DULUTH, MN MANILA, PH
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Case Study 2026

Clinical decision support

Currently usingClinical AI

Summary. I use clinical decision support every shift. Drug-interaction checkers in the EHR. Validated risk calculators for VTE and readmission. Imaging algorithms that flag findings I might miss on a tired Sunday. None of these replace my judgment; all of them sharpen it. The question is never “is the tool cool” — it is “has this tool been validated on patients who look like mine, and is the workflow honest about who owns the decision when the model is wrong.”

The three questions I ask of any decision-support tool

  1. Was it validated on a population that resembles mine? Rural Minnesota inpatients are not the same as an academic medical center cohort in California. A model trained on the latter and deployed against the former is a category error, not a feature.
  2. Has the validation been published, peer-reviewed, and replicated? Vendor white papers are not validation. A single internal study is not validation. I want at least one external replication before I rely on it.
  3. Who owns the workflow when the model is wrong? A risk score that nudges a clinician toward a different antibiotic is fine if I can override and document why. A risk score that triggers a unilateral action by another system is not fine.

Tools I currently use

Tools I am watching but not using

What I will not do

Use a decision-support tool that has been deployed institutionally without external validation, where the workflow does not give me a clear path to override and document. The fact that the tool exists in the EHR is not, on its own, evidence that it has been validated for my patients.


— Jeremy Tabernero, MD · More case studies · Get in touch