Predicting emergency readmissions for patients discharged from the medical service of a teaching hospital

Interventions to Reduce Repeat Presentations to Hospital Emergency Departments for Mental Health Concerns: A Scoping Review of the Literature

BACKGROUND

The number of readmissions to the emergency department (ED) for mental health services each year is significant, which increases healthcare costs and negatively affects the morale and quality of life of patients and their families.

OBJECT

This scoping review aimed to establish a better understanding of interventions that have been implemented to reduce psychiatric patient readmission and ED use within the ED, to identify areas for improvement, and therefore to assist in the development of more effective actions in the future.

METHOD

The scoping review was conducted on several bibliographic databases to identify relevant studies. Two researchers independently screened and reviewed titles, abstracts, and full-text articles that met the inclusion criteria. Using Covidence software, 26 out of 6951 studies were eligible for inclusion in this scoping review based on the PRISMA checklist. Data were extracted, collated, summarized, presented, and discussed.

RESULT

This review identified 26 studies which examined interventions aimed to reduce ED visits, such as the High Alert Program (HAP), the Patient-Centered Medical Home (PCMH), the Primary Behavioral Health Care Integration (PBHCI), and the Collaborative Care (CC) Program, etc. Twenty-three of the studies were conducted in North America, while the rest were conducted in Europe and Australia. A total of 16 studies examined interventions directed to any mental health conditions, while the rest addressed specific health conditions, such as substance use disorders, schizophrenia, anxiety, depression. Interventions involved comprehensive and multidisciplinary services, incorporation of evidence-based behavioral and pharmacological strategies, and emphasized the case management that was found to be effective. Additionally, there was a marked consideration for diverse mental health groups, such as those with substance use disorder and of young age. Most interventions showed a positive effect on reducing psychiatric ED visits.

CONCLUSION

Various initiatives have been implemented worldwide to reduce the number of emergency department visits and the associated burden on healthcare systems. This review highlights the greater need for developing more accessible interventions, as well as setting up a comprehensive community health care system aiming to reduce frequent ED presentations.

Association of glucose variability at the last day of hospitalization with 30-day readmission in adults with diabetes

OBJECTIVE

To evaluate whether increased glucose variability (GV) during the last day of inpatient stay is associated with increased risk of 30-day readmission in patients with diabetes.

RESEARCH DESIGN AND METHODS

A comprehensive list of clinical, pharmacy and utilization files were obtained from the Veterans Affairs (VA) Central Data Warehouse to create a nationwide cohort including 1 042 150 admissions of patients with diabetes over a 14-year study observation period. Point-of-care glucose values during the last 24 hours of hospitalization were extracted to calculate GV (measured as SD and coefficient of variation (CV)). Admissions were divided into 10 categories defined by progressively increasing SD and CV. The primary outcome was 30-day readmission rate, adjusted for multiple covariates including demographics, comorbidities and hypoglycemia.

RESULTS

As GV increased, there was an overall increase in the 30-day readmission rate ratio. In the fully adjusted model, admissions with CV in the 5th-10th CV categories and admissions with SD in the 4th-10th categories had a statistically significant progressive increase in 30-day readmission rates, compared with admissions in the 1st (lowest) CV and SD categories. Admissions with the greatest CV and SD values (10th category) had the highest risk for readmission (rate ratio (RR): 1.08 (95% CI 1.05 to 1.10), p<0.0001 and RR: 1.11 (95% CI 1.09 to 1.14), p<0.0001 for CV and SD, respectively).

CONCLUSIONS

Patients with diabetes who exhibited higher degrees of GV on the final day of hospitalization had higher rates of 30-day readmission.

TRIAL REGISTRATION NUMBER

NCT03508934, NCT03877068.

The Health System Costs of Potentially Inappropriate Prescribing: A Population-Based, Retrospective Cohort Study Using Linked Health Administrative Databases in Ontario, Canada

OBJECTIVE

The aim of this study was to determine the health system costs from hospitalizations, emergency department (ED) visits, and medications due to potentially inappropriate prescribing (PIP) in Ontario, Canada, at the population-level.

METHODS

A retrospective cohort of individuals ≥ 66 years of age and prescribed at least one medication from April 2002 to March 2015 was identified using linked population-level health administrative databases from Ontario, Canada. Patients were identified as having PIP or no PIP by applying a subset of the Screening Tool of Older Persons' Potentially Inappropriate Prescribing/Screening Tool to Alert Doctors to Right Treatment (STOPP/START) criteria. The number of days spent in hospital, new medications prescribed, and ED visits in the 90 days following PIP or patient index date were captured, as well as the total costs from each of these health services. Count regression models were used to generate incidence rate ratios (IRRs) for each outcome given the presence of PIP versus no PIP and combined with the prevalence of PIP to generate population attributable fractions (PAFs). The PAF was then multiplied by the cost for each health service to obtain the costs attributable to PIP in the whole cohort, and by age and sex.

RESULTS

PIP was associated with an increased rate of hospitalization (IRR 2.77, 95% confidence interval [CI] 2.72-2.82), ED visits (IRR 1.87, 95% CI 1.82-1.92), and newly prescribed medications (IRR 1.13, 95% CI 1.13-1.14), resulting in PAFs of 55.7, 37.9, and 5.0% for each outcome, respectively. PIP was associated with 38.8% of the total spent on these healthcare services ($1.22 billion) in the 90 days after PIP. Costs attributable to PIP decreased with age despite increasing prevalence.

CONCLUSIONS

PIP in older adults is a significant source of health system costs from healthcare service use beyond medication costs, with a significant portion of hospitalizations and ED visit costs attributable to PIP. Future work should focus on identifying strategies and priorities for intervention.

eIF5B gates the transition from translation initiation to elongation

Translation initiation determines both the quantity and identity of the protein encoded in an mRNA by establishing the reading frame for protein synthesis. In eukaryotic cells, numerous translation initiation factors (eIFs) prepare ribosomes for polypeptide synthesis, yet the underlying dynamics of this process remain enigmatic^1,2. A central question is how eukaryotic ribosomes transition from translation initiation to elongation. Here, we applied in vitro single-molecule fluorescence microscopy approaches to monitor directly in real time the pathways of late translation initiation and the transition to elongation using a purified yeast Saccharomyces cerevisiae translation system. This transition was remarkably slower in our eukaryotic system than that reported for Escherichia coli^3–5. The slow entry to elongation was defined by a long residence time of eIF5B on the 80S ribosome after joining of individual ribosomal subunits, which is catalyzed by this universally conserved initiation factor. Inhibition of eIF5B GTPase activity following subunit joining prevented eIF5B dissociation from the 80S complex, thereby preventing elongation. Our findings illustrate how eIF5B dissociation serves as a kinetic checkpoint for the transition from initiation to elongation, and its release may be governed by a conformation of the ribosome complex that triggers GTP hydrolysis.

Association of Glucose Concentrations at Hospital Discharge With Readmissions and Mortality: A Nationwide Cohort Study

CONTEXT

Low blood glucose concentrations during the discharge day may affect 30-day readmission and posthospital discharge mortality rates.

OBJECTIVE

To investigate whether patients with diabetes and low glucose values during the last day of hospitalization are at increased risk of readmission or mortality.

DESIGN AND OUTCOMES

Minimum point of care glucose values were collected during the last 24 hours of hospitalization. We used adjusted rates of 30-day readmission rate, 30-, 90-, and 180-day mortality rates, and combined 30-day readmission/mortality rate to identify minimum glucose thresholds above which patients can be safely discharged.

PATIENTS AND SETTING

Nationwide cohort study including 843,978 admissions of patients with diabetes at the Veteran Affairs hospitals 14 years.

RESULTS

The rate ratios (RRs) increased progressively for all five outcomes as the minimum glucose concentrations progressively decreased below the 90 to 99 mg/dL category, compared with the 100 to 109 mg/dL category: 30-day readmission RR, 1.01 to 1.45; 30-day readmission/mortality RR, 1.01 to 1.71; 30-day mortality RR, 0.99 to 5.82; 90-day mortality RR, 1.01 to 2.40; 180-day mortality RR, 1.03 to 1.91. Patients with diabetes experienced greater 30-day readmission rates, 30-, 90- and 180-day postdischarge mortality rates, and higher combined 30-day readmission/mortality rates, with glucose levels <92.9 mg/dL, <45.2 mg/dL, 65.8 mg/dL, 67.3 mg/dL, and <87.2 mg/dL, respectively.

CONCLUSION

Patients with diabetes who had hypoglycemia or near-normal glucose values during the last day of hospitalization had higher rates of 30-day readmission and postdischarge mortality.

Transitional care interventions reduce unplanned hospital readmissions in high-risk older adults

BACKGROUND

Acute hospital services account for the largest proportion of health care system budgets, and older adults are the most frequent users. As a result, older people who have been recently discharged from hospital may be at greater risk of readmission. This study aims to evaluate the comparative effectiveness of transitional care interventions on unplanned hospital readmissions within 28 days, 12 weeks and 24 weeks following hospital discharge.

METHOD

The present study was a randomised controlled trial (ACTRN12608000202369). The trial involved 222 participants who were recruited from medical wards in two metropolitan hospitals in Australia. Participants were eligible for inclusion if they were aged 65 years and over, admitted with a medical diagnosis and had at least one risk factor for readmission. Participants were randomised to one of four groups: standard care, exercise program only, Nurse Home visit and Telephone follow-up (N-HaT), or Exercise program and Nurse Home visit and Telephone follow-up (ExN-HaT). Socio-demographics, health and functional ability were assessed at baseline, 28 days, 12 weeks and 24 weeks. The primary outcome measure was unplanned hospital readmission which was defined as any hospital admission for an unforeseen or unplanned cause.

RESULTS

Participants in the ExN-HaT or the N-HaT groups were 3.6 times and 2.6 times respectively significantly less likely to have an unplanned readmission 28 days following discharge (ExN-HaT group HR 0.28, 95% CI 0.09-0.87, p = 0.029; N-HaT group HR 0.38, 95% CI 0.13-1.07, p = 0.067). Participants in the ExN-HaT or the N-HaT groups were 2.13 and 2.63 times respectively less likely to have an unplanned readmission in the 12 weeks after discharge (ExN-HaT group HR 0.47, 95% CI 0.23-0.97, p = 0.014; N-HaT group HR 0.38, 95% CI 0.18-0.82, p = 0.040). At 24 weeks after discharge, there were no significant differences between groups.

CONCLUSION

Multifaceted transitional care interventions across hospital and community settings are beneficial, with lower hospital readmission rates observed in those receiving more transitional intervention components, although only in first 12 weeks.

TRIAL REGISTRATION

Australian and New Zealand Clinical Trial Registry ( ACTRN12608000202369 ).

Health system costs of potentially inappropriate prescribing in Ontario, Canada: a protocol for a population-based cohort study

INTRODUCTION

Adverse drug events (ADEs) are common in older persons and contribute significantly to emergency department visits, hospitalisations and mortality. ADEs are often due to potentially inappropriate prescriptions (PIP) or potentially inappropriate omissions (PIO), and are avoidable if inappropriate prescriptions or omissions are identified and prevented. Identifying PIP/PIO at the population level through the application of PIP/PIO assessment tools to health administrative data can provide a unique opportunity to assess the economic burden of PIP/PIO on the healthcare system beyond medication costs which is yet to be done. The objective of this study is to assess the economic burden associated with PIP/PIO and to estimate the incremental costs associated with distinct PIP/PIO in the province of Ontario.

METHODS AND ANALYSIS

We will conduct a retrospective cohort study using Ontario's health administrative databases. Eligible patients aged 66 years and older who were prescribed at least one medication between 1 April 2003 and 31 March 2014 (approximately 2.4 million patients) will be included. Population attributable fraction methodology will be used to assess the overall burden of PIP in Ontario, while regression analyses will be used to estimate the incremental costs of having specific PIP criteria and aid in prioritising targets for intervention.

ETHICS AND DISSEMINATION

This study was approved by the Institutional Review Board at Sunnybrook Health Sciences Centre, Toronto, Canada. Dissemination will occur via publication, presentation at national and international conferences, and knowledge exchange with various stakeholders.

Correction to: Hospital Readmission of Patients with Diabetes

This article was originally published with errors that were introduced during the editing process. The corrected version of this article appears below.

Predicting Hospital Re-admissions from Nursing Care Data of Hospitalized Patients

Readmission rates in the hospitals are increasingly being used as a benchmark to determine the quality of healthcare delivery to hospitalized patients. Around three-fourths of all hospital re-admissions can be avoided, saving billions of dollars. Many hospitals have now deployed electronic health record (EHR) systems that can be used to study issues that trigger readmission.However, most of the EHRs are high dimensional and sparsely populated, and analyzing such data sets is a Big Data challenge. The effect of some of the well-known dimension reduction techniques is minimized due to presence of non-linear variables. We use association mining as a dimension reduction method and the results are used to develop models, using data from an existing nursing EHR system, for predicting risk of re-admission to the hospitals. These models can help in determining effective treatments for patients to minimize the possibility of re-admission, bringing down the cost and increasing the quality of care provided to the patients. Results from the models show significantly accurate predictions of patient re-admission.

Assessing potentially inappropriate prescribing (PIP) and predicting patient outcomes in Ontario's older population: a population-based cohort study applying subsets of the STOPP/START and Beers' criteria in large health administrative databases

INTRODUCTION

Adverse drug events (ADEs) are common in older people and contribute significantly to emergency department (ED) visits, unplanned hospitalisations, healthcare costs, morbidity and mortality. Many ADEs are avoidable if attention is directed towards identifying and preventing inappropriate drug use and undesirable drug combinations. Tools exist to identify potentially inappropriate prescribing (PIP) in clinical settings, but they are underused. Applying PIP assessment tools to population-wide health administrative data could provide an opportunity to assess the impact of PIP on individual patients as well as on the healthcare system. This would open new possibilities for interventions to monitor and optimise medication management on a broader, population-level scale.

METHODS AND ANALYSIS

The aim of this study is to describe the occurrence of PIP in Ontario's older population (aged 65 years and older), and to assess the health outcomes and health system costs associated with PIP-more specifically, the association between PIP and the occurrence of ED visits, hospitalisations and death, and their related costs. This will be done within the framework of a population-based retrospective cohort study using Ontario's large health administrative and population databases. Eligible patients aged 66 years and older who were issued at least 1 prescription between 1 April 2003 and 31 March 2014 (approximately 2 million patients) will be included.

ETHICS AND DISSEMINATION

Ethical approval was obtained from the Ottawa Health Services Network Ethical Review Board and from the Bruyère Research Institute Ethics Review Board. Dissemination will occur via publication, presentation at national and international conferences, and ongoing exchanges with regional, provincial and national stakeholders, including the Ontario Drug Policy Research Network and the Ontario Ministry of Health and Long-Term Care.

TRIAL REGISTRATION NUMBER

Registered with clinicaltrials.gov (registration number: NCT02555891).

Hospital readmission of patients with diabetes

Hospital readmission is a high-priority health care quality measure and target for cost reduction. Despite broad interest in readmission, relatively little research has focused on patients with diabetes. The burden of diabetes among hospitalized patients, however, is substantial, growing, and costly, and readmissions contribute a significant portion of this burden. Reducing readmission rates of diabetic patients has the potential to greatly reduce health care costs while simultaneously improving care. Risk factors for readmission in this population include lower socioeconomic status, racial/ethnic minority, comorbidity burden, public insurance, emergent or urgent admission, and a history of recent prior hospitalization. Hospitalized patients with diabetes may be at higher risk of readmission than those without diabetes. Potential ways to reduce readmission risk are inpatient education, specialty care, better discharge instructions, coordination of care, and post-discharge support. More studies are needed to test the effect of these interventions on the readmission rates of patients with diabetes.

A tool for prediction of risk of rehospitalisation and mortality in the hospitalised elderly: secondary analysis of clinical trial data

OBJECTIVES

To construct and internally validate a risk score, the '80+ score', for revisits to hospital and mortality for older patients, incorporating aspects of pharmacotherapy. Our secondary aim was to compare the discriminatory ability of the score with that of three validated tools for measuring inappropriate prescribing: Screening Tool of Older Person's Prescriptions (STOPP), Screening Tool to Alert doctors to Right Treatment (START) and Medication Appropriateness Index (MAI).

SETTING

Two acute internal medicine wards at Uppsala University hospital. Patient data were used from a randomised controlled trial investigating the effects of a comprehensive clinical pharmacist intervention.

PARTICIPANTS

Data from 368 patients, aged 80 years and older, admitted to one of the study wards.

PRIMARY OUTCOME MEASURE

Time to rehospitalisation or death during the year after discharge from hospital. Candidate variables were selected among a large number of clinical and drug-specific variables. After a selection process, a score for risk estimation was constructed. The 80+ score was internally validated, and the discriminatory ability of the score and of STOPP, START and MAI was assessed using C-statistics.

RESULTS

Seven variables were selected. Impaired renal function, pulmonary disease, malignant disease, living in a nursing home, being prescribed an opioid or being prescribed a drug for peptic ulcer or gastroesophageal reflux disease were associated with an increased risk, while being prescribed an antidepressant drug (tricyclic antidepressants not included) was linked to a lower risk of the outcome. These variables made up the components of the 80+ score. The C-statistics were 0.71 (80+), 0.57 (STOPP), 0.54 (START) and 0.63 (MAI).

CONCLUSIONS

We developed and internally validated a score for prediction of risk of rehospitalisation and mortality in hospitalised older people. The score discriminated risk better than available tools for inappropriate prescribing. Pending external validation, this score can aid in clinical identification of high-risk patients and targeting of interventions.

Reuse of clinical data

OBJECTIVES

To provide an overview of the benefits of clinical data collected as a by-product of the care process, the potential problems with large aggregations of these data, the policy frameworks that have been formulated, and the major challenges in the coming years.

METHODS

This report summarizes some of the major observations from AMIA and IMIA conferences held on this admittedly broad topic from 2006 through 2013. This report also includes many unsupported opinions of the author.

RESULTS

The benefits of aggregating larger and larger sets of routinely collected clinical data are well documented and of great societal benefit. These large data sets will probably never answer all possible clinical questions for methodological reasons. Non-traditional sources of health data that are patient-sources will pose new data science challenges.

CONCLUSIONS

If we ever hope to have tools that can rapidly provide evidence for daily practice of medicine we need a science of health data perhaps modeled after the science of astronomy.

Quality of transitions in older medical patients with frequent readmissions: opportunities for improvement

BACKGROUND

Medical patients with a recent previous hospitalisation are at very high risk of subsequent readmission. Evidence suggests that improving key transition processes may reduce hospital readmissions. This study describes quality of transition processes in frequently admitted medical patients, to inform system improvements for this high risk group.

METHODS

Retrospective records review of consecutive medical inpatients aged 50 years or older in a major metropolitan teaching hospital in Australia with a recent (within 6 months) prior hospitalisation. Information was sought on 4 key processes: discharge summary completed and sent within 2 weeks; discharge medication reconciliation; patient/carer discharge education; and timely scheduling of outpatient review with the treating team. Readmission rates were obtained from a state-wide admissions database.

RESULTS

Discharge processes for 209 live discharges in 164 patients were reviewed. Although timely discharge summary completion (81%) and discharge medication reconciliation by a pharmacist (81%) were high, there were major gaps in patient education (33%) and in timely outpatient review (12%). Outpatient systems appear poorly organised to support high quality transitions. Readmission rates were high (23% at 30 days and 58% at 180 days). Individual discharge quality processes did not predict readmissions.

DISCUSSION

Gaps in transitional care of frequently attending medical patients provide potential targets for improvement. In particular, opportunities for better patient/carer education and timely, structured outpatient review may inform design of improved transitions for this high risk group, to be tested in prospective controlled trials.

Hospital readmission among elderly patients

This study investigates the incidence and determinants of hospital readmissions among elderly patients in Norway. The analyses are based on registered data on inpatient admissions to public hospitals from 1999 to 2006. During this period, mean length of stay in hospital decreased, while readmission rates increased. Probit and instrumental variable regression models are applied for the analyses. The results indicate that longer length of stay in the hospital is associated with lower probability of readmission. A patient's age, comorbidities, and complexity of the treatment procedure are positively associated with readmissions, while higher number of diagnostic procedures negatively affects hospital readmission. Finally, patients discharged to institutions are more likely to be readmitted to the hospital.

Acute care hospital utilization among medical inpatients discharged with a substance use disorder diagnosis

OBJECTIVE

Hospital discharge may be an opportunity to intervene among patients with substance use disorders to reduce subsequent hospital utilization. This study determined whether having a substance use disorder diagnosis was associated with subsequent acute care hospital utilization.

METHODS

We conducted an observational cohort study among 738 patients on a general medical service at an urban, academic, safety-net hospital. The main outcomes were rate and risk of acute care hospital utilization (emergency department visit or hospitalization) within 30 days of discharge. The main independent variable was presence of a substance use disorder primary or secondary discharge diagnosis code at the index hospitalization.

RESULTS

At discharge, 17% of subjects had a substance use disorder diagnosis. These patients had higher rates of recurrent acute care hospital utilization than patients without substance use disorder diagnoses (0.63 vs 0.32 events per subject at 30 days, P < 0.01) and increased risk of any recurrent acute care hospital utilization (33% vs 22% at 30 days, P < 0.05). In adjusted Poisson regression models, the incident rate ratio at 30 days was 1.49 (95% confidence interval, 1.12-1.98) for patients with substance use disorder diagnoses compared with those without. In subgroup analyses, higher utilization was attributable to those with drug diagnoses or a combination of drug and alcohol diagnoses, but not to those with exclusively alcohol diagnoses.

CONCLUSIONS

Medical patients with substance use disorder diagnoses, specifically those with drug use-related diagnoses, have higher rates of recurrent acute care hospital utilization than those without substance use disorder diagnoses.

Depression is a risk factor for rehospitalization in medical inpatients

BACKGROUND

Rehospitalization occurs in approximately 20% of medical inpatients within 90 days of discharge. Rehospitalization accounts for considerable morbidity, mortality, and costs. Identification of risk factors could lead to interventions to reduce rehospitalization. The objective of the study was to determine if physical and mental health, substance abuse, and social support are risk factors for rehospitalization.

METHOD

This was a prospective cohort study in an innercity population conducted from September 2002 to September 2004. Participants included 144 adult inpatients with at least 1 hospital admission in the past 6 months. Measurements included age, length of stay, number of admissions in the past year, and medical comorbidity as well as measures of depression, alcohol and drug abuse, social support, and health-related quality of life. The outcome studied was the rehospitalization status of participants within 90 days of the index hospitalization.

RESULTS

The mean age of the subjects was 54.8 years; 48% were black and 78% spoke English as a primary language. Subjects were admitted a mean of 2.5 times in the year before the index admission. Sixty-four patients (44%) were subsequently rehospitalized within 90 days after the index admission. In bivariate analysis, rehospitalized patients had more prior admissions (median of 3.0 vs. 2.0 admissions, p = .002), greater medical comorbidity (mean Charlson Comorbidity Index score of 2.6 vs. 2.0, p = .04), and poorer physical functional status (mean SF-12 physical component score of 31.5 vs. 36.2, p = .03). A logistic regression model, including prior admissions in the last year, comorbidity, physical functional status, and depression, showed that depression tripled the odds of rehospitalization (odds ratio = 3.3, 95% CI = 1.2 to 9.3). This model had fair accuracy in identifying patients at greatest risk for rehospitalization (c statistic = 0.72).

CONCLUSIONS

Hospitalized patients with a history of prior hospitalization within 6 months who screen positive for depression are 3 times more likely to be rehospitalized within 90 days in this relatively high-risk population. Screening during hospitalization for depressive symptoms may identify those at risk for rehospitalization.

Recurrent readmissions in medical patients: a prospective study

BACKGROUND

Hospital readmissions are common and costly. A recent previous hospitalization preceding the index admission is a marker of increased risk of future readmission.

OBJECTIVES

To identify factors associated with an increased risk of recurrent readmission in medical patients with 2 or more hospitalizations in the past 6 months.

DESIGN

Prospective cohort study.

SETTING

Australian teaching hospital acute medical wards, February 2006-February 2007.

PARTICIPANTS

142 inpatients aged ≥ 50 years with a previous hospitalization ≤ 6 months preceding the index admission. Patients from residential care, with terminal illness, or with serious cognitive or language difficulties were excluded.

VARIABLES OF INTEREST

Demographics, previous hospitalizations, diagnosis, comorbidities and nutritional status were recorded in hospital. Participants were assessed at home within 2 weeks of hospital discharge using validated questionnaires for cognition, literacy, activities of daily living (ADL)/instrumental activities of daily living (IADL) function, depression, anxiety, alcohol use, medication adherence, social support, and financial status.

MAIN OUTCOME MEASURE

Unplanned readmission to the study hospital within 6 months.

RESULTS

A total of 55 participants (38.7%) had a further unplanned hospital admission within 6 months. In multivariate analysis, chronic disease (adjusted odds ratio [OR] 3.4; 95% confidence interval [CI], 1.3-9.3, P = 0.002), depressive symptoms (adjusted OR, 3.0; 95% CI, 1.3-6.8, P = 0.01), and underweight (adjusted OR, 12.7; 95% CI, 2.3-70.7, P = 0.004) were significant predictors of readmission after adjusting for age, length of stay and functional status.

CONCLUSIONS

In this high-risk patient group, multiple chronic conditions are common and predict increased risk of readmission. Post-hospital interventions should consider targeting nutritional and mood status in this population.

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, >or=1 admission(s) within the last year, and current length of stay >2 days. A cumulative risk score of >or=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.

Risk factors for 30-day hospital readmission in patients ≥65 years of age

The objective of the study was to develop and validate predictors of 30-day hospital readmission using readily available administrative data and to compare prediction models that use alternate comorbidity classifications. A retrospective cohort study was designed; the models were developed in a two-thirds random sample and validated in the remaining one-third sample. The study cohort consisted of 29,292 adults aged 65 or older who were admitted from July 2002 to June 2004 to any of seven acute care hospitals in the Dallas-Fort Worth metropolitan area affiliated with the Baylor Health Care System. Demographic variables (age, sex, race), health system variables (insurance, discharge location, medical vs surgical service), comorbidity (classified by the Elixhauser classification or the High-Risk Diagnoses in the Elderly Scale), and geographic variables (distance from patient's residence to hospital and median income) were assessed by estimating relative risk and risk difference for 30-day readmission. Population-attributable risk was calculated. Results showed that age 75 or older, male sex, African American race, medical vs surgical service, Medicare with no other insurance, discharge to a skilled nursing facility, and specific comorbidities predicted 30-day readmission. Models with demographic, health system, and either comorbidity classification covariates performed similarly, with modest discrimination (C statistic, 0.65) and acceptable calibration (Hosmer-Lemeshow χ² = 6.08; P > 0.24). Models with demographic variables, health system variables, and number of comorbid conditions also performed adequately. Discharge to long-term care (relative risk, 1.94; 95% confidence interval, 1.80- 2.09) had the highest population-attributable risk of 30-day readmission (12.86%). A 25% threshold of predicted probability of 30-day readmission identified 4.1 % of patients ≥65 years old as priority patients for improved discharge planning. We conclude that elders with a high risk of 30-day hospital readmission can be identified early in their hospital course.

Early readmissions to the department of medicine as a screening tool for monitoring quality of care problems

With growing awareness of medical fallibility, researchers need to develop tools to identify and study medical mistakes. We examined the utility of hospital readmissions for this purpose in a prospective case-control study in a large academic medical center in Israel. All patients with nonelective readmissions to 2 departments of medicine within 30 days of discharge were interviewed, and their medical records were carefully examined with emphasis on the index admission. Patient data were compared to data for age- and sex-matched controls (n = 140) who were not readmitted. Medical records of readmitted and control patients were blindly evaluated by 2 senior clinicians who independently identified potential quality of care (QOC) problems during the index admission. Inhospital and late mortality was determined 6 months after discharge.Over a period of 3 months there were 1988 urgent admissions; 1913 discharges and subsequently 271 unplanned readmissions occurred (14.1% of discharges). Readmissions occurred an average of 10 days after discharge, and readmitted patients were sicker than controls (mean, 4.3 vs. 3.3 diagnoses per patient), although their length of stay was similarly short (3.4 +/- 2.8 d). Analysis of all readmissions revealed QOC problems in 90/271 (33%) of readmissions, 4.5% of hospitalizations. All were deemed preventable. Interobserver agreement was good (83%, kappa = 0.67). Among matched controls, only 8/140 admissions revealed QOC problems (6%, p < 0.001) (k = 0.77). The preventable readmissions mostly involved a vascular event or congestive heart failure; they occurred within a mean of 10 +/- 8 days of the index admission, and their inpatient mortality was 6.7% vs. 1.7% among readmissions that had no QOC problems (odds ratio, 4.1; 95% confidence interval, 1.0-16.7). The main pitfalls identified during the index admission included incomplete workup (33%), too short hospital stay (31%), inappropriate medication (44%), diagnostic error (16%), and disregarding a significant laboratory result (12%). In many patients more than 1 pitfall was identified (mean, 1.5 per patient). Risk factors for preventable readmission include older age and living in an institution (p < 0.05). Almost two-thirds of the readmitted patients with QOC problems were discharged after spending 2 days or fewer at the hospital. In conclusion, unplanned readmissions within 30 days of discharge are frequent, more prevalent in sicker patients, and possibly associated with increased mortality. In a third of readmitted patients a QOC problem can be identified, and these problems are preventable. Thus, readmission may be used as a screening tool for potential QOC problems in the department of medicine. Routine monitoring of all readmissions may provide a simple cost-effective means of identifying and addressing medical mistakes.

The readmission of elderly people to hospital in an inner-city health district

The hospital sector in Britain has, over the last decade, achieved substantial reductions in the average length of stay for patients aged 65 and over. One consequence of this may be increased readmission rates. Furthermore, readmission rates are increasingly being proposed as a surrogate measure of outcome after hospital treatment. All admissions of people aged 65 + to two inner-London hospitals in May 1988 were monitored for 6 months after discharge and readmission rates calculated. Of the 386 patients discharged, 130 (38%) were readmitted within 6 months. The 1 week readmission rate was 6%; at 1 month it was 18%. Readmissions showed no variation regarding age or sex of patients but were related to specialty of treatment and length of stay. Consistently, those readmitted has a shorter length of stay than those not readmitted. If readmission rates are to have any utility as a surrogate outcome indicator they must be calculated on a common basis which relates to unplanned readmissions occurring fairly rapidly after discharge, and are related to the index admission.

Continuity of care and patient outcomes after hospital discharge

BACKGROUND

Patients are often treated in hospital by physicians other than their regular community doctor. After they are discharged, their care is often returned to their regular community doctor and patients may not see the hospital physician. Transfer of information between physicians can be poor. We determined whether early postdischarge outcomes changed when patients were seen after discharge by physicians who treated them in the hospital.

METHODS

This cohort study used population-based administrative databases to follow 938833 adults from Ontario, Canada, after they were discharged alive from a nonelective medical or surgical hospitalization between April 1, 1995, and March 1, 2000. We determined when patients were seen after discharge by physicians who treated them in the hospital, physicians who treated them 3 months prior to admission (community physicians), and specialists. The outcome of interest was 30-day death or nonelective readmission to hospital.

RESULTS

Of patients studied, 7.7% died or were readmitted. The adjusted relative risk of death or readmission decreased by 5% (95% confidence interval [CI], 4% to 5%) and 3% (95% CI, 2% to 3%) with each additional visit to a hospital physician rather than a community physician or specialist, respectively. The effect of hospital physician visits was cumulative, with the adjusted risk of 30-day death or nonelective readmission reduced to 7.3%, 7.0%, and 6.7% if patients had 1, 2, or 3 visits, respectively, with a hospital rather than a community physician. The effect was consistent across important subgroups.

CONCLUSIONS

Patient outcomes could be improved if their early postdischarge visits were with physicians who treated them in hospital rather than with other physicians. Follow-up visits with a hospital physician, rather than another physician, could be a modifiable factor to improve patient outcomes following discharge from hospital.

Predictors of early hospital readmission in HIV-infected patients with pneumonia

OBJECTIVE

Although hospitalization patterns have been studied, little is known about hospital readmission among HIV-infected patients in the era of highly active antiretroviral therapy. We explored the risk factors for early readmission to a tertiary care inner-city hospital among HIV-infected patients with pneumonia in Vancouver, Canada.

DESIGN

Case-control study.

SETTING

Tertiary care, university-affiliated, inner-city hospital.

PARTICIPANTS

All HIV-infected patients who were hospitalized with Pneumocystis carinii pneumonia (PCP) or bacterial pneumonia (BP) between January 1997 and December 2000. Case patients included those who had early readmissions, defined as being readmitted within 2 weeks of discharge (N = 131). Control patients were randomly selected HIV-infected patients admitted during the study period who were not readmitted within 2 weeks of discharge (N = 131), matched to the cases by proportion of PCP to BP.

MEASUREMENTS

Sociodemographic, HIV risk category, and clinical data were compared using chi2 test for categorical variables, and the Wilcoxon rank-sum test was used for continuous variables. Multivariable logistic regression was performed to determine the factors independently associated with early readmission. We also reviewed the medical records of 132 patients admitted to the HIV/AIDS ward during the study period and collected more detailed clinical data for a subanalysis.

MAIN RESULTS

Patients were at significantly increased odds of early readmission if they left the hospital against medical advice (AMA) (adjusted odds ratio [OR], 4.26; 95% confidence interval [95% CI], 2.13 to 8.55), lived in the poorest urban neighborhood (OR, 2.03; 95% CI, 1.09 to 3.77), were hospitalized in summer season (May though October, OR, 2.36; 95% CI, 1.36 to 4.10), or had been admitted in the preceding 6 months (OR, 2.55; 95% CI, 1.46 to 4.47). Gender, age, history of AIDS-defining illness, and injection drug use status were not significantly associated with early readmission.

CONCLUSIONS

Predictors of early readmission of HIV-infected patients with pneumonia included: leaving hospital AMA, living in the poorest urban neighborhood, being hospitalized in the preceding 6 months and during the summer months. Interventions involving social work may address some of the underlying reasons why these patients leave hospital AMA and should be further studied.

Effect of discharge summary availability during post-discharge visits on hospital readmission

OBJECTIVE

To determine if the delivery of hospital discharge summaries to follow-up physicians decreases the risk of hospital readmission.

SUBJECTS

Eight hundred eighty-eight patients discharged from a single hospital following treatment for an acute medical illness.

SETTING

Teaching hospital in a universal health-care system.

DESIGN

We determined the date that each patient's discharge summary was printed and the physicians to whom it was sent. Summary receipt was confirmed by survey and phoning each physician's office. Each patient's hospital chart was reviewed to determine their acute and chronic medical conditions as well as their course in hospital. Using population-based administrative databases, all post-hospitalization visits were identified. For each of these visits, we determined whether the summary was available.

MAIN OUTCOME MEASURES

Time to nonelective hospital readmission during 3 months following discharge.

RESULTS

The discharge summary was available for only 568 of 4,639 outpatient visits (12.2%). Overall, 240 (27.0%) of patients were urgently readmitted to hospital. After adjusting for significant patient and hospitalization factors, we found a trend toward a decreased risk of readmission for patients who were seen in follow-up by a physician who had received a summary (relative risk 0.74, 95% confidence interval 0.50 to 1.11).

CONCLUSIONS

The risk of rehospitalization may decrease when patients are assessed following discharge by physicians who have received the discharge summary. Further research is required to determine if better continuity of patient information improves patient outcomes.

Predicting non-elective hospital readmissions: a multi-site study. Department of Veterans Affairs Cooperative Study Group on Primary Care and Readmissions

OBJECTIVE

To determine clinical and patient-centered factors predicting non-elective hospital readmissions.

DESIGN

Secondary analysis from a randomized clinical trial.

CLINICAL SETTING

Nine VA medical centers.

PARTICIPANTS

Patients discharged from the medical service with diabetes mellitus, congestive heart failure, and/or chronic obstructive pulmonary disease (COPD).

MAIN OUTCOME MEASUREMENT

Non-elective readmission within 90 days.

RESULTS

Of 1378 patients discharged, 23.3% were readmitted. After controlling for hospital and intervention status, risk of readmission was increased if the patient had more hospitalizations and emergency room visits in the prior 6 months, higher blood urea nitrogen, lower mental health function, a diagnosis of COPD, and increased satisfaction with access to emergency care assessed on the index hospitalization.

CONCLUSIONS

Both clinical and patient-centered factors identifiable at discharge are related to non-elective readmission. These factors identify high-risk patients and provide guidance for future interventions. The relationship of patient satisfaction measures to readmission deserves further study.

Risk factors for early hospital readmission in patients with AIDS and pneumonia

OBJECTIVE

To determine risk factors for early readmission to the hospital in patients with AIDS and pneumonia.

DESIGN

Case-control analysis.

SETTING

A municipal teaching hospital serving an indigent population.

PATIENTS

Case patients were all AIDS patients hospitalized with Pneumocystis carinii pneumonia or bacterial pneumonia between January 1992 and March 1995 who were readmitted for any nonelective reason within 2 weeks of discharge (n = 90). Control patients were randomly selected AIDS patients admitted during the study period who were not early readmissions (n = 87), matched by proportion of Pneumocystis carinii to bacterial pneumonia.

MEASUREMENTS AND MAIN RESULTS

Demographics, social support, health-related behaviors, clinical aspects of the acute hospitalization, and general medical status were the main predictors measured.

RESULTS

Patients were at significantly increased risk of early readmission if they left the hospital unaccompanied by family or friend (odds ratio [OR] 4.76; 95% confidence interval [CI] 2.06, 11.0; p =.0003), used crack cocaine (OR 3.40; 95% CI 1.02, 11.3; p =. 046), had one or more coincident AIDS diagnoses (OR 3.65; 95% CI 1. 44, 9.26; p =.0065), or had been admitted in the preceding 6 months (OR 2.82; 95% CI 1.21, 6.57; p =.016). Demographic characteristics, alcoholism, intravenous drug use, illness severity on admission, and length of hospitalization did not predict early readmission.

CONCLUSIONS

Absence of companion at discharge and crack use were important risk factors for early readmission in patients with AIDS and pneumonia. Additional AIDS comorbidity and recent antecedent hospitalization were also risk factors; however, demographics and measures of acute illness during index hospitalization did not predict early readmission.

Risk factors for nonelective hospital readmissions

We previously reported a predictive model that identified potentially modifiable risk factors for nonelective readmission to a county hospital. The objectives of this study were to determine if those risk factors were generalizable to a different population. We found that the previously reported risk factors were generalizable, and other potentially modifiable risk factors were identified in this population of veterans. However, further research is needed to establish whether or not the risk factors can be modified and whether or not modification improves outcomes.

Risk factors associated with multiple hospital readmissions

The percentage of multiple hospital readmissions averages between 21% and 27% in the United States today. The reasons for this readmission rate and, more important, how readmissions can be prevented, are not clear. In this integrative review we examine 13 research articles in an attempt to identify specific factors leading to the readmission of medical patients. Risk factors continually researched throughout the articles were dependence, patient age, stage of illness, length of hospital stay, prior hospitalization, care after discharge, and mobility status. Congestive heart failure and chronic obstructive pulmonary disease were the medical conditions responsible for most readmissions. No single factor was found to universally predict readmission, although several items were found to be statistically significant.

Lack of association between patients' measured burden of disease and risk for hospital readmission

Identifying patients at increased risk for hospital readmission is important for clinicians, health policy-makers, hospital administrators, and researchers. We used a retrospective case-control design to compare the clinimetric properties of five validated indices that measure a patient's disease burden. The study was conducted on a random sample of patients discharged from the general medicine service at the Durham Department of Veterans Affairs Medical Center. Trained observers (two research assistants, one nurse, and two physicians) blinded to readmission status abstracted the required data elements from the medical record for three indices (Charlson, Kaplan-Feinstein, Index of Coexistent Disease). The hospital's computer provided data elements for two indices (Smith, adapted Charlson). Indices varied in the time required to complete, the ability to capture individual heterogeneity, and inter-observer variability. However, none of the indices discriminated among patients who did and those who did not have 6-month hospital readmissions. Factors other than summary scores derived from these indices should be used to identify patients at high risk for readmission.

Recidivism in a helicopter emergency medical service

This study was designed to determine the frequency of recidivism (patients using a service more than once for the same or different disease episodes) in a helicopter emergency service, with the premise that high levels of recidivism may constitute grounds for improving quality of care or patient education programs. A retrospective chart review was performed on records from June 1985 to September 1990. Patients were included if they required helicopter transport on more than one occasion for either different disease episodes (true recidivists) or for multiple transports during a single hospital admission. Twenty-one (0.6%) of the 3,543 patients transported were true recidivists and 20 (0.6%) patients required secondary transport during the same admission. Of the latter group, 17 secondary transports were within 24 hours of admission. This study showed that recidivism in this helicopter emergency service is low. Patients who were air transported more than once for the same illness or injury within a 24-hour period occurred in less than 1% of transports, well within the helicopter program's pre-established less than 2% threshold.

Factors predicting readmission of older general medicine patients

OBJECTIVES

1) Identify demographic, clinical social support, functional, and psychological factors about which data are available within 24 hours of hospital admission associated with emergent unscheduled readmission for a group of older general medicine patients; 2) develop a model to predict emergent readmission.

DESIGN

Interview- and chart-based study of emergent admissions that occurred within 60 days of discharge.

SETTING

General medicine wards of the Memphis Veterans Affairs Medical Center, an 862-bed university-affiliated tertiary care facility.

PATIENTS/PARTICIPANTS

General medicine patients greater than or equal to 65 years old (n = 173). Inclusion criteria were willingness to participate, written consent (patient or family member), and patient interview within 36 hours of admission.

MEASUREMENTS AND MAIN RESULTS

The dependent variable was emergent readmission within 60 days of discharge from the hospital. Independent variables included demographic (age, race, income, education), social support (marital status, living arrangements), psychological (cognition, depression), activities of daily living functioning, and clinical (diagnoses, type and source of admission, length of stay, numbers of hospitalizations and days of hospitalizations in the past year, illness severity) parameters. Readmitted patients were emergently admitted and more severely ill, had more diagnoses of chronic obstructive pulmonary disease (COPD) or congestive heart failure (CHF), less ischemic heart disease, and more hospitalizations and hospital days in the past year (all p less than 0.05). Logistic regression identified diagnostic group (COPD or CHF), emergent admission, and admission severity of illness as predictive of readmission. The likelihood of being readmitted was 5.4. Accuracy of the three-variable model was 76%, predicted value positive, 73%, and predictive value negative, 77%.

CONCLUSIONS

Chronically ill patients who are severely ill at index admission and who have had several hospitalizations in the past year tend to be readmitted. Using this model, high-risk patients may be prospectively targeted to reduce readmissions.

Risk factors for early unplanned hospital readmission in the elderly

STUDY OBJECTIVE

To determine the prevalence of early (in 14 days or less) readmissions to the hospital, and to identify risk factors for readmission.

DESIGN

Matched case-control. Cases (n = 155) were readmitted to the hospital within 14 days of a hospital discharge, while controls (n = 155) were not. Controls and cases were matched by week of hospital discharge.

PATIENTS

Two-year sequential sample of male veterans aged 65 years and over admitted to the Seattle Veterans Affairs (VA) Medical Center.

MEASUREMENTS

Data about 31 potential risk factors were abstracted from the medical records.

RESULTS

Three risk factors associated with readmission risk were identified and include two or more hospital admissions in the previous year [odds ratio (OR) = 3.06], any medication dosage change in the 48 hours prior to discharge (OR = 2.34), and a visiting nurse referral for follow-up (OR = 2.78). One protective factor--discharge from the geriatric evaluation unit (GEU) (OR = 0.09)--was also determined.

CONCLUSIONS

Early unplanned readmissions were frequent at this VA facility. Since the strongest risk factor for readmission was the number of admissions in the previous year, readmissions appeared most commonly among high utilizers of inpatient VA care. This risk factor and others may be useful in identifying a group at high readmission risk, which could be targeted in intervention studies. The reduced readmission rate associated with the GEU suggests one potential intervention to decrease readmission risk.

Using routinely collected data for clinical research

Clinical research involving prospective data collection in randomized controlled trials is not always feasible. Increasingly, hospitals are developing large clinical databases that are waiting to be mined. We have developed a computer program, ClinQuery, that facilitates such exploration and analysis. We have also shown in a series of studies that the use of clinical data is a powerful tool in health services research. In some cases, we have shown that coded data are inaccurate and that alternative clinical data are preferable. In other cases, a combination of clinical data and coded discharge diagnoses is preferable.