Johannes O Ferstad1, Jacqueline J Vallon1, Daniel Jun1, Angela Gu2, Anastasiya Vitko2, Dianelys P Morales1, Jeannine Leverenz3, Ming Yeh Lee3, Brianna Leverenz3, Christos Vasilakis4, Esli Osmanlliu3,5, Priya Prahalad3,6, David M Maahs3,6,7, Ramesh Johari1,6, David Scheinker1,3,8. 1. Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California, USA. 2. Department of Computer Science, Stanford University School of Engineering, Stanford, California, USA. 3. Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA. 4. Centre for Healthcare Innovation and Improvement (CHI2), School of Management, University of Bath, Bath, UK. 5. Department of Pediatrics, Montreal Children's Hospital, McGill University Health Centre, Montreal, Canada. 6. Stanford Diabetes Research Center, Stanford University, Stanford, California, USA. 7. Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA. 8. Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, California, USA.
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 ± 0.24 min 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 = 0.6-16.9 pp) greater time-in-range (70-180 mg/dl) glucoses compared to 25 control patients who did not qualify at 12 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.
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 ± 0.24 min 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 = 0.6-16.9 pp) greater time-in-range (70-180 mg/dl) glucoses compared to 25 control patients who did not qualify at 12 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.
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