Improving Provision of Preanesthetic Information Through Use of the Digital Conversational Agent "MyAnesth": Prospective Observational Trial

Feasibility study of the digital tool Max for the patient-provided medication list in the medication reconciliation process prior to hospitalisation: patient willingness and usability, time saved and reliability

PURPOSE

More than 20% of prescription errors in hospitals are due to an incomplete medication history. Medication reconciliation is a solution to decrease unintentional discrepancies between medications taken at home and hospital prescriptions. It is a normalised clinical activity but it is time consuming. Medication reconciliation usually uses three sources of information for an optimised medical synthesis, one of which is the patient. A conversational robot for patients could be a solution to assist. Numerous digital applications are designed for patients and need to be tested for usability, satisfaction, reliability and time saved.

METHOD

We analysed Max, a conversational robot for patients scheduled for surgery in Toulouse University Hospital, using routinely collected health data in three successive steps. We examined willingness, compliance and patient satisfaction of usability with a Likert questionnaire and measured the time spent with Max and without. Finally, the reliability has been explored.

RESULTS

The three successive observational steps were assessment of willingness and compliance (79 patients), time saved (61 patients) and reliability of the tool (68 patients). 71% agreed to use Max after a telephone call but only 73% of patients completed Max entirely. Max was well received and the overall satisfaction of usability was high for ease of use, readability, relevance and number of questions. Max saved a few minutes by optimised medical synthesis compared with a conventional telephone call. However, the reliability appeared to be lower than the human conventional telephone call. Randomised controlled trials are needed to confirm this feasibility study.

CONCLUSION

Max was appreciated by patients and appeared to be suitable for assisting pharmacists in medication reconciliation. The tool established the list of treatments taken by the patient at home but reliability appeared to be lower than a conventional telephone call, recommending a 'double check' on the patient's arrival.

Artificial Intelligence in Perioperative Care: Opportunities and Challenges

Artificial intelligence (AI) applications have great potential to enhance perioperative care. This paper explores promising areas for AI in anesthesiology; expertise, stakeholders, and infrastructure for development; and barriers and challenges to implementation.

Perioperative application of chatbots: a systematic review and meta-analysis

BACKGROUND AND OBJECTIVES

Patient-clinician communication and shared decision-making face challenges in the perioperative period. Chatbots have emerged as valuable support tools in perioperative care. A simultaneous and complete comparison of overall benefits and harm of chatbot application is conducted.

MATERIALS

MEDLINE, EMBASE and the Cochrane Library were systematically searched for studies published before May 2023 on the benefits and harm of chatbots used in the perioperative period. The major outcomes assessed were patient satisfaction and knowledge acquisition. Untransformed proportion (PR) with a 95% CI was used for the analysis of continuous data. Risk of bias was assessed using the Cochrane Risk of Bias assessment tool version 2 and the Methodological Index for Non-Randomised Studies.

RESULTS

Eight trials comprising 1073 adults from four countries were included. Most interventions (n = 5, 62.5%) targeted perioperative care in orthopaedics. Most interventions use rule-based chatbots (n = 7, 87.5%). This meta-analysis found that the majority of the participants were satisfied with the use of chatbots (mean proportion=0.73; 95% CI: 0.62 to 0.85), and agreed that they gained knowledge in their perioperative period (mean proportion=0.80; 95% CI: 0.74 to 0.87).

CONCLUSION

This review demonstrates that perioperative chatbots are well received by the majority of patients with no reports of harm to-date. Chatbots may be considered as an aid in perioperative communication between patients and clinicians and shared decision-making. These findings may be used to guide the healthcare providers, policymakers and researchers for enhancing perioperative care.

Robotic Anesthesia: A Vision for 2050

The last 2 decades have brought important developments in anesthetic technology, including robotic anesthesia. Anesthesiologists titrate the administration of pharmacological agents to the patients' physiology and the needs of surgery, using a variety of sophisticated equipment (we use the term "pilots of the human biosphere"). In anesthesia, increased safety seems coupled with increased technology and innovation. This article gives an overview of the technological developments over the past decades, both in terms of pharmacological and mechanical robots, which have laid the groundwork for robotic anesthesia: target-controlled drug infusion systems, closed-loop administration of anesthesia and sedation, mechanical robots for intubation, and the latest development in the world of communication with the arrival of artificial intelligence (AI)-derived chatbots are presented.

Perioperative Risk Assessment of Patients Using the MyRISK Digital Score Completed Before the Preanesthetic Consultation: Prospective Observational Study

BACKGROUND

The ongoing COVID-19 pandemic has highlighted the potential of digital health solutions to adapt the organization of care in a crisis context.

OBJECTIVE

Our aim was to describe the relationship between the MyRISK score, derived from self-reported data collected by a chatbot before the preanesthetic consultation, and the occurrence of postoperative complications.

METHODS

This was a single-center prospective observational study that included 401 patients. The 16 items composing the MyRISK score were selected using the Delphi method. An algorithm was used to stratify patients with low (green), intermediate (orange), and high (red) risk. The primary end point concerned postoperative complications occurring in the first 6 months after surgery (composite criterion), collected by telephone and by consulting the electronic medical database. A logistic regression analysis was carried out to identify the explanatory variables associated with the complications. A machine learning model was trained to predict the MyRISK score using a larger data set of 1823 patients classified as green or red to reclassify individuals classified as orange as either modified green or modified red. User satisfaction and usability were assessed.

RESULTS

Of the 389 patients analyzed for the primary end point, 16 (4.1%) experienced a postoperative complication. A red score was independently associated with postoperative complications (odds ratio 5.9, 95% CI 1.5-22.3; P=.009). A modified red score was strongly correlated with postoperative complications (odds ratio 21.8, 95% CI 2.8-171.5; P=.003) and predicted postoperative complications with high sensitivity (94%) and high negative predictive value (99%) but with low specificity (49%) and very low positive predictive value (7%; area under the receiver operating characteristic curve=0.71). Patient satisfaction numeric rating scale and system usability scale median scores were 8.0 (IQR 7.0-9.0) out of 10 and 90.0 (IQR 82.5-95.0) out of 100, respectively.

CONCLUSIONS

The MyRISK digital perioperative risk score established before the preanesthetic consultation was independently associated with the occurrence of postoperative complications. Its negative predictive strength was increased using a machine learning model to reclassify patients identified as being at intermediate risk. This reliable numerical categorization could be used to objectively refer patients with low risk to teleconsultation.