and for all acute admissions. SETTING: The eight major acute care hospitals in Manitoba. PARTICIPANTS: Manitoba residents admitted to any one of the eight hospitals during the fiscal year 1989-90, 1990-91 or 1991-92. Patients transferred to or from another institution, those with atypically long stays and those who died in hospital were excluded. OUTCOME MEASURE: Length of hospital stay. RESULTS: The length of stay was strongly influenced by hospital of admission, even after adjustment for key patient characteristics. Excluding the most seriously ill patients and those with the longest stays, approximately 186 beds could potentially have been saved if each hospital had discharged its patients as efficiently as the hospital with the shortest overall length of stay. CONCLUSIONS: A substantial proportion of days currently invested in treating acute care patients could be eliminated. At least some bed closures in Manitoba hospitals could be accommodated simply through more efficient treatment of patients in the remaining beds, without decreasing access to hospital care.

ABSTRACT

BACKGROUND

There have been no prior population-based studies of variation in performance of hospitalists.

OBJECTIVE

To measure the variation in performance of hospitalists.

DESIGN

Retrospective research design of 100 % Texas Medicare data using multilevel, multivariable models.

SUBJECTS

131,710 hospitalized patients cared for by 1,099 hospitalists in 268 hospitals from 2006–2009.

MAIN MEASURES

We calculated, for each hospitalist, adjusted for patient and disease factors (case mix), their patients' average length of stay, rate of discharge home or to skilled nursing facility (SNF) and rate of 30-day mortality, readmissions and emergency room (ER) visits.

KEY RESULTS

In two-level models (admission and hospitalist), there was significant variation in average length of stay and discharge location among hospitalists, but very little variation in 30-day mortality, readmission or emergency room visit rates. There was stability over time (2008–2009 vs. 2006–2007) in hospitalist performance. In three-level models including admissions, hospitalists and hospitals, the variation among hospitalists was substantially reduced. For example, hospitals, hospitalists and case mix contributed 1.02 %, 0.75 % and 42.15 % of the total variance in 30-day mortality rates, respectively.

CONCLUSIONS

There is significant variation among hospitalists in length of stay and discharge destination of their patients, but much of the variation is attributable to the hospitals where they practice. The very low variation among hospitalists in 30-day readmission rates suggests that hospitalists are not important contributors to variations in those rates among hospitals.

Objective

To use statistical process control charts to monitor in-hospital outcomes at the hospital level for a wide range of procedures and diagnoses.

Data Sources

Routine English hospital admissions data.

Study Design

Retrospective analysis using risk-adjusted log-likelihood cumulative sum (CUSUM) charts, comparing each hospital with the national average and its peers for in-hospital mortality, length of stay, and emergency readmission within 28 days.

Data Collection

Data were derived from the Department of Health administrative hospital admissions database, with monthly uploads from the clearing service.

Principal Findings

The tool is currently being used by nearly 100 hospitals and also a number of primary care trusts responsible for purchasing hospital care. It monitors around 80 percent of admissions and in-hospital deaths. Case-mix adjustment gives values for the area under the receiver operating characteristic curve between 0.60 and 0.86 for mortality, but the values were poorer for readmission.

Conclusions

CUSUMs are a promising management tool for managers and clinicians for driving improvement in hospital performance for a range of outcomes, and interactive presentation via a web-based front end has been well received by users. Our methods act as a focus for intelligently directed clinical audit with the real potential to improve outcomes, but wider availability and prospective monitoring are required to fully assess the method's utility.

We have audited the effects of day surgery on the workload of primary and community care teams in Portsmouth. A modified version of the Audit Commission's 'Patients' Experiences of Surgery' questionnaire was given to all patients admitted for an elective surgical procedure from 16 general practices to the two local hospitals between February and November 1996; 487 completed replies were received. In all, 50% patients consulted primary or community health care staff within 21 days of discharge from hospital. The average total patient contact rate with these staff increased with length of hospital stay from 0.39 contacts/patient for day case to 1.83 contacts/patient for longer stay admissions. Contacts with most members of the primary and community health teams increased with length of hospital stay. The postoperative visit rate by general practitioners and district nurses to day case patients was very low. We conclude that day case surgery at its present level in Portsmouth appears to create less workload for primary and community health services than inpatient surgery.

Background

Up to now, costs attributable to adverse events (AEs) and preventable AEs in the Netherlands were unknown. We assessed the total direct medical costs associated with AEs and preventable AEs in Dutch hospitals to gain insight in opportunities for cost savings.

Methods

Trained nurses and physicians retrospectively reviewed 7926 patient records in 21 hospitals. Additional patient information of 7889 patients was received from the Dutch registration of hospital information. Direct medical costs attributable to AEs were assessed by measuring excess length of stay and additional medical procedures after an AE occurred. Costs were valued using Dutch standardized cost prices.

Results

The annual direct medical costs in Dutch hospitals were estimated at a total of euro 355 million for all AEs and euro 161 million for preventable AEs in 2004. The total number of hospital admissions in which a preventable AE occurred was 30,000 (2.3% of all admissions) and more than 300,000 (over 3% of all bed days) bed days were attributable to preventable AEs in 2004. Multilevel analysis showed that variance in direct medical costs was not determined by differences between hospitals or hospital departments.

Conclusion

The estimates of the total preventable direct medical costs of AEs indicate that they form a substantial part (1%) of the expenses of the national health care budget and are of importance to hospital management. The cost driver of the direct medical costs is the excess length of stay (including readmissions) in a hospital. Insight in which determinants are associated with high preventable costs will offer useful information for policymakers and hospital management to determine starting points for interventions to reduce the costs of preventable AEs.

Background

Adverse drug reactions (ADR) are a substantial cause of hospital admissions. We conducted a nationwide study to estimate the burden of hospital admissions for ADRs in Spain during a six-year period (2001-2006) along with the associated total health cost.

Methods

Data were obtained from the national surveillance system for hospital data (Minimum Basic Data Set) maintained by the Ministry of Health and Consumer Affairs, and covering more than 95% of Spanish hospitals. From these admissions we selected all hospitalization that were code as drug-related (ICD-9-CM codes E), but intended forms of overdoses, errors in administration and therapeutics failure were excluded. The average number of hospitalizations per year, annual incidence of hospital admissions, average length of stay in the hospital, and case-fatality rate, were calculated.

Results

During the 2001-2006 periods, the total number of hospitalized patients with ADR diagnosis was 350,835 subjects, 1.69% of all acute hospital admissions in Spain. The estimated incidence of admissions due to ADR decreased during the period 2001-2006 (p < 0.05). More than five percent of patients (n = 19,734) died during an ADR-related hospitalization. The drugs most commonly associated with ADR-related hospitalization were antineoplastic and immunosuppressive drugs (n = 75,760), adrenal cortical steroids (n = 47,539), anticoagulants (n = 26,546) and antibiotics (n = 22,144). The costs generated by patients in our study increased by 19.05% between 2001 and 2006.

Conclusions

Approximately 1.69% of all acute hospital admissions were associated with ADRs. The rates were much higher for elderly patients. The total cost of ADR-related hospitalization to the Spanish health system is high and has increased between 2001 and 2006. ADRs are an important cause of admission, resulting in considerable use of national health system beds and a significant number of deaths.

To identify the clinical conditions associated with substantial time spent in hospital by children aged 1-14 years, records of children admitted to hospital in 1975, 1979, and 1984 were studied. Analysis was by linkage of abstracts of routine records of hospital inpatient care in six districts in southern England covered by the Oxford record linkage study. The total time spend in hospital in the acute specialties each year was calculated by summing the lengths of stay of all episodes of care for each child in each year. First, admissions with long median times in hospital per child admitted were identified. These included, notably, fracture of femur and, in the later years, leukaemia, other malignant neoplasms, and congenital disorders of metabolism. Second conditions were identified which accounted for large numbers of children with lengths of stay of five days or more. These included, in particular, congenital anomalies, asthma, and appendicitis. Third, conditions were identified which accounted for the largest numbers of bed days used. These included congenital anomalies, hypertrophy of tonsils and adenoids, asthma, otitis media, appendicitis, and head injury. Median time spent in hospital per child admitted declined for most conditions but increased for leukaemia, other malignant neoplasms, and congenital disorders of metabolism. Admission rates for children who spent five days or more in hospital each year declined for all common conditions except asthma which increased. Total numbers of beds used increased for asthma and otitis media but declined for all other common conditions.

Objectives

To assess how reductions in length of stay associated with hospitalist care vary by patient and hospital characteristics and explore whether these reductions in length of stay changed over time in the Medicare population.

Design

Retrospective cohort study using data from a 5% national sample of Medicare beneficiaries.

Setting

Hospital.

Participants

To examine temporal trends, 1,981,654 Medicare admissions in 2001 to 2006 at 5036 U.S. hospitals were used. To examine the influence of patient and hospital characteristics, 314,590 admissions in 2006 were used.

Measurements

Hospital length of stay.

Results

In multivariable analyses controlling for patient and hospital characteristics, the reductions in length of stay associated with hospitalist care increased from 0.02 days in 2001-02 to 0.22 days in 2003-04, and 0.35 days in 2005-06. For 2006 admissions, reductions in length of stay were greater in older patients and patients with a higher DRG weight. The reductions were three times greater for medical than for surgical DRGs, with greater reductions in length of stay at non-profit vs. for profit hospitals, and at community vs. teaching hospitals.

Conclusion

The reductions in length of stay associated with hospitalist care would appear to be greatest in older, complicated, non-surgical patients cared for at community hospitals.

BACKGROUND: Patients of GPs who have access to community hospitals (CHs) as well as district general hospitals (DGHs) tend to spend on average more days in hospital each year. Increasing attention is being paid to the efficient management of medical admissions; however, there has been no previous prospective study investigating the appropriateness of CH admissions. AIM: To develop a protocol to assess the clinical appropriateness of admission and length of stay of patients in CHs and to simultaneously compare the appropriateness of admissions to all DGHs and CHs in the county. DESIGN OF STUDY: A protocol named Community Hospital Appropriateness Evaluation Protocol (CHAEP) was developed to assess CH admissions through a process of consultation and a series of pilot studies. The appropriateness evaluation protocol (AEP) was also reviewed and used to assess DGH admissions. SETTING: A prospective cohort of 440 DGH admissions from five DGH sites and 440 CH admissions from nine CHs. METHODS: The admissions were assessed and followed for 28 days. If an admission failed to satisfy any of the criteria then the researcher interviewed the clinician to decide whether it was justified to override the protocol and still classify the admission as appropriate. To assess validity, a proportion of these 'clinical overrides' and the researcher's classifications were reviewed retrospectively by a clinical panel. The kappa statistic was used to assess the level of agreement. RESULTS: Applying the CHAEP, 82% of CH admissions satisfied a criterion for admission and a further 3% were given clinical overrides. A lower intensity of care was required for the majority of the remainder while three admissions required DGH care according to AEP criteria. Sixty-eight per cent of bed days satisfied day-of-care criteria within CHAEP and only a further 2% were given clinical override. These results were similar to those found with the AEP at the DGHs where 75% of admissions (plus 16% given clinical override) and 55% of days-of-care (plus 20% given clinical override) satisfied the AEP criteria. The review panel generally did not agree with the clinician's use of the clinical override at the CHs. Agreement between research nurse and review panel was better for the AEP and DGH (kappa = 0.9, 95% confidence interval (CI) = 0.7-1.0) than for the CHAEP and CH (kappa = 0.37, 95% CI = 0.1-0.8). CONCLUSIONS: The CHAEP could be used to audit the appropriateness of admission and length of stay in CHs. Other health communities would need to review the CHAEP before it could be applied within their context.

The article evaluates the impact of Medicare and Medicaid DRG prospective payment on utilization in Philadelphia area hospitals. These hospitals began a combined Medicare-Medicaid DRG prospective payment at the same time after a common cost-based reimbursement history. Particular attention is paid to the hospital-driven as opposed to physician-driven explanations of declining inpatient utilization. The evaluation of the Tax Equity and Fiscal Responsibility Act (TEFRA) and Diagnosis-Related Group (DRG) interventions uses an ARIMA model that removes both seasonal and autoregressive effects. Both TEFRA and the DRG payment system produced significant reductions in average length of stay, total hospital days, and hospital occupancy rates. Neither, however, had a significant effect on admissions. Hospitals with a higher proportion of Medicare and Medicaid discharges reduced their average length of stay more than other facilities. Hospitals with a higher proportion of outpatient visits to inpatient admissions also reduced inpatient length of stay more. Hospitals with higher than expected overall admissions after the introduction of the DRG program tended to have lower than expected average lengths of stay. The results lend support to the "hospital-driven" interpretation of declines in average length of stay. They fail to support the contention that the DRG system will produce automatic counteracting increases in admissions in the system as a whole.

Background

This article reviews the performance of our hospital's inpatient insulin pump policy.

Methods

Twenty-five hospital admissions of 21 unique patients receiving outpatient insulin pump therapy were reviewed.

Results

Between November 1, 2005, and November 30, 2006, there were 25 hospital admissions involving 21 patients receiving outpatient insulin pump therapy. The average age and duration of diabetes among these 21 patients was 50 and 29 years, respectively; 67% were women, 90% had type 1 diabetes, and all were white. The mean length of hospital stay was 4 days, and the average reported length of insulin pump therapy was 4 years. Patients in 16 of the admissions were identified as candidates for continued use of the insulin pump during the hospital stay. Over 90% of patients remaining on the insulin pump had documentation by nursing of the presence of the pump at the time of admission; 100% of the patients had an admission glucose recorded; 88% had a record of signed patient consent; 81% had evidence of completed preprinted insulin pump orders; 75% received a required endocrine consultation; and 75% of cases had documentation of completed bedside flow sheet. A high frequency of both hypoglycemic and hyperglycemic events occurred in the patients; however, no adverse events were related directly to the insulin pump.

Conclusions

Insulin pump therapy can be safely continued in the hospital setting. While staff compliance with required procedures was high, there was still room for improvement. More data are needed, however, on whether this method of insulin delivery is effective for controlling hyperglycemia in hospitalized patients.

INTRODUCTION

Recent studies have shown that closure of loop ileostomy can be performed in the day-case setting, reducing the length and cost of hospitalisation. By analysing our patients who have undergone reversal, we aimed to determine the length of hospital stay and potential factors behind stays beyond 24 h.

PATIENTS AND METHODS

A database of patients undergoing closure of loop ileostomy at one colorectal unit was examined. The times taken to discharge, morbidity and re-admission rates were recorded.

RESULTS

Eighty patients underwent reversal of ileostomy between January 2001 and January 2006. Median age was 63 years (range, 22–81 years). The median length of stay was 4 days (range, 2–32 days). The median length of stay in patients without complications was 4 days. Many appeared able to be discharged earlier. Seventy-two patients (90%) were able to tolerate a solid diet within 48 h and 54 (67.5%) had bowel function within 3 days. Six patients went home before bowel function; none of these were re-admitted. Twenty patients (25%) developed complications, which included wound infection (8%), small bowel obstruction/ileus (6%), enterocutaneous fistula (1%), anastomotic leak (1%), and late abdominal wall abscess (1%). Of the patients, 16% stayed longer than 5 days despite having no postoperative complications.

CONCLUSIONS

The majority of patients undergoing loop ileostomy reversal at our institution can be discharged earlier than they are at present. Support in the community and the implementation of modified UK day-case surgery protocols are suggested to help shorten patients' length of stay.

Summary

Even if calculating the exact cost of burn treatment is a very hard task, the study of cost analysis provides financial perspective. We performed a cost analysis study in our burn centre to respond to questions about total patient treatment cost and the length of hospital stay. We reviewed all patients admitted to the Gulhane Military Medical Academy Burn Centre in Ankara, Turkey, between March 2005 and August 2008. Forty-three patients with major burns were identified on the basis of the study criteria. The data regarding total treatment cost and the length of hospital stay for each type of burn (flame, scald, electric) were collected at the end of the study. The average total body surface area burned was 36 ± 7%.. The average duration of hospital stay was 73 ± 33 days. Patients with electrical burns stayed longer in hospital than patients with other types of burn injuries. Each one per cent of burn corresponded to a mean hospital stay of two days. The overall mean total cost was $US 15,250. The mean total cost of electrical burns was the highest, with $US 22,501 ± 24,039. Even if the costs associated with burn injury are higher than some other well-known health-related problems, they have not been much studied. Reports have produced different results, but it should be kept in mind that although the results of cost analysis studies may vary they must be performed in all newly established burn centres in order to form a financial overview.

Using hospital discharge abstract data for fiscal year 1984 for all acute care hospitals treating Medicare patients (age greater than or equal to 65), we measured four mortality rates: inpatient deaths, deaths within 30 days after discharge, and deaths within two fixed periods following admission (30 days, and the 95th percentile length of stay for each condition). The metric of interest was the probability that a hospital would have as many deaths as it did (taking age, race, and sex into account). Differences among hospitals in inpatient death rates were large and significant (p less than .05) for 22 of 48 specific conditions studied and for all conditions together; among these 22 "high-variation" conditions, medical conditions accounted for far more deaths than did surgical conditions. We compared pairs of conditions in terms of hospital rankings by probability of observed numbers of inpatient deaths; we found relatively low correlations (Spearman correlation coefficients of 0.3 or lower) for most comparisons except between a few surgical conditions. When we compared different pairs of the four death measures on their rankings of hospitals by probabilities of the observed numbers of deaths, the correlations were moderate to high (Spearman correlation coefficients of 0.54 to 0.99). Hospitals with low probabilities of the number of observed deaths were not distributed randomly geographically; a small number of states had significantly more than their share of these hospitals (p less than .01). Information from hospital discharge abstract data is insufficient to determine the extent to which differences in severity of illness or quality of care account for this marked variability, so data on hospital death rates cannot now be used to draw inferences about quality of care. The magnitude of variability in death rates and the geographic clustering of facilities with low probabilities, however, both argue for further study of hospital death rates. These data may prove most useful as a screening mechanism to identify patterns of potentially poor quality of care. Careful choice of the mortality measure used is needed, however, to maximize the probability of identifying those hospitals, and only those hospitals, warranting more in-depth review.

Introduction

Severe sepsis is a dreaded consequence of infection and necessitates intensive care treatment. Severe sepsis has a profound impact on mortality and on hospital costs, but recent incidence data from The Netherlands are not available. The purpose of the present study was to determine the prevalence and incidence of severe sepsis occurring during the first 24 hours of admission in Dutch intensive care units (ICUs).

Methods

Forty-seven ICUs in The Netherlands participated in a point prevalence survey and included patients with infection at the time of ICU admission. Clinical symptoms of severe sepsis during the first 24 hours of each patient's ICU stay were recorded and the prevalence of severe sepsis was calculated. Then, the annual incidence of severe sepsis in The Netherlands was estimated, based on the prevalence, the estimated length of stay, and the capacity of the participating ICUs relative to the national intensive care capacity.

Results

The participating ICUs had 442 beds available for admissions, which was estimated to be 42% of the national ICU capacity. At the time of the survey, 455 patients were currently admitted and 151 were included in the analysis; 134 (29.5%) patients met criteria for severe sepsis. The most common failing organ system was the respiratory system (90%), and most patients were admitted following surgery (37%) and were admitted because of acute infection (62%). The most prevalent source of infection was the lung (47%). The estimated duration of ICU stay for severe sepsis patients was 13.3 ± 1.1 days.

Conclusion

The annual number of admissions for severe sepsis in Dutch ICUs was calculated at 8643 ± 929 cases/year, which is 0.054% of the population, 0.61% of hospital admissions and 11% of ICU admissions.

To determine reasons for variations in length of stay (LOS) for surgical patients, a comprehensive statistical model was specified and estimated using 1978 discharge abstract data from New Jersey. The model distinguished preoperative LOS from postoperative LOS, and analyzed differences in the impacts of each determining factor on each segment of a hospital stay. The model included a large set of control variables, but the focus of discussion in this article is on factors which reflect the preferences, policies, and organizational routines of hospitals. The empirical findings suggest strategies that hospital managers and regulators can use for reducing average LOS. For example, afternoon admissions often result in extra preoperative days of care even after adjusting for severity of illness. Apparent scarcity of posthospital care in New Jersey also seems to translate into longer hospital stays. Using a comprehensive model and a large, reliable data set, the analysis confirms many hypotheses concerning reasons for LOS variation that have been suggested by earlier research. However, the analysis also raises questions concerning the interpretation of other earlier findings.

Background

Hospitalization costs in clinical trials are typically derived by multiplying the length of stay (LOS) by an average per-diem (PD) cost from external sources. This assumes that PD costs are independent of LOS. Resource utilization in early days of the stay is usually more intense, however, and thus, the PD cost for a short hospitalization may be higher than for longer stays. The shape of this relationship is unlikely to be linear, as PD costs would be expected to gradually plateau. This paper describes how to model the relationship between PD cost and LOS using flexible statistical modelling techniques.

Methods

An example based on a clinical study of clevidipine for the treatment of peri-operative hypertension during hospitalizations for cardiac surgery is used to illustrate how inferences about cost-savings associated with good blood pressure (BP) control during the stay can be affected by the approach used to derive hospitalization costs.

Data on the cost and LOS of hospitalizations for coronary artery bypass grafting (CABG) from the Massachusetts Acute Hospital Case Mix Database (the MA Case Mix Database) were analyzed to link LOS to PD cost, factoring in complications that may have occurred during the hospitalization or post-discharge. The shape of the relationship between LOS and PD costs in the MA Case Mix was explored graphically in a regression framework. A series of statistical models including those based on simple logarithmic transformation of LOS to more flexible models using LOcally wEighted Scatterplot Smoothing (LOESS) techniques were considered. A final model was selected, using simplicity and parsimony as guiding principles in addition traditional fit statistics (like Akaike’s Information Criterion, or AIC). This mapping was applied in ECLIPSE to predict an LOS-specific PD cost, and then a total cost of hospitalization. These were then compared for patients who had good vs. poor peri-operative blood-pressure control.

Results

The MA Case Mix dataset included data from over 10,000 patients. Visual inspection of PD vs. LOS revealed a non-linear relationship. A logarithmic model and a series of LOESS and piecewise-linear models with varying connection points were tested. The logarithmic model was ultimately favoured for its fit and simplicity. Using this mapping in the ECLIPSE trials, we found that good peri-operative BP control was associated with a cost savings of $5,366 when costs were derived using the mapping, compared with savings of $7,666 obtained using the traditional approach of calculating the cost.

Conclusions

PD costs vary systematically with LOS, with short stays being associated with high PD costs that drop gradually and level off. The shape of the relationship may differ in other settings. It is important to assess this and model the observed pattern, as this may have an impact on conclusions based on derived hospitalization costs.

Background

Mortality is a widely used, but often criticised, quality indicator for hospitals. In many countries, mortality is calculated from in-hospital deaths, due to limited access to follow-up data on patients transferred between hospitals and on discharged patients. The objectives were to: i) summarize time, place and cause of death for first time acute myocardial infarction (AMI), stroke and hip fracture, ii) compare case-mix adjusted 30-day mortality measures based on in-hospital deaths and in-and-out-of hospital deaths, with and without patients transferred to other hospitals.

Methods

Norwegian hospital data within a 5-year period were merged with information from official registers. Mortality based on in-and-out-of-hospital deaths, weighted according to length of stay at each hospital for transferred patients (W30D), was compared to a) mortality based on in-and-out-of-hospital deaths excluding patients treated at two or more hospitals (S30D), and b) mortality based on in-hospital deaths (IH30D). Adjusted mortalities were estimated by logistic regression which, in addition to hospital, included age, sex and stage of disease. The hospitals were assigned outlier status according to the Z-values for hospitals in the models; low mortality: Z-values below the 5-percentile, high mortality: Z-values above the 95-percentile, medium mortality: remaining hospitals.

Results

The data included 48 048 AMI patients, 47 854 stroke patients and 40 142 hip fracture patients from 55, 59 and 58 hospitals, respectively. The overall relative frequencies of deaths within 30 days were 19.1% (AMI), 17.6% (stroke) and 7.8% (hip fracture). The cause of death diagnoses included the referral diagnosis for 73.8-89.6% of the deaths within 30 days. When comparing S30D versus W30D outlier status changed for 14.6% (AMI), 15.3% (stroke) and 36.2% (hip fracture) of the hospitals. For IH30D compared to W30D outlier status changed for 18.2% (AMI), 25.4% (stroke) and 27.6% (hip fracture) of the hospitals.

Conclusions

Mortality measures based on in-hospital deaths alone, or measures excluding admissions for transferred patients, can be misleading as indicators of hospital performance. We propose to attribute the outcome to all hospitals by fraction of time spent in each hospital for patients transferred between hospitals to reduce bias due to double counting or exclusion of hospital stays.

OBJECTIVE: To examine the lengths of stay of chronic status patients in an acute care hospital, to identify discharge stages that contribute to excessive stays, to estimate the length of stay at each discharge stage and to link hospital bed-day utilization by the discharge stage to the experience of the patient. DESIGN: Two-year prospective cohort study. The number of hospital days retrospective to the date of the current admission were included in the analysis. SETTING: University hospital. PATIENTS: All 115 inpatients formally declared as achieving chronic status by July 31, 1987. OUTCOME MEASURES: Lengths of stay (total days and days at acute and chronic status) for chronic status patients, including those still in hospital at the end of the study period. Each bed-day was assigned to a discharge stage that corresponded to the patient's status. The disposition of each patient by the end of the study period was reviewed. RESULTS: The study population spent a total of 101 585 days in hospital. The total length of stay per patient was nearly four times that stated in the hospital's annual report, in which the figure was calculated only on the basis of discharge data. On average only 77.2 (8.7%) of the days were spent in acute care. The remaining days were at the chronic level: 24.1% were spent waiting for completion of an application to a long-term care facility, 25.3% for application approval and 41.9% for an available bed in the assigned long-term care institution. For 30 patients no initiation of the discharge process was ever undertaken. As the number of patients in each progressive discharge stage decreased, the wait per patient increased. By the end of the study period only 32 patients had been transferred to a public long-term care facility; 22 were still in hospital, and 35 had died waiting for placement. CONCLUSIONS: Although considered to be a useful measure of hospital efficiency, length of stay determined from discharge data creates an iceberg effect when applied to chronic status patients in acute care hospitals. Lack of access to the assigned resource is the most important reason for a delay in discharge. Interventions, whether undertaken at the patient, hospital or provincial level, must to some degree address this issue. Further study is required to determine which risk factors will predict lags at each discharge stage. Since our discharge staging reflects not only the experience of the patient but also the utilization of hospital bed-days and access to provincial resources, it provides a common language for clinicians, hospital administrators and systems planners.

Maryland is the first state to regulate reimbursement of some of its hospitals on the basis of admissions, instead of services, with adjustments for case mix. This form of reimbursement is intended to provide incentives for increasing efficiency and reducing service intensity (i.e., length of stay and use of ancillaries). Any savings achieved by hospitals may be retained, while losses must be absorbed. Existing management information systems (MIS) are not oriented toward producing data on admissions, with the exception of the Uniform Hospital Discharge Abstract Data Set (UHDADS). Using UHDADS, supplemented with detailed charge data, two issues are being addressed in the Department of Medicine, The Johns Hopkins Hospital:

(1) can charges per admission be predicted from a minimal data set?

(2) can diagnostic specific trends in utilization and charges be detected on a timely basis?

Results of multivariate analysis have shown that significant variations in length of stay among diagnostic categories are frequently not associated with significant variations in charges. Through a detailed analysis of selected diagnostic groups, the need for a treatment pattern classification to compliment the diagnostic classification of admissions has been identified. A classification system based on level and type of treatment charges has been tested. Effectiveness is being measured by its capability to detect trends in charges, and to characterize resource use patterns for management reporting.

Background

The effect of statin therapy on mortality in critically ill patients is controversial, with some studies suggesting a benefit and others suggesting no benefit or even potential harm. The objective of this study was to evaluate the association between statin therapy during intensive care unit (ICU) admission and all-cause mortality in critically ill patients.

Methods

This was a nested cohort study within two randomised controlled trials conducted in a tertiary care ICU. All 763 patients who participated in the two trials were included in this study. Of these, 107 patients (14%) received statins during their ICU stay. The primary endpoint was all-cause ICU and hospital mortality. Secondary endpoints included the development of sepsis and severe sepsis during the ICU stay, the ICU length of stay, the hospital length of stay, and the duration of mechanical ventilation. Multivariate logistic regression was used to adjust for clinically and statistically relevant variables.

Results

Statin therapy was associated with a reduction in hospital mortality (adjusted odds ratio [aOR] = 0.60, 95% confidence interval [CI] 0.36-0.99). Statin therapy was associated with lower hospital mortality in the following groups: patients >58 years of age (aOR = 0.58, 95% CI 0.35-0.97), those with an acute physiology and chronic health evaluation (APACHE II) score >22 (aOR = 0.54, 95% CI 0.31-0.96), diabetic patients (aOR = 0.52, 95% CI 0.30-0.90), patients on vasopressor therapy (aOR = 0.53, 95% CI 0.29-0.97), those admitted with severe sepsis (aOR = 0.22, 95% CI 0.07-0.66), patients with creatinine ≤100 μmol/L (aOR = 0.14, 95% CI 0.04-0.51), and patients with GCS ≤9 (aOR = 0.34, 95% CI 0.17-0.71). When stratified by statin dose, the mortality reduction was mainly observed with statin equipotent doses ≥40 mg of simvastatin (aOR = 0.53, 95% CI 0.28-1.00). Mortality reduction was observed with simvastatin (aOR = 0.37, 95% CI 0.17-0.81) but not with atorvastatin (aOR = 0.80, 95% CI 0.84-1.46). Statin therapy was not associated with a difference in any of the secondary outcomes.

Conclusion

Statin therapy during ICU stay was associated with a reduction in all-cause hospital mortality. This association was especially noted in high-risk subgroups. This potential benefit needs to be validated in a randomised, controlled trial.

Objective

To test whether there is an association between hospital operating conditions such as average length of stays (LOS) and staffing ratio, and elderly patients' risk of readmission.

Data Sources

The main data source was a national patient database of admissions to all acute-care Norwegian hospitals during the year of 1996.

Study Design

It is a cross-sectional study, where Cox' regression analysis was used to test the factors acting on the probability of early unplanned readmission (within 30 days), and later occurring ones. The principal hospital variables included average hospital LOS and staffing ratio (discharges per man-years of personnel). Adjusting patient variables in the model included age, gender, and cost-weights of the Diagnosis Related Groups (DRGs).

Data Extraction Methods

The selected material included discharges from 59 hospitals, and 113,055 elderly patients (≥67 years). Multiple admissions to the same hospital were linked together chronologically, and additional hospital data were matched on. To maximize the association between the index stay and the defined outcome (unplanned readmission), no intervening planned admission was accepted.

Principal Findings

Being admitted to a hospital with relatively short average LOS increased the patient's risk of early readmission significantly. In addition it was found that more intensive care (more staff) could have a compensatory effect. Furthermore, the predictive factors were shown to be time dependent, as hospital variables had much less impact on readmissions occurring late (within 90–180 days).

Conclusions

The results give support to the assumption of a link between hospital operating conditions and patient outcome.

The accuracy of diagnosis of femoral hernia in referrals to a district general hospital over a period of 5 years has been studied and related to clinical outcome. A correct diagnosis was made in only 36 of 98 cases (60 urgent, 38 routine) before admission to hospital. A correct pre-operative diagnosis was ultimately made in 85 cases. Four patients, all urgent admissions with incarcerated bowel, died within 30 days of operation. In none of these cases was a correct diagnosis made before admission to hospital. The median length of post-operative stay of urgent admissions was 7 days (range 4-50) when a correct initial diagnosis was made and 10 days (range 4-50) when the initial diagnosis was incorrect (P = 0.07, Mann-Whitney test). When strangulated small bowel was found at operation, 70% of those with an incorrect initial diagnosis (n = 23) required resection, as compared with 20% of those with a correct initial diagnosis (n = 10, P = 0.014, chi 2 with Yates' correction). Femoral hernias are frequently incorrectly diagnosed before hospital admission and this is associated with worsened outcome in urgent cases.