Public Opinions About Surgery in Older Adults

Dharmasukrit, Charlie; Ramaiyer, Malini; Dillon, Ellis C; Russell, Marcia M.; Dutt, Meghan; Colley, Alexis; Tang, Victoria

doi:10.1097/sla.0000000000005286

Cited by 4 publications

(3 citation statements)

References 13 publications

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“…However, setting patient expectations has been shown to be an essential part of patient-physician trust and relationships. 40 There were no significant differences in ChatGPT- vs surgeon-generated RBAs along these specific metrics, suggesting that the rubric was not unfairly penalizing to surgeons. Third, our study evaluated the LLM-based chatbot -generated RBAs without physician alteration.…”

Section: Discussionmentioning

confidence: 85%

“…Some surgeons may not have known to include this information in their documentation of risks. However, setting patient expectations has been shown to be an essential part of patient-physician trust and relationships . There were no significant differences in ChatGPT- vs surgeon-generated RBAs along these specific metrics, suggesting that the rubric was not unfairly penalizing to surgeons.…”

Section: Discussionmentioning

confidence: 91%

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Large Language Model−Based Chatbot vs Surgeon-Generated Informed Consent Documentation for Common Procedures

Decker,

Trang,

Ramirez

et al. 2023

JAMA Netw Open

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ImportanceInformed consent is a critical component of patient care before invasive procedures, yet it is frequently inadequate. Electronic consent forms have the potential to facilitate patient comprehension if they provide information that is readable, accurate, and complete; it is not known if large language model (LLM)-based chatbots may improve informed consent documentation by generating accurate and complete information that is easily understood by patients.ObjectiveTo compare the readability, accuracy, and completeness of LLM-based chatbot- vs surgeon-generated information on the risks, benefits, and alternatives (RBAs) of common surgical procedures.Design, Setting, and ParticipantsThis cross-sectional study compared randomly selected surgeon-generated RBAs used in signed electronic consent forms at an academic referral center in San Francisco with LLM-based chatbot-generated (ChatGPT-3.5, OpenAI) RBAs for 6 surgical procedures (colectomy, coronary artery bypass graft, laparoscopic cholecystectomy, inguinal hernia repair, knee arthroplasty, and spinal fusion).Main Outcomes and MeasuresReadability was measured using previously validated scales (Flesh-Kincaid grade level, Gunning Fog index, the Simple Measure of Gobbledygook, and the Coleman-Liau index). Scores range from 0 to greater than 20 to indicate the years of education required to understand a text. Accuracy and completeness were assessed using a rubric developed with recommendations from LeapFrog, the Joint Commission, and the American College of Surgeons. Both composite and RBA subgroup scores were compared.ResultsThe total sample consisted of 36 RBAs, with 1 RBA generated by the LLM-based chatbot and 5 RBAs generated by a surgeon for each of the 6 surgical procedures. The mean (SD) readability score for the LLM-based chatbot RBAs was 12.9 (2.0) vs 15.7 (4.0) for surgeon-generated RBAs (P = .10). The mean (SD) composite completeness and accuracy score was lower for surgeons’ RBAs at 1.6 (0.5) than for LLM-based chatbot RBAs at 2.2 (0.4) (P &lt; .001). The LLM-based chatbot scores were higher than the surgeon-generated scores for descriptions of the benefits of surgery (2.3 [0.7] vs 1.4 [0.7]; P &lt; .001) and alternatives to surgery (2.7 [0.5] vs 1.4 [0.7]; P &lt; .001). There was no significant difference in chatbot vs surgeon RBA scores for risks of surgery (1.7 [0.5] vs 1.7 [0.4]; P = .38).Conclusions and RelevanceThe findings of this cross-sectional study suggest that despite not being perfect, LLM-based chatbots have the potential to enhance informed consent documentation. If an LLM were embedded in electronic health records in a manner compliant with the Health Insurance Portability and Accountability Act, it could be used to provide personalized risk information while easing documentation burden for physicians.

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Section: Discussionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 91%

Large Language Model−Based Chatbot vs Surgeon-Generated Informed Consent Documentation for Common Procedures

Decker,

Trang,

Ramirez

et al. 2023

JAMA Netw Open

Self Cite

View full text Add to dashboard Cite

show abstract

“…Public opinion about surgery in older adults includes distrust of surgery and complaints of ineffective communication and allowing unrealistic expectations; recommended solutions are multidisciplinary teams and patient-centered communication. 10 All of these themes are ameliorated by the GSV. In our experience, every older adult patient has welcomed our “leave no stone unturned” evaluation and increased postoperative attention.…”

Section: Benefits Of the Gsvmentioning

confidence: 99%

Making a Financial Case for the Geriatric Surgery Verification Program

Katlic,

Wolf,

Demos

et al. 2024

Annals of Surgery Open

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Patient-Centered Outcomes and Preoperative Discussions

Rajasingh,

Wren

2024

JAMA Surg

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Surgeons and patients can have differing opinions about whether a procedure will improve patients' health or quality of life. 1 Not infrequently, patients or families advocate for an intervention even when surgeons recommend otherwise. The Joint Commission defines informed consent as follows: "A patient must be apprised of the nature, risks, and alternatives," a definition that excludes any obligation to discuss functional outcomes. 2 The nextlevel metric, patient-centered outcomes (PCO), such as days spent at home, shifts the focus to a patient's quality of life and ability to return to their preoperative functional status. 3,4 Jacobs and colleagues 5 described a novel PCO-patient trajectories after surgery measured by "days elsewhere from home." Notably, in Jacobs and colleagues' analysis, trajectories start 275 days before a procedure and end 365 days after, highlighting the impact of surgery beyond the traditional 30-day end point. They identified 5 trajectories in a population of patients receiving care at Veterans Affairs hospitals undergoing both elective and nonelective surgery. Three of these trajectories (T1, T2, and T3) reflected varying speeds of recovery and return to baseline, and another (T5) reflected patients who were chronically dependent preoperatively and, unsurprisingly, remained so postoperatively. The final trajectory (T4) represented the 1.3% to 5.2% of patients who were independent preoperatively and bec ame dependent in the yearlong postoperative period. Significantly, irrespective of age, available preoperative variables, such as health factors, frailty, and

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Public Opinions About Surgery in Older Adults

Cited by 4 publications

References 13 publications

Large Language Model−Based Chatbot vs Surgeon-Generated Informed Consent Documentation for Common Procedures

Large Language Model−Based Chatbot vs Surgeon-Generated Informed Consent Documentation for Common Procedures

Making a Financial Case for the Geriatric Surgery Verification Program

Patient-Centered Outcomes and Preoperative Discussions

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