From patient data to a plan people can act on

Identifying a patient who may need additional support is the beginning of the work. A clinician needs to review the information, agree on a plan with the patient, and establish what happens next.

Precision’s design brings patient identification, clinician-reviewed protocols, practical Action Plans and ongoing follow-through into one connected model. For a medical practice, the aim is to make clear which patients need attention, what the agreed next step is, who is responsible and what happened afterward. Over time, reviewing those results can help refine both the care protocols and how care is delivered.

Across a practice, authorized patient information can help care teams identify where closer review may be useful. For an individual patient, the next step depends on clinical judgment, existing care and what is workable in daily life. A risk flag starts a review; it does not settle the care decision.

An Action Plan should make that decision usable. It should explain what the patient and care team have agreed to do, who is responsible, and when to check progress. Affordability, understanding and practical barriers belong in that conversation.

Consider a result that reaches a practice but has not yet been reviewed. Technology can help make that unfinished work visible. The care team still needs to interpret the result, communicate with the patient and document the next step. Sometimes the appropriate decision is to make no change.

AI can assist with organizing information and supporting coordination. Its usefulness needs to be tested in the setting where people will use it. The FUTURE-AI consensus guideline emphasizes usability, traceability and monitoring throughout the life of a healthcare AI system. [1]

Following a plan also creates opportunities to learn. Was the recommendation appropriate? Did the patient understand it? Was the agreed action completed, and what happened afterward? With appropriate permissions and review, those observations can inform future protocols and care coordination.

AHRQ describes learning health systems as combining experience from care with external evidence and putting that knowledge into practice. [2] That is the direction we are working toward: a clear connection between patient information, an agreed plan and follow-through that the practice can review.