%0 Journal Article %@ 1438-8871 %I JMIR Publications %V 25 %N %P e43404 %T Advancing Digital Health Innovation in Oncology: Priorities for High-Value Digital Transformation in Cancer Care %A Patel,Smit %A Goldsack,Jennifer C %A Cordovano,Grace %A Downing,Andrea %A Fields,Karen K %A Geoghegan,Cindy %A Grewal,Upinder %A Nieva,Jorge %A Patel,Nikunj %A Rollison,Dana E %A Sah,Archana %A Said,Maya %A Van De Keere,Isabel %A Way,Amanda %A Wolff-Hughes,Dana L %A Wood,William A %A Robinson,Edmondo J %+ Digital Medicine Society, 90 Canal St, 4th Floor, Boston, MA, 02114, United States, 1 765 234 3463, jennifer@dimesociety.org %K digital health %K innovation %K oncology %K cancer care %K cancer %K patient journey %K digital transformation %K digital divide %K health care delivery %K service delivery %K equity %K patient-reported outcome %K PROM %K biomarker %K digital innovation %D 2023 %7 4.1.2023 %9 Viewpoint %J J Med Internet Res %G English %X Although health care delivery is becoming increasingly digitized, driven by the pursuit of improved access, equity, efficiency, and effectiveness, progress does not appear to be equally distributed across therapeutic areas. Oncology is renowned for leading innovation in research and in care; digital pathology, digital radiology, real-world data, next-generation sequencing, patient-reported outcomes, and precision approaches driven by complex data and biomarkers are hallmarks of the field. However, remote patient monitoring, decentralized approaches to care and research, “hospital at home,” and machine learning techniques have yet to be broadly deployed to improve cancer care. In response, the Digital Medicine Society and Moffitt Cancer Center convened a multistakeholder roundtable discussion to bring together leading experts in cancer care and digital innovation. This viewpoint highlights the findings from these discussions, in which experts agreed that digital innovation is lagging in oncology relative to other therapeutic areas. It reports that this lag is most likely attributed to poor articulation of the challenges in cancer care and research best suited to digital solutions, lack of incentives and support, and missing standardized infrastructure to implement digital innovations. It concludes with suggestions for actions needed to bring the promise of digitization to cancer care to improve lives. %M 36598811 %R 10.2196/43404 %U https://www.jmir.org/2023/1/e43404 %U https://doi.org/10.2196/43404 %U http://www.ncbi.nlm.nih.gov/pubmed/36598811 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 24 %N 5 %P e35951 %T Measuring Health-Related Quality of Life With Multimodal Data: Viewpoint %A Clay,Ieuan %A Cormack,Francesca %A Fedor,Szymon %A Foschini,Luca %A Gentile,Giovanni %A van Hoof,Chris %A Kumar,Priya %A Lipsmeier,Florian %A Sano,Akane %A Smarr,Benjamin %A Vandendriessche,Benjamin %A De Luca,Valeria %+ Novartis Institutes for Biomedical Research, Fabrikstrasse 2, Basel, 4056, Switzerland, 41 79 540 3272, valeria.de_luca@novartis.com %K digital measures %K quality of life %K machine learning %K digital health %K digital product %K digital wellness %K digital therapeutics %K digital therapy %K multimodal technology %K drug development %K care delivery %K data integration %D 2022 %7 26.5.2022 %9 Viewpoint %J J Med Internet Res %G English %X The ability to objectively measure aspects of performance and behavior is a fundamental pillar of digital health, enabling digital wellness products, decentralized trial concepts, evidence generation, digital therapeutics, and more. Emerging multimodal technologies capable of measuring several modalities simultaneously and efforts to integrate inputs across several sources are further expanding the limits of what digital measures can assess. Experts from the field of digital health were convened as part of a multi-stakeholder workshop to examine the progress of multimodal digital measures in two key areas: detection of disease and the measurement of meaningful aspects of health relevant to the quality of life. Here we present a meeting report, summarizing key discussion points, relevant literature, and finally a vision for the immediate future, including how multimodal measures can provide value to stakeholders across drug development and care delivery, as well as three key areas where headway will need to be made if we are to continue to build on the encouraging progress so far: collaboration and data sharing, removal of barriers to data integration, and alignment around robust modular evaluation of new measurement capabilities. %M 35617003 %R 10.2196/35951 %U https://www.jmir.org/2022/5/e35951 %U https://doi.org/10.2196/35951 %U http://www.ncbi.nlm.nih.gov/pubmed/35617003 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 11 %P e34493 %T Sensor Data Integration: A New Cross-Industry Collaboration to Articulate Value, Define Needs, and Advance a Framework for Best Practices %A Clay,Ieuan %A Angelopoulos,Christian %A Bailey,Anne Lord %A Blocker,Aaron %A Carini,Simona %A Carvajal,Rodrigo %A Drummond,David %A McManus,Kimberly F %A Oakley-Girvan,Ingrid %A Patel,Krupal B %A Szepietowski,Phillip %A Goldsack,Jennifer C %+ Digital Medicine Society (DiMe), 90 Canal Street, Boston, MA, 02114, United States, 1 765 234 3463, ieuan@dimesociety.org %K digital measures %K data integration %K patient centricity %K utility %D 2021 %7 9.11.2021 %9 Viewpoint %J J Med Internet Res %G English %X Data integration, the processes by which data are aggregated, combined, and made available for use, has been key to the development and growth of many technological solutions. In health care, we are experiencing a revolution in the use of sensors to collect data on patient behaviors and experiences. Yet, the potential of this data to transform health outcomes is being held back. Deficits in standards, lexicons, data rights, permissioning, and security have been well documented, less so the cultural adoption of sensor data integration as a priority for large-scale deployment and impact on patient lives. The use and reuse of trustworthy data to make better and faster decisions across drug development and care delivery will require an understanding of all stakeholder needs and best practices to ensure these needs are met. The Digital Medicine Society is launching a new multistakeholder Sensor Data Integration Tour of Duty to address these challenges and more, providing a clear direction on how sensor data can fulfill its potential to enhance patient lives. %M 34751656 %R 10.2196/34493 %U https://www.jmir.org/2021/11/e34493 %U https://doi.org/10.2196/34493 %U http://www.ncbi.nlm.nih.gov/pubmed/34751656 %0 Journal Article %@ 1438-8871 %I JMIR Publications %V 23 %N 9 %P e29875 %T Recent Academic Research on Clinically Relevant Digital Measures: Systematic Review %A Shandhi,Md Mobashir Hasan %A Goldsack,Jennifer C %A Ryan,Kyle %A Bennion,Alexandra %A Kotla,Aditya V %A Feng,Alina %A Jiang,Yihang %A Wang,Will Ke %A Hurst,Tina %A Patena,John %A Carini,Simona %A Chung,Jeanne %A Dunn,Jessilyn %+ Department of Biomedical Engineering, Duke University, 1427 FCIEMAS, Box 90281, Durham, NC, 27708, United States, 1 919 660 5131, jessilyn.dunn@duke.edu %K digital clinical measures %K academic research %K funding %K biosensor %K digital measures %K digital health %K health outcomes %D 2021 %7 15.9.2021 %9 Review %J J Med Internet Res %G English %X Background: Digital clinical measures collected via various digital sensing technologies such as smartphones, smartwatches, wearables, ingestibles, and implantables are increasingly used by individuals and clinicians to capture health outcomes or behavioral and physiological characteristics of individuals. Although academia is taking an active role in evaluating digital sensing products, academic contributions to advancing the safe, effective, ethical, and equitable use of digital clinical measures are poorly characterized. Objective: We performed a systematic review to characterize the nature of academic research on digital clinical measures and to compare and contrast the types of sensors used and the sources of funding support for specific subareas of this research. Methods: We conducted a PubMed search using a range of search terms to retrieve peer-reviewed articles reporting US-led academic research on digital clinical measures between January 2019 and February 2021. We screened each publication against specific inclusion and exclusion criteria. We then identified and categorized research studies based on the types of academic research, sensors used, and funding sources. Finally, we compared and contrasted the funding support for these specific subareas of research and sensor types. Results: The search retrieved 4240 articles of interest. Following the screening, 295 articles remained for data extraction and categorization. The top five research subareas included operations research (research analysis; n=225, 76%), analytical validation (n=173, 59%), usability and utility (data visualization; n=123, 42%), verification (n=93, 32%), and clinical validation (n=83, 28%). The three most underrepresented areas of research into digital clinical measures were ethics (n=0, 0%), security (n=1, 0.5%), and data rights and governance (n=1, 0.5%). Movement and activity trackers were the most commonly studied sensor type, and physiological (mechanical) sensors were the least frequently studied. We found that government agencies are providing the most funding for research on digital clinical measures (n=192, 65%), followed by independent foundations (n=109, 37%) and industries (n=56, 19%), with the remaining 12% (n=36) of these studies completely unfunded. Conclusions: Specific subareas of academic research related to digital clinical measures are not keeping pace with the rapid expansion and adoption of digital sensing products. An integrated and coordinated effort is required across academia, academic partners, and academic funders to establish the field of digital clinical measures as an evidence-based field worthy of our trust. %M 34524089 %R 10.2196/29875 %U https://www.jmir.org/2021/9/e29875 %U https://doi.org/10.2196/29875 %U http://www.ncbi.nlm.nih.gov/pubmed/34524089 %0 Journal Article %@ 2291-5222 %I JMIR Publications %V 9 %N 2 %P e24570 %T Digital Medicine Community Perspectives and Challenges: Survey Study %A Bent,Brinnae %A Sim,Ida %A Dunn,Jessilyn P %+ Department of Biostatistics & Bioinformatics, Duke University Medical Center, 2424 Erwin Road, Durham, NC, 27705, United States, 1 9196689798, jessilyn.dunn@duke.edu %K digital medicine %K digital health %K interoperability %K mHealth %K wearables %K sensors %D 2021 %7 3.2.2021 %9 Original Paper %J JMIR Mhealth Uhealth %G English %X Background: The field of digital medicine has seen rapid growth over the past decade. With this unfettered growth, challenges surrounding interoperability have emerged as a critical barrier to translating digital medicine into practice. In order to understand how to mitigate challenges in digital medicine research and practice, this community must understand the landscape of digital medicine professionals, which digital medicine tools are being used and how, and user perspectives on current challenges in the field of digital medicine. Objective: The primary objective of this study is to provide information to the digital medicine community that is working to establish frameworks and best practices for interoperability in digital medicine. We sought to learn about the background of digital medicine professionals and determine which sensors and file types are being used most commonly in digital medicine research. We also sought to understand perspectives on digital medicine interoperability. Methods: We used a web-based survey to query a total of 56 digital medicine professionals from May 1, 2020, to July 10, 2020, on their educational and work experience, the sensors, file types, and toolkits they use professionally, and their perspectives on interoperability in digital medicine. Results: We determined that the digital medicine community comes from diverse educational backgrounds and uses a variety of sensors and file types. Sensors measuring physical activity and the cardiovascular system are the most frequently used, and smartphones continue to be the dominant source of digital health information collection in the digital medicine community. We show that there is not a general consensus on file types in digital medicine, and data are currently handled in multiple ways. There is consensus that interoperability is a critical impediment in digital medicine, with 93% (52) of survey respondents in agreement. However, only 36% (20) of respondents currently use tools for interoperability in digital medicine. We identified three key interoperability needs to be met: integration with electronic health records, implementation of standard data schemas, and standard and verifiable methods for digital medicine research. We show that digital medicine professionals are eager to adopt new tools to solve interoperability problems, and we suggest tools to support digital medicine interoperability. Conclusions: Understanding the digital medicine community, the sensors and file types they use, and their perspectives on interoperability will enable the development and implementation of solutions that fill critical interoperability gaps in digital medicine. The challenges to interoperability outlined by this study will drive the next steps in creating an interoperable digital medicine community. Establishing best practices to address these challenges and employing platforms for digital medicine interoperability will be essential to furthering the field of digital medicine. %M 33533721 %R 10.2196/24570 %U http://mhealth.jmir.org/2021/2/e24570/ %U https://doi.org/10.2196/24570 %U http://www.ncbi.nlm.nih.gov/pubmed/33533721