Hospital Quality Indicators Research Articles

Association of dementia with early rehospitalization among Medicare beneficiaries

Preventable hospital readmissions have been recognized as indicators of hospital quality, a source of increased healthcare expenditures, and a burden for patients, families, and caregivers. Despite growth of initiatives targeting risk factors associated with potentially avoidable hospital readmissions, the impact of dementia on the likelihood of rehospitalization is poorly characterized. Therefore, the primary objective of this retrospective cohort study was to investigate whether dementia was an independent predictor of 30-day readmissions. Administrative claims data for all admissions to Rhode Island hospitals in 2009 was utilized to identify hospitalizations of Medicare fee-for-service beneficiaries with a diagnosis of Alzheimer's Disease or other dementias. Demographics, measures of comorbid disease burden, and other potential confounders were extracted from the data and the odds of 30-day readmission to any United States hospital was calculated from conditional logistic regression models. From a sample of 25,839 hospitalizations, there were 3908 index admissions of Medicare beneficiaries who fulfilled the study criteria for a dementia diagnosis. Nearly 20% of admissions (n=5133) were followed by a readmission within thirty days. Hospitalizations of beneficiaries with a dementia diagnosis were more likely to be followed by a readmission within thirty days (adjusted odds ratio (AOR) 1.18; 95% CI, 1.08, 1.29), compared to hospitalizations of those of without dementia. Controlling for discharge site of care did not attenuate the association (AOR 1.21; 95% CI, 1.10, 1.33).

Understanding Differences Between High- And Low-Price Hospitals: Implications For Efforts To Rein In Costs

Private insurers pay widely varying prices for inpatient care across hospitals. Previous research indicates that certain hospitals use market clout to obtain higher payment rates, but there have been few in-depth examinations of the relationship between hospital characteristics and pricing power. This study used private insurance claims data to identify hospitals receiving inpatient prices significantly higher or lower than the median in their market. High-price hospitals, compared to other hospitals, tend to be larger; be major teaching hospitals; belong to systems with large market shares; and provide specialized services, such as heart transplants and Level I trauma care. High-price hospitals also receive significant revenues from nonpatient sources, such as state Medicaid disproportionate-share hospital funds, and they enjoy healthy total financial margins. Quality indicators for high-price hospitals were mixed: High-price hospitals fared much better than low-price hospitals did in U.S. News & World Report rankings, which are largely based on reputation, while generally scoring worse on objective measures of quality, such as postsurgical mortality rates. Thus, insurers may face resistance if they attempt to steer patients away from high-price hospitals because these facilities have good reputations and offer specialized services that may be unique in their markets.

Developing a national framework of quality indicators for public hospitals

The current study describes the development of a preliminary set of quality indicators for public Greek National Health System (GNHS) hospitals, which were used in the "Health Monitoring Indicators System: Health Map" (Ygeionomikos Chartis) project, with the purpose that these quality indicators would assess the quality of all the aspects relevant to public hospital healthcare workforce and services provided. A literature review was conducted in the MEDLINE database to identify articles referring to international and national hospital quality assessment projects, together with an online search for relevant projects. Studies were included if they were published in English, from 1980 to 2010. A consensus panel took place afterwards with 40 experts in the field and tele-voting procedure. Twenty relevant projects and their 1698 indicators were selected through the literature search, and after the consensus panel process, a list of 67 indicators were selected to be implemented for the assessment of the public hospitals categorized under six distinct dimensions: Quality, Responsiveness, Efficiency, Utilization, Timeliness, and Resources and Capacity. Data gathered and analyzed in this manner provided a novel evaluation and monitoring system for Greece, which can assist decision-makers, healthcare professionals, and patients in Greece to retrieve relevant information, with the long-term goal to improve quality in care in the GNHS hospital sector.

Ambulatory and Hospital-based Quality Improvement Methods in Israel.

This review article compares ambulatory and hospital-based quality improvement methods in Israel. Data were collected from: reports of the National Program for Quality Indicators in community, the National Program for Quality Indicators in Hospitals, and from the Organization for Economic Cooperation and Development (OECD) Reviews of Health Care Quality.

Disability as a covariate in risk adjustment models for predicting hospital deaths

Risk-adjusted hospital mortality rates are frequently used as putative indicators of hospital quality. These figures could become increasingly important as efforts escalate to contain U.S. health care costs while simultaneously maintaining or improving quality of care. Most risk adjustment methods today employ coded diagnostic information sometimes supplemented with more detailed clinical data obtained from medical records. This article considers whether risk-adjusted hospital mortality rates should account for baseline patient disability. Accounting for baseline disability when calculating hospital mortality rates makes clinical sense, especially for conditions such as heart failure or coronary artery bypass grafting surgery, where patients' cardiac-related functional status strongly predicts their imminent outcomes. A small body of research suggests the strength of disability in predicting hospital mortality, even in comparison with indicators of acute physiologic status and comorbid illness. However, the feasibility of obtaining complete and accurate data on patients' baseline disability will be challenging and requires further investigation. The risk of not adjusting for baseline disability could be efforts by physicians and hospitals to avoid treating patients with significant disabilities.

Conjunto de indicadores de calidad y seguridad para hospitales de la Agencia Valenciana de Salud

ObjectivesTo prepare a set of quality and safety indicators for Hospitals of the «Agencia Valenciana de Salud». Material and methodsThe qualitative technique Metaplan® was applied in order to gather proposals on sustainability and nursing. The catalogue of the «Spanish Society of Quality in Healthcare» was adopted as a starting point for clinical indicators. Using the Delphi technique, 207 professionals were invited to participate in the selecting the most reliable and feasible indicators. Lastly, the resulting proposal was validated with the managers of 12 hospitals, taking into account the variability, objectivity, feasibility, reliability and sensitivity, of the indicators. ResultsParticipation rates varied between 66.67% and 80.71%. Of the 159 initial indicators, 68 were prioritized and selected (21 economic or management indicators, 22 nursing indicators, and 25 clinical or hospital indicators). Three of them were common to all three categories and two did not match the specified criteria during the validation phase, thus obtaining a final catalogue of 63 indicators. ConclusionsA set of quality and safety indicators for Hospitals was prepared. They are currently being monitored using the hospital information systems.

A Quantitative Method for Assessment of Prescribing Patterns Using Electronic Health Records

BackgroundMost available quality indicators for hospitals are represented by simple ratios or proportions, and are limited to specific events. A generalized method that can be applied to diverse clinical events has not been developed. The aim of this study was to develop a simple method of evaluating physicians' prescription patterns for diverse events and their level of awareness of clinical practice guidelines.Methods and FindingsWe developed a quantitative method called Prescription pattern Around Clinical Event (PACE), which is applicable to electronic health records (EHRs). Three discrete prescription patterns (intervention, maintenance, and discontinuation) were determined based on the prescription change index (PCI), which was calculated by means of the increase or decrease in the prescription rate after a clinical event. Hyperkalemia and Clostridium difficile-associated diarrhea (CDAD) were used as example cases. We calculated the PCIs of 10 drugs related to hyperkalemia, categorized them into prescription patterns, and then compared the resulting prescription patterns with the known standards for hyperkalemia treatment. The hyperkalemia knowledge of physicians was estimated using a questionnaire and compared to the prescription pattern. Prescriptions for CDAD were also determined and compared to clinical knowledge. Clinical data of 1698, 348, and 1288 patients were collected from EHR data. The physicians prescribing behaviors for hyperkalemia and CDAD were concordant with the standard knowledge. Prescription patterns were well correlated with individual physicians' knowledge of hyperkalemia (κ = 0.714). Prescribing behaviors according to event severity or clinical condition were plotted as a simple summary graph.ConclusionThe algorithm successfully assessed the prescribing patterns from the EHR data. The prescription patterns were well correlated with physicians' knowledge. We expect that this algorithm will enable quantification of prescribers' adherence to clinical guidelines and be used to facilitate improved prescribing practices.

Readmissions in internal medicine: Epidemiology and determinants

Introduction: Readmissions are frequent in Internal Medicine wards and can be considered a score for quality and efficiency in health care. The readmission rate (RR) is commonly used as a hospital quality indicator and is frequently included in the evaluation of hospital performance. Penalties based on RR are being introduced in several models for hospital financing. The effective value of RR and the time frame to be considered are not consensual. Objective: Quantify the RR, characterize the readmission episodes and identify its determinants. Methods: Retrospective study of all the admissions in an Internal Medicine ward of a university central hospital in 2011. Data source: Diagnostic Related Categories (DRG) electronic administrative database. Readmission was defined as any new admission following an index admission during the year 2011. Variables: patient demographics, diagnosis (ICD-9 and Clinical Classification Software — AHRQ, U.S.A.) and DRG, length of stay (LoS), comorbidity indexes (CoI) (Charlson and Elixhauser) and chronic conditions (Chronic Condition Indicator — AHRQ, U.S.A.). Results: From 3826 admissions, 663 (17.3%) were readmissions (5.8% below 7 days and 11.9% below 30 days). There were no differences in gender distribution. The average age of the readmission episodes was greater than the non-readmissions (74.2 vs 72.7 years, p = 0.001). The LoS of the index episodes preceding a readmission was higher than those without a subsequent readmission (9.3 vs 7.3 days, p < 0.001). Only 22.5% of the readmissions were coded in the same DRG and only 24.1% in the same main diagnosis of the index preceding admission. The cases associated with a readmission had a higher number of coded diagnosis (7.5 vs 6.8) and chronic conditions (4.7 vs 4.1). The CoI were also greater in the readmission index cases (Elixhauser: 2.8 vs 2.3 and Charlson: 2.9 vs 2.2) (p < 0.001). In the logistic regression multivariate model for the occurrence of readmission after an initial episode, age, LoS and CoI were identified as significant variables. Conclusions: Readmissions have relevant weight in inpatient hospital activity in Internal Medicine. In our study the early RR was low and 3/4 of the readmissions occurred in clinical contexts distinct from the initial index episode. We stress the inverse relation between the shorter LoS and the probability of readmission and emphasize the higher complexity associated with readmission episodes. Extra-hospitalar facts, namely individual patient characteristics, are relevant determinants for readmission. In Internal Medicine the raw RR should not be used as a quality, performance or efficiency indicator, as RR should be adjusted to the patient clinical complexity, gaged accordingly to the nosological profile of the institution and weighted taking into account the efficiency of community health care.

The impact of academic calendar cycle on coronary artery bypass outcomes: a comparison of teaching and non-teaching hospitals

BackgroundThe commencement of new academic cycle in July is presumed to be associated with poor patient outcomes, although supportive evidence is limited for cardiac surgery patients. We sought to determine if the new academic cycle affected the outcomes of patients undergoing Coronary Artery Bypass Grafting.MethodsA retrospective analysis was performed on 10-year nationwide in-hospital data from 1998–2007. Only patients who underwent CABG in the first and final academic 3-month calendar quarter were included. Generalized multivariate regression was used to assess indicators of hospital quality of care such as risk-adjusted mortality, total complications and “failure to rescue“ (FTOR) - defined as death after a complication.ResultsOf the 1,056,865 CABG operations performed in the selected calendar quarters, 698,942 were at teaching hospitals. The risk-adjusted mortality, complications and FTOR were higher in the beginning of the academic year [Odds ratio = 1.14, 1.04 and 1.19 respectively; p < 0.001 for all] irrespective of teaching status. However, teaching status was associated with lower mortality (OR 0.9) despite a higher complication rate (OR 1.02); [p < 0.05 for both]. The July Effect thus contributed to only a 2.4% higher FTOR in teaching hospitals compared to 19% in non teaching hospitals.ConclusionsThe July Effect is reflective of an overall increase in morbidity in all hospitals at the beginning of the academic cycle and it had a pronounced effect in non-teaching hospitals. Teaching hospitals were associated with lower mortality despite higher complication rates in the beginning of the academic cycle compared to non-teaching hospitals. The July effect thus cannot be attributed to presence of trainees alone.Ultramini abstractThis study compares the July effect in teaching and non-teaching hospitals and demonstrates that this effect is not unique to teaching hospitals for CABG patients. In fact, teaching hospitals have somewhat better outcomes at the beginning of the academic cycle and the July effect is a much broader seasonal variation.

Emergency Department Visits After Surgery Are Common For Medicare Patients, Suggesting Opportunities To Improve Care

Considerable attention is being paid to hospital readmission as a marker of poor postdischarge care coordination. However, little is known about another potential marker: emergency department (ED) use. We examined ED visits for Medicare patients within thirty days of discharge for six common inpatient surgeries. We found that these visits were widespread and showed extensive variation across facilities. For example, 17.3 percent of these patients experienced at least one ED visit within the postdischarge period, and 4.4 percent of patients had multiple ED visits. Among those patients who were readmitted, 56.5 percent were readmitted from the ED. There was substantial variation-as much as fourfold-in hospital-level ED use for these patients across all six procedures. The variation might signify a failure in upstream coordination of care and therefore might represent a novel hospital quality indicator. In addition, the postdischarge ED visit is an opportunity to ensure that care is properly coordinated and is the last best chance to avoid preventable readmissions.

COSTS ASSOCIATED WITH READMISSIONS IN BELGIAN ACUTE-CARE HOSPITALS

Introduction: Internationally, hospital readmissions have a great appeal as an indicator of hospital quality. Since possibilities in prevention and control exist, reducing rates of hospital readmission has attracted attention of policymakers as a way to improve quality of care while simultaneously reducing costs. Therefore reducing the number of readmissions is considered to be a pillar of more cost-effective hospital care. The goal of this study was to estimate the cost of hospital readmissions at a national level, describe differences in readmission rates between hospitals and to calculate the potential monetary savings of reducing excess readmissions.Methods: Stays data were obtained from the Minimum Basic Data Set 2008 in a sample of 45 hospitals representing 16,141 beds. Readmissions were identified as a second admission for the same patient with the same APR-DRG code within 1 month or 3 months after discharge. Hospital type, diagnosis-related group, age and gender were used as matching factors in comparing readmission rates. Specific types of readmissions that occur naturally in each other’s proximity due to the repeating nature of the therapy were excluded from the analysis. The costs per readmission were then calculated by linking the stays data with the cost data per APR-DRG and per severity index using the 2008 national feedback. The results of our sample were then extrapolated to all Belgian hospitals in order to calculate the total cost of readmissions. By means of a sensitivity analysis we estimated potential monetary savings when a reduction in the incidence of readmissions in hospitals having a higher readmission rate in comparison to other hospitals would be realized.Results: In our sample 1.5% readmissions within 1 month after discharge and 2.1% within 3 months after discharge were identified. The additional weighted mean cost of these readmissions was € 3,495.58 within 1 month and € 3,572.20 within 3 months. The total financial burden, as extrapolated to the Belgian setting, is estimated at € 280,091,471. The wide variability between hospitals in incidence of readmissions (1.17-6.40%) indicates a potential for improvement. For instance, if all Belgian hospitals having a higher readmission rate improve their rate to the level of the hospital corresponding to percentile 75 (= 2.4% readmissions) savings would amount to € 14,118,509.Conclusion: The observed incidence of readmissions is associated with important additional healthcare costs. Although not all readmissions can be prevented, there is clearly a potential to increase cost-effectiveness of hospital care delivery.

Item generation in the development of an inpatient experience questionnaire: a qualitative study

BackgroundPatient experience is a key feature of quality improvement in modern health-care delivery. Measuring patient experience is one of several tools used to assess and monitor the quality of health services. This study aims to develop a tool for assessing patient experience with inpatient care in public hospitals in Hong Kong.MethodsBased on the General Inpatient Questionnaire (GIQ) framework of the Care Quality Commission as a discussion guide, a qualitative study involving focus group discussions and in-depth individual interviews with patients was employed to develop a tool for measuring inpatient experience in Hong Kong.ResultsAll participants agreed that a patient satisfaction survey is an important platform for collecting patients’ views on improving the quality of health-care services. Findings of the focus group discussions and in-depth individual interviews identified nine key themes as important hospital quality indicators: prompt access, information provision, care and involvement in decision making, physical and emotional needs, coordination of care, respect and privacy, environment and facilities, handling of patient feedback, and overall care from health-care professionals and quality of care. Privacy, complaint mechanisms, patient involvement, and information provision were further highlighted as particularly important areas for item revision by the in-depth individual interviews. Thus, the initial version of the Hong Kong Inpatient Experience Questionnaire (HKIEQ), comprising 58 core items under nine themes, was developed.ConclusionsA set of dimensions and core items of the HKIEQ was developed and the instrument will undergo validity and reliability tests through a validation survey. A valid and reliable tool is important in accurately assessing patient experience with care delivery in hospitals to improve the quality of health-care services.

The use of quality information by general practitioners: does it alter choices? A randomized clustered study

BackgroundFollowing the introduction of elements of managed competition in the Netherlands in 2006, General Practitioners (GPs) and patients were given the role to select treatment hospital using public quality information. In this study we investigate to what extent hospital preferences of GP’s are affected by performance indicators on medical effectiveness and patient experiences. We selected three conditions: breast cancer, cataract surgery, and hip and knee replacement.MethodsAfter an inquiry 26 out of 226 GPs in the region signed up to participate in our study. After a 2:1 randomization, we analyzed the referral patterns in the region using three groups of GPs: GPs (n=17) who used the report cards and received personal clarification, GPs that signed up for the study but were assigned to the control group (n=9), and the GPs outside the study (n=200).We conducted a difference in differences analysis where the choice for a particular hospital was the dependent variable and time (2009 or 2010), the sum score of the CQI, the sum score of the PI’s and dummy variables for the individual hospitals were used as independent variables.ResultsThe analysis of the conditions together and cataract surgery and hip and knee replacement separately, showed no significant relationships between the scores on the report cards and the referral patterns of the GPs. For breast cancer our analysis revealed that GPs in the intervention group refer 1.0% (p=0.01) more to hospitals that score one percent point better on the indicators for medical effectiveness.ConclusionOur study provides empirical evidence that GP referral patterns were unaffected by the available quality information, except for the outcome indicators for breast cancer care that were presented. This finding was surprising since our study was designed to identify changes in hospital preference (1) amongst the most motivated GP’s, (2) that received personal clarification of the performance indicators, and (3) selected indicators/conditions from a large set of indicators that they believed were most important. This finding may differ when quality information is based on outcome indicators with a clinically relevant difference, as shown by our indicators for breast cancer treatment. We believe that the current set of (largely process) hospital quality indicators do not serve the GP’s information needs and consequently quality plays little role in the selection of hospitals for treatment.

Limits Of Readmission Rates In Measuring Hospital Quality Suggest The Need For Added Metrics

Recent national policies use risk-standardized readmission rates to measure hospital performance on the theory that readmissions reflect dimensions of the quality of patient care that are influenced by hospitals. In this article our objective was to assess readmission rates as a hospital quality measure. First we compared quartile rankings of hospitals based on readmission rates in 2009 and 2011 to see whether hospitals maintained their relative performance or whether shifts occurred that suggested either changes in quality or random variation. Next we examined the relationship between readmission rates and several commonly used hospital quality indicators, including risk-standardized mortality rates, volume, teaching status, and process-measure performance. We found that quartile rankings fluctuated and that readmission rates for lower-performing hospitals in 2009 tended to improve by 2011, while readmission rates for higher-performing hospitals tended to worsen. Regression to the mean (a form of statistical noise) accounted for a portion of the changes in hospital performance. We also found that readmission rates were higher in teaching hospitals and were weakly correlated with the other indicators of hospital quality. Policy makers should consider augmenting the use of readmission rates with other measures of hospital performance during care transitions and should build on current efforts that take a communitywide approach to the readmissions issue.

The Effect of Competition on Process and Outcome Quality of Hospital Care in the Netherlands

We examine the impact of competition on outcome and process indicators of hospital quality. While earlier literature on the relationship between competition and hospital quality mainly focused on outcome indicators, we argue that the inclusion of process indicators in the analysis can provide supplementary information about the effect of competitive pressure on hospitals’ incentives. In particular, these indicators are less noisy than outcome indicators and are important as a management tool. Our panel dataset covers all Dutch general and academic hospitals in the period 2004–2008, during which the transparency of hospital quality information increased substantially due to the disclosure of hospitals’ quality indicators. We find that competition among hospitals located within the hospital catchment area explains differences in several process indicators, but fails to explain differences in outcome indicators. The results suggest that hospitals facing more competition organize diagnostic processes more efficiently; however, they have more operation cancellations at short notice and more delays of hip fracture injury operations for elderly patients. This suggests that competition affects the allocation of hospital personnel efforts even when outcomes have not been affected.

Do Patients “Like” Good Care? Measuring Hospital Quality via Facebook

With the growth of Facebook, public health researchers are exploring the platform's uses in health care. However, little research has examined the relationship between Facebook and traditional hospital quality measures. The authors conducted an exploratory quantitative analysis of hospitals' Facebook pages to assess whether Facebook "Likes" were associated with hospital quality and patient satisfaction. The 30-day mortality rates and patient recommendation rates were used to quantify hospital quality and patient satisfaction; these variables were correlated with Facebook data for 40 hospitals near New York, NY. The results showed that Facebook "Likes" have a strong negative association with 30-day mortality rates and are positively associated with patient recommendation. These exploratory findings suggest that the number of Facebook "Likes" for a hospital may serve as an indicator of hospital quality and patient satisfaction. These findings have implications for researchers and hospitals looking for a quick and widely available measure of these traditional indicators.

Composite Measures for Profiling Hospitals on Surgical Morbidity

Although risk-adjusted morbidity is widely used as a surgical quality indicator, it may not always be a reliable indicator of hospital quality. In this study, we assess the value of a novel composite measure for improving the reliability of hospital morbidity rankings. Using data from the American College of Surgeons' National Surgical Quality Improvement Program (ACS-NSQIP), we studied all patients undergoing 4 surgical procedures (2008-2009): colectomy, ventral hernia repair, abdominal aortic aneurysm repair, and lower extremity bypass surgery. For these procedures, we created a composite measure by combining quality indicators from several distinct domains of quality: morbidity, reoperation, length of stay, and morbidity with other potentially related procedures. We empirically weighted each measure and adjusted for reliability using empirical Bayes techniques. To validate this approach, we assessed how well composite measures from 1 year (2008) predict morbidity in the next year (2009) compared with the standard ACS-NSQIP approach for assessing hospital rates of risk-adjusted morbidity. For all 4 operations, the composite measures explained a higher proportion of hospital-level variation in morbidity than the standard approach: ventral hernia repair (58% for the composite vs 8% for the standard approach), colon resection (33% vs 14%), abdominal aortic aneurysm repair (51% vs 38%), and lower extremity bypass surgery (32% vs 3%). When evaluating the ability to discriminate future performance, the composite approach performed best for ventral hernia repair. For this procedure, the bottom 20% of hospitals based on the composite approach had nearly threefold higher (odds ratio: 2.65; 95% confidence interval: 1.83-3.85) morbidity rates than the top 20% of hospitals. However, when using the standard approach, there was only a 1.3-fold difference (odds ratio: 1.30; 95% confidence interval: 0.87-1.96). Although the differences were smaller in magnitude, the composite measure also outperformed the standard approach for the other 3 procedures. Composite measures better reflect hospital quality than simple rates of risk-adjusted morbidity. In the context of ACS-NSQIP, composite measures would give hospitals a better sense of where they stand and help identify truly exemplary hospitals for benchmarking.

Data mining of hospital characteristics in online publication of medical quality information

Information disclosure can reduce information asymmetry between health care providers and patients, thus improving both patient safety and medical quality. The National Bureau of Health Insurance (NBHI) inTaiwancurrently publishes health-related information online in order to enhance service efficiency and enable the public to monitor the country’s medical system. A data mining technique, classification and regression tree (CART), is used in this work to investigate online public quality information to compare the characteristics of hospital. The hospital quality indicators and characteristics data are available on the websites of the NBHI (http://www.nhi.gov.tw/AmountInfoWeb/Index.aspx) and the Department of Health (http://www.doh.gov.tw/). The full classification and regression tree presented in this work, grown using the hospitals’ quality medical indicators and characteristic values, classifies all hospitals into seven groups. The rate of stays longer than 30 days, which is the dependent variable in this study, is most influenced by the number of medical staff. This reflects the fact that the fewer medical staffs that are employed, the smaller the hospital is, and patients who are likely to have longer stays tend to go to the medium or large hospitals. Policy makers should work to decrease or eliminate persistent healthcare disparities among different socioeconomic groups and offer more online healthrelated services to reduce information asymmetry between health care providers and patients.

Comparing hospital mortality – how to count does matter for patients hospitalized for acute myocardial infarction (AMI), stroke and hip fracture

BackgroundMortality 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.MethodsNorwegian 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.ResultsThe 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.ConclusionsMortality 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.

The Role of an Acute Care for the Elderly Unit in Achieving Hospital Quality Indicators While Caring for Frail Hospitalized Elders

Acute Care for the Elderly (ACE) units have successfully decreased length of stay, hospital costs, and readmission rates. Furthermore, patients return home with increased functional capacity and improved satisfaction with their hospital stay. The ACE unit concept was geared toward patients returning to independent living, but the average hospitalized geriatric patient is increasingly more frail, vulnerable, and dependent. The purpose of this study is 2-fold: (1) to determine if the ACE unit continues to offer the same benefit to the frail, often bedbound elderly, and (2) to determine if such a unit is able to maintain standard hospital quality indicators. A total of 1096 cases discharged from the Memorial-Hermann ACE unit between July 2008 and June 2010 were compared to a sample of 383 patients with similar illness severity who were discharged between July 2007 and June 2008. Metrics measured include: average length of stay (ALOS), case mix index (CMI), case mix adjusted average length of stay (CMI adj ALOS), average direct costs per case, and readmission rate. Patient satisfaction was measured using Hospital Consumer Assessment of Healthcare Providers and Systems and Press-Ganey surveys; quality and safety data were provided by Memorial-Hermann's Quality and Safety Department. The ACE unit resulted in a statistically significant decrease in ALOS and CMI adj LOS with a simultaneous increase in Health Care Financing Administration CMI, indicating that the unit was serving a sicker, more frail population. The readmission rate was 11.95%. The decrease in length of stay, readmission rate, and direct cost translates into a decrease in cost per case. Furthermore, the ACE unit successfully met hospital quality indicators.