This paper is in the following e-collection/theme issue:

Digital Health, Telehealth and e-Innovation in Clinical Settings (401) Adoption and Change Management of eHealth Systems (712) mHealth in a Clinical Setting (615) Usability Evaluation Case Studies (191) Medication Reconciliation (11)

Published on in Vol 5, No 2 (2016): Apr-Jun

An Observational Study to Evaluate the Usability and Intent to Adopt an Artificial Intelligence–Powered Medication Reconciliation Tool

An Observational Study to Evaluate the Usability and Intent to Adopt an Artificial Intelligence–Powered Medication Reconciliation Tool

An Observational Study to Evaluate the Usability and Intent to Adopt an Artificial Intelligence–Powered Medication Reconciliation Tool

Authors of this article:

Ju Long1 Author Orcid Image ;   Michael Juntao Yuan2 Author Orcid Image ;   Robina Poonawala3 Author Orcid Image
Article Authors Cited by (17) Tweetations (4) Metrics

Journals

  1. Choudhury A, Asan O. Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review. JMIR Medical Informatics 2020;8(7):e18599 View
  2. Rozenblum R, Rodriguez-Monguio R, Volk L, Forsythe K, Myers S, McGurrin M, Williams D, Bates D, Schiff G, Seoane-Vazquez E. Using a Machine Learning System to Identify and Prevent Medication Prescribing Errors: A Clinical and Cost Analysis Evaluation. The Joint Commission Journal on Quality and Patient Safety 2020;46(1):3 View
  3. Marien S, Legrand D, Ramdoyal R, Nsenga J, Ospina G, Ramon V, Boland B, Spinewine A. A web application to involve patients in the medication reconciliation process: a user-centered usability and usefulness study. Journal of the American Medical Informatics Association 2018;25(11):1488 View
  4. Liu Y, Chu L, Su H, Tsai K, Kao P, Chen J, Hsieh H, Lin H, Hsu C, Huang C. Impact of Computer‐Based and Pharmacist‐Assisted Medication Review Initiated in the Emergency Department. Journal of the American Geriatrics Society 2019;67(11):2298 View
  5. Frament J, Hall R, Manley H. Medication Reconciliation: The Foundation of Medication Safety for Patients Requiring Dialysis. American Journal of Kidney Diseases 2020;76(6):868 View
  6. DeAntonio J, Leichtle S, Hobgood S, Boomer L, Aboutanos M, Mangino M, Wijesinghe D, Jayaraman S. Medication Reconciliation and Patient Safety in Trauma: Applicability of Existing Strategies. Journal of Surgical Research 2020;246:482 View
  7. Gordo C, Núñez‐Córdoba J, Mateo R. Root causes of adverse drug events in hospitals and artificial intelligence capabilities for prevention. Journal of Advanced Nursing 2021;77(7):3168 View
  8. Zhang Z, Citardi D, Wang D, Genc Y, Shan J, Fan X. Patients’ perceptions of using artificial intelligence (AI)-based technology to comprehend radiology imaging data. Health Informatics Journal 2021;27(2) View
  9. Zhang Z, Genc Y, Wang D, Ahsen M, Fan X. Effect of AI Explanations on Human Perceptions of Patient-Facing AI-Powered Healthcare Systems. Journal of Medical Systems 2021;45(6) View
  10. Babel A, Taneja R, Mondello Malvestiti F, Monaco A, Donde S. Artificial Intelligence Solutions to Increase Medication Adherence in Patients With Non-communicable Diseases. Frontiers in Digital Health 2021;3 View
  11. Hermann M, Holt M, Kjome R, Teigen A. Medication reconciliation—is it possible to speed up without compromising quality? A before–after study in the emergency department. European Journal of Hospital Pharmacy 2023;30(6):310 View
  12. Tulk Jesso S, Kelliher A, Sanghavi H, Martin T, Henrickson Parker S. Inclusion of Clinicians in the Development and Evaluation of Clinical Artificial Intelligence Tools: A Systematic Literature Review. Frontiers in Psychology 2022;13 View
  13. Wong A, Wentz E, Palisano N, Dirani M, Elsamadisi P, Qashou F, Celi L, Badawi O, Nazer L. Role of artificial intelligence in pharmacy practice: A narrative review. JACCP: JOURNAL OF THE AMERICAN COLLEGE OF CLINICAL PHARMACY 2023;6(11):1237 View
  14. Mohammad‐Rahimi H, Sohrabniya F, Ourang S, Dianat O, Aminoshariae A, Nagendrababu V, Dummer P, Duncan H, Nosrat A. Artificial intelligence in endodontics: Data preparation, clinical applications, ethical considerations, limitations, and future directions. International Endodontic Journal 2024;57(11):1566 View
  15. Rakers M, van Buchem M, Kucenko S, de Hond A, Kant I, van Smeden M, Moons K, Leeuwenberg A, Chavannes N, Villalobos-Quesada M, van Os H. Availability of Evidence for Predictive Machine Learning Algorithms in Primary Care. JAMA Network Open 2024;7(9):e2432990 View
  16. Keuper J, van Tuyl L, de Geit E, Rijpkema C, Vis E, Batenburg R, Verheij R. The impact of eHealth use on general practice workload in the pre-COVID-19 era: a systematic review. BMC Health Services Research 2024;24(1) View

Books/Policy Documents

  1. Belagodu Sridhar S, Karattuthodi M, Parakkal S. Application of Artificial Intelligence in Neurological Disorders. View