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Research Article Open access CC BY 4.0

Digital Framework Strategy for Patient Medication Adherence and Improvement in Medical Healthcare Centre Offa, Kwara State

O. Alabi Abdullateef, AbdulAkeem O. Otunola, A. T. Abdullateef, M. K. Lawal

Asian Journal of Research in Computer Science · pp. 10–22 · Published 11 Jul 2022

10.9734/ajrcos/2022/v14i230331

Abstract

The prospect behind digital transformation strategies makes the healthcare systems more  safe, affordable, and accessible with remarkable opportunities. The reasons why patients decided to have negative medication adherence became a critical challenge in the healthcare system. This study presents a Digital Framework Strategy DSF for patient medication adherence    to improve patient health during the treatment regime. A case study of the Federal Polytechnic Medical centre and clinical activities of Offa General Hospital examines the existing treatment of chronic diseases. The cloud-based server revolves around Convolution Neural Network (CNN) feature to perform a real-time collection of data and analytics of patient information. When thoroughly combined, the CNN of the neural network has a model of the application, which will form part of the desired output. This output presents a level of patient medication adherence within the parameters—the data around the enclosed sources. The approach  data was acquired with the patient wearing a sensor and smartphone devices. The model throughputs presented detailed analytics of individual patient adherence behaviors. The result of CNN performance revealed 96.99% accuracy of medication adherence level on a tested dataset collation, and the essence of digital framework analytics helped the healthcare workers (HCW) and healthcare providers to make prompt decisions on patients’ medical  conditions.

Digital transformation medication adherence healthcare provider chronic diseases

References (33)

  1. 1 Design Science in Information Systems Research1 [DOI]
  2. 2 Key Factors Associated with Adherence to Physical Exercise in Patients with Chronic Diseases and Older Adults: An Umbrella Review [DOI]
  3. 3 A human pilot trial of ingestible electronic capsules capable of sensing different gases in the gut [DOI]
  4. 4 Implications of big data analytics in developing healthcare frameworks – A review [DOI]
  5. 5 Digital adherence technologies for the management of tuberculosis therapy: mapping the landscape and research priorities [DOI]
  6. 6 Big Data in Medicine Is Driving Big Changes [DOI]
  7. 7 Thinking Outside the Pillbox A System-wide Approach to Improving Patient Medication Adherence for Chronic Disease
  8. 8 A Review of Medication Adherence Monitoring Technologies [DOI]
  9. 9 Smart drugs:Improving healthcare using Smart Pill Box for Medicine Reminder and Monitoring System [DOI]
  10. 10 A novel context-aware multimodal framework for persian sentiment analysis [DOI]
  11. 11 Using NFC-Enabled Mobile Phones for Public Health in Developing Countries [DOI]
  12. 12 Opening the black box of the group‐based trajectory modeling process to analyze medication adherence patterns: An example using real‐world statin adherence data [DOI]
  13. 13 Responsible Design, Implementation and Use of Information and Communication Technology [DOI]
  14. 14 Ingestible Biosensors for Real-Time Medical Adherence Monitoring: MyTMed [DOI]
  15. 15 TEOS: A framework for constructing operational definitions of medication adherence based on Timelines–Events–Objectives–Sources [DOI]
  16. 16 Digitization of Measurement-Based Care Pathways in Mental Health Through REDCap and Electronic Health Record Integration: Development and Usability Study [DOI]
  17. 17 Feasibility of a short message service (SMS) intervention to deliver tuberculosis testing results in peri-urban and rural Uganda [DOI]
  18. 18 Prevalence of hypertension in Kegbara-Dere, a rural community in the Niger Delta region, Nigeria.
  19. 19 A Conceptual Model of Treatment Adherence in a Behavior Analytic Framework [DOI]
  20. 20 Measuring effects of medication adherence on time-varying health outcomes\nusing Bayesian dynamic linear models [DOI]
  21. 21 Using Process Indicators to Monitor Documentation of Patient-Centred Variables in an Integrated Oncology and Palliative Care Pathway—Results from a Cluster Randomized Trial [DOI]
  22. 22 Treatment Adherence in Chronic Conditions during Ageing: Uses, Functionalities, and Cultural Adaptation of the Assistant on Care and Health Offline (ACHO) in Rural Areas [DOI]
  23. 23 Applications of Machine Learning in Disease Pre-screening [DOI]
  24. 24 Applications of Machine Learning in Disease Pre-screening [DOI]
  25. 25 An Activity Recognition System for Taking Medicine Using In-The-Wild Data to Promote Medication Adherence [DOI]
  26. 26 Perceived Usefulness of Big-Data for Store Layout: Evidence for Organized Retailers of Karachi [DOI]
  27. 27 Public knowledge of heart attack symptoms and prevalence of self-reported cardiovascular risk factors in Ilorin, Nigeria [DOI]
  28. 28 A Digital Collaborative Framework for Improved Tuberculosis Treatment Adherence of Patients in Rural Settings [DOI]
  29. 29 Acceptability of the Medication Adherence for Patients Support intervention to improve adherence to patients prescribed medications for hypertension or comorbidities, as an adjunct to primary care: A qualitative study [DOI]
  30. 30 Measuring effects of medication adherence on time-varying health outcomes using Bayesian dynamic linear models [DOI]
  31. 31 Smart System for Monitoring Daily Medication Using Swallowable Digital Pill [DOI]
  32. 32 Proceedings of the Annual Hawaii International Conference on System Sciences
  33. 33 MedSensor: Medication Adherence Monitoring Using Neural Networks on Smartwatch Accelerometer Sensor Data [DOI]

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