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March 7, 20260 citationsOpen Access

Adoption Patterns of Electronic Prescription Systems in Nigerian Public Hospitals: A Cost-Effectiveness Analysis

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OAOlumide AyoolaCOChinedu Okezue ObinnaAOAdeniyi Olayinka

Key Points

  • This research aims to analyze the adoption patterns and cost-effectiveness of electronic prescription systems in Nigerian public hospitals.
  • Mixed-method approach including surveys of hospital administrators
  • Cost-benefit analysis model to assess financial impact
  • Data collection on EPS implementation and regional variations
  • 45% of surveyed public hospitals have adopted electronic prescription systems
  • EPS implementation can reduce administrative costs by up to 20%
  • Net present value of adopting EPS is estimated at $1.5 million over five years

Abstract

Electronic prescription systems (EPSs) have been adopted in various healthcare settings to improve efficiency and patient safety. However, their adoption patterns vary significantly across different regions and contexts. A mixed-method approach was employed, including surveys of hospital administrators and a cost-benefit analysis model to assess the financial impact of adopting an EPS system. Surveys revealed that while 45% of public hospitals in Nigeria have implemented EPSs, there is significant variation in adoption rates across different regions. Cost-benefit analyses indicated that implementing an EPS could reduce administrative costs by up to 20%, with a net present value (NPV) of 1. 5 million over five years. The results suggest that while cost-effectiveness varies, the introduction of EPSs in Nigerian public hospitals has the potential to significantly reduce operational costs and improve efficiency. Given the positive findings, it is recommended that policy makers consider incentivizing or mandating the adoption of EPS systems in public hospitals to maximise benefits for both healthcare providers and patients. Treatment effect was estimated with logit (pᵢ) =₀+^ Xᵢ, and uncertainty reported using confidence-interval based inference.

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Cite This Study

Ayoola et al. (2008) studied this question.

synapsesocial.com/papers/69abc2725af8044f7a4ec06bhttps://doi.org/10.5281/zenodo.18875400
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