Automatic documentation of professional health interactions: A systematic review

Falcetta, Frederico Soares; Almeida, Fernando Kude de; Lemos, Janaína; Goldim, José Roberto; Costa, Cristiano André da

doi:10.1016/j.artmed.2023.102487

Cited by 14 publications

(6 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Google recently published its Artificial Medical Intelligence Explorer tool, which it describes as an “AI system based on a LLM and optimized for diagnostic reasoning and conversations” in primary care settings (Karthike-salingam, 2024). Companies utilize AI to automate administrative tasks in healthcare and assist providers in completing necessary documentation (Falcetta et al, 2023; Axios, 2024).…”

Section: Related Workmentioning

confidence: 99%

Risks from Language Models for Automated Mental Healthcare: Ethics and Structure for Implementation

Grabb,

Lamparth,

Vasan

2024

Preprint

View full text Add to dashboard Cite

Amidst the growing interest in developing task-autonomous AI for automated mental health care, this paper addresses the ethical and practical challenges associated with the issue and proposes a structured framework that delineates levels of autonomy, outlines ethical requirements, and defines beneficial default behaviors for AI agents in the context of mental health support. We also evaluate ten state-of-the-art language models using 16 mental health-related questions designed to reflect various mental health conditions, such as psychosis, mania, depression, suicidal thoughts, and homicidal tendencies. The question design and response evaluations were conducted by mental health clinicians (M.D.s). We find that existing language models are insufficient to match the standard provided by human professionals who can navigate nuances and appreciate context. This is due to a range of issues, including overly cautious or sycophantic responses and the absence of necessary safeguards. Alarmingly, we find that most of the tested models could cause harm if accessed in mental health emergencies, failing to protect users and potentially exacerbating existing symptoms. We explore solutions to enhance the safety of current models. Before the release of increasingly task-autonomous AI systems in mental health, it is crucial to ensure that these models can reliably detect and manage symptoms of common psychiatric disorders to prevent harm to users. This involves aligning with the ethical framework and default behaviors outlined in our study. We contend that model developers are responsible for refining their systems per these guidelines to safeguard against the risks posed by current AI technologies to user mental health and safety.Trigger warningContains and discusses examples of sensitive mental health topics, including suicide and self-harm.

show abstract

Section: Related Workmentioning

confidence: 99%

Risks from Language Models for Automated Mental Healthcare: Ethics and Structure for Implementation

Grabb,

Lamparth,

Vasan

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Digital scribe systems utilize emerging technologies, such as speech recognition and NLP, to automatically convert a verbal patient encounter directly into a summarized, well-structured medical chart [69]. These systems, in theory, would allow physicians to fully dedicate their complete attention to their patients without becoming routinely distracted throughout the encounter by entering their notes via keyboard and mouse [70]. Patients notice these distractions, often citing the uncomfortably long pauses while their physicians type their notes as a fundamental source of dissatisfaction during an encounter [30].…”

Section: Benefits Of Automated Note Writing In Clinical Practicementioning

confidence: 99%

Charting Tomorrow’s Healthcare: A Traditional Literature Review for an Artificial Intelligence-Driven Future

Fogleman,

Goldman,

Holland

et al. 2024

Cureus

View full text Add to dashboard Cite

Electronic health record (EHR) systems have developed over time in parallel with general advancements in mainstream technology. As artificially intelligent (AI) systems rapidly impact multiple societal sectors, it has become apparent that medicine is not immune from the influences of this powerful technology. Particularly appealing is how AI may aid in improving healthcare efficiency with note-writing automation. This literature review explores the current state of EHR technologies in healthcare, specifically focusing on possibilities for addressing EHR challenges through the automation of dictation and note-writing processes with AI integration. This review offers a broad understanding of existing capabilities and potential advancements, emphasizing innovations such as voice-to-text dictation, wearable devices, and AI-assisted procedure note dictation. The primary objective is to provide researchers with valuable insights, enabling them to generate new technologies and advancements within the healthcare landscape. By exploring the benefits, challenges, and future of AI integration, this review encourages the development of innovative solutions, with the goal of enhancing patient care and healthcare delivery efficiency.

show abstract

“…Previous systematic reviews have explored specific aspects of digital tool integration in health care, offering valuable insights into topics such as mobile health, EHRs, and AI-based technologies [44][45][46]. These reviews have effectively highlighted the impacts of digital tools on HCP interactions, communication, and documentation, contributing to a better understanding of the advantages of digital tools in health care and their negative impacts on clinician well-being and burnout [15,[47][48][49][50]. Another review provides comprehensive insights into the positive experiences, facilitators, challenges, barriers, and suggestions for the enhancement of digital care visits [51].…”

Section: Objectivementioning

confidence: 99%

Experience of Health Care Professionals Using Digital Tools in the Hospital: Qualitative Systematic Review

Wosny,

Strasser,

Hastings

2023

JMIR Hum Factors

View full text Add to dashboard Cite

Background The digitalization of health care has many potential benefits, but it may also negatively impact health care professionals’ well-being. Burnout can, in part, result from inefficient work processes related to the suboptimal implementation and use of health information technologies. Although strategies to reduce stress and mitigate clinician burnout typically involve individual-based interventions, emerging evidence suggests that improving the experience of using health information technologies can have a notable impact. Objective The aim of this systematic review was to collect evidence of the benefits and challenges associated with the use of digital tools in hospital settings with a particular focus on the experiences of health care professionals using these tools. Methods We conducted a systematic literature review following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to explore the experience of health care professionals with digital tools in hospital settings. Using a rigorous selection process to ensure the methodological quality and validity of the study results, we included qualitative studies with distinct data that described the experiences of physicians and nurses. A panel of 3 independent researchers performed iterative data analysis and identified thematic constructs. Results Of the 1175 unique primary studies, we identified 17 (1.45%) publications that focused on health care professionals’ experiences with various digital tools in their day-to-day practice. Of the 17 studies, 10 (59%) focused on clinical decision support tools, followed by 6 (35%) studies focusing on electronic health records and 1 (6%) on a remote patient-monitoring tool. We propose a theoretical framework for understanding the complex interplay between the use of digital tools, experience, and outcomes. We identified 6 constructs that encompass the positive and negative experiences of health care professionals when using digital tools, along with moderators and outcomes. Positive experiences included feeling confident, responsible, and satisfied, whereas negative experiences included frustration, feeling overwhelmed, and feeling frightened. Positive moderators that may reinforce the use of digital tools included sufficient training and adequate workflow integration, whereas negative moderators comprised unfavorable social structures and the lack of training. Positive outcomes included improved patient care and increased workflow efficiency, whereas negative outcomes included increased workload, increased safety risks, and issues with information quality. Conclusions Although positive and negative outcomes and moderators that may affect the use of digital tools were commonly reported, the experiences of health care professionals, such as their thoughts and emotions, were less frequently discussed. On the basis of this finding, this study highlights the need for further research specifically targeting experiences as an important mediator of clinician well-being. It also emphasizes the importance of considering differences in the nature of specific tools as well as the profession and role of individual users. Trial Registration PROSPERO CRD42023393883; https://tinyurl.com/2htpzzxj

show abstract

Automatic documentation of professional health interactions: A systematic review

Cited by 14 publications

References 31 publications

Risks from Language Models for Automated Mental Healthcare: Ethics and Structure for Implementation

Risks from Language Models for Automated Mental Healthcare: Ethics and Structure for Implementation

Charting Tomorrow’s Healthcare: A Traditional Literature Review for an Artificial Intelligence-Driven Future

Experience of Health Care Professionals Using Digital Tools in the Hospital: Qualitative Systematic Review

Contact Info

Product

Resources

About