The U.S. Department of Health and Human Services (HHS), through the Assistant Secretary for Technology Policy and Office of the National Coordinator for Health Information Technology (ASTP), announced two awards totaling $2 million under the Leading Edge Acceleration Projects in Health Information Technology (LEAP in Health IT) funding opportunity. The LEAP in Health IT awardees aim to create methods and tools to improve care delivery, advance research capabilities, and address emerging challenges related to interoperable health IT.

The May 2024 Special Emphasis Notice sought applications for two areas of interest: developing innovative ways to evaluate and improve the quality of health care data used by artificial intelligence (AI) tools in health care, and accelerating adoption of health IT in behavioral health settings.

“AI and behavioral health are two high priority areas for HHS. We hope that the funding each awardee receives supercharges their entrepreneurial spirit and positions them to make a real impact in people’s lives,” said Steve Posnack, principal deputy assistant secretary for technology policy. “We are cheering them on and look forward to their future results.”

The 2024 LEAP in Health IT awardees are:

**Area 1: Develop innovative ways to evaluate and improve the quality of health care data used by artificial intelligence (AI) tools in health care**

**Awardee:** The Trustees of Columbia University in the City of New York, the governing board of Columbia University in New York City.

**Project:** Scalable, Shareable, and Computable Clinical Knowledge for AI-Based Processing of Hospital-Based Nursing Data (SC2K)

Overview: Advanced AI methods will increasingly use data documented by nurses. Insufficient knowledge of nursing practice, nurse decision-making, and nursing workflows risks both inaccurate and undiscovered data signals. The proposed study seeks to harness nursing knowledge systematically to better capture the nuances of nursing data, leading to more comprehensive, accurate, and transparent algorithms. Additionally, the study aims to develop scalable computational approaches to evaluate and improve the quality of data recorded by inpatient nurses used in AI algorithms.

Objectives:

- Test and validate different computational methods within a health care process modeling (HPM) framework applied to two AI-based use cases.

- Generate and validate a set of applicable knowledge graphs related to HPMs that are generalizable for the two AI-based use cases.

- Extend multi-modal approaches across five additional AI-based use cases leveraging inpatient nursing data.

- Build an open-source pipeline to share and reuse these computational processes combined with knowledge graphs.

**Area 2: Accelerate adoption of health IT in behavioral health settings**

**Awardee:** Oregon Health & Science University (OHSU). OHSU is a system of hospitals and clinics across Oregon and southwest Washington.

**Project:** Behavioral Health eCarePlan Collaborative Project

Overview: This project seeks to adapt an open-source SMART on Fast Health Interoperability Resources® (FHIR®) application based on the HL7® Multiple Chronic Condition (MCC) care plan effort for three behavioral health use cases. It will pilot this application in stand-alone behavioral health clinics with challenges in exchanging health information.

Objectives:

- Fine-tune MyCarePlanner/eCarePlanner applications to improve structured behavioral health data exchange.

- Connect MyCarePlanner/eCarePlanner applications with EHRs having limited information exchange capabilities.

- Perform formal evaluations for three key behavioral health use cases.

- Share results with key groups focused on open-source tools including HL7, peer support networks, and cross-agency management groups.