Why One-Size-Fits-All AI Is Failing Healthcare And How Andor Health Is Defining a More Accurate Model With Pritesh Patel

Healthcare has spent years assuming that artificial intelligence could be applied uniformly across clinical roles. The success of physician-facing ambient tools made that belief tempting. Those applications delivered neatly structured narrative notes and reduced after-hours charting, which led many health systems to imagine that the same approach could translate directly into nursing.

A closer look at real clinical operations reveals the flaw in that logic. Nursing documentation does not resemble physician documentation. It does not rely on a single narrative arc. It is built on hundreds of unique assessments that demand precision rather than conversational flow. The gap between how work actually happens and how AI currently operates has become too large to ignore.

This is the problem that Pritesh Patel, Chief Operating Officer of Andor Health, has been focused on solving. Nursing workflows require context, sequence and certainty. Missing even one required question during an assessment can reshape the care plan and disrupt downstream actions.

Patel describes the misconception clearly, “AI is not one size fits all. You cannot take something used for physician documentation and apply it to nursing in an effective manner.” That single clarification frames the entire challenge facing health systems that attempt to scale AI without understanding the structural differences between documentation types.

Andor’s approach reflects that understanding. Its AI-first platform, ThinkAndor(r), begins by identifying a patient’s status within the electronic medical record. It determines which assessments are required, recognises what has already been completed at the bedside, and surfaces only the remaining components. This happens within the parameters of each health system’s own forms and policies. No universal template is imposed.

As the #1 agentic AI software infrastructure for healthcare™, ThinkAndor(r) adapts to the institution rather than expecting the institution to adapt to the platform. Documentation is treated as a workflow rather than a transcript. It carries the implication that accuracy, completeness and consistency matter more than simple speed.

The distinction becomes more pronounced when considering the chain of actions that follow each assessment. A nursing assessment can trigger a consultation, a diagnostic protocol, or an escalation to a physician. ThinkAndor does more than capture the data . It orchestrates what comes next.

This is the part of clinical work that often remains invisible. The correctness of each step depends on the clarity of the previous one. Patel’s view is that healthcare technology should understand those relationships and support them without adding new complexity.

Another core principle guides Andor’s deployment model. Providers should not be asked to switch interfaces or learn new environments. Guidance and next steps arrive on the devices they already use. The platform meets clinicians where they work. It also preserves their oversight. That combination of respect for existing workflows and responsible use of AI aligns with how frontline clinicians make decisions in practice.

The larger industry landscape is beginning to recognise the same truth. The next phase of AI is not about perfecting transcription. It is about understanding structure, context and policy. It is about reflecting the work as it exists rather than as software designers imagine it.

In that environment, Patel’s perspective positions Andor as a company building for the realities of inpatient care rather than the generalities of the market. Healthcare does not operate on a single type of note. True intelligence begins with understanding the workflow itself.