Identifying and mitigating factors contributing to 30-day hospital readmission in high risk patient populations.
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ABSTRACT:
SUBMITTER: Rogers MP
PROVIDER: S-EPMC8640920 | biostudies-literature | 2021 Nov
REPOSITORIES: biostudies-literature
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Identifying and mitigating factors contributing to 30-day hospital readmission in high risk patient populations.
Rogers Michael P MP Kuo Paul C PC
Annals of translational medicine 20211101 21
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Project description:BackgroundLowering hospital readmission rates has become a primary target for the Centers for Medicare & Medicaid Services, but studies of the relationship between adherence to the recommended hospital care processes and readmission rates have provided inconsistent and inconclusive results.ObjectiveTo examine the association between hospital performance on Medicare's Hospital Compare process quality measures and 30-day readmission rates for patients with acute myocardial infarction (AMI), heart failure and pneumonia, and for those undergoing major surgery.Design, setting and participantsWe assessed hospital performance on process measures using the 2007 Hospital Inpatient Quality Reporting Program. The process measures for each condition were aggregated in two separate measures: Overall Measure (OM) and Appropriate Care Measure (ACM) scores. Readmission rates were calculated using Medicare claims.Main outcome measureRisk-standardized 30-day all-cause readmission rate was calculated as the ratio of predicted to expected rate standardized by the overall mean readmission rate. We calculated predicted readmission rate using hierarchical generalized linear models and adjusting for patient-level factors.ResultsAmong patients aged ≥ 66 years, the median OM score ranged from 79.4 % for abdominal surgery to 95.7 % for AMI, and the median ACM scores ranged from 45.8 % for abdominal surgery to 87.9 % for AMI. We observed a statistically significant, but weak, correlation between performance scores and readmission rates for pneumonia (correlation coefficient R = 0.07), AMI (R = 0.10), and orthopedic surgery (R = 0.06). The difference in the mean readmission rate between hospitals in the 1st and 4th quartiles of process measure performance was statistically significant only for AMI (0.25 percentage points) and pneumonia (0.31 percentage points). Performance on process measures explained less than 1 % of hospital-level variation in readmission rates.ConclusionsHospitals with greater adherence to recommended care processes did not achieve meaningfully better 30-day hospital readmission rates compared to those with lower levels of performance.
| S-EPMC3579957 | biostudies-literature
Project description:ImportanceThirty-day readmission rates have been suggested as a marker for quality of care. By investigating the factors associated with readmissions in all otolaryngology subspecialties we provide data relevant for the development of risk stratification systems to improve outcomes.ObjectiveTo establish the association of surgical and hospital volume and patient characteristics with 30-day readmission rates to guide the development of otolaryngology-specific risk stratification models.Design, setting, and participantsA retrospective cohort study including adult patients who underwent inpatient otolaryngology surgery in New York State between 1995 and 2015 was conducted using the Statewide Planning and Research Cooperative System (SPARCS). Regression techniques were used to describe relationships of patient-level factors, hospital, and surgeon volume to 30-day readmission rates in New York State.Main outcomes and measuresThe main outcome measures were patient-, surgeon-, and hospital-level risk factors for readmission. Secondary outcome measures were rate of readmissions by subspecialty procedure and by diagnosis on readmission.ResultsWe identified 254 257 cases of otolaryngology surgery (147 065 women [58%], mean [SD] age 50 [17] years). The 30-day readmission rate was 6%. In a multivariable model, odds ratios (ORs) identified Medicaid insurance (OR, 1.46; 99% CI, 1.36-1.57), Medicare insurance (OR, 1.32; 99% CI, 1.24-1.42), bottom quartile income (OR, 1.08; 99% CI, 1.01-1.15), patient comorbidities measured by the Charlson Comorbidity Index (CCI) (CCI >1; OR, 2.31; 99% CI, 2.16-2.47), length of stay (LOS) (LOS >10 days; OR, 2.29; 99% CI, 2.00-2.45), rhinology (OR, 1.37; 99% CI, 1.24-1.51), laryngology (OR, 1.98; 99% CI, 1.62-2.43), and head and neck cancer (OR, 1.27; 99% CI, 1.17-1.37) procedures as readmission predictors. High-volume surgeons were protective of 30-day readmission (OR, 0.67; 99% CI, 0.635-0.708) relative to low volume. Hospital volume was not significantly associated to readmissions. The most common causes of readmission included wound- (2682 patients, 18%), respiratory- (1776 patients, 12%), cardiovascular- (1210 patients, 8%), and volume- (1089 patients, 7%) related disorders.Conclusions and relevanceThis study evaluated the combined effects of patient-, surgeon-, and hospital-level factors on 30-day readmission after otolaryngology surgery. Socioeconomic factors, patient comorbidities, surgeon volumes, and procedure were significantly associated with 30-day readmission. Though the cause of 30-day readmission is multifactorial, a large portion is driven by socioeconomic factors. Addressing these disparities at the system level is necessary to address the described readmission disparities. The development of risk-stratification models based on patient-, procedure-, and surgeon-level factors may help facilitate resource distribution.
| S-EPMC6481432 | biostudies-literature
Project description:ObjectiveTo assess the reliability of risk-standardized readmission rates (RSRRs) for medical conditions and surgical procedures used in the Hospital Readmission Reduction Program (HRRP).Data sourcesState Inpatient Databases for six states from 2011 to 2013 were used to identify patient cohorts for the six conditions used in the HRRP, which was augmented with hospital characteristic and HRRP penalty data.Study designHierarchical logistic regression models estimated hospital-level RSRRs for each condition, the reliability of each RSRR, and the extent to which socioeconomic and hospital factors further explain RSRR variation. We used publicly available data to estimate payments for excess readmissions in hospitals with reliable and unreliable RSRRs.Principal findingsOnly RSRRs for surgical procedures exceeded the reliability benchmark for most hospitals, whereas RSRRs for medical conditions were typically below the benchmark. Additional adjustment for socioeconomic and hospital factors modestly explained variation in RSRRs. Approximately 25 percent of payments for excess readmissions were tied to unreliable RSRRs.ConclusionsMany of the RSRRs employed by the HRRP are unreliable, and one quarter of payments for excess readmissions are associated with unreliable RSRRs. Unreliable measures blur the connection between hospital performance and incentives, and threaten the success of the HRRP.
| S-EPMC5134200 | biostudies-literature
Project description:Hospital readmission prediction models often perform poorly, but most only use information collected until the time of hospital discharge. In this clinical trial, we randomly assigned 500 patients discharged from hospital to home to use either a smartphone or wearable device to collect and transmit remote patient monitoring (RPM) data on activity patterns after hospital discharge. Analyses were conducted at the patient-day level using discrete-time survival analysis. Each arm was split into training and testing folds. The training set used fivefold cross-validation and then final model results are from predictions on the test set. A standard model comprised data collected up to the time of discharge including demographics, comorbidities, hospital length of stay, and vitals prior to discharge. An enhanced model consisted of the standard model plus RPM data. Traditional parametric regression models (logit and lasso) were compared to nonparametric machine learning approaches (random forest, gradient boosting, and ensemble). The main outcome was hospital readmission or death within 30 days of discharge. Prediction of 30-day hospital readmission significantly improved when including remotely-monitored patient data on activity patterns after hospital discharge and using nonparametric machine learning approaches. Wearables slightly outperformed smartphones but both had good prediction of 30-day hospital-readmission.
| S-EPMC10203290 | biostudies-literature
Project description:Pneumonia is the leading cause of hospitalization in pediatric patients. Disease severity greatly influences pneumonia progression and adverse health outcomes such as hospital readmission. Hospital readmissions have become a measure of healthcare quality to reduce excess expenditures. The aim of this study was to examine 30-day all-cause readmission rates and evaluate the association between pneumonia severity and readmission among pediatric pneumonia hospitalizations. Using 2018 Nationwide Readmissions Database (NRD), we conducted a cross-sectional study of pediatric hospitalizations for pneumonia. Pneumonia severity was defined by the presence of respiratory failure, sepsis, mechanical ventilation, dependence on long-term supplemental oxygen, and/or respiratory intubation. Outcomes of interest were 30-day all-cause readmission, length of stay, and cost. The rate of 30-day readmission for the total sample was 5.9%, 4.7% for non-severe pneumonia, and 8.7% for severe pneumonia (p < 0.01). Among those who were readmitted, hospitalizations for severe pneumonia had a longer length of stay (6.5 vs. 5.4 days, p < 0.01) and higher daily cost (USD 3246 vs. USD 2679, p < 0.01) than admissions for non-severe pneumonia. Factors associated with 30-day readmission were pneumonia severity, immunosuppressive conditions, length of stay, and hospital case volume. To reduce potentially preventable readmissions, clinical interventions to improve the disease course and hospital system interventions are necessary.
| S-EPMC8911283 | biostudies-literature
Project description:BackgroundThirty-day hospital readmissions are common among maintenance dialysis patients. Prior studies have evaluated easily measurable readmission risk factors such as comorbid conditions, laboratory results, and hospital discharge day. We undertook this prospective study to investigate the associations between hospital-assessed depression, health literacy, social support, and self-rated health (separately) and 30-day hospital readmission among dialysis patients.MethodsParticipants were recruited from the University of North Carolina Hospitals, 2014-2016. Validated depression, health literacy, social support, and self-rated health screening instruments were administered during index hospitalizations. Multivariable logistic regression models with 30-day readmission as the dependent outcome were used to examine readmission risk factors.ResultsOf the 154 participants, 58 (37.7%) had a 30-day hospital readmission. In unadjusted analyses, individuals with positive screening for depression, lower health literacy, and poorer social support were more likely to have a 30-day readmission (vs. negative screening). Positive depression screening and poorer social support remained significantly associated with 30-day readmission in models adjusted for race, heart failure, admitting service, weekend discharge day, and serum albumin: adjusted OR (95% CI) 2.33 (1.02-5.15) for positive depressive symptoms and 2.57 (1.10-5.91) for poorer social support. The area under the receiver operating characteristic curve (AUC) of the multivariable model adjusted for social support status was significantly greater than the AUC of the multivariable model without social support status (test for equality; p value = 0.04).ConclusionPoor social support and depressive symptoms identified during hospitalizations may represent targetable readmission risk factors among dialysis patients. Our findings suggest that hospital-based assessments of select psychosocial factors may improve readmission risk prediction.
| S-EPMC5483850 | biostudies-literature
Project description:ImportanceAccurate identification of hospital readmission risk during a current hospitalization may enhance decision-making, facilitate targeted systems-level interventions, and avoid preventable readmissions.ObjectiveTo temporally and externally validate a suite of readmission risk prediction models across 48 children's hospitals to assess their generalizability and feasibility for future clinical implementation.Design, setting, and participantsThis prognostic study analyzed data from the Pediatric Health Information System (PHIS) database, which contains billing and resource use data from 48 US children's hospitals, including the derivation hospital (DH) and 47 hospitals participating in the PHIS database (hereafter other PHIS hospitals). Children aged 18 years or younger discharged from these hospitals between January 1, 2016, to December 31, 2019, were included. This cohort was divided as specified into the 3 prediction models at the DH: 6 months or older with no recent hospitalizations (new admission model [NAM]), 6 months or older with 1 or more prior hospitalizations within the last 6 months (recent admission model [RAM]), and 6 months or younger (young infant model [YIM]). Data were analyzed from August 9 to December 1, 2023.Main outcomes and measuresThe primary validation outcome was hospital-level discrimination measured with area under the receiver operating characteristic curve (AUROC). Predictors included demographic, clinical, and utilization variables. All-cause 30-day readmission was modeled for each hospital using logistic regression and parameter estimates from the DH. Calibration plots examined observed vs predicted outcome frequencies for each hospital.ResultsIn external validation, a total of 851 499 children were discharged from 48 hospitals (16 330 DH discharges and 835 169 other PHIS hospital discharges). The largest group of children was aged 5 to 14 years (281 193 [33.0%]). In temporal validation, the DH PHIS 2016-2018 cohort included 45 682 discharges. All-cause 30-day readmission rates were 7.2% for NAM, 35.5% for RAM, and 11.7% for YIM. The 2019 DH PHIS cohort included 16 330 discharges. All cause 30-day readmision rates were 7.2% for NAM, 35.1% for RAM, and 11.1% for YIM. Temporal validation demonstrated reduced discrimination across all 3 models (median AUROC, 0.65 [95% CI 0.62-0.67] for the NAM; 0.73 [95% CI 0.72-0.75) for RAM; 0.67 [95% CI 0.63-0.70) for the YIM compared with the original estimates (median AUROC 0.76 [95% CI 0.85-0.78] for the NAM; 0.84 [95% CI 0.83-0.84] for the RAM; 0.79 [95% CI 0.77-0.80] for the YIM). Overall readmission rates were 5.9% for NAM, 30.1% for RAM, and 7.6% for YIM. External validation yielded similiar findings as the temporal validation, although with demonstrable variation in performance across hospitals (median [range] AUROC, 0.64 [0.60-0.68] for the NAM; 0.73 [0.64-0.80] for the RAM; 0.65 [0.53-0.74] for the YIM). Most hospitals were poorly calibrated, with both significant overestimation and underestimation of observed risk. Of 47 other PHIS hospitals, only 3 for the RAM (6.4%) and 9 for both the NAM and YIM (19.1%) were adequately calibrated.Conclusions and relevanceThis prognostic study found that the readmission risk prediction models had reduced predictive accuracy across time and variability in hospital-level performance. These findings stress the importance of local validation prior to clinical implementation and suggest opportunities to improve generalizability, including multicenter derivation and expansion of candidate predictors.
| S-EPMC11826366 | biostudies-literature
Project description:Avoidable hospital readmissions not only contribute to the high costs of healthcare in the US, but also have an impact on the quality of care for patients. Large scale adoption of Electronic Health Records (EHR) has created the opportunity to proactively identify patients with high risk of hospital readmission, and apply effective interventions to mitigate that risk. To that end, in the past, numerous machine-learning models have been employed to predict the risk of 30-day hospital readmission. However, the need for an accurate and real-time predictive model, suitable for hospital setting applications still exists. Here, using data from more than 300,000 hospital stays in California from Sutter Health's EHR system, we built and tested an artificial neural network (NN) model based on Google's TensorFlow library. Through comparison with other traditional and non-traditional models, we demonstrated that neural networks are great candidates to capture the complexity and interdependency of various data fields in EHRs. LACE, the current industry standard, showed a precision (PPV) of 0.20 in identifying high-risk patients in our database. In contrast, our NN model yielded a PPV of 0.24, which is a 20% improvement over LACE. Additionally, we discussed the predictive power of Social Determinants of Health (SDoH) data, and presented a simple cost analysis to assist hospitalists in implementing helpful and cost-effective post-discharge interventions.
| S-EPMC5510858 | biostudies-other
Project description:BackgroundCardiovascular disease (CVD) is the leading cause of hospitalization in older adults and high readmission rates have attracted considerable attention as actionable targets to promote efficiency in care and to reduce costs. Despite a plethora of research over the past decade, current strategies to predict readmissions have been largely ineffective and efforts to identify novel clinical predictors have been largely unsuccessful.ObjectiveThe objective of this study is to examine a wide array of socioeconomic, psychosocial, behavioral, and clinical factors to predict risks of 30-day hospital readmission in cardiovascular patients.MethodsThe study includes patients (aged 18 years and older) admitted for the treatment of cardiovascular-related illnesses at the Duke Heart Center, which is among the nation's largest and top-ranked cardiovascular care hospitals. The study uses a novel standardized survey to ascertain data on a comprehensive array of patient characteristics that will be linked to their electronic medical records. A series of univariate and multivariate models will be used to estimate the associations between the patient-level factors and 30-day readmissions. The performance of the risk models will be examined based on 2 components of accuracy-model calibration and discrimination-to determine how closely the predicted outcome agrees with the observed (actual) outcome and how well the model distinguishes patients who were readmitted and those who were not. The purpose of this paper is to present the protocol for the implementation of this study.ResultsThe study was launched in February 2014 and is actively recruiting patients from the Heart Center. Approximately 550 patients have been enrolled to date and the study is expected to continue recruitment until February 2018. Preliminary results show that participants in the study were aged 63.6 years on average (SD 14.0), predominately male (61.2%), and primarily non-Hispanic white (64.6%) or non-Hispanic black (31.7%). The demographic characteristics of study participants were not significantly different from all patients admitted to the Heart Center during this period with an average age of 65.0 years (SD 15.3) and predominately male (58.6%), non-Hispanic white (62.9%) or non-Hispanic black (31.8%) The integration of the interview data with clinical data from the patient electronic medical records is currently underway. The study has received funding and ethical approval.ConclusionsMany US hospitals continue to struggle with high readmission rates in patients with cardiovascular disease. The primary objective of this study is to collect and integrate a comprehensive array of patient attributes to develop a powerful yet parsimonious model to stratify risks of rehospitalization in cardiovascular patients. The results of this research also have the potential to identify actionable targets for tailored interventions to improve patient outcomes.
| S-EPMC5491895 | biostudies-literature
Project description:Total knee arthroplasty (TKA) is a highly effective procedure for advanced osteoarthritis of the knee. Thirty-day hospital readmission is an adverse outcome related to complications, which can be mitigated by identifying associated risk factors. We aimed to identify patient-related characteristics associated with unplanned 30-day readmission following TKA, and to determine the effect size of the association between these risk factors and unplanned 30-day readmission. We searched MEDLINE and EMBASE from inception to 8 September 2020 for English language articles. Reference lists of included articles were searched for additional literature. Patients of interest were TKA recipients (primary and revision) compared for 30-day readmission to any institution, due to any cause, based on patient risk factors; case series were excluded. Two reviewers independently extracted data and carried out critical appraisal. In-hospital complications during the index admission were the strongest risk factors for 30-day readmission in both primary and revision TKA patients, suggesting discharge planning to include closer post-discharge monitoring to prevent avoidable readmission may be warranted. Further research could determine whether closer monitoring post-discharge would prevent unplanned but avoidable readmissions. Increased comorbidity burden correlated with increased risk, as did specific comorbidities. Body mass index was not strongly correlated with readmission risk. Demographic risk factors included low socioeconomic status, but the impact of age on readmission risk was less clear. These risk factors can also be included in predictive models for 30-day readmission in TKA patients to identify high-risk patients as part of risk reduction programs.
| S-EPMC7795505 | biostudies-literature