Evolution of Healthcare with ChatGPT: A Word of Caution

Assessing the Impact on Electronic Health Record Burden After Five Years of Physician Engagement in a Canadian Mental Health Organization: Mixed-Methods Study

Background

The burden caused by the use of electronic health record (EHR) systems continues to be an important issue for health care organizations, especially given human resource shortages in health care systems globally. As physicians report spending 2 hours documenting for every hour of patient care, there has been strong interest from many organizations to understand and address the root causes of physician burnout due to EHR burden.

Objective

This study focuses on evaluating physician burnout related to EHR usage and the impact of a physician engagement strategy at a Canadian mental health organization 5 years after implementation.

Methods

A cross-sectional survey was conducted to assess the perceived impact of the physician engagement strategy on burnout associated with EHR use. Physicians were invited to participate in a web-based survey that included the Mini-Z Burnout questionnaire, along with questions about their perceptions of the EHR and the effectiveness of the initiatives within the physician engagement strategy. Descriptive statistics were applied to analyze the quantitative data, while thematic analysis was used for the qualitative data.

Results

Of the 254 physicians invited, 128 completed the survey, resulting in a 50% response rate. Among the respondents, 26% (33/128) met the criteria for burnout according to the Mini-Z questionnaire, with 61% (20/33) of these attributing their burnout to EHR use. About 52% of participants indicated that the EHR improves communication (67/128) and 38% agreed that the EHR enables high-quality care (49/128). Regarding the physician engagement strategy initiatives, 39% (50/128) agreed that communication through the strategy is efficient, and 75% (96/128) felt more proficient in using the EHR. However, additional areas for improvement within the EHR were identified, including (1) medication reconciliation and prescription processes; (2) chart navigation and information retrieval; (3) longitudinal medication history; and (4) technology infrastructure challenges.

Conclusions

This study highlights the potential impact of EHRs on physician burnout and the effectiveness of a unique physician engagement strategy in fostering positive perceptions and improving EHR usability among physicians. By evaluating this initiative in a real-world setting, the study contributes to the broader literature on strategies aimed at enhancing physician experience following large-scale EHR implementation. However, the findings indicate a continued need for system-level improvements to maximize the value and usage of EHRs. The physician engagement strategy demonstrates the potential to enhance physicians' EHR experience. Future efforts should prioritize system-level advancements to increase the EHR's impact on quality of care and develop standardized approaches for engaging physicians on a broader Canadian scale.

Role of ChatGPT-4 for Medical Researchers

The robustness of artificial intelligence (AI) in medical research has gotten a lot of attention in the current era. In particular, ChatGPT-4 is emerging as a notable AI language model. Basically, ChatGPT-4 is a state-of-the-art language model that mimics human responses to natural language inputs using AI-based deep learning methods and was created by OpenAI. ChatGPT-4 allows medical researchers to instantly share and receive up-to-date information on best practices and guidelines. Moreover, practitioners can explore this technology in various fields such as recognizing patterns, predicting outcomes, and providing important insights by entering patient data, medical reports, and other text-based information. No doubt, ChatGPT-4 is in the trending era but still need to know more about this tool. Therefore, this letter highlights the contributions, benefits, and challenges of the ChatGPT-4 tool in the medical field that may help medical professionals or researchers in their future work.

Implementation of Chatbot Technology in Health Care: Protocol for a Bibliometric Analysis

BACKGROUND

Chatbots have the potential to increase people's access to quality health care. However, the implementation of chatbot technology in the health care system is unclear due to the scarce analysis of publications on the adoption of chatbot in health and medical settings.

OBJECTIVE

This paper presents a protocol of a bibliometric analysis aimed at offering the public insights into the current state and emerging trends in research related to the use of chatbot technology for promoting health.

METHODS

In this bibliometric analysis, we will select published papers from the databases of CINAHL, IEEE Xplore, PubMed, Scopus, and Web of Science that pertain to chatbot technology and its applications in health care. Our search strategy includes keywords such as "chatbot," "virtual agent," "virtual assistant," "conversational agent," "conversational AI," "interactive agent," "health," and "healthcare." Five researchers who are AI engineers and clinicians will independently review the titles and abstracts of selected papers to determine their eligibility for a full-text review. The corresponding author (ZN) will serve as a mediator to address any discrepancies and disputes among the 5 reviewers. Our analysis will encompass various publication patterns of chatbot research, including the number of annual publications, their geographic or institutional distribution, and the number of annual grants supporting chatbot research, and further summarize the methodologies used in the development of health-related chatbots, along with their features and applications in health care settings. Software tool VOSViewer (version 1.6.19; Leiden University) will be used to construct and visualize bibliometric networks.

RESULTS

The preparation for the bibliometric analysis began on December 3, 2021, when the research team started the process of familiarizing themselves with the software tools that may be used in this analysis, VOSViewer and CiteSpace, during which they consulted 3 librarians at the Yale University regarding search terms and tentative results. Tentative searches on the aforementioned databases yielded a total of 2340 papers. The official search phase started on July 27, 2023. Our goal is to complete the screening of papers and the analysis by February 15, 2024.

CONCLUSIONS

Artificial intelligence chatbots, such as ChatGPT (OpenAI Inc), have sparked numerous discussions within the health care industry regarding their impact on human health. Chatbot technology holds substantial promise for advancing health care systems worldwide. However, developing a sophisticated chatbot capable of precise interaction with health care consumers, delivering personalized care, and providing accurate health-related information and knowledge remain considerable challenges. This bibliometric analysis seeks to fill the knowledge gap in the existing literature on health-related chatbots, entailing their applications, the software used in their development, and their preferred functionalities among users.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/54349.

Leveraging ChatGPT for Human Behavior Assessment: Potential Implications for Mental Health Care

This letter explores the capability of AI, specifically OpenAI's ChatGPT, in interpreting human behavior and its potential implications for mental health care. Data were collected from the Reddit forum "AmItheAsshole" (AITA) to assess the congruence between AI's verdict and the collective human opinion on this platform. AITA, with its vast range of interpersonal situations, provides rich insights into human behavioral evaluation and perception. Two key research questions were addressed: the degree of alignment between ChatGPT's judgment and collective verdicts of Redditors, and the consistency of ChatGPT in evaluating the same AITA post repeatedly. The results exhibited a promising level of agreement between ChatGPT and human verdicts. It also demonstrated high consistency across repeated evaluations of the same posts. These findings hint at the significant potential of AI in mental health care provision, underscoring the importance of continued research and development in this field.