Flory L. Nkoy, MD, MS, MPH

Dr. Nkoy is a Research Professor at the University of Utah, Pediatric Department, and an Adjunct Faculty in the Biomedical Informatics Department. In addition to clinical medicine, Dr. Nkoy’s background includes training in health services research, public health, quality improvement, bioinformatics, and dissemination/implementation research. His work utilizes these skills to pioneer innovative approaches to improve care of children with chronic diseases. Dr. Nkoy has been the Principal Investigator (PI) and co-Investigator (co-I) of several federal grants. As a PI, he led an AHRQ R18 grant to study contextual factors that influence successful implementation of a care process model (CPM) for pediatric asthma at multiple hospitals. He was the PI of an AHRQ R18 grant that supported the development and initial evaluation of the electronic-AsthmaTracker (e-AT), a novel self-monitoring tool for preventing exacerbations and emergency department (ED)/hospital admissions in children with asthma. He received 2 R01 equivalent awards from PCORI to evaluate e-AT impact at 11 pediatric clinics, and to disseminate the e-AT to multiple clinics in Utah (to date >50 clinics). Via an HRSA grant, Dr. Nkoy led the development of MyChildCMC, a novel app for home monitoring of children with medical complexity (CMC) to help prevent ED/hospital admissions. He demonstrated in a pilot trial the feasibility of MyChildCMC use and its ability to detect early signs of CMC’s health deteriorations up to 14 days prior to hospital admissions, with preliminary results showing improvement in CMC outcomes. He received a new NIH R01 award to evaluate MyChildCMC’s efficacy in a multicenter study. If successful, MyChildCMC will be a new model for improving CMC care and reducing costs, with potential for national dissemination. Dr. Nkoy’s work and experience have prompted multiple collaborative and translational research projects (as co-I) funded by NIH, to 1) assess the impact of CYP3A genetic variations on inhaled corticosteroid (ICS) effectiveness in asthmatics; 2) develop a platform integrating clinical, environmental and air pollution sensor data to support pediatric asthma research; 3) evaluate TRP gene polymorphisms and cellular responses to air pollution in asthmatics; 4) develop/test machine learning (ML) models for predicting appropriate bronchiolitis admissions from the ED; 5) develop/test ML models for predicting high-risk asthmatics for referral to care management; and 6) assess early life environmental exposure impacts on child respiratory outcomes. His experience in dissemination/implementation research led (as co-I) to 2 SAMHSA funded projects to: 7) disseminate a CPM for childhood traumatic stress at multiple primary care clinics in Utah; and 8) further disseminate the CPM at 300 primary care clinics and child’s advocacy centers across 20 states. Dr. Nkoy has recently initiated a novel study and is working on a NIH R01 application to use ML and causal inference to predict optimal ICS response in asthmatics. Dr. Nkoy also serves as a grant reviewer and has reviewed for several study sections for NHLBI, NINR, AHRQ, NIAID and PCORI.

Research Statement

I use multidisciplinary approaches to pioneer novel approaches to improve care of children with chronic diseases. As a PI, I led an AHRQ R18 grant to study contextual factors influencing successful implementation of a care process model for pediatric asthma at multiple hospitals. I led an AHRQ R18 grant that supported the development and initial evaluation of the electronic-AsthmaTracker (e-AT), a novel self-monitoring tool for preventing emergency department (ED)/ hospital admissions in children with asthma. I received 2 PCORI awards to evaluate e-AT impact at 11 pediatric clinics, and to disseminate it to multiple clinics in Utah. Via an HRSA grant, I led the development of MyChildCMC, a novel app for home monitoring of children with medical complexity (CMC). I demonstrated in a pilot trial the feasibility of MyChildCMC use and its ability to detect early signs of CMC’s health deteriorations up to 14 days prior to hospitalizations. I received a new NIH R01 award to evaluate MyChildCMC’s efficacy in a multicenter study. My work has prompted multiple collaborative research projects (as co-I), leading to >30 million in NIH funding. I recently initiated a novel study to use machine learning to predict optimal response to inhaled corticosteroids in asthmatics.