AI/QI Incubator

This project will improve maternal health and perinatal outcomes by understanding maternal social determinants of health (SDoH) through patient and geospatial assessments, predicting adverse perinatal outcomes using AI models, and shifting measurable postpartum interventions into prenatal phases of care (e.g., clinic access, social work referrals) for at-risk moms. 

The primary AI tool being used is the geospatial data & social determinants of health (including social and economic conditions impacting health, such as food insecurity).

This project will improve PSI indicators (e.g., sepsis) by predicting sepsis-related postoperative outcomes, mapping sepsis-related postoperative outcomes to antecedent, latent, high-risk clinical care pathways, and developing internal, candidate process measures linked to sepsis-related PSI outcome measures. 

The primary AI tool being used is the patient safety graph & care pathway optimization using graph neural networks.

This project will help clinicians identify high-risk women for lactation failure and provide targeted interventions to promote lactation success, increasing the number of postpartum women providing any human milk at discharge.

The primary AI tools being used are geospatial data & social determinants of health (including social and economic conditions impacting health, such as food insecurity) and natural language processing (NLP) of clinical text documents.

This project will develop personalized bleeding and transfusion risk scores to update policies surrounding perioperative transfusion of blood products, decreasing the volume of preoperative labs, crossmatched blood products, and unwarranted transfusions. 

The primary AI tool being used is the geospatial data & social determinants of health (including social and economic conditions impacting health, such as food insecurity).

This project will improve access to ECT, transcranial magnetic stimulation, and intranasal ketamine for older adults with treatment-resistant depression.

The primary AI tools being used are geospatial data & social determinants of health (including social and economic conditions impacting health, such as food insecurity), the patient safety graph & care pathway optimization using graph neural networks and natural language processing (NLP) of clinical text documents.

This project will use AI and computer vision to understand surgical instrument utilization and decrease waste and associated costs of infrequently used instruments and trays.

The primary AI tool being used is computer vision.

This project will use predictive and prescriptive modeling strategies to 1) understand which ambulatory surgery nephrolithiasis patients may be at risk for unplanned postoperative healthcare encounters (baseline rate of 15%), 2) which patients may benefit from elective postoperative admission.

The primary AI tools being used are geospatial data & social determinants of health (including social and economic conditions impacting health, such as food insecurity), the patient safety graph & care pathway optimization using graph neural networks and natural language processing (NLP) of clinical text documents.

This project will combine patient and clinical process factors in models to predict and decrease the risk of readmission following hospitalization for heart failure intervention. Interventions include clinical process modification and patient-specific, education-based interventions linked to specific cause(s) of HF readmission.

The primary AI tool being used is the geospatial data & social determinants of health (including social and economic conditions impacting health, such as food insecurity).

This project will decrease gaps in equitable access to appropriate diagnosis and effective therapeutic interventions for women with endometriosis and adenomyosis through the development of SDoH-aware patient care pathways.

The primary AI tools being used are geospatial data & social determinants of health (including social and economic conditions impacting health, such as food insecurity), the patient safety graph & care pathway optimization using graph neural networks and natural language processing (NLP) of clinical text documents.

This project examines QI opportunities related to surgical management of aortic disease, including reducing post-op sepsis and post-op respiratory failure.

The primary AI tools being used are the patient safety graph & care pathway optimization using graph neural networks and natural language processing (NLP) of clinical text documents.

This project will improve patient readiness to provide informed consent for breast reduction surgery and overall satisfaction with the surgery outcome.

The primary AI tool being used is image processing.

This project will improve neurodevelopment of children with congenital heart disease by starting therapies early in patient’s life. Therapies will be guided by AI prediction of neurodevelopment challenges.

The primary AI tools being used are geospatial data & social determinants of health (including social and economic conditions impacting health, such as food insecurity), the patient safety graph & care pathway optimization using graph neural networks and natural language processing (NLP) of clinical text documents.

This project will improve management of pain outcomes after spine surgery by optimizing time and type of surgery.

The primary AI tools being used are the patient safety graph & care pathway optimization using graph neural networks, natural language processing (NLP) of clinical text documents and image processing.

This project will improve patient flow following discharge from the Neuro ICU.

The primary AI tool being used is the patient safety graph & care pathway optimization using graph neural networks.

This project optimizes workflows and safety concerns in the radiation therapy clinics and treatment suites using the patient safety graph.

The primary AI tools being used are the patient safety graph & care pathway optimization using graph neural networks and natural language processing (NLP) of clinical text documents.

ALPS links PHI to GPU-accelerated, high-performance computing infrastructure within a secure enclave. This permits AI analyses to be performed on identifiable data. For QI and hospital operations, this allows us to extend research-level AI models to operational datasets.

ALPS is principally managed by Ray Opoku, MSc., in consultation with UF Research Computing and Ron Ison, M.S., P.M.P., in the Department of Anesthesiology.

ALPS was a key advance in allowing us to develop temporal graph neural networks (GNNs) for use with PHI.  Because temporal GNNs use date/timestamps of hospital events and require GPU acceleration for creation, and because attempts to anonymize through various obfuscation methods were deemed impermissible by the IRB, temporal GNN models would not be feasible outside of an ALPS-like enclave.

A sub-project within ALPS seeks to develop a core resource for examining social disparities using geospatial analysis. Using this core, we can attach social determinants of health to all modeling efforts, and back-translate findings to maps of disparities and social vulnerabilities. This is an operationally-enabled resource developed by Rulman Pebe, MSc., in consultation with Jiang Bian, Ph.D..