Managerial and Organizational Challenges in the Age of AI

Obradovich, Nick; Johnson, Tim; Paulus, Martin P.

doi:10.1001/jamapsychiatry.2023.5247

Cited by 2 publications

(2 citation statements)

References 8 publications

(15 reference statements)

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“…In a recent example of this possibility, Jiang et al [19] demonstrated how an LLM trained on clinical notes could successfully predict patients' readmission, in-hospital mortality, comorbidity index, length of stay, and insurance denial in the NYU Langone Health System. On its face, the findings relate solely to prognosis, but viewed more broadly the results provide grounds for "systems solutions"-redesigns of the hospital's management and processes [20] that enhance decision-making via acting on the implications of AI predictions [18]. Operational decisions about personnel schedules and equipment needs could draw on the readmission and length of stay predictions reported in the paper -that is, a prediction that a wave of patients might require readmission or longer stays could activate increases in staffing levels and hospital material orders.…”

Section: Potential Implications Of Llms For Mental Healthmentioning

confidence: 99%

Opportunities and risks of large language models in psychiatry

Obradovich,

Khalsa,

Khan

et al. 2024

NPP—Digit Psychiatry Neurosci

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The integration of large language models (LLMs) into mental healthcare and research heralds a potentially transformative shift, one offering enhanced access to care, efficient data collection, and innovative therapeutic tools. This paper reviews the development, function, and burgeoning use of LLMs in psychiatry, highlighting their potential to enhance mental healthcare through improved diagnostic accuracy, personalized care, and streamlined administrative processes. It is also acknowledged that LLMs introduce challenges related to computational demands, potential for misinterpretation, and ethical concerns, necessitating the development of pragmatic frameworks to ensure their safe deployment. We explore both the promise of LLMs in enriching psychiatric care and research through examples such as predictive analytics and therapy chatbots and risks including labor substitution, privacy concerns, and the necessity for responsible AI practices. We conclude by advocating for processes to develop responsible guardrails, including red-teaming, multi-stakeholder-oriented safety, and ethical guidelines/frameworks, to mitigate risks and harness the full potential of LLMs for advancing mental health.

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Section: Potential Implications Of Llms For Mental Healthmentioning

confidence: 99%

Opportunities and risks of large language models in psychiatry

Obradovich,

Khalsa,

Khan

et al. 2024

NPP—Digit Psychiatry Neurosci

Self Cite

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“…It would be desirable if LLM could automate the process of informing patients and obtaining consent for medical procedures [12]. This could save cost-intensive personnel resources and allow a focus on core competencies [13]. By using NLP, information documents could be created individually and automatically.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of AI ChatBots for the Creation of Patient-Informed Consent Sheets

Raimann,

Neef,

Hennighausen

et al. 2024

MAKE

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Introduction: Large language models (LLMs), such as ChatGPT, are a topic of major public interest, and their potential benefits and threats are a subject of discussion. The potential contribution of these models to health care is widely discussed. However, few studies to date have examined LLMs. For example, the potential use of LLMs in (individualized) informed consent remains unclear. Methods: We analyzed the performance of the LLMs ChatGPT 3.5, ChatGPT 4.0, and Gemini with regard to their ability to create an information sheet for six basic anesthesiologic procedures in response to corresponding questions. We performed multiple attempts to create forms for anesthesia and analyzed the results checklists based on existing standard sheets. Results: None of the LLMs tested were able to create a legally compliant information sheet for any basic anesthesiologic procedure. Overall, fewer than one-third of the risks, procedural descriptions, and preparations listed were covered by the LLMs. Conclusions: There are clear limitations of current LLMs in terms of practical application. Advantages in the generation of patient-adapted risk stratification within individual informed consent forms are not available at the moment, although the potential for further development is difficult to predict.

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Managerial and Organizational Challenges in the Age of AI

Abstract: This Viewpoint discusses the managerial and organizational challenges that could result from the use of artificial intelligence systems in psychiatric research and care.

Cited by 2 publications

References 8 publications

Opportunities and risks of large language models in psychiatry

Opportunities and risks of large language models in psychiatry

Evaluation of AI ChatBots for the Creation of Patient-Informed Consent Sheets

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