Performance of Pediatric Readmission Measures

Validity of different algorithmic methods to identify hospital readmissions from routinely coded medical data

BACKGROUND

Hospital readmission rates are used for quality and pay-for-performance initiatives. To identify readmissions from administrative data, two commonly employed methods are focusing either on unplanned readmissions (used by the Centers for Medicare & Medicaid Services, CMS) or potentially avoidable readmissions (used by commercial vendors such as SQLape or 3 M). However, it is not known which of these methods has higher criterion validity and can more accurately identify actually avoidable readmissions.

OBJECTIVES

A manual record review based on data from seven hospitals was used to compare the validity of the methods by CMS and SQLape.

METHODS

Seven independent reviewers reviewed 738 single inpatient stays. The sensitivity, specificity, positive predictive value (PPV), and F1 score were examined to characterize the ability of an original CMS method, an adapted version of the CMS method, and the SQLape method to identify unplanned, potentially avoidable, and actually avoidable readmissions.

RESULTS

Both versions of the CMS method had greater sensitivity (92/86% vs. 62%) and a higher PPV (84/91% vs. 71%) than the SQLape method, in terms of identifying their outcomes of interest (unplanned vs. potentially avoidable readmissions, respectively). To distinguish actually avoidable readmissions, the two versions of the CMS method again displayed higher sensitivity (90/85% vs. 66%), although the PPV did not differ significantly between the different methods.

CONCLUSIONS

Thus, the CMS method has both higher criterion validity and greater sensitivity for identifying actually avoidable readmissions, compared with the SQLape method. Consequently, the CMS method should primarily be used for quality initiatives.

Garnering effective telehealth to help optimize multidisciplinary team engagement (GET2HOME) for children with medical complexity: Protocol for a pragmatic randomized control trial

BACKGROUND

Children and young adults with medical complexity (CMC) experience high rates of healthcare reutilization following hospital discharge. Prior studies have identified common hospital-to-home transition failures that may increase the risk for reutilization, including medication, technology and equipment issues, financial concerns, and confusion about which providers can help with posthospitalization needs. Few interventions have been developed and evaluated for CMC during this transition period.

OBJECTIVE

We will compare the effectiveness of the garnering effective telehealth 2 help optimize multidisciplinary team engagement (GET2HOME) transition bundle intervention to the standard hospital-based care coordination discharge process by assessing healthcare reutilization and patient- and family-centered outcomes.

DESIGNS, SETTINGS, AND PARTICIPANTS

We will conduct a pragmatic 2-arm randomized controlled trial (RCT) comparing the GET2HOME bundle intervention to the standard hospital-based care discharge process on CMC hospitalized and discharged from hospital medicine at two sites of our pediatric medical center between November 2022 and February 2025. CMC of any age will be identified as having complex chronic disease using the Pediatric Medical Complexity Algorithm tool. We will exclude CMC who live independently, live in skilled nursing facilities, are in custody of the county, or are hospitalized for suicidal ideation or end-of-life care.

INTERVENTION

We will randomize participants to the bundle intervention or standard hospital-based care coordination discharge process. The bundle intervention includes (1) predischarge telehealth huddle with inpatient providers, outpatient providers, patients, and their families; (2) care management discharge task tracker; and (3) postdischarge telehealth huddle with similar participants within 7 days of discharge. As part of the pragmatic design, families will choose if they want to complete the postdischarge huddle. The standard hospital-based discharge process includes a pharmacist, social worker, and care management support when consulted by the inpatient team but does not include huddles between providers and families.

MAIN OUTCOME AND MEASURES

Primary outcome will be 30-day urgent healthcare reutilization (unplanned readmission, emergency department, and urgent care visits). Secondary outcomes include 7-day urgent healthcare reutilization, patient- and family-reported transition quality, quality of life, and time to return to baseline using electronic health record and surveys at 7, 30, 60, and 90 days following discharge. We will also evaluate heterogeneity of treatment effect for the intervention across levels of financial strain and for CMC with high-intensity neurologic impairment. The primary analysis will follow the intention-to-treat principle with logistic regression used to study reutilization outcomes and generalized linear mixed modeling to study repeated measures of patient- and family-reported outcomes over time.

RESULTS

This pragmatic RCT is designed to evaluate the effectiveness of enhanced discharge transition support, including telehealth huddles and a care management discharge tool, for CMC and their families. Enrollment began in November 2022 and is projected to complete in February 2025. Primary analysis completion is anticipated in July 2025 with reporting of results following.

Incidence of moral hazards among health care providers in the implementation of social health insurance toward universal health coverage: evidence from rural province hospitals in Indonesia

Objective

To identify the incidence of moral hazards among health care providers and its determinant factors in the implementation of national health insurance in Indonesia.

Methods

Data were derived from 360 inpatient medical records from six types C public and private hospitals in an Indonesian rural province. These data were accumulated from inpatient medical records from four major disciplines: medicine, surgery, obstetrics and gynecology, and pediatrics. The dependent variable was provider moral hazards, which included indicators of up-coding, readmission, and unnecessary admission. The independent variables are Physicians' characteristics (age, gender, and specialization), coders' characteristics (age, gender, education level, number of training, and length of service), and patients' characteristics (age, birth weight, length of stay, the discharge status, and the severity of patient's illness). We use logistic regression to investigate the determinants of moral hazard.

Results

We found that the incidences of possible unnecessary admissions, up-coding, and readmissions were 17.8%, 11.9%, and 2.8%, respectively. Senior physicians, medical specialists, coders with shorter lengths of service, and patients with longer lengths of stay had a significant relationship with the incidence of moral hazard.

Conclusion

Unnecessary admission is the most common form of a provider's moral hazard. The characteristics of physicians and coders significantly contribute to the incidence of moral hazard. Hospitals should implement reward and punishment systems for doctors and coders in order to control moral hazards among the providers.

Annual Variation in 30-Day Risk-Adjusted Readmission Rates in U.S. Children's Hospitals

OBJECTIVE

Reducing pediatric readmissions has become a national priority; however, the use of readmission rates as a quality metric remains controversial. The goal of this study was to examine short-term stability and long-term changes in hospital readmission rates.

METHODS

Data from the Pediatric Health Information System were used to compare annual 30-day risk-adjusted readmission rates (RARRs) in 47 US children's hospitals from 2016 to 2017 (short-term) and 2016 to 2019 (long-term). Pearson correlation coefficients and weighted Cohen's Kappa statistics were used to measure correlation and agreement across years for hospital-level RARRs and performance quartiles.

RESULTS

Median (IQR) 30-day RARRs remained stable from 7.7% (7.0-8.3) in 2016 to 7.6% (7.0-8.1) in 2019. Individual hospital RARRs in 2016 were strongly correlated with the same hospital's 2017 rate (R2 = 0.89 [95% confidence interval (CI) 0.80-0.94]) and moderately correlated with those in 2019 (R2 = 0.49 [95%CI 0.23-0.68]). Short-term RARRs (2016 vs 2017) were more highly correlated for medical conditions than surgical conditions, but correlations between long-term medical and surgical RARRs (2016 vs 2019) were similar. Agreement between RARRs was higher when comparing short-term changes (0.73 [95%CI 0.59-0.86]) than long-term changes (0.45 [95%CI 0.27-0.63]). From 2016 to 2019, RARRs increased by ≥1% in 7 (15%) hospitals and decreased by ≥1% in 6 (13%) hospitals. Only 7 (15%) hospitals experienced reductions in RARRs over the short and long-term.

CONCLUSIONS

Hospital-level performance on RARRs remained stable with high agreement over the short-term suggesting stability of readmission measures. There was little evidence of sustained improvement in hospital-level performance over multiple years.

Variation in Condition-Specific Readmission Rates Across US Children's Hospitals

OBJECTIVE

Despite extensive efforts, overall readmission rates at US children's hospitals have not materially declined over the past decade, raising questions about how to direct future efforts. Using measures of prevalence and performance variation we describe readmission rates by condition and identify priority conditions for future intervention.

METHODS

Retrospective cohort study of 49 US children's hospitals in the Pediatric Health Information System in 2017. Conditions were classified using All Patients Refined Diagnosis Related Groups. 30-day unadjusted and risk-adjusted readmission rates were calculated for each hospital/condition using the Pediatric All Cause Readmission measure. We ranked the highest volume conditions by rate variation (RV, interquartile range divided by the median) for each condition across hospitals.

RESULTS

The sample included 811,434 index hospitalizations with 50,196 (6.2%) 30-day readmissions. The RV across hospitals/conditions was between 0 and 2.8 (median = 0.7). Common reasons for admission had low RVs across hospitals, for example, bronchiolitis (readmission rate = 5.6%, RV = 0.4), seizure (readmission rate = 6.6%, RV = 0.3), and asthma (readmission rate = 3.1%, RV = 0.4). We identified 33 conditions with high variation in readmission rates across hospitals, which accounted for 18% of all discharges and 11% of all pediatric readmissions. These conditions may serve as candidates for future readmission reduction activities.

CONCLUSIONS

Many common childhood conditions have little variation in readmission rates across children's hospitals, suggesting limited future improvement opportunities. Conditions with high rate variation may provide opportunities for quality improvement; however, these conditions account for a relatively small share of total discharges suggesting modest potential impacts on national rates.

Applicability of predictive models for 30-day unplanned hospital readmission risk in paediatrics: a systematic review

OBJECTIVES

To summarise multivariable predictive models for 30-day unplanned hospital readmissions (UHRs) in paediatrics, describe their performance and completeness in reporting, and determine their potential for application in practice.

DESIGN

Systematic review.

DATA SOURCE

CINAHL, Embase and PubMed up to 7 October 2021.

ELIGIBILITY CRITERIA

English or German language studies aiming to develop or validate a multivariable predictive model for 30-day paediatric UHRs related to all-cause, surgical conditions or general medical conditions were included.

DATA EXTRACTION AND SYNTHESIS

Study characteristics, risk factors significant for predicting readmissions and information about performance measures (eg, c-statistic) were extracted. Reporting quality was addressed by the 'Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis' (TRIPOD) adherence form. The study quality was assessed by applying six domains of potential biases. Due to expected heterogeneity among the studies, the data were qualitatively synthesised.

RESULTS

Based on 28 studies, 37 predictive models were identified, which could potentially be used for determining individual 30-day UHR risk in paediatrics. The number of study participants ranged from 190 children to 1.4 million encounters. The two most common significant risk factors were comorbidity and (postoperative) length of stay. 23 models showed a c-statistic above 0.7 and are primarily applicable at discharge. The median TRIPOD adherence of the models was 59% (P₂₅-P₇₅, 55%-69%), ranging from a minimum of 33% to a maximum of 81%. Overall, the quality of many studies was moderate to low in all six domains.

CONCLUSION

Predictive models may be useful in identifying paediatric patients at increased risk of readmission. To support the application of predictive models, more attention should be placed on completeness in reporting, particularly for those items that may be relevant for implementation in practice.

Characteristics and Outcomes of Pediatric Nonindex Readmission: Evidence From Florida Hospitals

OBJECTIVES

Increasing pediatric care regionalization may inadvertently fragment care if children are readmitted to a different (nonindex) hospital rather than the discharge (index) hospital. Therefore, this study aimed to assess trends in pediatric nonindex readmission rates, examine the risk factors, and determine if this destination difference affects readmission outcomes.

METHODS

In this retrospective cohort study, we use the Healthcare Cost and Utilization Project State Inpatient Database to include pediatric (0 to 18 years) admissions from 2010 to 2017 across Florida hospitals. Risk factors of nonindex readmissions were identified by using logistic regression analyses. The differences in outcomes between index versus nonindex readmissions were compared for in-hospital mortality, morbidity, hospital cost, length of stay, against medical advice discharges, and subsequent hospital visits by using generalized linear regression models.

RESULTS

Among 41 107 total identified readmissions, 5585 (13.6%) were readmitted to nonindex hospitals. Adjusted nonindex readmission rate increased from 13.3% in 2010% to 15.4% in 2017. Patients in the nonindex readmissions group were more likely to be adolescents, live in poor neighborhoods, have higher comorbidity scores, travel longer distances, and be discharged at the postacute facility. After risk adjusting, no difference in in-hospital mortality was found, but morbidity was 13% higher, and following unplanned emergency department visits were 28% higher among patients with nonindex readmissions. Length of stay, hospital costs, and against medical advice discharges were also significantly higher for nonindex readmissions.

CONCLUSIONS

A substantial proportion of children experienced nonindex readmissions and relatively poorer health outcomes compared with index readmission. Targeted strategies for improving continuity of care are necessary to improve readmission outcomes.