Patients As Partners: Reimaging Healthcare Relationships

Assoc Prof Devanand Anantham's Topic: And we became more like friends: Reimagining doctor-patient relationships

Abstract:
This lecture delves into the concept of "medical friendship," exploring the relational aspects of the doctor-patient relationship from both philosophical and practical perspectives. This role for doctors challenges transactional and narrow conceptions patient-centred models and advocates for a more humanistic approach to the practice of medicine. I present qualitative data from COPD patients in an ambulatory setting in Singapore on what meaningful doctor-patient relationships would like in the context of making medical decisions for the management of chronic diseases. Themes that are identified from interpretive analysis include the relevance of mutual care, personal connection, and shared activities between doctors and patients. I also discuss the roles of doctors in providing not just clinical care but also emotional support, emphasising the significance of friendliness and effective communication. Through patient and doctor narratives, I illustrate how the  “like a friend” role for doctors can enhance patient satisfaction and outcomes. This role underscores the need for doctors to balance technical competence with genuine care and concern, ultimately reimagining the doctor-patient relationship as a partnership.

Dr Shizuko Takahashi's Topic: Reevaluating ‘Seriousness’ in Genetic Conditions: Balancing Clinical Criteria and Lived Experiences in Japan

Abstract:

How should PGT-M criteria be determined? We begin with a Japanese case that catalyzed debate under a policy permitting only 16 conditions until 2022 (now ~36), which the Japanese Society of Obstetrics and Gynecology still finds ethically difficult to adjudicate. Building on Kleiderman et al., criteria should pair clinical rigor with lived-experience evidence in a transparent, revisable, publicly accountable process. In our Japan public survey (n=455), acceptance tracked perceived “seriousness” along a spectrum—avoid disability → avoid disease → select for health → select for enhancement → select for disability—with preferences clustering at the preventive end: high support to avoid disability (>70%) and hereditary cancers (66%); mixed for ADHD (62%); lower for appearance-related traits (48%). Endorsement of enhancement was rare (7%) and declined after testimony. These gradients map onto clinical dimensions (burden, age of onset, treatability, stigma) and can inform weights in a multi-criteria matrix. Procedurally, fairness and patient-centred impacts require multidisciplinary panels—including clinicians, ethicists, disability advocates, and people with lived experience—and structured testimony. Our stakeholder workshop aligns: 71% would choose PGT-M for hereditary cancer; 89% favored introducing PGT-M information at diagnosis; testimonies shifted views from “societal good” to reproductive rights (p=0.005). We therefore recommend formal public-and-patient involvement in setting and revising PGT-M criteria.

Learning Outcomes:

1. Distinguish clinical “seriousness” criteria from lived-experience domains and apply a seriousness–autonomy matrix to evaluate test eligibility and counseling.
2. Identify cultural and structural biases that shape severity judgments and articulate one actionable adjustment to clinical framing.
3. Determine when patient & public involvement is essential and outline principled boundaries for statutory limits on what genetic tests should be offered.

Assoc Prof Brian Earp's Topic: Digital Psychological Twins in Healthcare

Abstract:

Rapid advances in artificial intelligence are making it possible to create digital “psychological twins” of individual patients—AI models fine-tuned on a person’s own writings, speech, and behavioural data to approximate their distinctive values, preferences, and reasoning patterns. In healthcare, such systems could help clinicians anticipate what a patient would want in complex or uncertain circumstances, especially when the patient cannot speak for themselves. One proposed application is the Personalised Patient Preference Predictor (P4), which aims to model individual decision tendencies with greater nuance than population-level surrogates. This talk explores the technical promise and ethical implications of such AI “mind mirrors.” It considers questions of consent, authenticity, and moral responsibility, asking whether these systems extend or distort patient autonomy, and how they might be responsibly designed, validated, and governed within healthcare decision-making. Ultimately, digital twins could humanise medicine—or mechanise it—depending on how we proceed.