Evaluation of Patient Willingness to Adopt Remote Digital Monitoring for Diabetes Management

Oikonomidi, Theodora; Ravaud, Philippe; Cosson, E.; Montori, Víctor M.; Tran, Viet-Thi

doi:10.1001/jamanetworkopen.2020.33115

Cited by 13 publications

(6 citation statements)

References 45 publications

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“…[33][34][35] In addition, people with diabetes are increasingly open to remote monitoring, with the promise of receiving additional support from health care teams. 36 Although our survey found infrequent remote monitoring of CGM data, these data were collected at the onset of the COVID-19 pandemic, and we expect to see more frequent and consistent use of remote monitoring going forward. As the national health care landscape increasingly shifts toward value-based and risk-based reimbursement models, passively shared, connected device data may be key to proactive, population-based diabetes management.…”

Section: Discussionmentioning

confidence: 79%

Diabetes Specialists Value Continuous Glucose Monitoring Despite Challenges in Prescribing and Data Review Process

Kompala

Wong

Neinstein

2022

J Diabetes Sci Technol

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Background: Diabetes clinicians are key facilitators of continuous glucose monitoring (CGM) provision, but data on provider behavior related to CGM use and CGM generated data are limited. Methods: We conducted a national survey of providers caring for people with diabetes on CGM-related opinions, facilitators and barriers to prescription, and data review practices. Results: Of 182 survey respondents, 73.2% worked at academic centers, 70.6% were endocrinologists, and 70.7% practiced in urban settings. Nearly 70% of providers reported CGM use in the majority of their patients with type 1 diabetes. Half of the providers reported CGM use in 10% to 50% of their patients with type 2 diabetes. All respondents believed CGM improved quality of life and could optimize diabetes control. We found no differences in reported rates of CGM use based on providers’ years of experience, patient volume, practice setting, or clinic type. Most providers reviewed CGM data each visit (97.7%) and actively involved patients in the data interpretation (98.8%). Only 14.1% of clinicians reported reviewing CGM data without any prompting from patients or their family members outside of visits. Most providers (80.7%) reported their CGM data review was valued by patients although only half reported having adequate time (45.1%) or an efficient process (56.1%) to do so. Conclusions: Despite uniform support for CGM by providers, ongoing challenges related to cost, insurance coverage, and difficulties with prescription were major barriers to CGM use. Increased use of CGM in appropriate populations will necessitate improvements in data access and integration, clearly defined workflows, and decreased administrative burden to obtain CGM.

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Section: Discussionmentioning

confidence: 79%

Diabetes Specialists Value Continuous Glucose Monitoring Despite Challenges in Prescribing and Data Review Process

Kompala

Wong

Neinstein

2022

J Diabetes Sci Technol

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“…First, of the 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. The assistance of GenAI was used for disease detection (19/161, 11.8%) [ 58 , 63 , 67 , 69 , 71 , 73 , 77 , 90 , 97 - 107 ], diagnosis (14/161, 8.7%) [ 100 , 108 - 120 ], and screening processes (12/161, 7.5%) [ 65 , 86 , 87 , 93 , 121 - 127 , 168 , 169 ] in the areas of radiology (17/161, 10.6%) [ 49 - 63 , 65 , 66 ], cardiology (12/161, 7.5%) [ 67 - 72 , 74 , 76 - 79 , 129 ], gastrointestinal medicine (4/161, 2.5%) [ 81 - 84 ], and diabetes (6/161, 3.7%) [ 86 - 91 ]. Thus, although the use of GenAI has percolated across almost all disease-relevant and main service–relevant areas in health care, it is mainly for assisting through knowledge access, collation, and filtering.…”

Section: Resultsmentioning

confidence: 99%

Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review

Yim,

Khuntia,

Parameswaran

et al. 2024

JMIR Med Inform

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Background Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients’ families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. Objective This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. Methods We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. Results Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. Conclusions GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service–level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.

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“…For example, not all patients who are targeted for a digital health intervention will take action as a result. Although in one study, 65% of adults with diabetes expressed willingness to use a digital health tool to manage their diabetes even if it had a minimal effect on their outcomes [ 38 ], the intention-behavior gap is well-documented [ 39 ] and it is well-known that digital health adoption in general lags expectations. Therefore, we recommend adjusting any economic impact estimates to reflect a portion of the population that may take action, especially if using the model as part of a pricing or sales exercise.…”

Section: Discussionmentioning

confidence: 99%

An Economic Impact Model for Estimating the Value to Health Systems of a Digital Intervention for Diabetes Primary Care: Development and Usefulness Study

Powers¹,

Bucher²

2022

JMIR Form Res

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Background Diabetes is associated with significant long-term costs for both patients and health systems. Regular primary care visits aligned with American Diabetes Association guidelines could help mitigate those costs while generating near-term revenue for health systems. Digital interventions prompting primary care visits among unengaged patients could provide significant economic value back to the health system as well as individual patients, but only few economic models have been put forth to understand this value. Objective Our objective is to establish a data-based method to estimate the economic impact to a health system of interventions promoting primary care visits for people with diabetes who have been historically unengaged with their care. The model was built with a focus on a specific digital health intervention, Precision Nudging, but can be used to quantify the value of other interventions driving primary care usage among patients with diabetes. Methods We developed an economic model to estimate the financial value of a primary care visit of a patient with diabetes to the health system. This model requires segmenting patients with diabetes according to their level of blood sugar control as measured by their most recent hemoglobin A1c value to understand how frequently they should be visiting a primary care provider. The model also accounts for the payer mix among the population with diabetes, documenting the percentage of insurance coverage through a commercial plan, Medicare, or Medicaid, as these influence the reimbursement rates for the services. Then, the model takes into consideration the population base rates of comorbid conditions for patients with diabetes and the associated current procedural terminology codes to understand what a provider can bill as well as the expected inpatient revenue from a subset of patients likely to require hospitalization based on the national hospitalization rates for people with diabetes. Physician reimbursement is subtracted from the total. Finally, the model also accounts for the level of patient engagement with the intervention to ensure a realistic estimate of the impact. Results We present a model to prospectively estimate the economic impact of a digital health intervention to encourage patients with documented diabetes diagnoses to attend primary care visits. The model leverages both publicly available and health system data to calculate the per appointment value (revenue) to the health system. The model offers a method to understand and test the financial impact of Precision Nudging or other primary care–focused diabetes interventions inclusive of costs driven by comorbid conditions. Conclusions The proposed economic model can help health systems understand and evaluate the estimated economic benefits of interventions focused on primary care and prevention for patients with diabetes as well as help intervention developers determine pricing for their product.

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Evaluation of Patient Willingness to Adopt Remote Digital Monitoring for Diabetes Management

Cited by 13 publications

References 45 publications

Diabetes Specialists Value Continuous Glucose Monitoring Despite Challenges in Prescribing and Data Review Process

Diabetes Specialists Value Continuous Glucose Monitoring Despite Challenges in Prescribing and Data Review Process

Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review

An Economic Impact Model for Estimating the Value to Health Systems of a Digital Intervention for Diabetes Primary Care: Development and Usefulness Study

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