# Strategies for Engineering Reliable Value Sets (SERVS)

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2022 · $386,698

## Abstract

Project Summary/Abstract
Significant evidence suggests that CDS, when used effectively, can improve health care quality, safety,
and effectiveness. However, despite its potential, CDS can cause significant adverse events due to mal-
functions. In our previous work, we identified that a significant source of CDS malfunctions is related to
problems with maintaining accurate and consistent value sets. Value sets are “lists of codes and corre-
sponding terms, from NLM-hosted standard clinical vocabularies (such as SNOMED CT®, RxNorm,
LOINC® and others), that define clinical concepts.” They are commonly used in both clinical decision
support and clinical quality measures (CQMs) to define complex concepts. Creating and maintaining
value sets is inherently challenging, and value set errors can lead to errors in both CDS and quality
measurement. We have found that these errors are widespread and have a variety of causes. In the
MALDIVES (Machine Learning-Driven Interactive Value Set Enhancement System) project, we propose
the first comprehensive study of value set creation and maintenance, the development of novel and inno-
vative machine learning and ontology-based approaches for improving value sets, and the creation of
new, open-source tools to help value set authors and users. MALDIVES relies on a mix of qualitative
methods, development of novel ontology and machine learning-based methods for improvement of value
sets, and new open-source tools, and we anticipate that it will yield new theory, innovations in clinical ap-
plications of machine learning, and practical tools and processes for value set authors and users. These
advances will improve clinical decision support and quality measurement, reduce alert fatigue and con-
tribute to improvements in patient safety and healthcare quality.

## Key facts

- **NIH application ID:** 10417435
- **Project number:** 1R01LM013995-01
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** ADAM T WRIGHT
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $386,698
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10417435

## Citation

> US National Institutes of Health, RePORTER application 10417435, Strategies for Engineering Reliable Value Sets (SERVS) (1R01LM013995-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10417435. Licensed CC0.

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