Predictors of Subspecialty Appointment Scheduling and Completion for Patients Referred From a Pediatric Primary Care Clinic.

Failure to complete subspecialty referrals decreases access to subspecialty care and may endanger patient safety. We conducted a retrospective analysis of new patient referrals made to the 14 most common referral departments at Boston Children's Hospital from January 1 to December 31, 2017. The sample included 2031 patient referrals. The mean wait time between referral and appointment date was 39.6 days. In all, 87% of referrals were scheduled and 84% of scheduled appointments attended, thus 73% of the original referrals were completed. In multivariate analysis, younger age, medical complexity, being a non-English speaker, and referral to a surgical subspecialty were associated with a higher likelihood of referral completion. Black and Hispanic/Latino race/ethnicity, living in a Census tract with Social Vulnerability Index (SVI) ≥ 90th percentile, and longer wait times were associated with a lower likelihood of appointment attendance. Future interventions should consider both health care system factors such as appointment wait times and community-level barriers to referral completion.

A Coproduced Family Reporting Intervention to Improve Safety Surveillance and Reduce Disparities.

OBJECTIVES: Examine family safety-reporting after implementing a parent-nurse-physician-leader coproduced, health literacy-informed, family safety-reporting intervention for hospitalized families of children with medical complexity. METHODS: We implemented an English and Spanish mobile family-safety-reporting tool, staff and family education, and process for sharing comments with unit leaders on a dedicated inpatient complex care service at a pediatric hospital. Families shared safety concerns via predischarge surveys (baseline and intervention) and mobile tool (intervention). Three physicians with patient safety expertise classified events. We compared safety-reporting baseline (via survey) versus intervention (via survey and/or mobile tool) with generalized estimating equations and sub-analyzed data by COVID-19-era and educational attainment. We also compared mobile tool-detected event rates with hospital voluntary incident reporting. RESULTS: 232 baseline and 208 intervention parents participated (78.2% consented); 29.5% of baseline families versus 38.2% of intervention families reported safety concerns (P = .09). Adjusted odds ratio (95% CI) of families reporting safety concerns intervention versus baseline was 1.6 (1.0-2.6) overall, 2.6 (1.3-5.4) for those with < college education, and 3.1 (1.3-7.3) in the COVID-19-era subgroup. Safety concerns reported via mobile tool (34.6% of enrolled parents) included 42 medical errors, 43 nonsafety-related quality issues, 11 hazards, and 4 other. 15% of mobile tool concerns were also detected with voluntary incident reporting. CONCLUSIONS: Family safety-reporting was unchanged overall after implementing a mobile reporting tool, though reporting increased among families with lower educational attainment and during the COVID-19 pandemic. The tool identified many events not otherwise captured by staff-only voluntary incident reporting. Hospitals should proactively engage families in reporting to improve safety, quality, and equity.

Pediatric readmission prevalence and variability across hospitals.

IMPORTANCE: Readmission rates are used as an indicator of the quality of care that patients receive during a hospital admission and after discharge. OBJECTIVE: To determine the prevalence of pediatric readmissions and the magnitude of variation in pediatric readmission rates across hospitals. DESIGN, SETTING, AND PATIENTS: We analyzed 568,845 admissions at 72 children's hospitals between July 1, 2009, and June 30, 2010, in the National Association of Children's Hospitals and Related Institutions Case Mix Comparative data set. We estimated hierarchical regression models for 30-day readmission rates by hospital, accounting for age and Chronic Condition Indicators. Hospitals with adjusted readmission rates that were 1 SD above and below the mean were defined as having "high" and "low" rates, respectively. MAIN OUTCOME MEASURES: Thirty-day unplanned readmissions following admission for any diagnosis and for the 10 admission diagnoses with the highest readmission prevalence. Planned readmissions were identified with procedure codes from the International Classification of Diseases, Ninth Revision, Clinical Modification. RESULTS: The 30-day unadjusted readmission rate for all hospitalized children was 6.5% (n = 36,734). Adjusted rates were 28.6% greater in hospitals with high vs low readmission rates (7.2% [95% CI, 7.1%-7.2%] vs 5.6% [95% CI, 5.6%-5.6%]). For the 10 admission diagnoses with the highest readmission prevalence, the adjusted rates were 17.0% to 66.0% greater in hospitals with high vs low readmission rates. For example, sickle cell rates were 20.1% (95% CI, 20.0%-20.3%) vs 12.7% (95% CI, 12.6%-12.8%) in high vs low hospitals, respectively. CONCLUSIONS AND RELEVANCE: Among patients admitted to acute care pediatric hospitals, the rate of unplanned readmissions at 30 days was 6.5%. There was significant variability in readmission rates across conditions and hospitals. These data may be useful for hospitals' quality improvement efforts.

Improving Use of Antibiotics for Pediatric Pneumonia in Community Hospital Emergency Departments.

BACKGROUND AND OBJECTIVES: The majority of pediatric patients present to community-hospital emergency departments (EDs). Pneumonia is among the most common reasons for ED visits; however, prescribing narrow-spectrum antibiotics occurs at rates below established best practices. We sought to increase prescription of narrow-spectrum antibiotics for pediatric pneumonia in 5 community hospital EDs using an interdisciplinary learning collaborative. We aimed to increase use of narrow spectrum antibiotics from 60% to 80% by December 2018. METHODS: A collaborative of 5 community hospitals developed quality improvement teams who held quarterly meetings over a 1 year period engaging teams in Plan-Do-Study-Act cycles. Interventions included deployment of an evidenced based guideline, educational interventions, and order set modification. Preintervention data were collected for 12 months. Using a standardized data form, teams collected monthly data during the intervention period and for an additional year after to assess for sustainability. Teams evaluated data using statistical process control charts and included any patient 3 months to 18 years with a diagnosis of pneumonia. RESULTS: The aggregated rate of narrow-spectrum antibiotic prescriptions increased from 60% during the baseline period to 78% during the intervention period. During the year after active implementation, this aggregate rate increased to 92%. Differences in prescribing patterns were noted by provider type, but narrow-spectrum antibiotic use improved for both general emergency medicine and pediatric providers. No return visits to the ED for failure of antibiotic treatment within 72 hours occurred. CONCLUSIONS: An interdisciplinary community hospital learning collaborative increased prescribing narrow-spectrum antibiotics by both general and pediatric ED providers.

Patient Experience with Virtual Preoperative Consultations in Pediatric Surgical Specialties.

BACKGROUND: A cross-sectional study was conducted to assess the comparative effectiveness of virtual visits for preoperative evaluation and surgical decision-making in three pediatric surgical subspecialties. METHODS: Patients who underwent surgical procedures in the departments of Urology, Ophthalmology, and Plastic and Oral Surgery at a tertiary care pediatric hospital over a one-year period during the COVID-19 pandemic were included. Patients were assigned to one of three clinical pathways based on their preoperative visit(s): only in-person visit(s) (IP), a combination of in-person and virtual visit(s) (IP/VV), and only virtual visit(s) (VV). Demographics, procedure information, and patient experience survey results were collected. We then assessed variations in procedure types and patient experience scores in these three patient groups. RESULTS: There were 431 patients who completed the modified patient experience survey. The most common procedures were circumcision (17%), excision of lesion (16%), and strabismus repair (11%). Survey results were positive, with 90% of participants rating that they would recommend the service to others. No significant differences were found among groups in their demographics, overall care rating, and duration between preoperative clinic visit and procedure. Post-hoc power analysis indicated 87% power to detect a 10% difference in survey ratings between IP and VV cases, confirming non-inferiority in patient satisfaction for virtual preoperative visits. CONCLUSION: This study demonstrated the non-inferiority of preoperative virtual visits in three pediatric surgical subspecialties as measured by patient experience scores. Additional studies with more granular scope are necessary to further elucidate telemedicine's safety and efficacy for select diagnoses. LEVEL OF EVIDENCE: III.

Impairment Types and Combinations Among Adolescents and Young Adults with Disabilities: Colorado 2014-2018.

OBJECTIVE: Understanding the types of functional challenges faced by adolescents and young adults with disabilities (AYA-WD) can help payers, clinicians, community-based service providers, and policymakers recognize and meet needs. This paper describes state-level prevalence rates for 1) AYA-WD overall and for 2) impairment types singly and in combinations; and 3) examines how rates may differ between those insured by Medicaid versus commercial insurance. METHODS: This descriptive study uses Colorado's All Payer Claims Dataset 2014-2018 to identify insured 10- to 26-year-olds (Medicaid only: 333,931; commercially only: 392,444). It then applies the previously validated Children with Disabilities Algorithm (CWDA) and its companion, the Diagnosis-to-Impairment-Type Algorithm (DITA), to compare state-level prevalence rates by insurance source for disability overall and for each of five impairment types singly and in combination. RESULTS: Disability prevalence was greater among the Medicaid-insured AYA-WD by +7.6% points (pp)-Medicaid: 11.9% (47,654/333,931), commercial: 4.3% (16,907/392,444). Most AYA-WD had a single impairment, but the prevalence of AYA-WD with two or more impairments was greater among the Medicaid-insured than the commercially insured (+9.9 pp; Medicaid: 33.5% [15,963/47,654], commercial: 23.7% [3992/16, 907]), as was the prevalence of impairment types that were physical (+6.7 pp; Medicaid: 54.7% [26,054/47,654], commercial: 48.0% [8121/16,907]); developmental (+4.1 pp; Medicaid: 35.4% [16,874/47,654], commercial: 31.3% [5290/16,907]); psychiatric (+6.7 pp; Medicaid 21.3% [10,175/47,654], commercial: 14.6% [2470/16,907]), and intellectual (+9.3 pp; Medicaid: 26.2% [12,501/47,654], commercial: 16.9% [2858/16,907]). CONCLUSIONS: CWDA and DITA can be used to understand the rates at which impairment types and combinations occur in a population with childhood-onset disabilities.

Evaluation of a protocol for eliciting narrative accounts of pediatric inpatient experiences of care.

OBJECTIVE: To evaluate the measurement properties of a set of six items designed to elicit narrative accounts of pediatric inpatient experience. DATA SOURCES: Data came from 163 participants recruited from a probability-based online panel of U.S. adults. Participants were family members of a child who had an overnight hospital stay in the past 12 months. STUDY DESIGN: Cross-sectional survey with follow-up phone interviews. DATA COLLECTION/EXTRACTION METHODS: Participants completed an online (n = 129) or phone (n = 34) survey about their child's hospitalization experience. The survey contained closed-ended items from the Child Hospital Consumer Assessment of Healthcare Providers and Systems (Child HCAHPS) survey, followed by the six narrative items. Approximately 2 weeks after completing the survey, 47 participants additionally completed a one-hour, semi-structured phone interview, the results of which served as a "gold standard" for evaluating the fidelity of narrative responses. Qualitative content analysis was used to code narrative and interview responses for domains of patient experience and actionability. PRINCIPAL FINDINGS: The average narrative was 248 words (SD = 319). Seventy-nine percent of narratives mentioned a topic included in the Child HCAHPS survey; 89% mentioned a topic not covered by that survey; and 75% included at least one detailed description of an actionable event. Overall, there was 66% correspondence between narrative and interview responses. Correspondence was higher on the phone than in the online condition (75% vs. 59%). CONCLUSIONS: Narratives elicited from rigorously designed multi-item sets can provide detailed, substantive information about pediatric inpatient experiences that hospitals could use to improve child and family experiences during pediatric hospitalization. They add context to closed-ended survey item responses and provide information about experiences of care important to children and families that are not included in quantitative surveys.

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.

Understanding Caregiver Perspectives on an Electronic Consultation and Referral System.

This study examined caregiver impressions of an electronic consultation and referral (ECR) system. Participants included 56 caregivers of primary care patients referred through the ECR system. Semistructured interviews and surveys were conducted between August 2018 and April 2019. Transcripts were coded and themes developed using thematic content analysis. A total of 51% of caregivers stated that they would prefer to see their child's primary care provider (PCP) for a specialty issue if they could receive the same quality of care. All caregivers who received an electronic consult (n = 28) said that they would utilize that process again. Three themes emerged: (1) caregivers expect immediate action prior to or instead of a specialty referral; (2) caregiver preferences for PCP versus specialist are mediated by both child and provider characteristics; (3) caregiver attitudes toward the ECR system are influenced by external considerations and experiences with the system. Results suggest caregivers value enhanced communication and immediate access to specialty input facilitated by the ECR system.

Understanding Racial, Ethnic, and Socioeconomic Differences in the Ambulatory Care Experience.

BACKGROUND AND OBJECTIVE: Racial and ethnic and socioeconomic differences in patient experience are prevalent and can negatively impact health outcomes. Our objective was to examine differences in family experience of care in the pediatric ambulatory setting. METHODS: We conducted interviews with parents of patients from different demographic groups who had received care at 1 of 3 clinics at a quaternary children's hospital. Multidisciplinary team conducted inductive and deductive thematic analysis of transcribed interviews. Sentiments and recurring themes were compared within and between racial and ethnic groups, insurance status, and language. RESULTS: Eighty parents were interviewed. Three primary themes were identified: (1) mitigation of system issues: parents' mixed experiences with staff or clinicians mitigating system issues impacted their overall perceptions of care; (2) pivotal role of personal interactions: clinicians' interactions positively influenced family-clinician relationships and offset negative experiences; (3) effective explanations: clinicians' clear and thorough explanations were crucial in enhancing parent confidence in care. As an overarching theme, discrimination and disrespect by staff undermined trust in care, affecting all aspects of experience. With the exception of explanations, a higher proportion of publicly-insured parents reported negative experiences across all themes compared to those with private insurance. Asian parents with public insurance had the highest proportion of interviews that were mainly negative in sentiment. CONCLUSIONS: Our findings offer nuanced insights into differences in the experience of ambulatory care. Insurance status emerged as an important marker of differential perceptions of care. Our study points to areas for improvement and highlights family-clinician interactions as vital to overall positive experience.

Considerations When Aggregating Data to Measure Performance Across Levels of the Health Care System.

BACKGROUND: Measuring quality at varying levels of the health care system requires attribution, a process of determining the patients and services for which each level is responsible. However, it is important to ensure that attribution approaches are equitable; otherwise, individuals may be assigned differentially based upon social determinants of health. METHODS: First, we used Medicaid claims (2010-2018) from Michigan to assess the proportion of children with sickle cell anemia who had less than 12 months enrollment within a single Medicaid health plan and could therefore not be attributed to a specific health plan. Second, we used the Medicaid Analytic eXtract data (2008-2009) from 26 states to simulate adapting the 30-Day Pediatric All-Condition Readmission measure to the Accountable Care Organization (ACO) level and examined the proportion of readmissions that could not be attributed. RESULTS: For the sickle cell measure, an average of 300 children with sickle cell anemia were enrolled in Michigan Medicaid each year. The proportion of children that could not be attributed to a Medicaid health plan ranged from 12.2% to 89.0% across years. For the readmissions measure, of the 1,051,365 index admissions, 22% were excluded in the ACO-level analysis because of being unable to attribute the patient to a health plan for the 30 days post discharge. CONCLUSIONS: When applying attribution models, it is essential to consider the potential to induce health disparities. Differential attribution may have unintentional consequences that deepen health disparities, particularly when considering incentive programs for health plans to improve the quality of care.

An Innovative Model for Providing Dermatology Services Within Primary Care.

OBJECTIVE: Dermatologic complaints are common in outpatient pediatrics. However, pediatric dermatology specialty care can be difficult to access. We aimed to test the feasibility of co-locating dermatology services within primary care and increase the proportion of patients treated for basic skin complaints within the medical home while decreasing wait times. METHODS: The Rapid Assessment of Skin Health (RASH) clinic was created within a hospital-based primary care clinic in 11/2013. The clinic was staffed by 2 pediatricians trained in the dermatology department and supported with specialist advice as needed. Referral volume and wait times to dermatology and RASH clinic were tracked for visits between 11/1/12 and 10/31/18. A chart review was also conducted on a subset of RASH clinic visits. Primary care providers (PCPs) were surveyed about their experiences. RESULTS: Fifty-eight percent of patients referred for a dermatologic complaint were scheduled in RASH clinic. Wait times for new patient appointments in RASH clinic were significantly shorter than for new dermatology appointments in the previous 12 months (mean 36 days vs 65 days, P < .001). The monthly number of referrals to dermatology also decreased significantly after the RASH clinic opened (24/month vs 12/month, P < .001). Ten percent of RASH patients were referred on to dermatology. In a survey of PCPs (N = 67), 76% said the RASH clinic was "extremely/very helpful." CONCLUSIONS: Providing dermatologic care to low or moderate complexity patients within the medical home is feasible and leads to better access to care. This innovative model could be spread to other clinics and subspecialties.

Trends in 30-Day Readmission for Medicaid and Privately Insured Pediatric Patients: 2010-2017.

BACKGROUND: Children insured by Medicaid have higher readmission rates than privately insured children. However, little is known about whether this disparity has changed over time. METHODS: Data from the 2010 to 2017 Healthcare Cost and Utilization Project Nationwide Readmissions Database were used to compare trends in 30-day readmission rates for children insured by Medicaid and private insurers. Patient-level crude and risk-adjusted readmission rates were compared by using Poisson regression. Hospital-level risk-adjusted readmission rates were compared between Medicaid- and privately insured patients within a hospital by using linear regression. RESULTS: Approximately 60% of pediatric admissions were covered by Medicaid. From 2010 to 2017, the percentage of children with a complex or chronic condition increased for both Medicaid- and privately insured patients. Readmission rates were consistently higher for Medicaid beneficiaries from 2010 to 2017. Readmission rates declined slightly for both Medicaid- and privately insured patients; however, they declined faster for privately insured patients (rate ratio: 0.988 [95% confidence interval: 0.986-0.989] vs 0.995 [95% confidence interval: 0.994-0.996], P for interaction <.001]). After adjustment, readmission rates for Medicaid- and privately insured patients declined at a similar rate (P for interaction = .87). Risk-adjusted hospital readmission rates were also consistently higher for Medicaid beneficiaries. The within-hospital difference in readmission rates for Medicaid versus privately insured patients remained stable over time (slope for difference: 0.015 [SE 0.011], P = .019). CONCLUSIONS: Readmission rates for Medicaid- and privately insured pediatric patients declined slightly from 2010 to 2017 but remained substantially higher among Medicaid beneficiaries suggesting a persistence of the disparity by insurance status.

Factors Associated With Family Experience in Pediatric Inpatient Care.

BACKGROUND: Hospitals are rapidly increasing efforts to improve the pediatric inpatient experience. However, hospitals often do not know what to target for improvement. To determine what matters most to families, we assessed which aspects of experience have the strongest relationships with parents' willingness to recommend a hospital. METHODS: Cross-sectional study of 17 727 surveys completed from November 2012 to January 2014 by parents of children hospitalized at 69 hospitals in 34 states using the Child Hospital Consumer Assessment of Healthcare Providers and Systems Survey. Hierarchical logistic regressions predicted the "top box" for willingness to recommend from measures of specific care dimensions (nurse-parent communication, doctor-parent communication, communication about medicines, keeping parents informed about the child's care, privacy with providers, preparing to leave the hospital, mistakes and concerns, child comfort, cleanliness, and quietness), adjusting for parent-child characteristics. Relative importance was assessed by using partially standardized adjusted odds ratios (aORs). RESULTS: Child comfort (aOR 1.50; 95% confidence interval 1.41-1.60) and nurse-parent communication (aOR 1.50; 95% confidence interval 1.42-1.58) showed the strongest relationships with willingness to recommend, followed by preparing to leave the hospital, doctor-parent communication, and keeping parents informed. Privacy and quietness were not significantly associated with willingness to recommend in multivariate analysis. CONCLUSIONS: Our study uncovered highly valued dimensions that are distinct to pediatric care. Hospitals that care for children should consider using dedicated pediatric instruments to measure and track their performance. Improvement efforts should focus on creating an age-appropriate environment, improving the effectiveness of provider interactions, and engaging parents to share their values and concerns.

Suitability of elderly adult hospital readmission rates for profiling readmissions in younger adult and pediatric populations.

OBJECTIVE: To determine the correlation between hospital 30-day risk-standardized readmission rates (RSRRs) in elderly adults and those in nonelderly adults and children. DATA SOURCES/STUDY SETTING: US hospitals (n = 1760 hospitals admitting adult patients and 235 hospitals admitting both adult and pediatric patients) in the 2013-2014 Nationwide Readmissions Database. STUDY DESIGN: Cross-sectional analysis comparing 30-day RSRRs for elderly adult (≥65 years), middle-aged adult (40-64 years), young adult (18-39 years), and pediatric (1-17 years) patients. PRINCIPAL FINDINGS: Hospital elderly adult RSRRs were strongly correlated with middle-aged adult RSRRs (Pearson R2 .69 [95% confidence interval (CI) 0.66-0.71]), moderately correlated with young adult RSRRs (Pearson R2 .44 [95% CI 0.40-0.47]), and weakly correlated with pediatric RSRRs (Pearson R2 .28 [95% CI 0.17-0.38]). Nearly identical findings were observed with measures of interquartile agreement and Kappa statistics. This stepwise relationship between age and strength of correlation was consistent across every hospital characteristic. CONCLUSIONS: Hospital readmission rates in elderly adults, which are currently used for public reporting and hospital comparisons, may reflect broader hospital readmission performance in middle-aged and young adult populations; however, they are not reflective of hospital performance in pediatric populations.

Trends in Pediatric Hospitalizations and Readmissions: 2010-2016.

BACKGROUND: Health reform and policy initiatives over the last 2 decades have led to significant changes in pediatric clinical practice. However, little is known about recent trends in pediatric hospitalizations and readmissions at a national level. METHODS: Data from the 2010-2016 Healthcare Cost and Utilization Project Nationwide Readmissions Database and National Inpatient Sample were analyzed to characterize patient-level and hospital-level trends in annual pediatric (ages 1-17 years) admissions and 30-day readmissions. Poisson regression was used to evaluate trends in pediatric readmissions over time. RESULTS: From 2010 to 2016, the total number of index admissions decreased by 21.3%, but the percentage of admissions for children with complex chronic conditions increased by 5.7%. Unadjusted pediatric 30-day readmission rates increased over time from 6.26% in 2010 to 7.02% in 2016 with a corresponding increase in numbers of admissions for patients with complex chronic conditions. When stratified by complex or chronic conditions, readmission rates declined or remained stable across patient subgroups. Mean risk-adjusted hospital readmission rates increased over time overall (6.46% in 2010 to 7.14% in 2016) and in most hospital subgroups but decreased over time in metropolitan teaching hospitals. CONCLUSIONS: Pediatric admissions declined from 2010 to 2016 as 30-day readmission rates increased. The increase in readmission rates was associated with greater numbers of admissions for children with chronic conditions. Hospitals serving pediatric patients need to account for the rising complexity of pediatric admissions and develop strategies for reducing readmissions in this high-risk population.

Improving Response Rates and Representation of Hard-to-Reach Groups in Family Experience Surveys.

OBJECTIVE: Most US hospitals conduct patient experience surveys by mail or telephone after discharge to assess patient/family centeredness of care. Pediatric response rates are usually very low, especially for black, Latino, and low-income respondents. We investigated whether day of discharge surveying using tablets improves response rates and respondent representativeness. METHODS: This was a quasi-experimental study of parents of patients discharged from 4 units of a children's hospital. Parents were assigned to receive the Child Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) via an audio-enabled tablet before discharge or via mail at approximately 1 week postdischarge. Intervention and control conditions alternated by week. We compared response rates, child/respondent characteristics, and mean top-box scores between tablet and mail only arms. RESULTS: Administering Child HCAHPS on a tablet was administratively feasible and did not interfere with the discharge process (median completion time, 12.4 minutes). The response rate was 71.1% (424 of 596) for tablet versus 16.3% (96 of 588) for mail only. Although the tablet response rate was higher in every subgroup, tablet respondents were more likely to be fathers (20.4% vs 6.4%; P = .006), more likely to have a high school education or less (17.5% vs 8.4%; P = .002), less likely to be white (56.8% vs 71.9%; P = .006), and more likely to be publicly insured (31.4% vs 19.8%; P = .02). Tablet scores were significantly higher than mail only scores for 3 of 17 measures. CONCLUSIONS: The response rate for day of discharge tablet survey administration was >4-fold higher than with single-wave mail-only administration, with greater participation of hard-to-reach groups. These findings suggest tablet administration before discharge shows great promise for real-time feedback and QI and may transform the field of inpatient survey administration.

Racial, Ethnic, and Socioeconomic Disparities in Patient Safety Events for Hospitalized Children.

OBJECTIVES: Previous studies have revealed racial/ethnic and socioeconomic disparities in quality of care and patient safety. However, these disparities have not been examined in a pediatric inpatient environment by using a measure of clinically confirmed adverse events (AEs). In this study, we do so using the Global Assessment of Pediatric Patient Safety (GAPPS) Trigger Tool. METHODS: GAPPS was applied to medical records of randomly selected pediatric patients discharged from 16 hospitals in the Pediatric Research in Inpatient Settings Network across 4 US regions from January 2007 to December 2012. Disparities in AEs for hospitalized children were identified on the basis of patient race/ethnicity (black, Latino, white, or other; N = 17 336 patient days) and insurance status (public, private, or self-pay/no insurance; N = 19 030 patient days). RESULTS: Compared with hospitalized non-Latino white children, hospitalized Latino children experienced higher rates of all AEs (Latino: 30.1 AEs per 1000 patient days versus white: 16.9 AEs per 1000 patient days; P ≤ .001), preventable AEs (Latino: 15.9 AEs per 1000 patient days versus white: 8.9 AEs per 1000 patient days; P = .002), and high-severity AEs (Latino: 12.6 AEs per 1000 patient days versus white: 7.7 AEs per 1000 patient days; P = .02). Compared with privately insured children, publicly insured children experienced higher rates of preventable AEs (public: 12.1 AEs per 1000 patient days versus private: 8.5 AEs per 1000 patient days; P = .02). No significant differences were observed among other groups. CONCLUSIONS: The GAPPS analysis revealed racial and/or ethnic and socioeconomic disparities in rates of AEs experienced by hospitalized children across a broad range of geographic and hospital settings. Further investigation may reveal underlying mechanisms of these disparities and could help hospitals reduce harm.

Care Quality and Spending Among Commercially Insured Children With Disabilities.

OBJECTIVE: To identify opportunities to improve care value for children with disabilities (CWD), we examined CWD prevalence within a commercially insured population and compared outpatient care quality and annual health plan spending levels for CWD relative to children with complex medical conditions without disabilities; children with chronic conditions that are not complex; and children without disabling, complex, or chronic conditions. METHODS: This cross-sectional study comprised 1,118,081 person-years of Blue Cross Blue Shield Massachusetts data for beneficiaries aged 1 to 19years old during 2008 to 2012. We combined the newly developed and validated Children with Disabilities Algorithm with the Pediatric Medical Complexity Algorithm to identify CWD and non-CWD subgroups. We used 14 validated or National Quality Forum-endorsed measures to assess outpatient care quality and paid claims to examine annual plan spending levels and components. RESULTS: CWD constituted 4.5% of all enrollees. Care quality for CWD was between 11% and 59% for 8 of 14 quality measures and >80% for the 6 remaining measures and was generally comparable to that for non-CWD subgroups. Annual plan spending among CWD was a median and mean 23% and 53% higher than that for children with complex medical conditions without disabilities, respectively; CWD mean and median values were higher than for all other groups as well. CONCLUSIONS: CWD were prevalent in our commercially insured population. CWD experienced suboptimal levels of care, but those levels were comparable to non-CWD groups. Improving the care value for CWD involves a deeper understanding of what higher spending delivers and additional aspects of care quality.

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.