Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/16651, first published .
Applying A/B Testing to Clinical Decision Support: Rapid Randomized Controlled Trials

Applying A/B Testing to Clinical Decision Support: Rapid Randomized Controlled Trials

Applying A/B Testing to Clinical Decision Support: Rapid Randomized Controlled Trials

Journals

  1. Last B, Buttenheim A, Futterer A, Livesey C, Jaeger J, Stewart R, Reilly M, Press M, Peifer M, Wolk C, Beidas R. A pilot study of participatory and rapid implementation approaches to increase depression screening in primary care. BMC Family Practice 2021;22(1) View
  2. Ziemann A, Sibley A, Scarbrough H, Tuvey S, Robens S. Academic health science networks' experiences with rapid implementation practice during the COVID-19 pandemic in England. Frontiers in Health Services 2022;2 View
  3. Ehrmann D, Gallant S, Nagaraj S, Goodfellow S, Eytan D, Goldenberg A, Mazwi M. Evaluating and reducing cognitive load should be a priority for machine learning in healthcare. Nature Medicine 2022;28(7):1331 View
  4. Dorr D, D'Autremont C, Richardson J, Bobo M, Terndrup C, Dunne M, Cheng A, Rope R. Patient-Facing Clinical Decision Support for High Blood Pressure Control: Patient Survey. JMIR Cardio 2023;7:e39490 View
  5. Gaysynsky A, Heley K, Chou W. An Overview of Innovative Approaches to Support Timely and Agile Health Communication Research and Practice. International Journal of Environmental Research and Public Health 2022;19(22):15073 View
  6. Wilson M, Palmer E, Asselbergs F, Harris S. Integrated rapid-cycle comparative effectiveness trials using flexible point of care randomisation in electronic health record systems. Journal of Biomedical Informatics 2023;137:104273 View
  7. Horwitz L, Krelle H. Using Rapid Randomized Trials to Improve Health Care Systems. Annual Review of Public Health 2023;44(1):445 View
  8. Ramakrishnaiah Y, Macesic N, Webb G, Peleg A, Tyagi S. EHR-QC: A streamlined pipeline for automated electronic health records standardisation and preprocessing to predict clinical outcomes. Journal of Biomedical Informatics 2023;147:104509 View
  9. Garabedian P, Rui A, Volk L, Neville B, Lipsitz S, Healey M, Bates D. A Multiyear Survey Evaluating Clinician Electronic Health Record Satisfaction. Applied Clinical Informatics 2023;14(04):632 View
  10. Larsen N, Stallrich J, Sengupta S, Deng A, Kohavi R, Stevens N. Statistical Challenges in Online Controlled Experiments: A Review of A/B Testing Methodology. The American Statistician 2023:1 View
  11. Rabbani N, Ho M, Dash D, Calway T, Morse K, Chadwick W. Pseudorandomized Testing of a Discharge Medication Alert to Reduce Free-Text Prescribing. Applied Clinical Informatics 2023;14(03):470 View
  12. Kearney L, Jansen E, Kathuria H, Steiling K, Jones K, Walkey A, Cordella N. Efficacy of Digital Outreach Strategies for Collecting Smoking Data: Pragmatic Randomized Trial. JMIR Formative Research 2024;8:e50465 View
  13. Wolcott M, McLaughlin J. Exploring user experience (UX) research methods in health professions education. Currents in Pharmacy Teaching and Learning 2024;16(2):144 View
  14. Ge J, Fontil V, Ackerman S, Pletcher M, Lai J. Clinical decision support and electronic interventions to improve care quality in chronic liver diseases and cirrhosis. Hepatology 2023 View
  15. Kim M, Patrick K, Nebeker C, Godino J, Stein S, Klasnja P, Perski O, Viglione C, Coleman A, Hekler E. The Digital Therapeutics Real World Evidence Framework: An approach for guiding evidence-based DTx design, development, testing, and monitoring (Preprint). Journal of Medical Internet Research 2023 View
  16. Spithoff S, McPhail B, Vesely L, Rowe R, Mogic L, Grundy Q. How the commercial virtual care industry gathers, uses and values patient data: a Canadian qualitative study. BMJ Open 2024;14(2):e074019 View

Books/Policy Documents

  1. Turchioe M, Creber R. Personal Health Informatics. View