Count Medications by Drug Class in a Clinical Record with Revdoku

Step 1: Open the medication list.

The Bay Area Medical Center Active Medications table is open, listing Lisinopril (ACE Inhibitor), Metformin (Antidiabetic), Atorvastatin (Statin), Amlodipine, Aspirin, Omeprazole, and more — each with drug class, dose, route, frequency, and start date.

Open the medication list. The Bay Area Medical Center Active Medications table is open, listing Lisinopril (ACE Inhibitor), Metformin (Antidiabetic), Atorvastatin (Statin), Amlodipine, Aspirin, Omeprazole, and more — each with drug class, dose, route, frequency, and start date.

Step 2: Scroll through the 10 rows of active medications.

A Pharmacy Note at the bottom flags two concurrent antibiotic courses and two overlapping antidiabetic agents.

Scroll through the 10 rows of active medications. A Pharmacy Note at the bottom flags two concurrent antibiotic courses and two overlapping antidiabetic agents.

Step 3: Run the Medication Count by Drug Class checklist.

Click Review, select Medication Count by Drug Class (1 rules) in the dialog, then click Run Review.

Run the Medication Count by Drug Class checklist. Click Review, select Medication Count by Drug Class (1 rules) in the dialog, then click Run Review.

Step 4: Read the drug class tally.

The result reports 10 medications across 7 classes — analgesic 2, antibiotic 2, antidiabetic 2, plus one each of ACE inhibitor, calcium channel blocker, proton pump inhibitor, and statin. Six issues flag the drug-interaction concerns from the Pharmacy Note.

Read the drug class tally. The result reports 10 medications across 7 classes — analgesic 2, antibiotic 2, antidiabetic 2, plus one each of ACE inhibitor, calcium channel blocker, proton pump inhibitor, and statin. Six issues flag the drug-interaction concerns from the Pharmacy Note.


What You Learned

This case showed how Revdoku can take a document, apply a structured checklist, and surface specific findings — all in under a minute. No manual line-by-line reading required.

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Disclosure: Sample documents in this use case were generated using AI and contain intentional errors for demonstration purposes. All names, companies, and details are fictional.

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