Correction to: 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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ABSTRACT:
SUBMITTER: Singotani RG
PROVIDER: S-EPMC6880343 | biostudies-literature | 2019 Nov
REPOSITORIES: biostudies-literature
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Correction to: 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 20191127 1
Due to an error introduced during copyediting of this article [1], following corrections need to be made. ...[more]
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Project description:BackgroundSeveral 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.MethodsA 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.ResultsForty-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.ConclusionsThis 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.
| S-EPMC6778387 | biostudies-literature
Project description:OBJECTIVES:The aim of this study was to describe the rates and causes of unplanned readmissions at different time periods following percutaneous coronary intervention (PCI). BACKGROUND:The rates and causes of readmission at different time periods after PCI remain incompletely elucidated. METHODS:Patients undergoing PCI between 2010 and 2014 in the U.S. Nationwide Readmission Database were evaluated for the rates, causes, predictors, and costs of unplanned readmission between 0 and 7 days, 8 and 30 days, 31 and 90 days, and 91 and 180 days after index discharge. RESULTS:This analysis included 2,412,000 patients; 2.5% were readmitted between 0 and 7 days, 7.6% between 8 and 30 days, 8.9% between 31 and 90 days, and 8.0% between 91 and 180 days (cumulative rates 2.5%, 9.9%, 18.0%, and 24.8%, respectively). The majority of readmissions during each time period were due to noncardiac causes (53.1% to 59.6%). Nonspecific chest pain was the most common identifiable noncardiac cause for readmission during each time period (14.2% to 22.7% of noncardiac readmissions). Coronary artery disease including angina was the most common cardiac cause for readmission during each time period (37.4% to 39.3% of cardiac readmissions). The second most common cardiac cause for readmission was acute myocardial infarction between 0 and 7 days (27.6% of cardiac readmissions) and heart failure during all subsequent time periods (22.2% to 23.7% of cardiac readmissions). CONCLUSIONS:Approximately 25% of patients following PCI have unplanned readmissions within 6 months. Causes of readmission depend on the timing at which they are assessed, with noncardiovascular causes becoming more important at longer time points.
| S-EPMC6843988 | biostudies-literature
Project description:OBJECTIVES/HYPOTHESIS:Examine rates of readmission after total laryngectomy and determine primary etiologies, timing, and risk factors for unplanned readmission. STUDY DESIGN:Retrospective cohort study. METHODS:The Nationwide Readmissions Database was queried for patients who underwent total laryngectomy between January 2013 and November 2013. Patient-, procedure-, admission-, and institution-level characteristics were compared for patients with and without unplanned 30-day readmission. Outcomes of interest included rates, etiology, and timing of readmission. Multivariate logistic regression was used to identify predictors of 30-day readmission. RESULTS:There were 2,931 total laryngectomies performed in 2013 with an unplanned readmission rate of 17.5%. Postoperative fistula accounted for 13.7% of readmissions. The odds of readmission were elevated for patients undergoing concurrent procedures, including primary tracheoesophageal fistulization (adjusted odds ratio [aOR]: 2.44, 95% confidence interval [CI]: 1.15-5.18, P = .02) and/or pedicle graft or flap procedures (aOR: 1.73, 95% CI: 1.13-2.66, P = .01). Additionally, patients with comorbid coagulopathy (aOR: 3.04, 95% CI: 1.13-8.22, P = .03), liver disease (aOR: 2.48, 95% CI: 1.08-5.71, P = .03), and valvular heart disease (aOR: 3.18, 95% CI: 1.20-8.41, P = .02) had increased risk for unplanned 30-day readmission. Private insurance and longer lengths of stay were associated with decreased odds of readmission. CONCLUSIONS:Nearly one-fifth of total laryngectomy patients are readmitted to the hospital within 30 days of discharge. Risk factors identified in this nationally representative cohort should be carefully considered during the postoperative period to reduce preventable readmissions after total laryngectomy. LEVEL OF EVIDENCE:2c Laryngoscope, 1842-1850, 2018.
| S-EPMC5960601 | biostudies-literature
Project description:OBJECTIVES:To determine the occurrence rate of unplanned readmissions to PICUs within 1 year and examine risk factors associated with repeated readmission. DESIGN:Retrospective cohort analysis. SETTING:Seventy-six North American PICUs that participated in the Virtual Pediatric Systems, LLC (VPS, LLC, Los Angeles, CA). PATIENTS:Ninety-three thousand three hundred seventy-nine PICU patients discharged between 2009 and 2010. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:Index admissions and unplanned readmissions were characterized and their outcomes compared. Time-to-event analyses were performed to examine factors associated with readmission within 1 year. Eleven percent (10,233) of patients had 15,625 unplanned readmissions within 1 year to the same PICU; 3.4% had two or more readmissions. Readmissions had significantly higher PICU mortality and longer PICU length of stay, compared with index admissions (4.0% vs 2.5% and 2.5 vs 1.6 d; all p < 0.001). Median time to readmission was 30 days for all readmissions, 3.5 days for readmissions during the same hospitalization, and 66 days for different hospitalizations. Having more complex chronic conditions was associated with earlier readmission (adjusted hazard ratio, 2.9 for one complex chronic condition; hazard ratio, 4.8 for two complex chronic conditions; hazard ratio, 9.6 for three or more complex chronic conditions; all p < 0.001 compared no complex chronic condition). Most specific complex chronic condition conferred a greater risk of readmission, and some had considerably higher risk than others. CONCLUSIONS:Unplanned readmissions occurred in a sizable minority of PICU patients. Patients with complex chronic conditions and particular conditions were at much higher risk for readmission.
| S-EPMC5541898 | biostudies-literature
Project description:To determine the rate of unplanned PICU readmissions, examine the characteristics of index admissions associated with readmission, and compare outcomes of readmissions versus index admissions.Retrospective cohort analysis.Ninety North American PICUs that participated in the Virtual Pediatric Intensive Care Unit Systems.One hundred five thousand four hundred thirty-seven admissions between July 2009 and March 2011.None.Unplanned PICU readmission within 48 hours of index discharge was the primary outcome. Summary statistics, bivariate analyses, and mixed-effects logistic regression model with random effects for each hospital were performed.There were 1,161 readmissions (1.2%). The readmission rate varied among PICUs (0-3.3%), and acute respiratory (56%), infectious (35%), neurological (28%), and cardiovascular (20%) diagnoses were often present on readmission. Readmission risk increased in patients with two or more complex chronic conditions (adjusted odds ratio, 1.72; p < 0.001), unscheduled index admission (adjusted odds ratio, 1.37; p < 0.001), and transfer to an intermediate unit (adjusted odds ratio, 1.29; p = 0.004, compared with ward). Trauma patients had a decreased risk of readmission (adjusted odds ratio, 0.67; p = 0.003). Gender, race, insurance, age more than 6 months, perioperative status, and nighttime transfer were not associated with readmission. Compared with index admissions, readmissions had longer median PICU length of stay (3.1 vs 1.7 d, p < 0.001) and higher mortality (4% vs 2.5%, p = 0.002).Unplanned PICU readmissions were relatively uncommon, but were associated with worse outcomes. Several patient and admission characteristics were associated with readmission. These data help identify high-risk patient groups and inform risk-adjustment for standardized readmission rates.
| S-EPMC4327890 | biostudies-literature
Project description:A hospital readmission is when a patient who was discharged from the hospital is admitted again for the same or related care within a certain period. Hospital readmissions are a significant problem in the healthcare domain, as they lead to increased hospitalization costs, decreased patient satisfaction, and increased risk of adverse outcomes such as infections, medication errors, and even death. The problem of hospital readmissions is particularly acute in intensive care units (ICUs), due to the severity of the patients' conditions, and the substantial risk of complications. Predicting Unplanned Readmissions in ICUs is a challenging task, as it involves analyzing different data modalities, such as static data, unstructured free text, sequences of diagnoses and procedures, and multivariate time-series. Here, we investigate the effectiveness of each data modality separately, then alongside with others, using state-of-the-art machine learning approaches in time-series analysis and natural language processing. Using our evaluation process, we are able to determine the contribution of each data modality, and for the first time in the context of readmission, establish a hierarchy of their predictive value. Additionally, we demonstrate the impact of Temporal Abstractions in enhancing the performance of time-series approaches to readmission prediction. Due to conflicting definitions in the literature, we also provide a clear definition of the term Unplanned Readmission to enhance reproducibility and consistency of future research and to prevent any potential misunderstandings that could result from diverse interpretations of the term. Our experimental results on a large benchmark clinical data set show that Discharge Notes written by physicians, have better capabilities for readmission prediction than all other modalities.
| S-EPMC10507073 | biostudies-literature
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.
| S-EPMC6352831 | biostudies-literature
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.
| S-EPMC5628712 | biostudies-literature
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.
| S-EPMC7732713 | biostudies-literature
Project description:In the United States, national efforts to reduce hospital readmissions have been enacted, including the application of substantial insurance reimbursement penalties for hospitals with elevated rates. Readmissions after severe sepsis remain understudied and could possibly signify lapses in care and missed opportunities for intervention. We sought to characterize 7- and 30-day readmission rates following hospital admission for severe sepsis as well as institutional variations in readmission.Retrospective analysis of 345,657 severe sepsis discharges from University HealthSystem Consortium hospitals in 2012.United States.We applied the commonly cited method described by Angus et al for identification of severe sepsis, including only discharges with sepsis present at admission.None.We identified unplanned, all-cause readmissions within 7 and 30 days of discharge using claims-based algorithms. Using mixed-effects logistic regression, we determined factors associated with 30-day readmission. We used risk-standardized readmission rates to assess institutional variations. Among 216,328 eligible severe sepsis discharges, there were 14,932 readmissions within 7 days (6.9%; 95% CI, 6.8-7.0) and 43,092 within 30 days (19.9%; 95% CI, 19.8-20.1). Among those readmitted within 30 days, 66.9% had an infection and 40.3% had severe sepsis at readmission. Patient severity, length of stay, and specific diagnoses were associated with increased odds of 30-day readmission. Observed institutional 7-day readmission rates ranged from 0% to 12.3%, 30-day rates from 3.6% to 29.1%, and 30-day risk-standardized readmission rates from 14.1% to 31.1%. Greater institutional volume, teaching status, trauma services, location in the Northeast, and lower ICU rates were associated with poor risk-standardized readmission rate performance.Severe sepsis readmission places a substantial burden on the healthcare system, with one in 15 and one in five severe sepsis discharges readmitted within 7 and 30 days, respectively. Hospitals and clinicians should be aware of this important sequela of severe sepsis.
| S-EPMC4537666 | biostudies-literature