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Ramesh Johari | Stanford HAI

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peopleFaculty

Ramesh Johari

Associate Professor of Management Science and Engineering and, by Courtesy, of Computer Science and of Electrical Engineering

External Bio
Latest Work
Equitable Implementation of a Precision Digital Health Program for Glucose Management in Individuals with Newly Diagnosed Type 1 Diabetes
Ramesh Johari, Ananta Addala, Franziska K Bishop, Korey Hood, Ming Yeh Lee, Victoria Y Ding, Priya Prahalad, Dessi P Zaharieva, Johannes Ferstad, Manisha Desai, David Scheinker, David Maahs
Jul 30
Research
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Few young people with type 1 diabetes (T1D) meet glucose targets. Continuous glucose monitoring improves glycemia, but access is not equitable. We prospectively assessed the impact of a systematic and equitable digital-health-team-based care program implementing tighter glucose targets (HbA1c < 7%), early technology use (continuous glucose monitoring starts <1 month after diagnosis) and remote patient monitoring on glycemia in young people with newly diagnosed T1D enrolled in the Teamwork, Targets, Technology, and Tight Control (4T Study 1). Primary outcome was HbA1c change from 4 to 12 months after diagnosis; the secondary outcome was achieving the HbA1c targets. The 4T Study 1 cohort (36.8% Hispanic and 35.3% publicly insured) had a mean HbA1c of 6.58%, 64% with HbA1c < 7% and mean time in the range (70-180 mg dl-1) of 68% at 1 year after diagnosis. Clinical implementation of the 4T Study 1 met the prespecified primary outcome and improved glycemia without unexpected serious adverse events. The strategies in the 4T Study 1 can be used to implement systematic and equitable care for individuals with T1D and translate to care for other chronic diseases.

Smart Start—Designing Powerful Clinical Trials Using Pilot Study Data
Emily Fox, Ramesh Johari, Priya Prahalad, Dessi P Zaharieva, Johannes Ferstad, Manisha Desai, David Scheinker, David Maahs
Jan 22
Research
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BACKGROUND

Digital health interventions may be optimized before evaluation in a randomized clinical trial. Although many digital health interventions are deployed in pilot studies, the data collected are rarely used to refine the intervention and the subsequent clinical trials.

METHODS

We leverage natural variation in patients eligible for a digital health intervention in a remote patient-monitoring pilot study to design and compare interventions for a subsequent randomized clinical trial.

RESULTS

Our approach leverages patient heterogeneity to identify an intervention with twice the estimated effect size of an unoptimized intervention.

CONCLUSIONS

Optimizing an intervention and clinical trial based on pilot data may improve efficacy and increase the probability of success. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT04336969.)

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