Digital Therapeutics

Medical Informatics, mHealth and Digital Therapeutics (DTx)

Digital therapeutics such as mobile health applications (mHealth apps) are becoming part of patients’ treatment programs. Ensuring patients actually use (adhere to) an app as prescribed, effectively measuring and interpreting usage, and detecting clinical non-compliance are fundamental to effective treatment. Clinicians are not currently prepared to deal with issues of patients’ adherence to digital therapeutics (DTx). Our studies analyse potential frameworks for clinician-patient dialogue about DTx adherence using leading adherence frameworks, one at each of the micro (patient), mesa (physician), and macro (system) levels of healthcare. The ABC taxonomy of adherence stages; Osterberg and Blaschke’s medication adherence framework; and the Morisky Medication Adherence Scale-8 (MMAS8). Each framework is deconstructed and analysed from the perspective of DTx adherence.  A strong basis for studying and measuring DTx adherence exists in existing treatment adherence research and practice, and can help guide policy. However, important adaptations are needed to ensure the development of methods for use in clinical environments.

Our other research in this field focus on machine learning (ML) techniques to distill meaningful and impactful insights from medical datasets.

Related publications include:

  1. Danay, L., Ramon-Gonen, R., Gorodetzki, M.& Schwartz, D.G., Evaluating the Effectiveness of a Sliding Window Technique in Machine Learning Models for Mortality Prediction in ICU Cardiac Arrest Patients, International Journal of Medical Informatics, 191, 105565, 2024. https://doi.org/10.1016/j.ijmedinf.2024.105565

  2. Schwartz, D. G., Spitzer, S., Khalemsky, M., Cano-Bejar, A. H., Ray, S., Chiou, J.-Y., Sakhnini, R., Lanin, R., Meir, M. M., & Tsai, M.-C., Apps don’t work for patients who don't use them: Towards frameworks for digital therapeutics adherence. Health Policy and Technology, 100848, 2024.. https://doi.org/10.1016/j.hlpt.2024.100848

  3. Wu, L.; Chen, X.; Khalemsky, A.; Li, D.; Zoubeidi, T.; Lauque, D.; Alsabri, M.; Boudi, Z.; Kumar, V.A.; Paxton, J.;...Schwartz, D. et al. The Association between Emergency Department Length of Stay and In-Hospital Mortality in Older Patients Using Machine Learning: An Observational Cohort Study. Journal of Clinical Medicine. 2023; 12(14):4750. https://doi.org/10.3390/jcm12144750 

  4. Lauque D, Khalemsky A, Boudi Z, Östlundh L, Xu C, Alsabri M, Onyeji C, Cellini J, Intas G, Soni KD, Junhasavasdikul D, Cabello JJT, Rathlev NK, Liu SW, Camargo CA Jr., Slagman A, Christ M, Singer AJ, Houze-Cerfon C-H, Aburawi EH, Tazarourte K, Kurland L, Levy PD, Paxton JH, Tsilimingras D, Kumar VA, Schwartz DG, Lang E, Bates DW, Savioli G, Grossman SA, Bellou A. Length-of-Stay in the Emergency Department and In-Hospital Mortality: A Systematic Review and Meta-Analysis. Journal of Clinical Medicine, 2022.  https://doi.org/10.3390/jcm12010032