The Potential of Evidence-Based Clinical Intake Tools to Discover or Ground Prevalence of Symptoms Using Real-Life Digital Health Encounters: Retrospective Cohort Study.

BACKGROUND: Evidence-based clinical intake tools (EBCITs) are structured assessment tools used to gather information about patients and help health care providers make informed decisions. The growing demand for personalized medicine, along with the big data revolution, has rendered EBCITs a promising solution. EBCITs have the potential to provide comprehensive and individualized assessments of symptoms, enabling accurate diagnosis, while contributing to the grounding of medical care. OBJECTIVE: This work aims to examine whether EBCITs cover data concerning disorders and symptoms to a similar extent as physicians, and thus can reliably address medical conditions in clinical settings. We also explore the potential of EBCITs to discover and ground the real prevalence of symptoms in different disorders thereby expanding medical knowledge and further supporting medical diagnoses made by physicians. METHODS: Between August 1, 2022, and January 15, 2023, patients who used the services of a digital health care (DH) provider in the United States were first assessed by the Kahun EBCIT. Kahun platform gathered and analyzed the information from the sessions. This study estimated the prevalence of patients' symptoms in medical disorders using 2 data sets. The first data set analyzed symptom prevalence, as determined by Kahun's knowledge engine. The second data set analyzed symptom prevalence, relying solely on data from the DH patients gathered by Kahun. The variance difference between these 2 prevalence data sets helped us assess Kahun's ability to incorporate new data while integrating existing knowledge. To analyze the comprehensiveness of 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 (NMCAS). To assess Kahun's diagnosis accuracy, physicians independently diagnosed 250 of Kahun-DH's sessions. Their diagnoses were compared with Kahun's diagnoses. RESULTS: In this study, 2550 patients used Kahun to complete a full assessment. Kahun proposed 108,523 suggestions related to symptoms during the intake process. At the end of the intake process, 6496 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 the 2019 NMCAS. In 90% (224/250) of the sessions, both physicians and Kahun suggested at least one identical disorder, with a 72% (367/507) total accuracy rate. Kahun's engine yielded 519 prevalences while the Kahun-DH cohort yielded 599; 156 prevalences were unique to the latter and 443 prevalences were shared by both data sets. CONCLUSIONS: ECBITs, such as Kahun, encompass extensive amounts of knowledge and could serve as a reliable database for inferring medical insights and diagnoses. Using this credible database, the potential prevalence of symptoms in different disorders was 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.

The prevalence of abnormal right ventricle speckle strain in the setting of acute myocarditis and preserved left ventricle function.

BACKGROUND: Acute myocarditis has a wide spectrum of clinical presentation, from subclinical disease to acute heart failure, and sudden cardiac death. Two-dimensional speckle tracking echocardiography (2D-STE) has been proven effective in early diagnosis of subclinical cardiac injury, however, there is a limited data regarding the right ventricle (RV) involvement among patients with acute myocarditis. PURPOSE: We evaluated the prevalence of early subclinical RV injury assessed by 2D-STE, among patients with acute myocarditis and preserved left ventricle (LV) function. METHODS: We performed a retrospective single-center study at Tel-Aviv Sourasky Medical Center, including all adult patients hospitalized with acute myocarditis, who presented with preserved LV function. 2D-STE analysis of the RV was performed offline, assessing both the RV four-chamber longitudinal strain peak systolic (RV4CLS PK) and the free wall longitudinal strain peak systolic (RVFWLS PK). The myocarditis group was compared to a healthy control group. RESULTS: From 2011 to 2020, a total of 90 patients included in the study and were compared to 70 healthy subjects. RV 2D-STE emerged as significantly lower for both the RV4CLS PK (-21.8 ± 4.2 vs. -24.9 ± 4.8, P < 0.001) and RVFWLS PK (-24.7 ± 4.9 vs. -28.4 ± 5, P < 0.001), and remained significant in a multivariate analysis. CONCLUSION: We presented for the first time the presence of subclinical RV dysfunction, assessed by 2D-STE, in patients diagnosed with acute myocarditis, in the presence of preserved LV function. Further studies are needed to evaluate its' role in the development of LV dysfunction, heart failure and mortality.

Assessing data gathering of chatbot based symptom checkers - a clinical vignettes study.

BACKGROUND: The burden on healthcare systems is mounting continuously owing to population growth and aging, overuse of medical services, and the recent COVID-19 pandemic. This overload is also causing reduced healthcare quality and outcomes. One solution gaining momentum is the integration of intelligent self-assessment tools, known as symptom-checkers, into healthcare-providers' systems. To the best of our knowledge, no study so far has investigated the data-gathering capabilities of these tools, which represent a crucial resource for simulating doctors' skills in medical-interviews. OBJECTIVES: The goal of this study was to evaluate the data-gathering function of currently available chatbot symptom-checkers. METHODS: We evaluated 8 symptom-checkers using 28 clinical vignettes from the repository of MSD-Manual case studies. The mean number of predefined pertinent findings for each case was 31.8 ± 6.8. The vignettes were entered into the platforms by 3 medical students who simulated the role of the patient. For each conversation, we obtained the number of pertinent findings retrieved and the number of questions asked. We then calculated the recall-rates (pertinent-findings retrieved out of all predefined pertinent-findings), and efficiency-rates (pertinent-findings retrieved out of the number of questions asked) of data-gathering, and compared them between the platforms. RESULTS: The overall recall rate for all symptom-checkers was 0.32(2,280/7,112;95 %CI 0.31-0.33) for all pertinent findings, 0.37(1,110/2,992;95 %CI 0.35-0.39) for present findings, and 0.28(1140/4120;95 %CI 0.26-0.29) for absent findings. Among the symptom-checkers, Kahun platform had the highest recall rate with 0.51(450/889;95 %CI 0.47-0.54). Out of 4,877 questions asked overall, 2,280 findings were gathered, yielding an efficiency rate of 0.46(95 %CI 0.45-0.48) across all platforms. Kahun was the most efficient tool 0.74 (95 %CI 0.70-0.77) without a statistically significant difference from Your.MD 0.69(95 %CI 0.65-0.73). CONCLUSION: The data-gathering performance of currently available symptom checkers is questionable. From among the tools available, Kahun demonstrated the best overall performance.