Hospital readmission prevalence and analysis of those potentially avoidable in southern Italy.
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ABSTRACT:
SUBMITTER: Bianco A
PROVIDER: S-EPMC3487865 | biostudies-other | 2012
REPOSITORIES: biostudies-other
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Project description:BackgroundReadmission prediction models have been developed and validated for targeted in-hospital preventive interventions. We aimed to externally validate the Potentially Avoidable Readmission-Risk Score (PAR-Risk Score), a 12-items prediction model for internal medicine patients with a convenient scoring system, for our local patient cohort.MethodsA cohort study using electronic health record data from the internal medicine ward of a Swiss tertiary teaching hospital was conducted. The individual PAR-Risk Score values were calculated for each patient. Univariable logistic regression was used to predict potentially avoidable readmissions (PARs), as identified by the SQLape algorithm. For additional analyses, patients were stratified into low, medium, and high risk according to tertiles based on the PAR-Risk Score. Statistical associations between predictor variables and PAR as outcome were assessed using both univariable and multivariable logistic regression.ResultsThe final dataset consisted of 5,985 patients. Of these, 340 patients (5.7%) experienced a PAR. The overall PAR-Risk Score showed rather poor discriminatory power (C statistic 0.605, 95%-CI 0.575-0.635). When using stratified groups (low, medium, high), patients in the high-risk group were at statistically significant higher odds (OR 2.63, 95%-CI 1.33-5.18) of being readmitted within 30 days compared to low risk patients. Multivariable logistic regression identified previous admission within six months, anaemia, heart failure, and opioids to be significantly associated with PAR in this patient cohort.ConclusionThis external validation showed a limited overall performance of the PAR-Risk Score, although higher scores were associated with an increased risk for PAR and patients in the high-risk group were at significantly higher odds of being readmitted within 30 days. This study highlights the importance of externally validating prediction models.
| S-EPMC8610256 | biostudies-literature
Project description:Abstract Background and Objectives Readmission-related health care reforms have shifted their focus from all-cause readmissions (ACR) to potentially avoidable readmissions (PAR). However, little is known about the utility of analytic tools from administrative data in predicting PAR. This study determined whether 30-day ACR or 30-day PAR is more predictable using tools that assess frailty, comorbidities, and activities of daily living (ADL) from administrative data. Research Design and Methods This retrospective cohort study was conducted at a large general acute care hospital in Tokyo, Japan. We analyzed patients aged ≥70 years who had been admitted to and discharged from the subject hospital between July 2016 and February 2021. Using administrative data, we assessed each patient’s Hospital Frailty Risk Score, Charlson Comorbidity Index, and Barthel Index on admission. To determine the influence of each tool on readmission predictions, we constructed logistic regression models with different combinations of independent variables for predicting unplanned ACR and PAR within 30 days of discharge. Results Among 16 313 study patients, 4.1% experienced 30-day ACR and 1.8% experienced 30-day PAR. The full model (including sex, age, annual household income, frailty, comorbidities, and ADL as independent variables) for 30-day PAR showed better discrimination (C-statistic: 0.79, 95% confidence interval: 0.77–0.82) than the full model for 30-day ACR (0.73, 0.71–0.75). The other prediction models for 30-day PAR also had consistently better discrimination than their corresponding models for 30-day ACR. Discussion and Implications PAR is more predictable than ACR when using tools that assess frailty, comorbidities, and ADL from administrative data. Our PAR prediction model may contribute to the accurate identification of at-risk patients in clinical settings who would benefit from transitional care interventions.
| S-EPMC10278982 | biostudies-literature
Project description:Identification of patients at a high risk of potentially avoidable readmission allows hospitals to efficiently direct additional care transitions services to the patients most likely to benefit.To externally validate the HOSPITAL score in an international multicenter study to assess its generalizability.International retrospective cohort study of 117?065 adult patients consecutively discharged alive from the medical department of 9 large hospitals across 4 different countries between January 2011 and December 2011. Patients transferred to another acute care facility were excluded.The HOSPITAL score includes the following predictors at discharge: hemoglobin, discharge from an oncology service, sodium level, procedure during the index admission, index type of admission (urgent), number of admissions during the last 12 months, and length of stay.30-day potentially avoidable readmission to the index hospital using the SQLape algorithm.Overall, 117?065 adults consecutively discharged alive from a medical department between January 2011 and December 2011 were studied. Of all medical discharges, 16?992 of 117?065 (14.5%) were followed by a 30-day readmission, and 11?307 (9.7%) were followed by a 30-day potentially avoidable readmission. The discriminatory power of the HOSPITAL score to predict potentially avoidable readmission was good, with a C statistic of 0.72 (95% CI, 0.72-0.72). As in the derivation study, patients were classified into 3 risk categories: low (n?=?73?031 [62.4%]), intermediate (n?=?27?612 [23.6%]), and high risk (n?=?16?422 [14.0%]). The estimated proportions of potentially avoidable readmission for each risk category matched the observed proportion, resulting in an excellent calibration (Pearson ?2 test P?=?.89).The HOSPITAL score identified patients at high risk of 30-day potentially avoidable readmission with moderately high discrimination and excellent calibration when applied to a large international multicenter cohort of medical patients. This score has the potential to easily identify patients in need of more intensive transitional care interventions to prevent avoidable hospital readmissions.
| S-EPMC5070968 | biostudies-literature
Project description:IntroductionHospitalizations for ambulatory care sensitive conditions, of which chronic obstructive pulmonary disease (COPD) is among the most common, represent an indirect measure of the healthcare system to manage chronic disease. Research has pointed to disparities in various COPD-related outcomes between persons of lower versus higher income; however, few studies have examined the influence of patients' social context on potentially avoidable COPD admissions.ObjectiveThe research explores the use of linked population census and administrative health data to assess the influence of income inequalities on the risk of hospitalization and rehospitalization for COPD among Canadian adults.MethodsThis analysis utilizes data from the 2006 Census linked longitudinally to the 2006/07-2008/09 Discharge Abstract Database. Multiple logistic regressions were conducted to assess the independent influence of income inequality on the risks of hospitalization and of six-month readmission due to COPD among the population aged 30-69, controlling for age, sex, education and other sociodemographic characteristics.ResultsCompared with adults in the most affluent income quintile, the adjusted odds of COPD hospitalization were significantly greater in the 4th highest income quintile (OR: 1.38; 95%CI: 1.30-1.47), and peaked for those in the least affluent quintile (OR: 2.92; 95%CI: 2.77-3.09). Among individuals who had been hospitalized at least once for COPD in the study period, and compared with the most affluent group, the adjusted odds of readmission were highest in the least affluent group (OR: 1.39; 95%CI: 1.22-1.58).ConclusionsDespite Canada's system of universal coverage for physician and hospital care, a clear income gradient in the risk of being hospitalized and, to some extent, rehospitalized for COPD, is found. Income inequalities may be contributing to excess hospitalizations, reinforcing the importance of integrating social and economic issues in primary care to meet the ambulatory needs of this population.
| S-EPMC8110890 | biostudies-literature
Project description:BACKGROUND:Admissions of infants in England have increased substantially but there is little evidence whether this is across the first year or predominately in neonates; and for all or for specific causes. We aimed to characterise this increase, especially those admissions that may be avoidable in the context of postnatal care provision. METHODS:A cross sectional analysis of 1,387,677 infants up to age one admitted to English hospitals between April 2008 and April 2014 using Hospital Episode Statistics and live birth denominators for England from Office for National Statistics. Potentially avoidable conditions were defined through a staged process with a panel. RESULTS:The rate of hospital admission in the first year of life for physiological jaundice, feeding difficulties and gastroenteritis, the three conditions identified as potentially preventable in the context of postnatal care provision, increased by 39% (39.55 to 55.33 per 1000 live births) relative to an overall increase of 6% (334.97 to 354.55 per 1000 live births). Over the first year the biggest increase in admissions occurred in the first 0-6?days (RR 1.26, 95% CI 1.24 to 1.29) and 85% of the increase (12.36 to 18.23 per 1000 live births) in this period was for the three potentially preventable conditions. CONCLUSIONS:Most of the increase in infant hospital admissions was in the early neonatal period, the great majority being accounted for by three potentially avoidable conditions especially jaundice and feeding difficulties. This may indicate missed opportunities within the postnatal care pathway and given the enormous NHS cost and parental distress from hospital admission of infants, requires urgent attention.
| S-EPMC6302406 | biostudies-literature
Project description:BackgroundAvoidable hospital readmission is a major problem among health systems. Although there are effective peri-discharge interventions for reducing avoidable hospital readmission, successful implementation is challenging. This systematic review of qualitative studies aimed to identify barriers and facilitators to implementing peri-discharge interventions from providers' and service users' perspectives.MethodsWe searched four databases for potentially eligible qualitative studies from databases' inception to March 2020, and updated literature search for studies published between January 2020 to October 2021. Barriers and facilitators to implementing peri-discharge interventions were identified and mapped onto the Consolidated Framework for Implementation Research (CFIR) constructs. Inductive analysis of the CFIR constructs was performed to yield thematic areas that illustrated the relationship between various facilitators and barriers, generating practical insights to key implementation issues.ResultsThirteen qualitative studies were included in this systematic review. Key issues were clustered in the CFIR constructs of Design Quality and Complexity of the intervention, strength of Network and Communication, being responsive to Patient Needs with sufficient Resource support, and External Incentives. The three thematic areas were rationality of the interventions, readiness and effort of multidisciplinary implementation teams, and influence of external stakeholders. Common barriers included (i) limited resources, (ii) poor communication among team members, (iii) incompatibility between the new intervention and existing work routine, (iv) complicated implementation process, (v) low practicality of supporting instruments, and (vi) lack of understanding about the content and effectiveness of the new interventions. Common facilitators were (i) information sharing via regular meetings on implementation issues, (ii) organizational culture that values quality and accountability, (iii) financial penalties for hospitals with high avoidable readmissions rates, (iv) external support offered via quality improvement programs and community resources, and (v) senior leadership support.ConclusionThis study synthesized commonly-presenting barriers and facilitators to implementing peri-discharge interventions among different healthcare organizations. Findings may inform development of implementation strategies in different health systems after appropriate tailoring, based on a consensus-based formative research process.
| S-EPMC10125127 | biostudies-literature
Project description:ObjectivesEarly hospital readmissions or deaths are key healthcare quality measures in pay-for-performance programs. Predictive models could identify patients at higher risk of readmission or death and target interventions. However, existing models usually do not incorporate social determinants of health (SDH) information, although this information is of great importance to address health disparities related to social risk factors. The objective of this study is to examine the impact of social determinants of health on predictive models for potentially avoidable 30-day readmission.MethodsWe extracted electronic health record data for 19,941 hospital admissions between January 2015 and November 2017 at an academic medical center in New York City. We applied the Simplified HOSPITAL score model to predict potentially avoidable 30-day readmission or death and examined if incorporating individual- and community-level SDH could improve the prediction using cross-validation. We calculated the C-statistic for discrimination, Brier score for accuracy, and Hosmer-Lemeshow test for calibration for each model using logistic regression. Analysis was conducted for all patients and three subgroups that may be disproportionately affected by social risk factors, namely Medicaid patients, patients who are 65 or older, and obese patients.ResultsThe Simplified HOSPITAL score model achieved similar performance in our sample compared to previous studies. Adding SDH did not improve the prediction among all patients. However, adding individual- and community-level SDH at the US census tract level significantly improved the prediction for all three subgroups. Specifically, C-statistics improved from 0.70 to 0.73 for Medicaid patients, from 0.66 to 0.68 for patients 65 or older, and from 0.70 to 0.73 for obese patients.ConclusionsPatients from certain subgroups may be more likely to be affected by social risk factors. Incorporating SDH into predictive models may be helpful to identify these patients and reduce health disparities associated with vulnerable social conditions.
| S-EPMC7316307 | biostudies-literature
Project description:BACKGROUND:Identifying patients at high risk of hospital preventable readmission is an essential step towards selecting those who might benefit from specific transitional interventions. OBJECTIVE:Derive and validate a predictive risk score for potentially avoidable readmission (PAR) based on analysis of readmissions, with a focus on medication. DESIGN/SETTING/PARTICIPANTS:Retrospective analysis of all hospital admissions to internal medicine wards between 2011 and 2014. Comparison between patients readmitted within 30 days and non-readmitted patients, as identified using a specially designed algorithm. Univariate and multivariate regression analyses of demographic data, clinical diagnoses, laboratory results, and the medication data of patients admitted during the first period (2011-2013), to identify factors associated with PAR. Using these, derive a predictive score with a regression coefficient-based scoring method. Subsequently, validate this score with a second cohort of patients admitted in 2013-2014. Variables were identified at hospital discharge. RESULTS:The derivation cohort included 7,317 hospital stays. Multivariate logistic regressions found significant associations with PAR for: [adjusted OR (95% CI)] hospital length of stay > 4 days [1.3 (1.1-1.7)], admission in previous 6 months [2.3 (1.9-2.8)], heart failure [1.3 (1.0-1.7)], chronic ischemic heart disease [1.7 (1.2-2.3)], diabetes with organ damage [2.2 (1.3-3.8)], cancer [1.4 (1.0-1.9)], metastatic carcinoma [1.9 (1.3-3.0)], anemia [1.2 (1.0-1.5)], hypertension [1.3 (1.1-1.7)], arrhythmia [1.3 (1.0-1.6)], hyperkalemia [1.4 (1.0-1.7)], opioid drug prescription [1.3 (1.1-1.6)], and acute myocardial infarction [0.6 (0.4-0.9)]. The PAR-Risk Score, derived from these results, demonstrated fair discriminatory and calibration power (C-statistic = 0.699; Brier Score = 0.069). The results for the validation cohort's operating characteristics were similar (C-statistic = 0.687; Brier Score = 0.064). CONCLUSION:This study identified routinely-available factors that were significantly associated with PAR. A predictive score was derived and internally validated.
| S-EPMC6629067 | biostudies-literature
Project description:BackgroundIdentifying potentially avoidable admissions to Canadian hospitals is an important health system goal. With general internal medicine (GIM) accounting for 40% of hospital admissions, we sought to develop a method to identify potentially avoidable admissions and characterize patient, provider and health system factors.MethodsWe conducted an observational study of GIM admissions at our institution from August 2019 to February 2020. We defined potentially avoidable admissions as admissions that could be managed in an appropriate and safe manner in the emergency department or ambulatory setting and asked staff physicians to screen admissions daily and flag candidates as potentially avoidable admissions. For each candidate, we prepared a case review and debriefed with members of the admitting team. We then reviewed each candidate with our research team, assigned an avoidability score (1 [low] to 4 [high]) and identified contributing factors for those with scores of 3 or more.ResultsWe screened 601 total admissions and staff physicians flagged 117 (19.5%) of these as candidate potential avoidable admissions. Consensus review identified 67 candidates as potentially avoidable admissions (11.1%, 95% confidence interval 8.8%-13.9%); these patients were younger (mean age 65 yr v. 72 yr), had fewer comorbidities (Canadian Institute for Health Information Case Mix Group+ 0.42 v. 1.14), had lower resource-intensity weighting scores (0.72 v. 1.50) and shorter hospital lengths of stay (29 h v. 105 h) (p < 0.01). Common factors included diagnostic and therapeutic uncertainty, perceived need for short-term monitoring, government directive of a 4-hour limit for admission decision-making and subspecialist request to admit.InterpretationOur prospective method of screening, flagging and case review showed that 1 in 9 GIM admissions were potentially avoidable. Other institutions could consider adapting this methodology to ascertain their rate of potentially avoidable admissions and to understand contributing factors to inform improvement endeavours.
| S-EPMC9981162 | biostudies-literature
Project description:Introduction30-day readmission rate is considered an adverse outcome reflecting suboptimal quality of care during index hospitalisation for community-acquired pneumonia (CAP). However, potentially avoidable readmission would be a more relevant metric than all-cause readmission for tracking quality of hospital care for CAP. The objectives of this study are (1) to estimate potentially avoidable 30-day readmission rate and (2) to develop a risk prediction model intended to identify potentially avoidable readmissions for CAP.Methods and analysisThe study population consists of consecutive patients admitted in two hospitals from the community or nursing home setting with pneumonia. To qualify for inclusion, patients must have a primary or secondary discharge diagnosis code of pneumonia. Data sources include routinely collected administrative claims data as part of diagnosis-related group prospective payment system and structured chart reviews. The main outcome measure is potentially avoidable readmission within 30 days of discharge from index hospitalisation. The likelihood that a readmission is potentially avoidable will be quantified using latent class analysis based on independent structured reviews performed by four panellists. We will use a two-stage approach to develop a claims data-based model intended to identify potentially avoidable readmissions. The first stage implies deriving a clinical model based on data collected through retrospective chart review only. In the second stage, the predictors comprising the medical record model will be translated into International Classification of Diseases, 10th revision discharge diagnosis codes in order to obtain a claim data-based risk model.The study sample consists of 1150 hospital stays with a diagnosis of CAP. 30-day index hospital readmission rate is 17.5%.Ethics and disseminationThe protocol was reviewed by the Comité de Protection des Personnes Sud Est V (IRB#6705). Efforts will be made to release the primary study results within 6 months of data collection completion.Trial registration numberClinicalTrials.gov Registry (NCT02833259).
| S-EPMC7661353 | biostudies-literature