Time Burden of Electronic Medical Records on Nurses and Physicians in Saudi Arabia: Occurrence, Predictors, and Challenges—A Mixed-Methods Study

<b>Background:</b> Electronic Medical Records improve decision-making but add administrative burdens for healthcare providers, such as physicians and nurses. While the rate of adoption is high in Saudi Arabia, the concrete temporary impact and reasoning behind their adoption are understudied. <b>Objectives:</b> This study is a Mixed-Methods Study designed to ascertain the number of hours of EMR use among physicians and nurses, the predictors of using EMRs for extended periods, perceived barriers and clinical impacts. <b>Methods:</b> A sequential mixed-methods study was performed in three hospitals in Riyadh, Dammam, and Makkah. Quantitative data from 503 clinicians were analyzed using inferential statistics, followed by thematic analysis of 10 semi-structured interviews. <b>Results:</b> A total of 503 professionals (162 physicians, 341 nurses) participated. The majority were females (67.2%), aged 30 to 40 years (44.9%), and non-Saudi (62%). Nurses reported a significantly higher daily EMR workload than physicians with 5.43 h (45.25%) versus 4.34 h (36.17%), with a mean difference of 1.09 h (t = -5.76, <i>p</i> = 0.001). Ordinal logistic regression identified female gender, non-Saudi nationality, nursing position, and lack of advanced education (Masters/Doctorate) as high-significance predictors of prolonged usage (all <i>p</i> < 0.005). Additionally, years of experience (<i>p</i> = 0.001) and EMR training (<i>p</i> = 0.003) were significant factors. Perceived barriers were moderate but significantly predicted by professional position (<i>p</i> = 0.004), work region (<i>p</i> = 0.017), and training duration (<i>p</i> = 0.001). Qualitatively, thematic analysis revealed four major barrier categories: system performance, infrastructure issues, lack of IT support, and increased workflow burdens. While EMRs improved professional practice and patient safety by solving handwriting issues and structuring data, they forced work routine adjustments that significantly reduced bedside patient interaction and assessment time.