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Impact of Clinical Decision Support Systems on Medication Prescribing Errors in Nigerian Hospitals

Abstract

Medication prescribing errors remain an important patient safety concern in Nigerian hospitals and may result from incorrect drug selection, inappropriate dosage, incorrect frequency, drug interactions, contraindications, duplication of therapy, incomplete patient information, and illegible or poorly documented prescriptions. Such errors may increase the risk of adverse drug events, treatment failure, prolonged hospitalization, increased healthcare costs, and preventable patient harm. Clinical Decision Support Systems (CDSS) provide healthcare professionals with computerized tools that integrate patient information with clinical knowledge to support prescribing and treatment decisions. Features such as drug–drug interaction alerts, allergy warnings, dosage recommendations, contraindication alerts, duplicate-therapy notifications, and clinical guidelines may assist prescribers in identifying potential medication-related problems before prescriptions are finalized. Against this background, this study investigates the impact of Clinical Decision Support Systems on medication prescribing errors in Nigerian hospitals. The study will be anchored on the Donabedian Model of Healthcare Quality, Systems Theory of Patient Safety, and the Technology Acceptance Model. The Donabedian Model explains healthcare quality through structure, process, and outcome, with CDSS representing a technological component of healthcare infrastructure and medication prescribing errors representing a measurable outcome of the prescribing process. Systems Theory of Patient Safety views medication errors as outcomes of interactions among healthcare professionals, patients, information systems, medications, organizational procedures, and environmental factors. The Technology Acceptance Model explains how perceived usefulness and perceived ease of use influence healthcare professionals' acceptance and utilization of CDSS technology. Collectively, these theoretical perspectives provide a suitable framework for explaining how Clinical Decision Support Systems may influence medication prescribing errors in Nigerian hospitals. The study will adopt a quantitative quasi-experimental or analytical comparative research design. The study population will comprise medical doctors and other authorized prescribers working in selected public and private hospitals in Nigeria, while medication prescriptions and relevant patient records will also be reviewed. A multistage sampling technique will be used to select states, local government areas, hospitals, clinical departments, prescribers, and eligible prescriptions. Clinical Decision Support Systems will be assessed using indicators such as availability of computerized prescribing systems, drug–drug interaction alerts, allergy alerts, dosage warnings, contraindication alerts, duplicate-therapy alerts, clinical guideline support, medication-related reminders, alert response functionality, system accessibility, frequency of use, and availability of technical support and user training. Medication prescribing errors will be assessed using indicators such as incorrect medication selection, inappropriate dosage, incorrect frequency, incorrect route, drug–drug interactions, contraindications, therapeutic duplication, allergy-related prescribing, inappropriate treatment duration, incomplete prescriptions, and other potentially preventable prescribing errors. Data will be collected using structured questionnaires, electronic prescribing records, patient medical records, pharmacy records, medication charts, CDSS alert logs, and hospital medication-safety reports. Prescribing errors will be assessed before and after implementation of CDSS or compared between hospitals with and without functional CDSS, depending on the selected research design. Descriptive statistics will be used to summarize prescribers' characteristics, CDSS utilization, prescription patterns, and medication-error rates. Inferential statistical techniques, including chi-square tests, t-tests, correlation analysis, and logistic or multiple regression analysis where appropriate, will be used to determine the impact of CDSS on medication prescribing errors. Diagnostic tests will also be conducted to assess the reliability, validity, and robustness of the findings. The study is expected to find that effective Clinical Decision Support Systems have a significant negative impact on medication prescribing errors in Nigerian hospitals, indicating that appropriate use of CDSS reduces the occurrence of prescribing errors. Hospitals with functional CDSS are expected to demonstrate fewer errors involving drug selection, dosage, drug interactions, contraindications, allergies, and therapeutic duplication compared with hospitals without such systems. Automated alerts and evidence-based prescribing recommendations may help prescribers identify potential medication-related problems before medicines are administered to patients. CDSS may also improve access to patient-specific information and standardize prescribing decisions. However, excessive or poorly designed alerts, alert fatigue, inadequate training, system downtime, poor integration with hospital information systems, unreliable electricity or internet connectivity, incomplete patient data, and prescriber resistance to technology may limit its effectiveness. The study therefore expects well-designed, user-friendly, adequately supported, and properly integrated Clinical Decision Support Systems to contribute significantly to reducing medication prescribing errors in Nigerian hospitals. The study is expected to contribute to the literature on Clinical Decision Support Systems, medication prescribing errors, digital health, medication safety, electronic prescribing, patient safety, healthcare information systems, clinical decision-making, and healthcare quality in Nigeria. The findings will provide useful information to the Federal Ministry of Health and Social Welfare, National Health Information Technology authorities, hospitals, healthcare administrators, physicians, pharmacists, nurses, health informatics professionals, patient safety committees, technology providers, development partners, and policymakers regarding strategies for improving medication safety through digital health technologies. The study will also provide evidence-based recommendations for expanding CDSS implementation, improving system interoperability, strengthening user training, optimizing clinical alerts, reducing alert fatigue, improving data quality, strengthening technical support, and integrating CDSS into hospital medication-safety and quality-improvement programmes across Nigeria.

Keywords: Clinical Decision Support Systems, medication prescribing errors, medication safety, digital health, electronic prescribing, patient safety, clinical decision-making, healthcare information systems, hospitals, Nigeria, public health.