Electronic health records (EHRs) are notorious for reducing the face-to-face time with patients while increasing the screen-time for clinicians leading to burnout. This is especially problematic for psychiatry care in which maintaining consistent eye-contact and nonverbal cues are just as important as the spoken words. In this ongoing work, we explore the feasibility of automatically generating psychiatric EHR case notes from digital transcripts of doctor-patient conversation using a two-step approach: (1) predicting semantic topics for segments of transcripts using supervised machine learning, and (2) generating formal text of those segments using natural language processing. Through a series of preliminary experimental results obtained through a collection of synthetic and real-life transcripts, we demonstrate the viability of this approach.

Institutes are required to catalog their articles with proper subject headings so that the users can easily retrieve relevant articles from the institutional repositories. However, due to the rate of proliferation of the number of articles in these repositories, it is becoming a challenge to manually catalog the newly added articles at the same pace. To address this challenge, we explore the feasibility of automatically annotating articles with Library of Congress Subject Headings (LCSH). We first use web scraping to extract keywords for a collection of articles from the Repository Analytics and Metrics Portal (RAMP). Then, we map these keywords to LCSH names for developing a goldstandard dataset. As a case study, using the subset of Biology-related LCSH concepts, we develop predictive models by formulating this task as a multi-label classification problem. Our experimental results demonstrate the viability of this approach for predicting LCSH for scholarly articles.

Current health care systems require clinicians to spend a substantial amount of time to digitally document their interactions with their patients through the use of electronic health records (EHRs), limiting the time spent on face-to-face patient care. Moreover, the use of EHRs is known to be highly inefficient due to additional time it takes for completion, which also leads to clinician burnout. In this project, we explore the feasibility of developing an automated case notes system for psychiatrists using text mining techniques that will listen to doctor-patient conversations, generate digital transcripts using speech-to-text conversion, classify information from the transcripts into relevant categories, and automatically generate structured case notes. In our preliminary work, we develop a human-powered doctor-patient conversation transcript annotator and obtain a gold standard dataset through the National Alliance of Mental Illness (NAMI) Montana. We model the task of classifying parts of conversations into six broad categories such as medical and family history as a supervised classification problem and apply several popular machine learning algorithms. According to our preliminary experimental results obtained through 5-fold cross-validation, Support Vector Machines are able to classify an unseen transcript with an average AUROC (area under the receiver operating characteristic curve) score of 89%. Finally, we use part-of-speech (POS) tagging, grammatical rules of English language and verb conjugation, we generate written versions of the pieces of text belonging to different categories. These formal text are aggregated in to filling different sections of the EHR forms.

This study provides a snapshot of the current vaccine business ecosystem, including practices, challenges, beliefs, and expectations of vaccine providers. Our team focused on providers’ firsthand experience with administering vaccines to determine if an oral vaccine (e.g. pill or oral-drop) would be well-received. We interviewed 135 healthcare providers and vaccine specialists across the US, focusing questions on routine vaccinations, not COVID-19 vaccines. Improving workflow efficiency is a top concern among vaccine providers due to shrinking reimbursement rates—determined by pharmacy benefit managers (PBMs)—and the time-intensiveness of injectable vaccines. Administering injectable vaccines takes 23 minutes/patient on average, while dispensing pills takes only 5 minutes/patient. An average of 24% of patients express needle-fear, which further lengthens the processing time. Misaligned incentives between providers and PBMs could reduce the quality and availability of vaccine-related care. The unavailability of single-dose orders prevents some rural providers from offering certain vaccines. Most interviewees (74%) believe an oral vaccine would improve patient–provider experience, patient-compliance, and workflow efficiency, while detractors (26%) worry about the taste, vaccine absorption, and efficacy. Additional research could investigate whether currently non-vaccinating pharmacies would be willing to offer oral vaccines, and the impact of oral vaccines on vaccine acceptance.

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