Mapping Markets
February 5, 2025

Autonomous Patient Assessment Market Map: Optimizing the Medical Interview

Patrick Wingo's headshot
Patrick Wingo
Head of Research, Elion

This is part of Elions weekly market map series where we break down critical vendor categories and the key players in them. For more, become a member and sign up for our email here.

Patients and providers alike are aware that they have precious little time together during consultations, with the average office visit taking only 20.3 minutes. Autonomous patient assessment tools offer an opportunity to collect patient data before, during, and after clinical encounters, capturing the nuanced details that inform the clinical decision-making process (much like a traditional medical interview) in greater detail and less time.

While AI symptom checkerscovered last week—are focused on triage and scheduling patients with the right care, autonomous patient assessment tools are focused on data collection for patients already connected to a provider.

These solutions encompass a range of technologies that automate the collection of important details, including a patient’s history of present illness, family background, behavioral health, and social determinants of health. The goal is to support clinicians by assembling a comprehensive picture of the patient’s condition. Unlike static questionnaires, many of these systems use adaptive questioning techniques that adjust in real time based on the patient’s responses. This dynamic approach helps ensure that critical areas are probed without burdening the patient with redundant or irrelevant questions.

Augmenting the Interview with AI

The traditional medical interview has long been the cornerstone of patient care. It is a structured conversation where clinicians systematically explore the patient’s chief complaint, history of present illness, past medical and family history, and social context. This process is both an art and a science—clinicians must use active listening, empathy, and their clinical experience to extract subtle cues and integrate disparate pieces of information.

However, even the most skilled interviewers can be challenged by factors such as patient anxiety, incomplete recall, or time constraints during busy clinical schedules. These challenges can sometimes lead to gaps in the collected information, potentially impacting the accuracy of the diagnosis and the formulation of an effective treatment plan.

Emerging AI solutions employ large language models to analyze patient responses as they are given. This analysis can identify ambiguities or inconsistencies that might otherwise be missed, prompting the system to ask follow-up questions in real time. For example, if a patient describes chest discomfort, the system may automatically dive into related symptoms such as shortness of breath or palpitations, ensuring a thorough exploration of cardiovascular risk factors. Some platforms even incorporate 2D avatars that interact with patients in a more personable manner, which can help reduce anxiety and encourage more candid responses.

Convergence of Clinical Tools

Much like AI scribes that automatically document patient-clinician interactions, these platforms use automated speech recognition and large language models to capture and analyze patient narratives in real time. They synthesize the collected data in a manner similar to clinical summarization tools, distilling complex information into concise, actionable insights. They reference medical literature to decide which questions to ask based on what they’ve discovered so far. In other words, these solutions appear to be one of the natural convergences of these other tools that are already gaining broader adoption.

Vendor Landscape: Who’s Leading the Way

The vendor landscape for Autonomous Patient Assessment reflects a growing diversity in approaches and specializations.

Looking ahead, the integration of AI into the medical interview process promises to enhance both the efficiency and quality of patient care. As these autonomous systems mature, they are likely to become indispensable tools in both outpatient and chronic care settings. By providing clinicians with richer, more comprehensive data and by automating routine aspects of the patient interview, these technologies have the potential to free up valuable time for deeper clinical reasoning and personalized care planning.

Role: Vice President, Digital Products and Innovation