Patients going home after a hospitalization face many challenges. This transition period exposes patients to unnecessary risks related to inadequate preparation prior to leaving the hospital, potentially leading to errors and patient harm. Although patients engaging in self-management have better health outcomes and increased self-efficacy, little is known about the processes in place to support and develop these skills for patients leaving the hospital. Through qualitative interviews and observations of 28 patients during and after their hospitalizations, we explore the challenges they face transitioning from hospital care to self-management. We identify three key elements in this process: knowledge, resources, and self-efficacy. We describe how both system and individual factors contribute to breakdowns leading to ineffective patient management. This work expands our understanding of the unique challenges faced by patients during this difficult transition and uncovers important design opportunities for supporting crucial yet unmet patient needs.

BackgroundThe rarity of pediatric glomerular disease makes it difficult to identify sufficient numbers of participants for clinical trials. This leaves limited data to guide improvements in care for these patients.MethodsThe authors developed and tested an electronic health record (EHR) algorithm to identify children with glomerular disease. We used EHR data from 231 patients with glomerular disorders at a single center to develop a computerized algorithm comprising diagnosis, kidney biopsy, and transplant procedure codes. The algorithm was tested using PEDSnet, a national network of eight children’s hospitals with data on >6.5 million children. Patients with three or more nephrologist encounters (n=55,560) not meeting the computable phenotype definition of glomerular disease were defined as nonglomerular cases. A reviewer blinded to case status used a standardized form to review random samples of cases (n=800) and nonglomerular cases (n=798).ResultsThe final algorithm consisted of two or more diagnosis codes from a qualifying list or one diagnosis code and a pretransplant biopsy. Performance characteristics among the population with three or more nephrology encounters were sensitivity, 96% (95% CI, 94% to 97%); specificity, 93% (95% CI, 91% to 94%); positive predictive value (PPV), 89% (95% CI, 86% to 91%); negative predictive value, 97% (95% CI, 96% to 98%); and area under the receiver operating characteristics curve, 94% (95% CI, 93% to 95%). Requiring that the sum of nephrotic syndrome diagnosis codes exceed that of glomerulonephritis codes identified children with nephrotic syndrome or biopsy-based minimal change nephropathy, FSGS, or membranous nephropathy, with 94% sensitivity and 92% PPV. The algorithm identified 6657 children with glomerular disease across PEDSnet, ≥50% of whom were seen within 18 months.ConclusionsThe authors developed an EHR-based algorithm and demonstrated that it had excellent classification accuracy across PEDSnet. This tool may enable faster identification of cohorts of pediatric patients with glomerular disease for observational or prospective studies.

An automated, hospital-based influenza vaccination program integrated into the EMR can increase vaccinations of hospitalized patients and provide insight into the vaccination history and declination reasons for children not receiving the vaccine.