- Provider scoring ranks HCP accounts for planning; it is not a rating of any clinician's skill or care.
- H2O Driverless AI builds the scores and returns reason codes, so field leads and compliance reviewers see what drove each one.
- Scores set where approved information is most relevant and which channel to use; they are never tied to anything of value.
- H2O can be used for this job; no life sciences customer result is claimed.
Every team scores HCPs differently
Your sales, marketing and medical affairs teams each keep their own view of which healthcare professionals matter. The deciles disagree, and nobody can say why an account moved.
Static segments also age fast. Patient mix, formulary access and practice structure change during the year, and a list built at planning time can be wrong by the second quarter. You need one score, refreshed on a schedule, with reasons attached that a field lead and a compliance reviewer can both read.
How AI provider scoring works
H2O can be used to build predictive AI models that score each HCP account.
Step 1: agree the data
Your compliance and privacy teams approve the sources: licensed prescription and claims data, specialty, practice setting, formulary access and past engagement with approved materials.
Step 2: frame the scores
A classification model predicts patient-population fit with your approved indications, and a second model predicts which channel each HCP responds to.
Step 3: build the models
H2O Driverless AI runs feature engineering and model search, and documents each model with AutoDoc.
Step 4: explain each score
Reason codes from explainable AI show which factors drove each account's score.
Step 5: plan and refresh
Your teams set call plans and channel mix from the scores, rescore on a schedule, and retrain when model drift appears.
A planning score, not a judgment on a clinician
A provider score tells your team where approved information is most relevant and how to deliver it. It does not rate a clinician's care, decide what a rep says, or set payments, samples or anything of value.
Keep the models to approved indications and to data sources your compliance team has reviewed. Every score carries its reasons, so your reviewers can audit it, and licensed data stays where your contracts require across on-premises, air-gapped, private cloud or cloud deployment.