Enhancing Patient Care Through AI Integration in Record Keeping: Analyzing Barriers and Strategies for Effective Adoption in Midlands Province's Healthcare Facilities in Zimbabwe.

Abstract: This study seeks to investigate the incorporation of artificial intelligence (AI) in patient record management within private healthcare institutions in the Midlands Province of Zimbabwe. It examines the obstacles to adoption and techniques for successful implementation to improve patient care. A qualitative study methodology was utilized, incorporating semi-structured interviews with 37 healthcare practitioners from diverse private institutions. The interviews, ranging from 30 to 60 minutes, sought to elicit detailed insights into the problems and attitudes associated with AI integration. Thematic analysis was employed to discern reoccurring themes, with ethical approval and informed permission safeguarding participant confidentiality.
The investigation identified significant obstacles to AI adoption, such as technological proficiency, worries around job security, and a preference for conventional record-keeping practices. Prominent topics encompassed insufficient training, significant implementation expenses, and concerns around data protection. In contrast, participants recognized the prospective advantages of AI, including enhanced accuracy, efficiency in recordkeeping, and accelerated access to patient information. These observations underscored the influence of existing practices on the quality of patient treatment. The results reveal substantial obstacles and prospects in the incorporation of AI into healthcare record management. Resolving challenges such as insufficient training, elevated expenses, and privacy issues is essential for fostering an atmosphere favorable to AI adoption. Through the implementation of targeted tactics, such as advanced training programs and partnerships with technology providers, healthcare facilities may fully leverage AI's capabilities, hence enhancing patient outcomes and operating efficiencies.

Artificial Intelligence, Patient record keeping, Healthcare Integration, Barriers to adoption

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