Hospital E-prescribing Implementation in Reducing Medication Error

I. Putu Artawan & Ida Ayu Oka Martini

International Journal of Business and Economics2020https://doi.org/10.31295/ijbem.v3n1.128article
ABDC B
Weight
0.34

Abstract

The purpose of this study was to describe and analyze hospital e-prescribing implementation in reducing medication errors. The hospital information system was an information system that was used for hospitals wherein this information system allows data flow from a hospital can be done electronically. The service to patients can be done more quickly, accurately, and transparently which in the end could provide satisfaction to patients. Medication errors often occurred in hospitals. One of the causes was prescription errors or prescribing errors. The wrong medical decision had an impact on the safety and quality of the patient's health. Therefore, the efforts were needed to prevent and reduce errors by e-prescribing. This study used a qualitative approach in which the research conducted was descriptive. That was how the implementation of e-prescribing in RSUP Sanglah Denpasar in reducing medication error. The research results obtained from interviews, observations, and documentation were the implementation of e-prescribing in Sanglah Hospital going well by involving interviews with management, IT, pharmacists, doctors, and nurses. However, the obstacle faced was the internet network was sometimes not strong and the number of devices a computer was lacking. It can interfere with the process of drug service.

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https://doi.org/https://doi.org/10.31295/ijbem.v3n1.128

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@article{i.2020,
  title        = {{Hospital E-prescribing Implementation in Reducing Medication Error}},
  author       = {I. Putu Artawan & Ida Ayu Oka Martini},
  journal      = {International Journal of Business and Economics},
  year         = {2020},
  doi          = {https://doi.org/https://doi.org/10.31295/ijbem.v3n1.128},
}

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Evidence weight

0.34

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.00 × 0.4 = 0.00
M · momentum0.80 × 0.15 = 0.12
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.