Objective:To improve our understanding of the information needs of patients/caregivers when accessing anatomic pathology reports (APRs) via the patient portals (PPs) without further clarification by a clinician and to evaluate their opinion toward accessing APRs through a PP. Materials and Methods:We conducted an exploratory mixed-method study using a questionnaire and a think-aloud protocol based on three mock APRs of gastrointestinal specimens. Participants verbalized the cognitive processes used to understand report content, discussed concerns/questions, underlined all unknown terms/phrases, and summarized each APR in their own words. Interviews were transcribed and thematic analysis was performed. APR summaries and term descriptions were assessed for accuracy. Finally, the underlined terms/phrases were grouped to create an ontology. Results:All participants ( n = 15) had difficulty with most of the medical and technical terms in the APRs. Of the terms that participants said they were confident of knowing, only 48% were accurate and accuracy declined to 38% when guessed. The thematic analysis identified six main themes: information sources, interpreting the APR, emotional reactions, finding answers, opinion on accessing the APR on the portal, and preference for doctor-patient communication. The underlined terms were categorized into 12 domains creating the ontology of unknown terms in APRs. Conclusion:This study's findings suggest that patients, regardless of the diagnosis, have difficulties in understanding APRs, mainly due to complex medical terminology. Providing access to APRs via PPs alone is not sufficient to improve patient understanding. Further research is necessary to develop effective solutions that improve patients' understanding of these documents.
Background:In November 2021, the Washington State Department of Health launched WA Verify, a quick-response (QR) code-based tool that allows users to share their coronavirus disease 2019 (COVID-19) vaccine status using a mobile phone. Objectives:This study aimed to gather an unbiased sample of Washington residents to collect information about their experiences and opinions on digital public health interventions, with a particular focus on WA Verify. Materials and Methods:The WA Verify evaluation team designed and implemented a statewide survey using an address-based sample to assess respondents' knowledge, attitudes, and practices regarding COVID-19, WA Verify, and technology. To facilitate comparisons with known characteristics of the Washington State population, the survey included questions also found in state and national surveys and the U.S. Census. Weights based on demographic characteristics were developed to allow for weighted analyses in the future. Results:The unweighted demographic distribution of respondents mostly matched that of the Washington State population, with the largest discrepancy in education; 58.9% of respondents reported having a four-year degree or higher versus 37.3% of the state population. Across race, education level, age and gender, there were differences in reported experiences with and views on requirements to provide vaccination verification and awareness and use of WA Verify. Conclusion:Despite differences in experiences and views on WA Verify, there is broad interest in the tool, suggesting opportunities to expand its adoption. Data and information gathered with this survey, conducted using an address-based sample, provide a strong foundation for further analyses to inform public health interventions, particularly informatics-focused projects.
Background:Adoption of a common data model (CDM), such as the Observational Medical Outcomes Partnership (OMOP) CDM, is a critical component of health information exchange, but the extent to which CDMs facilitate patient-level interoperability is unclear. We sought to determine the feasibility of using a CDM for health information exchange between two healthcare organizations. Materials and Methods:We executed a single Structured Query Language (SQL) query on OMOP-transformed data from the University of California, San Francisco (UCSF) and the San Francisco VA Healthcare System for five patients. Results:The SQL query was successfully executed, and complementary healthcare information was obtained for five of five patients, but interoperability was limited by (1) lack of uniform patient identifiers, (2) use of different coding vocabularies, and (3) variations in mapping of source data to the CDM. Conclusion:Although transformation of EHR data to a CDM can facilitate health information exchange, our study suggests that patient-level interoperability of a CDM may require further alignment of both semantics and syntax.
Background:Project management is crucial in academic hospitals due to the intensive involvement in research and clinical trials. Designing an application in a user-centered and workflow-compatible manner can prevent issues arising from hospital's limited resources and various risks (e.g., financial risk and ethical misconduct). Objectives:This study aimed to (1) develop a hybrid method combining user-centered design (UCD) and agile software development (ASD) principles, (2) apply the hybrid model to develop a Clinical Research Project Management System (CRPMS), (3) assess the usability of the CRPMS and iteratively refine the application. Materials and Methods:A CRPMS was developed following the UCD and ASD principles and supported by a research core in a pediatric heart institute. In Phase 0, semi-structured interviews were conducted to understand processes and bottlenecks. In Phase 1, the project management and budgeting sections of the CRPMS were simultaneously developed. In Phase 2, the Principal Investigator dashboard and the intake section of the CRPMS were developed. Usability evaluation metrics included the System Usability Scale (SUS), Single Ease Question, and severity scores. Results:In Phase 1, the average SUS score was 88.65. There were 126 usability issues and 68 were considered high severity due to the SUS score cutoff (>1.5). In Phase 2, the average SUS score was 87.1. All of the 71 usability issues were addressed during the iterative refinements. Conclusion:A workflow-compatible and highly user-friendly CRPMS was developed by employing a hybrid Agile UCD model. Future work includes continuing development and expanding the CRPMS within and outside the institute.
Background A common cause of preventable harm is the failure to detect and appropriately respond to clinical deterioration. Timely intervention is needed, particularly in medically complex patients, to mitigate the effects of adverse events, disease progression, and medical error. This challenging problem requires clinical surveillance, early recognition, timely notification of the appropriate clinicians, and effective intervention. Objectives We determined the feasibility of designing, developing, and implementing the tools and processes to create a surveillance-and-risk prediction system to detect clinical deterioration in cancer outpatients. Methods We used systems engineering and iterative human-centered design to develop a functional prototype of a surveillance-and-risk prediction system. The system includes passive surveillance involving wearable sensors, active surveillance involving patient event and symptom reporting as well as extraction of selected patient data from the electronic health record (EHR), a predictive model, and communication of estimated risk to clinicians. System usability was evaluated using patient and clinician interviews and clinician ratings using the System Usability Scale (SUS). Results Fifty of 71 recruited patients enrolled in the feasibility study. Patient-reported outcome measures and clinical data extracted from the EHR were the best predictors of a patient's 7-day risk of experiencing unplanned treatment events (UTEs, i.e., emergency room visits, hospital admissions, or major treatment changes). Deep learning neural network models using these predictors demonstrated modest performance in predicting 7-day UTE risk (PROMS, F-measure: 0.900, area under the receiver operating characteristic curve [AUC-ROC]: 0.983; clinical data from EHR F-measure: 0.625, AUC-ROC: 0.983). Patient risk scores were communicated to clinicians using a risk communication prototype rated favorably by clinicians with a SUS score of 76 out of 100 (median = 80; range: 60-85). Conclusion We demonstrate the feasibility of a surveillance-and-risk prediction system for detecting and reporting clinical deterioration in cancer outpatients. Future research is needed to fully implement and evaluate system adoption and effectiveness under different clinical situations.
