ABSTRACT
OBJECTIVES: This manuscript reviews the current state of veterinary medical electronic health records and the ability to aggregate and analyze large datasets from multiple organizations and clinics. We also review analytical techniques as well as research efforts into veterinary informatics with a focus on applications relevant to human and animal medicine. Our goal is to provide references and context for these resources so that researchers can identify resources of interest and translational opportunities to advance the field. METHODS AND RESULTS: This review covers various methods of veterinary informatics including natural language processing and machine learning techniques in brief and various ongoing and future projects. After detailing techniques and sources of data, we describe some of the challenges and opportunities within veterinary informatics as well as providing reviews of common One Health techniques and specific applications that affect both humans and animals. DISCUSSION: Current limitations in the field of veterinary informatics include limited sources of training data for developing machine learning and artificial intelligence algorithms, siloed data between academic institutions, corporate institutions, and many small private practices, and inconsistent data formats that make many integration problems difficult. Despite those limitations, there have been significant advancements in the field in the last few years and continued development of a few, key, large data resources that are available for interested clinicians and researchers. These real-world use cases and applications show current and significant future potential as veterinary informatics grows in importance. Veterinary informatics can forge new possibilities within veterinary medicine and between veterinary medicine, human medicine, and One Health initiatives.
ABSTRACT
BACKGROUND: To impart principles of antimicrobial stewardship (AS) and infection prevention and control (IPC), we developed a curriculum tailored to the diverse aptitudes of learners at our medical center. METHODS: We integrated case-based modules, group learning activities, smartphone applications (apps), decision support tools, and prescription audit and feedback into curricula of the medical school, medicine residency program, infectious diseases (ID) fellowship program, and hospital medicine program operations. Interventions were implemented in 2012-2016 using a quasi-experimental before-and-after study design, and this was assessed using pre- and postintervention surveys or audit of antibiotic prescriptions. RESULTS: Over 180 medical students participated in the AS and IPC seminars. After smartphone app introduction, 69% reported using the app as their preferred source of antibiotic information. Approximately 70% of students felt comfortable prescribing antibiotics for a known infection compared with 40% at baseline (P = .02), and approximately 83% were able to identify the appropriate personal protective equipment for specific scenarios. Approximately 99% agreed that they have a role in promoting patient safety and preventing healthcare-associated infections as medical students. At 20 months, appropriateness of trainee antibiotic prescriptions increased by 20% (P < .01). Almost all ID fellows indicated that the AS and IPC seminar was a vital training supplement. Uptake of internist antibiotic recommendations using AS decision support tools was approximately 70%. CONCLUSIONS: All 5 interventions addressed learning objectives and knowledge gaps and are applicable across a range of environments. Evaluating long-term impact of our curriculum is the focus of future study.
