Teledermatology in India During the Peri–COVID-19 Outbreak Period: Advantages, Shortcomings, and Challenges
Bibliographic record
Abstract
Background Telemedicine is defined as the use of electronic information and communication technologies for health care professionals to provide care to patients. Although available since the pre–COVID-19 era, a huge surge in teledermatology consultations occurred during the COVID-19 outbreak. As access to health care became limited and difficult due to repeated lockdowns, teledermatology helped us provide health care to our patients. Moreover, as dermatology is a visual field, it was even more suitable for teleconsultations. Objective The objectives of this study were to investigate the advantages, shortcomings, and challenges of teledermatology in India during the peri–COVID-19 outbreak period. Methods This was a single-center, retrospective, observational study conducted at a tertiary care hospital in India. Teledermatology consultation data from April 1, 2020, till September 2021 (18 months) were included. All modes, including real-time (RT) video, asynchronous store and forward (SAF), and hybrid, were used to conduct teledermatology consultations. Statistical analyses were performed using SPSS software (IBM Corp). Results During these 18 months, a total of 4280 patients took teledermatology consultations at our center. The mean age of the patients was 34.19 years, with most of them (36.4%) in the age group of 31-40 years. The patient population comprised a mix of urban (55%) and rural (45%) individuals. Overall, 70% of consultations were conducted in the SAF mode; hybrid mode, 16%; and RT video consultations, 14%. Diagnosis was established in 89.1% of the cases, and the most common diagnosis was superficial fungal infection (28%), followed by eczema (16%) and acne (8.6%). Hospital visits were required in the remaining 10.9% of cases for the following reasons: lack of clear pictures and technical errors (5.57%). Additional diagnostic tests were required in 1.3% of cases, physical examination in 1.05% of cases, and 0.39% of patients had life-threatening conditions requiring hospitalization. The advantages of teledermatology include decreased need for hospital visits among 89.1% of patients, which played a very important role in decreasing overcrowding. Also, this helped us provide expert health care to the rural population of India. Owing to shortcomings including the lack of good-quality pictures (4.2%; more so in SAF teleconsultations) and technical errors (1.37%), teledermatology cannot be used to manage life-threatening conditions (0.39%), and, in particular, RT video consultations are more time-consuming (14%). Challenges faced by dermatologists during teledermatology consultations were mainly operational, such as the lack of good internet access leading to interrupted consultations (1.37%), poor quality of pictures (4.2%), and difficulty in extracting history in cases of SAF consultations. Conclusions Teledermatology serves as a triage platform and helps reduce hospital visits. It helps to cater to the rural population, which otherwise has limited access to health care. Some technical challenges are the dependence of teledermatology on pictures and information sent by the patient for establishing the diagnosis. Also, sometimes patients faced difficulty in conveying problems clearly to the doctors. Because of the ease and advantages, several dermatologists have continued to use teledermatology along with the physical consultations in the post–COVID-19 era. With a few advancements, teledermatology will certainly remain a successful and useful model for consultations, more so for catering to the population with the lack of access to specialist services. Conflicts of Interest None declared.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".