Feasibility of Implementation of a Mobile Digital Personal Health Record to Coordinate Care for Children and Youth With Special Health Care Needs in Primary Care: Protocol for a Mixed Methods Study.

BACKGROUND: Electronic health record (EHR)-integrated digital personal health records (PHRs) via Fast Healthcare Interoperability Resources (FHIR) are promising digital health tools to support care coordination (CC) for children and youth with special health care needs but remain widely unadopted; as their adoption grows, mixed methods and implementation research could guide real-world implementation and evaluation. OBJECTIVE: This study (1) evaluates the feasibility of an FHIR-enabled digital PHR app for CC for children and youth with special health care needs, (2) characterizes determinants of implementation, and (3) explores associations between adoption and patient- or family-reported outcomes. METHODS: This nonrandomized, single-arm, prospective feasibility trial will test an FHIR-enabled digital PHR app's use among families of children and youth with special health care needs in primary care settings. Key app features are FHIR-enabled access to structured data from the child's medical record, families' abilities to longitudinally track patient- or family-centered care goals, and sharing progress toward care goals with the child's primary care provider via a clinician dashboard. We shall enroll 40 parents or caregivers of children and youth with special health care needs to use the app for 6 months. Inclusion criteria for children and youth with special health care needs are age 0-16 years; primary care at a participating site; complex needs benefiting from CC; high hospitalization risk in the next 6 months; English speaking; having requisite technology at home (internet access, Apple iOS mobile device); and an active web-based EHR patient portal account to which a parent or caregiver has full proxy access. Digital prescriptions will be used to disseminate study recruitment materials directly to eligible participants via their existing EHR patient portal accounts. We will apply an intervention mixed methods design to link quantitative and qualitative (semistructured interviews and family engagement panels with parents of children and youth with special health care needs) data and characterize implementation determinants. Two CC frameworks (Pediatric Care Coordination Framework; Patient-Centered Medical Home) and 2 evaluation frameworks (Consolidated Framework for Implementation Research; Technology Acceptance Model) provide theoretical foundations for this study. RESULTS: Participant recruitment began in fall 2022, before which we identified >300 potentially eligible patients in EHR data. A family engagement panel in fall 2021 generated formative feedback from family partners. Integrated analysis of pretrial quantitative and qualitative data informed family-centered enhancements to study procedures. CONCLUSIONS: Our findings will inform how to integrate an FHIR-enabled digital PHR app for children and youth with special health care needs into clinical care. Mixed methods and implementation research will help strengthen implementation in diverse clinical settings. The study is positioned to advance knowledge of how to use digital health innovations for improving care and outcomes for children and youth with special health care needs and their families. TRIAL REGISTRATION: ClinicalTrials.gov NCT05513235; https://clinicaltrials.gov/study/NCT05513235. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/46847.

Digital solution for follow-up in congenital cardiac surgery.

BACKGROUND: In this era of public scrutiny, there is an ongoing need for innovative methods for patient follow-up. OBJECTIVES: As part of a quality initiative, we developed an automated post-operative follow-up system for patients following discharge after cardiac surgery at Boston Children's Hospital. METHODS: Discharge Communication (DisCo) is a web-based system developed at Boston Children's Hospital. An automated text and e-mail with a link to a health status survey are sent at 30 days and 1 year post-discharge in English/Spanish. If there is no response, surveys are completed via phone calls to the patient/patient's physician or chart review. Responses are stored in the DisCo database and the patient's medical record. Patients who underwent cardiac surgery and survived to hospital discharge from October, 2016 received the surveys. RESULTS: Overall, 3345 30-day and 2563 1-year surveys were sent between October, 2016 and June, 2020. Of 3345 30-day surveys, there were 3191 responses (95%). Of 2563 1-year surveys, there were 1807 responses (71%). Most patients/families responded directly to the link at 30 days (65% for paediatrics/75% for adults) and at 1 year (72% for paediatrics/78% for adults). Multi-variable logistic regression revealed that higher complexity of cardiac lesion, presence of major non-cardiac anomalies and presence of major residua were associated with readmission and catheter/surgical reinterventions. Non-cardiac anomalies were associated with increased need for services for learning, development or behaviour. CONCLUSIONS: DisCo provides a successful web-based health status assessment of patients following congenital cardiac surgery. It helps to identify high-risk patients who need closer follow-up.

Identifying patterns in administrative tasks through structural topic modeling: A study of task definitions, prevalence, and shifts in a mental health practice's operations during the COVID-19 pandemic.

OBJECTIVE: This case study illustrates the use of natural language processing for identifying administrative task categories, prevalence, and shifts necessitated by a major event (the COVID-19 [coronavirus disease 2019] pandemic) from user-generated data stored as free text in a task management system for a multisite mental health practice with 40 clinicians and 13 administrative staff members. MATERIALS AND METHODS: Structural topic modeling was applied on 7079 task sequences from 13 administrative users of a Health Insurance Portability and Accountability Act-compliant task management platform. Context was obtained through interviews with an expert panel. RESULTS: Ten task definitions spanning 3 major categories were identified, and their prevalence estimated. Significant shifts in task prevalence due to the pandemic were detected for tasks like billing inquiries to insurers, appointment cancellations, patient balances, and new patient follow-up. CONCLUSIONS: Structural topic modeling effectively detects task categories, prevalence, and shifts, providing opportunities for healthcare providers to reconsider staff roles and to optimize workflows and resource allocation.

Underrepresentation of Phenotypic Variability of 16p13.11 Microduplication Syndrome Assessed With an Online Self-Phenotyping Tool (Phenotypr): Cohort Study.

BACKGROUND: 16p13.11 microduplication syndrome has a variable presentation and is characterized primarily by neurodevelopmental and physical phenotypes resulting from copy number variation at chromosome 16p13.11. Given its variability, there may be features that have not yet been reported. The goal of this study was to use a patient "self-phenotyping" survey to collect data directly from patients to further characterize the phenotypes of 16p13.11 microduplication syndrome. OBJECTIVE: This study aimed to (1) discover self-identified phenotypes in 16p13.11 microduplication syndrome that have been underrepresented in the scientific literature and (2) demonstrate that self-phenotyping tools are valuable sources of data for the medical and scientific communities. METHODS: As part of a large study to compare and evaluate patient self-phenotyping surveys, an online survey tool, Phenotypr, was developed for patients with rare disorders to self-report phenotypes. Participants with 16p13.11 microduplication syndrome were recruited through the Boston Children's Hospital 16p13.11 Registry. Either the caregiver, parent, or legal guardian of an affected child or the affected person (if aged 18 years or above) completed the survey. Results were securely transferred to a Research Electronic Data Capture database and aggregated for analysis. RESULTS: A total of 19 participants enrolled in the study. Notably, among the 19 participants, aggression and anxiety were mentioned by 3 (16%) and 4 (21%) participants, respectively, which is an increase over the numbers in previously published literature. Additionally, among the 19 participants, 3 (16%) had asthma and 2 (11%) had other immunological disorders, both of which have not been previously described in the syndrome. CONCLUSIONS: Several phenotypes might be underrepresented in the previous 16p13.11 microduplication literature, and new possible phenotypes have been identified. Whenever possible, patients should continue to be referenced as a source of complete phenotyping data on their condition. Self-phenotyping may lead to a better understanding of the prevalence of phenotypes in genetic disorders and may identify previously unreported phenotypes.

Pediatrician Attitudes toward Digital Voice Assistant Technology Use in Clinical Practice.

OBJECTIVE: Digital voice assistant technology provides unique opportunities to enhance clinical practice. We aimed to understand factors influencing pediatric providers' current and potential use of this technology in clinical practice. METHODS: We surveyed pediatric providers regarding current use and interest in voice technology in the workplace. Regression analyses evaluated provider characteristics associated with voice technology use. Among respondents not interested in voice technology, we elicited individual concerns. RESULTS: Among 114 respondents, 19 (16.7%) indicated current use of voice technology in clinical practice, and 51 (44.7%) indicated use of voice technology for nonclinical purposes. Fifty-four (47.4%) reported willingness to try digital voice assistant technology in the clinical setting. Providers who had longer clinic visits (odds ratio [OR], 3.11, 95% confidence interval [CI], 1.04, 9.33, p = 0.04), fewer patient encounters per day (p = 0.02), and worked in hospital-based practices (OR, 2.95, 95% CI, 1.08, 8.07, p = 0.03) were more likely to currently use voice technology in the office. Younger providers (p = 0.02) and those confident in the accuracy of voice technology (OR, 3.05, 95% CI, 1.38, 6.74, p = 0.005) were more willing to trial digital voice assistants in the clinical setting. Among respondents unwilling or unsure about trying voice assistant technology, the most common reasons elicited were concerns related to its accuracy (35%), efficiency (33%), and privacy (28%). CONCLUSION: This national survey evaluating use and attitudes toward digital voice assistant technology by pediatric providers found that while only one-eighth of pediatric providers currently use digital voice assistant technology in the clinical setting, almost half are interested in trying it in the future. Younger provider age and confidence in the accuracy of voice technology are associated with provider interest in using voice technology in the clinical setting. Future development of voice technology for clinical use will need to consider accuracy of information, efficiency of use, and patient privacy for successful integration into the workplace.

Issues Identified by Postdischarge Contact after Pediatric Hospitalization: A Multisite Study.

BACKGROUND: Many hospitals are considering contacting hospitalized patients soon after discharge to help with issues that arise. OBJECTIVE: To (1) describe the prevalence of contactidentified postdischarge issues (PDI) and (2) assess characteristics of children with the highest likelihood of having a PDI. DESIGN, SETTING, PATIENTS: A retrospective analysis of hospital-initiated follow-up contact for 12,986 children discharged from January 2012 to July 2015 from 4 US children's hospitals. Contact was made within 14 days of discharge by hospital staff via telephone call, text message, or e-mail. Standardized questions were asked about issues with medications, appointments, and other PDIs. For each hospital, patient characteristics were compared with the likelihood of PDI by using logistic regression. RESULTS: Median (interquartile range) age of children at admission was 4.0 years (0-11); 59.9% were nonHispanic white, and 51.0% used Medicaid. The most common reasons for admission were bronchiolitis (6.3%), pneumonia (6.2%), asthma (5.1%), and seizure (4.9%). Twenty-five percent of hospitalized children (n=3263) reported a PDI at contact (hospital range: 16.0%-62.8%). Most (76.3%) PDIs were related to follow-up appointments (eg, difficulty getting one); 20.8% of PDIs were related to medications (eg, problems filling a prescription). Patient characteristics associated with the likelihood of PDI varied across hospitals. Older age (age 10-18 years vs <1 year) was significantly (P<.001) associated with an increased likelihood of PDI in 3 of 4 hospitals. CONCLUSIONS: PDIs were identified often through hospital-initiated follow-up contact. Most PDIs were related to appointments. Hospitals caring for children may find this information useful as they strive to optimize their processes for follow-up contact after discharge.

A Mobile App Development Guideline for Hospital Settings: Maximizing the Use of and Minimizing the Security Risks of "Bring Your Own Devices" Policies.

BACKGROUND: Hospitals today are introducing new mobile apps to improve patient care and workflow processes. Mobile device adoption by hospitals fits with present day technology behavior; however, requires a deeper look into hospital device policies and the impact on patients, staff, and technology development. Should hospitals spend thousands to millions of dollars to equip all personnel with a mobile device that is only used in a hospital environment? Allowing health care professionals to use personal mobile devices at work, known as bring-your-own-device (BYOD), has the potential to support both the hospital and its employees to deliver effective and efficient care. OBJECTIVE: The objectives of this research were to create a mobile app development guideline for a BYOD hospital environment, apply the guideline to the development of an in-house mobile app called TaskList, pilot the TaskList app within Boston Children's Hospital (BCH), and refine the guideline based on the app pilot. TaskList is an Apple operating system (iOS)-based app designed for medical residents to monitor, create, capture, and share daily collaborative tasks associated with patients. METHODS: To create the BYOD guidelines, we developed TaskList that required the use of mobile devices among medical resident. The TaskList app was designed in four phases: (1) mobile app guideline development, (2) requirements gathering and developing of TaskList fitting the guideline, (3) deployment of TaskList using BYOD with end-users, and (4) refinement of the guideline based on the TaskList pilot. Phase 1 included understanding the existing hospital BYOD policies and conducting Web searches to find best practices in software development for a BYOD environment. Phase 1 also included gathering subject matter input from the Information Services Department (ISD) at BCH. Phase 2 involved the collaboration between the Innovation Acceleration Program at BCH, the ISD Department and the TaskList Clinical team in understanding what features should be built into the app. Phase 3 involved deployment of TaskList on a clinical floor at BCH. Lastly, Phase 4 gathered the lessons learned from the pilot to refine the guideline. RESULTS: Fourteen practical recommendations were identified to create the BCH Mobile Application Development Guideline to safeguard custom applications in hospital BYOD settings. The recommendations were grouped into four categories: (1) authentication and authorization, (2) data management, (3) safeguarding app environment, and (4) remote enforcement. Following the guideline, the TaskList app was developed and then was piloted with an inpatient ward team. CONCLUSIONS: The Mobile Application Development guideline was created and used in the development of TaskList. The guideline is intended for use by developers when addressing integration with hospital information systems, deploying apps in BYOD health care settings, and meeting compliance standards, such as Health Insurance Portability and Accountability Act (HIPAA) regulations.