Dataset Information

Towards a patient journey perspective on causes of unplanned readmissions using a classification framework: results of a systematic review with narrative synthesis.

ABSTRACT:

Background

Several literature reviews have been published focusing on the prevalence and/or preventability of hospital readmissions. To our knowledge, none focused on the different causes which have been used to evaluate the preventability of readmissions. Insight into the range of causes is crucial to understand the complex nature of readmissions. We conducted a systematic review to: (1) evaluate the range of causes of unplanned readmissions in a patient journey, and (2) present a cause classification framework that can support future readmission studies.

Methods

A literature search was conducted in PUBMED and EMBASE using "readmission" and "avoidability" or "preventability" as key terms. Studies that specified causes of unplanned readmissions were included. The causes were classified into eight preliminary root causes: Technical, Organization (integrated care), Organization (hospital department level), Human (care provider), Human (informal caregiver), Patient (self-management), Patient (disease), and Other. The root causes were based on expert opinions and the root cause analysis tool of PRISMA (Prevention and Recovery Information System for Monitoring and Analysis). The range of different causes were analyzed using Microsoft Excel.

Results

Forty-five studies that reported 381 causes of readmissions were included. All studies reported causes related to organization of care at the hospital department level. These causes were often reported as preventable. Twenty-two studies included causes related to patient's self-management and 19 studies reported causes related to patient's disease. Studies differed in which causes were seen as preventable or unpreventable. None reported causes related to technical failures and causes due to integrated care issues were reported in 18 studies.

Conclusions

This review showed that causes for readmissions were mainly evaluated from a hospital perspective. However, causes beyond the scope of the hospital can also play a major role in unplanned readmissions. Opinions regarding preventability seem to depend on contextual factors of the readmission. This study presents a cause classification framework that could help future readmission studies to gain insight into a broad range of causes for readmissions in a patient journey.

SUBMITTER: Singotani RG

PROVIDER: S-EPMC6778387 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Publications

Towards a patient journey perspective on causes of unplanned readmissions using a classification framework: results of a systematic review with narrative synthesis.

Singotani R G RG Karapinar F F Brouwers C C Wagner C C de Bruijne M C MC

BMC medical research methodology 20191004 1

<h4>Background</h4>Several literature reviews have been published focusing on the prevalence and/or preventability of hospital readmissions. To our knowledge, none focused on the different causes which have been used to evaluate the preventability of readmissions. Insight into the range of causes is crucial to understand the complex nature of readmissions. We conducted a systematic review to: (1) evaluate the range of causes of unplanned readmissions in a patient journey, and (2) present a cause ...[more]

PMID: 31585528

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Project description:OBJECTIVE:To synthesise evidence on risk factors associated with paediatric unplanned hospital readmissions (UHRs). DESIGN:Systematic review. DATA SOURCE:CINAHL, EMBASE (Ovid) and MEDLINE from 2000 to 2017. ELIGIBILITY CRITERIA:Studies published in English with full-text access and focused on paediatric All-cause, Surgical procedure and General medical condition related UHRs were included. DATA EXTRACTION AND SYNTHESIS:Characteristics of the included studies, examined variables and the statistically significant risk factors were extracted. Two reviewers independently assessed study quality based on six domains of potential bias. Pooling of extracted risk factors was not permitted due to heterogeneity of the included studies. Data were synthesised using content analysis and presented in narrative form. RESULTS:Thirty-six significant risk factors were extracted from the 44 included studies and presented under three health condition groupings. For All-cause UHRs, ethnicity, comorbidity and type of health insurance were the most frequently cited factors. For Surgical procedure related UHRs, specific surgical procedures, comorbidity, length of stay (LOS), age, the American Society of Anaesthesiologists class, postoperative complications, duration of procedure, type of health insurance and illness severity were cited more frequently. The four most cited risk factors associated with General medical condition related UHRs were comorbidity, age, health service usage prior to the index admission and LOS. CONCLUSIONS:This systematic review acknowledges the complexity of readmission risk prediction in paediatric populations. This review identified four risk factors across all three health condition groupings, namely comorbidity; public health insurance; longer LOS and patients<12 months or between 13-18 years. The identification of risk factors, however, depended on the variables examined by each of the included studies. Consideration should be taken into account when generalising reported risk factors to other institutions. This review highlights the need to develop a standardised set of measures to capture key hospital discharge variables that predict unplanned readmission among paediatric patients.

Project description:Background and objectivesPatients on hemodialysis have high 30-day unplanned readmission rates. Using a national all-payer administrative database, we describe the epidemiology of 30-day unplanned readmissions in patients on hemodialysis, determine concordance of reasons for initial admission and readmission, and identify predictors for readmission.Design, setting, participants, & measurementsThis is a retrospective cohort study using the Nationwide Readmission Database from the year 2013 to identify index admissions and readmission in patients with ESRD on hemodialysis. The Clinical Classification Software was used to categorize admission diagnosis into mutually exclusive clinically meaningful categories and determine concordance of reasons for admission on index hospitalizations and readmissions. Survey logistic regression was used to identify predictors of at least one readmission.ResultsDuring 2013, there were 87,302 (22%) index admissions with at least one 30-day unplanned readmission. Although patient and hospital characteristics were statistically different between those with and without readmissions, there were small absolute differences. The highest readmission rate was for acute myocardial infarction (25%), whereas the lowest readmission rate was for hypertension (20%). The primary reasons for initial hospitalization and subsequent 30-day readmission were discordant in 80% of admissions. Comorbidities that were associated with readmissions included depression (odds ratio, 1.10; 95% confidence interval [95% CI], 1.05 to 1.15; P<0.001), drug abuse (odds ratio, 1.41; 95% CI, 1.31 to 1.51; P<0.001), and discharge against medical advice (odds ratio, 1.57; 95% CI, 1.45 to 1.70; P<0.001). A group of high utilizers, which constituted 2% of the population, was responsible for 20% of all readmissions.ConclusionsIn patients with ESRD on hemodialysis, nearly one quarter of admissions were followed by a 30-day unplanned readmission. Most readmissions were for primary diagnoses that were different from initial hospitalization. A small proportion of patients accounted for a disproportionate number of readmissions.

Project description:BackgroundTotal joint replacements are high-volume and high-cost procedures that should be monitored for cost and quality control. Models that can identify patients at high risk of readmission might help reduce costs by suggesting who should be enrolled in preventive care programs. Previous models for risk prediction have relied on structured data of patients rather than clinical notes in electronic health records (EHRs). The former approach requires manual feature extraction by domain experts, which may limit the applicability of these models.ObjectiveThis study aims to develop and evaluate a machine learning model for predicting the risk of 30-day readmission following knee and hip arthroplasty procedures. The input data for these models come from raw EHRs. We empirically demonstrate that unstructured free-text notes contain a reasonably predictive signal for this task.MethodsWe performed a retrospective analysis of data from 7174 patients at Partners Healthcare collected between 2006 and 2016. These data were split into train, validation, and test sets. These data sets were used to build, validate, and test models to predict unplanned readmission within 30 days of hospital discharge. The proposed models made predictions on the basis of clinical notes, obviating the need for performing manual feature extraction by domain and machine learning experts. The notes that served as model inputs were written by physicians, nurses, pathologists, and others who diagnose and treat patients and may have their own predictions, even if these are not recorded.ResultsThe proposed models output readmission risk scores (propensities) for each patient. The best models (as selected on a development set) yielded an area under the receiver operating characteristic curve of 0.846 (95% CI 82.75-87.11) for hip and 0.822 (95% CI 80.94-86.22) for knee surgery, indicating reasonable discriminative ability.ConclusionsMachine learning models can predict which patients are at a high risk of readmission within 30 days following hip and knee arthroplasty procedures on the basis of notes in EHRs with reasonable discriminative power. Following further validation and empirical demonstration that the models realize predictive performance above that which clinical judgment may provide, such models may be used to build an automated decision support tool to help caretakers identify at-risk patients.

Project description:INTRODUCTION/BACKGROUND:The choice between endoscopic injection (EI) and ureteroneocystotomy (UNC) for surgical correction of vesicoureteral reflux (VUR) is controversial. OBJECTIVE:To compare postoperative outcomes of EI vs UNC. STUDY DESIGN:This study reviewed linked inpatient (SID), ambulatory surgery (SASD), and emergency department (SEDD) data from five states in the United States (2007-10) to identify pediatric patients with primary VUR undergoing EI or UNC as an initial surgical intervention. Unplanned readmissions, additional procedures, and emergency room (ER) visits were extracted. Statistical analysis was performed using multivariate logistic regression using generalized estimating equation (GEE) to adjust for hospital-level clustering. RESULTS:The study identified 2556 UNC and 1997 EI procedures. Compared with patients undergoing EI, those who underwent UNC were more likely to be younger (4.6 vs 6.0 years, P < 0.001), male (30 vs 20%, P < 0.001), and publicly insured (34 vs 29%, P < 0.001). As shown in Summary Figure, compared with EI, UNC patients had lower rates of additional anti-reflux procedures within 12 months (25 (1.0) vs 121 (6.1%), P < 0.001), but a higher rate of 30-day and 90-day readmissions and ER visits. On multivariate analysis, patients treated by UNC remained at higher odds of being readmitted (OR = 4.45; 2.69 in 30 days; 90 days, P < 0.001) and to have postoperative ER visits (OR = 3.33; 2.26 in 30 days; 90 days, P < 0.001); however, EI had significantly higher odds of repeat anti-reflux procedures in the subsequent year (OR = 7.12, P < 0.001). DISCUSSIONS:Endoscopic injection constituted nearly half of initial anti-reflux procedures in children. However, patients treated with UNC had significantly lower odds of requiring re-treatment in the first year relative to those treated with EI. By contrast, patients treated with UNC had more than twice the odds of being readmitted or visiting an ER postoperatively. Although the available data were amongst the largest and most well validated, the major limitation was the retrospective nature of the administrative database. The practice setting may not be generalizable to states not included in the analysis. CONCLUSIONS:Postoperative readmissions and ER visits were uncommon after any surgical intervention for VUR, but were more common among children undergoing UNC. The EI patients had a more than seven-fold increased risk of surgical re-treatment within 1 year.

Dataset Information

Towards a patient journey perspective on causes of unplanned readmissions using a classification framework: results of a systematic review with narrative synthesis.

Background

Methods

Results

Conclusions

Publications

Towards a patient journey perspective on causes of unplanned readmissions using a classification framework: results of a systematic review with narrative synthesis.

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