The patient-clinician relationship remains central to the success of diagnostic teams. Patient-clinician dyads have been associated with advantages for both patients (e.g., improved quality of life, satisfaction with care) and clinicians (e.g., higher quality of care, better job satisfaction).8-10 However, patients and clinicians face multiple challenges to developing strong relationships.
First, not all patients have access to clinicians, either at all (e.g., inadequate access to primary care) or when in need (e.g., urgent care that may result in overuse of emergency departments). When clinicians are available, they may have insufficient time to probe all aspects of a patient’s history or to consider all potential data points to develop high-quality diagnostic or therapeutic plans.11 The expanded use of telemedicine increases access for some patients but also reduces opportunities to incorporate physical examination findings into diagnostic decision making.12
Opportunities exist for AI to offer solutions that address some of these barriers and perhaps even strengthen the patient-clinician relationship.
