Can video-based telehealth examinations of the abdomen safely determine the need for imaging?

Estimating the Proportion of Telehealth-Able United States Emergency Department Visits

STUDY OBJECTIVE

We use national emergency department (ED) data to identify the proportion of "telehealth-able" ED visits, defined as potentially conductible by Video Only or Video Plus (with limited outpatient testing).

METHODS

We used ED visits by patients 4 years of age and older from the 2019 National Hospital Ambulatory Medical Care Survey and applied survey weighting for national representativeness. Two raters categorized patient-described Reasons for Visit (RFV) as telehealth-able (yes, no, uncertain) for both Video Only and Video Plus visits. This categorization was stratified by age (4 to 17 years old, 18 to 35, 36 to 64, and 65 and older). Visit characteristics that were used to remove further nontelehealth-able visits included admission, procedures, diagnostic testing, acuity level, and pain score.

RESULTS

Our sample included 133.6 million United States ED visits in 2019 for patients aged 4 years or older. Of those, between 3.4% and 8.8% of visits were telehealth-able by Video Only and between 5.0% and 9.7% by Video Plus, considering only the first RFV. Visits by younger patients were more often telehealth-able, with the proportion of telehealth-able visits decreasing with advancing age. Considering all RFVs, between 0% to 6.6% of ED visits were telehealth-able with Video Only and 0.02% to 7.6% with Video Plus.

CONCLUSION

Between 3% and 10% of United States ED visits may be potentially telehealth-able for patients aged 4 years and older, considering the first listed RFV and ED visit characteristics. Fewer visits may be telehealth-able when all reasons for visits are considered.

iApp: An Autonomous Inspection, Auscultation, Percussion, and Palpation Platform

Disease symptoms often contain features that are not routinely recognized by patients but can be identified through indirect inspection or diagnosis by medical professionals. Telemedicine requires sufficient information for aiding doctors' diagnosis, and it has been primarily achieved by clinical decision support systems (CDSSs) utilizing visual information. However, additional medical diagnostic tools are needed for improving CDSSs. Moreover, since the COVID-19 pandemic, telemedicine has garnered increasing attention, and basic diagnostic tools (e.g., classical examination) have become the most important components of a comprehensive framework. This study proposes a conceptual system, iApp, that can collect and analyze quantified data based on an automatically performed inspection, auscultation, percussion, and palpation. The proposed iApp system consists of an auscultation sensor, camera for inspection, and custom-built hardware for automatic percussion and palpation. Experiments were designed to categorize the eight abdominal divisions of healthy subjects based on the system multi-modal data. A deep multi-modal learning model, yielding a single prediction from multi-modal inputs, was designed for learning distinctive features in eight abdominal divisions. The model's performance was evaluated in terms of the classification accuracy, sensitivity, positive predictive value, and F-measure, using epoch-wise and subject-wise methods. The results demonstrate that the iApp system can successfully categorize abdominal divisions, with the test accuracy of 89.46%. Through an automatic examination of the iApp system, this proof-of-concept study demonstrates a sophisticated classification by extracting distinct features of different abdominal divisions where different organs are located. In the future, we intend to capture the distinct features between normal and abnormal tissues while securing patient data and demonstrate the feasibility of a fully telediagnostic system that can support abnormality diagnosis.