Health Informatics Journal最新文献

Introduction: Patients increasingly use chatbots to obtain medical information, a trend that has provoked both optimism and pessimism. Numerous studies have evaluated the quality and readability of these outputs. This study synthesizes these findings through a cross-sectional meta-synthesis. Methods: We identified studies that evaluated responses using the DISCERN instrument, designed to assess the quality of written material. Additionally, we only included studies that also evaluated readability. We recorded the chatbot used, DISCERN scores, the number of words in each question, the number of questions asked, the number of DISCERN evaluators, the readability of responses, and the year the study was conducted. We also assessed the influence of each publication's journal ranking using the Journal Citation Indicator. Results: We identified 42 studies that conducted 86 tests. Chatbot response readability decreased as response quality increased. Forty-nine tests produced responses ranked "good" or better, and only 10 scored below college-level readability. We significantly increased readability by adding the phrase "write responses at sixth-grade reading level" to prompts that previously produced post-graduate reading level responses in published studies. Discussion: Variable quality and poor readability of chatbot responses reinforce pessimism about their utility. Nevertheless, appropriate "prompt engineering" provides scope to enhance response quality and readability.

Objectives: Digital patient-reported outcome (PRO) tools, though beneficial for managing inflammatory bowel disease (IBD), remain underutilized in Australia. This study aimed to investigate a group of Australian patients' readiness to engage with digital PRO tools and identify potential barriers to their implementation. Methods: We assessed 58 patients from a tertiary IBD clinic in Melbourne, Australia, using the Readiness and Enablement Index for Health Technology (ReadHy) tool, and compared the results to those from a Danish study. Results: Compared to the Danish cohort, our patients were younger with more frequent users of electronic devices, showed higher readiness across most ReadHy dimensions, except in the "heiQ8 Emotional Distress" dimension. Conclusion: These findings suggest a generally favourable environment for implementing digital PRO tools at an Australian tertiary IBD clinic, though attention should be paid to emotional well-being to improve adoption. This study also provides a framework for other centres to evaluate their patients' readiness for digital PRO engagement.

Objective: This study evaluates the feasibility and performance of a cloud-based healthcare blockchain framework that integrates Fully Homomorphic Encryption (FHE), Extended Secure Searchable Encryption (ESSE), and Attribute-Based Signature (ABS) for managing encrypted Personal Health Records (PHRs). Methods: A synthetic dataset of 1,000 anonymized health records, modeled after the publicly available Cell-Phone Brain Tumour dataset (Kaggle), was generated in MATLAB. The dataset emulated attributes typically collected by IoT-enabled or mobile health devices (e.g., usage duration, radiation exposure), although no physical IoT integration was implemented. The cryptographic modules-FHE, ESSE, and ABS-were implemented and simulated in a MATLAB-based cloud environment. System evaluation focused on encryption latency, query throughput, access control accuracy, and overall operational efficiency. Results: The FHE module achieved an average encryption time of 2.5 s and a computation time of 4.8 s per 1 KB record. ESSE sustained 20 encrypted queries per second with an 85% success rate. ABS enforced decentralized access with 97% accuracy and a false positive rate of 0.002%. When integrated, the system reached 94% operational efficiency across simulated healthcare workloads. Conclusion: The proposed FHE-ESSE-ABS framework advances existing healthcare blockchain solutions by enabling encrypted computation, privacy-preserving search, and fine-grained access control. These findings confirm its feasibility for secure cloud-based healthcare data management and establish a foundation for future real-world deployment in health informatics.

To achieve useful interoperability between electronic health record (EHR) systems, many approaches have been proposed. To date, none has prevailed as a clear solution. This scoping review studies 24 publications from 2014 to 2023. The aim is to streamline the understanding of current EHR interoperability expectations, practices, and problems, highlight learnings from the Levels of Conceptual Interoperability Model (LCIM), and suggest means for expediting EHR interoperability. Four interoperability levels are visible in EHR compared to seven in the LCIM: technical/foundational, syntactic/structural, semantic, and process/organization. Semantic interoperability-preserving meaning of exchanged data-is the main focus and the problem to solve. Its many expectations cause implementation difficulty. Standardization of data structures, transfer protocols, terminologies, vocabularies, and ontologies are the most common approach, but there is a lack of consensus on standards. Emerging approaches include fuzzy ontologies, natural language processing, and bidirectional transformation. Standardized data structure is not a prerequisite to useful EHR interoperability. Focusing on the state of health records rather than full system integration can expedite interoperability. Different use cases can benefit from various approaches. Artificial intelligence shows promise for handling semi-structured or unstructured data. Stronger regulations may be necessary to guide ongoing integrations.

Objective: To develop and compare the predictive accuracy of machine learning (ML) models for coronary artery calcium (CAC) prediction among firefighters and to evaluate their cross-validated performance against traditional binary logistic regression (BLR). Methods: This study utilized health records from 416 firefighters who underwent comprehensive health screenings at Ascension Public Safety Medical. CAC was assessed using cardiac computed tomography scans. The degree of CAC was measured using the Agatston scores. 17 clinical and lifestyle related risk variables were collected. Machine learning models, including XGBoost, Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and K Nearest Neighbor (KNN), were developed and compared. Additionally, the performance of these ML models was evaluated against traditional binary logistic regression (BLR). Results: Among the 416 firefighters, age (r = 0.28, p < 0.0001), glucose levels (r = 0.13, p = 0.001), monocyte percentages (r = 0.13, p = 0.001), and resting systolic blood pressure (r = 0.13, p = 0.009) were positively associated with CAC. While sodium levels (r = -0.11, p = 0.038), GFR (r = -0.17, p = 0.021), and maximum oxygen volumes (r = -0.19, p = 0.0002) were inversely associated with CAC. XGBoost achieved the highest cross-validated area under the curve (AUC) of 0.770, outperforming NB (0.768), SVM (0.765), RF (0.749), KNN (0.671), and BLR (0.658). Conclusion: Our research demonstrates the efficacy of ML algorithms, particularly XGBoost, in enhancing early detection and preventive strategies for CAC among firefighters. These advancements are crucial for proactive health management in this high-risk group, potentially mitigating risks associated with their demanding profession.

Objective: The study aimed to assess the predictors and the reasons for refusal to participate in a digitally supported remote maintenance pulmonary rehabilitation programme (M-PRP). Methods: Patients contacted to integrate a 12-month M-PRP were assessed for clinical and sociodemographic characteristics and completed a series of 11 questionnaires including digital literacy (MDPQ-16), personality traits (BFI-10) and reasons for refusal. Results: Of the 136 patients included, 78 accepted the M-PRP and 58 refused (43%). The likelihood of refusal was associated with low forced expiratory volume in 1s (FEV₁), body mass index, neuroticism (BFI-10) and MDPQ-16. Main reasons for refusal were programme constraints (47%), intention to continue physical activity alone (45%), and lack of information technologies (IT) equipment (29%). Conclusion: Digital M-PRP rejection is a common problem. Disease severity and technology issues are main barriers. Particular attention should be paid to patients who state they intend to continue on their own, given conflicting literature data.

A novel patient monitoring system is introduced, designed to support early risk mitigation in pancreatic cancer through personalized health interventions. The system aims to strengthen patient engagement and proactive care by enabling healthcare practitioners (HCP) to assign dynamic, data-driven and personalized mitigation plans. Central to this system is a user interface that allows HCP to review and assign tailored mitigation plans to patients. These plans are formulated based on primary clinical data and enriched with secondary behavioral data, such as wearable-derived metrics and self-reported inputs. These inputs are continuously collected, transformed into Holistic Health Records (HHRs), and stored in a scalable platform for integration with ML-based trend analysis and visualization. The article outlines the system's technical architecture, patient data evaluation logic, and user experience across both the HCP interface and patient app. Evaluation by end users via questionnaires demonstrated improved adherence to plans and higher-quality behavioral data. This monitoring platform offers a promising tool for facilitating early risk intervention in pancreatic cancer care. By integrating multi-source patient data into actionable strategies and fostering bidirectional engagement, it bridges the gap between clinical insight and patient participation, contributing to holistic health management.

Background: Human Digital Twins (HDTs) have recently emerged, especially in the context of healthcare. With the growing emphasis on preventive healthcare beyond diagnosis, pervasive sensing has become essential which enables continuous monitoring through real-world data captured from wearables and/or mobile devices. Objective: This scoping review investigates how pervasive sensing technologies have been utilized in the implementation of HDTs for healthcare, with a focus on understanding the twinning methods, identifying their advantages and limitations, and uncovering key challenges encountered in real-world applications. Methods: We proposed an analytical framework to examine how pervasive sensing technologies are utilized in the implementation of HDTs for personal health management. Using this framework, we conducted a comprehensive literature search across PubMed, Scopus, IEEE Xplore, Web of Science, and Google Scholar. Results: A total of 39 eligible papers were reviewed. We present an analysis of these studies and provide a discussion on the potential and limitations of HDTs in the context of pervasive healthcare. Conclusions: The key takeaway is that the integration of HDTs and pervasive sensing provides a foundation for realizing pervasive healthcare by enabling not one-time digital replication, but continuous and comprehensive monitoring of individuals, including their surrounding environments and behavioral changes.

Background: Insomnia is common among psychiatric outpatients in Taiwan and often coexists with anxiety and depression. Early insomnia changes may predict long-term depression. Although CBT-I is effective, face-to-face therapy requires many resources. This study evaluated the effectiveness of a chatbot to enhance access to sleep training. Methods: This study recruited 80 patients from a psychosomatic outpatient clinic in Taiwan and randomly assigned them 1:1 to the intervention or control group. Due to withdrawals or incomplete assessments, 35 in the intervention group and 31 in the control group completed all procedures. The intervention group used a CBT-I chatbot for 4 weeks, while the control group received basic sleep education via a website. Sleep quality and mental health were assessed using the PSQI, BSRS-5, PHQ-9, BDI, and BAI. Results: The intervention group showed significant PSQI improvement (t (34) = 3.80, p < .001) and reduced BSRS-5, PHQ-9, BDI, and BAI scores (p < .05). The control group showed no significant changes. Conclusions: A CBT-I chatbot significantly enhances sleep and mental health, offering accessible, effective support with broad clinical potential.

Background: Digital health technologies are often subject to regulatory requirements. Regulatory auditing processes are complex but necessary to guarantee quality, efficacy and safety of patients. Evolvements such as digitalized clinical trials, and digital biomarkers require a constant adaption of regulatory frameworks. Objective: This review aims to provide an overview on current regulations and standards for digital therapeutics and digital biomarkers, from technical development to market access. Methods: We conducted an unstructured literature review to identify the relevant guidelines, policies and standards for software based digital therapeutics and digital biomarkers. Results: The principal regulations governing software as a medical device are outlined in Chapter 21 of the Code of Federal Regulations by the US Food and Drug Administration, as well as the European Medical Device Regulation 2017/745. Regulatory pathways, such as the DiGA, are in the process of development, particularly for digital therapeutics, which fall within the purview of software as a medical device. Qualification of (digital) biomarkers is typically voluntary but can play a significant role in the development and approval of digital therapeutics. Conclusions: Fragmented, lacking and diverse regulations around digital biomarkers and digital therapeutics highlight the urge to harmonize and foster regulatory frameworks on an international level.