A comparison of hospital readmission rates between two general physicians with different outpatient review practices

BACKGROUND: There has been a relentless increase in emergency medical admissions in the UK over recent years. Many of these patients suffer with chronic conditions requiring continuing medical attention. We wished to determine whether conventional outpatient clinic follow up after discharge has any impact on the rate of readmission to hospital. METHODS: Two consultant general physicians with the same patient case-mix but markedly different outpatient follow-up practice were chosen. Of 1203 patients discharged, one consultant saw twice as many patients in the follow-up clinic than the other (Dr A 9.8% v Dr B 19.6%). The readmission rate in the twelve months following discharge was compared in a retrospective analysis of hospital activity data. Due to the specialisation of the admitting system, patients mainly had cardiovascular or cerebrovascular disease or had taken an overdose. Few had respiratory or infectious diseases. Outpatient follow-up was focussed on patients with cardiac disease. RESULTS: Risk of readmission increased significantly with age and length of stay of the original episode and was less for digestive system and musculo-skeletal disorders. 28.7% of patients discharged by Dr A and 31.5 % of those discharged by Dr B were readmitted at least once. Relative readmission risk was not significantly different between the consultants and there was no difference in the length of stay of readmissions. CONCLUSIONS: Increasing the proportion of patients with this age- and case-mix who are followed up in a hospital general medical outpatient clinic is unlikely to reduce the demand for acute hospital beds

Benchmarking and reducing length of stay in Dutch hospitals

<p>Abstract</p> <p>Background</p> <p>To assess the development of and variation in lengths of stay in Dutch hospitals and to determine the potential reduction in hospital days if all Dutch hospitals would have an average length of stay equal to that of benchmark hospitals.</p> <p>Methods</p> <p>The potential reduction was calculated using data obtained from 69 hospitals that participated in the National Medical Registration (LMR). For each hospital, the average length of stay was adjusted for differences in type of admission (clinical or day-care admission) and case mix (age, diagnosis and procedure). We calculated the number of hospital days that theoretically could be saved by (i) counting unnecessary clinical admissions as day cases whenever possible, and (ii) treating all remaining clinical patients with a length of stay equal to the benchmark (15^thpercentile length of stay hospital).</p> <p>Results</p> <p>The average (mean) length of stay in Dutch hospitals decreased from 14 days in 1980 to 7 days in 2006. In 2006 more than 80% of all hospitals reached an average length of stay shorter than the 15th percentile hospital in the year 2000. In 2006 the mean length of stay ranged from 5.1 to 8.7 days. If the average length of stay of the 15^thpercentile hospital in 2006 is identified as the standard that other hospitals can achieve, a 14% reduction of hospital days can be attained. This percentage varied substantially across medical specialties. Extrapolating the potential reduction of hospital days of the 69 hospitals to all 98 Dutch hospitals yielded a total savings of 1.8 million hospital days (2006). The average length of stay in Dutch hospitals if all hospitals were able to treat their patients as the 15^thpercentile hospital would be 6 days and the number of day cases would increase by 13%.</p> <p>Conclusion</p> <p>Hospitals in the Netherlands vary substantially in case mix adjusted length of stay. Benchmarking – using the method presented – shows the potential for efficiency improvement which can be realized by decreasing inputs (e.g. available beds for inpatient care). Future research should focus on the effect of length of stay reduction programs on outputs such as quality of care.</p

Avoidable readmission in Hong Kong - system, clinician, patient or social factor?

<p>Abstract</p> <p>Background</p> <p>Studies that identify reasons for readmissions are gaining importance in the light of the changing demographics worldwide which has led to greater demand for hospital beds. It is essential to profile the prevalence of avoidable readmissions and understand its drivers so as to develop possible interventions for reducing readmissions that are preventable. The aim of this study is to identify the magnitude of avoidable readmissions, its contributing factors and costs in Hong Kong.</p> <p>Methods</p> <p>This was a retrospective analysis of 332,453 inpatient admissions in the Medical specialty in public hospital system in Hong Kong in year 2007. A stratified random sample of patients with unplanned readmission within 30 days after discharge was selected for medical record reviews. Eight physicians reviewed patients' medical records and classified whether a readmission was avoidable according to an assessment checklist. The results were correlated with hospital inpatient data.</p> <p>Results</p> <p>It was found that 40.8% of the 603 unplanned readmissions were judged avoidable by the reviewers. Avoidable readmissions were due to: clinician factor (42.3%) including low threshold for admission and premature discharge etc.; patient factor (including medical and health factor) (41.9%) such as relapse or progress of previous complaint, and compliance problems etc., followed by system factor (14.6%) including inadequate discharge planning, inadequate palliative care/terminal care, etc., and social factor (1.2%) such as carer system, lack of support and community services. After adjusting for patients' age, gender, principal diagnosis at previous discharge and readmission hospitals, the risk factors for avoidable readmissions in the total population i.e. all acute care admissions irrespective of whether there was a readmission or not, included patients with a longer length of stay, and with higher number of hospitalizations and attendance in public outpatient clinics and Accident and Emergency departments in the past 12 months. In the analysis of only unplanned readmissions, it was found that the concordance of the principal diagnosis for admission and readmission, and shorter time period between discharge and readmission were associated with avoidable readmissions.</p> <p>Conclusions</p> <p>Our study found that almost half of the readmissions could have been prevented. They had been mainly due to clinician and patient factors, in particular, both of which were intimately related to clinical management and patient care. These readmissions could be prevented by a system of ongoing clinical review to examine the clinical practice/decision for discharge, and improving clinical care and enhancing patient knowledge of the early warning signs for relapse. The importance of adequate and appropriate ambulatory care to support the patients in the community was also a key finding to reduce avoidable readmissions. Education on patient self-management should also be enhanced to minimize the patient factors with regard to avoidable readmission. Our findings thus provide important insights into the development of an effective discharge planning system which should place patients and carers as the primacy focus of care by engaging them along with the healthcare professionals in the whole discharge planning process.</p

Unplanned readmission rates, length of hospital stay, mortality, and medical costs of ten common medical conditions: a retrospective analysis of Hong Kong hospital data

<p>Abstract</p> <p>Background</p> <p>Studies on readmissions attributed to particular medical conditions, especially heart failure, have generally not addressed the factors associated with readmissions and the implications for health outcomes and costs. This study aimed to investigate the factors associated with 30-day unplanned readmission for 10 common conditions and to determine the cost implications.</p> <p>Methods</p> <p>This population-based retrospective cohort study included patients admitted to all public hospitals in Hong Kong in 2007. The sample consisted of 337,694 hospitalizations in Internal Medicine. The disease-specific risk-adjusted odd ratio (OR), length of stay (LOS), mortality and attributable medical costs for the year were examined for unplanned readmissions for 10 medical conditions, namely malignant neoplasms, heart diseases, cerebrovascular diseases, pneumonia, injury and poisoning, nephritis and nephrosis, diabetes mellitus, chronic liver disease and cirrhosis, septicaemia, and aortic aneurysm.</p> <p>Results</p> <p>The overall unplanned readmission rate was 16.7%. Chronic liver disease and cirrhosis had the highest OR (1.62, 95% confidence interval (CI) 1.39-1.87). Patients with cerebrovascular disease had the longest LOS, with mean acute and rehabilitation stays of 6.9 and 3.0 days, respectively. Malignant neoplasms had the highest mortality rate (30.8%) followed by aortic aneurysm and pneumonia. The attributed medical cost of readmission was highest for heart disease (US$3 199 418, 95$2 579 443-803 393).</p> <p>Conclusions</p> <p>Our findings showed variations in readmission rates and mortality for different medical conditions which may suggest differences in the quality of care provided for various medical conditions. In-hospital care, comprehensive discharge planning, and post-discharge community support for patients need to be reviewed to improve the quality of care and patient health outcomes.</p

The immediate effects of the severe acute respiratory syndrome (SARS) epidemic on childbirth in Taiwan

BACKGROUND: When an emerging infectious disease like severe acute respiratory syndrome (SARS) strikes suddenly, many wonder the public's overwhelming fears of SARS may deterred patients from seeking routine care from hospitals and/or interrupt patient's continuity of care. In this study, we sought to estimate the influence of pregnant women's fears of severe acute respiratory syndrome (SARS) on their choice of provider, mode of childbirth, and length of stay (LOS) for the delivery during and after the SARS epidemic in Taiwan. METHODS: The National Health Insurance data from January 01, 2002 to December 31, 2003 were used. A population-based descriptive analysis was conducted to assess the changes in volume, market share, cesarean rate, and average LOS for each of the 4 provider levels, before, during and after the SARS epidemic. RESULTS: Compared to the pre-SARS period, medical centers and regional hospitals dropped 5.2% and 4.1% in market share for childbirth services during the peak SARS period, while district hospitals and clinics increased 2.1% and 7.1%, respectively. For changes in cesarean rates, only a significantly larger increase was observed in medical centers (2.2%) during the peak SARS period. In terms of LOS, significant reductions in average LOS were observed in all hospital levels except for clinics. Average LOS was shortened by 0.21 days in medical centers (5.6%), 0.21 days in regional hospitals (5.8%), and 0.13 days in district hospitals (3.8%). CONCLUSION: The large amount of patients shifting from the maternity wards of more advanced hospitals to those of less advanced hospitals, coupled with the substantial reduction in their length of maternity stay due to their fears of SARS could also lead to serious concerns for quality of care, especially regarding a patient's accessibility to quality providers and continuity of care

Reduction of missed appointments at an urban primary care clinic: a randomised controlled study

<p>Abstract</p> <p>Background</p> <p>Missed appointments are known to interfere with appropriate care and to misspend medical and administrative resources. The aim of this study was to test the effectiveness of a sequential intervention reminding patients of their upcoming appointment and to identify the profile of patients missing their appointments.</p> <p>Methods</p> <p>We conducted a randomised controlled study in an urban primary care clinic at the Geneva University Hospitals serving a majority of vulnerable patients. All patients booked in a primary care or HIV clinic at the Geneva University Hospitals were sent a reminder 48 hrs prior to their appointment according to the following sequential intervention: 1. Phone call (fixed or mobile) reminder; 2. If no phone response: a Short Message Service (SMS) reminder; 3. If no available mobile phone number: a postal reminder. The rate of missed appointment, the cost of the intervention, and the profile of patients missing their appointment were recorded.</p> <p>Results</p> <p>2123 patients were included: 1052 in the intervention group, 1071 in the control group. Only 61.7% patients had a mobile phone recorded at the clinic. The sequential intervention significantly reduced the rate of missed appointments: 11.4% (n = 122) in the control group and 7.8% (n = 82) in the intervention group (p < 0.005), and allowed to reallocate 28% of cancelled appointments. It also proved to be cost effective in providing a total net benefit of 1846. - EUR/3 months. A satisfaction survey conducted with 241 patients showed that 93% of them were not bothered by the reminders and 78% considered them to be useful. By multivariate analysis, the following characteristics were significant predictors of missed appointments: younger age (OR per additional decade 0.82; CI 0.71-0.94), male gender (OR 1.72; CI 1.18-2.50), follow-up appointment >1year (OR 2.2; CI: 1.15-4.2), substance abuse (2.09, CI 1.21-3.61), and being an asylum seeker (OR 2.73: CI 1.22-6.09).</p> <p>Conclusion</p> <p>A practical reminder system can significantly increase patient attendance at medical outpatient clinics. An intervention focused on specific patient characteristics could further increase the effectiveness of appointment reminders.</p

Hospital Readmission in General Medicine Patients: A Prediction Model

Background: Previous studies of hospital readmission have focused on specific conditions or populations and generated complex prediction models. Objective: To identify predictors of early hospital readmission in a diverse patient population and derive and validate a simple model for identifying patients at high readmission risk. Design: Prospective observational cohort study. Patients: Participants encompassed 10,946 patients discharged home from general medicine services at six academic medical centers and were randomly divided into derivation (n = 7,287) and validation (n = 3,659) cohorts. Measurements: We identified readmissions from administrative data and 30-day post-discharge telephone follow-up. Patient-level factors were grouped into four categories: sociodemographic factors, social support, health condition, and healthcare utilization. We performed logistic regression analysis to identify significant predictors of unplanned readmission within 30 days of discharge and developed a scoring system for estimating readmission risk. Results: Approximately 17.5% of patients were readmitted in each cohort. Among patients in the derivation cohort, seven factors emerged as significant predictors of early readmission: insurance status, marital status, having a regular physician, Charlson comorbidity index, SF12 physical component score, ≥1 admission(s) within the last year, and current length of stay >2 days. A cumulative risk score of ≥25 points identified 5% of patients with a readmission risk of approximately 30% in each cohort. Model discrimination was fair with a c-statistic of 0.65 and 0.61 for the derivation and validation cohorts, respectively. Conclusions: Select patient characteristics easily available shortly after admission can be used to identify a subset of patients at elevated risk of early readmission. This information may guide the efficient use of interventions to prevent readmission

Fatores de atraso na alta hospitalar em hospitais de ensino

OBJETIVO Analisar os motivos de atraso na alta hospitalar de pacientes internados em enfermarias de clínica médica. MÉTODOS Foram analisados 395 prontuários de pacientes consecutivos das enfermarias de clínica médica de dois hospitais públicos de ensino: Hospital das Clínicas da Universidade Federal de Minas Gerais e Hospital Odilon Behrens. Foi utilizado o Appropriateness Evaluation Protocol para definir o momento a partir do qual as anotações do prontuário permitiam concluir que a permanência no hospital não mais era adequada. O intervalo entre esse momento e a data da alta hospitalar efetivada definiu o total de dias de atraso na alta hospitalar. Foi utilizado, sistematicamente, instrumento para categorizar os motivos de atraso da alta hospitalar, tendo sido realizada análise de frequências. RESULTADOS O atraso na alta hospitalar ocorreu em 60,0% das 207 internações do Hospital das Clínicas e em 58,0% das 188 internações do Hospital Odilon Behrens. O atraso por paciente foi em média de 4,5 dias no primeiro e 4,1 dias no segundo, o que corresponde à taxa de ocupação de 23,0% e 28,0% em cada hospital, respectivamente. Os principais motivos de atraso nos dois hospitais foram, respectivamente: espera para realização de exames complementares (30,6% e 34,7%) ou para liberação dos laudos dos exames (22,4% e 11,9%) e os relacionados à responsabilidade médica (36,2% e 26,1%), compreendendo a demora na discussão do caso clínico e na tomada de decisão clínica e dificuldades nas interconsultas, respectivamente (20,4% e 9,1%). CONCLUSÕES Foi constatado percentual elevado de atraso na alta hospitalar nos dois hospitais. O atraso foi devido principalmente a fatores relacionados a processos, que podem ser melhorados por intervenções da equipe assistencial e dos gestores. O impacto na média de permanência hospitalar e na taxa de ocupação foi expressivo e preocupante, num cenário de relativa escassez de leitos e longas esperas por internação

Prognostic Factors in Patients Hospitalized for Heart Failure

Each year, there are over one million hospitalizations for heart failure in the United States, with a similar number in Western Europe. Although these patients respond to initial therapies, they have very high short and intermediate term (2-6 months) mortality and readmission rates, while the healthcare system incurs substantial costs. Several risk prediction models that can accurately identify high-risk patients have been developed using data from clinical trials, large registries or administrative databases. Use of multi-variable risk models at the time of hospital admission or discharge offers better risk stratification and should be encouraged, as it allows for appropriate allocation of existing resources and development of clinical trials testing new treatment strategies for patients admitted with heart failure