# PA-20-070 "Development of evidence-based decision support for the management of COVID19"

> **NIH AHRQ R01** · BECKMAN RESEARCH INSTITUTE/CITY OF HOPE · 2021 · $399,999

## Abstract

SUMMARY
A key to optimal management of COVID-19 is development of evidence-based recommendations and
associated strategies to ensure implementation of treatment recommendations. This is particularly important
when evidence is emerging as rapidly as is the case for COVID-19. GRADE (Grading of Recommendations
Assessment, Development and Evaluation) has emerged as the leading system for rating the quality of
evidence and strength of recommendations. Endorsed by more than 110 professional organizations, GRADE
has codified key normative factors that clinical practice guidelines (CPGs) panels ought to take into
consideration. During activities conducted over the last 4 years on our parent R01 grant (5R01HS024917), we
have discovered that in addition to normative GRADE factors, important non-GRADE factors affect the group
judgment of CPG panels. We now propose to leverage these findings from the parent R01 grant to help
generate optimal management strategies for COVID-19. To provide the most rational framework for
managing COVID-19 patients, we propose to develop GRADE-based CPGs that we will implement in the
electronic medical record (EMR) at the point-of-care within the Rush health system in Chicago. An increasingly
popular strategy for improving patient care is to standardize care by translating CPGs into clinical pathways
(CPs), which typically use flow charts or clinical algorithms to provide detailed steps about a course of
management for a particular clinical problem or an entire spectrum of care. However, despite the promise of
CPs and their increasing use, no theoretical framework has been developed to guide their development. This
means it is not possible to rigorously analyze the efficiency of CPGs/CPs, nor their influence on patient health
outcomes. We hypothesize that solid theoretical grounds for developing CPGs/CPs can be provided by
converting them into fast-and-frugal decision trees (FFTs). FFTs are constructed as a series of sequentially-
ordered, clinical information or “cues” whose relation is defined by a series of if–then statements. Every cue in
an FFT can correctly or incorrectly classify a signal (e.g., patient has COVID-19) vs. noise (e.g., patient does
not have COVID-19) and this classification pattern can be measured (e.g., is the signal a true positive or
negative). This property of FFTs allows them to be integrated within a broader theoretical framework of signal
detection and related theories which, in turn, allows the accuracy of the clinical strategies they represent to be
evaluated. In this application, we propose to develop GRADE CPGs for COVID-19 (Aim 1), translate the CPGs
into CPs, and, convert the CPs into FFTs. Subsequently, we will implement FFTs in the Rush EMR (Aim 2),
and conduct an interrupted time series to evaluate the effect of GRADE-based FFTs on management of
patients with COVID-19. The proposed application is directly informed by the parent R01 grant and has
potential for immediate and sustained impact ...

## Key facts

- **NIH application ID:** 10175925
- **Project number:** 3R01HS024917-06S1
- **Recipient organization:** BECKMAN RESEARCH INSTITUTE/CITY OF HOPE
- **Principal Investigator:** Benjamin Djulbegovic
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** AHRQ
- **Fiscal year:** 2021
- **Award amount:** $399,999
- **Award type:** 3
- **Project period:** 2016-09-30 → 2022-12-14

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10175925

## Citation

> US National Institutes of Health, RePORTER application 10175925, PA-20-070 "Development of evidence-based decision support for the management of COVID19" (3R01HS024917-06S1). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10175925. Licensed CC0.

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