Academic Appointments

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  • Use of Voice-Based Conversational Artificial Intelligence for Basal Insulin Prescription Management Among Patients With Type 2 Diabetes: A Randomized Clinical Trial. JAMA network open Nayak, A., Vakili, S., Nayak, K., Nikolov, M., Chiu, M., Sosseinheimer, P., Talamantes, S., Testa, S., Palanisamy, S., Giri, V., Schulman, K. 2023; 6 (12): e2340232


    Optimizing insulin therapy for patients with type 2 diabetes can be challenging given the need for frequent dose adjustments. Most patients receive suboptimal doses and do not achieve glycemic control.To examine whether a voice-based conversational artificial intelligence (AI) application can help patients with type 2 diabetes titrate basal insulin at home to achieve rapid glycemic control.In this randomized clinical trial conducted at 4 primary care clinics at an academic medical center from March 1, 2021, to December 31, 2022, 32 adults with type 2 diabetes requiring initiation or adjustment of once-daily basal insulin were followed up for 8 weeks. Statistical analysis was performed from January to February 2023.Participants were randomized in a 1:1 ratio to receive basal insulin management with a voice-based conversational AI application or standard of care.Primary outcomes were time to optimal insulin dose (number of days needed to achieve glycemic control), insulin adherence, and change in composite survey scores measuring diabetes-related emotional distress and attitudes toward health technology and medication adherence. Secondary outcomes were glycemic control and glycemic improvement. Analysis was performed on an intent-to-treat basis.The study population included 32 patients (mean [SD] age, 55.1 [12.7] years; 19 women [59.4%]). Participants in the voice-based conversational AI group more quickly achieved optimal insulin dosing compared with the standard of care group (median, 15 days [IQR, 6-27 days] vs >56 days [IQR, >29.5 to >56 days]; a significant difference in time-to-event curves; P = .006) and had better insulin adherence (mean [SD], 82.9% [20.6%] vs 50.2% [43.0%]; difference, 32.7% [95% CI, 8.0%-57.4%]; P = .01). Participants in the voice-based conversational AI group were also more likely than those in the standard of care group to achieve glycemic control (13 of 16 [81.3%; 95% CI, 53.7%-95.0%] vs 4 of 16 [25.0%; 95% CI, 8.3%-52.6%]; difference, 56.3% [95% CI, 21.4%-91.1%]; P = .005) and glycemic improvement, as measured by change in mean (SD) fasting blood glucose level (-45.9 [45.9] mg/dL [95% CI, -70.4 to -21.5 mg/dL] vs 23.0 [54.7] mg/dL [95% CI, -8.6 to 54.6 mg/dL]; difference, -68.9 mg/dL [95% CI, -107.1 to -30.7 mg/dL]; P = .001). There was a significant difference between the voice-based conversational AI group and the standard of care group in change in composite survey scores measuring diabetes-related emotional distress (-1.9 points vs 1.7 points; difference, -3.6 points [95% CI, -6.8 to -0.4 points]; P = .03).In this randomized clinical trial of a voice-based conversational AI application that provided autonomous basal insulin management for adults with type 2 diabetes, participants in the AI group had significantly improved time to optimal insulin dose, insulin adherence, glycemic control, and diabetes-related emotional distress compared with those in the standard of care group. These findings suggest that voice-based digital health solutions can be useful for medication Identifier: NCT05081011.

    View details for DOI 10.1001/jamanetworkopen.2023.40232

    View details for PubMedID 38039007

  • Comparison of History of Present Illness Summaries Generated by a Chatbot and Senior Internal Medicine Residents. JAMA internal medicine Nayak, A., Alkaitis, M. S., Nayak, K., Nikolov, M., Weinfurt, K. P., Schulman, K. 2023

    View details for DOI 10.1001/jamainternmed.2023.2561

    View details for PubMedID 37459091

  • Pap, Normal and Abnormal in Nonpregnant Women Ages 25 Years and Older. The 5-Minute Clinical Consult 2023 Ha, E., Nayak, K. edited by Domino, F. Wolters Kluwer. 2022; 31st
  • Developing a Telemedicine Curriculum for a Family Medicine Residency. PRiMER (Leawood, Kan.) Ha, E., Zwicky, K., Yu, G., Schechtman, A. 2020; 4: 21


    Introduction: Telemedicine has rapidly become an essential part of primary care due to the COVID-19 pandemic. However, formal training in telemedicine during residency is lacking. We developed and implemented a telemedicine curriculum for a family medicine residency program and investigated its effect on resident confidence levels in practicing telemedicine.Methods: We designed a process map of the telemedicine visit workflow at the residency clinic to identify key topics to cover in the curriculum. We created a live 50-minute didactic lecture on best practices in telemedicine, along with a quick-reference handout. We distributed pre- and postintervention surveys to current residents (N=24) to assess the effect of the educational intervention on their confidence in practicing telemedicine.Results: Fourteen residents (58% response rate) completed all aspects of the study including both surveys and participation in the educational intervention. Confidence levels in conducting telemedicine visits increased in three of five domains: (1) virtual physical exam ( P=.04), (2) visit documentation (P=.03), and (3) virtually staffing with an attending ( P=.04). Resident interest in using telemedicine after residency also increased following the educational intervention.Conclusion: Telemedicine requires a unique skill set. Formal education on best practices improves resident confidence levels and interest in practicing telemedicine. Primary care residency programs should incorporate telemedicine training to adequately prepare their graduates for clinical practice.

    View details for DOI 10.22454/PRiMER.2020.126466

    View details for PubMedID 33111048