Pharmacogenomic knowledge gaps and educational resource needs among physicians in selected specialties

Figures & data

Table 1 Characteristics of survey population (n=300)

Characteristics	Total respondents (n)
Sex
Male	68.7% (206)
Female	31.3% (94)
Age, years
25–34	24.3% (73)
35–44	53.7% (161)
45–54	3.7% (11)
55–64	18.3% (55)
Years in practice (postresidency/fellowship)	10.3±1.59
Hours of direct patient care per week	48.6±7.51
Medical specialty
Family medicine*	38.3% (115)
General medicine*	0.7% (2)
Internal medicine*	21.3% (64)
Psychiatry	20.0% (60)
Cardiology	19.7% (59)
Region
Northeast	21.3% (64)
Midwest	19.3% (58)
South	36.7% (110)
West	22.7% (68)
Practice setting
Physician office, solo	14.3% (43)
Physician office, single-specialty group	53.3% (160)
Multispecialty group practice	32.3% (97)
Uses an EMR
Primary care physicians	77.3% (140/181)a
Psychiatrists	51.7% (31/60)
Cardiologists	72.9% (43/59)
Devices used to access health care-related information
Desktop computer	75.7% (227)
Laptop computer	76.3% (229)
Tablet computer	33.3% (100)
Smartphone	80.0% (240)

Notes:

* Grouped together as primary care physicians;

a significantly more primary care physicians than psychiatrists reported using an EMR (P<0.05).

Abbreviation: EMR, electronic medical record.

Figure 1 Factors cited as extremely or very important by physician respondents when choosing appropriate drug therapy or dosage.

Notes: ^aPsychiatrists were significantly less likely than primary care physicians and cardiologists to report that labeled indication is extremely or very important when making prescribing decisions (38.3% versus 66.9% and 72.9%, respectively, P<0.05).

Figure 2 Familiarity with, confidence in and knowledge of, and training in pharmacogenomics, as reported by physician respondents. “Formal training” was defined as medical school, residency, or continuing medical education.

Table 2 Proportion of respondents indicating that the drug listed could elicit a substantially variable response due to a patient’s genetic background

Drug	Biomarker(s)	Total % (n)	PCP % (n)	Psychiatrists % (n)	Cardiologists % (n)
Warfarin†	CYP2C9 VKORC1	47.7 (143)	51.9 (94)a	18.3 (11)	64.4 (38)a
Clopidogrel (Plavix^®)†	CYP2C19	47.0 (141)	47.0 (85)a	8.3 (5)	86.4 (51)c
Atorvastatin (Lipitor^®)†	LDL-R	24.7 (74)	28.2 (51)a	11.7 (7)	27.1 (16)
Carbamazepine (Tegretol^®)†	HLA-B*1502	21.0 (63)	18.2 (33)	43.3 (26)b	6.8 (4)
Quinidine		14.3 (43)	16.0 (29)	8.3 (5)	15.3 (9)
Captopril (Capoten^®)		13.3 (40)	14.9 (27)	6.7 (4)	15.3 (9)
Fluconazole (Diflucan^®)		13.0 (39)	14.9 (27)	10.0 (6)	10.2 (6)
Hydrochlorothiazide		10.7 (32)	13.8 (25)	8.3 (5)	3.4 (2)
Levothyroxine (Synthroid^®)		10.7 (32)	14.4 (26)	6.7 (4)	3.4 (2)
Atomoxetine (Straterra^®)†	CYP2D6	7.7 (23)	5.0 (9)	21.7 (13)b	1.7 (1)
Metoprolol (Lopressor^®)†	CYP2D6	6.3 (13)	7.2 (13)	5.0 (3)	5.1 (3)
Don’t know		21.3 (64)	19.3 (35)	41.7 (25)b	6.8 (4)
Total		(300)	(181)	(60)	(59)

Notes:

† Correct answer (drug elicits substantially variable response due to genetic variation); drugs included on this list are those that are among the most frequently prescribed by the specialties participating in the survey; those considered correct answers have clear pharmacogenomic information in the drug labeling, as well as substantial evidence of pharmacogenomic influence published in the literature;

a value significantly higher than corresponding value in “Psychiatrists” column (P<0.05);

b value significantly higher than corresponding values in “PCP” and “Cardiologists” columns (P<0.05);

c value significantly higher than corresponding values in “PCP” and “Psychiatrists” columns (P<0.05). Manufacturer details are as follows: Plavix^® (Sanofi, Bridgewater, New Jersey, USA); Lipitor^® and Diflucan^® (Pfizer, Inc., New York, NY, USA); Tegretol^® and Lopressor® (Novartis International AG, Basel, Switzerland); Capoten^® (Par Pharmaceutical, Spring Valley, NY, USA); Synthroid^® (Abbott Laboratories, North Chicago, IL, USA); Straterra^® (Eli Lilly and Company, Indianapolis, IN, USA).

Abbreviation: PCP, primary care physician.

Figure 3 Reasons most commonly cited by physician respondents for not ordering a pharmacogenomic test in the past year, and for not anticipating ordering a pharmacogenomic test in the next year.

Notes: ^aSignificantly more primary care physicians than cardiologists reported that they did not know what test to order (75.0% versus 52.5%, P<0.05); ^bsignificantly more primary care physicians and psychiatrists than cardiologists reported that they would not know what test to order (84.6% and 83.9%, respectively, versus 44.8%; P<0.05).

Table 3 Resources currently consulted when questions arise about pharmacogenomics

Resources	Total respondents (n)
Scientific literature	58.0% (174)
Internet (Google searches, WebMD, etc)	49.3% (148)
Professional society literature/guidelines/recommendations	47.3% (142)
Peer discussion	41.7% (125)
Laboratory directors/personnel	25.0% (75)a
Drug labeling	20.7% (62)
FDA website	17.7% (53)b
Insurance company/payer	11.3% (34)
Have not consulted resources	14.3% (43)
Do currently available resources enable you to access the pharmacogenomic information you need or want to know?
Yes	43.0% (129)
No	57.0% (171)

Notes:

a Primary care physicians were significantly more likely than cardiologists to have consulted a laboratory director/personnel (29.8% versus 11.9%, P<0.05);

b primary care physicians and psychiatrists were significantly more likely than cardiologists to have consulted the US Food and Drug Administration (FDA) website (18.8% and 26.7%, respectively, versus 5.1%; P<0.05).

Table 4 Preferred characteristics of an ideal pharmacogenomic educational resource

Characteristics	Total respondents (n)
Content
How to interpret pharmacogenomic test results	88.4% (260)a
Recommendations for prescribing	88.1% (259)
Effect of genetic variation on mechanism of drug action	79.9% (235)
Demographics of populations likely to carry variations	76.9% (226)
References (such as scientific literature)	69.0% (203)
List of laboratories offering testing	63.9% (188)
Description of pharmacogenomic information in drug labeling	62.2% (183)a
Format*
Web-based	67.7% (199)
Mobile application (for smartphone or tablet)	56.2% (165)
Incorporated within EMR	34.0% (100)b
Pop-up reminders within prescribing system	23.4% (69)
Print materials	18.7% (55)
Source (organization or institution)
Health care-related software company (eg, Epocrates)	66.9% (200)
Professional/specialty society	19.4% (58)
Government	6.7% (20)
Health insurance company	5.7% (17)

Notes:

* Values represent combined ratings of 1 or 2 on a scale of 1–5, with 1 representing most preferred and 5 representing least preferred;

a significantly more psychiatrists than cardiologists indicated that content on how to interpret pharmacogenomic test results (96.7% versus 82.5%, P<0.05) and a description of pharmacogenomic information in the drug labeling (75.0% versus 52.5%, P<0.05) should be included;

b primary care physicians were significantly more interested in EMR incorporation than were psychiatrists (41.2% rating it as a 1 or 2 versus 16.6% rating it as a 1 or 2, P<0.05). Epocrates, Inc. (San Mateo, CA, USA).

Abbreviation: EMR, electronic medical record.

Table

		Extremely important	Very important	Somewhat important	Not very important	Not at all important
10.	Medical history	○	○	○	○	○
11.	Age	○	○	○	○	○
12.	Sex	○	○	○	○	○
13.	Genetic information	○	○	○	○	○
14.	Labeled indication	○	○	○	○	○
15.	Adverse effects	○	○	○	○	○
16.	Insurance coverage	○	○	○	○	○

Table

		1	2	3	4	5
31.	Web-based	○	○	○	○	○
32.	Mobile application (for smartphone/tablet)	○	○	○	○	○
33.	Print materials	○	○	○	○	○
34.	Incorporated within an EMR system	○	○	○	○	○
35.	Pop-up reminders/information at time of prescription order	○	○	○	○	○