Evidence-based healthcare practice adoption: The impact of electronic health records
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| Publicado en: | Africa's Public Service Delivery and Performance Review vol. 12, no. 1 (2024) |
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| Autor principal: | |
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| Publicado: |
AOSIS (Pty) Ltd
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text - PDF |
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| LEADER | 00000nab a2200000uu 4500 | ||
|---|---|---|---|
| 001 | 3106671966 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2310-2195 | ||
| 022 | |a 2310-2152 | ||
| 024 | 7 | |a 10.4102/apsdpr.v12i1.728 |2 doi | |
| 035 | |a 3106671966 | ||
| 045 | 2 | |b d20240101 |b d20241231 | |
| 100 | 1 | |a Motsi, Lovemore | |
| 245 | 1 | |a Evidence-based healthcare practice adoption: The impact of electronic health records | |
| 260 | |b AOSIS (Pty) Ltd |c 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Background:For healthcare institutions, proper documentation of the upkeep of patient medical records is imperative. In addition, without a record of the patient’s medical history, the doctors are unable to demonstrate that the treatment was delivered correctly.Aim:The primary objective of this research was to determine the influence of electronic health records (EHR) towards the adoption of evidence-based healthcare practice (EBHP) in South African public healthcare.Methods:The study used a quantitative methodology, and a self-administered questionnaire was used to collect data from 300 healthcare professionals. In all, 450 questionnaires were distributed, and of those, 150 were unfit for data analysis because of insufficient data, leaving a total of 300 responses. Data were analysed using exploratory factor analysis (EFA) to identify latent constructs. Confirmatory factor analysis (CFA) was used to assess the validity and reliability of these constructs. The appropriateness of the measurement model was then assessed using fit indices for a structural equation model.Results:The findings show EHR had a direct influence on information quality, medical error reduction, diagnosis and treatment of diseases as well as better coordination of patient’s care. In addition, the results show that EHR-based clinical decision support is crucial for practising evidence-based healthcare and plays a significant role in the quality of healthcare, particularly in the management of diseases and preventative care. As all requirements for validity and reliability (root mean square error of approximation [RMSEA] = 0.085, comparative fit index [CFI] = 0.956 and χ2/ df = 2.513) have been satisfied, the model is considered valid and reliable.Conclusion:When healthcare professions such as doctors and nurses accurately record patients’ medical histories, they are able to make successful medical decisions and prescribe medications based on the patients’ past and present medical histories. Electronic health records systems facilitate the easier and more efficient exchange of patient data between medical schools, research labs, specialists, pharmacies and other healthcare institutions. Furthermore, they provide medical professionals with resources and up-to-date information to help them deliver EBHP that can benefit patients by reducing or even eliminating medical errors.Contribution:The study contributes theoretically to the field of information systems by outlining a model that includes the variables that affect the adoption of EBHPs in public hospitals. | |
| 653 | |a Patients | ||
| 653 | |a Electronic health records | ||
| 653 | |a Discriminant analysis | ||
| 653 | |a Evidence-based medicine | ||
| 653 | |a Hospitals | ||
| 653 | |a Quantitative analysis | ||
| 653 | |a Practitioner patient relationship | ||
| 653 | |a Reliability | ||
| 653 | |a Errors | ||
| 653 | |a Confirmatory factor analysis | ||
| 653 | |a Quality of care | ||
| 653 | |a Questionnaires | ||
| 653 | |a Professions | ||
| 653 | |a Medical diagnosis | ||
| 653 | |a Exploratory factor analysis | ||
| 653 | |a Physicians | ||
| 653 | |a Medical records | ||
| 653 | |a Archives & records | ||
| 653 | |a Medical schools | ||
| 653 | |a Adoption of innovations | ||
| 653 | |a Information systems | ||
| 653 | |a Pharmacies | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Measurement | ||
| 653 | |a Specialists | ||
| 653 | |a Medical personnel | ||
| 653 | |a Medical history | ||
| 653 | |a Preventive medicine | ||
| 653 | |a Data | ||
| 653 | |a Coordination | ||
| 653 | |a Critical incidents | ||
| 653 | |a Variables | ||
| 653 | |a Prevention | ||
| 653 | |a Documentation | ||
| 653 | |a Indexes | ||
| 653 | |a Information | ||
| 653 | |a Data analysis | ||
| 653 | |a Health services | ||
| 653 | |a Structural equation modeling | ||
| 653 | |a Nurses | ||
| 653 | |a Factor analysis | ||
| 653 | |a Medical decision making | ||
| 653 | |a Professionals | ||
| 653 | |a Clinical decision making | ||
| 653 | |a Disease | ||
| 653 | |a Information technology | ||
| 653 | |a Public schools | ||
| 653 | |a Information sources | ||
| 653 | |a Medical treatment | ||
| 653 | |a Computerized medical records | ||
| 653 | |a Health records | ||
| 653 | |a Decision support systems | ||
| 700 | 1 | |a Chimbo, Bester | |
| 773 | 0 | |t Africa's Public Service Delivery and Performance Review |g vol. 12, no. 1 (2024) | |
| 786 | 0 | |d ProQuest |t Political Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3106671966/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3106671966/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |