Johannes O Ferstad

All Publications

• The Association between Patient Characteristics and the Efficacy of Remote Patient Monitoring and Messaging Ferstad, J., Prahalad, P., Maahs, D. M., Fox, E., Johari, R., Scheinker, D. AMER DIABETES ASSOC. 2022

  View details for DOI 10.2337/db22-1009-P

  View details for Web of Science ID 000854899301502

• Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities. Frontiers in endocrinology Zaharieva, D. P., Senanayake, R., Brown, C., Watkins, B., Loving, G., Prahalad, P., Ferstad, J. O., Guestrin, C., Fox, E. B., Maahs, D. M., Scheinker, D. 2022; 13: 1096325

  Abstract

  Algorithm-enabled patient prioritization and remote patient monitoring (RPM) have been used to improve clinical workflows at Stanford and have been associated with improved glucose time-in-range in newly diagnosed youth with type 1 diabetes (T1D). This novel algorithm-enabled care model currently integrates continuous glucose monitoring (CGM) data to prioritize patients for weekly reviews by the clinical diabetes team. The use of additional data may help clinical teams make more informed decisions around T1D management. Regular exercise and physical activity are essential to increasing cardiovascular fitness, increasing insulin sensitivity, and improving overall well-being of youth and adults with T1D. However, exercise can lead to fluctuations in glycemia during and after the activity. Future iterations of the care model will integrate physical activity metrics (e.g., heart rate and step count) and physical activity flags to help identify patients whose needs are not fully captured by CGM data. Our aim is to help healthcare professionals improve patient care with a better integration of CGM and physical activity data. We hypothesize that incorporating exercise data into the current CGM-based care model will produce specific, clinically relevant information such as identifying whether patients are meeting exercise guidelines. This work provides an overview of the essential steps of integrating exercise data into an RPM program and the most promising opportunities for the use of these data.

  View details for DOI 10.3389/fendo.2022.1096325

  View details for PubMedID 36714600

• Population-level management of Type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health. Pediatric diabetes Ferstad, J. O., Vallon, J. J., Jun, D., Gu, A., Vitko, A., Morales, D. P., Leverenz, J., Lee, M. Y., Leverenz, B., Vasilakis, C., Osmanlliu, E., Prahalad, P., Maahs, D. M., Johari, R., Scheinker, D. 2021

  Abstract

  OBJECTIVE: To develop and scale algorithm-enabled patient prioritization to improve population-level management of type 1 diabetes (T1D) in a pediatric clinic with fixed resources, using telemedicine and remote monitoring of patients via continuous glucose monitor (CGM) data review.RESEARCH DESIGN AND METHODS: We adapted consensus glucose targets for T1D patients using CGM to identify interpretable clinical criteria to prioritize patients for weekly provider review. The criteria were constructed to manage the number of patients reviewed weekly and identify patients who most needed provider contact. We developed an interactive dashboard to display CGM data relevant for the patients prioritized for review.RESULTS: The introduction of the new criteria and interactive dashboard was associated with a 60% reduction in the mean time spent by diabetes team members who remotely and asynchronously reviewed patient data and contacted patients, from 3.2±0.20 to 1.3±0.24minutes per patient per week. Given fixed resources for review, this corresponded to an estimated 147% increase in weekly clinic capacity. Patients who qualified for and received remote review (n=58) have associated 8.8 percentage points (pp) (95% CI=0.6-16.9pp) greater time-in-range (70-180mg/dL) glucoses compared to 25 control patients who did not qualify at twelve months after T1D onset.CONCLUSIONS: An algorithm-enabled prioritization of T1D patients with CGM for asynchronous remote review reduced provider time spent per patient and was associated with improved time-in-range. This article is protected by copyright. All rights reserved.

  View details for DOI 10.1111/pedi.13256

  View details for PubMedID 34374183

• Stanford Medicine-Engineering Partnership Launches an Interactive Model to Facilitate COVID-19 Response Planning for Hospital and Regional Leaders (4/1) Health Management, Policy and Innovation (HMPI.org) Ferstad, J. O., Gu, A., Lee, R. Y., Thapa, I., Schulman, K., Scheinker, D., Shin, A. Y. 2020; 5 (1)
• A model to forecast regional demand for COVID-19 related hospital beds Ferstad, J. O., Gu, A. J., Lee, R. Y., Thapa, I., Shin, A. Y., Salomon, J. A., Glynn, P., Shah, N. H., Milstein, A., Schulman, K., Scheinker, D. medRxiv. medRxiv. 2020

  Abstract

  COVID-19 threatens to overwhelm hospital facilities throughout the United States. We created an interactive, quantitative model that forecasts demand for COVID-19 related hospitalization based on county-level population characteristics, data from the literature on COVID-19, and data from online repositories. Using this information as well as user inputs, the model estimates a time series of demand for intensive care beds and acute care beds as well as the availability of those beds. The online model is designed to be intuitive and interactive so that local leaders with limited technical or epidemiological expertise may make decisions based on a variety of scenarios. This complements high-level models designed for public consumption and technically sophisticated models designed for use by epidemiologists. The model is actively being used by several academic medical centers and policy makers, and we believe that broader access will continue to aid community and hospital leaders in their response to COVID-19. Link to online model: https://surf.stanford.edu/covid-19-tools/covid-19/