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Selmer C, Green A, Madsen S, Johannesen M, Jensen MT, Nørgaard K. Stenopool: A Comprehensive Platform for Consolidating Diabetes Device Data. J Diabetes Sci Technol 2024:19322968241264761. [PMID: 39044480 DOI: 10.1177/19322968241264761] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/25/2024]
Abstract
BACKGROUND The growing adoption of diabetes devices has highlighted the need for integrated platforms to consolidate data from various vendors and device types, enhancing the patient experience and treatment. This shift could pave the way for a transition from conventional outpatient diabetes clinics to advanced home monitoring and virtual care methods. Overall, we wished to empower individuals with diabetes and healthcare providers to interpret and utilize information from diabetes devices more effectively. METHODS Stenopool integrates most diabetes devices for glucose monitoring and insulin administration in our clinic. The platform was initially developed with inspiration from open-source software, and the current version is a unique digital platform for managing and analyzing diabetes device data. The development process, outcomes, and status are described. RESULTS Since November 2021, Stenopool has been used in our outpatient clinic to integrate over 30 different diabetes devices from around 7000 individuals. Data are primarily uploaded via wired connections, but also using semi-automated and automated cloud-to-cloud data transfers. The platform offers a streamlined workflow for healthcare providers and displays data from various glucose meter, insulin pump, and continuous glucose monitor (CGM) vendors on a single screen in a manner that healthcare providers can modify. A data warehouse with data from Stenopool and electronical health records is nearing completion, preparing the development of tools for population health management, quality assessment, and risk stratification of patients. CONCLUSION Using Stenopool, we aimed to enhance diabetes device data management, facilitate the future for virtual patient care pathways, and improve outcomes. This article outlines the platform's development process and challenges.
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Affiliation(s)
- Christian Selmer
- Steno Diabetes Center Copenhagen, Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
- Department of Endocrinology, Bispebjerg and Frederiksberg Hospital, Copenhagen University Hospital, Copenhagen, Denmark
| | - Allan Green
- Steno Diabetes Center Copenhagen, Copenhagen University Hospital, Herlev, Denmark
| | | | | | - Magnus T Jensen
- Steno Diabetes Center Copenhagen, Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Kirsten Nørgaard
- Steno Diabetes Center Copenhagen, Copenhagen University Hospital, Herlev, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
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Prahalad P, Scheinker D, Desai M, Ding VY, Bishop FK, Lee MY, Ferstad J, Zaharieva DP, Addala A, Johari R, Hood K, Maahs DM. Equitable implementation of a precision digital health program for glucose management in individuals with newly diagnosed type 1 diabetes. Nat Med 2024; 30:2067-2075. [PMID: 38702523 DOI: 10.1038/s41591-024-02975-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 04/03/2024] [Indexed: 05/06/2024]
Abstract
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. ClinicalTrials.gov registration: NCT04336969 .
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Affiliation(s)
- Priya Prahalad
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA.
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA.
| | - David Scheinker
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
- Clinical Excellence Research Center, Stanford University, Stanford, CA, USA
| | - Manisha Desai
- Department of Medicine, Quantitative Sciences Unit, Stanford University, Stanford, CA, USA
| | - Victoria Y Ding
- Department of Medicine, Quantitative Sciences Unit, Stanford University, Stanford, CA, USA
| | - Franziska K Bishop
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Ming Yeh Lee
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
| | - Johannes Ferstad
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Dessi P Zaharieva
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
| | - Ananta Addala
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - Ramesh Johari
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Korey Hood
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - David M Maahs
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Department of Health Research and Policy (Epidemiology), Stanford University, Stanford, CA, USA
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Gilliam LK, Parker MM, Moffet HH, Lee AK, Karter AJ. Continuous Glucose Monitor Metrics Are Associated with Emergency Department Visits and Hospitalizations for Hypoglycemia and Hyperglycemia, but Have Low Predictive Value. Diabetes Technol Ther 2024; 26:298-306. [PMID: 38277155 PMCID: PMC11058412 DOI: 10.1089/dia.2023.0493] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/27/2024]
Abstract
Objective: Determine whether continuous glucose monitor (CGM) metrics can provide actionable advance warning of an emergency department (ED) visit or hospitalization for hypoglycemic or hyperglycemic (dysglycemic) events. Research Design and Methods: Two nested case-control studies were conducted among insulin-treated diabetes patients at Kaiser Permanente, who shared their CGM data with their providers. Cases included dysglycemic events identified from ED and hospital records (2016-2021). Controls were selected using incidence density sampling. Multiple CGM metrics were calculated among patients using CGM >70% of the time, using CGM data from two lookback periods (0-7 and 8-14 days) before each event. Generalized estimating equations were specified to estimate odds ratios and C-statistics. Results: Among 3626 CGM users, 108 patients had 154 hypoglycemic events and 165 patients had 335 hyperglycemic events. Approximately 25% of patients had no CGM data during either lookback; these patients had >2 × the odds of a hypoglycemic event and 3-4 × the odds of a hyperglycemic event. While several metrics were strongly associated with a dysglycemic event, none had good discrimination. Conclusion: Several CGM metrics were strongly associated with risk of dysglycemic events, and these can be used to identify higher risk patients. Also, patients who are not using their CGM device may be at elevated risk of adverse outcomes. However, no CGM metric or absence of CGM data had adequate discrimination to reliably provide actionable advance warning of an event and thus justify a rapid intervention.
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Affiliation(s)
- Lisa K. Gilliam
- Kaiser Northern California Diabetes Program, Endocrinology and Internal Medicine, Kaiser Permanente, South San Francisco Medical Center, South San Francisco, California, USA
| | | | - Howard H. Moffet
- Division of Research, Kaiser Permanente, Oakland, California, USA
| | - Alexandra K. Lee
- Division of Geriatrics, University of California, San Francisco, San Francisco, California, USA
| | - Andrew J. Karter
- Division of Research, Kaiser Permanente, Oakland, California, USA
- Department of General Internal Medicine, University of California, San Francisco, San Francisco, California, USA
- Department of Health Systems and Population Health, University of Washington, Seattle, Washington, USA
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Giammarino F, Senanayake R, Prahalad P, Maahs DM, Scheinker D. A Machine Learning Model for Week-Ahead Hypoglycemia Prediction From Continuous Glucose Monitoring Data. J Diabetes Sci Technol 2024:19322968241236208. [PMID: 38445628 DOI: 10.1177/19322968241236208] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/07/2024]
Abstract
BACKGROUND Remote patient monitoring (RPM) programs augment type 1 diabetes (T1D) care based on retrospective continuous glucose monitoring (CGM) data. Few methods are available to estimate the likelihood of a patient experiencing clinically significant hypoglycemia within one week. METHODS We developed a machine learning model to estimate the probability that a patient will experience a clinically significant hypoglycemic event, defined as CGM readings below 54 mg/dL for at least 15 consecutive minutes, within one week. The model takes as input the patient's CGM time series over a given week, and outputs the predicted probability of a clinically significant hypoglycemic event the following week. We used 10-fold cross-validation and external validation (testing on cohorts different from the training cohort) to evaluate performance. We used CGM data from three different cohorts of patients with T1D: REPLACE-BG (226 patients), Juvenile Diabetes Research Foundation (JDRF; 355 patients) and Tidepool (120 patients). RESULTS In 10-fold cross-validation, the average area under the receiver operating characteristic curve (ROC-AUC) was 0.77 (standard deviation [SD]: 0.0233) on the REPLACE-BG cohort, 0.74 (SD: 0.0188) on the JDRF cohort, and 0.76 (SD: 0.02) on the Tidepool cohort. In external validation, the average ROC-AUC across the three cohorts was 0.74 (SD: 0.0262). CONCLUSIONS We developed a machine learning algorithm to estimate the probability of a clinically significant hypoglycemic event within one week. Predictive algorithms may provide diabetes care providers using RPM with additional context when prioritizing T1D patients for review.
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Affiliation(s)
| | | | - Priya Prahalad
- Division of Endocrinology, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA, USA
- Stanford Children's Health, Lucile Packard Children's Hospital, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - David M Maahs
- Division of Endocrinology, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA, USA
- Stanford Children's Health, Lucile Packard Children's Hospital, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
| | - David Scheinker
- Division of Endocrinology, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA, USA
- Stanford Children's Health, Lucile Packard Children's Hospital, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, USA
- Department of Management Science and Engineering, School of Engineering, Stanford University, Stanford, CA, USA
- Clinical Excellence Research Center, School of Medicine, Stanford University, Stanford, CA, USA
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5
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Ferstad JO, Prahalad P, Maahs DM, Zaharieva DP, Fox E, Desai M, Johari R, Scheinker D. Smart Start - Designing Powerful Clinical Trials Using Pilot Study Data. NEJM EVIDENCE 2024; 3:EVIDoa2300164. [PMID: 38320487 DOI: 10.1056/evidoa2300164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
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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Affiliation(s)
- Johannes O Ferstad
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA
| | - Priya Prahalad
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, CA
| | - David M Maahs
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, CA
| | - Dessi P Zaharieva
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, CA
| | - Emily Fox
- Department of Statistics, Stanford University, Stanford, CA
- Department of Computer Science, Stanford University, Stanford, CA
- Chan Zuckerberg Biohub, San Francisco
| | - Manisha Desai
- Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Ramesh Johari
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA
| | - David Scheinker
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, CA
- Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA
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6
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Leverenz JC, Leverenz B, Prahalad P, Bishop FK, Sagan P, Martinez-Singh A, Conrad B, Chmielewski A, Senaldi J, Scheinker D, Maahs DM. Role and Perspective of Certified Diabetes Care and Education Specialists in the Development of the 4T Program. Diabetes Spectr 2024; 37:153-159. [PMID: 38756427 PMCID: PMC11093765 DOI: 10.2337/ds23-0010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/18/2024]
Affiliation(s)
- Jeannine C. Leverenz
- Lucile Packard Children’s Hospital, Division of Pediatric Endocrinology, Palo Alto, CA
| | - Brianna Leverenz
- Stritch School of Medicine, Loyola University Chicago, Maywood, IL
| | - Priya Prahalad
- Lucile Packard Children’s Hospital, Division of Pediatric Endocrinology, Palo Alto, CA
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford, CA
| | - Franziska K. Bishop
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA
| | - Piper Sagan
- Lucile Packard Children’s Hospital, Division of Pediatric Endocrinology, Palo Alto, CA
| | - Anjoli Martinez-Singh
- Lucile Packard Children’s Hospital, Division of Pediatric Endocrinology, Palo Alto, CA
| | - Barry Conrad
- Lucile Packard Children’s Hospital, Division of Pediatric Endocrinology, Palo Alto, CA
| | - Annette Chmielewski
- Lucile Packard Children’s Hospital, Division of Pediatric Endocrinology, Palo Alto, CA
| | - Julianne Senaldi
- Lucile Packard Children’s Hospital, Division of Pediatric Endocrinology, Palo Alto, CA
| | - David Scheinker
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA
| | - David M. Maahs
- Lucile Packard Children’s Hospital, Division of Pediatric Endocrinology, Palo Alto, CA
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford, CA
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Franceschi R, Tornese G. Sustaining telehealth in pediatric diabetology beyond COVID-19: How to set the tone. Digit Health 2024; 10:20552076241249272. [PMID: 39156051 PMCID: PMC11329951 DOI: 10.1177/20552076241249272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 04/08/2024] [Indexed: 08/20/2024] Open
Abstract
In the post-COVID-19 era, telehealth experience and knowledge must be structured to deliver high-quality care. Type 1 diabetes is a chronic disease that lends itself to being a model for telehealth diffusion, especially in the pediatric setting where the use of cloud-connected technologies is widespread. Here, we present "how to set the tone" and manage a telemedicine session according to our experiences and those reported in the literature, according to the health professional perspective. A practical workflow on how healthcare professionals can structure a virtual diabetes clinic is reported, as well as critical issues related to limits in physical examination, communication registers, relationships, and visit settings. A proactive virtual visit model could be feasible, stratifying patients according to continuous glucose monitoring metrics, and personalized interventions can be provided to each patient. Analysis of benefits and hassles due to telehealth for each patient has to be considered, as well as their personal perspective, expectations, and reported barriers, mainly related to connection issues and digital literacy.
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Affiliation(s)
| | - Gianluca Tornese
- Pediatric Department, Institute for Maternal and Child Health IRCCS Burlo Garofolo, Trieste, Italy
- Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
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Prahalad P, Maahs DM. Roadmap to Continuous Glucose Monitoring Adoption and Improved Outcomes in Endocrinology: The 4T (Teamwork, Targets, Technology, and Tight Control) Program. Diabetes Spectr 2023; 36:299-305. [PMID: 37982062 PMCID: PMC10654131 DOI: 10.2337/dsi23-0003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/21/2023]
Abstract
Glucose monitoring is essential for the management of type 1 diabetes and has evolved from urine glucose monitoring in the early 1900s to home blood glucose monitoring in the 1980s to continuous glucose monitoring (CGM) today. Youth with type 1 diabetes struggle to meet A1C goals; however, CGM is associated with improved A1C in these youth and is recommended as a standard of care by diabetes professional organizations. Despite their utility, expanding uptake of CGM systems has been challenging, especially in minoritized communities. The 4T (Teamwork, Targets, Technology, and Tight Control) program was developed using a team-based approach to set consistent glycemic targets and equitably initiate CGM and remote patient monitoring in all youth with new-onset type 1 diabetes. In the pilot 4T study, youth in the 4T cohort had a 0.5% improvement in A1C 12 months after diabetes diagnosis compared with those in the historical cohort. The 4T program can serve as a roadmap for other multidisciplinary pediatric type 1 diabetes clinics to increase CGM adoption and improve glycemic outcomes.
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Affiliation(s)
- Priya Prahalad
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA
| | - David M. Maahs
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA
- Stanford Diabetes Research Center, Stanford University, Stanford, CA
- Department of Health Research and Policy (Epidemiology), Stanford University, Stanford, CA
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Cui EH, Goldfine AB, Quinlan M, James DA, Sverdlov O. Investigating the value of glucodensity analysis of continuous glucose monitoring data in type 1 diabetes: an exploratory analysis. FRONTIERS IN CLINICAL DIABETES AND HEALTHCARE 2023; 4:1244613. [PMID: 37753312 PMCID: PMC10518413 DOI: 10.3389/fcdhc.2023.1244613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 08/14/2023] [Indexed: 09/28/2023]
Abstract
Introduction Continuous glucose monitoring (CGM) devices capture longitudinal data on interstitial glucose levels and are increasingly used to show the dynamics of diabetes metabolism. Given the complexity of CGM data, it is crucial to extract important patterns hidden in these data through efficient visualization and statistical analysis techniques. Methods In this paper, we adopted the concept of glucodensity, and using a subset of data from an ongoing clinical trial in pediatric individuals and young adults with new-onset type 1 diabetes, we performed a cluster analysis of glucodensities. We assessed the differences among the identified clusters using analysis of variance (ANOVA) with respect to residual pancreatic beta-cell function and some standard CGM-derived parameters such as time in range, time above range, and time below range. Results Distinct CGM data patterns were identified using cluster analysis based on glucodensities. Statistically significant differences were shown among the clusters with respect to baseline levels of pancreatic beta-cell function surrogate (C-peptide) and with respect to time in range and time above range. Discussion Our findings provide supportive evidence for the value of glucodensity in the analysis of CGM data. Some challenges in the modeling of CGM data include unbalanced data structure, missing observations, and many known and unknown confounders, which speaks to the importance of--and provides opportunities for--taking an approach integrating clinical, statistical, and data science expertise in the analysis of these data.
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Affiliation(s)
- Elvis Han Cui
- Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, United States
| | - Allison B. Goldfine
- Division of Translational Medicine, Cardiometabolic Disease, Novartis Institutes for Biomedical Research, Cambridge, MA, United States
| | - Michelle Quinlan
- Early Development Analytics, Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States
| | - David A. James
- Methodology and Data Science, Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States
| | - Oleksandr Sverdlov
- Early Development Analytics, Novartis Pharmaceuticals Corporation, East Hanover, NJ, United States
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Chang A, Gao MZ, Ferstad JO, Dupenloup P, Zaharieva DP, Maahs DM, Prahalad P, Johari R, Scheinker D. A quantitative model to ensure capacity sufficient for timely access to care in a remote patient monitoring program. Endocrinol Diabetes Metab 2023; 6:e435. [PMID: 37345227 PMCID: PMC10495556 DOI: 10.1002/edm2.435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 05/31/2023] [Accepted: 06/03/2023] [Indexed: 06/23/2023] Open
Abstract
INTRODUCTION Algorithm-enabled remote patient monitoring (RPM) programs pose novel operational challenges. For clinics developing and deploying such programs, no standardized model is available to ensure capacity sufficient for timely access to care. We developed a flexible model and interactive dashboard of capacity planning for whole-population RPM-based care for T1D. METHODS Data were gathered from a weekly RPM program for 277 paediatric patients with T1D at a paediatric academic medical centre. Through the analysis of 2 years of observational operational data and iterative interviews with the care team, we identified the primary operational, population, and workforce metrics that drive demand for care providers. Based on these metrics, an interactive model was designed to facilitate capacity planning and deployed as a dashboard. RESULTS The primary population-level drivers of demand are the number of patients in the program, the rate at which patients enrol and graduate from the program, and the average frequency at which patients require a review of their data. The primary modifiable clinic-level drivers of capacity are the number of care providers, the time required to review patient data and contact a patient, and the number of hours each provider allocates to the program each week. At the institution studied, the model identified a variety of practical operational approaches to better match the demand for patient care. CONCLUSION We designed a generalizable, systematic model for capacity planning for a paediatric endocrinology clinic providing RPM for T1D. We deployed this model as an interactive dashboard and used it to facilitate expansion of a novel care program (4 T Study) for newly diagnosed patients with T1D. This model may facilitate the systematic design of RPM-based care programs.
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Affiliation(s)
- Annie Chang
- Icahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Management Science and EngineeringStanford UniversityStanfordCaliforniaUSA
| | - Michael Z. Gao
- Department of Management Science and EngineeringStanford UniversityStanfordCaliforniaUSA
| | - Johannes O. Ferstad
- Department of Management Science and EngineeringStanford UniversityStanfordCaliforniaUSA
| | - Paul Dupenloup
- Department of Management Science and EngineeringStanford UniversityStanfordCaliforniaUSA
| | - Dessi P. Zaharieva
- Department of Paediatric, Division of Paediatric EndocrinologyStanford UniversityStanfordCaliforniaUSA
| | - David M. Maahs
- Department of Paediatric, Division of Paediatric EndocrinologyStanford UniversityStanfordCaliforniaUSA
- Stanford Diabetes Research CentreStanford UniversityStanfordCaliforniaUSA
| | - Priya Prahalad
- Department of Paediatric, Division of Paediatric EndocrinologyStanford UniversityStanfordCaliforniaUSA
| | - Ramesh Johari
- Department of Management Science and EngineeringStanford UniversityStanfordCaliforniaUSA
- Stanford Diabetes Research CentreStanford UniversityStanfordCaliforniaUSA
| | - David Scheinker
- Department of Management Science and EngineeringStanford UniversityStanfordCaliforniaUSA
- Department of Paediatric, Division of Paediatric EndocrinologyStanford UniversityStanfordCaliforniaUSA
- Stanford Diabetes Research CentreStanford UniversityStanfordCaliforniaUSA
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Aleppo G, Chmiel R, Zurn A, Bandoske R, Creamer P, Neubauer N, Wong J, Andrade SB, Hauptman A. Integration of Continuous Glucose Monitoring Data into an Electronic Health Record System: Single-Center Implementation. J Diabetes Sci Technol 2023:19322968231196168. [PMID: 37644816 DOI: 10.1177/19322968231196168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Managing data from continuous glucose monitoring (CGM) systems presents challenges to health care provider teams that rely on the electronic health record (EHR) during patient visits. A method of integrating CGM data with the EHR that relies on the Dexcom API was developed by Northwestern Medicine and Dexcom to address these challenges. Here, we describe the data management steps and user interface of the integrated system. Providers can access patients' historical and latest daily CGM data in the form of modal day plots and stacked columns showing time in various glucose concentration ranges. The integration facilitates the acquisition, storage, analysis, and display of CGM data within an EHR system and may be appropriate for deployment in other health care facilities.
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Affiliation(s)
| | | | | | | | | | | | - Jo Wong
- Dexcom, Inc., San Diego, CA, USA
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12
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Lee MY, Gloyn AL, Maahs DM, Prahalad P. Management of Neonatal Diabetes due to a KCNJ11 Mutation with Automated Insulin Delivery System and Remote Patient Monitoring. Case Rep Endocrinol 2023; 2023:8825724. [PMID: 37664823 PMCID: PMC10468271 DOI: 10.1155/2023/8825724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 07/25/2023] [Accepted: 08/16/2023] [Indexed: 09/05/2023] Open
Abstract
Neonatal diabetes mellitus (NDM) is a monogenic form of diabetes. Management of hyperglycemia in neonates with subcutaneous insulin is challenging because of frequent feeding, variable quantity of milk intake with each feed, low insulin dose requirements, and high risk for hypoglycemia and associated complications in this population. We present a case of NDM in a proband initially presenting with focal seizures and diabetic ketoacidosis due to a pathologic mutation in the beta cell potassium ATP channel gene KCNJ11 c.679G > A (p.E227K). We describe the use of continuous glucose monitoring (CGM), insulin pump, automated insulin delivery system, and remote patient monitoring technologies to facilitate rapid and safe outpatient cross-titration from insulin to oral sulfonylurea. Our case highlights the safety and efficacy of these technologies for infants with diabetes, including improvements in glycemia, quality of life, and cost-effectiveness by shortening hospital stay.
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Affiliation(s)
- Ming Yeh Lee
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, CA, USA
| | - Anna L. Gloyn
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - David M. Maahs
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford University School of Medicine, Stanford, CA, USA
| | - Priya Prahalad
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, CA, USA
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Addala A, Ding V, Zaharieva DP, Bishop FK, Adams AS, King AC, Johari R, Scheinker D, Hood KK, Desai M, Maahs DM, Prahalad P. Disparities in Hemoglobin A1c Levels in the First Year After Diagnosis Among Youths With Type 1 Diabetes Offered Continuous Glucose Monitoring. JAMA Netw Open 2023; 6:e238881. [PMID: 37074715 PMCID: PMC10116368 DOI: 10.1001/jamanetworkopen.2023.8881] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Accepted: 03/05/2023] [Indexed: 04/20/2023] Open
Abstract
Importance Continuous glucose monitoring (CGM) is associated with improvements in hemoglobin A1c (HbA1c) in youths with type 1 diabetes (T1D); however, youths from minoritized racial and ethnic groups and those with public insurance face greater barriers to CGM access. Early initiation of and access to CGM may reduce disparities in CGM uptake and improve diabetes outcomes. Objective To determine whether HbA1c decreases differed by ethnicity and insurance status among a cohort of youths newly diagnosed with T1D and provided CGM. Design, Setting, and Participants This cohort study used data from the Teamwork, Targets, Technology, and Tight Control (4T) study, a clinical research program that aims to initiate CGM within 1 month of T1D diagnosis. All youths with new-onset T1D diagnosed between July 25, 2018, and June 15, 2020, at Stanford Children's Hospital, a single-site, freestanding children's hospital in California, were approached to enroll in the Pilot-4T study and were followed for 12 months. Data analysis was performed and completed on June 3, 2022. Exposures All eligible participants were offered CGM within 1 month of diabetes diagnosis. Main Outcomes and Measures To assess HbA1c change over the study period, analyses were stratified by ethnicity (Hispanic vs non-Hispanic) or insurance status (public vs private) to compare the Pilot-4T cohort with a historical cohort of 272 youths diagnosed with T1D between June 1, 2014, and December 28, 2016. Results The Pilot-4T cohort comprised 135 youths, with a median age of 9.7 years (IQR, 6.8-12.7 years) at diagnosis. There were 71 boys (52.6%) and 64 girls (47.4%). Based on self-report, participants' race was categorized as Asian or Pacific Islander (19 [14.1%]), White (62 [45.9%]), or other race (39 [28.9%]); race was missing or not reported for 15 participants (11.1%). Participants also self-reported their ethnicity as Hispanic (29 [21.5%]) or non-Hispanic (92 [68.1%]). A total of 104 participants (77.0%) had private insurance and 31 (23.0%) had public insurance. Compared with the historical cohort, similar reductions in HbA1c at 6, 9, and 12 months postdiagnosis were observed for Hispanic individuals (estimated difference, -0.26% [95% CI, -1.05% to 0.43%], -0.60% [-1.46% to 0.21%], and -0.15% [-1.48% to 0.80%]) and non-Hispanic individuals (estimated difference, -0.27% [95% CI, -0.62% to 0.10%], -0.50% [-0.81% to -0.11%], and -0.47% [-0.91% to 0.06%]) in the Pilot-4T cohort. Similar reductions in HbA1c at 6, 9, and 12 months postdiagnosis were also observed for publicly insured individuals (estimated difference, -0.52% [95% CI, -1.22% to 0.15%], -0.38% [-1.26% to 0.33%], and -0.57% [-2.08% to 0.74%]) and privately insured individuals (estimated difference, -0.34% [95% CI, -0.67% to 0.03%], -0.57% [-0.85% to -0.26%], and -0.43% [-0.85% to 0.01%]) in the Pilot-4T cohort. Hispanic youths in the Pilot-4T cohort had higher HbA1c at 6, 9, and 12 months postdiagnosis than non-Hispanic youths (estimated difference, 0.28% [95% CI, -0.46% to 0.86%], 0.63% [0.02% to 1.20%], and 1.39% [0.37% to 1.96%]), as did publicly insured youths compared with privately insured youths (estimated difference, 0.39% [95% CI, -0.23% to 0.99%], 0.95% [0.28% to 1.45%], and 1.16% [-0.09% to 2.13%]). Conclusions and Relevance The findings of this cohort study suggest that CGM initiation soon after diagnosis is associated with similar improvements in HbA1c for Hispanic and non-Hispanic youths as well as for publicly and privately insured youths. These results further suggest that equitable access to CGM soon after T1D diagnosis may be a first step to improve HbA1c for all youths but is unlikely to eliminate disparities entirely. Trial Registration ClinicalTrials.gov Identifier: NCT04336969.
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Affiliation(s)
- Ananta Addala
- Division of Pediatric Endocrinology, Department of Pediatrics, Stanford University, Stanford, California
| | - Victoria Ding
- Division of Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California
| | - Dessi P. Zaharieva
- Division of Pediatric Endocrinology, Department of Pediatrics, Stanford University, Stanford, California
| | - Franziska K. Bishop
- Division of Pediatric Endocrinology, Department of Pediatrics, Stanford University, Stanford, California
| | - Alyce S. Adams
- Division of Pediatric Endocrinology, Department of Pediatrics, Stanford University, Stanford, California
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
- Department of Health Policy, Stanford University School of Medicine, Stanford, California
- Stanford Diabetes Research Center, Stanford University, Stanford, California
| | - Abby C. King
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
- Stanford Prevention Research Center Division, Department of Medicine, Stanford University School of Medicine, Stanford, California
| | - Ramesh Johari
- Clinical Excellence Research Center, Stanford University, Stanford, California
| | - David Scheinker
- Division of Pediatric Endocrinology, Department of Pediatrics, Stanford University, Stanford, California
- Stanford Diabetes Research Center, Stanford University, Stanford, California
- Clinical Excellence Research Center, Stanford University, Stanford, California
- Department of Management Science and Engineering, Stanford University, Stanford, California
| | - Korey K. Hood
- Division of Pediatric Endocrinology, Department of Pediatrics, Stanford University, Stanford, California
- Stanford Diabetes Research Center, Stanford University, Stanford, California
| | - Manisha Desai
- Division of Biomedical Informatics Research, Department of Medicine, Stanford University, Stanford, California
| | - David M. Maahs
- Division of Pediatric Endocrinology, Department of Pediatrics, Stanford University, Stanford, California
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, California
- Stanford Diabetes Research Center, Stanford University, Stanford, California
| | - Priya Prahalad
- Division of Pediatric Endocrinology, Department of Pediatrics, Stanford University, Stanford, California
- Stanford Diabetes Research Center, Stanford University, Stanford, California
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Nimri R, Phillip M, Kovatchev B. Closed-Loop and Artificial Intelligence-Based Decision Support Systems. Diabetes Technol Ther 2023; 25:S70-S89. [PMID: 36802182 DOI: 10.1089/dia.2023.2505] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Affiliation(s)
- Revital Nimri
- Diabetes Technology Center, Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Moshe Phillip
- Diabetes Technology Center, Jesse Z and Sara Lea Shafer Institute for Endocrinology and Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Boris Kovatchev
- University of Virginia Center for Diabetes Technology, University of Virginia School of Medicine, Charlottesville, VA, USA
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15
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Clements M, Kaufman N, Mel E. Using Digital Health Technology to Prevent and Treat Diabetes. Diabetes Technol Ther 2023; 25:S90-S108. [PMID: 36802181 DOI: 10.1089/dia.2023.2506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Affiliation(s)
- Mark Clements
- Children's Mercy Hospital, Kansas City, MO, USA
- University of Missouri-Kansas City, Kansas City, MO, USA
| | - Neal Kaufman
- Fielding School of Public Health, Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Canary Health Inc., Los Angeles, CA, USA
| | - Eran Mel
- Jesse Z. and Sara Lea Shafer Institute for Endocrinology and Diabetes, National Center for Childhood Diabetes, Schneider Children's Medical Center of Israel, Petah Tikva, Israel
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16
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Crossen SS, Romero CC, Lewis C, Glaser NS. Remote glucose monitoring is feasible for patients and providers using a commercially available population health platform. Front Endocrinol (Lausanne) 2023; 14:1063290. [PMID: 36817610 PMCID: PMC9931729 DOI: 10.3389/fendo.2023.1063290] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 01/16/2023] [Indexed: 02/05/2023] Open
Abstract
OBJECTIVE Remote patient monitoring (RPM) holds potential to enable more individualized and effective care for patients with type 1 diabetes (T1D), but requires population analytics to focus limited clinical resources on patients most in need. We explored the feasibility of RPM from patient and provider standpoints using a commercially available data analytic platform (glooko Population Health) among a cohort of youth with T1D. STUDY DESIGN Patients aged 1-20 years with established T1D (≥12 months) and CGM use (≥3 months) were recruited to participate. Participants' CGM devices were connected to the glooko app and linked to the research team's glooko account during a one-month baseline period. This was followed by a six-month intervention period during which participants with >15% of glucose values >250 mg/dl or >5% of values <70 mg/dl each month were contacted with personalized diabetes management recommendations. Participants were surveyed about their experiences, and effects on glycemic control were estimated via change in glucose management indicator (GMI) generated from CGM data at baseline and completion. Changes in time spent within various glucose ranges were also evaluated, and all glycemic metrics were compared to a non-randomized control group via difference-in-difference regression, adjusting for baseline characteristics. RESULTS Remote data-sharing was successful for 36 of 39 participants (92%). Between 33%-66% of participants merited outreach each month, and clinician outreach required a median of 10 minutes per event. RPM was reported to be helpful by 94% of participants. RPM was associated with a GMI change of -0.25% (P=0.047) for the entire cohort, and stratified analysis revealed greatest treatment effects among participants with baseline GMI of 8.0-9.4% (GMI change of -0.68%, P=0.047; 19.84% reduction in time spent >250 mg/dl, P=0.005). CONCLUSIONS This study demonstrates the feasibility of RPM for patients with T1D using a commercially available population health platform, and suggests that RPM with clinician-initiated outreach may be particularly beneficial for patients with suboptimal glycemic control at entry. However, larger randomized studies are needed to fully explore the glycemic impact of RPM. CLINICAL TRIAL REGISTRATION https://clinicaltrials.gov/ct2/show/NCT04696640, identifier NCT04696640.
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Affiliation(s)
- Stephanie S. Crossen
- Department of Pediatrics, University of California Davis, Sacramento, CA, United States
- Center for Healthcare Policy and Research, University of California Davis, Sacramento, CA, United States
- *Correspondence: Stephanie S. Crossen,
| | - Crystal C. Romero
- Department of Pediatrics, University of California Davis, Sacramento, CA, United States
| | - Carrie Lewis
- Center for Healthcare Policy and Research, University of California Davis, Sacramento, CA, United States
| | - Nicole S. Glaser
- Department of Pediatrics, University of California Davis, Sacramento, CA, United States
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17
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Denker K, Dean M, Chapman D, Sump C. Using a centralized nursing team to implement multi-specialty pediatric remote patient monitoring programs. J Pediatr Nurs 2022; 69:10-17. [PMID: 36592607 PMCID: PMC9803966 DOI: 10.1016/j.pedn.2022.12.028] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 12/19/2022] [Accepted: 12/26/2022] [Indexed: 01/01/2023]
Abstract
BACKGROUND The increase in telehealth usage has sustained since the beginning of the COVID-19 pandemic. While Remote Patient Monitoring (RPM) programs are abundantly used in the management of adults, pediatric RPM programs remain rare. METHODS An RPM department was developed to serve several, multi-specialty pediatric programs. This department uses a centralized nursing team that manages all patients enrolled in RPM programs. Each program is unique and created in partnership with the centralized nurses and the ambulatory care teams. The various programs allow for transmission of patient- and caregiver-generated health data and consistent communication between the patient or caregiver and the managing providers, allowing for real-time plan adaptation. FINDINGS Over 1200 patients have been managed through the 18 various RPM programs. Approximately 300 patients are monitored each month by the centralized nursing team. Patient and caregiver experience has been high due to resources offered including on-demand video visits and text messaging with the nursing team. DISCUSSION Multi-specialty RPM departments help to expand the reach of an institution and provide care to more patients. Quality improvement must be ongoing to ensure equity of participation and perceived benefit of the programs for both providers and patients and caregivers. APPLICATION TO PRACTICE Pediatric RPM programs can improve patient care delivery by decreasing days away from home while improving access to care. Ensuring equitable opportunity for patient participation is imperative in achieving success for an RPM department.
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Affiliation(s)
- Kylee Denker
- Center for Telehealth, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH 45229, United States of America.
| | - Micah Dean
- Center for Telehealth, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH 45229, United States of America.
| | - DaVona Chapman
- The James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH 45229, United States of America.
| | - Courtney Sump
- Division of Hospital Medicine, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, Cincinnati, OH 45229, United States of America; Department of Pediatrics, College of Medicine, University of Cincinnati, 3230 Eden Ave, Cincinnati, OH 45267, United States of America.
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18
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Dupenloup P, Pei RL, Chang A, Gao MZ, Prahalad P, Johari R, Schulman K, Addala A, Zaharieva DP, Maahs DM, Scheinker D. A model to design financially sustainable algorithm-enabled remote patient monitoring for pediatric type 1 diabetes care. Front Endocrinol (Lausanne) 2022; 13:1021982. [PMID: 36440201 PMCID: PMC9691757 DOI: 10.3389/fendo.2022.1021982] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 10/21/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Population-level algorithm-enabled remote patient monitoring (RPM) based on continuous glucose monitor (CGM) data review has been shown to improve clinical outcomes in diabetes patients, especially children. However, existing reimbursement models are geared towards the direct provision of clinic care, not population health management. We developed a financial model to assist pediatric type 1 diabetes (T1D) clinics design financially sustainable RPM programs based on algorithm-enabled review of CGM data. Methods Data were gathered from a weekly RPM program for 302 pediatric patients with T1D at Lucile Packard Children's Hospital. We created a customizable financial model to calculate the yearly marginal costs and revenues of providing diabetes education. We consider a baseline or status quo scenario and compare it to two different care delivery scenarios, in which routine appointments are supplemented with algorithm-enabled, flexible, message-based contacts delivered according to patient need. We use the model to estimate the minimum reimbursement rate needed for telemedicine contacts to maintain revenue-neutrality and not suffer an adverse impact to the bottom line. Results The financial model estimates that in both scenarios, an average reimbursement rate of roughly $10.00 USD per telehealth interaction would be sufficient to maintain revenue-neutrality. Algorithm-enabled RPM could potentially be billed for using existing RPM CPT codes and lead to margin expansion. Conclusion We designed a model which evaluates the financial impact of adopting algorithm-enabled RPM in a pediatric endocrinology clinic serving T1D patients. This model establishes a clear threshold reimbursement value for maintaining revenue-neutrality, as well as an estimate of potential RPM reimbursement revenue which could be billed for. It may serve as a useful financial-planning tool for a pediatric T1D clinic seeking to leverage algorithm-enabled RPM to provide flexible, more timely interventions to its patients.
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Affiliation(s)
- Paul Dupenloup
- Department of Management Science and Engineering, Stanford University, Stanford, CA, United States
| | - Ryan Leonard Pei
- Department of Management Science and Engineering, Stanford University, Stanford, CA, United States
| | - Annie Chang
- Department of Management Science and Engineering, Stanford University, Stanford, CA, United States
| | - Michael Z. Gao
- Department of Management Science and Engineering, Stanford University, Stanford, CA, United States
| | - Priya Prahalad
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States
| | - Ramesh Johari
- Department of Management Science and Engineering, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States
| | - Kevin Schulman
- Clinical Excellence Research Center, Stanford University, Stanford, CA, United States
- Graduate School of Business, Stanford University, Stanford, CA, United States
| | - Ananta Addala
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, United States
| | - Dessi P. Zaharieva
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, United States
| | - David M. Maahs
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States
| | - David Scheinker
- Department of Management Science and Engineering, Stanford University, Stanford, CA, United States
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, United States
- Clinical Excellence Research Center, Stanford University, Stanford, CA, United States
- Department of Medicine, Division of Biomedical Informatics Research, Stanford University, Stanford, CA, United States
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19
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Zaharieva DP, Addala A, Prahalad P, Leverenz B, Arrizon-Ruiz N, Ding VY, Desai M, Karger AB, Maahs DM. An Evaluation of Point-of-Care HbA1c, HbA1c Home Kits, and Glucose Management Indicator: Potential Solutions for Telehealth Glycemic Assessments. DIABETOLOGY 2022; 3:494-501. [PMID: 37163187 PMCID: PMC10166120 DOI: 10.3390/diabetology3030037] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
During the COVID-19 pandemic, fewer in-person clinic visits resulted in fewer point-of-care (POC) HbA1c measurements. In this sub-study, we assessed the performance of alternative glycemic measures that can be obtained remotely, such as HbA1c home kits and Glucose Management Indicator (GMI) values from Dexcom Clarity. Home kit HbA1c (n = 99), GMI, (n = 88), and POC HbA1c (n = 32) were collected from youth with T1D (age 9.7 ± 4.6 years). Bland-Altman analyses and Lin's concordance correlation coefficient (ρc) were used to characterize the agreement between paired HbA1c measures. Both the HbA1c home kit and GMI showed a slight positive bias (mean difference 0.18% and 0.34%, respectively) and strong concordance with POC HbA1c (ρc = 0.982 [0.965, 0.991] and 0.823 [0.686, 0.904], respectively). GMI showed a slight positive bias (mean difference 0.28%) and fair concordance (ρc = 0.750 [0.658, 0.820]) to the HbA1c home kit. In conclusion, the strong concordance of GMI and home kits to POC A1c measures suggest their utility in telehealth visits assessments. Although these are not candidates for replacement, these measures can facilitate telehealth visits, particularly in the context of other POC HbA1c measurements from an individual.
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Affiliation(s)
- Dessi P. Zaharieva
- Division of Endocrinology, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94304, USA
- Correspondence: ; Tel.: +1-(628)-238-9420; Fax: +1-(650)-475-8375
| | - Ananta Addala
- Division of Endocrinology, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94304, USA
| | - Priya Prahalad
- Division of Endocrinology, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94304, USA
- Stanford Diabetes Research Center, Stanford, CA 94304, USA
| | - Brianna Leverenz
- Division of Endocrinology, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94304, USA
| | - Nora Arrizon-Ruiz
- Division of Endocrinology, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94304, USA
| | - Victoria Y. Ding
- Quantitative Sciences Unit, Division of Biomedical Informatics Research, Stanford University, Stanford, CA 94305, USA
| | - Manisha Desai
- Quantitative Sciences Unit, Division of Biomedical Informatics Research, Stanford University, Stanford, CA 94305, USA
| | - Amy B. Karger
- Department of Laboratory Medicine and Pathology, School of Medicine, University of Minnesota, Minneapolis, MN 55455, USA
| | - David M. Maahs
- Division of Endocrinology, Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA 94304, USA
- Stanford Diabetes Research Center, Stanford, CA 94304, USA
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20
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Zaharieva DP, Bishop FK, Maahs DM. Advancements and future directions in the teamwork, targets, technology, and tight control-the 4T study: improving clinical outcomes in newly diagnosed pediatric type 1 diabetes. Curr Opin Pediatr 2022; 34:423-429. [PMID: 35836400 PMCID: PMC9298953 DOI: 10.1097/mop.0000000000001140] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
PURPOSE OF REVIEW The benefits of intensive diabetes management have been established by the Diabetes Control and Complications Trial. However, challenges with optimizing glycemic management in youth with type 1 diabetes (T1D) remain across pediatric clinics in the United States. This article will review our Teamwork, Targets, Technology, and Tight Control (4T) study that implements emerging diabetes technology into clinical practice with a team approach to sustain tight glycemic control from the onset of T1D and beyond to optimize clinical outcomes. RECENT FINDINGS During the 4T Pilot study and study 1, our team-based approach to intensive target setting, education, and remote data review has led to significant improvements in hemoglobin A1c throughout the first year of T1D diagnosis in youth, as well as family and provider satisfaction. SUMMARY The next steps include refinement of the current 4T study 1, developing a business case, and broader implementation of the 4T study. In study 2, we are including a more pragmatic cadence of remote data review and disseminating exercise education and activity tracking to both English- and Spanish-speaking families. The overall goal is to create and implement a translatable program that can facilitate better outcomes for pediatric clinics across the USA.
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Affiliation(s)
- Dessi P. Zaharieva
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA
| | - Franziska K. Bishop
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA
| | - David M. Maahs
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA
- Stanford Diabetes Research Center, Stanford, CA
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21
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Scheinker D, Prahalad P, Johari R, Maahs DM, Majzun R. A New Technology-Enabled Care Model for Pediatric Type 1 Diabetes. NEJM CATALYST INNOVATIONS IN CARE DELIVERY 2022; 3:10.1056/CAT.21.0438. [PMID: 36544715 PMCID: PMC9767424 DOI: 10.1056/cat.21.0438] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
In July 2018, pediatric type 1 diabetes (T1D) care at Stanford suffered many of the problems that plague U.S. health care. Patient outcomes lagged behind those of peer European nations, care was delivered primarily on a fixed cadence rather than as needed, continuous glucose monitors (CGMs) were largely unavailable for individuals with public insurance, and providers' primary access to CGM data was through long printouts. Stanford developed a new technology-enabled, telemedicine-based care model for patients with newly diagnosed T1D. They developed and deployed Timely Interventions for Diabetes Excellence (TIDE) to facilitate as-needed patient contact with the partially automated analysis of CGM data and used philanthropic funding to facilitate full access to CGM technology for publicly insured patients, for whom CGM is not readily available in California. A study of the use of CGM for patients with new-onset T1D (pilot Teamwork, Targets, and Technology for Tight Control [4T] study), which incorporated the use of TIDE, was associated with a 0.5%-point reduction in hemoglobin A1c compared with historical controls and an 86% reduction in screen time for providers reviewing patient data. Based on this initial success, Stanford expanded the use of TIDE to a total of 300 patients, including many outside the pilot 4T study, and made TIDE freely available as open-source software. Next steps include expanding the use of TIDE to support the care of approximately 1,000 patients, improving TIDE and the associated workflows to scale their use to more patients, incorporating data from additional sensors, and partnering with other institutions to facilitate their deployment of this care model.
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Affiliation(s)
- David Scheinker
- Associate Professor, Pediatrics, Stanford University, Stanford, California, USA,Executive Director, Lucile Packard Children’s Hospital Stanford, Palo Alto, California, USA,Faculty, Clinical Excellence Research Center, Stanford University, California, USA
| | - Priya Prahalad
- Associate Professor, Pediatrics, Stanford University, Stanford, California, USA
| | - Ramesh Johari
- Professor, Management Science and Engineering, Stanford University, Stanford, California, USA
| | - David M. Maahs
- Professor, Pediatrics, Stanford University, Stanford, California, USA
| | - Rick Majzun
- Chief Operating Officer, Lucile Packard Children’s Hospital Stanford, Palo Alto, California, USA
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22
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Prahalad P, Ding VY, Zaharieva DP, Addala A, Johari R, Scheinker D, Desai M, Hood K, Maahs DM. Teamwork, Targets, Technology, and Tight Control in Newly Diagnosed Type 1 Diabetes: the Pilot 4T Study. J Clin Endocrinol Metab 2022; 107:998-1008. [PMID: 34850024 PMCID: PMC8947228 DOI: 10.1210/clinem/dgab859] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Indexed: 11/19/2022]
Abstract
CONTEXT Youth with type 1 diabetes (T1D) do not meet glycated hemoglobin A1c (HbA1c) targets. OBJECTIVE This work aimed to assess HbA1c outcomes in children with new-onset T1D enrolled in the Teamwork, Targets, Technology and Tight Control (4T) Study. METHODS HbA1c levels were compared between the 4T and historical cohorts. HbA1c differences between cohorts were estimated using locally estimated scatter plot smoothing (LOESS). The change from nadir HbA1c (month 4) to 12 months post diagnosis was estimated by cohort using a piecewise mixed-effects regression model accounting for age at diagnosis, sex, ethnicity, and insurance type. We recruited 135 youth with newly diagnosed T1D at Stanford Children's Health. Starting July 2018, all youth within the first month of T1D diagnosis were offered continuous glucose monitoring (CGM) initiation and remote CGM data review was added in March 2019. The main outcomes measure was HbA1c. RESULTS HbA1c at 6, 9, and 12 months post diagnosis was lower in the 4T cohort than in the historic cohort (-0.54% to -0.52%, and -0.58%, respectively). Within the 4T cohort, HbA1c at 6, 9, and 12 months post diagnosis was lower in those patients with remote monitoring than those without (-0.14%, -0.18% to -0.14%, respectively). Multivariable regression analysis showed that the 4T cohort experienced a significantly lower increase in HbA1c between months 4 and 12 (P < .001). CONCLUSION A technology-enabled, team-based approach to intensified new-onset education involving target setting, CGM initiation, and remote data review statistically significantly decreased HbA1c in youth with T1D 12 months post diagnosis.
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Affiliation(s)
- Priya Prahalad
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, California 94304, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, California 94304, USA
- Correspondence: Priya Prahalad, MD, PhD, Department of Pediatrics, Division of Pediatric Endocrinology, Center for Academic Medicine, 453 Quarry Rd, Palo Alto, CA 94304, USA.
| | - Victoria Y Ding
- Department of Medicine, Division of Biomedical Informatics Research, Stanford University, Stanford, California 94304, USA
| | - Dessi P Zaharieva
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, California 94304, USA
| | - Ananta Addala
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, California 94304, USA
| | - Ramesh Johari
- Stanford Diabetes Research Center, Stanford University, Stanford, California 94304, USA
- Department of Management Science and Engineering, Stanford University, Stanford, California 94304, USA
| | - David Scheinker
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, California 94304, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, California 94304, USA
- Department of Management Science and Engineering, Stanford University, Stanford, California 94304, USA
- Clinical Excellence Research Center, Stanford University, Stanford, California 94304, USA
| | - Manisha Desai
- Department of Medicine, Division of Biomedical Informatics Research, Stanford University, Stanford, California 94304, USA
| | - Korey Hood
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, California 94304, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, California 94304, USA
| | - David M Maahs
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, California 94304, USA
- Stanford Diabetes Research Center, Stanford University, Stanford, California 94304, USA
- Department of Health Research and Policy (Epidemiology) Stanford University, Stanford, California 94304, USA
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23
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Zaharieva DP, Senanayake R, Brown C, Watkins B, Loving G, Prahalad P, Ferstad JO, Guestrin C, Fox EB, Maahs DM, Scheinker D. Adding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities. Front Endocrinol (Lausanne) 2022; 13:1096325. [PMID: 36714600 PMCID: PMC9877334 DOI: 10.3389/fendo.2022.1096325] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 12/15/2022] [Indexed: 01/15/2023] Open
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.
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Affiliation(s)
- Dessi P. Zaharieva
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA, United States
- *Correspondence: Dessi P. Zaharieva,
| | - Ransalu Senanayake
- Department of Computer Science, Stanford University, Stanford, CA, United States
| | - Conner Brown
- Stanford Children’s Health, Lucile Packard Children’s Hospital, Stanford, CA, United States
| | - Brendan Watkins
- Stanford Children’s Health, Lucile Packard Children’s Hospital, Stanford, CA, United States
| | - Glenn Loving
- Stanford Children’s Health, Lucile Packard Children’s Hospital, Stanford, CA, United States
| | - Priya Prahalad
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States
| | - Johannes O. Ferstad
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Carlos Guestrin
- Department of Computer Science, Stanford University, Stanford, CA, United States
| | - Emily B. Fox
- Department of Computer Science, Stanford University, Stanford, CA, United States
- Chan Zuckerberg Biohub, San Francisco, CA, United States
- Department of Statistics, Stanford University, Stanford, CA, United States
| | - David M. Maahs
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States
- Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, United States
| | - David Scheinker
- Division of Endocrinology, Department of Pediatrics, Stanford University, School of Medicine, Stanford, CA, United States
- Stanford Children’s Health, Lucile Packard Children’s Hospital, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA, United States
- Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, CA, United States
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24
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Scheinker D, Gu A, Grossman J, Ward A, Ayerdi O, Miller D, Leverenz J, Hood K, Lee MY, Maahs DM, Prahalad P. Algorithm-Enabled, Personalized Glucose Management for Type 1 Diabetes at the Population Scale: A Prospective Evaluation in Clinical Practice (Preprint). JMIR Diabetes 2021; 7:e27284. [PMID: 35666570 PMCID: PMC9210201 DOI: 10.2196/27284] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 05/19/2021] [Accepted: 02/22/2022] [Indexed: 01/04/2023] Open
Abstract
Background The use of continuous glucose monitors (CGMs) is recommended as the standard of care by the American Diabetes Association for individuals with type 1 diabetes (T1D). Few hardware-agnostic, open-source, whole-population tools are available to facilitate the use of CGM data by clinicians such as physicians and certified diabetes educators. Objective This study aimed to develop a tool that identifies patients appropriate for contact using an asynchronous message through electronic medical records while minimizing the number of patients reviewed by a certified diabetes educator or physician using the tool. Methods We used consensus guidelines to develop timely interventions for diabetes excellence (TIDE), an open-source hardware-agnostic tool to analyze CGM data to identify patients with deteriorating glucose control by generating generic flags (eg, mean glucose [MG] >170 mg/dL) and personalized flags (eg, MG increased by >10 mg/dL). In a prospective 7-week study in a pediatric T1D clinic, we measured the sensitivity of TIDE in identifying patients appropriate for contact and the number of patients reviewed. We simulated measures of the workload generated by TIDE, including the average number of time in range (TIR) flags per patient per review period, on a convenience sample of eight external data sets, 6 from clinical trials and 2 donated by research foundations. Results Over the 7 weeks of evaluation, the clinical population increased from 56 to 64 patients. The mean sensitivity was 99% (242/245; SD 2.5%), and the mean reduction in the number of patients reviewed was 42.6% (182/427; SD 10.9%). The 8 external data sets contained 1365 patients with 30,017 weeks of data collected by 7 types of CGMs. The rates of generic and personalized TIR flags per patient per review period were, respectively, 0.15 and 0.12 in the data set with the lowest average MG (141 mg/dL) and 0.95 and 0.22 in the data set with the highest average MG (207 mg/dL). Conclusions TIDE is an open-source hardware-agnostic tool for personalized analysis of CGM data at the clinical population scale. In a pediatric T1D clinic, TIDE identified 99% of patients appropriate for contact using an asynchronous message through electronic medical records while reducing the number of patients reviewed by certified diabetes care and education specialists by 43%. For each of the 8 external data sets, simulation of the use of TIDE produced fewer than 0.25 personalized TIR flags per patient per review period. The use of TIDE to support telemedicine-based T1D care may facilitate sensitive and efficient guideline-based population health management.
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Affiliation(s)
- David Scheinker
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, United States
- Lucile Packard Children's Hospital, Stanford University, Stanford, CA, United States
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States
| | - Angela Gu
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Joshua Grossman
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Andrew Ward
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Oseas Ayerdi
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Daniel Miller
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, CA, United States
| | - Jeannine Leverenz
- Lucile Packard Children's Hospital, Stanford University, Stanford, CA, United States
| | - Korey Hood
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States
| | - Ming Yeh Lee
- Lucile Packard Children's Hospital, Stanford University, Stanford, CA, United States
| | - David M Maahs
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States
- Department of Health Research and Policy, Stanford University, Stanford, CA, United States
| | - Priya Prahalad
- Department of Pediatrics, Division of Pediatric Endocrinology, Stanford University, Stanford, CA, United States
- Stanford Diabetes Research Center, Stanford University, Stanford, CA, United States
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