Research Buddy partnership in a MD-PhD program: lessons learned

BACKGROUND AND AIMS

There is increasing recognition of the importance of patient involvement in research. In recent years, there has also been growing interest in patient partnerships with doctoral studies students. However, it can be difficult to know where to start and how to go about such involvement activities. The purpose of this perspective piece was to share experiential insight of the experience of a patient involvement program such that others can learn from this experience. BODY: This is a co-authored perspective piece centred on the experience of MGH, a patient who has had hip replacement surgery, and DG, a medical student completing a PhD, participating in a Research Buddy partnership over the course of over 3 years. The context in which this partnership took place was also described to facilitate comparison with readers' own circumstances and contexts. DG and MGH met regularly to discuss, and work together on, various aspects of DG's PhD research project. Reflexive thematic analysis was conducted on reflections from DG and MGH regarding their experience in the Research Buddy program to synthesise nine lessons which were then corroborated with reference to published literature on patient involvement in research. These lessons were: learn from experience; tailor the program; get involved early; embrace uniqueness; meet regularly; build rapport; ensure mutual benefit; broad involvement; regularly reflect and review.

CONCLUSIONS

In this perspective piece, a patient and a medical student completing a PhD reflected upon their experience co-designing a Research Buddy partnership within a patient involvement program. A series of nine lessons was identified and presented to inform readers seeking to develop or enhance their own patient involvement programs. Researcher-patient rapport is foundational to all other aspects of the patient's involvement.

Patients' Views on AI for Risk Prediction in Shared Decision-Making for Knee Replacement Surgery: Qualitative Interview Study

BACKGROUND

The use of artificial intelligence (AI) in decision-making around knee replacement surgery is increasing, and this technology holds promise to improve the prediction of patient outcomes. Ambiguity surrounds the definition of AI, and there are mixed views on its application in clinical settings.

OBJECTIVE

In this study, we aimed to explore the understanding and attitudes of patients who underwent knee replacement surgery regarding AI in the context of risk prediction for shared clinical decision-making.

METHODS

This qualitative study involved patients who underwent knee replacement surgery at a tertiary referral center for joint replacement surgery. The participants were selected based on their age and sex. Semistructured interviews explored the participants' understanding of AI and their opinions on its use in shared clinical decision-making. Data collection and reflexive thematic analyses were conducted concurrently. Recruitment continued until thematic saturation was achieved.

RESULTS

Thematic saturation was achieved with 19 interviews and confirmed with 1 additional interview, resulting in 20 participants being interviewed (female participants: n=11, 55%; male participants: n=9, 45%; median age: 66 years). A total of 11 (55%) participants had a substantial postoperative complication. Three themes captured the participants' understanding of AI and their perceptions of its use in shared clinical decision-making. The theme Expectations captured the participants' views of themselves as individuals with the right to self-determination as they sought therapeutic solutions tailored to their circumstances, needs, and desires, including whether to use AI at all. The theme Empowerment highlighted the potential of AI to enable patients to develop realistic expectations and equip them with personalized risk information to discuss in shared decision-making conversations with the surgeon. The theme Partnership captured the importance of symbiosis between AI and clinicians because AI has varied levels of interpretability and understanding of human emotions and empathy.

CONCLUSIONS

Patients who underwent knee replacement surgery in this study had varied levels of familiarity with AI and diverse conceptualizations of its definitions and capabilities. Educating patients about AI through nontechnical explanations and illustrative scenarios could help inform their decision to use it for risk prediction in the shared decision-making process with their surgeon. These findings could be used in the process of developing a questionnaire to ascertain the views of patients undergoing knee replacement surgery on the acceptability of AI in shared clinical decision-making. Future work could investigate the accuracy of this patient group's understanding of AI, beyond their familiarity with it, and how this influences their acceptance of its use. Surgeons may play a key role in finding a place for AI in the clinical setting as the uptake of this technology in health care continues to grow.