Adding Social Determinant Data Changes Children's Hospitals' Readmissions Performance

Implementing Inpatient Social Needs Screening in an Urban Tertiary Care Children's Hospital

BACKGROUND AND OBJECTIVES

The American Academy of Pediatrics recommends screening for unmet social needs, and the literature on inpatient screening implementation is growing. Our aim was to use quality improvement methods to implement standardized social needs screening in hospitalized pediatric patients.

METHODS

We implemented inpatient social needs screening using the Model for Improvement. An interprofessional team trialed interventions in a cyclical manner using plan-do-study-act cycles. Interventions included a structured screening questionnaire, standardized screening and referrals workflows, electronic health record (EHR) modifications, and house staff education, deliberate practice, and feedback. The primary outcome measure was the percentage of discharged patients screened for social needs. Screening for social needs was defined as a completed EHR screening questionnaire or a full social work evaluation. Process and balancing measures were collected to capture data on screening questionnaire completion and social work consultations. Data were plotted on statistical process control charts and analyzed for special cause variation.

RESULTS

The mean monthly percentage of patients screened for social needs improved from 20% at baseline to 51% during the intervention period. Special cause variation was observed for the percentage of patients with completed social needs screening, EHR-documented screening questionnaires, and social work consults.

CONCLUSIONS

Social needs screening during pediatric hospitalization can be implemented by using quality improvement methods. The next steps should be focused on sustainability and the spread of screening. Interventions with greater involvement of interdisciplinary health care team members will foster process sustainability and allow for the spread of screening interventions to the wider hospitalized pediatric population.

Realizing the Potential of Social Determinants Data: A Scoping Review of Approaches for Screening, Linkage, Extraction, Analysis and Interventions

Background

Social determinants of health (SDoH) like socioeconomics and neighborhoods strongly influence outcomes, yet standardized SDoH data is lacking in electronic health records (EHR), limiting research and care quality.

Methods

We searched PubMed using keywords "SDOH" and "EHR", underwent title/abstract and full-text screening. Included records were analyzed under five domains: 1) SDoH screening and assessment approaches, 2) SDoH data collection and documentation, 3) Use of natural language processing (NLP) for extracting SDoH, 4) SDoH data and health outcomes, and 5) SDoH-driven interventions.

Results

We identified 685 articles, of which 324 underwent full review. Key findings include tailored screening instruments implemented across settings, census and claims data linkage providing contextual SDoH profiles, rule-based and neural network systems extracting SDoH from notes using NLP, connections found between SDoH data and healthcare utilization/chronic disease control, and integrated care management programs executed. However, considerable variability persists across data sources, tools, and outcomes.

Discussion

Despite progress identifying patient social needs, further development of standards, predictive models, and coordinated interventions is critical to fulfill the potential of SDoH-EHR integration. Additional database searches could strengthen this scoping review. Ultimately widespread capture, analysis, and translation of multidimensional SDoH data into clinical care is essential for promoting health equity.

A state-level analysis of macro-level factors associated with hospital readmissions

Investigation of the factors that contribute to hospital readmissions has focused largely on individual level factors. We extend the knowledge base by exploring macrolevel factors that may contribute to readmissions. We point to environmental, behavioral, and socioeconomic factors that are emerging as correlates to readmissions. Data were taken from publicly available reports provided by multiple agencies. Partial Least Squares-Structural Equation Modeling was used to test the association between economic stability and environmental factors on opioid use which was in turn tested for a direct association with hospital readmissions. We also tested whether hospital access as measured by the proportion of people per hospital moderates the relationship between opioid use and hospital readmissions. We found significant associations between Negative Economic Factors and Opioid Use, between Environmental Factors and Opioid Use, and between Opioid Use and Hospital Readmissions. We found that Hospital Access positively moderates the relationship between Opioid Use and Readmissions. A priori assumptions about factors that influence hospital readmissions must extend beyond just individualistic factors and must incorporate a holistic approach that also considers the impact of macrolevel environmental factors.

Identifying the Prevalence and Causes of 30-Day Hospital Readmission in Children: A Case Study from a Tertiary Pediatric Hospital

Introduction

The objectives of this study were to determine the prevalence of unplanned readmissions in the pediatric population within 30 days of discharge, identify the possible reasons behind them, and develop a predictive model for unplanned admissions.

Methods

A retrospective chart review study of 25,211 patients was conducted to identify the prevalence of readmissions occurring within 30 days of discharge from the King Abdullah Specialized Children's Hospital (KASCH) in Riyadh, Saudi Arabia, between Jan 1, 2019, and Dec 31, 2021. The data were collected using the BestCare electronic health records system and analyzed using Jamovi statistical software version 1.6.

Results

Among the 25,211 patients admitted to the hospital during the study period, the prevalence of unplanned readmission within 30 days was 1291 (5.12%). Of the 1291 patients, 1.91% had subsequent unplanned readmissions. In 57.8% of the cases, the cause of the first unplanned readmission was related to the cause of the first admission, and in 90.64% of the cases, the cause of the subsequent unplanned readmission was related to the cause of the first unplanned readmission. The most common reason for the first unplanned readmission was postoperative complications (18.75%), whereas pneumonia (10.81%) was the most common reason for subsequent unplanned readmissions. Most patients with subsequent unplanned readmissions were also found to have either isolated central nervous system pathology or chronic complex medical conditions.

Conclusion

Internationally, the rate of unplanned readmissions in pediatric patients has been estimated to be 6.5% within 30 days, which is comparable to the results of our study (5.12%). Most of the causes of first and subsequent unplanned readmission were found to be related to primary admission. The diagnosis/causes of readmission vary depending on the patient's age. A predictive model for pediatric readmission should be established so that preventive measures can be implemented.

Social and Medical Care Integration Practices Among Children's Hospitals

OBJECTIVES

In response to evidence linking social risk factors and adverse health outcomes, new incentives have emerged for hospitals to screen for adverse social determinants of health (SDOH). However, little information is available about the current state of social risk-related care practices among children's hospitals. To address outstanding knowledge gaps, we sought to describe social risk-related care practices among a national sample of children's hospitals.

METHODS

We analyzed responses to the 2020 American Hospital Association Annual Survey. Among children's hospitals, we calculated the prevalence of screening for social needs, strategies to address social risks/needs, partnerships with community-based organizations to address social risks/needs at the individual and community level, and rates of impact assessments of how social risk-related interventions affect outcomes. We also used χ2 tests to compare results by hospital characteristics. We weighted results to adjust for nonresponse.

RESULTS

The sample included 82 children's hospitals. A total of 79.6% screened for and 96.0% had strategies to address at least 1 social risk factor, although rates varied by SDOH domain. Children's hospitals more commonly partnered with community-based organizations to address patient-level social risks than to participate in community-level initiatives. A total of 39.2% of hospitals assessed SDOH intervention effectiveness. Differences in social risk-related care practices commonly varied by hospital ownership and Medicaid population but not by region.

CONCLUSIONS

We found wide variability in social risk-related care practices among children's hospitals based on the risk domain and hospital characteristics. Findings can be used to monitor whether social risk-related care practices change in the setting of new incentives.

Prevalence of social risk factors and social needs in a Medicaid Accountable Care Organization (ACO)

Background

Health-related social needs (HRSN) are associated with higher chronic disease prevalence and healthcare utilization. Health systems increasingly screen for HRSN during routine care. In this study, we compare the differential prevalence of social risk factors and social needs in a Medicaid Accountable Care Organization (ACO) and identify the patient and practice characteristics associated with reporting social needs in a different domain from social risks.

Methods

Cross-sectional study of patient responses to HRSN screening February 2019-February 2020. HRSN screening occurred as part of routine primary care and assessed social risk factors in eight domains and social needs by requesting resources in these domains. Participants included adult and pediatric patients from 114 primary care practices. We measured patient-reported social risk factors and social needs from the HRSN screening, and performed multivariable regression to evaluate patient and practice characteristics associated with reporting social needs and concordance to social risks. Covariates included patient age, sex, race, ethnicity, language, and practice proportion of patients with Medicaid and/or Limited English Proficiency (LEP).

Results

Twenty-seven thousand four hundred thirteen individuals completed 30,703 screenings, including 15,205 (55.5%) caregivers of pediatric patients. Among completed screenings, 13,692 (44.6%) were positive for ≥ 1 social risk factor and 2,944 (9.6%) for ≥ 3 risks; 5,861 (19.1%) were positive for social needs and 4,848 (35.4%) for both. Notably, 1,013 (6.0%) were negative for social risks but positive for social needs. Patients who did not identify as non-Hispanic White or were in higher proportion LEP or Medicaid practices were more likely to report social needs, with or without social risks. Patients who were non-Hispanic Black, Hispanic, preferred non-English languages or were in higher LEP or Medicaid practices were more likely to report social needs without accompanying social risks.

Conclusions

Half of Medicaid ACO patients screened for HRSN reported social risk factors or social needs, with incomplete overlap between groups. Screening for both social risks and social needs can identify more individuals with HRSN and increase opportunities to mitigate negative health outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12913-022-08721-9.

Development and Validation of an Integrated Suite of Prediction Models for All-Cause 30-Day Readmissions of Children and Adolescents Aged 0 to 18 Years

IMPORTANCE

Readmission is often considered a hospital quality measure, yet no validated risk prediction models exist for children.

OBJECTIVE

To develop and validate a tool identifying patients before hospital discharge who are at risk for subsequent readmission, applicable to all ages.

DESIGN, SETTING, AND PARTICIPANTS

This population-based prognostic analysis used electronic health record-derived data from a freestanding children's hospital from January 1, 2016, to December 31, 2019. All-cause 30-day readmission was modeled using 3 years of discharge data. Data were analyzed from June 1 to November 30, 2021.

MAIN OUTCOMES AND MEASURES

Three models were derived as a complementary suite to include (1) children 6 months or older with 1 or more prior hospitalizations within the last 6 months (recent admission model [RAM]), (2) children 6 months or older with no prior hospitalizations in the last 6 months (new admission model [NAM]), and (3) children younger than 6 months (young infant model [YIM]). Generalized mixed linear models were used for all analyses. Models were validated using an additional year of discharges.

RESULTS

The derivation set contained 29 988 patients with 48 019 hospitalizations; 50.1% of these admissions were for children younger than 5 years and 54.7% were boys. In the derivation set, 4878 of 13 490 admissions (36.2%) in the RAM cohort, 2044 of 27 531 (7.4%) in the NAM cohort, and 855 of 6998 (12.2%) in the YIM cohort were followed within 30 days by a readmission. In the RAM cohort, prior utilization, current or prior procedures indicative of severity of illness (transfusion, ventilation, or central venous catheter), commercial insurance, and prolonged length of stay (LOS) were associated with readmission. In the NAM cohort, procedures, prolonged LOS, and emergency department visit in the past 6 months were associated with readmission. In the YIM cohort, LOS, prior visits, and critical procedures were associated with readmission. The area under the receiver operating characteristics curve was 83.1 (95% CI, 82.4-83.8) for the RAM cohort, 76.1 (95% CI, 75.0-77.2) for the NAM cohort, and 80.3 (95% CI, 78.8-81.9) for the YIM cohort.

CONCLUSIONS AND RELEVANCE

In this prognostic study, the suite of 3 prediction models had acceptable to excellent discrimination for children. These models may allow future improvements in tailored discharge preparedness to prevent high-risk readmissions.

Hospital-Level Neighborhood Opportunity and Rehospitalization for Common Diagnoses at US Children's Hospitals

OBJECTIVE

Neighborhood conditions influence child health outcomes, but data examining association between local factors and hospital utilization are lacking. We determined if hospitals' mix of patients by neighborhood opportunity correlates with rehospitalization for common diagnoses at US children's hospitals.

METHODS

We analyzed all discharges in 2018 for children ≤18 years at 47 children's hospitals for 14 common diagnoses. The exposure was hospital-level mean neighborhood opportunity - measured by Child Opportunity Index (COI) - for each diagnosis. The outcome was same-cause rehospitalization within 365 days. We measured association via Pearson correlation coefficient. For diagnoses with significant associations, we also examined shorter rehospitalization time windows and compared unadjusted and COI-adjusted rehospitalization rates.

RESULTS

There were 256,871 discharges included. Hospital-level COI ranged from 17th to 70th percentile nationally. Hospitals serving lower COI neighborhoods had more frequent rehospitalization for asthma (ρ -0.34 [95% confidence interval -0.57, -0.06]) and diabetes (ρ -0.33 [-0.56, -0.04]), but fewer primary mental health rehospitalizations (ρ 0.47 [0.21, 0.67]). There was no association for 11 other diagnoses. Secondary timepoint analysis revealed increasing correlation over time, with differences by diagnosis. Adjustment for hospital-level COI resulted in 26%, 32%, and 45% of hospitals changing >1 decile in rehospitalization rank order for diabetes, asthma, and mental health diagnoses, respectively.

CONCLUSIONS

Children's hospitals vary widely in their mix of neighborhoods served. Asthma, diabetes, and mental health rehospitalization rates correlate with COI, suggesting that neighborhood factors may influence outcome disparities for these conditions. Hospital outcomes may be affected by neighborhood opportunity, which has implications for benchmarking.

Adjusting for Social Risk Factors in Pediatric Quality Measures: Adding to the Evidence Base

BACKGROUND

Outcome and utilization quality measures are adjusted for patient case-mix including demographic characteristics and comorbid conditions to allow for comparisons between hospitals and health plans. However, controversy exists around whether and how to adjust for social risk factors.

OBJECTIVE

To assess an approach to incorporating social risk variables into a pediatric measure of utilization from the Pediatric Quality Measures Program (PQMP).

METHODS

We used data from California Medicaid claims (2015-16) and Massachusetts All Payer Claims Database (2014-2015) to calculate health plan performance using measure specifications from the Pediatric Asthma Emergency Department Use measure. Health plan performance categories were assessed using mixed effect negative binomial models with and without adjustment for social risk factors, with both models adjusting for age, gender and chronic condition category. Mixed effects linear models were then used to compare patient social risk for health plans that changed performance categories to patient social risk for health plans that did not.

RESULTS

Of 133 health plans, serving 404,649 pediatric patients with asthma, 7% to 13% changed performance categories after social risk adjustment. Health plans that moved to higher performance categories cared for lower socioeconomic status (SES) patients whereas those that moved to lower performance categories cared for higher SES patients.

CONCLUSIONS

Adjustment for social risk factors changed performance rankings on the PQMP Pediatric Asthma Emergency Department Use measure for a substantial number of health plans. Some health plans caring for higher risk patients performed more poorly when social risk factors were not included in risk adjustment models. In light of this, social risk factors are incorporated into the National Quality Forum-endorsed measure; whether to incorporate social risk factors into pediatric quality measures will differ depending on the use case.

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.

Association Between Neighborhood Disadvantage and Pediatric Readmissions

OBJECTIVE

Although individual-level social determinants of health (SDH) are known to influence 30-day readmission risk, contextual-level associations with readmission are poorly understood among children. This study explores associations between neighborhood disadvantage measured by Area Deprivation Index (ADI) and pediatric 30-day readmissions.

METHODS

This retrospective cohort study included discharges of patients aged < 20 years from Maryland's 2013-2016 all-payer dataset. The ADI, which quantifies 17 indicators of neighborhood socioeconomic disadvantage within census block groups, is used as a proxy for contextual-level SDH. Readmissions were identified with the 30-day Pediatric All-Condition Readmissions measure. Associations between ADI and readmission were identified with generalized estimating equations adjusted for patient demographics and clinical severity (Chronic Condition Indicator [CCI], Pediatric Medical Complexity Algorithm [PMCA], Index Hospital All Patients Refined Diagnosis Related Groups [APR-DRG]), and hospital discharge volume.

RESULTS

Discharges (n = 138,998) were mostly female (52.7%), publicly insured (55.1%), urban-dwelling (93.0%), with low clinical severity levels (0-1 CCIs [82.3%], minor APR-DRG severity [48.4%]). Overall readmission rate was 4.0%. Compared to the least disadvantaged ADI quartile, readmissions for the most disadvantaged quartile were significantly more likely (aOR 1.19, 95% CI 1.09-1.30). After adjustment, readmissions were associated with public insurance and indicators of medical complexity (higher number of CCIs, complex-chronic disease PMCA, and APR-DRG severity).

CONCLUSION

In this all-payer, statewide sample, living in the most socioeconomically disadvantaged neighborhoods independently predicted pediatric readmission. While the relative magnitude of neighborhood disadvantage was modest compared to medical complexity, disadvantage is modifiable and thus represents an important consideration for prevention and risk stratification efforts.

The Association of the Childhood Opportunity Index on Pediatric Readmissions and Emergency Department Revisits

OBJECTIVE

Reutilization following discharge is costly to families and the health care system. Singular measures of the social determinants of health (SDOH) have been shown to impact utilization; however, the SDOH are multifactorial. The Childhood Opportunity Index (COI) is a validated approach for comprehensive estimation of the SDOH. Using the COI, we aimed to describe the association between SDOH and 30-day revisit rates.

METHODS

This retrospective study included children 0 to 17 years within 48 children's hospitals using the Pediatric Health Information System from 1/1/2019 to 12/31/2019. The main exposure was a child's ZIP code level COI. The primary outcome was unplanned readmissions and emergency department (ED) revisits within 30 days of discharge. Primary outcomes were summarized by COI category and compared using chi-square or Kruskal-Wallis tests. Adjusted analysis used generalized linear mixed effects models with adjustments for demographics, clinical characteristics, and hospital clustering.

RESULTS

Of 728,997 hospitalizations meeting inclusion criteria, 30-day unplanned returns occurred for 96,007 children (13.2%). After adjustment, the patterns of returns were significantly associated with COI. For example, 30-day returns occurred for 19.1% (95% confidence interval [CI]: 18.2, 20.0) of children living within very low opportunity areas, with a gradient-like decrease as opportunity increased (15.5%, 95% CI: 14.5, 16.5 for very high). The relative decrease in utilization as COI increased was more pronounced for ED revisits.

CONCLUSIONS

Children living in low opportunity areas had greater 30-day readmissions and ED revisits. Our results suggest that a broader approach, including policy and system-level change, is needed to effectively reduce readmissions and ED revisits.

Targeted Clinical Interventions for Reducing Pediatric Readmissions

BACKGROUND

In this interventional study, we addressed the selection and application of clinical interventions on pediatric patients identified as at risk by a predictive model for readmissions.

METHODS

A predictive model for readmissions was implemented, and a team of providers expanded corresponding clinical interventions for at-risk patients at a freestanding children's hospital. Interventions encompassed social determinants of health, outpatient care, medication reconciliation, inpatient and discharge planning, and postdischarge calls and/or follow-up. Statistical process control charts were used to compare readmission rates for the 3-year period preceding adoption of the model and clinical interventions with those for the 2-year period after adoption of the model and clinical interventions. Potential financial savings were estimated by using national estimates of the cost of pediatric inpatient readmissions.

RESULTS

The 30-day all-cause readmission rates during the periods before and after predictive modeling (and corresponding 95% confidence intervals [CI]) were 12.5% (95% CI: 12.2%-12.8%) and 11.1% (95% CI: 10.8%-11.5%), respectively. More modest but similar improvements were observed for 7-day readmissions. Statistical process control charts indicated nonrandom reductions in readmissions after predictive model adoption. The national estimate of the cost of pediatric readmissions indicates an associated health care savings due to reduced 30-day readmission during the 2-year predictive modeling period at $2 673 264 (95% CI: $2 612 431-$2 735 364).

CONCLUSIONS

A combination of predictive modeling and targeted clinical interventions to improve the management of pediatric patients at high risk for readmission was successful in reducing the rate of readmission and reducing overall health care costs. The continued prioritization of patients with potentially modifiable outcomes is key to improving patient outcomes.

Prevalence of Social Risks on Inpatient Screening and Their Impact on Pediatric Care Use

OBJECTIVES

Screening for social determinants of health in the inpatient setting is uncommon. However, social risk factors documented in billing and electronic medical record data are associated with increased pediatric care use. We sought to describe (1) the epidemiology of social risks and referral acceptance and (2) association between social risks identified through routine inpatient screening and care use.

METHODS

Parents of children ages 0 to 18 admitted to a general pediatric floor at an academic children's hospital completed a psychosocial screening survey from October 2017 to June 2019. The survey covered the following domains: finances, housing, food security, medications, and benefits. Patient characteristics and care use outcomes were abstracted from the electronic medical record and compared by using Pearson's χ² or the Wilcoxon rank test and logistic regression analyses.

RESULTS

Of 374 screened families, 141 (38%) had a positive screen result, of whom 78 (55%) reported >1 need and 64 (45%) accepted a community resource. In bivariate analyses, patients with a positive screen result had higher 30-day readmission (10% vs 5%; P = .05), lower median household income ($62 321 vs $71 460; P < .01), lower parental education (P < .01), public insurance (57% vs 43%; P < .01), lived in a 1-parent household (30 vs 12%; P < .01), and had a complex chronic condition (35% vs 23%; P = .01) compared with those with a negative screen result. There was no difference in care reuse by screening status in adjusted analyses.

CONCLUSIONS

Social risks are common in the pediatric inpatient setting. Children with medical complexity offer a good target for initial screening efforts.

Social determinants of health in electronic health records and their impact on analysis and risk prediction: A systematic review

OBJECTIVE

This integrative review identifies and analyzes the extant literature to examine the integration of social determinants of health (SDoH) domains into electronic health records (EHRs), their impact on risk prediction, and the specific outcomes and SDoH domains that have been tracked.

MATERIALS AND METHODS

In accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a literature search in the PubMed, CINAHL, Cochrane, EMBASE, and PsycINFO databases for English language studies published until March 2020 that examined SDoH domains in the context of EHRs.

RESULTS

Our search strategy identified 71 unique studies that are directly related to the research questions. 75% of the included studies were published since 2017, and 68% were U.S.-based. 79% of the reviewed articles integrated SDoH information from external data sources into EHRs, and the rest of them extracted SDoH information from unstructured clinical notes in the EHRs. We found that all but 1 study using external area-level SDoH data reported minimum contribution to performance improvement in the predictive models. In contrast, studies that incorporated individual-level SDoH data reported improved predictive performance of various outcomes such as service referrals, medication adherence, and risk of 30-day readmission. We also found little consensus on the SDoH measures used in the literature and current screening tools.

CONCLUSIONS

The literature provides early and rapidly growing evidence that integrating individual-level SDoH into EHRs can assist in risk assessment and predicting healthcare utilization and health outcomes, which further motivates efforts to collect and standardize patient-level SDoH information.

Race, Ethnicity, and Insurance: the Association with Opioid Use in a Pediatric Hospital Setting

BACKGROUND

This study examined the association between race/ethnicity and health insurance payer type with pediatric opioid and non-opioid ordering in an inpatient hospital setting.

METHODS

Cross-sectional inpatient encounter data from June 2013 to June 2018 was retrieved from a pediatric children's hospital in Southern California (N = 55,944), and statistical analyses were performed to determine associations with opioid ordering.

RESULTS

There was a significant main effect of race/ethnicity on opioid and non-opioid orders. Physicians ordered significantly fewer opioid medications, but a greater number of non-opioid medications, for non-Hispanic African American children than non-Hispanic Asian, Hispanic/Latinx, and non-Hispanic White pediatric patients. There was also a main effect of health insurance payer type on non-opioid orders. Patients with government-sponsored plans (e.g., Medi-Cal, Medicare) received fewer non-opioid prescriptions compared with patients with both HMO and PPO coverage. Additionally, there was a significant race/ethnicity by insurance interaction on opioid orders. Non-Hispanic White patients with "other" insurance coverage received the greatest number of opioid orders. In non-Hispanic African American patients, children with PPO coverage received fewer opioids than those with government-sponsored and HMO insurance. For non-Hispanic Asian patients, children with PPO were prescribed more opioids than those with government-sponsored and HMO coverage.

CONCLUSION

Findings suggest that the relationship between race/ethnicity, insurance type, and physician decisions opioid prescribing is complex and multifaceted. Given that consistency in opioid prescribing should be seen regardless of patient background characteristics, future studies should continue to assess and monitor unequitable differences in care.

HealtheDataLab - a cloud computing solution for data science and advanced analytics in healthcare with application to predicting multi-center pediatric readmissions

BACKGROUND

There is a shortage of medical informatics and data science platforms using cloud computing on electronic medical record (EMR) data, and with computing capacity for analyzing big data. We implemented, described, and applied a cloud computing solution utilizing the fast health interoperability resources (FHIR) standardization and state-of-the-art parallel distributed computing platform for advanced analytics.

METHODS

We utilized the architecture of the modern predictive analytics platform called Cerner® HealtheDataLab and described the suite of cloud computing services and Apache Projects that it relies on. We validated the platform by replicating and improving on a previous single pediatric institution study/model on readmission and developing a multi-center model of all-cause readmission for pediatric-age patients using the Cerner® Health Facts Deidentified Database (now updated and referred to as the Cerner Real World Data). We retrieved a subset of 1.4 million pediatric encounters consisting of 48 hospitals' data on pediatric encounters in the database based on a priori inclusion criteria. We built and analyzed corresponding random forest and multilayer perceptron (MLP) neural network models using HealtheDataLab.

RESULTS

Using the HealtheDataLab platform, we developed a random forest model and multi-layer perceptron model with AUC of 0.8446 (0.8444, 0.8447) and 0.8451 (0.8449, 0.8453) respectively. We showed the distribution in model performance across hospitals and identified a set of novel variables under previous resource utilization and generic medications that may be used to improve existing readmission models.

CONCLUSION

Our results suggest that high performance, elastic cloud computing infrastructures such as the platform presented here can be used for the development of highly predictive models on EMR data in a secure and robust environment. This in turn can lead to new clinical insights/discoveries.

Association of Medicaid Eligibility With Surgical Readmission Among Medicare Beneficiaries

IMPORTANCE

The Centers for Medicare & Medicaid Services is beginning to consider adjusting for social risk factors, such as dual eligibility for Medicare and Medicaid, when evaluating hospital performance under value-based purchasing programs. It is unknown whether dual eligibility represents a unique domain of social risk or instead represents clinical risk unmeasured by variables available in traditional Medicare claims.

OBJECTIVE

To assess how dual eligibility for Medicare and Medicaid is associated with risk-adjusted readmission rates after surgery.

DESIGN, SETTING, AND PARTICIPANTS

A retrospective cohort study was conducted of 55 651 Medicare beneficiaries undergoing general, vascular, and gynecologic surgery at 62 hospitals in Michigan between January 1, 2014, and December 1, 2016. Representative cohorts were derived from traditional Medicare claims (n = 29 710) and the Michigan Surgical Quality Collaborative (MSQC) clinical registry (n = 25 941), which includes additional measures of clinical risk. Statistical analysis was conducted between April 10 and July 15, 2019. The association between dual eligibility and risk-adjusted 30-day readmission rates after surgery was compared between models inclusive and exclusive of additional measurements of clinical risk. The study also examined how dual eligibility is associated with hospital profiling using risk-adjusted readmission rates.

EXPOSURES

Dual eligibility for Medicare and Medicaid.

MAIN OUTCOMES AND MEASURES

Risk-adjusted all-cause 30-day readmission after surgery.

RESULTS

There were a total of 3986 dual-eligible beneficiaries in the Medicare claims cohort (2554 women; mean [SD] age, 72.9 [6.9] years) and 1608 dual-eligible beneficiaries in the MSQC cohort (990 women; mean [SD] age, 72.9 [6.8] years). In both data sets, higher proportions of dual-eligible beneficiaries were younger, female, and nonwhite than Medicare-only beneficiaries (Medicare claims cohort: female, 2554 of 3986 [64.1%] vs 12 879 of 25 724 [50.1%]; nonwhite, 1225 of 3986 [30.7%] vs 2783 of 25 724 [10.8%]; MSQC cohort: female, 990 of 1608 [61.6%] vs 12 578 of 24 333 [51.7%]; nonwhite, 416 of 1608 [25.9%] vs 2176 of 24 333 [8.9%]). In the Medicare claims cohort, dual-eligible beneficiaries were more likely to be readmitted (15.5% [95% CI, 13.7%-17.3%]) than Medicare-only beneficiaries (13.3% [95% CI, 12.7%-13.9%]; difference, 2.2 percentage points [95% CI, 0.4-3.9 percentage points]). In the MSQC cohort, after adjustment for more granular measures of clinical risk, dual eligibility was not significantly associated with readmission (difference, 0.6 percentage points [95% CI, -1.0 to 2.2 percentage points]). In both the Medicare claims and MSQC cohorts, adding dual eligibility to risk-adjustment models had little association with hospital ranking using risk-adjusted readmission rates.

CONCLUSIONS AND RELEVANCE

This study suggests that dual eligibility for Medicare and Medicaid may reflect unmeasured clinical risk of readmission in claims data. Policy makers should consider incorporating more robust measures of social risk into risk-adjustment models used by value-based purchasing programs.

Readmissions among NICU graduates: Who, when and why?

Readmission amongst previous neonatal intensive care unit (NICU) graduates, especially for preterm infants, is common and remains a significant risk for these infants beyond the neonatal period. This review explores risk factors for readmissions, common reasons for requiring rehospitalization and explores opportunities for improving the transition from discharge to home with the ultimate goal of reducing readmissions for these high risk infants.

Factors Associated With Return Visits at 7 Days After Hospital Discharge

OBJECTIVE

To identify variables associated with return visits to the hospital within 7 days after discharge.

METHODS

We performed a retrospective study of 7-day revisits and readmissions between October 2012 and September 2015 using the Pediatric Health Information System database supplemented by electronic medical record data from a tertiary-care children's hospital. We examined factors associated with revisits among the top 10 most frequent indications for hospitalization using generalized estimating equations.

RESULTS

There were 736 (4.2%) revisits and 416 (2.3%) readmissions within 7 days. Predictors of 7-day revisits and readmissions included age, length of hospital stay, and presence of a chronic medical condition. In addition, insurance status was associated with risk of revisits and race was associated with risk of readmissions in the bivariate analysis.

CONCLUSIONS

In this study, we identified patient characteristics that may be associated with a higher risk of early return to the emergency department and/or readmissions. Early identification of this at-risk group of patients may provide opportunities for intervention and enhanced care coordination at discharge.

A Quality Improvement Intervention Bundle to Reduce 30-Day Pediatric Readmissions

Supplemental Digital Content is available in the text.

Introduction:

Pediatric hospital readmissions can represent gaps in care quality between discharge and follow-up, including social factors not typically addressed by hospitals. This study aimed to reduce the 30-day pediatric readmission rate on 2 general pediatric services through an intervention to enhance care spanning the hospital stay, discharge, and follow-up process.

Methods:

A multidisciplinary team developed an intervention bundle based on a needs assessment and evidence-based models of transitional care. The intervention included pre-discharge planning with a transition coordinator, screening and intervention for adverse social determinants of health (SDH), medication reconciliation after discharge, communication with the primary care provider, access to a hospital-based transition clinic, and access to a 24-hour direct telephone line staffed by hospital attending pediatricians. These were implemented sequentially from October 2013 to February 2017. The primary outcome was the readmission rate within 30 days of index discharge. The length of stay was a balancing measure.

Results:

During the intervention, the included services discharged 4,853 children. The pre-implementation readmission rate of 10.3% declined to 7.4% and remained stable during a 4-month post-intervention observation period. Among 1,394 families screened for adverse SDH, 48% reported and received assistance with ≥ 1 concern. The length of stay increased from 4.10 days in 2013 to 4.30 days in 2017.

Conclusions:

An intervention bundle, including SDH, was associated with a sustained reduction in readmission rates to 2 general pediatric services. Transitional care that addresses multiple domains of family need during a child’s health crisis can help reduce pediatric readmissions.

Care of Pediatric High-Cost Hospitalizations Across Hospital Types

BACKGROUND

High-cost hospitalizations (HCHs) account for a substantial proportion of pediatric health care expenditures. We aimed to (1) describe the distribution of pediatric HCHs across hospital types caring for children and (2) compare characteristics of pediatric HCHs by hospital type.

METHODS

Cross-sectional analysis of all pediatric hospitalizations in the 2012 Kids' Inpatient Database. HCHs were defined as costs >$40 000 (94th percentile). Hospitals were categorized as children's, small general, and large general.

RESULTS

Approximately 166 000 HCHs were responsible for 50.8% of aggregate hospital costs ($18.1 of $35.7 billion) and were mostly at children's hospitals (65%). Children with an HCH were largely neonates (45%), had public insurance (50%), and had ≥1 chronic condition (74%). A total of 131 children's hospitals cared for a median of 559 HCHs per hospital (interquartile range [IQR]: 355-1153) compared to 76 HCHs per hospital (IQR: 32-151) at 397 large general hospitals and 5 HCHs per hospital (IQR: 2-22) at 3581 small general hospitals. The median annual aggregate cost for HCHs was $60 million (IQR: $36-$135) per children's hospital compared to $6.6 million (IQR: $2-$15) per large general hospital and $300 000 (IQR: $116 000-$1.5 million) per small general hospital. HCHs from children's hospitals encompassed nearly 5 times as many unique clinical conditions as large general hospitals and >30 times as many as small general hospitals.

CONCLUSIONS

Children's hospitals cared for a disproportionate volume, cost, and diversity of HCHs compared to general hospitals. Future studies should characterize the factors driving cost, resources, and reimbursement practices for HCH to ensure the long-term financial viability of the pediatric health care system.

A Statistical-Learning Model for Unplanned 7-Day Readmission in Pediatrics

OBJECTIVES

The rate of pediatric 7-day unplanned readmissions is often seen as a measure of quality of care, with high rates indicative of the need for improvement of quality of care. In this study, we used machine learning on electronic health records to study predictors of pediatric 7-day readmissions. We ranked predictors by clinical significance, as determined by the magnitude of the least absolute shrinkage and selection operator regression coefficients.

METHODS

Data consisting of 50 241 inpatient and observation encounters at a single tertiary pediatric hospital were retrieved; 50% of these patients' data were used for building a least absolute shrinkage and selection operator regression model, whereas the other half of the data were used for evaluating model performance. The categories of variables included were demographics, social determinants of health, severity of illness and acuity, resource use, diagnoses, medications, psychosocial factors, and other variables such as primary care no show.

RESULTS

Previous hospitalizations and readmissions, medications, multiple comorbidities, longer current and previous lengths of stay, certain diagnoses, and previous emergency department use were the most significant predictors modifying a patient's risk of 7-day pediatric readmission. The model achieved an area under the curve of 0.778 (95% confidence interval 0.763-0.793).

CONCLUSIONS

Predictors such as medications, previous and current health care resource use, history of readmissions, severity of illness and acuity, and certain psychosocial factors modified the risk of unplanned 7-day readmissions. These predictors are mostly unmodifiable, indicating that intervention plans on high-risk patients may be developed through discussions with patients and parents to identify underlying modifiable causal factors of readmissions.

Combining Nonclinical Determinants of Health and Clinical Data for Research and Evaluation: Rapid Review

Background

Nonclinical determinants of health are of increasing importance to health care delivery and health policy. Concurrent with growing interest in better addressing patients’ nonmedical issues is the exponential growth in availability of data sources that provide insight into these nonclinical determinants of health.

Objective

This review aimed to characterize the state of the existing literature on the use of nonclinical health indicators in conjunction with clinical data sources.

Methods

We conducted a rapid review of articles and relevant agency publications published in English. Eligible studies described the effect of, the methods for, or the need for combining nonclinical data with clinical data and were published in the United States between January 2010 and April 2018. Additional reports were obtained by manual searching. Records were screened for inclusion in 2 rounds by 4 trained reviewers with interrater reliability checks. From each article, we abstracted the measures, data sources, and level of measurement (individual or aggregate) for each nonclinical determinant of health reported.

Results

A total of 178 articles were included in the review. The articles collectively reported on 744 different nonclinical determinants of health measures. Measures related to socioeconomic status and material conditions were most prevalent (included in 90% of articles), followed by the closely related domain of social circumstances (included in 25% of articles), reflecting the widespread availability and use of standard demographic measures such as household income, marital status, education, race, and ethnicity in public health surveillance. Measures related to health-related behaviors (eg, smoking, diet, tobacco, and substance abuse), the built environment (eg, transportation, sidewalks, and buildings), natural environment (eg, air quality and pollution), and health services and conditions (eg, provider of care supply, utilization, and disease prevalence) were less common, whereas measures related to public policies were rare. When combining nonclinical and clinical data, a majority of studies associated aggregate, area-level nonclinical measures with individual-level clinical data by matching geographical location.

Conclusions

A variety of nonclinical determinants of health measures have been widely but unevenly used in conjunction with clinical data to support population health research.

Potential Impact of Initial Clinical Data on Adjustment of Pediatric Readmission Rates

OBJECTIVE

Comparison of readmission rates requires adjustment for case-mix (ie, differences in patient populations), but previously only claims data were available for this purpose. We examined whether incorporation of relatively readily available clinical data improves prediction of pediatric readmissions and thus might enhance case-mix adjustment.

METHODS

We examined 30-day readmissions using claims and electronic health record data for patients ≤18 years and 29 days of age who were admitted to 3 children's hospitals from February 2011 to February 2014. Using the Pediatric All-Condition Readmission Measure and starting with a model including age, gender, chronic conditions, and primary diagnosis, we examined whether the addition of initial vital sign and laboratory data improved model performance. We employed machine learning to evaluate the same variables, using the L2-regularized logistic regression with cost-sensitive learning and convolutional neural network.

RESULTS

Controlling for the core model variables, low red blood cell count and mean corpuscular hemoglobin concentration and high red cell distribution width were associated with greater readmission risk, as were certain interactions between laboratory and chronic condition variables. However, the C-statistic (0.722 vs 0.713) and McFadden's pseudo R² (0.085 vs 0.076) for this and the core model were similar, suggesting minimal improvement in performance. In machine learning analyses, the F-measure (harmonic mean of sensitivity and positive predictive value) was similar for the best-performing model (containing all variables) and core model (0.250 vs 0.243).

CONCLUSIONS

Readily available clinical variables do not meaningfully improve the prediction of pediatric readmissions and would be unlikely to enhance case-mix adjustment unless their distributions varied widely across hospitals.

Social determinants of health priorities of state Medicaid programs

Background

Growing understanding of the influence of social determinants of health (SDH) on healthcare costs and outcomes for low income populations is leading State Medicaid agencies to consider incorporating SDH into their program design. This paper explores states’ current approaches to SDH.

Methods

A mixed-methods approach combined a web-based survey sent through the Medicaid Medical Director Network (MMDN) listserv and semi-structured interviews conducted at the MMDN Annual Meeting in November 2017.

Results

Seventeen MMDs responded to the survey and 14 participated in an interview. More than half reported current collection of SDH data and all had intentions for future collection. Most commonly reported SDH screening topics were housing instability and food insecurity. In-depth interviews underscored barriers to optimal SDH approaches.

Conclusion

These results demonstrate that Medicaid leaders recognize the importance of SDH in improving health, health equity, and healthcare costs for the Medicaid population but challenges for sustainable implementation remain.

Integrating Social and Behavioral Determinants of Health into Population Health Analytics: A Conceptual Framework and Suggested Road Map

There is growing recognition that social and behavioral risk factors impact population health outcomes. Interventions that target these risk factors can improve health outcomes. This study presents a review of existing literature and proposes a conceptual framework for the integration of social and behavioral data into population health analytics platforms. The authors describe several use cases for these platforms at the patient, health system, and community levels, and align these use cases with the different types of prevention identified by the Centers for Disease Control and Prevention. They then detail the potential benefits of these use cases for different health system stakeholders and explore currently available and potential future sources of social and behavioral domains data. Also noted are several potential roadblocks for these analytic platforms, including limited data interoperability, expense of data acquisition, and a lack of standardized technical terminology for socio-behavioral factors.

Developing Prediction Models for 30-Day Unplanned Readmission Among Children With Medical Complexity

OBJECTIVES

To target interventions to prevent readmission, we sought to develop clinical prediction models for 30-day readmission among children with complex chronic conditions (CCCs).

METHODS

After extracting sociodemographic and clinical characteristics from electronic health records for children with CCCs admitted to an academic medical center, we constructed a multivariable logistic regression model to predict readmission from characteristics obtainable at admission and then a second model adding hospitalization and discharge variables to the first model. We assessed model performance using c-statistic and calibration curves and internal validation using bootstrapping. We then created readmission risk scoring systems from final model β-coefficients.

RESULTS

Of the 2296 index admissions involving children with CCCs, 188 (8.2%) had unplanned 30-day readmissions. The model with admission characteristics included previous admissions, previous emergency department visits, number of CCC categories, and medical versus surgical admission (c-statistic 0.65). The model with hospitalization and discharge factors added discharge disposition, length of stay, and weekday discharge to the admission variables (c-statistic 0.67). Bootstrap samples had similar c-statistics, and slopes did not suggest significant overfitting for either model. Readmission risk was 3.6% to 4.9% in the lowest risk quartile versus 15.9% to 17.6% in the highest risk quartile (or 3.6-4.5 times higher) for both models.

CONCLUSIONS

Clinical variables related to the degree of medical complexity and illness severity can stratify children with CCCs into groups with clinically meaningful differences in the risk of readmission. Future research will explore whether these models can be used to target interventions and resources aimed at decreasing readmissions.

Annual Report on Health Care for Children and Youth in the United States: Focus on 30-Day Unplanned Inpatient Readmissions, 2009 to 2014

OBJECTIVE

To describe trends in unplanned 30-day all-condition hospital readmissions for children aged 1 to 17 years between 2009 and 2014.

METHODS

Analysis was conducted with the 2009-14 Nationwide Readmissions Database from the Agency for Healthcare Research and Quality's Healthcare Cost and Utilization Project. Annual hospital readmission rates, resource use, and the most common reasons for readmission were calculated for the 2009-14 period.

RESULTS

The rate of readmission for children aged 1 to 17 years was essentially stable between 2009 and 2014 (5.5% in 2009 and 5.9% in 2014). In 2009, the most common reason (principal diagnosis) for readmission was sickle cell anemia, whereas in 2014 the most common reason was epilepsy. Pneumonia fell from the second to the sixth most common reason for readmission over this period (from 3832 to 2418 stays). Other respiratory infections were among the top 10 principal readmission diagnoses in 2009, but not in 2014. Septicemia was among the 10 most common reasons for readmission in 2014, but not in 2009. Although the average cost of index (ie, initial) stays with a subsequent readmission were similar in 2009 and 2014, the average cost of index stays without a readmission and cost of readmission stays increased by approximately 23%. In both 2009 and 2014, the average cost of the index stays with a subsequent readmission was 73% to 89% higher than that of the index stays of children who were not readmitted within 30 days. The average cost of index stays preceding a readmission was 33% to 45% higher than average costs for readmitted stays. In 2014, the aggregate cost of index stays plus readmissions was $1.58 billion, with 42.9% of the costs attributable to readmissions. Regarding the average costs and lengths of stay for the 10 most common readmission diagnoses, in 2009 the average cost per stay for complications of devices, implants, or grafts was nearly 5 times greater than that of asthma ($21,200 vs $4500, respectively). In 2014, average cost per stay ranged from $5500 for asthma to $39,500 for septicemia. In 2009, the average length of stay (LOS) for complications of devices, implants, or grafts was more than 3 three times higher than that for asthma (7.8 days vs 2.5 days, respectively), and in 2014, the average LOS for septicemia was nearly 4 times higher than that for asthma (10.4 days vs. 2.6 days).

CONCLUSIONS

This study provides a baseline assessment for examining trends in 30-day unplanned pediatric readmissions, an important quality metric as the provisions of the Children's Health Insurance Program Reauthorization Act and the Affordable Care Act are changed and implemented in the future. More than 50,000 pediatric hospital stays in 2014 occurred within 30 days of a previous hospitalization, with an average cost of $13,800. This report is timely, as the health care system works to become more patient-centered and public and private payers grapple with how to pay for quality care for children. The report provides baseline information that can be used to further explore ways to reduce unplanned readmissions.

A Novel Model for Enhanced Prediction and Understanding of Unplanned 30-Day Pediatric Readmission

OBJECTIVES

To develop a model to assist clinicians in reducing 30-day unplanned pediatric readmissions and to enhance understanding of risk factors leading to such readmissions.

METHODS

Data consisting of 38 143 inpatient clinical encounters at a tertiary pediatric hospital were retrieved, and 50% were used for training on a multivariate logistic regression model. The pediatric Rothman Index (pRI) was 1 of the novel candidate predictors considered. Multivariate model selection was conducted by minimization of Akaike Information Criteria. The area under the receiver operator characteristic curve (AUC) and values for sensitivity, specificity, positive predictive value, relative risk, and accuracy were computed on the remaining 50% of the data.

RESULTS

The multivariate logistic regression model of readmission consists of 7 disease diagnosis groups, 4 measures of hospital resource use, 3 measures of disease severity and/or medical complexities, and 2 variables derived from the pRI. Four of the predictors are novel, including history of previous 30-day readmissions within last 6 months (P < .001), planned admissions (P < .001), the discharge pRI score (P < .001), and indicator of whether the maximum pRI occurred during the last 24 hours of hospitalization (P = .005). An AUC of 0.79 (0.77-0.80) was obtained on the independent test data set.

CONCLUSIONS

Our model provides significant performance improvements in the prediction of unplanned 30-day pediatric readmissions with AUC higher than the LACE readmission model and other general unplanned 30-day pediatric readmission models. The model is expected to provide an opportunity to capture 39% of readmissions (at a selected operating point) and may therefore assist clinicians in reducing avoidable readmissions.

Lost Earnings and Nonmedical Expenses of Pediatric Hospitalizations

BACKGROUND AND OBJECTIVES

Hospitalization-related nonmedical costs, including lost earnings and expenses such as transportation, meals, and child care, can lead to challenges in prioritizing postdischarge decisions. In this study, we quantify such costs and evaluate their relationship with sociodemographic factors, including family-reported financial and social hardships.

METHODS

This was a cross-sectional analysis of data collected during the Hospital-to-Home Outcomes Study, a randomized trial designed to determine the effects of a nurse home visit after standard pediatric discharge. Parents completed an in-person survey during the child's hospitalization. The survey included sociodemographic characteristics of the parent and child, measures of financial and social hardship, household income and also evaluated the family's total nonmedical cost burden, which was defined as all lost earnings plus expenses. A daily cost burden (DCB) standardized it for a 24-hour period. The daily cost burden as a percentage of daily household income (DCBi) was also calculated.

RESULTS

Median total cost burden for the 1372 households was $113, the median DCB was $51, and the median DCBi was 45%. DCB and DCBi varied across many sociodemographic characteristics. In particular, single-parent households (those with less work flexibility and more financial hardships experienced significantly higher DCB and DCBi. Those who reported ≥3 financial hardships lost or spent 6-times more of their daily income on nonmedical costs than those without hardships. Those with ≥1 social hardships lost or spent double their daily income compared with those without social hardships.

CONCLUSIONS

Nonmedical costs place burdens on families of children who are hospitalized, disproportionately affecting those with competing socioeconomic challenges.

Insurance Coverage and Acute Care Revisits After Pediatric Ambulatory Tonsillectomy

We investigated whether patterns of health insurance coverage were associated with 30-day all-cause acute care revisits after ambulatory tonsillectomy at a free-standing quaternary-care pediatric hospital. Insurance patterns were classified from past encounters as continuous private, continuous Medicaid, Medicaid-to-private change, or private-to-Medicaid change. Among 478/675 boys/girls (age 9 ± 4 years) selected for analysis, 148 (13%) had 30-day revisits, whereas 96 (8%) changed from Medicaid to private insurance, and 99 (9%) changed from private insurance to Medicaid. Revisits were most common in the private-to-Medicaid group, compared with continuous private coverage (19% vs 10%; 95% CI of difference: 1%-18%; P = .007). The private-to-Medicaid group was most likely to be overweight, have symptoms of sleep disordered breathing, and have more past clinical encounters. In multivariable analysis, the greater risk of acute care revisits among children with private-to-Medicaid change in coverage was attributable to greater comorbidity burden and past health care utilization.

Using electronic health record data for environmental and place based population health research: a systematic review

PURPOSE

We conducted a systematic review of literature published on January 2000-May 2017 that spatially linked electronic health record (EHR) data with environmental information for population health research.

METHODS

We abstracted information on the environmental and health outcome variables and the methods and data sources used.

RESULTS

The automated search yielded 669 articles; 128 articles are included in the full review. The number of articles increased by publication year; the majority (80%) were from the United States, and the mean sample size was approximately 160,000. Most articles used cross-sectional (44%) or longitudinal (40%) designs. Common outcomes were health care utilization (32%), cardiometabolic conditions/obesity (23%), and asthma/respiratory conditions (10%). Common environmental variables were sociodemographic measures (42%), proximity to medical facilities (15%), and built environment and land use (13%). The most common spatial identifiers were administrative units (59%), such as census tracts. Residential addresses were also commonly used to assign point locations, or to calculate distances or buffer areas.

CONCLUSIONS

Future research should include more detailed descriptions of methods used to geocode addresses, focus on a broader array of health outcomes, and describe linkage methods. Studies should also explore using longitudinal residential address histories to evaluate associations between time-varying environmental variables and health outcomes.

Importance and Feasibility of Transitional Care for Children With Medical Complexity: Results of a Multistakeholder Delphi Process

BACKGROUND

Children with medical complexity (CMC) account for disproportionate hospital utilization and adverse outcomes after discharge, and several gaps exist regarding the quality of hospital to home transitional care for this population. We conducted an expert elicitation process to identify important and feasible hospital to home transitional care interventions for CMC from the perspectives of parents and health care professionals.

METHODS

We conducted a 2-round electronic Delphi process to identify important and feasible transitional care interventions. Panelists included parents of CMC and multidisciplinary health care professionals. In the first round, panelists rated the importance and feasibility of 39 transitional care interventions on a 9-point Likert scale; agreement between panelists was defined according to RAND/UCLA Appropriateness Methods. The second round of data collection evaluated 16 interventions that panelists did not agree on in the first round and 8 new or revised interventions, accompanied by quantitative and qualitative data summaries.

RESULTS

A total of 29 parents of CMC and 37 health care professionals participated in the Delphi process (response rate 75%). Both stakeholder panels endorsed most interventions as important; health care professionals were less likely to rate several interventions as feasible compared with the parent panel. Over 2 rounds of data collection, the 2 stakeholder panels endorsed 25 interventions as important as well as feasible. These interventions related to family engagement during the hospitalization, care coordination and social support assessment, predischarge education, and written materials.

CONCLUSIONS

Parents and health care professionals considered several transitional care interventions important as well as feasible. This research might inform hospitals' transitional care programs and policies.