More Like this

1. Building a Diabetes Educator Program for Remote Patient Monitoring Billing

   https://doi.org/10.1177/19322968241308920  

   Bishop, Franziska_K ; Chmielewski, Annette ; Leverenz, Jeannine ; Lin, Shannon ; Conrad, Barry ; Martinez-Singh, Anjoli ; Pike, Laura ; Scheinker, David ; Prahalad, Priya ; Maahs, David_M ( December 2024 , Journal of Diabetes Science and Technology)

   Develop workflows and billing processes for a Certified Diabetes Care and Education Specialist (CDCES)-led remote patient monitoring (RPM) program to transition the Teamwork, Targets, Technology, and Tight Control (4T) Study to our clinic’s standard of care.

   We identified stakeholders within a pediatric endocrinology clinic (hospital compliance, billing specialists, and clinical informatics) to identify, discuss, and approve billing codes and workflow. The group evaluated billing code stipulations, such as the timing of continuous glucose monitor (CGM) interpretation, scope of work, providers’ licensing, and electronic health record (EHR) documentation to meet billing compliance standards. We developed a CDCES workflow for asynchronous CGM interpretation and intervention and initiated an RPM billing pilot.

   We built a workflow for CGM interpretation (billing code: 95251) with the CDCES as the service provider. The workflow includes data review, patient communications, and documentation. Over the first month of the pilot, RPM billing codes were submitted for 52 patients. The average reimbursement rate was $110.33 for commercial insurance (60% of patients) and $46.95 for public insurance (40% of patients) per code occurrence.

   Continuous involvement of CDCES and hospital stakeholders was essential to operationalize all relevant aspects of clinical care, workflows, compliance, documentation, and billing. CGM interpretation with RPM billing allows CDCES to work at the top of their licensing credential, increase clinical care touch points, and provide a business case for expansion. As evidence of the clinical benefits of RPM increases, the processes developed here may facilitate broader adoption of revenue-generating CDCES-led care to fund RPM.

    

   more » « less
2. Personalized Diabetes Management Using Electronic Medical Records

   https://doi.org/10.2337/dc16-0826  

   Bertsimas, Dimitris ; Kallus, Nathan ; Weinstein, Alexander_M ; Zhuo, Ying_Daisy ( December 2016 , Diabetes Care)

   Current clinical guidelines for managing type 2 diabetes do not differentiate based on patient-specific factors. We present a data-driven algorithm for personalized diabetes management that improves health outcomes relative to the standard of care.

   We modeled outcomes under 13 pharmacological therapies based on electronic medical records from 1999 to 2014 for 10,806 patients with type 2 diabetes from Boston Medical Center. For each patient visit, we analyzed the range of outcomes under alternative care using a k-nearest neighbor approach. The neighbors were chosen to maximize similarity on individual patient characteristics and medical history that were most predictive of health outcomes. The recommendation algorithm prescribes the regimen with best predicted outcome if the expected improvement from switching regimens exceeds a threshold. We evaluated the effect of recommendations on matched patient outcomes from unseen data.

   Among the 48,140 patient visits in the test set, the algorithm’s recommendation mirrored the observed standard of care in 68.2% of visits. For patient visits in which the algorithmic recommendation differed from the standard of care, the mean posttreatment glycated hemoglobin A1c (HbA1c) under the algorithm was lower than standard of care by 0.44 ± 0.03% (4.8 ± 0.3 mmol/mol) (P < 0.001), from 8.37% under the standard of care to 7.93% under our algorithm (68.0 to 63.2 mmol/mol).

   A personalized approach to diabetes management yielded substantial improvements in HbA1c outcomes relative to the standard of care. Our prototyped dashboard visualizing the recommendation algorithm can be used by providers to inform diabetes care and improve outcomes.

    

   more » « less
3. Perceived Penalties for Sharing Patient Beliefs with Health Care Providers

   https://doi.org/10.1177/0272989X241262241  

   Marsh, Jessecae_K ; Asan, Onur ; Kleinberg, Samantha ( August 2024 , Medical Decision Making)

   Health care interactions may require patients to share with a physician information they believe but is incorrect. While a key piece of physicians’ work is educating their patients, people’s concerns of being seen as uninformed or incompetent by physicians may lead them to think that sharing incorrect health beliefs comes with a penalty. We tested people’s perceptions of patients who share incorrect information and how these perceptions vary by the reasonableness of the belief and its centrality to the patient’s disease.

   We recruited 399 United States Prolific.co workers (357 retained after exclusions), 200 Prolific.co workers who reported having diabetes (139 after exclusions), and 244 primary care physicians (207 after exclusions). Participants read vignettes describing patients with type 2 diabetes sharing health beliefs that were central or peripheral to the management of diabetes. Beliefs included true and incorrect statements that were reasonable or unreasonable to believe. Participants rated how a doctor would perceive the patient, the patient’s ability to manage their disease, and the patient’s trust in doctors.

   Participants rated patients who shared more unreasonable beliefs more negatively. There was an extra penalty for incorrect statements central to the patient’s diabetes management (sample 1). These results replicated for participants with type 2 diabetes (sample 2) and physician participants (sample 3).

   Participants believed that patients who share incorrect information with their physicians will be penalized for their honesty. Physicians need to be educated on patients’ concerns so they can help patients disclose what may be most important for education.

   Understanding how people think they will be perceived in a health care setting can help us understand what they may be wary to share with their physicians. People think that patients who share incorrect beliefs will be viewed negatively. Helping patients share incorrect beliefs can improve care.

    

   more » « less
4. A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study

   https://doi.org/10.2196/50328  

   Hendawi, Rasha ; Li, Juan ; Roy, Souradip ( January 2023 , JMIR Formative Research)

   Machine learning approaches, including deep learning, have demonstrated remarkable effectiveness in the diagnosis and prediction of diabetes. However, these approaches often operate as opaque black boxes, leaving health care providers in the dark about the reasoning behind predictions. This opacity poses a barrier to the widespread adoption of machine learning in diabetes and health care, leading to confusion and eroding trust.

   This study aimed to address this critical issue by developing and evaluating an explainable artificial intelligence (AI) platform, XAI4Diabetes, designed to empower health care professionals with a clear understanding of AI-generated predictions and recommendations for diabetes care. XAI4Diabetes not only delivers diabetes risk predictions but also furnishes easily interpretable explanations for complex machine learning models and their outcomes.

   XAI4Diabetes features a versatile multimodule explanation framework that leverages machine learning, knowledge graphs, and ontologies. The platform comprises the following four essential modules: (1) knowledge base, (2) knowledge matching, (3) prediction, and (4) interpretation. By harnessing AI techniques, XAI4Diabetes forecasts diabetes risk and provides valuable insights into the prediction process and outcomes. A structured, survey-based user study assessed the app’s usability and influence on participants’ comprehension of machine learning predictions in real-world patient scenarios.

   A prototype mobile app was meticulously developed and subjected to thorough usability studies and satisfaction surveys. The evaluation study findings underscore the substantial improvement in medical professionals’ comprehension of key aspects, including the (1) diabetes prediction process, (2) data sets used for model training, (3) data features used, and (4) relative significance of different features in prediction outcomes. Most participants reported heightened understanding of and trust in AI predictions following their use of XAI4Diabetes. The satisfaction survey results further revealed a high level of overall user satisfaction with the tool.

   This study introduces XAI4Diabetes, a versatile multi-model explainable prediction platform tailored to diabetes care. By enabling transparent diabetes risk predictions and delivering interpretable insights, XAI4Diabetes empowers health care professionals to comprehend the AI-driven decision-making process, thereby fostering transparency and trust. These advancements hold the potential to mitigate biases and facilitate the broader integration of AI in diabetes care.

    

   more » « less
5. Disparities in patient portal access by US adults before and during the COVID-19 pandemic

   https://doi.org/10.1093/jamiaopen/ooac104  

   Nishii, Akira ; Campos-Castillo, Celeste ; Anthony, Denise ( December 2022 , JAMIA Open)

   Online patient portals become important during disruptions to in-person health care, like when cases of coronavirus disease 2019 (COVID-19) and other respiratory viruses rise, yet underlying structural inequalities associated with race, socio-economic status, and other socio-demographic characteristics may affect their use. We analyzed a population-based survey to identify disparities within the United States in access to online portals during the early period of COVID-19 in 2020.

   The National Cancer Institute fielded the 2020 Health and Information National Trends Survey from February to June 2020. We conducted multivariable analysis to identify socio-demographic characteristics of US patients who were offered and accessed online portals, and reasons for nonuse.

   Less than half of insured adult patients reported accessing an online portal in the prior 12 months, and this was less common among patients who are male, are Hispanic, have less than a college degree, have Medicaid insurance, have no regular provider, or have no internet. Reasons for nonuse include: wanting to speak directly to a provider, not having an online record, concerns about privacy, and discomfort with technology.

   Despite the rapid expansion of digital health technologies due to COVID-19, we found persistent socio-demographic disparities in access to patient portals. Ensuring that digital health tools are secure, private, and trustworthy would address some patient concerns that are barriers to portal access.

   Expanding the use of online portals requires explicitly addressing fundamental inequities to prevent exacerbating existing disparities, particularly during surges in cases of COVID-19 and other respiratory viruses that tax health care resources.

    

   more » « less