JMIR Publications

ABSTRACT

Background:

Evidence-based clinical intake tools (EBCITs) are structured assessment tools used to gather information about patients and help healthcare providers make more informed decisions. The growing demand for personalized medicine, along with the big data revolution, have rendered EBCITs a promising solution. EBCITs have the potential to provide comprehensive and individualized assessments of symptoms, enabling accurate and timely diagnosis, while contributing to the grounding of medical care.

Objective:

This work examines whether EBCITs cover data concerning disorders and symptoms to a similar extent as physicians, thus can reliably address medical conditions in clinical settings. We also explore the potential of EBCITs to discover and ground real prevalence of symptoms in different disorders thereby expanding medical knowledge and further support medical diagnoses made by physicians.

Methods:

Between August 1, 2022, and January 15, 2023, patients who used the services of a virtual healthcare (VH) provider in the USA were first assessed by the Kahun EBCIT. Kahun platform gathered, documented, and analyzed the information from the sessions and its clinical findings. In this study, we estimated the prevalence of patients' symptoms in medical disorders, using two datasets. The first set analyzed symptoms prevalence, as determined by the Kahun's knowledge engine. The second set analyzed symptoms prevalence, relying solely on data from the VH patients gathered by Kahun. The difference in variance between these two prevalence datasets, helped us assess Kahun's ability to incorporate new data, while integrating existing knowledge. To analyze the comprehensiveness of the Kahun's knowledge engine, we compared how well it covers weighted data for the symptoms and disorders found in the 2019 National Ambulatory Medical Care Survey (NAMCS). To assess Kahun's diagnosis accuracy, physicians independently diagnosed 250 of Kahun-VH’s sessions. Their diagnoses were compared with Kahun's diagnoses.

Results:

As part of this work, 2,550 patients used Kahun to complete a full assessment, among them 1,714 females and 836 males. Kahun collected 314 different chief complaints and proposed 108,523 suggestions related to symptoms during the intake process. At the end of the intake process, 6,496 conditions were presented to the caregiver. Kahun covered 94% (526,157,569/562,150,572) of the weighted symptoms and 91% (1,582,637,476/173,4783,244) of the weighted disorders in NAMCS 2019. In 90% (224/250) of the sessions, at least one identical disorder suggested by both the physicians and Kahun, with total accuracy rate of 72% (367/507). Kahun’s engine yielded 519 prevalences while the Kahun-VH cohort yielded 599; 156 prevalences were unique to the latter and 443 prevalences were shared by both databases.

Conclusions:

ECBITs, such as Kahun, encompass extensive amounts of knowledge and could serve as a reliable database for inferring medical insights and diagnosis. Using this credible database, potential prevalence of symptoms in different disorders were discovered or grounded. This highlights the ability of ECBITs to refine the understanding of relationships between disorders and symptoms, which further supports physicians in medical diagnosis.