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Artificial intelligence (AI) models trained on audio data may have the potential to rapidly perform clinical tasks, enhancing medical decision-making and potentially improving outcomes through early detection. Existing technologies depend on limited datasets collected with expensive recording equipment in high-income countries, which challenges deployment in resource-constrained, high-volume settings where audio data may have a profound impact on health equity. This report introduces a novel protocol for audio data collection and a corresponding application that captures health information through guided questions. To demonstrate the potential of Voice EHR as a biomarker of health, initial experiments on data quality and multiple case studies are presented in this report. Large language models (LLMs) were used to compare transcribed Voice EHR data with data (from the same patients) collected through conventional techniques like multiple choice questions. Information contained in the Voice EHR samples was consistently rated as equally or more relevant to a health evaluation. The HEAR application facilitates the collection of an audio electronic health record (\"Voice EHR\") that may contain complex biomarkers of health from conventional voice/respiratory features, speech patterns, and spoken language with semantic meaning and longitudinal context-potentially compensating for the typical limitations of unimodal clinical datasets.

The Diabetes Control and Complications Trial and the U.K. Prospective Diabetes Study demonstrated the effectiveness of intensive glycemic control in the reduction of microvascular complications in patients with type 1 and type 2 diabetes, respectively.2,3 The American Diabetes Association (ADA) publishes annual clinical practice guidelines that recommend a general hemoglobin A1 (AlC) goal of < 7%, a total cholesterol goal of < 200 mg/dl, an HDL cholesterol goal of > 40 mg/dl for men and > 50 mg/dl for women, a triglyceride goal of < 150 mg/dl, an LDL cholesterol goal of < 100 mg/dl, a systolic blood pressure goal of < 130 mmHg, and a diastolic blood pressure goal of < 80 mmHg.4 Despite the publication and wide distribution of these recommendations, many patients with diabetes still do not meet recommended treatment goals.5 Numerous barriers to care exist in the primary care setting. Some of these include intensifying the treatment regimen, identifying barriers to adherence, and increasing frequency of patient contact.4 Multifaceted interventions targeting providers, patients, and organizations have been shown to improve the chronic care of patients with diabetes.8,10 Efforts, should ultimately be focused on increasing patients' knowledge, skills, and confidence in managing their disease.11 Several studies have reported improvements in glycemic control through pharmacist intervention within collaborative practices,12-1 s case management,16 and interdisciplinary teams.17 Pharmacists have been involved in diabetes care for many years within the Regional Medical Center in Memphis, Tenn., through a referral-based diabetes management clinic and an ADA-certified group diabetes self-management education (DSME) program.

Artificial intelligence (AI) models trained on audio data may have the potential to rapidly perform clinical tasks, enhancing medical decision-making and potentially improving outcomes through early detection. Existing technologies depend on limited datasets collected with expensive recording equipment in high-income countries, which challenges deployment in resource-constrained, high-volume settings where audio data may have a profound impact on health equity. This report introduces a novel data type and a corresponding collection system that captures health data through guided questions using only a mobile/web application. The app facilitates the collection of an audio electronic health record (Voice EHR) which may contain complex biomarkers of health from conventional voice/respiratory features, speech patterns, and spoken language with semantic meaning and longitudinal context, potentially compensating for the typical limitations of unimodal clinical datasets. This report presents the application used for data collection, initial experiments on data quality, and case studies which demonstrate the potential of voice EHR to advance the scalability/diversity of audio AI.