# Clinical outcomes for asynchronous teledermatology

> **NIH VA I01** · VETERANS AFFAIRS MED CTR SAN FRANCISCO · 2022 · —

## Abstract

Background: Store-and-forward teledermatology is a significant part of Department of Veterans Affairs’
telehealth portfolio. While considerable evidence supports teledermatology’s potential to provide timely access
to expert dermatologic care, its effectiveness in achieving clinical outcomes that are equivalent to usual in-
person care has not been as well documented due to the lack of objective outcome measures for many skin
diseases. Clinicians typically document skin diseases using non-standardized qualitative language. Manual
review to extract meaningful outcomes data from relatively unstructured text is typically prohibitive.
Significance/Impact: Natural language processing (NLP) offers a previously unexplored approach to
objectively and systematically identify relevant text in the electronic medical record to gauge patients’ clinical
responses following either in-person dermatology and asynchronous teledermatology consultation. This
project will leverage NLP to follow clinical courses of important skin conditions in the medical record and to
compare the outcomes and effectiveness of teledermatology relative to usual office-based dermatology
consultation. It will also serve as a test for other outcome measures such as access times that are often
assumed to be proxies for quality of care for Veterans. The results may help influence VA telehealth strategy
and policies to enhance access of patients to high quality skin care and to improve patient safety.
Innovation: This project represents a novel application of NLP methods to understand how key clinicians
document skin conditions and to provide a large-scale, systematic and rigorous assessment of
teledermatology’s effectiveness in caring for Veterans with a variety of skin diseases. The project will also
result in NLP systems which may be translatable to create practical operational quality management tools for
monitoring the quality of follow-up care of both dermatology and teledermatology patients in VA.
Specific Aims: Aim 1 will survey expert and non-expert clinicians to learn how each group evaluates and
documents clinical change in five common skin diagnostic categories. We will test novel annotation methods,
and identify differences between clinician groups in annotated survey responses. Aim 2 will use our annotated
data sets to train and validate NLP models to extract concepts and relationships for our five diagnostic
categories from actual VA clinical notes. This information will be used to create a document classifier capable
of assigning a clinical change status to follow-up notes. Aim 3 will integrate output from our NLP tools to
assign an overall clinical outcome to dermatology and teledermatology referrals. Other important clinical
events and activities available as structured data will be correlated with NLP outcomes to further interpret their
significance. Commonly used access outcome measures will also be compared as a test of their validity.
Methodology: Aim 1 will survey derm...

## Key facts

- **NIH application ID:** 10426001
- **Project number:** 1I01HX003344-01A2
- **Recipient organization:** VETERANS AFFAIRS MED CTR SAN FRANCISCO
- **Principal Investigator:** DENNIS H OH
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2022
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2022-06-01 → 2026-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10426001

## Citation

> US National Institutes of Health, RePORTER application 10426001, Clinical outcomes for asynchronous teledermatology (1I01HX003344-01A2). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10426001. Licensed CC0.

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