2024 LEAP in Health IT Projects
The Leading Edge Acceleration Projects (LEAP) in Health IT program addresses fast emerging challenges that inhibit the development, use, and advancement of interoperable health IT. Information about 2024 LEAP projects can be found below.
Project Dates: 2024 – 2026 (est.)
Scalable, Shareable, and Computable Clinical Knowledge for AI-Based Processing of Hospital-Based Nursing Data (SC2K)
Project Overview
The Trustees of Columbia University in the City of New York seek to harness nursing knowledge in a systematic way to better capture the nuances of nursing data, leading to more comprehensive, accurate, and transparent algorithms. Additionally, the study seeks to develop scalable computational approaches to evaluate and improve the quality of data recorded by inpatient nurses and used in AI algorithms. 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.
Project Goals
The goals of this project are to:
- Test and validate different computational methods (e.g., LLM, logistic regression, neural network) within a healthcare process modeling (HPM) framework applied to two AI-based use cases (classifying missing data versus missed care; classifying implicit biases) that leverage inpatient nursing and multi-modal data ready for integration with knowledge graphs. The HPM framework moves data science methods beyond transactional data analytics to model clinical knowledge, decision making, and behavior to classify and make predictions about patients that are consistent with and can enhance the quality of the data captured used to discover previously unknown patterns.
- Generate and validate a set of applicable knowledge graphs related to HPMs that are generalizable and valuable for the two AI-based use cases that leverage inpatient nursing and multi-modal data.
- Extend multi-modal approaches to HPM informed scalable computational processes combined with knowledge graphs across five additional AI-based use cases that leverage inpatient nursing and multi-modal data.
- Build an open-source pipeline to share and reuse the HPM informed scalable computational processes combined with knowledge graphs.
Project Dates: 2024 – 2026 (est.)
Behavioral Health eCarePlan Collaborative Project
Project Overview
Oregon Health & Science University (OHSU) 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 and pilot the application in stand-alone behavioral health clinics with challenges in exchanging health information.
Project Goals
The goals of this project are to:
- Fine tune the MyCarePlanner/eCarePlanner applications to improve the exchange of structured behavioral health data, enabling both standard storage to a supplemental data store and write-back to any electronic health record (EHR) available. The system is built to allow any structured data collection form to be incorporated and translated into FHIR questionnaire queries.
- Connect and pilot the MyCarePlanner/eCarePlanner applications to a set of behavioral health providers with EHRs with limited health information exchange capabilities.
- Perform a formal evaluation of the applications’ capabilities for three key behavioral health use cases.
- The results will be shared not only with the behavioral health sites and their patients, but also with a number of key groups focused on open-source tools, including HL7, behavioral health peer support networks, and the eCarePlan cross-agency management group.