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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 2024:10.1038/s41591-024-02975-y. [PMID: 38702523 DOI: 10.1038/s41591-024-02975-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [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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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 Evid 2024; 3:EVIDoa2300164. [PMID: 38320487 DOI: 10.1056/evidoa2300164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Using Pilot Study Data to Design Clinical TrialsDigital health interventions are often studied in a pilot trial before full evaluation in a randomized controlled trial. The authors introduce Smart Start, a framework for using pilot study data to optimize the intervention and design the subsequent randomized controlled trial to maximize the chance of success.
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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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Bunning BJ, Hedlin H, Chen JH, Ciolino JD, Ferstad JO, Fox E, Garcia A, Go A, Johari R, Lee J, Maahs DM, Mahaffey KW, Opsahl-Ong K, Perez M, Rochford K, Scheinker D, Spratt H, Turakhia MP, Desai M. The evolving role of data & safety monitoring boards for real-world clinical trials. J Clin Transl Sci 2023; 7:e179. [PMID: 37745930 PMCID: PMC10514684 DOI: 10.1017/cts.2023.582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/20/2023] [Accepted: 06/24/2023] [Indexed: 09/26/2023] Open
Abstract
Introduction Clinical trials provide the "gold standard" evidence for advancing the practice of medicine, even as they evolve to integrate real-world data sources. Modern clinical trials are increasingly incorporating real-world data sources - data not intended for research and often collected in free-living contexts. We refer to trials that incorporate real-world data sources as real-world trials. Such trials may have the potential to enhance the generalizability of findings, facilitate pragmatic study designs, and evaluate real-world effectiveness. However, key differences in the design, conduct, and implementation of real-world vs traditional trials have ramifications in data management that can threaten their desired rigor. Methods Three examples of real-world trials that leverage different types of data sources - wearables, medical devices, and electronic health records are described. Key insights applicable to all three trials in their relationship to Data and Safety Monitoring Boards (DSMBs) are derived. Results Insight and recommendations are given on four topic areas: A. Charge of the DSMB; B. Composition of the DSMB; C. Pre-launch Activities; and D. Post-launch Activities. We recommend stronger and additional focus on data integrity. Conclusions Clinical trials can benefit from incorporating real-world data sources, potentially increasing the generalizability of findings and overall trial scale and efficiency. The data, however, present a level of informatic complexity that relies heavily on a robust data science infrastructure. The nature of monitoring the data and safety must evolve to adapt to new trial scenarios to protect the rigor of clinical trials.
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Affiliation(s)
- Bryan J. Bunning
- Quantitative Sciences Unit, Stanford University, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University, Stanford, CA, USA
| | - Haley Hedlin
- Quantitative Sciences Unit, Stanford University, Stanford, CA, USA
| | - Jonathan H. Chen
- Stanford Center for Biomedical Informatics Research, Stanford University, Stanford, CA, USA
| | - Jody D. Ciolino
- Department of Preventative Medicine – Biostatistics, Northwestern University, Chicago, IL, USA
| | | | - Emily Fox
- Department of Statistics, Stanford University, Stanford, CA, USA
- Kaiser Permanente Northern California Division of Research, Kaiser Permanente, Oakland, CA, USA
| | - Ariadna Garcia
- Quantitative Sciences Unit, Stanford University, Stanford, CA, USA
| | - Alan Go
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Ramesh Johari
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - Justin Lee
- Quantitative Sciences Unit, Stanford University, Stanford, CA, USA
| | - David M. Maahs
- Department of Pediatrics, Stanford Medicine Children’s Hospital, Stanford, CA, USA
| | - Kenneth W. Mahaffey
- Stanford Center for Clinical Research, Stanford University, Stanford, CA, USA
| | - Krista Opsahl-Ong
- Department of Pediatrics, Stanford Medicine Children’s Hospital, Stanford, CA, USA
| | - Marco Perez
- Department of Medicine, Cardiovascular Medicine, Stanford Medicine, Stanford, CA, USA
| | - Kaylin Rochford
- Department of Management Science and Engineering, Stanford University, Stanford, CA, USA
| | - David Scheinker
- Systems Design and Collaborative Research, Stanford Medicine Children’s Hospital, Stanford, CA, USA
| | - Heidi Spratt
- Department of Preventative Medicine & Community Health, University of Texas Medical Branch, Galveston, TX, USA
| | - Mintu P. Turakhia
- Stanford Center for Clinical Research, Stanford University, Stanford, CA, USA
| | - Manisha Desai
- Quantitative Sciences Unit, Stanford University, 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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Scheinker D, Prahalad P, Johari R, Maahs DM, Majzun R. A New Technology-Enabled Care Model for Pediatric Type 1 Diabetes. NEJM Catal Innov Care Deliv 2022; 3:10.1056/CAT.21.0438. [PMID: 36544715 PMCID: PMC9767424 DOI: 10.1056/cat.21.0438] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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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: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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9
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Ferstad JO, Vallon JJ, Jun D, Gu A, Vitko A, Morales DP, Leverenz J, Lee MY, Leverenz B, Vasilakis C, Osmanlliu E, Prahalad P, Maahs DM, Johari R, Scheinker D. Population-level management of type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health. Pediatr Diabetes 2021; 22:982-991. [PMID: 34374183 PMCID: PMC8635792 DOI: 10.1111/pedi.13256] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 07/28/2021] [Indexed: 01/17/2023] Open
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.
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Affiliation(s)
- Johannes O. Ferstad
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California, USA
| | - Jacqueline J. Vallon
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California, USA
| | - Daniel Jun
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California, USA
| | - Angela Gu
- Department of Computer Science, Stanford University School of Engineering, Stanford, California, USA
| | - Anastasiya Vitko
- Department of Computer Science, Stanford University School of Engineering, Stanford, California, USA
| | - Dianelys P. Morales
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California, USA
| | - Jeannine Leverenz
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA
| | - Ming Yeh Lee
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA
| | - Brianna Leverenz
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA
| | - Christos Vasilakis
- Centre for Healthcare Innovation and Improvement (CHI), School of Management, University of Bath, Bath, UK
| | - Esli Osmanlliu
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA,Department of Pediatrics, Montreal Children’s Hospital, McGill University Health Centre, Montreal, Canada
| | - Priya Prahalad
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA,Stanford Diabetes Research Center, Stanford University, Stanford, California, USA
| | - David M. Maahs
- Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA,Stanford Diabetes Research Center, Stanford University, Stanford, California, USA,Department of Health Research and Policy, Stanford University School of Medicine, Stanford, California, USA
| | - Ramesh Johari
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California, USA,Stanford Diabetes Research Center, Stanford University, Stanford, California, USA
| | - David Scheinker
- Department of Management Science and Engineering, Stanford University School of Engineering, Stanford, California, USA,Division of Pediatric Endocrinology, Stanford University School of Medicine, Stanford, California, USA,Clinical Excellence Research Center, Stanford University School of Medicine, Stanford, California, USA
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10
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Abstract
We study dynamic pricing policies for ridesharing platforms such as Lyft and Uber. On one hand these platforms are two-sided: this requires economic models that capture the incentives of both drivers and passengers. On the other hand, these platforms support high temporal-resolution for data collection and pricing: this requires stochastic models that capture the dynamics of drivers and passengers in the system.
We summarize our main results from [Banerjee et al. 2015], in which we study the role of dynamic pricing in ridesharing platforms using a queueing-theoretic economic model. We build a model of two-sided ridesharing platforms that captures both the stochastic dynamics of the marketplace and the strategic decisions of drivers, passengers and the platform. We show how our model can help explain the success of dynamic pricing in practice: in particular, we argue that the benefit of dynamic pricing over static pricing is not in the optimal performance, but rather, in the robustness of its performance to uncertainty in system parameters.
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11
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12
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Abstract
We study learning in a dynamic setting where identical copies of a good are sold over time through a sequence of second price auctions. Each agent in the market has an
unknown
independent private valuation which determines the distribution of the reward she obtains from the good; for example, in sponsored search settings, advertisers may initially be unsure of the value of a click. Though the induced dynamic game is complex, we simplify analysis of the market using an approximation methodology known as
mean field equilibrium
(MFE). The methodology assumes that agents optimize only with respect to long run average estimates of the distribution of other players' bids. We show a remarkable fact: in a mean field equilibrium, the agent has an optimal strategy where she bids truthfully according to a
conjoint valuation
. The conjoint valuation is the sum of her current expected valuation, together with an overbid amount that is exactly the expected marginal benefit to one additional observation about her true private valuation. Under mild conditions on the model, we show that an MFE exists, and that it is a good approximation to a
rational
agent's behavior as the number of agents increases. We conclude by discussing the implications of the auction format and design on the auctioneer's revenue. In particular, we establish a dynamic version of the revenue equivalence theorem, and discuss optimal selection of reserve prices in dynamic auctions.
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13
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Abstract
ISPs are increasingly selling "tiered" contracts, which offer Internet connectivity to wholesale customers in bundles, at rates based on the cost of the links that the traffic in the bundle is traversing. Although providers have already begun to implement and deploy tiered pricing contracts, little is known about how to structure them. While contracts that sell connectivity on finer granularities improve market efficiency, they are also more costly for ISPs to implement and more difficult for customers to understand. Our goal is to analyze whether current tiered pricing practices in the wholesale transit market yield optimal profits for ISPs and whether better bundling strategies might exist. In the process, we deliver two contributions: 1) we develop a novel way of mapping traffic and topology data to a demand and cost model, and 2) we fit this model on three large real-world networks: an European transit ISP, a content distribution network, and an academic research network, and run counterfactuals to evaluate the effects of different bundling strategies. Our results show that the common ISP practice of structuring tiered contracts according to the cost of carrying the traffic flows (
e.g.
, offering a discount for traffic that is local) can be suboptimal and that dividing contracts based on
both traffic demand and the cost of carrying it
into
only three or four tiers
yields near-optimal profit for the ISP.
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14
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Aperjis C, Johari R. Designing aggregation mechanisms for reputation systems in online marketplaces. SIGecom Exch 2010. [DOI: 10.1145/1980534.1980537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
A seller in an online marketplace with an effective reputation mechanism should expect that dishonest behavior results in higher payments now, while honest behavior results in higher reputation---and thus higher payments---in the future. We briefly survey recent results on the Window Aggregation Mechanism, a widely used class of mechanisms that shows the average value of the seller's ratings within some fixed window of past transactions. We suggest approaches for choosing the window size that maximizes the range of parameters for which it is optimal for the seller to be truthful. We show that mechanisms that use information from a larger number of past transactions tend to provide incentives for patient sellers to be more truthful, but for higher quality sellers to be less truthful. We then discuss a broader class of aggregation mechanisms that weight recent ratings more heavily and show that the same insight applies.
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15
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Sherwood R, Chan M, Covington A, Gibb G, Flajslik M, Handigol N, Huang TY, Kazemian P, Kobayashi M, Naous J, Seetharaman S, Underhill D, Yabe T, Yap KK, Yiakoumis Y, Zeng H, Appenzeller G, Johari R, McKeown N, Parulkar G. Carving research slices out of your production networks with OpenFlow. SIGCOMM Comput Commun Rev 2010. [DOI: 10.1145/1672308.1672333] [Citation(s) in RCA: 69] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Affiliation(s)
- Rob Sherwood
- Deutsche Telekom Inc., R&D Lab, Los Altos, CA, USA
| | | | | | - Glen Gibb
- Stanford University, Palo Alto, CA, USA
| | | | | | | | | | | | - Jad Naous
- Stanford University, Palo Alto, CA, USA
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16
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Dixit MD, Johari R, Dubey A, Gan M, Prabhu P, Nishanimath N, Vagaral A, Patil S, Sabade S, Dhulkhed V, Dayal A. VSD-closure using double-patch technique in cases of VSD with severe pulmonary artery hypertension (PAH)—Our experience. Indian J Thorac Cardiovasc Surg 2006. [DOI: 10.1007/s12055-006-0548-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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17
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Dixit MD, Johari R, Dubey A, Gan M, Prabhu P, Nishanimath N, Vagarali A, Patil S, Sabade S, Dhulkhed V, Dayal A. Long term results of lad endarterectomy-evaluation of surgical technique of LIMA LAD anastamous vs LIMA over vein angioplasty. Indian J Thorac Cardiovasc Surg 2006. [DOI: 10.1007/s12055-006-0618-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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18
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Zakaria Z, Sulaiman M, Arifah A, Jais AM, Somchit M, Kirisnaven K, Punnitharr D, Safarul M, Fatimah C, Johari R. The Anti-inflammatory and Antipyretic Activities of Corchorus olitorius in Rats. ACTA ACUST UNITED AC 2006. [DOI: 10.3923/jpt.2006.139.146] [Citation(s) in RCA: 13] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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19
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Abstract
Thousands of competing autonomous systems must cooperate with each other to provide global Internet connectivity. Each autonomous system (AS) encodes various economic, business, and performance decisions in its routing policy. The current interdomain routing system enables each AS to express policy using
rankings
that determine how each router inthe AS chooses among different routes to a destination, and
filters
that determine which routes are hidden from each neighboring AS. Because the Internet is composed of many independent, competing networks, the interdomain routing system should provide
autonomy
, allowing network operators to set their rankings independently, and to have no constraints on allowed filters. This paper studies routing protocol stability under these conditions. We first demonstrate that certain rankings that are commonly used in practice may not ensure routing stability. We then prove that, when providers can set rankings and filters autonomously, guaranteeing that the routing system will converge to a stable path assignment essentially requires ASes to rank routes based on AS-path lengths. We discuss the implications of these results for the future of interdomain routing.
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