Patient-centered care aspires to be sensitive to and respectful of the needs and values of each patient. It emphasizes patients’ rights to choice and control over medical decisions and views them as active participants in the healthcare process. Shared decision-making, which identifies the treatment best suited to each patient’s circumstance, is a crucial part of patient-centered care. It entails a direct exchange of information between the patient and the doctor during which the patient discusses their values and priorities and the clinician tells them of the potential risks and benefits of various treatment options.
As dialogue aids, several evidence-based tools have been created to assist collaborative decision-making. Contrary to patient decision aids, which the patient uses to prepare for the clinical encounter, discussion aids are intended to help the patient and clinician make decisions together while in the clinical encounter. They consider well-established medical facts about their conditions and, by synthesizing the information already available, they can assist patients in comprehending their unique risks and outcomes, exploring their options, and choosing the course of action that best suits their objectives and priorities.
Clinicians are unable to completely explain to patients how specific results or suggestions were arrived at because they lack a thorough understanding of the inner workings and computations of the decision aid. That is where explainable AI comes into the picture.
By offering clinicians and patients a customized dialogue aid based on the patient’s unique traits and risk factors, Explainability can address this problem. An explainable AI decision aid could support physicians in eliciting patient values and preferences by simulating the effects of various treatment or lifestyle interventions. This would help patients become more conscious of their choices. Explainability offers a graphic depiction or a natural language explanation of how various elements influenced the final risk assessment. However, patients rely on the clinician’s capacity to comprehend and communicate these explanations in a way that is correct and understandable to evaluate system-derived explanations and probabilities. Explainable AI decision support systems may help patients feel more competent and well-informed if they are used properly. This may increase patients’ motivation to participate in shared decision-making and act on risk-relevant information by fostering more accurate risk perceptions.
A Shift Towards Opaque Algorithms in CDSS – A Future:
Notably, such shifts could unintentionally result in a resurgence of paternalistic care concepts that reduce patients to mere bystanders in the course of medical decision-making. It might also usher in a brand-new branch of medicine where, to minimize negative effects on a patient’s health, the tool’s output shall aid doctors exponentially. Explainability can help to ensure that patients remain at the center of care and that they can make informed and autonomous decisions about their health in conjunction with clinicians, but its omission from clinical decision support systems threatens core medical values and may have negative effects on both individual and public health.
Samridhhi Mandawat, Consultant (Strategy) – Healthark Insights