Hasil untuk "Computer applications to medicine. Medical informatics"

Dazhen Li, Yuanli Guo, Keke Ma et al.

Abstract Background In China, the informed consent for thrombolysis in patients with AIS tends to be primarily signed by proxy decision-makers, generally for the family members of patients. The decision-making process is considerably influenced by various factors, including environmental conditions, individual characteristics, and clinical contexts, all of which exhibit substantial effects on the quality and efficiency of the decision-making process. Objective To conduct an in-depth analysis of the core and bridging factors that influence the decision-making processes of proxy decision-makers, aimed at establishing a foundation for the development of targeted decision support interventions. Methods A cross-sectional study was conducted from January 2024 to December 2024, involving the recruitment of volunteers from the Emergency Department of a tertiary hospital. The Cognitive Appraisal Orientation Test (CAOT), State-Trait Anxiety Inventory (STAI), Chinese version of the Decisional Conflict Scale (C-DCS), Wake Forest Physician Trust Scale (WFPTS), and Perceived Social Support Scale (SSS) were employed to evaluate the effects of the individual and environmental factors. Additionally, network analysis was applied in the visualization of the interrelationships among these variables, alongside the analysis of the pertinent indicators of the network structure. Results 855 valid questionnaires were obtained for subsequent analysis. Within the network structure, the value clarity dimension of C-DCS (C-DCS-val) (rs = 17.313, rc = 0.095) exhibited the highest strength and closeness centrality. Specifically, C-DCS-val from the C-DCS, the benevolent dimension of WFPTS (WFPTS-love) (rs = 11.421, rc = 0.088) from the WFPTS, and the positive dimension of CAOT (CAOT-pos) (rs = 8.312, rc = 0.067) from the CAOT were identified to exhibit the greatest strength centrality within their respective clusters. Additionally, WFPTS-love could function as a bridging factor between the multiple clusters within the network structure. Conclusion C-DCS-val serves as the central determinant, while WFPTS-love functions as the bridge that influences the decision-making process. This study highlights the critical role of healthcare professionals in aiding proxy decision-makers to clarify value preferences, alongside the enhancement of the effectiveness of their communication strategies.

Nandini Krishappa, Girisha Gowdra Shivappa, Sharon Zachariah et al.

Genomic data sharing remains a core problem in precision medicine because genomic data are highly sensitive and unchangeable. In this article, we propose a blockchain-based framework that utilizes zero-knowledge proofs (ZKPs), smart contracts, and off-chain storage to facilitate secure, privacy-preserving data sharing within health record systems. We implemented and evaluated a proof-of-concept prototype in Python on a simulated genomic dataset. The prototype uses a hybrid storage system where metadata is retained on a blockchain and encrypted data are placed in an emulated InterPlanetary File System (IPFS). Rule-based access is controlled using smart contracts, while privacy and security are achieved using ZKPs with interactive Schnorr protocol and elliptic curve cryptography (ECC). Empirical analysis using real-time testing over 100 iterations reported an average zero-knowledge proof with blockchain (ZKPB) query latency of 5.83 ms with a 90.00% accuracy, smart contract latency of under 0.01 ms with 90.00% accuracy, blockchain query time of 0.01 ms with 90.00% accuracy, and ECC latency of 8.72 ms with 90.00% accuracy. These empirical findings validate the effectiveness and privacy guarantees of the framework, which can be utilized in healthcare research, clinical genomics, and personalized medicine workflows. In the age of precision medicine, genomic data are becoming central to powering customized diagnosis and therapy. However, its permanent and sensitive nature raises concerns over privacy, misuse, and unauthorized exploitation. Legacy centralized architecture remains vulnerable to breaches, thus necessitating more resilient alternatives. Recent advances have turned towards blockchain for its decentralization and permanence but remain incomplete in terms of scalability and privacy. New research also combines federated learning, smart contracts, and consent mechanisms, but few attempt to adequately address the complexity of genomic data privacy, actual-world scalability, or data protection regulations compliance. We present Secure Chain, a decentralized, privacy-enhancing infrastructure for genomic data sharing with security. By drawing on blockchain, zero-knowledge proofs (ZKPs), off-chain storage (e.g. IPFS), and homomorphic encryption, the system provides confidentiality, verifiability, and scalability. The goal here is to compare this hybrid architecture’s performance on parameters such as security, computational cost, and query response time with full compliance with law (Health Insurance Portability and Accountability Act [HIPAA] and General Data Protection Regulation [GDPR]). By comparative outputs, the framework shall prove that combining ZKPs and blockchain provides an optimal trade-off between privacy and efficiency in making Secure Chain a feasible, practical solution for safe, regulation-compliant genomic data exchange.

Lauren Micalizzi, Lindy K Howe, Cynthia L Battle et al.

BackgroundPostpartum depression (PPD) rates in the United States are among the highest globally, and PPD can pose significant, long-term risks to families. Concurrently, perinatal cannabis use is increasing in prevalence and may exacerbate PPD. Although evidence links cannabis use with PPD, little is known about its impact on immediate depressive symptoms or depression trajectories across the perinatal period. Moreover, the potential impact of cannabis use on mother-child attachment, bonding, and emotional availability could intensify the effects of cannabis on PPD. ObjectiveThis protocol study is a longitudinal investigation aimed at detecting initial signals of the daily and long-term associations between cannabis use, PPD symptoms, and the mother-infant relationship. MethodsParticipants (N=20) were individuals carrying a singleton pregnancy who reported using cannabis at least twice weekly. Recruitment was through community outreach and online advertisements. Study participation began with a baseline laboratory assessment during pregnancy, which included surveys on mental health and substance use. Follow-ups were conducted virtually at 6 weeks post partum and in the laboratory at 6 months post partum and included additional surveys on infant development, aspects of the mother-infant relationship (eg, attachment), as well as behavioral interaction tasks. Each assessment was paired with a 2-week ecological momentary assessment burst, resulting in three bursts. To support retention, brief check-in visits were completed during the second and third trimesters (depending on gestational age at enrollment), and a postdelivery phone call was conducted within 2 weeks of delivery. A 2-level linear mixed-effect models will be used to examine both event-level and person-level associations of cannabis use with momentary negative affect, PPD symptoms, and attachment, bonding, and emotional availability. Interaction models will test whether these characteristics of the mother-child relationship intensify the association between cannabis use and PPD symptoms. ResultsThis project received institutional review board approval on December 19, 2022, and was awarded funding on February 1, 2023. The recruitment goal of 20 participants was reached on September 4, 2024. Recruitment challenges were encountered early in the study, leading to successful adaptations in recruitment and data collection protocols. Follow-up data collection is ongoing, with completion expected by October 2025 and results anticipated by April 2026. Retention rates approach 100% at follow-up, and ecological momentary assessment compliance rates exceed those observed in nonpregnant samples (ie, >80%). ConclusionsThis protocol study demonstrates our ability to collect momentary and longitudinal data to examine the daily and cumulative impact of cannabis use on PPD and the mother-infant relationship. These data are well-positioned to provide preliminary evidence on how cannabis use may shape depressive symptoms during a particularly high-risk period for maternal mental health. The findings will inform a larger-scale study and advance understanding of the potential effects of cannabis use on perinatal mental health. International Registered Report Identifier (IRRID)DERR1-10.2196/71302

Elisavet Andrikopoulou, Philip Scott

Mobile health (mHealth) applications play an increasingly significant role in enhancing healthcare delivery and supporting the self-management of long-term conditions. This study aims to identify the essential features of mHealth applications that improve medication adherence and health outcomes in adults with long-term conditions. Using qualitative methods, we recruited 42 participants, including 17 with diabetes, and analysed interview data to generate six core themes: blood glucose improvement, convenience of use, decision-making support, emotional wellbeing, clinician-patient relationship enhancement, and patient empowerment. Key features identified include daily patterns, trend analysis, data sharing, and reminders. The findings underscore the importance of usable and meaningful solutions in developing effective and trustworthy mHealth applications.

Kai Ling Chin, Eraniyah Jastan Suing, Ruhini Andong et al.

Spinal tuberculosis, also referred to as Pott's disease, presents a significant risk of severe paralysis if not promptly detected and treated, owing to complications such as spinal cord compression and deformity. This article presents the genetic analysis of a Mycobacterium tuberculosis STB-T1A strain, isolated from the spine of a 29-year-old female diagnosed with spinal tuberculosis. Genomic DNA was extracted from pure culture and subjected to sequencing using the Illumina NovaSeq 6000 sequencing system. The genome of the M. tuberculosis STB-T1A strain spans 4,367,616 base pairs with a G+C content of 65.56 % and 4174 protein-coding genes. Comparative genomic analysis, conducted via single nucleotide polymorphism (SNP)-based phylogenetic analysis using the Maximum Likelihood method, revealed that the strain falls within the Indo-Oceanic lineage (Lineage 1). It clusters with the M. tuberculosis 43-16836 strain, which was isolated from the cerebrospinal fluid of a patient with tuberculous meningitis in Thailand. The complete genome sequence has been deposited at the National Center for Biotechnology Information (NCBI) GenBank database with the accession number JBBMVZ000000000.

Kyrylo S. Malakhov, Sergii V. Kotlyk, Mykola G. Petrenko

This article explores the theoretical aspects of transdisciplinary research, with a specific focus on its application to telerehabilitation. The integration of multiple disciplines – ranging from medicine, digital health, and informatics to engineering and the social sciences – is increasingly necessary to address the complex challenges of delivering effective remote rehabilitation services. The article begins by outlining the conceptual framework, distinguishing between disciplinary, interdisciplinary, multidisciplinary, and transdisciplinary approaches, and highlighting the importance of transcending traditional disciplinary boundaries. The theoretical foundations discussed provide a basis for understanding how the convergence of diverse fields can lead to innovative solutions in telerehabilitation. The integration of disciplines is examined in detail, illustrating how collaborative efforts across medicine, technology, and behavioral sciences can enhance patient outcomes, improve accessibility, and foster the development of personalized rehabilitation plans. The article also covers the practical implications for clinical practice, emphasizing the need for a more collaborative model of care delivery and the potential for cost-effective, scalable solutions. Looking toward the future, the article  identifies key areas for research, including the development of advanced technologies, exploration of new therapeutic modalities, and consideration of ethical and social impacts. The need for standardization and interoperability in telerehabilitation systems is also underscored, as these will be critical to ensuring the seamless integration of various technologies and platforms.  

Michael Suesserman, Samantha Gorny, Daniel Lasaga et al.

Abstract Background Fraud, Waste, and Abuse (FWA) in medical claims have a negative impact on the quality and cost of healthcare. A major component of FWA in claims is procedure code overutilization, where one or more prescribed procedures may not be relevant to a given diagnosis and patient profile, resulting in unnecessary and unwarranted treatments and medical payments. This study aims to identify such unwarranted procedures from millions of healthcare claims. In the absence of labeled examples of unwarranted procedures, the study focused on the application of unsupervised machine learning techniques. Methods Experiments were conducted with deep autoencoders to find claims containing anomalous procedure codes indicative of FWA, and were compared against a baseline density-based clustering model. Diagnoses, procedures, and demographic data associated with healthcare claims were used as features for the models. A dataset of one hundred thousand claims sampled from a larger claims database is used to initially train and tune the models, followed by experimentations on a dataset with thirty-three million claims. Experimental results show that the autoencoder model, when trained with a novel feature-weighted loss function, outperforms the density-based clustering approach in finding potential outlier procedure codes. Results Given the unsupervised nature of our experiments, model performance was evaluated using a synthetic outlier test dataset, and a manually annotated outlier test dataset. Precision, recall and F1-scores on the synthetic outlier test dataset for the autoencoder model trained on one hundred thousand claims were 0.87, 1.0 and 0.93, respectively, while the results for these metrics on the manually annotated outlier test dataset were 0.36, 0.86 and 0.51, respectively. The model performance on the manually annotated outlier test dataset improved further when trained on the larger thirty-three million claims dataset with precision, recall and F1-scores of 0.48, 0.90 and 0.63, respectively. Conclusions This study demonstrates the feasibility of leveraging unsupervised, deep-learning methods to identify potential procedure overutilization from healthcare claims.

Gregor Petrič, Marjan Cugmas, Rok Petrič et al.

Objective Informational social support is one of the main reasons for patients to visit online health communities (OHCs). Calls have been made to investigate the objective quality of such support in the light of a worrying number of inaccurate online health-related information. The main aim of this study is to conceptualize the Quality of Informational Social Support (QISS) and develop and test a measure of QISS for content analysis. A further aim is to investigate the level of QISS in cancer-related messages in the largest OHC in Slovenia and examine the differences among various types of discussion forums, namely, online consultation forums, online support group forums, and socializing forums. Methods A multidimensional measurement instrument was developed, which included 20 items in a coding scheme for a content analysis of cancer-related messages. On a set of almost three million posts published between 2015 and 2019, a machine-learning algorithm was used to detect cancer-related discussions in the OHC. We then identified the messages providing informational social support, and through quantitative content analysis, three experts coded a random sample of 403 cancer-related messages for the QISS. Results The results demonstrate a good level of interrater reliability and agreement for a QISS scale with six dimensions, each demonstrating good internal consistency. The results reveal large differences among the social support, socializing, and consultation forums, with the latter recording significantly higher quality in terms of accuracy (M = 4.48, P  < .001), trustworthiness (M = 4.65, P  < .001), relevance (M = 3.59, P  < .001), and justification (M = 3.81, P  = .05) in messages providing informational social support regarding cancer-related issues. Conclusions This study provides the research field with a valid tool to further investigate the factors and consequences of varying quality of information exchanged in supportive communication. From a practical perspective, OHCs should dedicate more resources and develop mechanisms for the professional moderation of health-related topics in socializing forums and thereby suppress the publication and dissemination of low-quality information among OHC users and visitors.

Kyoungyeon Back, Nayeon Kim, Kyuwoong Kim et al.

Merete Røineland Benestad, Jorunn Drageset, Geir Egil Eide et al.

Abstract Purpose To study development trajectories to 34 years of age of health-related quality of life (HRQoL) and subjective health complaints in extremely preterm (EP) born subjects with and without disability, and to compare with term-born controls. Methods A Norwegian longitudinal population-based cohort of subjects born in 1982–85 at gestational age ≤ 28 weeks or with birth weight ≤ 1000 g and matched term-born controls completed the Norwegian version of the Short Form Health Survey-36 at ages 24 and 34 and the Health Behaviour in School-aged Children–Symptom Checklist at ages 17, 24 and 34 years. Data were analysed by unadjusted and adjusted mixed effects analyses with time by subject group as interaction term. Results A total of 35/49 (73%) surviving EP-born and 36/46 (78%) term-born controls participated at this third follow-up. EP-born subjects with severe disability reported clinical significant lower mean score in all domains compared to the term-born controls. Healthy EP-born subjects reported significantly lower mean scores for vitality, role emotional and mental health, and significantly higher mean score for total and psychological health complaints compared to term-born controls. There were no significant interactions with age regarding HRQoL and somatic health complaints, while there were significant differences in psychological health complaints; the EP-born scored higher at age 24 and lower at age 34. Conclusions EP-born adults at age 34 reported inferior HRQoL versus term-born peers, especially in the mental health domains, indicating that the negative differences observed at 24 years remained unchanged.

Edward H. Shortliffe, Michael F. Chiang

A.Karim Jabali

The implementation of medical technology in everyday clinical practice is not as well perceived in some countries when compared to others. Electronic Health Records (EHRs) reflect some evidence of how the technology has been adopted and which is the overall attitude of medical staff toward healthcare technology in hospitals and healthcare supplying organizations. In developing countries including Saudi Arabia, only a little amount of attention has been paid to exploring the level at which anesthesiologists, as end-users, perceive EHRs systems and subsequently apply them in clinical practice. Objectives: To explore anesthesiologists' perceptions about the impact, benefits, easy of use of EHRs and anesthesia specific features. The set of predictors affecting these features (age, experience, job rank, and EHRs experience) were investigated and comprehensively analyzed. Methods: An exploratory study was conducted targeting anesthesiologists from different hospitals of Saudi Arabia using a comprehensive questionnaire about the adoption and perception of EHRs. Responses of participants were collected and analyzed with various statistical methods. Results: Anesthesiologists overall demonstrated a positive perception with regard to the impact, benefits, easy of use and anesthesia-specific features of EHRs to themselves and to their specific specialty. Statistically significant correlations were found among the age, experience rank of respondents’ EHRs experience and the attitude toward using different tools and features of the available EHRs. The general perceived attitude toward anesthesiology-oriented features of EHRs was clearly expressed and the comparisons between subgroups of each prediction factor showed a clear variability in use among participants. These results can be used not only in planning but in designing and improving of healthcare management as well.

Areeba TARIQ, Osama Asif AZIZ, Faiza Khadim ARAIN et al.

Telemedicine integrates communication technologies with medicine, which allows healthcare professionals to monitor and treat patients remotely. During the coronavirus disease 2019 (COVID-19) pandemic, telemedicine has emerged as the most effective tool in the fight against such infectious diseases. Rapid advances have helped governments realize the flaws in current healthcare systems and have sparked a revolution in how these systems are managed and operated. The present work studies the changes realized within healthcare systems around the world, new regulations advocating these changes, and the barriers to widespread integration of telemedicine with current healthcare delivery systems. The Google Scholar, ScienceDirect and PubMed databases were used to find articles related to the aims of this study. In this systematic review based on the PRISMA guideline, we reviewed 61 studies to analyze the shift in trends within the telemedicine field due to the onset of the COVID-19 pandemic. Remote consultations, teleconferencing, and remote patient monitoring have experienced a significant increase in use and acceptance as a result of the pandemic. Furthermore, governments around the world have started to develop policies to expedite the integration of information and communication technologies with healthcare. The findings suggest that while a lot of progress has been made in terms of adopting such technology for healthcare delivery, several barriers, such as lack of legal framework and patient-physician acceptance in developing countries, still exist before telemedicine can be fully integrated with existing healthcare systems.

Reuter, Katja, Zhu, Yifan, Angyan, Praveen et al.

BackgroundSocial networks such as Twitter offer the clinical research community a novel opportunity for engaging potential study participants based on user activity data. However, the availability of public social media data has led to new ethical challenges about respecting user privacy and the appropriateness of monitoring social media for clinical trial recruitment. Researchers have voiced the need for involving users’ perspectives in the development of ethical norms and regulations. ObjectiveThis study examined the attitudes and level of concern among Twitter users and nonusers about using Twitter for monitoring social media users and their conversations to recruit potential clinical trial participants. MethodsWe used two online methods for recruiting study participants: the open survey was (1) advertised on Twitter between May 23 and June 8, 2017, and (2) deployed on TurkPrime, a crowdsourcing data acquisition platform, between May 23 and June 8, 2017. Eligible participants were adults, 18 years of age or older, who lived in the United States. People with and without Twitter accounts were included in the study. ResultsWhile nearly half the respondents—on Twitter (94/603, 15.6%) and on TurkPrime (509/603, 84.4%)—indicated agreement that social media monitoring constitutes a form of eavesdropping that invades their privacy, over one-third disagreed and nearly 1 in 5 had no opinion. A chi-square test revealed a positive relationship between respondents’ general privacy concern and their average concern about Internet research (P<.005). We found associations between respondents’ Twitter literacy and their concerns about the ability for researchers to monitor their Twitter activity for clinical trial recruitment (P=.001) and whether they consider Twitter monitoring for clinical trial recruitment as eavesdropping (P<.001) and an invasion of privacy (P=.003). As Twitter literacy increased, so did people’s concerns about researchers monitoring Twitter activity. Our data support the previously suggested use of the nonexceptionalist methodology for assessing social media in research, insofar as social media-based recruitment does not need to be considered exceptional and, for most, it is considered preferable to traditional in-person interventions at physical clinics. The expressed attitudes were highly contextual, depending on factors such as the type of disease or health topic (eg, HIV/AIDS vs obesity vs smoking), the entity or person monitoring users on Twitter, and the monitored information. ConclusionsThe data and findings from this study contribute to the critical dialogue with the public about the use of social media in clinical research. The findings suggest that most users do not think that monitoring Twitter for clinical trial recruitment constitutes inappropriate surveillance or a violation of privacy. However, researchers should remain mindful that some participants might find social media monitoring problematic when connected with certain conditions or health topics. Further research should isolate factors that influence the level of concern among social media users across platforms and populations and inform the development of more clear and consistent guidelines.

Rujipan Sampanna, Sanya Mitaim