Mayo Clinic Proceedings. Digital health最新文献

{"title":"Machine Learning Analysis of Facial Photographs for Predicting Bicuspid Aortic Valve","authors":"Atefeh Ghorbanzadeh MD , Naresh Prodduturi MS , Ana I. Casanegra MD, MS , Robert McBane MD , Paul Wennberg MD , Thom Rooke MD , David Liedl RN , Dennis Murphree PhD , Damon E. Houghton MD, MS","doi":"10.1016/j.mcpdig.2024.05.002","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.05.002","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 319-321"},"PeriodicalIF":0.0,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000373/pdfft?md5=323ed4c1e00b694f865a70ffa47c077d&pid=1-s2.0-S2949761224000373-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reinforcing Stereotypes in Health Care Through Artificial Intelligence–Generated Images: A Call for Regulation","authors":"Hannah van Kolfschooten LLM , Astrid Pilottin LLM","doi":"10.1016/j.mcpdig.2024.05.004","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.05.004","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 335-341"},"PeriodicalIF":0.0,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000397/pdfft?md5=6c14744f6113830d0aee54966003b0f0&pid=1-s2.0-S2949761224000397-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141429163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic Perspective of the Use of Wearables in Health Care: A Systematic Review","authors":"Gioacchino D. De Sario Velasquez MD ,&nbsp;Sahar Borna MD ,&nbsp;Michael J. Maniaci MD ,&nbsp;Jordan D. Coffey MBA ,&nbsp;Clifton R. Haider PhD ,&nbsp;Bart M. Demaerschalk MSc, MD ,&nbsp;Antonio Jorge Forte MD, PhD","doi":"10.1016/j.mcpdig.2024.05.003","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.05.003","url":null,"abstract":"<div><p>The objective of this study is to explore the current state of research concerning the cost-effectiveness of wearable health technologies, excluding hearing aids, owing to extensive previous investigation. A systematic review was performed using PubMed, EMBASE/MEDLINE, Google Scholar, and Cumulated Index to Nursing and Allied Health Literature to search studies evaluating the cost-effectiveness of wearable health devices in terms of quality-adjusted life years and incremental cost-effectiveness ratio. The search was conducted on March 28, 2023, and the date of publication did not limit the search. The search yielded 10 studies eligible for inclusion. These studies, published between 2012 and 2023, spanned various locations globally. The studies used data from hypothetical cohorts, existing research, randomized controlled trials, and meta-analyses. They covered a diverse range of wearable technologies applied in different health care settings, including respiratory rate monitors, pedometers, fall-prediction devices, hospital-acquired pressure injury prevention monitors, seizure detection devices, heart rate monitors, insulin therapy sensors, and wearable cardioverter defibrillators. The time horizons in the cost-effectiveness analyses ranged from less than a year to a lifetime. The studies indicate that wearable technologies can increase quality-adjusted life years and be cost-effective and potentially cost-saving. However, the cost-effectiveness depends on various factors, such as the type of device, the health condition being addressed, the specific perspective of the health economic analysis, local cost and payment structure, and willingness-to-pay thresholds. The use of wearables in health care promises improving outcomes and resource allocation. However, more research is needed to fully understand the long-term benefits and to strengthen the evidence base for health care providers, policymakers, and patients.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 299-317"},"PeriodicalIF":0.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000385/pdfft?md5=dcc6804bc580088be603b0023cca6ac3&pid=1-s2.0-S2949761224000385-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Staff Experiences Transitioning to Digital Dermatopathology in a Tertiary Academic Medical Center: Lessons Learned From Implementation Science","authors":"Celia C. Kamath PhD ,&nbsp;Erin O. Wissler Gerdes MA ,&nbsp;Barbara A. Barry PhD ,&nbsp;Sarah A. Minteer PhD ,&nbsp;Nneka I. Comfere MD ,&nbsp;Margot S. Peters MD ,&nbsp;Carilyn N. Wieland MD ,&nbsp;Elizabeth B. Habermann PhD ,&nbsp;Jennifer L. Ridgeway PhD","doi":"10.1016/j.mcpdig.2024.05.001","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.05.001","url":null,"abstract":"<div><p>Digital pathology (DP) transforms practice by replacing traditional glass slide review with digital whole slide images and workflows. Although digitization may improve accuracy and efficiency, transitioning to digital practice requires staff to learn new skills and adopt new ways of working and collaborating. In this study, we aimed to evaluate the experiences and perceptions of individuals involved in the day-to-day work of implementing DP in a tertiary academic medical center using Normalization Process Theory, a social theory that explains the processes by which innovations are operationalized and sustained in practice. Between September 2021 and June 2022, dermatopathologists, referring clinicians, and support staff at Mayo Clinic (Minnesota, Florida, and Arizona) participated in interviews (n=22) and completed surveys (n=34) concerning the transition. Normalization Process Theory informed the selection of validated survey items (Normalization Measure Development Questionnaire) and guided qualitative analysis. Participants reported high agreement with statements related to shared understanding and potential value of DP for workflow integration and working relationships. Qualitative themes reflecting the way organization and social context enable these processes were mapped onto implementation stages and related key activities. We found that earlier processes of implementation (understanding and working out participation) were better supported than later stages (doing it and reflecting on it). Our analysis helps identify targets for further intervention to hasten and help sustain implementation, including additional support in software and technological integration, workflows and work redesign, and regular monitoring and feedback systems. The use of implementation theory, such as Normalization Process Theory, may provide useful pointers to enable other similar digital system transition efforts.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 3","pages":"Pages 289-298"},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000361/pdfft?md5=029727f4e2c849485b54c16b291dce70&pid=1-s2.0-S2949761224000361-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Development and Performance of a Machine-Learning Based Mobile Platform for Visually Determining the Etiology of 5 Penile Diseases","authors":"Lao-Tzu Allan-Blitz MD, MPH ,&nbsp;Sithira Ambepitiya MD ,&nbsp;Raghavendra Tirupathi MD ,&nbsp;Jeffrey D. Klausner MD, MPH","doi":"10.1016/j.mcpdig.2024.04.006","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.04.006","url":null,"abstract":"<div><h3>Objective</h3><p>To develop a machine-learning visual classification algorithm for penile diseases in order to address disparities in access to sexual health services.</p></div><div><h3>Patients and Methods</h3><p>We developed an image data set using original and augmented images for 5 penile diseases: herpes lesions, syphilitic chancres, balanitis, penile cancer, and genital warts. We used a U-Net architecture model for semantic pixel segmentation into background or subject image, an Inception-ResNet version 2 neural architecture to classify each pixel as diseased or nondiseased, and a salience map using GradCAM++. We trained the model on a random 91% sample of the images and evaluated the model on the remaining 9%, assessing recall (or sensitivity), precision, specificity, and F1-score. As of July 1st 2022, the model has been in use via a mobile application platform; we assessed application usage between July and October 1, 2023.</p></div><div><h3>Results</h3><p>Of 239 images in the validation data set, 45 (18.8%) were of genital warts, 43 (18%) were of herpes simplex virus infection (ranging from early vesicles to ulcers), 29 (12.1%) were of penile cancer, 40 (16.7%) were of balanitis, 37 (15.5%) were of syphilitic chancres, and 45 (18.8%) were nondiseased images. The overall accuracy of the model for correctly classifying images was 0.944. There were 2640 unique submissions to the mobile platform; among a random sample (n=437), 271 (62%) were from the United States, 64 (14.6%) from Singapore, 41 (9.4%) from Canada, 40 (9.2%) from the United Kingdom, and 21 (4.8%) from Vietnam.</p></div><div><h3>Conclusion</h3><p>We report on the development of a machine-learning model for classifying 5 penile diseases, which exhibited excellent performance.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 280-288"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294976122400035X/pdfft?md5=82980c3f1cb70a53329b1e3241d7722b&pid=1-s2.0-S294976122400035X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141077698","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Impact of 3D Fast Spin Echo and Inversion Recovery Gradient Echo Sequences Magnetic Resonance Imaging Acquisition on Automated Brain Tumor Segmentation","authors":"Mana Moassefi MD ,&nbsp;Shahriar Faghani MD ,&nbsp;Sara Khanipour Roshan MD ,&nbsp;Gian Marco Conte MD, PhD ,&nbsp;Seyed Moein Rassoulinejad Mousavi MD ,&nbsp;Timothy J. Kaufmann MD ,&nbsp;Bradley J. Erickson MD, PhD","doi":"10.1016/j.mcpdig.2024.03.006","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.03.006","url":null,"abstract":"<div><h3>Objective</h3><p>To conduct a study comparing the performance of automated segmentation techniques using 2 different contrast-enhanced T1-weighted (CET1) magnetic resonance imaging (MRI) acquisition protocol.</p></div><div><h3>Patients and Methods</h3><p>We collected 100 preoperative glioblastoma (GBM) MRIs consisting of 50 IR-GRE and 50 3-dimensional fast spin echo (3D-FSE) image sets. Their gold-standard tumor segmentation mask was created based on the expert opinion of a neuroradiologist. Cases were randomly divided into training and test sets. We used the no new UNet (nnUNet) architecture pretrained on the 501-image public data set containing IR-GRE sequence image sets, followed by 2 training rounds with the IR-GRE and 3D-FSE images, respectively. For each patient, in the IR-GRE and 3D-FSE test sets, we had 2 prediction masks, one from the model fine-tuned with the IR-GRE training set and one with 3D-FSE. The dice similarity coefficients (DSCs) of the 2 sets of results for each case in the test sets were compared using the Wilcoxon tests.</p></div><div><h3>Results</h3><p>Models trained on 3D-FSE images outperformed IR-GRE models in lesion segmentation, with mean DSC differences of 0.057 and 0.022 in the respective test sets. For the 3D-FSE and IR-GRE test sets, the calculated <em>P</em> values comparing DSCs from 2 models were .02 and .61, respectively.</p></div><div><h3>Conclusion</h3><p>Including 3D-FSE MRI in the training data set improves segmentation performance when segmenting 3D-FSE images.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 231-240"},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000269/pdfft?md5=f561c94cea75e11c92b35216bd0136b8&pid=1-s2.0-S2949761224000269-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140554171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cautions and Considerations in Artificial Intelligence Implementation for Child Abuse: Lessons from Japan","authors":"Shotaro Kinoshita MD, PhD,&nbsp;Hiromi Yokoyama PhD,&nbsp;Taishiro Kishimoto MD, PhD","doi":"10.1016/j.mcpdig.2024.04.005","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.04.005","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Page 258"},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000348/pdfft?md5=32ba90c189da91ce77f88eea6d0242d1&pid=1-s2.0-S2949761224000348-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140894929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning–Based Prediction Modeling of Major Adverse Cardiovascular Events After Liver Transplantation","authors":"Ahmed Abdelhameed PhD ,&nbsp;Harpreet Bhangu MD ,&nbsp;Jingna Feng MS ,&nbsp;Fang Li PhD ,&nbsp;Xinyue Hu MS ,&nbsp;Parag Patel MD ,&nbsp;Liu Yang MD ,&nbsp;Cui Tao","doi":"10.1016/j.mcpdig.2024.03.005","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.03.005","url":null,"abstract":"<div><h3>Objective</h3><p>To validate deep learning models’ ability to predict post-transplantation major adverse cardiovascular events (MACE) in patients undergoing liver transplantation (LT).</p></div><div><h3>Patients and Methods</h3><p>We used data from Optum’s de-identified Clinformatics Data Mart Database to identify liver transplant recipients between January 2007 and March 2020. To predict post-transplantation MACE risk, we considered patients’ demographics characteristics, diagnoses, medications, and procedural data recorded back to 3 years before the LT procedure date (index date). MACE is predicted using the bidirectional gated recurrent units (BiGRU) deep learning model in different prediction interval lengths up to 5 years after the index date. In total, 18,304 liver transplant recipients (mean age, 57.4 years [SD, 12.76]; 7158 [39.1%] women) were used to develop and test the deep learning model’s performance against other baseline machine learning models. Models were optimized using 5-fold cross-validation on 80% of the cohort, and model performance was evaluated on the remaining 20% using the area under the receiver operating characteristic curve (AUC-ROC) and the area under the precision-recall curve (AUC-PR).</p></div><div><h3>Results</h3><p>Using different prediction intervals after the index date, the top-performing model was the deep learning model, BiGRU, and achieved an AUC-ROC of 0.841 (95% CI, 0.822-0.862) and AUC-PR of 0.578 (95% CI, 0.537-0.621) for a 30-day prediction interval after LT.</p></div><div><h3>Conclusion</h3><p>Using longitudinal claims data, deep learning models can efficiently predict MACE after LT, assisting clinicians in identifying high-risk candidates for further risk stratification or other management strategies to improve transplant outcomes based on important features identified by the model.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 221-230"},"PeriodicalIF":0.0,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000221/pdfft?md5=93eb32520224a4e9423e1f9cc6e1d49b&pid=1-s2.0-S2949761224000221-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140552567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Inherent Bias in Large Language Models: A Random Sampling Analysis","authors":"Noel F. Ayoub MD, MBA ,&nbsp;Karthik Balakrishnan MD, MPH ,&nbsp;Marc S. Ayoub MD ,&nbsp;Thomas F. Barrett MD ,&nbsp;Abel P. David MD ,&nbsp;Stacey T. Gray MD","doi":"10.1016/j.mcpdig.2024.03.003","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.03.003","url":null,"abstract":"<div><p>There are mounting concerns regarding inherent bias, safety, and tendency toward misinformation of large language models (LLMs), which could have significant implications in health care. This study sought to determine whether generative artificial intelligence (AI)-based simulations of physicians making life-and-death decisions in a resource-scarce environment would demonstrate bias. Thirteen questions were developed that simulated physicians treating patients in resource-limited environments. Through a random sampling of simulated physicians using OpenAI’s generative pretrained transformer (GPT-4), physicians were tasked with choosing only 1 patient to save owing to limited resources. This simulation was repeated 1000 times per question, representing 1000 unique physicians and patients each. Patients and physicians spanned a variety of demographic characteristics. All patients had similar a priori likelihood of surviving the acute illness. Overall, simulated physicians consistently demonstrated racial, gender, age, political affiliation, and sexual orientation bias in clinical decision-making. Across all demographic characteristics, physicians most frequently favored patients with similar demographic characteristics as themselves, with most pairwise comparisons showing statistical significance (<em>P</em>&lt;.05). Nondescript physicians favored White, male, and young demographic characteristics. The male doctor gravitated toward the male, White, and young, whereas the female doctor typically preferred female, young, and White patients. In addition to saving patients with their own political affiliation, Democratic physicians favored Black and female patients, whereas Republicans preferred White and male demographic characteristics. Heterosexual and gay/lesbian physicians frequently saved patients of similar sexual orientation. Overall, publicly available chatbot LLMs demonstrate significant biases, which may negatively impact patient outcomes if used to support clinical care decisions without appropriate precautions.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 186-191"},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949761224000208/pdfft?md5=895559f96cdc78e7afbad43c7d8d164a&pid=1-s2.0-S2949761224000208-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140542637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Therapeutic Content of Mobile Phone Applications for Substance Use Disorders: An Umbrella Review","authors":"Tyler S. Oesterle MD, MPH ,&nbsp;Daniel K. Hall-Flavin MD, MS ,&nbsp;Nicholas L. Bormann MD ,&nbsp;Larissa L. Loukianova MD, PhD ,&nbsp;David C. Fipps DO ,&nbsp;Scott A. Breitinger MD ,&nbsp;Wesley P. Gilliam PhD ,&nbsp;Tiffany Wu MD ,&nbsp;Sabrina Correa da Costa MD ,&nbsp;Stephan Arndt PhD ,&nbsp;Victor M. Karpyak MD, PhD","doi":"10.1016/j.mcpdig.2024.03.004","DOIUrl":"https://doi.org/10.1016/j.mcpdig.2024.03.004","url":null,"abstract":"<div><p>Mobile phone applications (MPAs) for substance use disorder (SUD) treatment are increasingly used by patients. Although pilot studies have shown promising results, multiple previous systematic reviews noted insufficient evidence for MPA use in SUD treatment—many of the previously published reviews evaluated different trials. Subsequently, we aimed to conduct an umbrella review of previously published reviews investigating the efficacy of MPAs for SUD treatment, excluding nicotine/tobacco because umbrella reviews have been done in this population and the nicotine/tobacco MPA approach often differs from SUD-focused MPAs. No previous reviews have included a statistical meta-analysis of clinical trials to quantify an estimated overall effect. Seven reviews met inclusion criteria, and 17 unique studies with available data were taken from those reviews for the meta-analysis. Overall, reviews reported a lack of evidence for recommending MPAs for SUD treatment. However, MPA-delivered recovery support services, cognitive behavioral therapy, and contingency management were identified across multiple reviews as having promising evidence for SUD treatment. Hedges <em>g</em> effect size for an MPA reduction in substance use–related outcomes relative to the control arm was insignificant (0.137; 95% CI, −0.056 to 0.330; <em>P</em>=.16). In subgroup analysis, contingency management (1.29; 95% CI, 1.088-1.482; <em>τ</em>²=0; <em>k</em>=2) and cognitive behavioral therapy (0.02; 95% CI, 0.001-0.030; <em>τ</em>²=0; <em>k</em>=2) were significant. Although contingency management’s effect was large, both trials were small (samples of 40 and 30). This review includes an adapted framework for the American Psychiatric Association’s MPA guidelines that clinicians can implement to review MPAs critically with patients.</p></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"2 2","pages":"Pages 192-206"},"PeriodicalIF":0.0,"publicationDate":"2024-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294976122400021X/pdfft?md5=ec649c717fe9aa97fc59b17250a82fe9&pid=1-s2.0-S294976122400021X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140542636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}