Fall Barometer — Data Analysis Brief (step-by-step)

For: Fall Barometer Patent Working Meeting · 2026-06-02 From: Gene Lang, PharmD — Director of Pharmacy Operations You do not need any pharmacy background to do this. Every term is defined below, and a companion file lists every drug you need to look for. If anything is unclear, ask Gene before guessing.

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0. The two files you'll work with

1. The data — the anchor-site de-identified prescription dataset (≈754 people, ≈189,000 rows). Each row = one prescription that was filled (a "fill"). Confirm with Gene which columns are which; you'll need at minimum:
2. a person ID (a code, no names — the de-identified member)
3. a drug name (and possibly a separate generic name, NDC, or RxCUI code)
4. a fill date (when it was picked up)
5. a days supply (how many days that fill is meant to last, e.g., 30 or 90)

6. an age (or birth year) so we can keep only people 65 and older

7. The drug list — Appendix A at the very end of this document is the answer key: it lists all 159 drugs we care about and, for each, flags what category it's in:

8. generic name, brand name(s), drug class
9. FRID (Yes/No — fall-risk drug)
10. Fall-risk OR (a risk number — higher = more fall risk)
11. Beers status (blank = not flagged; any text = flagged)
12. ACB (0–3 — anticholinergic strength; higher = stronger)

(The exact same table is also saved as a spreadsheet, Fall_Barometer_Drug_Lookup.csv, in this folder — use it instead of Appendix A if you'd rather join it to the data programmatically.)

The core technique for almost every question below = match each drug in the data to its row in Appendix A, then count. See §2.

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1. Glossary — read this first

Term	Plain-English meaning
Medication / drug	A prescription product someone takes.
Generic name	The drug's real chemical name (e.g., sertraline). What we match on.
Brand name	The marketed trade name for the same drug (e.g., Zoloft = sertraline). The same drug can have several brand names.
Active ingredient	The generic drug inside a product, ignoring its strength and form. Sertraline 50 mg tablet and sertraline 100 mg tablet are the same active ingredient (sertraline).
Drug class	A family of drugs that work the same way (e.g., all the "SSRI" antidepressants).
Fill (dispensing record)	One time a prescription was picked up. One person can have many fills of the same drug over time.
Days supply	How many days a fill is meant to last (30, 90, etc.).
"On" a drug / active	A person is "on" a drug if they filled it recently — see §2 for the exact rule.
Senior	A person age ≥ 65. We only analyze seniors.
FRID	Fall-Risk-Increasing Drug — a medication shown in research to raise the chance of falling. The list is in §3.1.
Polypharmacy	Taking many medications at once. We use ≥ 5 different drugs = polypharmacy, ≥ 10 = high polypharmacy.
Beers / PIM	The Beers Criteria is a famous published list of Potentially Inappropriate Medications — drugs that are usually best avoided in older adults. A drug is "Beers-flagged" if the beers_status column is not blank.
Anticholinergic / ACB	"Anticholinergic" is a side-effect drugs can have that may cause confusion, dizziness, dry mouth. ACB = Anticholinergic Cognitive Burden score, 0–3: 0 = none, 1 = mild, 2–3 = strong. You add up a person's ACB scores across their drugs.
DBI	Drug Burden Index — a measure of how much sedative + anticholinergic load a person carries. The dbi_classification column says whether a drug counts.
Drug–drug interaction / co-prescribing	When a person is on two drugs at the same time that are risky together (see §3.5 for the pairs).
Incident start ("new start")	The first time a person starts a drug they were not already on (no fill of it in the prior 180 days).
Orthostatic hypotension	Blood pressure dropping when you stand up → dizziness → falls. Antihypertensives (blood-pressure drugs) and alpha-blockers can cause it.
OR / HR	"Odds ratio" / "hazard ratio" — a risk multiplier. 1.0 = no extra risk; 1.5 = 50% more; 2.0 = double. You don't calculate these — they're already in the lookup/§3.5.

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2. Setup: clean the data and build the join (do this once)

1. Keep only seniors: filter the dataset to people age ≥ 65.
2. Identify each person's active medications. Rule (confirm the window with Gene): a person is "on" a drug if they have ≥ 1 fill of that drug's active ingredient in the look-back window (default = the most recent 180 days of their record). Collapse strengths/forms — sertraline 50 mg and sertraline 100 mg both = sertraline. Count each distinct active ingredient once per person.
3. Match each drug to its Appendix A row:
4. If the dataset has a generic name or RxCUI/NDC field, join on that (cleanest).
5. Otherwise, normalize the drug-name text: lowercase, remove the strength and form (the numbers, "mg", "tablet", "capsule", "ER", etc.), then match the result to generic_name or any name in brand_names in the lookup.
6. Result: every active drug per person now carries its flags (is_FRID, beers_status, acb_score, etc.).
7. Unmatched drugs: make a list of any dataset drugs that did NOT match the lookup, with how many people are on each. Do not guess their risk flags — hand that list to Gene; a pharmacist will classify them. (Our lookup has 159 drugs; the dataset will have more.)

Everything below is then just counting per person and summarizing across the 754.

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2A. Building it in Tableau — the easy way (the hard part is already done for you)

You've been handed a finished, pre-computed table: Fall_Barometer_PerPerson_Summary.csv — one row per senior (754 rows). All the messy joining + per-person counting from §2 is already done. You do not need to do the join or any advanced calculations — just connect this file to Tableau and make charts. Each row has:

• person_id, age, gender
• total_meds — how many different drugs the person is on
• frid_class_count — number of fall-risk drug classes
• beers_count — number of Beers-flagged ("avoid in older adults") drugs
• total_acb — anticholinergic points, added up
• on_opioid, on_benzo, on_gabapentinoid, on_antipsychotic, on_antihypertensive, on_alpha_blocker — 1 = yes, 0 = no
• cns_active_count — number of brain/nervous-system drugs
• int_opioid_benzo, int_opioid_gabapentinoid, int_opioid_antipsychotic, int_htn_alpha, int_3plus_cns — 1 = that risky combination is present
• new_frid_starts_last_quarter — how many new fall-risk drugs they started recently

(§3 below explains what each column means clinically. This CSV is person-level — keep it on the secure/local machine; only the charts and percentages you build get shared.)

The one trick that makes this trivial in Tableau: the 0/1 columns are flags, so the AVERAGE of a flag × 100 = the percentage of people. No "LOD" or fancy calculations needed.

Connect: Tableau → Connect → Text file → pick Fall_Barometer_PerPerson_Summary.csv. Then:

KPI	How to build it in Tableau (off this one table)
FRID burden	New calculated fields: IF [frid_class_count] >= 1 THEN 1 ELSE 0 END (repeat for >=2, >=3). Drag each to Rows as AVG, format as a %. (Bonus: a bar chart of COUNT(person_id) by frid_class_count shows the full 0/1/2/3/4+ spread.)
Polypharmacy	Calc IF [total_meds] >= 5 THEN 1 ELSE 0 END (and >=10) → AVG as %. Median = drag total_meds → set Measure to Median. Distribution = histogram of total_meds.
Beers / PIM	Calc IF [beers_count] >= 1 THEN 1 ELSE 0 END → AVG as %.
Anticholinergic (ACB)	Histogram of total_acb; calc IF [total_acb] >= 3 THEN 1 ELSE 0 END → AVG as %.
Interactions	Each int_* column is already 0/1 → drag as AVG → that's the % of seniors with that combo. One bar per interaction.
New FRID starts	Calc IF [new_frid_starts_last_quarter] >= 1 THEN 1 ELSE 0 END → AVG as %.

Chart types: a bar for each set of percentages, a histogram for the distributions (total_meds, total_acb), and a big-number (BAN) tile for each headline %.

Sanity check — your numbers should land near these (already computed from the same table; if yours are wildly different, something in the connection went wrong — ask Gene):

KPI	Expected
On ≥1 FRID class / ≥2 / ≥3	~72% / ~52% / ~34%
Polypharmacy ≥5 meds / ≥10 / median	~63% / ~37% / 7 meds
≥1 Beers drug	~71%
ACB ≥1 / ≥3	~48% / ~16%
Opioid+benzo / antihtn+alpha / ≥3 CNS-active	~2% / ~8% / ~10%
≥1 new FRID start last quarter	~18%

(The drug lookup now covers 159 drugs — 122 from the clinical database + 37 fall-relevant drugs added by drug-class analogy, pending a pharmacist's final sign-off (the rows marked † in Appendix A). ~339 lower-frequency or non-fall-relevant drugs remain unclassified in Fall_Barometer_Unmatched_Drugs.csv — most are correctly not fall-risk; the few that are can be added later.)

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3. The seven analyses (what each column means)

For each: What · Why · The drugs · How (steps) · Example · Output.

3.1 FRID burden (fall-risk drugs)

What: How many fall-risk drug classes each senior is on. Why: This is the population our device serves; it validates how we weight medications. The drugs (is_FRID = Yes — 76 drugs, by class):

• SSRI antidepressants: paroxetine (Paxil), sertraline (Zoloft), escitalopram (Lexapro), citalopram (Celexa), fluoxetine (Prozac)
• SNRI antidepressants: duloxetine (Cymbalta), venlafaxine (Effexor)
• TCA antidepressants: amitriptyline (Elavil), nortriptyline (Pamelor), doxepin >6mg (Sinequan), imipramine (Tofranil), desipramine (Norpramin)
• Other antidepressants: mirtazapine (Remeron), trazodone (Desyrel)
• Benzodiazepines: alprazolam (Xanax), lorazepam (Ativan), diazepam (Valium), clonazepam (Klonopin), temazepam (Restoril)
• Z-drugs (sleep): zolpidem (Ambien), eszopiclone (Lunesta), zaleplon (Sonata)
• Atypical antipsychotics: clozapine (Clozaril), quetiapine (Seroquel), risperidone (Risperdal), olanzapine (Zyprexa), aripiprazole (Abilify)
• Typical antipsychotics: haloperidol (Haldol), chlorpromazine (Thorazine)
• Opioids: oxycodone (OxyContin, Roxicodone), hydrocodone (Vicodin, Norco), morphine (MS Contin), codeine (Tylenol #3), tramadol (Ultram)
• Gabapentinoids: gabapentin (Neurontin), pregabalin (Lyrica)
• First-gen antihistamines: diphenhydramine (Benadryl), hydroxyzine (Vistaril/Atarax), chlorpheniramine (Chlor-Trimeton), promethazine (Phenergan)
• Muscle relaxants: cyclobenzaprine (Flexeril), methocarbamol (Robaxin), baclofen (Lioresal)
• Bladder anticholinergics: oxybutynin (Ditropan), tolterodine (Detrol)
• Antiparkinson anticholinergics: benztropine (Cogentin), trihexyphenidyl (Artane)
• Blood-pressure / heart (cause dizziness on standing): lisinopril (Zestril/Prinivil), enalapril (Vasotec), ramipril (Altace), losartan (Cozaar), valsartan (Diovan), irbesartan (Avapro), olmesartan (Benicar), amlodipine (Norvasc), nifedipine IR (Procardia), diltiazem (Cardizem), metoprolol succinate (Toprol-XL), metoprolol tartrate (Lopressor), carvedilol (Coreg)
• Alpha-1 blockers: tamsulosin (Flomax), doxazosin (Cardura), prazosin (Minipress)
• Central alpha agonist: clonidine (Catapres)
• Diuretics ("water pills"): furosemide (Lasix), hydrochlorothiazide (Microzide)
• Nitrates: nitroglycerin SL (Nitrostat), isosorbide mononitrate (Imdur)
• Diabetes (sulfonylureas — cause low blood sugar): glipizide (Glucotrol), glimepiride (Amaryl)
• Other: warfarin (Coumadin), digoxin (Lanoxin), phenytoin (Dilantin), carbamazepine (Tegretol), lithium (Lithobid), levodopa/carbidopa (Sinemet)

How: For each senior, look at their active drugs, keep the ones with is_FRID = Yes, and count the number of distinct drug classes they fall into (use the drug_class column; e.g., being on two SSRIs = 1 class). Example: Mrs. A is on sertraline (SSRI), lorazepam (Benzodiazepine), lisinopril (ACE Inhibitor) → 3 FRID classes. Output: % of the 754 on ≥1, ≥2, ≥3 FRID classes, plus a bar chart of the full distribution (0,1,2,3,4,5+).

3.2 Polypharmacy (how many drugs total)

What: How many different medications each senior is on. Why: More drugs = more fall risk + the reason for our quarterly med-review feature. The drugs: all of them — no list needed. How: For each senior, count the number of distinct active ingredients (from §2 — count each drug once, all drugs, not just FRID). Example: Mr. B has fills for 8 different drugs in the window → count = 8 → he's "polypharmacy" (≥5) but not "high" (≥10). Output: % on ≥5 and ≥10 drugs; median and mean drug count; distribution chart.

3.3 Beers / PIM prevalence (drugs best avoided in older adults)

What: How many seniors are on at least one "potentially inappropriate" drug. Why: Confirms the cohort is clinically relevant. The drugs: any drug where beers_status is not blank (69 drugs flagged), e.g., diphenhydramine, amitriptyline, the benzodiazepines, zolpidem, oxybutynin, glyburide-type diabetes drugs, etc. How: For each senior, count active drugs with a non-blank beers_status. Output: % of the 754 on ≥1 Beers-flagged drug; also the top 10 most common Beers drugs in the cohort (a ranked list).

3.4 Anticholinergic burden (ACB)

What: Add up each senior's anticholinergic load. Why: Anticholinergic load drives confusion/dizziness → falls; validates that piece of our model. The drugs (and their points): - ACB = 2 or 3 (strong): amitriptyline, nortriptyline, imipramine, doxepin >6mg, diphenhydramine, hydroxyzine, chlorpheniramine, promethazine, oxybutynin, tolterodine, solifenacin, dicyclomine, benztropine, trihexyphenidyl, chlorpromazine, clozapine, olanzapine, quetiapine, paroxetine, cyclobenzaprine, amantadine, atropine - ACB = 1 (mild): alprazolam, lorazepam, clonazepam, diazepam, temazepam, zolpidem, zaleplon, eszopiclone, gabapentin, pregabalin, tramadol, codeine, hydrocodone, morphine, oxycodone, trazodone, mirtazapine, haloperidol, risperidone, clonidine, carbamazepine, phenytoin, warfarin, metoprolol, cetirizine, loratadine, tiotropium, desipramine - (Exact per-drug scores are in the acb_score column — use that as the source of truth.) How: For each senior, add up the acb_score of all their active drugs = their total ACB. Example: Mrs. C on diphenhydramine (3) + oxybutynin (3) + lorazepam (1) → total ACB = 7 (very high). Output: distribution of total ACB across the 754 (e.g., % at 0, 1–2, 3–4, 5+); median total.

3.5 Risky drug combinations (interactions)

What: How many seniors are on a dangerous pair of drug classes at the same time. Why: Validates our "interaction" logic. The pairs to check (a person must be on BOTH classes concurrently): | Drug class A | Drug class B | Risk | |---|---|---| | Opioid | Benzodiazepine | HR 1.78 (FDA black-box) | | Opioid | Gabapentinoid (gabapentin/pregabalin) | OR 1.49 | | Opioid | Antipsychotic | OR 1.87 | | Antihypertensive (BP drug) | Alpha-1 blocker (tamsulosin/doxazosin/prazosin) | OR 1.94 | | Any ≥3 CNS-active drugs (opioids, benzos, Z-drugs, antipsychotics, antidepressants, gabapentinoids, muscle relaxants) | — | OR 2.0+ | | SSRI/SNRI antidepressant | tramadol | serotonin-syndrome risk | | Benzodiazepine | zolpidem (Z-drug) | extreme sedation | | Anticholinergic | Anticholinergic (two ACB drugs) | additive |

How: Use the drug_class column. For each pair, count seniors who have ≥1 active drug in class A AND ≥1 in class B. For the "≥3 CNS-active" row, count seniors with 3+ active drugs from the CNS list above. Output: count + % of the 754 for each pair; flag the "≥3 CNS-active" number prominently.

3.6 New fall-risk-drug starts (how often a new risk drug begins)

What: How often a senior starts a new FRID (a fall-risk drug they weren't already on). Why: Tells us how often the device's "new medication" watch would trigger. How: For each senior and each FRID drug (§3.1 list): a fill is a "new start" if there is no earlier fill of that same drug in the prior 180 days. Count new starts per senior per calendar quarter. Example: Mr. D fills gabapentin in March with nothing in the prior 6 months → 1 new FRID start in Q1. Output: average new FRID starts per senior per quarter, and % of seniors with ≥1 new FRID start in a quarter.

3.7 Per-person summary table (for Gene to score)

What: One row per senior combining the counts above. Why: Gene/the pharmacist will apply the scoring model — you don't need the model. How: Export a table: person_id, age, total_meds, FRID_class_count, beers_drug_count, total_ACB, interaction_pairs_present, new_FRID_starts_last_quarter. Output: the table (CSV) + a one-page summary of each column's distribution.

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4. Output, limits, and rules

• Aggregate only. Distributions, counts, %, medians, ranked top-10s. The one person-level table in §3.7 stays in the local/secure environment — do not put person-level rows in any shared deck or email.
• Label the source "anchor site" everywhere. Never write the partner pharmacy's name.
• No PHI / no re-identifiable detail in any output.
• The honest limit (please state it on your summary): this is dispensing data, not fall outcomes. It validates the medication engine and sizes the need — it cannot tell us who actually fell. Outcome validation is the pilot, not this analysis.
• When in doubt, ask Gene. Especially: the look-back window (default 180 days), which dataset column is which, and any drug that didn't match the lookup.

5. Suggested order

Do §2 (setup/join) first — it powers everything. Then 3.2 (polypharmacy, easiest) → 3.3 (Beers) → 3.1 (FRID) → 3.4 (ACB) → 3.5 (interactions) → 3.6 (new starts) → 3.7 (summary table).

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Appendix A - The complete drug list (all 159 drugs)

Look up any drug here. FRID = fall-risk drug (Yes/No). Fall-risk OR = risk multiplier (higher = more fall risk). Beers status = flagged as potentially inappropriate in older adults ("-" = not flagged). ACB = anticholinergic strength, 0-3 (higher = stronger). Drugs are listed alphabetically by generic name.

Rows marked † are the 37-drug Rx360 expansion set — fall-relevant drugs we added by drug-class analogy to broaden coverage. They are pending pharmacist (Matilda) sign-off: treat their class/FRID/Beers/ACB tags as provisional, not yet clinically ratified. The other 122 rows are from the validated master risk database.

Generic name	Brand name(s)	Drug class	FRID	Fall-risk OR	Beers status	ACB
albuterol	ProAir, Ventolin	SABA	No	-	-	0
alprazolam	Xanax	Benzodiazepine	Yes	1.48	Avoid	1
amantadine	Symmetrel	Dopaminergic	No	-	-	2
amitriptyline	Elavil	TCA	Yes	1.51	Avoid	3
amlodipine	Norvasc	CCB (DHP)	Yes	1.24	-	0
amphetamine †	-	Stimulant	No	-	Flagged	0
apixaban	Eliquis	DOAC	No	-	-	0
aripiprazole	Abilify	Atypical Antipsychotic	Yes	1.50	Avoid	0
aspirin 81mg	Bayer Low-Dose	Antiplatelet	No	-	Avoid primary prev	0
atenolol †	-	Beta Blocker	Yes	-	-	0
atorvastatin	Lipitor	Statin	No	-	-	0
atropine	(various)	Anticholinergic	No	-	-	3
baclofen	Lioresal	Muscle Relaxant	Yes	1.50	Avoid	0
benztropine	Cogentin	Anticholinergic/Antiparkinson	Yes	1.54	Avoid	3
bisoprolol †	-	Beta Blocker	Yes	-	-	0
brexpiprazole †	-	Atypical Antipsychotic	Yes	-	Flagged	0
bumetanide †	-	Loop Diuretic	Yes	-	-	0
buprenorphine †	-	Opioid	Yes	-	-	0
bupropion	Wellbutrin	NDRI	No	-	-	0
calcium carbonate	Tums, Caltrate	Mineral	No	-	-	0
canagliflozin	Invokana	SGLT2 Inhibitor	No	-	-	0
carbamazepine	Tegretol	Antiepileptic	Yes	1.55	Avoid	1
carbidopa †	-	Dopaminergic	Yes	-	-	0
cariprazine †	-	Atypical Antipsychotic	Yes	-	Flagged	0
carisoprodol †	-	Muscle Relaxant	Yes	-	Flagged	0
carvedilol	Coreg	Beta Blocker	Yes	0.88	-	0
celecoxib †	-	NSAID	No	-	Flagged	0
cetirizine	Zyrtec	Second-Gen Antihistamine	No	-	-	1
chlorpheniramine	Chlor-Trimeton	First-Gen Antihistamine	Yes	1.54	Avoid	3
chlorpromazine	Thorazine	Typical Antipsychotic	Yes	1.50	Avoid	3
citalopram	Celexa	SSRI	Yes	1.66	Caution	0
clonazepam	Klonopin	Benzodiazepine	Yes	1.48	Avoid	1
clonidine	Catapres	Central Alpha Agonist	Yes	1.50	Avoid as 1st line HTN	1
clopidogrel	Plavix	Antiplatelet	No	-	-	0
clozapine	Clozaril	Atypical Antipsychotic	Yes	1.50	Avoid	3
codeine	Tylenol #3	Opioid	Yes	1.60	Avoid	1
cyclobenzaprine	Flexeril	Muscle Relaxant	Yes	1.50	Avoid	2
cyclosporine	Neoral, Sandimmune	Immunosuppressant	No	-	-	0
dabigatran	Pradaxa	DOAC	No	-	Avoid if CrCl<30	0
dapagliflozin	Farxiga	SGLT2 Inhibitor	No	-	-	0
desipramine	Norpramin	TCA	Yes	1.51	Avoid	1
diazepam	Valium	Benzodiazepine	Yes	1.48	Avoid	1
diclofenac †	-	NSAID	No	-	Flagged	0
dicyclomine	Bentyl	GI Antispasmodic	No	-	Avoid	3
digoxin	Lanoxin	Cardiac Glycoside	Yes	2.06	Avoid >0.125mg	0
diltiazem	Cardizem	CCB (non-DHP)	Yes	1.24	Avoid	0
diphenhydramine	Benadryl	First-Gen Antihistamine	Yes	1.54	Avoid	3
docusate	Colace	Stool Softener	No	-	-	0
donepezil	Aricept	Cholinesterase Inhibitor	No	-	-	0
doxazosin	Cardura	Alpha-1 Blocker	Yes	1.40	Avoid for HTN	0
doxepin †	-	TCA	Yes	-	Flagged	3
doxepin >6mg	Sinequan	TCA	Yes	1.51	Avoid >6mg	3
dulaglutide	Trulicity	GLP-1 RA	No	-	-	0
duloxetine	Cymbalta	SNRI	Yes	1.66	Caution	0
empagliflozin	Jardiance	SGLT2 Inhibitor	No	-	-	0
enalapril	Vasotec	ACE Inhibitor	Yes	1.24	-	0
escitalopram	Lexapro	SSRI	Yes	1.66	Caution	0
eszopiclone	Lunesta	Z-Drug	Yes	1.54	Avoid	1
famotidine	Pepcid	H2 Blocker	No	-	-	0
fish oil	Lovaza	Omega-3	No	-	-	0
fluoxetine	Prozac	SSRI	Yes	1.66	Caution	0
furosemide	Lasix	Loop Diuretic	Yes	1.36	Caution	0
gabapentin	Neurontin	Gabapentinoid	Yes	1.55	Caution	1
glimepiride	Amaryl	Sulfonylurea	Yes	1.27	Avoid	0
glipizide	Glucotrol	Sulfonylurea	Yes	1.27	Avoid	0
glyburide †	-	Sulfonylurea	Yes	-	Flagged	0
haloperidol	Haldol	Typical Antipsychotic	Yes	1.50	Avoid	1
hydralazine †	-	Vasodilator	Yes	-	-	0
hydrochlorothiazide	Microzide	Thiazide	Yes	1.36	Caution	0
hydrocodone	Vicodin, Norco	Opioid	Yes	1.60	Avoid	1
hydroxyzine	Vistaril, Atarax	First-Gen Antihistamine	Yes	1.54	Avoid	3
ibuprofen †	-	NSAID	No	-	Flagged	0
imipramine	Tofranil	TCA	Yes	1.51	Avoid	3
indomethacin †	-	NSAID	No	-	Flagged	0
insulin †	-	Insulin	Yes	-	-	0
insulin glargine	Lantus, Basaglar	Basal Insulin	No	-	-	0
insulin lispro	Humalog	Rapid Insulin	No	-	-	0
irbesartan	Avapro	ARB	Yes	1.24	-	0
isosorbide dinitrate †	-	Nitrate	Yes	-	-	0
isosorbide mononitrate	Imdur	Nitrate	Yes	1.24	-	0
labetalol †	-	Beta Blocker	Yes	-	-	0
lamotrigine †	-	Antiepileptic	Yes	-	-	0
lansoprazole †	-	PPI	No	-	Flagged	0
levodopa/carbidopa	Sinemet	Dopaminergic	Yes	1.70	-	0
levothyroxine	Synthroid, Levoxyl	Thyroid Hormone	No	-	-	0
liraglutide	Victoza	GLP-1 RA	No	-	-	0
lisinopril	Zestril, Prinivil	ACE Inhibitor	Yes	1.24	-	0
lithium	Lithobid, Eskalith	Mood Stabilizer	Yes	1.50	Caution	0
loratadine	Claritin	Second-Gen Antihistamine	No	-	-	1
lorazepam	Ativan	Benzodiazepine	Yes	1.48	Avoid	1
losartan	Cozaar	ARB	Yes	1.24	-	0
lurasidone †	-	Atypical Antipsychotic	Yes	-	Flagged	0
meclizine †	-	First-Gen Antihistamine	Yes	-	-	2
melatonin	(OTC)	Supplement	No	-	-	0
meloxicam †	-	NSAID	No	-	Flagged	0
memantine	Namenda	NMDA Antagonist	No	-	-	0
metformin	Glucophage	Biguanide	No	-	-	0
methocarbamol	Robaxin	Muscle Relaxant	Yes	1.50	Avoid	0
methylphenidate †	-	Stimulant	No	-	Flagged	0
metoprolol succinate	Toprol-XL	Beta Blocker	Yes	0.88	-	1
metoprolol tartrate	Lopressor	Beta Blocker	Yes	0.88	-	1
mirtazapine	Remeron	NaSSA	Yes	1.66	Caution	1
morphine	MS Contin	Opioid	Yes	1.60	Avoid	1
multivitamin	Centrum, One-a-Day	Supplement	No	-	-	0
naproxen †	-	NSAID	No	-	Flagged	0
nebivolol †	-	Beta Blocker	Yes	-	-	0
nifedipine IR	Procardia	CCB (DHP)	Yes	1.24	Avoid IR	0
nitroglycerin SL	Nitrostat	Nitrate	Yes	1.24	-	0
nortriptyline	Pamelor	TCA	Yes	1.51	Avoid	2
olanzapine	Zyprexa	Atypical Antipsychotic	Yes	1.50	Avoid	3
olmesartan	Benicar	ARB	Yes	1.24	-	0
omeprazole	Prilosec	PPI	No	-	Avoid >8wk	0
oxybutynin	Ditropan	Anticholinergic/Bladder	Yes	1.54	Avoid	3
oxycodone	OxyContin, Roxicodone	Opioid	Yes	1.60	Avoid	1
pantoprazole	Protonix	PPI	No	-	Avoid >8wk	0
paroxetine	Paxil	SSRI	Yes	1.66	Avoid	3
phenytoin	Dilantin	Antiepileptic	Yes	1.55	Avoid	1
pioglitazone	Actos	TZD	No	-	-	0
polyethylene glycol	MiraLAX	Osmotic Laxative	No	-	-	0
pramipexole †	-	Dopaminergic	Yes	-	-	0
pravastatin	Pravachol	Statin	No	-	-	0
prazosin	Minipress	Alpha-1 Blocker	Yes	1.40	Avoid for HTN	0
pregabalin	Lyrica	Gabapentinoid	Yes	1.55	Caution	1
promethazine	Phenergan	Phenothiazine/Antihistamine	Yes	1.54	Avoid	3
propranolol †	-	Beta Blocker	Yes	-	-	0
quetiapine	Seroquel	Atypical Antipsychotic	Yes	1.50	Avoid	3
ramipril	Altace	ACE Inhibitor	Yes	1.24	-	0
risperidone	Risperdal	Atypical Antipsychotic	Yes	1.50	Avoid	1
rivaroxaban	Xarelto	DOAC	No	-	Avoid NVAF/VTE	0
ropinirole †	-	Dopaminergic	Yes	-	-	0
rosuvastatin	Crestor	Statin	No	-	-	0
scopolamine †	-	Anticholinergic	Yes	-	Flagged	3
semaglutide	Ozempic, Rybelsus	GLP-1 RA	No	-	-	0
sertraline	Zoloft	SSRI	Yes	1.66	Caution	0
simvastatin	Zocor	Statin	No	-	-	0
sitagliptin	Januvia	DPP-4 Inhibitor	No	-	-	0
solifenacin	Vesicare	Anticholinergic/Bladder	No	-	Avoid	2
sotalol †	-	Beta Blocker	Yes	-	-	0
spironolactone	Aldactone	K-Sparing Diuretic	No	-	-	0
tacrolimus	Prograf	Immunosuppressant	No	-	-	0
tamsulosin	Flomax	Alpha-1 Blocker	Yes	1.40	Caution	0
temazepam	Restoril	Benzodiazepine	Yes	1.48	Avoid	1
terazosin †	-	Alpha-1 Blocker	Yes	-	Flagged	0
tiotropium	Spiriva	LAMA	No	-	-	1
tirzepatide	Mounjaro	GIP/GLP-1 RA	No	-	-	0
tizanidine †	-	Muscle Relaxant	Yes	-	-	0
tolterodine	Detrol	Anticholinergic/Bladder	Yes	1.54	Avoid	2
torsemide †	-	Loop Diuretic	Yes	-	-	0
tramadol	Ultram	Opioid/SNRI	Yes	1.60	Avoid	1
trazodone	Desyrel	SARI	Yes	1.66	Avoid as hypnotic	1
triazolam †	-	Benzodiazepine	Yes	-	Flagged	1
trihexyphenidyl	Artane	Anticholinergic/Antiparkinson	Yes	1.54	Avoid	3
valsartan	Diovan	ARB	Yes	1.24	-	0
venlafaxine	Effexor	SNRI	Yes	1.66	Caution	0
vitamin D	(various)	Vitamin	No	-	-	0
vortioxetine †	-	Antidepressant	Yes	-	-	0
warfarin	Coumadin, Jantoven	Anticoagulant	Yes	1.42	Caution	1
zaleplon	Sonata	Z-Drug	Yes	1.54	Avoid	1
zolpidem	Ambien	Z-Drug	Yes	1.54	Avoid	1