Health Informatics Journal最新文献

Objectives: Dupuytren disease is a common condition that causes progressive finger contractures resulting in impaired hand function and difficulties in performing daily activities. Patient reported outcome measures (PROMs) are commonly used in research and clinical practice to evaluate treatment outcomes. Both general upper extremity PROMs and Dupuytren-specific PROMs are available, typically developed using conventional methodology based on classical test theory. However, most current PROMs have been shown to have low responsiveness and the relevance of included items have been questioned. In this study we aim to develop a new Dupuytren-specific PROM using modern measurement methodology based on item response theory (IRT). Methods: The study will be performed in three phases. In Phase 1, (completed), an expert group developed a questionnaire with a large number of potentially relevant items derived from existing PROMs and patient collaboration. In Phase 2, the questionnaire will be administered to 300 patients with Dupuytren disease, and their responses will be analyzed with IRT methodology to identify the best performing items to be included in the new PROM. In Phase 3, the new PROM will be administered to 300 additional patients for validation. Conclusion: This new Dupuytren-specific patient-reported outcome measure will help advance clinical research on Dupuytren disease.

Objective: To develop and test an NLP algorithm that accurately detects the presence of information reported from DXA scans containing femoral neck T-scores of the patients scanned. Methods: A rule-based NLP algorithm that iteratively built a collection of regular expressions in testing data consisting of 889 snippets of text pulled from DXA reports. This was manually checked by clinical experts to determine the proportion of manually verified annotations that contained T-score information detected by this algorithm called 'BoneScore'. Testing of 30- and 50-word lengths on each side of the key term 'femoral' were pursued until achievement of adequate accuracy. A separate clinical validation regressed the extracted T-score values on five risk factors with established associations. Results: BoneScore built a set of 20 regular expressions that in concert with a width of 50 words on each side of the key term yielded an accuracy of 98% in the testing data. The extracted T-scores, when modeled with multivariable linear regression, consistently exhibited associations supported by the literature. Conclusion: BoneScore uses regular expressions to accurately extract annotations of T-score values of bone mineral density with a width of 50 words on each side of the key term. The extracted T-scores exhibit clinical face validity.

Research indicates that recording medical consultations benefits patients by helping them recall information pertinent to their care. Cancer Care Alberta set out to develop a mobile recording app to enable patients to safely and securely record appointments and take notes. Stakeholder engagement was conducted with patients, healthcare providers, and the Alberta Health Services Legal & Privacy team. App testing was completed with patient and family advisors. The app was piloted in a clinic to assess workflow impacts before moving to a public launch. The app launched in late November 2018 and continues to be used by patients in the cancer program and beyond. Earlier in 2024, the app underwent additional testing with advisors and user-friendly improvements were made based on feedback and previous user reviews. This article summarizes the development, implementation, and sustainment of the My Care Conversations app. Implementation challenges and effective strategies are highlighted.

Objective: The current study aims to understand how Apple Watch helped users maintain wellness routines during the COVID-19 lockdown period, where access to public gyms and spaces was curtailed. We explore the effectiveness of biofeedback engagement aspects of Apple Watch: goals, alerts and notifications, and sociability aspects of the device or social interaction with other users. Methods: We report the results of a 2-week digital diary study based in the United States with 10 adults with 6 months or longer exposure to Apple Watch, followed by online survey responses gathered from 330 additional users. Results: The study findings show how Apple Watch transforms notifications from distractions into positive wellness tools. Data suggests that personal context (custom goals and supported intent) combined with motivational nudges from alerts and notifications as well as contextually triggered nudges contribute to Apple Watch user adoption and satisfaction. Conclusion: This study highlights how Apple Watch transforms notifications from distractions into positive wellness tools; emphasizing the importance of balancing nudging with customization with user control. Sociability and privacy remain crucial, especially with biofeedback-enabled fitness trackers. We conclude that Apple Watch enhances user engagement by triggering context-relevant interactions, nudging users to achieve their goals through small, motivated behaviors.

Objective: The Internet of Medical Things (IoMT) is transforming healthcare systems, but concerns about device integrity and sensitive data are growing. The study aims to develop a framework for evaluating and prioritizing integrity schemes in healthcare for IoT-based medical sensor devices, addressing the challenges of selecting the right authentication solution due to its complexity and intricacy. Methods: A unified health-hesitant fuzzy expert system for IoMT sensor integrity assessment in Saudi Arabia is described in this paper. Medical sensor integrity literature and professionals are contacted first. Delphi is used to gather attributes of integrity approaches while an Internet of Things medical sensor integrity specialist supervises the operation. After collecting characteristics, good assessment criteria are created and the hesitant fuzzy analytic network procedure is used to assess integrity. Results: Functional integrity and measurement accuracy are the biggest factors in IoMT sensor security and integrity, according to assessment. The framework achieves 93%, 94%, and 95% precision, accuracy, and recall compared to current approaches. The framework helps healthcare integrity security professionals and stakeholders assess and resolve IoT medical sensor authentication issues. Conclusion: This health-hesitant fuzzy expert system will let Saudi Arabian and international healthcare stakeholders safely deploy IoMT sensors in the changing healthcare landscape.

Objective: Mitigation of racism in artificial intelligence (AI) is needed to improve health outcomes, yet no consensus exists on how this might be achieved. Methods: At an international conference in 2022, experts gathered to discuss strategies for reducing bias in healthcare AI. Results: This paper delineates these strategies along with their corresponding strengths and weaknesses and reviews the existing literature on these strategies. Conclusions: Five major themes resulted: reducing dataset bias, accurate modeling of existing data, transparency of artificial intelligence, regulation of artificial intelligence and the people who develop it, and bringing stakeholders to the table.