Benefits of, Barriers to, and Needs for an Artificial Intelligence-Powered Medication Information Voice Chatbot for Older Adults: Interview Study With Geriatrics Experts.

BACKGROUND: One of the most complicated medical needs of older adults is managing their complex medication regimens. However, the use of technology to aid older adults in this endeavor is impeded by the fact that their technological capabilities are lower than those of much of the rest of the population. What is needed to help manage medications is a technology that seamlessly integrates within their comfort levels, such as artificial intelligence agents. OBJECTIVE: This study aimed to assess the benefits, barriers, and information needs that can be provided by an artificial intelligence-powered medication information voice chatbot for older adults. METHODS: A total of 8 semistructured interviews were conducted with geriatrics experts. All interviews were audio-recorded and transcribed. Each interview was coded by 2 investigators (2 among ML, PR, METR, and KR) using a semiopen coding method for qualitative analysis, and reconciliation was performed by a third investigator. All codes were organized into the benefit/nonbenefit, barrier/nonbarrier, and need categories. Iterative recoding and member checking were performed until convergence was reached for all interviews. RESULTS: The greatest benefits of a medication information voice-based chatbot would be helping to overcome the vision and dexterity hurdles experienced by most older adults, as it uses voice-based technology. It also helps to increase older adults' medication knowledge and adherence and supports their overall health. The main barriers were technology familiarity and cost, especially in lower socioeconomic older adults, as well as security and privacy concerns. It was noted however that technology familiarity was not an insurmountable barrier for older adults aged 65 to 75 years, who mostly owned smartphones, whereas older adults aged >75 years may have never been major users of technology in the first place. The most important needs were to be usable, to help patients with reminders, and to provide information on medication side effects and use instructions. CONCLUSIONS: Our needs analysis results derived from expert interviews clarify that a voice-based chatbot could be beneficial in improving adherence and overall health if it is built to serve the many medication information needs of older adults, such as reminders and instructions. However, the chatbot must be usable and affordable for its widespread use.

Patient and Provider Perspectives on Medication Non-adherence Among Patients with Depression and/or Diabetes in Diverse Community Settings - A Qualitative Analysis.

Background: Diabetes and depression affect a significant percentage of the world's total population, and the management of these conditions is critical for reducing the global burden of disease. Medication adherence is crucial for improving diabetes and depression outcomes, and research is needed to elucidate barriers to medication adherence, including the intentionality of non-adherence, to intervene effectively. The purpose of this study was to explore the perspectives of patients and health care providers on intentional and unintentional medication adherence among patients with depression and diabetes through a series of focus groups conducted across clinical settings in a large urban area. Methods: This qualitative study utilized a grounded theory approach to thematically analyze qualitative data using the framework method. Four focus groups in total were conducted, two with patients and two with providers, over a one-year period using a semi-structured facilitation instrument containing open-ended questions about experiences, perceptions and beliefs about medication adherence. Results: Across the focus groups, communication difficulties between patients and providers resulting in medication non-adherence was a primary theme that emerged. Concerns about medication side effects and beliefs about medication effectiveness were identified as perceptual barriers related to intentional medication non-adherence. Practical barriers to medication adherence, including medication costs, forgetting to take medications and polypharmacy, emerged as themes related to unintentional medication non-adherence. Conclusion: The study findings contribute to a growing body of research suggesting health system changes are needed to improve provider education and implement multicomponent interventions to improve medication adherence among patients with depression and/or diabetes, both chronic illnesses accounting for significant disease burden globally.

Generating sequential electronic health records using dual adversarial autoencoder.

OBJECTIVE: Recent studies on electronic health records (EHRs) started to learn deep generative models and synthesize a huge amount of realistic records, in order to address significant privacy issues surrounding the EHR. However, most of them only focus on structured records about patients' independent visits, rather than on chronological clinical records. In this article, we aim to learn and synthesize realistic sequences of EHRs based on the generative autoencoder. MATERIALS AND METHODS: We propose a dual adversarial autoencoder (DAAE), which learns set-valued sequences of medical entities, by combining a recurrent autoencoder with 2 generative adversarial networks (GANs). DAAE improves the mode coverage and quality of generated sequences by adversarially learning both the continuous latent distribution and the discrete data distribution. Using the MIMIC-III (Medical Information Mart for Intensive Care-III) and UT Physicians clinical databases, we evaluated the performances of DAAE in terms of predictive modeling, plausibility, and privacy preservation. RESULTS: Our generated sequences of EHRs showed the comparable performances to real data for a predictive modeling task, and achieved the best score in plausibility evaluation conducted by medical experts among all baseline models. In addition, differentially private optimization of our model enables to generate synthetic sequences without increasing the privacy leakage of patients' data. CONCLUSIONS: DAAE can effectively synthesize sequential EHRs by addressing its main challenges: the synthetic records should be realistic enough not to be distinguished from the real records, and they should cover all the training patients to reproduce the performance of specific downstream tasks.

Using FHIR to Construct a Corpus of Clinical Questions Annotated with Logical Forms and Answers.

This paper describes a novel technique for annotating logical forms and answers for clinical questions by utilizing Fast Healthcare Interoperability Resources (FHIR). Such annotations are widely used in building the semantic parsing models (which aim at understanding the precise meaning of natural language questions by converting them to machine-understandable logical forms). These systems focus on reducing the time it takes for a user to get to information present in electronic health records (EHRs). Directly annotating questions with logical forms is a challenging task and involves a time-consuming step of concept normalization annotation. We aim to automate this step using the normalized codes present in a FHIR resource. Using the proposed approach, two annotators curated an annotated dataset of 1000 questions in less than 1 week. To assess the quality of these annotations, we trained a semantic parsing model which achieved an accuracy of 94.2% on this corpus.

Insulinoma masquerading as a loss of consciousness in a teenage girl: case report and literature review.

BACKGROUND: Hypoglycemia due to a pancreatic beta cell neoplasm - insulinoma, is uncommon with only a few cases described. We report on a previously healthy 15-year-old Hispanic female with insulinoma who presented with a loss of consciousness due to hypoglycemia unawareness. CASE PRESENTATION: EM was first brought to the emergency department (ED) after she was found unresponsive at home with point of care (POC) glucose of 29 mg/dL(1.6 mmol/L) documented by emergency medical services (EMS) upon arrival. After treatment with dextrose and normal laboratory evaluation, including complete blood count, basal metabolic profile and urine drug screen, she was sent home with recommendations to follow-up the next day with an endocrinologist. Due to insurance issues, the family did not keep the appointment. Two days later, she returned to the ED with POC of 19 mg/dL (1.05 mmol/L). Detailed history review identified vague fatigue, excessive sleepiness, poor oral intake and weight gain for a 2-3 month period and no suspicion for drug, alcohol or prescription medication abuse. Family history of multiple endocrine neoplasia was negative. Physical examination revealed mild acanthosis nigricans and a body mass index of 32.8 kg/m2 (98th percentile). Laboratory evaluation showed elevated insulin with low cortisol and growth hormone levels at the time of hypoglycemia. Abdominal magnetic resonance imaging revealed a pancreatic mass, also supported by ultrasound, computed tomography and positron emission tomography scans. The patient underwent a partial pancreatectomy with removal of a well-circumscribed insulinoma from the anterior-superior aspect of the pancreatic neck confirmed by histology. Hypoglycemia resolved post-operatively and she remained euglycemic during a 48-h cure fast. At her 3-month follow-up visit, she had no symptoms of hypoglycemia. CONCLUSION: Documented hypoglycemia in an otherwise healthy adolescent should be fully investigated before discharging a patient. Even a short duration of symptoms should prompt, in-depth diagnostic evaluations to rule out a potentially life threatening diagnosis of insulinoma.