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Improving safety and quality of emergency care using machine learning-based clinical decision support at triage

Award Number: R01HS029179
ORGANIZATION: AGENCY FOR HEALTH CARE RESEARCH AND QUALITY
OPDIV: AHRQ
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 09/30/2023
PERIOD OF PERFORMANCE END DATE: 07/31/2028

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Improving safety and quality of emergency care using machine learning-based clinical decision support at triage - Abstract Current emergency department (ED) triage systems across the U.S. lead to mis-triage in up to one third of patient encounters, worsening ED crowding and contributing to delays and disparities in care. Early studies showed triage prediction models outperformed the subjective triage systems used in most EDs by prioritizing sicker patients, although significant gaps remain. Early models did not: 1) predict outcomes other than hospital admission, even though 80-90% of ED visits do not result in hospital admission; 2) include pediatric patients, even though pediatric patients contribute up to 25% of visits; 3) consider health equity in design or evaluation of prediction models; or 4) study impacts on key patient safety and quality measures, such as timeliness of care. Our proposal addresses these unmet needs and responds to two AHRQ Special Emphasis Notices, HS- 21-014 (Health Services Research to Advance Health Equity) and HS-22-004 (Research on Digital Healthcare Safety). Our study team has completed significant preliminary analyses, including: 1) study cohort build of over 6 million ED encounters across the 21 EDs in our health system; 2) assessment of significant limitations of triage in study setting; and 3) development of machine-learning models to predict patient acuity and resource needs at triage. In Aim 1, we will refine triage models that predict: 1) critical illness; 2) hospital admission; and 3) fast-track eligibility (<2 resources needed, no hospital admission or critical outcomes). We will measure algorithm biases and explore strategies to improve equity in triage model predictions. In Aim 2, we will map probability thresholds for each outcome into clinically relevant triage category recommendations. We will use a human factors framework and significant stakeholder engagement to design, build, and evaluate clinician- facing triage clinical decision support (CDS). Lastly, we will build the CDS into our electronic health record to efficiently display personalized risk predictions for each outcome as part of standard triage workflows. In Aim 3, we will assess the impact of the CDS in real time in a pragmatic, step-wedged cluster randomized trial across 21 hospital-based EDs and one free-standing ED. Our primary outcomes will be: 1) timeliness of care for critically ill patients; 2) appropriate early identification of fast-track eligible patients; and 3) ED length of stay. In addition, to test the equity-driven calibrations in our models, we will assess for bias by race, gender, and socioeconomic status among primary outcomes. Our secondary outcomes will be CDS reach, adoption, and implementation. Upon successful completion of the proposed research, we expect to demonstrate the extent to which a novel point-of-care digital technology that uses advanced predictive analytics can lead to safer, higher quality, and more equitable care.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2024 ( Subtotal = $394,156 )
2024	2024	KAISER FOUNDATION HOSPITALS	1800 HARRISON ST FL 16	OAKLAND	CA	94612	ALAMEDA	USA	Research on Healthcare Costs, Quality and Outcomes	002	2	8/9/2024	NON-COMPETING CONTINUATION	$394,156
2024	2023	KAISER FOUNDATION HOSPITALS	1800 HARRISON ST FL 16	OAKLAND	CA	94612	ALAMEDA	USA	Research on Healthcare Costs, Quality and Outcomes	001	1	7/15/2024	NEW	$0
2024	2023	KAISER FOUNDATION HOSPITALS	1800 HARRISON ST FL 16	OAKLAND	CA	94612	ALAMEDA	USA	Research on Healthcare Costs, Quality and Outcomes	000	1	10/12/2023	NEW	$0
														Subtotal = $394,156

Issue Date FY: 2023 ( Subtotal = $386,352 )
2023	2023	KAISER FOUNDATION HOSPITALS	1800 HARRISON ST FL 16	OAKLAND	CA	94612	ALAMEDA	USA	Research on Healthcare Costs, Quality and Outcomes	000	1	9/12/2023	NEW	$386,352
														Subtotal = $386,352

Grand Total All Awards = $780,508

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Improving safety and quality of emergency care using machine learning-based clinical decision support at triage

Award Number: R01HS029179

ORGANIZATION: AGENCY FOR HEALTH CARE RESEARCH AND QUALITY

OPDIV: AHRQ

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 09/30/2023

PERIOD OF PERFORMANCE END DATE: 07/31/2028

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