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Multi-modal intervention and prospective implementation of standardized sickle cell pain admission orders reduces 30-day readmission rate.

ABSTRACT: The National Association of Children's Hospitals (NACHRI) and the Centers for Medicare and Medicaid Services (CMS) recently introduced 30-day hospital readmission rate as a quality care indicator in children with sickle cell disease (SCD). Based on previous research identifying risk factors for 30-day readmission in our patient population, we designed and implemented a multi-modal intervention to reduce 30-day readmission rate in children with SCD and pain.A before-and-after study design was performed to evaluate an intervention containing three components: (1) standardized SCD-pain admission orders; (2) monthly SCD-pain in-service for house physicians for the first 6-months; and (3) continuous patient/caregiver education. Following order implementation, we prospectively collected data on all children admitted for SCD-pain over a 6-month period. We compared the 30-day readmission rate after the intervention to the rate during the same 6-month interval in the previous calendar year prior to the availability of pre-specified SCD-pain orders.A total of 89 admissions, in 68 individuals, were eligible for the standardized orders during the prospective time period and were compared to 85 admissions in 56 individuals during the control period. Pre-specified SCD-pain orders were used in 93% of eligible admissions during the intervention. Readmission rate within 30 days was lower for the intervention cohort than the control cohort, 11% (10/89) versus 28% (24/85), P = 0.007, 95% CI 0.1-0.7.A multi-modal intervention was successful in decreasing 30-day hospital readmission rate for children with SCD and pain. Provider education was the most important component of the multi-modal intervention.

SUBMITTER: Frei-Jones MJ

PROVIDER: S-EPMC2722244 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature

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Multi-modal intervention and prospective implementation of standardized sickle cell pain admission orders reduces 30-day readmission rate.

Frei-Jones Melissa J MJ Field Joshua J JJ DeBaun Michael R MR

Pediatric blood & cancer 20090901 3

<h4>Introduction</h4>The National Association of Children's Hospitals (NACHRI) and the Centers for Medicare and Medicaid Services (CMS) recently introduced 30-day hospital readmission rate as a quality care indicator in children with sickle cell disease (SCD). Based on previous research identifying risk factors for 30-day readmission in our patient population, we designed and implemented a multi-modal intervention to reduce 30-day readmission rate in children with SCD and pain.<h4>Methods</h4>A ...[more]

PMID: 19422031

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Project description:The 30-day readmission rate after hospitalization for a sickle cell crisis (SCC) is extremely high. Accurate information on readmission diagnoses, total readmission costs and factors associated with readmission is required to effectively plan resource allocation and to plan interventions to reduce readmission rates. The present study aimed to examine readmission diagnoses and factors associated with all-cause 30-day readmission after hospitalization for SCC. We analyzed 2016 nationwide readmission database (NRD) to identify patterns of 30-day readmission by patient demographic characteristics and time after hospitalization for SCC. We estimated the percentage and most common readmission diagnoses for 30-day and 7-day readmissions after discharge. We studied the relationship between risk factors and readmission and the impact of readmission on patient outcomes and resulting financial burden on health care in dollars. In 2016, of 67,887 discharges after index hospitalizations, 18099 (26.9%) were readmitted within 30-days. Of all readmissions, 5166 (7.6%) were readmitted within 7 days. The spectrum of readmission diagnoses was largely similar in both 30-day and 7-day readmission with more than 80% patients in both time periods readmitted with diagnoses related to SCC. The mean length of stay for readmitted patients was significantly longer than the index hospitalization (5.3 days (5.1-5.5) vs 4.9 days (CI 4.8-5.1, p?<?0.01). Also, the mean cost of hospitalization in readmitted patients $8485 was significantly higher than the index hospitalization $8064 p?<?0.01. In 2016, readmission among patients with SCC incurred an additional 95,445 hospitalization days resulting a total charge of $609 million and a total cost of $152 million in the US. On Multivariate analysis, age group 18-30 years, discharge against medical advice, higher Charlson comorbidity index, low socioeconomic status and admission at high volume centers were associated with a higher likelihood of 30-day readmission. Among patients hospitalized for SCC, 30-day readmissions were frequent throughout the month post hospitalization and resulted in an enormous financial burden on the United States healthcare system.

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.

Project description:Background and Purpose: Hospital readmissions impose a substantial burden on the healthcare system. Reducing readmissions after stroke could lead to improved quality of care especially since stroke is associated with a high rate of readmission. The goal of this study is to enhance our understanding of the predictors of 30-day readmission after ischemic stroke and develop models to identify high-risk individuals for targeted interventions. Methods: We used patient-level data from electronic health records (EHR), five machine learning algorithms (random forest, gradient boosting machine, extreme gradient boosting-XGBoost, support vector machine, and logistic regression-LR), data-driven feature selection strategy, and adaptive sampling to develop 15 models of 30-day readmission after ischemic stroke. We further identified important clinical variables. Results: We included 3,184 patients with ischemic stroke (mean age: 71 ± 13.90 years, men: 51.06%). Among the 61 clinical variables included in the model, the National Institutes of Health Stroke Scale score above 24, insert indwelling urinary catheter, hypercoagulable state, and percutaneous gastrostomy had the highest importance score. The Model's AUC (area under the curve) for predicting 30-day readmission was 0.74 (95%CI: 0.64-0.78) with PPV of 0.43 when the XGBoost algorithm was used with ROSE-sampling. The balance between specificity and sensitivity improved through the sampling strategy. The best sensitivity was achieved with LR when optimized with feature selection and ROSE-sampling (AUC: 0.64, sensitivity: 0.53, specificity: 0.69). Conclusions: Machine learning-based models can be designed to predict 30-day readmission after stroke using structured data from EHR. Among the algorithms analyzed, XGBoost with ROSE-sampling had the best performance in terms of AUC while LR with ROSE-sampling and feature selection had the best sensitivity. Clinical variables highly associated with 30-day readmission could be targeted for personalized interventions. Depending on healthcare systems' resources and criteria, models with optimized performance metrics can be implemented to improve outcomes.

Project description:BackgroundDriven by quality outcomes and economic incentives, predicting 30-day hospital readmissions remains important for healthcare systems. The Cleveland Clinic Health System (CCHS) implemented an internally validated readmission risk score in the electronic medical record (EMR).ObjectiveWe evaluated the predictive accuracy of the readmission risk score across CCHS hospitals, across primary discharge diagnosis categories, between surgical/medical specialties, and by race and ethnicity.DesignRetrospective cohort study.ParticipantsAdult patients discharged from a CCHS hospital April 2017-September 2020.Main measuresData was obtained from the CCHS EMR and billing databases. All patients discharged from a CCHS hospital were included except those from Oncology and Labor/Delivery, patients with hospice orders, or patients who died during admission. Discharges were categorized as surgical if from a surgical department or surgery was performed. Primary discharge diagnoses were classified per Agency for Healthcare Research and Quality Clinical Classifications Software Level 1 categories. Discrimination performance predicting 30-day readmission is reported using the c-statistic.ResultsThe final cohort included 600,872 discharges from 11 Northeast Ohio and Florida CCHS hospitals. The readmission risk score for the cohort had a c-statistic of 0.6875 with consistent yearly performance. The c-statistic for hospital sites ranged from 0.6762, CI [0.6634, 0.6876], to 0.7023, CI [0.6903, 0.7132]. Medical and surgical discharges showed consistent performance with c-statistics of 0.6923, CI [0.6807, 0.7045], and 0.6802, CI [0.6681, 0.6925], respectively. Primary discharge diagnosis showed variation, with lower performance for congenital anomalies and neoplasms. COVID-19 had a c-statistic of 0.6387. Subgroup analyses showed c-statistics of > 0.65 across race and ethnicity categories.ConclusionsThe CCHS readmission risk score showed good performance across diverse hospitals, across diagnosis categories, between surgical/medical specialties, and by patient race and ethnicity categories for 3 years after implementation, including during COVID-19. Evaluating clinical decision-making tools post-implementation is crucial to determine their continued relevance, identify opportunities to improve performance, and guide their appropriate use.

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Multi-modal intervention and prospective implementation of standardized sickle cell pain admission orders reduces 30-day readmission rate.

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Multi-modal intervention and prospective implementation of standardized sickle cell pain admission orders reduces 30-day readmission rate.

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