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JMIR Bioinformatics and Biotechnology Jun 2024Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing...
Health care is at a turning point. We are shifting from protocolized medicine to precision medicine, and digital health systems are facilitating this shift. By providing clinicians with detailed information for each patient and analytic support for decision-making at the point of care, digital health technologies are enabling a new era of precision medicine. Genomic data also provide clinicians with information that can improve the accuracy and timeliness of diagnosis, optimize prescribing, and target risk reduction strategies, all of which are key elements for precision medicine. However, genomic data are predominantly seen as diagnostic information and are not routinely integrated into the clinical workflows of electronic medical records. The use of genomic data holds significant potential for precision medicine; however, as genomic data are fundamentally different from the information collected during routine practice, special considerations are needed to use this information in a digital health setting. This paper outlines the potential of genomic data integration with electronic records, and how these data can enable precision medicine.
PubMed: 38935958
DOI: 10.2196/55632 -
JMIR Perioperative Medicine Jun 2024As of 2022, patient adherence to postoperative guidelines can reduce the risk of complications by up to 52.4% following laparoscopic abdominal surgery. With the... (Review)
Review
The Effectiveness of Patient Education on Laparoscopic Surgery Postoperative Outcomes to Determine Whether Direct Coaching Is the Best Approach: Systematic Review of Randomized Controlled Trials.
BACKGROUND
As of 2022, patient adherence to postoperative guidelines can reduce the risk of complications by up to 52.4% following laparoscopic abdominal surgery. With the availability of various preoperative education interventions (POEIs), understanding which POEI results in improvement in patient outcomes across the procedures is imperative.
OBJECTIVE
This study aims to determine which POEI could be the most effective on patient outcomes by systematically reviewing all the POEIs reported in the literature.
METHODS
In total, 4753 articles investigating various POEIs (eg, videos, presentations, mobile apps, and one-on-one education or coaching) were collected from the PubMed, Embase, and Scopus databases. Inclusion criteria were adult patients undergoing abdominal laparoscopic surgery, randomized controlled trials, and studies that provided postoperative outcomes. Exclusion criteria included studies not published in English and with no outcomes reported. Title and abstract and full-text articles with POEI randomized controlled studies were screened based on the above criteria through a blinded, dual review using Covidence (Veritas Health Innovation). Study quality was assessed through the Cochrane Risk of Bias tool. The included articles were analyzed for educational content, intervention timing, intervention type, and postoperative outcomes appropriate for a particular surgery.
RESULTS
Only 17 studies matched our criteria, with 1831 patients undergoing laparoscopic cholecystectomy, bariatric surgery (gastric bypass and gastric sleeve), and colectomy. In total, 15 studies reported a statistically significant improvement in at least 1 patient postoperative outcome. None of these studies were found to have an overall high risk of bias according to Cochrane standards. In total, 41% (7/17) of the included studies using direct individual education improved outcomes in almost all surgery types, while educational videos had the greatest statistically significant impact for anxiety, nausea, and pain postoperatively (P<.01). Direct group education demonstrated significant improvement in weight, BMI, exercise, and depressive symptoms in 33% (2/6) of the laparoscopic gastric bypass studies.
CONCLUSIONS
Direct education (individual or group based) positively impacts postoperative laparoscopic surgery outcomes.
TRIAL REGISTRATION
PROSPERO CRD42023438698; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=438698.
PubMed: 38935953
DOI: 10.2196/51573 -
Interactive Journal of Medical Research Jun 2024Degenerative cervical myelopathy (DCM) is a common neurological condition, with disease progression that is both variable and difficult to predict. Here, we present a...
Degenerative cervical myelopathy (DCM) is a common neurological condition, with disease progression that is both variable and difficult to predict. Here, we present a case of DCM in a gentleman in his late 60s with significant radiological disease progression without consequent change in clinical symptoms. The case serves as a reminder of an enduring medical aphorism that clinical history and examination should be prioritized above more complex data, such as imaging investigations. In addition, the case also highlights that guidelines should be contextualized within individual clinical circumstances.
PubMed: 38935951
DOI: 10.2196/48212 -
JMIR MHealth and UHealth Jun 2024Rising rates of psychological distress (symptoms of depression, anxiety, and stress) among adults in the United States necessitate effective mental wellness...
BACKGROUND
Rising rates of psychological distress (symptoms of depression, anxiety, and stress) among adults in the United States necessitate effective mental wellness interventions. Despite the prevalence of smartphone app-based programs, research on their efficacy is limited, with only 14% showing clinically validated evidence. Our study evaluates Noom Mood, a commercially available smartphone-based app that uses cognitive behavioral therapy and mindfulness-based programming. In this study, we address gaps in the existing literature by examining postintervention outcomes and the broader impact on mental wellness.
OBJECTIVE
Noom Mood is a smartphone-based mental wellness program designed to be used by the general population. This prospective study evaluates the efficacy and postintervention outcomes of Noom Mood. We aim to address the rising psychological distress among adults in the United States.
METHODS
A 1-arm study design was used, with participants having access to the Noom Mood program for 16 weeks (N=273). Surveys were conducted at baseline, week 4, week 8, week 12, week 16, and week 32 (16 weeks' postprogram follow-up). This study assessed a range of mental health outcomes, including anxiety symptoms, depressive symptoms, perceived stress, well-being, quality of life, coping, emotion regulation, sleep, and workplace productivity (absenteeism or presenteeism).
RESULTS
The mean age of participants was 40.5 (SD 11.7) years. Statistically significant improvements in anxiety symptoms, depressive symptoms, and perceived stress were observed by week 4 and maintained through the 16-week intervention and the 32-week follow-up. The largest changes were observed in the first 4 weeks (29% lower, 25% lower, and 15% lower for anxiety symptoms, depressive symptoms, and perceived stress, respectively), and only small improvements were observed afterward. Reductions in clinically relevant anxiety (7-item generalized anxiety disorder scale) and depression (8-item Patient Health Questionnaire depression scale) criteria were also maintained from program initiation through the 16-week intervention and the 32-week follow-up. Work productivity also showed statistically significant results, with participants gaining 2.57 productive work days from baseline at 16 weeks, and remaining relatively stable (2.23 productive work days gained) at follow-up (32 weeks). Additionally, effects across all coping, sleep disturbance (23% lower at 32 weeks), and emotion dysregulation variables exhibited positive and significant trends at all time points (15% higher, 23% lower, and 25% higher respectively at 32 weeks).
CONCLUSIONS
This study contributes insights into the promising positive impact of Noom Mood on mental health and well-being outcomes, extending beyond the intervention phase. Though more rigorous studies are necessary to understand the mechanism of action at play, this exploratory study addresses critical gaps in the literature, highlighting the potential of smartphone-based mental wellness programs to lessen barriers to mental health support and improve diverse dimensions of well-being. Future research should explore the scalability, feasibility, and long-term adherence of such interventions across diverse populations.
Topics: Humans; Prospective Studies; Male; Female; Adult; Middle Aged; Surveys and Questionnaires; Mobile Applications; Health Promotion; Cognitive Behavioral Therapy; Program Evaluation; United States; Mindfulness; Quality of Life
PubMed: 38935946
DOI: 10.2196/54634 -
JMIR Cancer Jun 2024Many supportive cancer care (SCC) services were teledelivered during COVID-19, but what facilitates patients' intentions to use teledelivered SCC is unknown.
Applying the Unified Theory of Acceptance and Use of Technology to Identify Factors Associated With Intention to Use Teledelivered Supportive Care Among Recently Diagnosed Breast Cancer Survivors During COVID-19 in Hong Kong: Cross-Sectional Survey.
BACKGROUND
Many supportive cancer care (SCC) services were teledelivered during COVID-19, but what facilitates patients' intentions to use teledelivered SCC is unknown.
OBJECTIVE
The study aimed to use the unified theory of acceptance and use of technology to investigate the factors associated with the intentions of breast cancer survivors (BCS) in Hong Kong to use various types of teledelivered SCC (including psychosocial care, medical consultation, complementary care, peer support groups). Favorable telehealth-related perceptions (higher performance expectancy, lower effort expectancy, more facilitating conditions, positive social influences), less technological anxiety, and greater fear of COVID-19 were hypothesized to be associated with higher intentions to use teledelivered SCC. Moreover, the associations between telehealth-related perceptions and intentions to use teledelivered SCC were hypothesized to be moderated by education level, such that associations between telehealth-related perceptions and intentions to use teledelivered SCC would be stronger among those with a higher education level.
METHODS
A sample of 209 (209/287, 72.8% completion rate) women diagnosed with breast cancer since the start of the COVID-19 outbreak in Hong Kong (ie, January 2020) were recruited from the Hong Kong Breast Cancer Registry to complete a cross-sectional survey between June 2022 and December 2022. Participants' intentions to use various types of teledelivered SCC (dependent variables), telehealth-related perceptions (independent variables), and sociodemographic variables (eg, education, as a moderator variable) were measured using self-reported, validated measures.
RESULTS
Hierarchical regression analysis results showed that greater confidence using telehealth, performance expectancy (believing telehealth helps with daily tasks), social influence (important others encouraging telehealth use), and facilitating conditions (having resources for telehealth use) were associated with higher intentions to use teledelivered SCC (range: β=0.16, P=.03 to β=0.34, P<.001). Moreover, 2-way interactions emerged between education level and 2 of the telehealth perception variables. Education level moderated the associations between (1) performance expectancy and intention to use teledelivered complementary care (β=0.34, P=.04) and (2) facilitating conditions and intention to use teledelivered peer support groups (β=0.36, P=.03). The positive associations between those telehealth perceptions and intentions were only significant among those with a higher education level.
CONCLUSIONS
The findings of this study implied that enhancing BCS' skills at using telehealth, BCS' and their important others' perceived benefits of telehealth, and providing assistance for telehealth use could increase BCS' intentions to use teledelivered SCC. For intentions to use specific types of SCC, addressing relevant factors (performance expectancy, facilitating conditions) might be particularly beneficial for those with a higher education level.
PubMed: 38935942
DOI: 10.2196/51072 -
Aging Jun 2024The primary objective of this study was to assess the diagnostic potential of galectin-3 (Gal-3), fractalkine (FKN), interleukin (IL)-6, microRNA(miR)-21, and cardiac...
OBJECTIVE
The primary objective of this study was to assess the diagnostic potential of galectin-3 (Gal-3), fractalkine (FKN), interleukin (IL)-6, microRNA(miR)-21, and cardiac troponin I (cTnI) in patients with ischemic cardiomyopathy (ICM).
METHOD
A total of 78 ICM patients (Case group) and 80 healthy volunteers (Control group) admitted to our hospital for treatment or physical examination from Aug. 2018 to Feb. 2020 were included in the current study. The serum concentration of Gal-3, FKN, IL-6, miR-21, and plasma expression of cTnI of both groups were determined. The severity of ICM was classified using New York Heart Association (NYHA) scale.
RESULTS
When compared with the control group, the case group had a significantly high blood concentration of Gal-3, FKN, IL-6, miR-21, and cTnI ( < 0.001). NYHA class II patients had lower blood levels of Gal-3, FKN, IL-6, miR-21, and cTnI than that in patients of NYHA class III and IV without statistical significance ( > 0.05). However, statistical significance could be achieved when comparing the above-analyzed markers in patients classified between class III and IV. Correlation analysis also revealed that serum levels of Gal-3, FKN, IL-6, miR-21, and cTnI were positively correlated with NYHA classification (R = 0.564, 0.621, 0.792, 0.981, < 0.05).
CONCLUSION
Our study revealed that up-regulated serum Gal-3, FKN, IL-6, miR-21, and cTnI levels were closely related to the progression of ICM. This association implies that these biomarkers have diagnostic potential, offering a promising avenue for early detection and monitoring of ICM progression.
Topics: Humans; Female; Male; Troponin I; Interleukin-6; MicroRNAs; Chemokine CX3CL1; Middle Aged; Galectin 3; Biomarkers; Aged; Myocardial Ischemia; Cardiomyopathies; Case-Control Studies; Galectins; Blood Proteins
PubMed: 38935941
DOI: 10.18632/aging.205953 -
JMIR Bioinformatics and Biotechnology May 2024The generative artificial intelligence (AI) model ChatGPT holds transformative prospects in medicine. The development of such models has signaled the beginning of a new...
The generative artificial intelligence (AI) model ChatGPT holds transformative prospects in medicine. The development of such models has signaled the beginning of a new era where complex biological data can be made more accessible and interpretable. ChatGPT is a natural language processing tool that can process, interpret, and summarize vast data sets. It can serve as a digital assistant for physicians and researchers, aiding in integrating medical imaging data with other multiomics data and facilitating the understanding of complex biological systems. The physician's and AI's viewpoints emphasize the value of such AI models in medicine, providing tangible examples of how this could enhance patient care. The editorial also discusses the rise of generative AI, highlighting its substantial impact in democratizing AI applications for modern medicine. While AI may not supersede health care professionals, practitioners incorporating AI into their practices could potentially have a competitive edge.
PubMed: 38935938
DOI: 10.2196/52700 -
Journal of Medical Internet Research Jun 2024Artificial intelligence, particularly chatbot systems, is becoming an instrumental tool in health care, aiding clinical decision-making and patient engagement. (Comparative Study)
Comparative Study
BACKGROUND
Artificial intelligence, particularly chatbot systems, is becoming an instrumental tool in health care, aiding clinical decision-making and patient engagement.
OBJECTIVE
This study aims to analyze the performance of ChatGPT-3.5 and ChatGPT-4 in addressing complex clinical and ethical dilemmas, and to illustrate their potential role in health care decision-making while comparing seniors' and residents' ratings, and specific question types.
METHODS
A total of 4 specialized physicians formulated 176 real-world clinical questions. A total of 8 senior physicians and residents assessed responses from GPT-3.5 and GPT-4 on a 1-5 scale across 5 categories: accuracy, relevance, clarity, utility, and comprehensiveness. Evaluations were conducted within internal medicine, emergency medicine, and ethics. Comparisons were made globally, between seniors and residents, and across classifications.
RESULTS
Both GPT models received high mean scores (4.4, SD 0.8 for GPT-4 and 4.1, SD 1.0 for GPT-3.5). GPT-4 outperformed GPT-3.5 across all rating dimensions, with seniors consistently rating responses higher than residents for both models. Specifically, seniors rated GPT-4 as more beneficial and complete (mean 4.6 vs 4.0 and 4.6 vs 4.1, respectively; P<.001), and GPT-3.5 similarly (mean 4.1 vs 3.7 and 3.9 vs 3.5, respectively; P<.001). Ethical queries received the highest ratings for both models, with mean scores reflecting consistency across accuracy and completeness criteria. Distinctions among question types were significant, particularly for the GPT-4 mean scores in completeness across emergency, internal, and ethical questions (4.2, SD 1.0; 4.3, SD 0.8; and 4.5, SD 0.7, respectively; P<.001), and for GPT-3.5's accuracy, beneficial, and completeness dimensions.
CONCLUSIONS
ChatGPT's potential to assist physicians with medical issues is promising, with prospects to enhance diagnostics, treatments, and ethics. While integration into clinical workflows may be valuable, it must complement, not replace, human expertise. Continued research is essential to ensure safe and effective implementation in clinical environments.
Topics: Humans; Clinical Decision-Making; Artificial Intelligence
PubMed: 38935937
DOI: 10.2196/54571 -
JCO Clinical Cancer Informatics Jun 2024The expanding presence of the electronic health record (EHR) underscores the necessity for improved interoperability. To test the interoperability within the field of...
Minimal Common Oncology Data Elements Genomics Pilot Project: Enhancing Oncology Research Through Electronic Health Record Interoperability at Vanderbilt University Medical Center.
PURPOSE
The expanding presence of the electronic health record (EHR) underscores the necessity for improved interoperability. To test the interoperability within the field of oncology research, our team at Vanderbilt University Medical Center (VUMC) enabled our Epic-based EHR to be compatible with the Minimal Common Oncology Data Elements (mCODE), which is a Fast Healthcare Interoperability Resources (FHIR)-based consensus data standard created to facilitate the transmission of EHRs for patients with cancer.
METHODS
Our approach used an extract, transform, load tool for converting EHR data from the VUMC Epic Clarity database into mCODE-compatible profiles. We established a sandbox environment on Microsoft Azure for data migration, deployed a FHIR server to handle application programming interface (API) requests, and mapped VUMC data to align with mCODE structures. In addition, we constructed a web application to demonstrate the practical use of mCODE profiles in health care.
RESULTS
We developed an end-to-end pipeline that converted EHR data into mCODE-compliant profiles, as well as a web application that visualizes genomic data and provides cancer risk assessments. Despite the complexities of aligning traditional EHR databases with mCODE standards and the limitations of FHIR APIs in supporting advanced statistical methodologies, this project successfully demonstrates the practical integration of mCODE standards into existing health care infrastructures.
CONCLUSION
This study provides a proof of concept for the interoperability of mCODE within a major health care institution's EHR system, highlighting both the potential and the current limitations of FHIR APIs in supporting complex data analysis for oncology research.
Topics: Humans; Electronic Health Records; Pilot Projects; Medical Oncology; Genomics; Academic Medical Centers; Neoplasms; Common Data Elements; Software; Health Information Interoperability
PubMed: 38935887
DOI: 10.1200/CCI.23.00249 -
American Society of Clinical Oncology... Jun 2024The landscape of prostate cancer care has rapidly evolved. We have transitioned from the use of conventional imaging, radical surgeries, and single-agent androgen... (Review)
Review
The landscape of prostate cancer care has rapidly evolved. We have transitioned from the use of conventional imaging, radical surgeries, and single-agent androgen deprivation therapy to an era of advanced imaging, precision diagnostics, genomics, and targeted treatment options. Concurrently, the emergence of large language models (LLMs) has dramatically transformed the paradigm for artificial intelligence (AI). This convergence of advancements in prostate cancer management and AI provides a compelling rationale to comprehensively review the current state of AI applications in prostate cancer care. Here, we review the advancements in AI-driven applications across the continuum of the journey of a patient with prostate cancer from early interception to survivorship care. We subsequently discuss the role of AI in prostate cancer drug discovery, clinical trials, and clinical practice guidelines. In the localized disease setting, deep learning models demonstrated impressive performance in detecting and grading prostate cancer using imaging and pathology data. For biochemically recurrent diseases, machine learning approaches are being tested for improved risk stratification and treatment decisions. In advanced prostate cancer, deep learning can potentially improve prognostication and assist in clinical decision making. Furthermore, LLMs are poised to revolutionize information summarization and extraction, clinical trial design and operations, drug development, evidence synthesis, and clinical practice guidelines. Synergistic integration of multimodal data integration and human-AI integration are emerging as a key strategy to unlock the full potential of AI in prostate cancer care.
Topics: Humans; Male; Prostatic Neoplasms; Artificial Intelligence
PubMed: 38935882
DOI: 10.1200/EDBK_438516