A Mixed-Methods Evaluation of Clinician Experiences and Adoption Patterns of an EHR-integrated Generative AI-based Clinical Decision Support in Kenya

Objective To quantify the adoption pattern of an LLM-based clinical decision support system across private primary health facilities in Kenya (operated by Penda Health), and explore factors influencing clinician uptake and overall experience.

A mixed-methods study combining quantitative analysis of CDSS metadata from all consultations that took place between 1st February and 1st October 2024, augmented by qualitative data from 42 staff members (26 clinical officers, 10 facility managers, four nurses, one quality assurance manager, and one business analyst). Data collection included journey mapping interviews (n=7), user-experience interviews (n=25), focus groups (n=2), and system utilization metrics. Quantitative data were summarized using descriptive statistics, and qualitative data analysed using thematic analysis (drawing on established theories of technology adoption and change management).

In total, there were 258,106 clinical episodes across all Penda Health facilities over the 8-month observation period, of which 56,050 (21.7%) were augmented by use of the ‘AI Consult’. ‘AI Consult’ use, aggregated across the 16 facilities, increased from 4% to 47% over 8 months. Feedback on the CDSS guidance was infrequent (only being provided in 31% of cases), but when it was, it was overwhelmingly positive (99.5%). The qualitative investigation identified five key themes associated with clinicians’ experiences with the AI Consult tool: (1) there are several value propositions to an ‘AI Consult’ style tool, (2) Clinicians’ confidence in the AI consult grew with time, (3) clinicians’ application of the ‘AI consult’ is influenced by case complexity, (4) responses from the AI consult are largely believed and valued by clinicians but several improvements are recommended, and (5) clinicians find the ‘AI consult’ easy to use but identified several pain points that warrant attention.

Successful GenAI/LLM-enhanced CDSS implementation in resource-constrained settings requires: (1) robust technological infrastructure, (2) localisation to reflect clinical guidelines, (3) structured change management with clinical champions, and (4) seamless workflow integration. Future product development exercises should specifically consider alternatives to active solicitation of CDSS input, as it is liable to overconfidence-related underutilization. What is already known? AI-powered clinical decision support systems face implementation challenges, especially in low-resource contexts. Studies from high-income countries suggest that workflow integration and perceived usefulness are key drivers of adoption. Limited research exists on the implementation of GenAI in resource-constrained healthcare environments. What are the new findings? It is possible to achieve non-trivial adoption of GenAI-based CDSS (4% to 47% over 8 months) when supported by structured change management. There are several value propositions for a GenAI-based CDSS, including improved quality of care and serving as a learning aid. Several factors influenced utilization/engagement, from perceived case complexity to ease of use and trust in the system. Clinicians’ behaviour and interactions with GenAI tools (as demonstrated by varying prompt use) mature over time based on perceived usefulness. What do the new findings imply? Sustainable AI implementation in low-resource settings requires addressing both technical infrastructure and human factors. Relying on active solicitation of inputs from an AI system is likely to suffer from overconfidence bias, which limits its overall utility, and instead, we should explore other methods of integrating CDSS into the relevant clinical workflows. Competing Interest Statement Robert Korom, Sarah Kiptinness and Najib Adan hold stock options in Penda Health. OpenAI (the proprietor of the LLM underpinning the CDSS evaluated in this study), provided in-kind support (in the form of cloud compute credits and guidance on how best to use the OpenAI API) to Penda Health for the development and optimization of the AI Consult. The decision to use OpenAIs product was made prior to the offer of in-kind support. OpenAI had no role in the design or undertaking of the described study. All other authors declare no potential, perceived or actual conflicts of interest. Funding Statement This research was supported by the Gates Foundation [INV-068056]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Maseno University Research and Ethics Committee reviewed and approved this study, approval number: MSU/DRPI/MUERC/00899/20 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes