Why One-Size-Fits-All AI Is Failing Healthcare And How Andor Health Is Defining a More Accurate Model With Pritesh Patel
Healthcare has traditionally believed that artificial intelligence (AI) could be uniformly applied across clinical roles, especially with the success of physician-facing ambient tools. However, this assumption is challenged by the distinct nature of…
Healthcare has traditionally believed that artificial intelligence (AI) could be uniformly applied across clinical roles, especially with the success of physician-facing ambient tools. However, this assumption is challenged by the distinct nature of nursing documentation, which requires precise assessments rather than narrative flow. The gap between current AI capabilities and actual clinical workflows has become apparent.
Pritesh Patel, Chief Operating Officer of Andor Health, is addressing this issue. Nursing workflows demand context, sequence, and certainty. Missing a question during an assessment can alter the care plan and disrupt subsequent actions. Patel emphasizes that AI solutions cannot be universally applied across different clinical documentation types.
Andor Health's AI-first platform, ThinkAndor®, is designed with this understanding. It evaluates a patient's status within the electronic medical record, identifies necessary assessments, and highlights remaining components that need completion. This process adheres to each health system's specific forms and policies, avoiding a one-size-fits-all template.
However, this assumption is challenged by the distinct nature of nursing documentation, which requires precise assessments rather than narrative flow.
ThinkAndor® adapts to the institution's existing systems, treating documentation as a workflow prioritizing accuracy, completeness, and consistency. Nursing assessments can trigger further consultations, diagnostic protocols, or escalations to physicians. The platform not only captures data but also orchestrates subsequent actions, emphasizing the interconnectedness of clinical tasks.
Andor Health's deployment model respects existing workflows, delivering guidance and next steps on devices clinicians already use. This approach aligns with frontline clinical decision-making, underscoring the importance of understanding structure, context, and policy in AI applications. Patel's perspective highlights the need for healthcare technology to support clinical workflows without introducing additional complexity.
Based on reporting by techround.co.uk.