Objectives

The number of surgeries performed in the United States has increased over the past two decades, with a shift to the ambulatory setting. Perioperative complications and mortality pose significant health care burdens. Inadequate preoperative assessment and documentation contribute to communication failure and poor patient outcomes. The aim of this quality improvement project was to design and implement a preoperative evaluation documentation template that not only improved communication during the perioperative pathway but also enhanced the overall user experience.

Methods

We implemented a revamped evidence-based documentation template in the electronic medical records of a health care organization across three internal medicine clinics on the downtown campus and seven satellite family medicine clinics. A pre- and postintervention design was used to assess the template utilization rate and clinician satisfaction.

Results

The preoperative template utilization rate increased from 51.2% at baseline to 66.5% after the revamped template "went live" (p < 0.001). Clinician satisfaction with the preoperative documentation template also significantly increased (30.6 vs. 80.0%, p < 0.001).

Conclusion

Adopting a user-friendly, evidence-based documentation template can enhance the standardization of preoperative evaluation documentation and reduce the documentation burden.

Background ● Artificial intelligence (AI) assisted clinical documentation tools are becoming increasingly available across outpatient clinical settings ● Voice to text recognition programs collect audio from patient-provider interactions and use AI to automatically generate notes documenting the encounter ● These notes can then be made available in the electronic health record within minutes ● Though these tools are available to attending physicians, there has been no research on attitudes regarding when such tools should be made available to medical trainees, such as medical students and residents ● We surveyed US medical students, residents, and attending physicians regarding when such tools should be introduced to learners, if at all, and concerns around such tools Study Design ● Cross-sectional survey ● Setting: Single institution in the Pacific region ● Study population: Medical students, resident physicians, fellows, and attending physicians (179 respondents) ● Study timeline: Survey available 5/9/24-6/10/24 ● Data collection: ○ Administered 15 question RedCap survey ○ Assessed respondents demographic and professional characteristics, including gender, highest level of training, percent of time spent in outpatient practice, time spent on patient interactions, and time spent on clinical documentation ● Study outcomes: ○ Relationship between respondents’ demographic and professional characteristics and what level of training they felt that AI-assisted clinical documentation tools should be available to trainees, should enter the medical record, and specific concerns with regard to their use (not meeting documentation milestones, detriment to patient-provider experience, accuracy, bias, violation of patient privacy, detriment to forming differential diagnoses, detriment to forming plans, and concerns about using personal devices) ● Statistical analysis: ○ Chi-squared analysis to determine association between demographic/professional characteristics and outcomes with significance level set at p < 0.05 ○ Cramer’s values calculated to determine strength of association ○ Descriptive characterisation of respondents Results 40% of female attending physicians believed that AI tools should not be available to trainees at all, compared to 25% of attending physicians overall and 17% of respondents in general Female respondents were far more likely to agree with the statement “I am concerned about AI-assisted clinical documentation violating patient privacy” and far less likely to agree with running the software on a personal device Summary ● Attending physicians, particularly female attending physicians, felt that AI assisted clinical documentation tools should be introduced to trainees later on and were more likely to believe that trainees should not be able to generate notes using such tools ● Female respondents were far more likely to have concerns about these tools adversely affecting trainees in achieving clinical documentation milestones and were far more likely to have concerns about privacy and use of personal devices surrounding these tools ● Respondents who spent more time with patients (P = 0.01, Cramer’s value = 0.19) and more time writing notes (P = 0.03, Cramer’s value = 0.20) were less likely to believe that that AI-assisted clinical documentation tools should be available to trainees Limitations ● Single institution survey ● Responses restricted to provided survey options ● Data not collected on experience with ambient clinical documentation tools within cohort Discussion ● Previous research has shown that physicians and medical students have positive attitudes and a willingness to learn about AI tools in healthcare ● However, our data suggests that attending physicians and female respondents in general have more concerns about privacy with regard to AI for clinical documentation tools and favor later introduction of such tools to trainees ● While trainees are amenable to learning about AI tools, faculty may recommended delaying the introduction of these tools to residency or later ● More research is needed to better understand why female physicians are more concerned about privacy with regard to AI tools and how these tools should be introduced to medical trainees ● Next steps: conducting interviews with respondents for more nuanced recommendations/understanding of concerns References AlZaabi A, AlMaskari S, AalAbdulsalam A. Are physicians and medical students ready for artificial intelligence applications in healthcare? DIGITAL HEALTH. 2023;9. doi:10.1177/20552076231152167 Giavina-Bianchi M, Amaro Jr E, Machado BS, Medical Expectations of Physicians on AI Solutions in Daily Practice: Cross-Sectional Survey Study, JMIRx Med 2024;5:e50803, doi: 10.2196/50803 Waheed MA, Liu L, Perceptions of Family Physicians About Applying AI in Primary Health Care: Case Study From a Premier Health Care Organization, JMIR AI 2024;3:e40781, doi: 10.2196/40781

Internal medicine physicians are increasingly interacting with systems that implement artificial intelligence (AI) and machine learning (ML) technologies. Some physicians and health care systems are even developing their own AI models, both within and outside of electronic health record (EHR) systems. These technologies have various applications throughout the provision of health care, such as clinical documentation, diagnostic image processing, and clinical decision support. With the growing availability of vast amounts of patient data and unprecedented levels of clinician burnout, the proliferation of these technologies is cautiously welcomed by some physicians. Others think it presents challenges to the patient-physician relationship and the professional integrity of physicians. These dispositions are understandable, given the "black box" nature of some AI models, for which specifications and development methods can be closely guarded or proprietary, along with the relative lagging or absence of appropriate regulatory scrutiny and validation. This American College of Physicians (ACP) position paper describes the College's foundational positions and recommendations regarding the use of AI- and ML-enabled tools and systems in the provision of health care. Many of the College's positions and recommendations, such as those related to patient-centeredness, privacy, and transparency, are founded on principles in the ACP Ethics Manual. They are also derived from considerations for the clinical safety and effectiveness of the tools as well as their potential consequences regarding health disparities. The College calls for more research on the clinical and ethical implications of these technologies and their effects on patient health and well-being.

Background

In 2011, the American Board of Medical Specialties established clinical informatics (CI) as a subspecialty in medicine, jointly administered by the American Board of Pathology and the American Board of Preventive Medicine. Subsequently, many institutions created CI fellowship training programs to meet the growing need for informaticists. Although many programs share similar features, there is considerable variation in program funding and administrative structures.

Objectives

The aim of our study was to characterize CI fellowship program features, including governance structures, funding sources, and expenses.

Methods

We created a cross-sectional online REDCap survey with 44 items requesting information on program administration, fellows, administrative support, funding sources, and expenses. We surveyed program directors of programs accredited by the Accreditation Council for Graduate Medical Education between 2014 and 2021.

Results

We invited 54 program directors, of which 41 (76%) completed the survey. The average administrative support received was $27,732/year. Most programs (85.4%) were accredited to have two or more fellows per year. Programs were administratively housed under six departments: Internal Medicine (17; 41.5%), Pediatrics (7; 17.1%), Pathology (6; 14.6%), Family Medicine (6; 14.6%), Emergency Medicine (4; 9.8%), and Anesthesiology (1; 2.4%). Funding sources for CI fellowship program directors included: hospital or health systems (28.3%), clinical departments (28.3%), graduate medical education office (13.2%), biomedical informatics department (9.4%), hospital information technology (9.4%), research and grants (7.5%), and other sources (3.8%) that included philanthropy and external entities.

Conclusion

CI fellowships have been established in leading academic and community health care systems across the country. Due to their unique training requirements, these programs require significant resources for education, administration, and recruitment. There continues to be considerable heterogeneity in funding models between programs. Our survey findings reinforce the need for reformed federal funding models for informatics practice and training.