Assessment of patient perceptions of technology and the use of machine-based learning in a clinical encounter
Background: Electronic health records (EHR) were implemented to improve patient care, reduce healthcare disparities, engage patients and families, improve care coordination, and maintain privacy and security. Unfortunately, the mandated use of EHR has also resulted in significantly increased clerical and administrative burden, with physicians spending an estimated three-fourths of their daily time interacting with the EHR, which negatively affects within-clinic processes and contributes to burnout. In-room scribes are associated with improvement in all aspects of physician satisfaction and increased productivity, though less is known about the use of other technologies such as Google Glass (GG), Natural Language Processing (NLP) and Machine-Based Learning (MBL) systems. Given the need to decrease administrative burden on clinicians, particularly in the utilization of the EHR, there is a need to explore the intersection between varying degrees of technology in the clinical encounter and their ability to meet the aforementioned goals of the EHR. Aims: The primary aim is to determine predictors of overall perception of care dependent on varying mechanisms used for documentation and medical decision-making in a routine clinical encounter. Secondary aims include comparing the perception of individual vignettes based on demographics of the participants and investigating any differences in perception questions by demographics of the participants. Methods: Video vignettes were shown to 498 OhioHealth Physician Group patients and to ResearchMatch volunteers during a 15-month period following IRB approval. Data included a baseline survey to gather demographic and background characteristics and then a perceptual survey where patients rated the physician in the video on 5 facets using a 1 to 5 Likert scale. The analysis included summarizing data of all continuous and categorical variables as well as overall perceptions analyzed using multivariate linear regression with perception score as the outcome variable. Results: Univariate modeling identified sex, education, and type of technology as three factors that were statistically significantly related to the overall perception score. Males had higher scores than females (p = 0.03) and those with lower education had higher scores (p < 0.001). In addition, the physician documenting outside of the room encounter had statistically significantly higher overall perception scores (mean = 22.2, p < 0.001) and the physician documenting in the room encounter had statistically significantly lower overall perception scores (mean = 15.3, p < 0.001) when compared to the other vignettes. Multivariable modeling identified all three of the univariably significant factors as independent factors related to overall perception score. Specifically, high school education had higher scores than associate/bachelor education (LSM = 21.6 vs. 19.9, p = 0.0002) and higher scores than master/higher education (LSM = 21.6 vs. 19.5, p < 0.0001). No differences between age groups were found on the individual perception scores. Males had higher scores than females on ‘The doctor clearly explained the diagnosis and treatment to the patient’ and ‘The doctor was sincere and trustworthy’. High school education had higher scores than associate/bachelor and master/higher on all five individual perception scores. Conclusion: The study found sex, education, and type of technology were significant indicators for overall perception of varying technologies used for documentation and medical decision-making in a routine clinical encounter. Importantly, the vignette depicting the least interaction with the EHR received the most positive overall perception score, while the vignette depicting the physician utilizing the EHR during the interaction received the least positive overall perception score. This suggests patients most value having the full attention of the physician and feel less strongly about differentiating the logistics of data transcription and medical decision-making, provided they feel engaged during the interaction. Therefore, the authors suggest maximizing face-to-face time in the integration of technology into the clinical encounter, allowing for increased perceptions of personal attention in the patient-physician interaction.
Bett, Ean S.; Frommeyer, Timothy C.; Reddy, Tejaswini; and Johnson, James “Ty”, "Assessment of patient perceptions of technology and the use of machine-based learning in a clinical encounter" (2023). Doctor of Osteopathic Medicine Program Open Access Publications. 18.