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Principal Investigator: Erica DaVonne Farrand
Organization: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
Fiscal Year: 2024
Award: $189,332
Funding agency: National Heart Lung and Blood Institute
PROJECT SUMMARY
Hybrid healthcare delivery is an integral part of the new healthcare landscape, connecting patients and clinicians
using a combination of in-person and telehealth modalities. Hybrid models transform traditional chronic disease
management, which is episodic, reactive, and expensive, to an approach that is adaptive, preventative, and
efficient. As such, they stand to have a significant impact on the care of 133 million US adults (roughly 1 in 2)
with at least one chronic illness. Unfortunately, the adoption, sustainability, and scalability of hybrid models in
chronic disease are threatened by the inability to monitor disease activity and progression during telehealth visits.
Such is the case in interstitial lung disease (ILD), a group of complex, chronic pulmonary diseases with
unpredictable disease courses. Fortunately, remote patient monitoring (RPM) is a feasible, acceptable, and
accurate approach to monitoring chronic disease activity and progression. In ILD clinical trials, remote spirometry
and oximetry can reliably monitor lung function and blood oxygen levels and predict disease progression and
clinic-based assessments. However, in the US, RPM has yet to be adopted into routine ILD practice. The
objective of this application is to bridge this gap by determining how to make RPM data accessible and actionable
in ILD hybrid care models. We propose to leverage the computational power of artificial intelligence to provide
RPM-informed clinical decision support (CDS) at the point of care. The specific aims are to: (1) identify provider
and health-system level barriers to an RPM-informed CDS tool, (2) evaluate the performance of ILD-risk
classification algorithms using RPM data (3) assess the implementation and effectiveness potential of an RPM-
informed CDS tool designed to identify patients at high risk for accelerated progression. The lessons learned in
ILD are likely to be generalizable to other chronic disease models, amplifying the impact of this research.
These aims will be achieved using implementation science, mixed methods, cognitive informatics, and
interventional study design and they address the NHLBI research priority areas to leverage emerging
opportunities in data science and to optimize implementation research. The studies are novel in their
application of complimentary methodologies to develop point-of-care CDS in ILD, with an eye towards broad
dissemination. The proposed studies are informed by Dr. Farrand’s expertise as an ILD physician and supported
by her research program which leverages digital health tools and implementation science methodologies to
improve the delivery of high-quality, safe, and effective care in chronic lung diseases. During this award, Dr.
Farrand will undertake training in four areas: 1) applying implementation science frameworks to design,
implement, and evaluate interventions, 2) conducting qualitative and mixed methods research, 3) applying
cognitive informatics to design and integrate artificial intelligence into clinical practice, and 4) designing
interventional studies. Dr. Farrand has assembled a mentorship team with content, methodologic, and practical
expertise to ensure completion of the proposed research and training and successful transition to independence.
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