Pub Date : 2025-10-10DOI: 10.1016/j.mcpdig.2025.100294
Sonya Makhni MD, MBA, MS , Jose Rico MBA , Paul Cerrato MA , Brenton Hill JD, MHA , Shauna Overgaard PhD , Jeffrey Wu MPH , Justin Fairbanks MS , Rachelann Tripp MPH , Justin Redziniak DPT , Roberto Blundo MPH , Clark Otley MD , John Halamka MD, MS
Integrating artificial intelligence (AI) into health care offers the potential to address critical challenges related to access to care, workforce burnout, and health inequities. Despite its promise, AI adoption remains limited due to safety, efficacy, and equity concerns. This paper presents a novel and comprehensive framework for responsible AI development, evaluation, and deployment in health care, encompassing four key phases: (1) AI Solution Design and Development, (2) AI Solution Qualification, (3) AI Solution Efficacy and Safety Evaluation, and (4) AI Solution Impact. By establishing rigorous standards for operational, clinical, and technical quality, the framework aims to guide AI developers and health care professionals toward creating AI solutions that are ethical, effective, and scalable. This structured approach fosters collaboration and mitigates risks to help AI achieve its full potential in improving patient outcomes and health care efficiency.
{"title":"A Comprehensive Approach to Responsible AI Development and Deployment","authors":"Sonya Makhni MD, MBA, MS , Jose Rico MBA , Paul Cerrato MA , Brenton Hill JD, MHA , Shauna Overgaard PhD , Jeffrey Wu MPH , Justin Fairbanks MS , Rachelann Tripp MPH , Justin Redziniak DPT , Roberto Blundo MPH , Clark Otley MD , John Halamka MD, MS","doi":"10.1016/j.mcpdig.2025.100294","DOIUrl":"10.1016/j.mcpdig.2025.100294","url":null,"abstract":"<div><div>Integrating artificial intelligence (AI) into health care offers the potential to address critical challenges related to access to care, workforce burnout, and health inequities. Despite its promise, AI adoption remains limited due to safety, efficacy, and equity concerns. This paper presents a novel and comprehensive framework for responsible AI development, evaluation, and deployment in health care, encompassing four key phases: (1) AI Solution Design and Development, (2) AI Solution Qualification, (3) AI Solution Efficacy and Safety Evaluation, and (4) AI Solution Impact. By establishing rigorous standards for operational, clinical, and technical quality, the framework aims to guide AI developers and health care professionals toward creating AI solutions that are ethical, effective, and scalable. This structured approach fosters collaboration and mitigates risks to help AI achieve its full potential in improving patient outcomes and health care efficiency.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100294"},"PeriodicalIF":0.0,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145466484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-09DOI: 10.1016/j.mcpdig.2025.100292
Taylor N. Anderson MD , Vishnu Mohan MD , David A. Dorr MD , Raj M. Ratwani PhD , Joshua M. Biro PhD , Jeffrey A. Gold MD
Objective
To evaluate and compare the quality and safety of ambient digital scribe (ADS) platforms using simulated ambulatory encounters.
Methods
Five ADS platforms were evaluated using audio recordings of fourteen simulated clinical encounters. Audio recordings were played on a laptop computer and captured by ADS platforms on a mobile phone. Generated transcripts were compared to professional transcriptions. Clinical notes were graded using rubrics of key elements for each case. Note errors were classified as omission, commission, or partially correct. Potential clinical harm was assessed using the agency for healthcare research and quality harm scale. Note quality was assessed using the 9-item Physician Documentation Quality Instrument (range 9-45). Statistical comparisons included Friedman and χ2 tests with a correction for multiple comparisons.
Results
Transcripts generated by platforms A through D contained an average of 13.9 (95% CI, 6.0-17.5) errors, with 19.5% of the transcript errors transmitted to the clinical note (95% CI, 6.6%-28.8%). For clinical notes, mean percent error across platforms was 26.3% (95% CI, 17.0%-31.0%) with a significantly higher proportion of errors in notes generated by platform E (P<.0053 for all comparisons). Of correctly reported elements, only 35.8%±11.3% were consistently correct across all platforms. An average of 3.0 (95% CI, 0-4, range 0-21) errors per case had potential for moderate-to-severe harm. The mean physician documentation quality instrument–9 score was 36±4, with significant variation between platforms.
Conclusion
Clinical notes generated by ADS platforms using simulated encounters reports important inter-platform and intra-platform variability in accuracy and quality. These findings indicate a need for standardized, objective evaluation and reporting.
{"title":"Evaluating the Quality and Safety of Ambient Digital Scribe Platforms Using Simulated Ambulatory Encounters","authors":"Taylor N. Anderson MD , Vishnu Mohan MD , David A. Dorr MD , Raj M. Ratwani PhD , Joshua M. Biro PhD , Jeffrey A. Gold MD","doi":"10.1016/j.mcpdig.2025.100292","DOIUrl":"10.1016/j.mcpdig.2025.100292","url":null,"abstract":"<div><h3>Objective</h3><div>To evaluate and compare the quality and safety of ambient digital scribe (ADS) platforms using simulated ambulatory encounters.</div></div><div><h3>Methods</h3><div>Five ADS platforms were evaluated using audio recordings of fourteen simulated clinical encounters. Audio recordings were played on a laptop computer and captured by ADS platforms on a mobile phone. Generated transcripts were compared to professional transcriptions. Clinical notes were graded using rubrics of key elements for each case. Note errors were classified as omission, commission, or partially correct. Potential clinical harm was assessed using the agency for healthcare research and quality harm scale. Note quality was assessed using the 9-item Physician Documentation Quality Instrument (range 9-45). Statistical comparisons included Friedman and χ<sup>2</sup> tests with a correction for multiple comparisons.</div></div><div><h3>Results</h3><div>Transcripts generated by platforms A through D contained an average of 13.9 (95% CI, 6.0-17.5) errors, with 19.5% of the transcript errors transmitted to the clinical note (95% CI, 6.6%-28.8%). For clinical notes, mean percent error across platforms was 26.3% (95% CI, 17.0%-31.0%) with a significantly higher proportion of errors in notes generated by platform E (<em>P</em><.0053 for all comparisons). Of correctly reported elements, only 35.8%±11.3% were consistently correct across all platforms. An average of 3.0 (95% CI, 0-4, range 0-21) errors per case had potential for moderate-to-severe harm. The mean physician documentation quality instrument–9 score was 36±4, with significant variation between platforms.</div></div><div><h3>Conclusion</h3><div>Clinical notes generated by ADS platforms using simulated encounters reports important inter-platform and intra-platform variability in accuracy and quality. These findings indicate a need for standardized, objective evaluation and reporting.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100292"},"PeriodicalIF":0.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417237","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-10-08DOI: 10.1016/j.mcpdig.2025.100293
Prajwal L. Salins MHA , Ganesh Anandan MSc , Basilio Duke Ananda MSc , Bhageerathy Reshmi MSc, PhD , Roshan David Jathanna MTech, PhD
Objective
To design, implement, and evaluate a digital indoor wayfinding web application (KH Wayfinder) for a tertiary care hospital, assessing its effects on spatial orientation and navigation-related stress among visitors.
Participants and Methods
A 3-phase study was conducted in a tertiary care hospital in coastal Karnataka, India, from April 1, 2023, through July 31, 2024. Phase 1 involved a cross-sectional survey (n=41) to assess user attitudes toward digital wayfinding. In phase 2, a browser-based application was developed using HyperText Markup Language, JavaScript, cascading style sheets, and Leaflet.js, covering 5 hospital floors with 52 destination points and 758 routes. Phase 3 consisted of usability testing with 54 participants using a validated questionnaire to assess performance, satisfaction, and ease of use.
Results
The majority of users 33 (80.5%) expressed willingness to use a digital Wayfinder. Postimplementation results showed that 46 (85.2%) found the tool easy to use, 47 (87%) reported a reduction in navigation time, and 45 (83.3%) experienced reduced psychological stress. Additionally, 51 (94.4%) preferred the digital system over traditional signage, and 54 (100%) would recommend it to others.
Conclusion
KH Wayfinder demonstrated high usability, effectiveness, and user satisfaction as a low-cost digital navigation solution. Its browser-based architecture and open-source design make it scalable and adaptable for broader use in smart hospital environments. Future enhancements may include real-time positioning, multilingual support, and accessibility features.
{"title":"Internet of Things-Based Wayfinding for Hospital Visitors: A Digital Solution for Complex Health Care Infrastructures","authors":"Prajwal L. Salins MHA , Ganesh Anandan MSc , Basilio Duke Ananda MSc , Bhageerathy Reshmi MSc, PhD , Roshan David Jathanna MTech, PhD","doi":"10.1016/j.mcpdig.2025.100293","DOIUrl":"10.1016/j.mcpdig.2025.100293","url":null,"abstract":"<div><h3>Objective</h3><div>To design, implement, and evaluate a digital indoor wayfinding web application (KH Wayfinder) for a tertiary care hospital, assessing its effects on spatial orientation and navigation-related stress among visitors.</div></div><div><h3>Participants and Methods</h3><div>A 3-phase study was conducted in a tertiary care hospital in coastal Karnataka, India, from April 1, 2023, through July 31, 2024. Phase 1 involved a cross-sectional survey (n=41) to assess user attitudes toward digital wayfinding. In phase 2, a browser-based application was developed using HyperText Markup Language, JavaScript, cascading style sheets, and Leaflet.js, covering 5 hospital floors with 52 destination points and 758 routes. Phase 3 consisted of usability testing with 54 participants using a validated questionnaire to assess performance, satisfaction, and ease of use.</div></div><div><h3>Results</h3><div>The majority of users 33 (80.5%) expressed willingness to use a digital Wayfinder. Postimplementation results showed that 46 (85.2%) found the tool easy to use, 47 (87%) reported a reduction in navigation time, and 45 (83.3%) experienced reduced psychological stress. Additionally, 51 (94.4%) preferred the digital system over traditional signage, and 54 (100%) would recommend it to others.</div></div><div><h3>Conclusion</h3><div>KH Wayfinder demonstrated high usability, effectiveness, and user satisfaction as a low-cost digital navigation solution. Its browser-based architecture and open-source design make it scalable and adaptable for broader use in smart hospital environments. Future enhancements may include real-time positioning, multilingual support, and accessibility features.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100293"},"PeriodicalIF":0.0,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-25DOI: 10.1016/j.mcpdig.2025.100270
Stephanie M. Helman PhD , Nathan T. Riek PhD , Susan M. Sereika PhD , Ahmad P. Tafti PhD , Robert Olsen BS , J. William Gaynor MD , Amy Jo Lisanti PhD , Salah S. Al-Zaiti PhD
Objective
To identify distinct postoperative temperature trajectories in neonates with congenital heart defects after cardiopulmonary bypass (CPB), using advanced unsupervised machine learning clustering methods, corroborate findings, and evaluate their prognostic value on outcomes.
Patients and Methods
A secondary cohort analysis of prospective data collected from a single pediatric referral center’s CardioAccess data registry, consistent of neonates who underwent CPB between January 1, 2015, and January 1, 2019, was performed. Postoperative temperatures were extracted from medical records (48 hours). Group-based trajectory modeling (GBTM) performance was compared with self-organizing maps (SOM) and k-means clustering. Cluster membership and model fit were optimized for 3 temperature clusters per method. The primary outcome was a composite of postoperative complications. Clustering techniques were compared and associated with outcomes using adjusted multivariable binary logistic regression.
Results
Neonates of ≥34 weeks’ gestation underwent CPB (N=450). GBTM, SOM, and k-means identified membership for 3 groups: (1) persistent hypothermia (n=38 [9%]; n=49 [11%]; and n=40 [9%], respectively); (2) resolving hypothermia (n=233 [51%]; n=227 [50%]; and n=147 [33%], respectively); and (3) normothermia (n=179 [40%]; n=174 [39%]; and n=263 [58%], respectively). Concordance between techniques found strong agreement between GBTM and SOM (κ=0.92) and weak agreement between GBTM and k-means (κ=0.41). After adjustment, persistently hypothermic neonates compared with normothermic neonates were associated with higher odds of the complication composite outcome in the GBTM (odds ratio [OR], 2.8; 95% CI, 1.0-7.3; P=.04) and SOM (OR, 2.3; 95% CI, 1.0-5.4; P=.04) models, but not in the k-means model (OR, 1.4; 95% CI, 0.7-3.1; P=.38).
Conclusion
Exploring concordance between different machine learning techniques shows that temperature in neonates after CPB follows unique trajectories. Those exhibiting persistent hypothermia trends are at higher risk of adverse outcomes.
{"title":"Exploring Novel Data-Driven Clustering Methods for Uncovering Patterns in Longitudinal Neonatal Postoperative Temperature Measurements","authors":"Stephanie M. Helman PhD , Nathan T. Riek PhD , Susan M. Sereika PhD , Ahmad P. Tafti PhD , Robert Olsen BS , J. William Gaynor MD , Amy Jo Lisanti PhD , Salah S. Al-Zaiti PhD","doi":"10.1016/j.mcpdig.2025.100270","DOIUrl":"10.1016/j.mcpdig.2025.100270","url":null,"abstract":"<div><h3>Objective</h3><div>To identify distinct postoperative temperature trajectories in neonates with congenital heart defects after cardiopulmonary bypass (CPB), using advanced unsupervised machine learning clustering methods, corroborate findings, and evaluate their prognostic value on outcomes.</div></div><div><h3>Patients and Methods</h3><div>A secondary cohort analysis of prospective data collected from a single pediatric referral center’s CardioAccess data registry, consistent of neonates who underwent CPB between January 1, 2015, and January 1, 2019, was performed. Postoperative temperatures were extracted from medical records (48 hours). Group-based trajectory modeling (GBTM) performance was compared with self-organizing maps (SOM) and k-means clustering. Cluster membership and model fit were optimized for 3 temperature clusters per method. The primary outcome was a composite of postoperative complications. Clustering techniques were compared and associated with outcomes using adjusted multivariable binary logistic regression.</div></div><div><h3>Results</h3><div>Neonates of ≥34 weeks’ gestation underwent CPB (<em>N</em>=450). GBTM, SOM, and k-means identified membership for 3 groups: (1) persistent hypothermia (n=38 [9%]; n=49 [11%]; and n=40 [9%], respectively); (2) resolving hypothermia (n=233 [51%]; n=227 [50%]; and n=147 [33%], respectively); and (3) normothermia (n=179 [40%]; n=174 [39%]; and n=263 [58%], respectively). Concordance between techniques found strong agreement between GBTM and SOM (κ=0.92) and weak agreement between GBTM and k-means (κ=0.41). After adjustment, persistently hypothermic neonates compared with normothermic neonates were associated with higher odds of the complication composite outcome in the GBTM (odds ratio [OR], 2.8; 95% CI, 1.0-7.3; <em>P</em>=.04) and SOM (OR, 2.3; 95% CI, 1.0-5.4; <em>P</em>=.04) models, but not in the k-means model (OR, 1.4; 95% CI, 0.7-3.1; <em>P</em>=.38).</div></div><div><h3>Conclusion</h3><div>Exploring concordance between different machine learning techniques shows that temperature in neonates after CPB follows unique trajectories. Those exhibiting persistent hypothermia trends are at higher risk of adverse outcomes.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100270"},"PeriodicalIF":0.0,"publicationDate":"2025-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145417123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-24DOI: 10.1016/j.mcpdig.2025.100269
Abigail Naa Amankwaa Abeo MS , Sophie Armstrong BSc , Michael Scriney PhD , Hannah Goss PhD
Objective
To systematically review the utilization of artificial intelligence (AI) in health literacy, highlighting limitations and future developments.
Methods
A systematic review, following PRISMA guidelines, was conducted searching 6 databases for studies published from January 1, 2014, through April 10, 2024. Data extracted included population characteristics, health literacy definitions and measurement, study objectives, AI techniques, and metrics. Risk of bias was assessed using an adapted checklist.
Results
From 1296 studies, 18 (1.4%) met inclusion criteria. These studies primarily evaluated text-based materials, including online articles, and electronic health records, with most materials in English, but also incorporated other languages. Artificial intelligence played various roles, including evaluating complexity, text simplification/readability enhancement, translation, and question-answering. Only 5 studies involved participant engagement. Seven studies provided a health literacy definition, consistently describing it as an individual’s ability to obtain, understand, and use health information for informed decisions, often linking it to external factors. However, only 1 study incorporated an individual level health literacy measurement tool, whereas organizational level health literacy measurement remained largely overlooked. The AI techniques used included traditional machine learning, deep learning, and transformer-based models. Evaluation metrics were categorized into human evaluation, readability, and machine learning metrics.
Conclusion
The review highlights AI’s dynamic application in relation to health literacy; however, measurement of health literacy, at both an individual and organizational level, to evidence AI's effectiveness remains limited. In addition, future work should not only measure health literacy outcomes more rigorously but also pursue research on enhancing AI model performance, robust evaluation, and their practical implementation in real-world settings.
{"title":"Artificial Intelligence Techniques and Health Literacy: A Systematic Review","authors":"Abigail Naa Amankwaa Abeo MS , Sophie Armstrong BSc , Michael Scriney PhD , Hannah Goss PhD","doi":"10.1016/j.mcpdig.2025.100269","DOIUrl":"10.1016/j.mcpdig.2025.100269","url":null,"abstract":"<div><h3>Objective</h3><div>To systematically review the utilization of artificial intelligence (AI) in health literacy, highlighting limitations and future developments.</div></div><div><h3>Methods</h3><div>A systematic review, following PRISMA guidelines, was conducted searching 6 databases for studies published from January 1, 2014, through April 10, 2024. Data extracted included population characteristics, health literacy definitions and measurement, study objectives, AI techniques, and metrics. Risk of bias was assessed using an adapted checklist.</div></div><div><h3>Results</h3><div>From 1296 studies, 18 (1.4%) met inclusion criteria. These studies primarily evaluated text-based materials, including online articles, and electronic health records, with most materials in English, but also incorporated other languages. Artificial intelligence played various roles, including evaluating complexity, text simplification/readability enhancement, translation, and question-answering. Only 5 studies involved participant engagement. Seven studies provided a health literacy definition, consistently describing it as an individual’s ability to obtain, understand, and use health information for informed decisions, often linking it to external factors. However, only 1 study incorporated an individual level health literacy measurement tool, whereas organizational level health literacy measurement remained largely overlooked. The AI techniques used included traditional machine learning, deep learning, and transformer-based models. Evaluation metrics were categorized into human evaluation, readability, and machine learning metrics.</div></div><div><h3>Conclusion</h3><div>The review highlights AI’s dynamic application in relation to health literacy; however, measurement of health literacy, at both an individual and organizational level, to evidence AI's effectiveness remains limited. In addition, future work should not only measure health literacy outcomes more rigorously but also pursue research on enhancing AI model performance, robust evaluation, and their practical implementation in real-world settings.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100269"},"PeriodicalIF":0.0,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145363565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Barriers to Radiomics Adoption for Urological Cancer Diagnosis in Low-Income and Middle-Income Countries: A Perspective from Pakistan","authors":"Awais Ayub MBBS, Hanan Mudassar MBBS, Maida Rizwan MBBS","doi":"10.1016/j.mcpdig.2025.100262","DOIUrl":"10.1016/j.mcpdig.2025.100262","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100262"},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-09DOI: 10.1016/j.mcpdig.2025.100263
Isaiah Z. Yao, Min Dong, William Y.K. Hwang MBBS, FRCP, FAMS, MBA
{"title":"In Reply: Barriers to Radiomics Adoption for Urological Cancer Diagnosis in Low-Income and Middle-Income Countries: A Perspective from Pakistan","authors":"Isaiah Z. Yao, Min Dong, William Y.K. Hwang MBBS, FRCP, FAMS, MBA","doi":"10.1016/j.mcpdig.2025.100263","DOIUrl":"10.1016/j.mcpdig.2025.100263","url":null,"abstract":"","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100263"},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145159891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-09DOI: 10.1016/j.mcpdig.2025.100264
Laura C. Zwiers MPhil , Duco Veen PhD , Marianna Mitratza PhD , Timo B. Brakenhoff PhD , Brianna M. Goodale PhD , Paul Klaver MSc , Kay Y. Hage MSc , Marcel van Willigen PhD , George S. Downward PhD , Peter Lugtig PhD , Leendert van Maanen PhD , Stefan Van der Stigchel PhD , Peter van der Heijden PhD , Maureen Cronin PhD , Diederick E. Grobbee PhD , COVID-RED Consortium
Objective
To present retention strategies implemented in the coronavirus disease 2019 (COVID-19) rapid early detection trial, a decentralized trial investigating the use of a wearable device for severe acute respiratory syndrome coronavirus 2 detection, and to provide insights into study retention and investigate determinants of discontinuation.
Patients and Methods
The COVID-2019 rapid early detection trial collected data from 17,825 participants from February 22, 2021 to November 18, 2021. Participants wore a wearable device overnight and synchronized it with a mobile application on waking. Retention strategies included common and personalized activities. Multivariable logistic regression was used to identify participants at high risk of discontinuation after 6 months in the trial. Results were combined with insights from behavioral theory to target participants with additional telephone calls.
Results
Total of 14,326 (80.4%) participants remained in the trial after 6 months and 12,208 (68.5%) until the end of the trial. Multivariable logistic regression identified age, employment situation, living situation, and COVID-19 vaccination status as predictors of discontinuation. Subgroups at high risk of discontinuation were identified, and behavioral assessments indicated that the subgroup of vaccinated pensioners would receive additional telephone calls. Their dropout rate was 11.4% after telephone calls.
Conclusion
This study describes how innovative and targeted data-driven retention strategies can be applied in a large decentralized clinical trial and presents the implemented retention strategies and discontinuation rates. Results can serve as a starting point for designing retention strategies in future decentralized trials.
{"title":"Increasing Retention in a Large-Scale Decentralized Clinical Trial: Learnings From the COVID-RED Trial","authors":"Laura C. Zwiers MPhil , Duco Veen PhD , Marianna Mitratza PhD , Timo B. Brakenhoff PhD , Brianna M. Goodale PhD , Paul Klaver MSc , Kay Y. Hage MSc , Marcel van Willigen PhD , George S. Downward PhD , Peter Lugtig PhD , Leendert van Maanen PhD , Stefan Van der Stigchel PhD , Peter van der Heijden PhD , Maureen Cronin PhD , Diederick E. Grobbee PhD , COVID-RED Consortium","doi":"10.1016/j.mcpdig.2025.100264","DOIUrl":"10.1016/j.mcpdig.2025.100264","url":null,"abstract":"<div><h3>Objective</h3><div>To present retention strategies implemented in the coronavirus disease 2019 (COVID-19) rapid early detection trial, a decentralized trial investigating the use of a wearable device for severe acute respiratory syndrome coronavirus 2 detection, and to provide insights into study retention and investigate determinants of discontinuation.</div></div><div><h3>Patients and Methods</h3><div>The COVID-2019 rapid early detection trial collected data from 17,825 participants from February 22, 2021 to November 18, 2021. Participants wore a wearable device overnight and synchronized it with a mobile application on waking. Retention strategies included common and personalized activities. Multivariable logistic regression was used to identify participants at high risk of discontinuation after 6 months in the trial. Results were combined with insights from behavioral theory to target participants with additional telephone calls.</div></div><div><h3>Results</h3><div>Total of 14,326 (80.4%) participants remained in the trial after 6 months and 12,208 (68.5%) until the end of the trial. Multivariable logistic regression identified age, employment situation, living situation, and COVID-19 vaccination status as predictors of discontinuation. Subgroups at high risk of discontinuation were identified, and behavioral assessments indicated that the subgroup of vaccinated pensioners would receive additional telephone calls. Their dropout rate was 11.4% after telephone calls.</div></div><div><h3>Conclusion</h3><div>This study describes how innovative and targeted data-driven retention strategies can be applied in a large decentralized clinical trial and presents the implemented retention strategies and discontinuation rates. Results can serve as a starting point for designing retention strategies in future decentralized trials.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100264"},"PeriodicalIF":0.0,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1016/j.mcpdig.2025.100261
Elvin Irihamye MSc , Justin Hadad MPhil , Natasha Ali MD, MSc , Bruno Holthof PhD, MD, MBA , Francis Wafula DPhil, MSc , Chris Paton DPhil, MBA , Mike English MBBChir , Shobhana Nagraj DPhil, MPhil, MBBS
This study explores challenges and potential strategies related to sustaining digital health business models and markets in low-income and low-middle-income countries using a critical interpretive synthesis approach. We extracted 21 articles from a database search that yielded over 1300 hits and used insights from 7 expert reviewers with experience operating or funding digital health companies in low-middle –income countries. Findings reveal 4 key challenges: (1) internal challenges related to managing value creation for complex stakeholder networks and external challenges related to (2) infrastructure, (3) financing, and (4) regulation. Entrepreneurs must address these through iterative business strategies, but broader market-shaping interventions remain essential. Such interventions could include facilitating strategic partnerships, fit-for-purpose regulation, enhancing public procurement, and innovative financing instruments. Health systems can tailor interventions around their unique contexts by prioritizing technologies, recruiting local market participants, analyzing shared barriers in the business environment, focusing on feasible interventions, and iterating to sustain a competitive environment.
{"title":"Sustainable by Design: Digital Health Business Models for Equitable Global Health Impact in Low-Income and Low-Middle-Income Countries","authors":"Elvin Irihamye MSc , Justin Hadad MPhil , Natasha Ali MD, MSc , Bruno Holthof PhD, MD, MBA , Francis Wafula DPhil, MSc , Chris Paton DPhil, MBA , Mike English MBBChir , Shobhana Nagraj DPhil, MPhil, MBBS","doi":"10.1016/j.mcpdig.2025.100261","DOIUrl":"10.1016/j.mcpdig.2025.100261","url":null,"abstract":"<div><div>This study explores challenges and potential strategies related to sustaining digital health business models and markets in low-income and low-middle-income countries using a critical interpretive synthesis approach. We extracted 21 articles from a database search that yielded over 1300 hits and used insights from 7 expert reviewers with experience operating or funding digital health companies in low-middle –income countries. Findings reveal 4 key challenges: (1) internal challenges related to managing value creation for complex stakeholder networks and external challenges related to (2) infrastructure, (3) financing, and (4) regulation. Entrepreneurs must address these through iterative business strategies, but broader market-shaping interventions remain essential. Such interventions could include facilitating strategic partnerships, fit-for-purpose regulation, enhancing public procurement, and innovative financing instruments. Health systems can tailor interventions around their unique contexts by prioritizing technologies, recruiting local market participants, analyzing shared barriers in the business environment, focusing on feasible interventions, and iterating to sustain a competitive environment.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100261"},"PeriodicalIF":0.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-03DOI: 10.1016/j.mcpdig.2025.100260
Joseph P. Deason MBA , Scott J. Adams MD, PhD, FRCPC , Ahmad Rahman MSc , Stacey Lovo PhD , Ivar Mendez MD, PhD, FRCSC
Objective
To develop and pilot a technology selection tool (TST) designed to evaluate and recommend virtual care technologies tailored to specific community clinical needs.
Patients and Methods
Developed through collaborations among clinicians, software developers, technology experts, and health administrators, the TST uses a multiple criteria decision analysis framework to recommend technologies based on clinical relevance and technical quality. Its functionality was tested in a pilot project that assessed 5 technologies for their application in virtual wound care to support a remote community in Saskatchewan, Canada. The pilot study was completed March 7, 2025, through July 28, 2025.
Results
The TST identified the TeleVU Glass View as the optimal technology for virtual wound care. The TST generated product scores for the TeleVU Glass View (71.67), Teladoc Xpress (70.10), 19 Labs GALE (50.67), and TytoCare TytoKit (47.00), whereas disqualifying the Teladoc Lite Cart for not meeting the pass–fail portability criterion. TeleVU’s high product score resulted primarily from its technological attribute quality scores for Telestration (10), Audio (9), Video (9), and Share Content (9), which were all determined as clinically relevant for virtual wound care. The pilot enabled real-time wound care support by connecting local clinicians with virtual teams.
Conclusion
The TST offers a practical and adaptable tool to support evidence-based decision making for selecting technologies for specific clinical applications.
{"title":"A Technology Selection Tool Applying Multiple Criteria Decision Analysis for Virtual Care Implementation","authors":"Joseph P. Deason MBA , Scott J. Adams MD, PhD, FRCPC , Ahmad Rahman MSc , Stacey Lovo PhD , Ivar Mendez MD, PhD, FRCSC","doi":"10.1016/j.mcpdig.2025.100260","DOIUrl":"10.1016/j.mcpdig.2025.100260","url":null,"abstract":"<div><h3>Objective</h3><div>To develop and pilot a technology selection tool (TST) designed to evaluate and recommend virtual care technologies tailored to specific community clinical needs.</div></div><div><h3>Patients and Methods</h3><div>Developed through collaborations among clinicians, software developers, technology experts, and health administrators, the TST uses a multiple criteria decision analysis framework to recommend technologies based on clinical relevance and technical quality. Its functionality was tested in a pilot project that assessed 5 technologies for their application in virtual wound care to support a remote community in Saskatchewan, Canada. The pilot study was completed March 7, 2025, through July 28, 2025.</div></div><div><h3>Results</h3><div>The TST identified the TeleVU Glass View as the optimal technology for virtual wound care. The TST generated product scores for the TeleVU Glass View (71.67), Teladoc Xpress (70.10), 19 Labs GALE (50.67), and TytoCare TytoKit (47.00), whereas disqualifying the Teladoc Lite Cart for not meeting the pass–fail portability criterion. TeleVU’s high product score resulted primarily from its technological attribute quality scores for Telestration (10), Audio (9), Video (9), and Share Content (9), which were all determined as clinically relevant for virtual wound care. The pilot enabled real-time wound care support by connecting local clinicians with virtual teams.</div></div><div><h3>Conclusion</h3><div>The TST offers a practical and adaptable tool to support evidence-based decision making for selecting technologies for specific clinical applications.</div></div>","PeriodicalId":74127,"journal":{"name":"Mayo Clinic Proceedings. Digital health","volume":"3 4","pages":"Article 100260"},"PeriodicalIF":0.0,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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