OBJECTIVES Hospitalized children are perceived to be increasingly medically complex, but no such trend has been documented. The objective of this study was to determine whether the proportion of pediatric inpatient use that is attributable to patients with a diagnosis of one or more complex chronic condition (CCC) has increased over time and to assess the degree to which CCC hospitalizations are associated with attributes that are consistent with heightened medical complexity. METHODS A retrospective observational study that used the 1997, 2000, 2003, and 2006 Kids Inpatient Databases examined US hospitalizations for children. Attributes of medical complexity included hospital admissions, length of stay, total charges, technology-assistance procedures, and mortality risk. RESULTS The proportion of inpatient pediatric admissions, days, and charges increased from 1997 to 2006 for any CCC and for every CCC group except hematology. CCCs accounted for 8.9% of US pediatric admissions in 1997 and 10.1% of admissions in 2006. These admissions used 22.7% to 26.1% of pediatric hospital days, used 37.1% to 40.6% of pediatric hospital charges, accounted for 41.9% to 43.2% of deaths, and (for 2006) used 73% to 92% of different forms of technology-assistance procedures. As the number of CCCs for a given admission increased, all markers of use increased. CONCLUSIONS CCC-associated hospitalizations compose an increasing proportion of inpatient care and resource use. Future research should seek to improve methods to identify the population of medically complex children, monitor their increasing inpatient use, and assess whether current systems of care are meeting their needs.

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Background As a major chronic disease, asthma causes many emergency department (ED) visits and hospitalizations each year. Predictive modeling is a key technology to prospectively identify high-risk asthmatic patients and enroll them in care management for preventive care to reduce future hospital encounters, including inpatient stays and ED visits. However, existing models for predicting hospital encounters in asthmatic patients are inaccurate. Usually, they miss over half of the patients who will incur future hospital encounters and incorrectly classify many others who will not. This makes it difficult to match the limited resources of care management to the patients who will incur future hospital encounters, increasing health care costs and degrading patient outcomes. Objective The goal of this study was to develop a more accurate model for predicting hospital encounters in asthmatic patients. Methods Secondary analysis of 334,564 data instances from Intermountain Healthcare from 2005 to 2018 was conducted to build a machine learning classification model to predict the hospital encounters for asthma in the following year in asthmatic patients. The patient cohort included all asthmatic patients who resided in Utah or Idaho and visited Intermountain Healthcare facilities during 2005 to 2018. A total of 235 candidate features were considered for model building. Results The model achieved an area under the receiver operating characteristic curve of 0.859 (95% CI 0.846-0.871). When the cutoff threshold for conducting binary classification was set at the top 10.00% (1926/19,256) of asthmatic patients with the highest predicted risk, the model reached an accuracy of 90.31% (17,391/19,256; 95% CI 89.86-90.70), a sensitivity of 53.7% (436/812; 95% CI 50.12-57.18), and a specificity of 91.93% (16,955/18,444; 95% CI 91.54-92.31). To steer future research on this topic, we pinpointed several potential improvements to our model. Conclusions Our model improves the state of the art for predicting hospital encounters for asthma in asthmatic patients. After further refinement, the model could be integrated into a decision support tool to guide asthma care management allocation. International Registered Report Identifier (IRRID) RR2-10.2196/resprot.5039

Implementation of the asthma CPM was associated with improved compliance with CAC-3 and with a delayed, yet significant and sustained decrease in hospital asthma readmission rates, validating CAC-3 as a quality measure. Due to high baseline compliance, CAC-1 and CAC-2 are of questionable value as quality measures.

In children with MCC admitted at our institution during the study period, no medication information source was optimally available, sensitive and specific. Admitting order medication errors affected more than half of patients, the most common being omissions. Efforts to improve medication reconciliation at hospital admission in this population must account for availability and accuracy of information sources and medication omissions at the time of hospital admission.