# Outcome-driven Order Set Content Development, Management, and Evaluation

> **NIH NIH K01** · WEILL MEDICAL COLL OF CORNELL UNIV · 2021 · $176,379

## Abstract

Project Summary
Candidate Goals and Objectives:
With a background in Information Systems and Management, and Biostatistics, Dr. Zhang has demonstrated
research records on electronic health record data mining to identify patterns of healthcare delivery that may be
used to inform patient-centered and evidence-based healthcare. The proposal will provide additional training
for Dr. Zhang on advanced machine learning, statistics, and evaluation methods in biomedical informatics for
applications on clinical decision support (CDS). Dr. Zhang's long-term goal is to bringing innovation CDS
development and evaluation through novel biomedical informatics and data science techniques.
Institutional Environment and Career Development:
Weill Cornell Medicine (WCM) provides ideal research facilities and training environment for Dr. Zhang. Dr.
Jyotishman Pathak, Chief of Division of Health Informatics at Department of Health Policy and Research, will
lead a multidisciplinary team of mentors: Drs. Jessica Ancker and Fei Wang at WCM, and Dr. Adam Wright at
Harvard Medical School. Dr. Zhang also has collaborators in WCM and NewYork-Presbyterian Hospital who
will support her in her training and research activities and provide clinical expertise.
Research Aims
Order sets are a type of CDS in computerized provider order entry (CPOE) to standardize decision making in
the ordering process and encourage compliance with clinical practice guidelines. Previous literature on order
set use has focused its effect on usability, workload, and physician satisfaction, but a knowledge gap remains
with respect to the effect of order sets on care outcomes. The overall goal of the research study is to create a
continuous improvement cycle for order sets with respect to a care outcome by rigorously learning from data.
Aim 1 of the study will apply computational phenotyping and subtyping algorithms to identify cohorts of heart
failure (HF) subtypes. Aim 2 will evaluate an existing order set intended for the care of HF patients on a care
outcome defined as 30-day all-cause, unplanned readmission with a hypothesis that the use of this order set is
associated with a better outcome. This will be achieved by building a range of outcome prediction models and
evaluating the strength of each order set order as a predictor. Aim 3 will optimize the existing order sets using
a metaheuristic optimization method such that its content collectively may have the largest positive effect on
the outcome of 30-day all-cause unplanned readmission. The effects of order set use on the care outcome is
measured using a causal inference technique in each iteration. The expected outcome is a framework to
develop and evaluate HF order sets which may eventually be generalized to other clinical areas. Training from
this proposal may lead to multi-site R01 studies of outcome-driven HF order sets and actual implementations.

## Key facts

- **NIH application ID:** 10242838
- **Project number:** 5K01LM013257-03
- **Recipient organization:** WEILL MEDICAL COLL OF CORNELL UNIV
- **Principal Investigator:** Yiye Zhang
- **Activity code:** K01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $176,379
- **Award type:** 5
- **Project period:** 2019-09-13 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10242838

## Citation

> US National Institutes of Health, RePORTER application 10242838, Outcome-driven Order Set Content Development, Management, and Evaluation (5K01LM013257-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10242838. Licensed CC0.

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