What if every medication decision, every risk score, and every drug interaction check had a safety net — one that catches what the human eye misses, deterministically, every time?
Your Parkinson's patient is on levodopa, a MAO-B inhibitor, an antidepressant, a blood pressure med, and a sleep aid. Checking every pairwise interaction manually is impossible at scale.
For every hour you spend with a patient, you spend two on EHR documentation. Clinical notes, medication reconciliation, referral letters — all manual, all error-prone.
Levodopa timing, wearing-off windows, dyskinesia thresholds — every Parkinson's patient is different. Adjustments are educated guesses informed by experience, not data.
{
"age": 72,
"conditions": ["parkinson's", "depression", "hypertension"],
"medications": [
{ "name": "carbidopa-levodopa", "dose": "25/100mg", "freq": "TID" },
{ "name": "selegiline", "dose": "5mg", "freq": "BID" },
{ "name": "sertraline", "dose": "100mg", "freq": "daily" },
{ "name": "amlodipine", "dose": "5mg", "freq": "daily" },
{ "name": "omeprazole", "dose": "20mg", "freq": "daily" },
{ "name": "diphenhydramine", "dose": "25mg", "freq": "PRN" },
{ "name": "atorvastatin", "dose": "20mg", "freq": "daily" }
]
}
{
"current_regimen": {
"drug": "carbidopa-levodopa",
"dose": "25/100mg",
"schedule": ["7:00", "12:00", "17:00"],
"daily_levodopa": 300
},
"symptoms": {
"wearing_off": true,
"wearing_off_minutes_before_dose": 90,
"dyskinesia": false,
"peak_dose_tremor_control": "good"
}
}
{
"age": 68,
"sex": "male",
"event": "transient right arm weakness, 40 min",
"bp_at_presentation": "158/92",
"history": {
"hypertension": true,
"diabetes": true,
"chf": false,
"prior_stroke": false,
"vascular_disease": true,
"atrial_fibrillation": true
}
}
| Criteria | Details | Points |
|---|---|---|
| Age ≥ 60 | 68 years old | 1 |
| Blood pressure ≥ 140/90 | 158/92 mmHg | 1 |
| Clinical features | Unilateral weakness | 2 |
| Duration ≥ 60 min | 40 minutes (10-59 min range) | 1 |
| Diabetes | Yes | 1 |
| ABCD2 Total | 6 / 7 | |
| Criteria | Present? | Points |
|---|---|---|
| CHF | No | 0 |
| Hypertension | Yes | 1 |
| Age ≥ 75 | No (68) | 0 |
| Diabetes | Yes | 1 |
| Stroke/TIA | Current TIA | 2 |
| Vascular disease | Yes | 1 |
| Age 65-74 | Yes (68) | 1 |
| Sex (female) | No | 0 |
| CHA₂DS₂-VASc Total | 6 / 9 | |
How much time do you spend checking interactions manually? Would an automated polypharmacy checker save meaningful time, or does your EHR already handle this well enough?
When you adjust Parkinson's meds, what data do you wish you had? Is the dosing-interval optimization useful, or is clinical intuition already sufficient here?
If AI could draft your clinical notes and safety-check them against the medication list, would you trust it? What would make you trust it?
What's the most tedious, error-prone part of your daily workflow that none of these address? What tool would you build if you could build anything?