running head: factors that influence - the npa master.doc · web viewthe factors that influence the...

Factors that Influence 1

Running head: FACTORS THAT INFLUENCE

The Factors that Influence the Prescribing Habits of Clinicians

Darren Schutt

D’Youville College


Abstract

An important part of the practice of medicine is a clinician’s use of prescription drugs.

Many factors influence how clinicians prescribe medication. An Internet survey utilizing

a Likert scale and direct questions will be employed to assess how local clinicians view

the factors that influence their prescribing habits. By using professional demographical

information and responses to the survey a better understanding will be gained of how

local clinicians make prescribing decisions. This better understanding of the factors that

shape prescribing habits can be used by educators, clinicians, and policy makers to

improve healthcare.


Table of Contents

Introduction ……………………………………………………………. p. 4

Background………………………………………………………………p. 5

Method…………………………………………………………………...p. 19

Results……………………………………………………………………p. 25

Discussion………………………………………………………………...p. 35 References………………………………………………………………...p. 43

Appendix………………………………………………………………….p. 48

Table……………………………………………………………………....p. 51

Figures……………………………………………………………………..p. 52


The Factors that Influence the Prescribing Habits of Clinicians

In today ‘s society the use of prescription drugs is prevalent. For example, in 2001,

Pancholi and Stgnitti (2004) reported people in the United States younger than 65

purchased a mean of 10.8 prescription drugs and those 65 or older purchased a mean of

26.5 prescription drugs. Due to the increase in the amount of medication prescribed,

spending on pharmaceuticals has accelerated greatly in recent years and is now the fastest

growing component of the health care budget (Heffler, Levit, & Smith, 2001). The ability

to write for prescription medications is an essential part of MDs, PAs NPs clinical

practice and a huge responsibility. Drug therapy can be used for a variety of purposes

such as treatment of an infection, pain relief, to slow down a degenerative process, as

prophylactic therapy, etc. When medications are used properly, the patient’s health will

be optimized. Due to the great amount of advancements made in pharmaceutical research

and production, clinicians now have a vast amount of options on what drugs they use,

how to use them and when they use them. As a consequence of the complex nature of

healthcare and the vast amount of medications on the market I have noticed that no two

clinicians have the same prescribing habits. This is due to many factors. Some of these

factors have clinical bases while others do not. An example of a factor that has clinical

bases is evidence-based medicine. Evidence-based medicine is the practice of using

relevant studies to determine when to prescribe a certain medicine. My particular interest

in this subject is due to several non-clinical factors that influence prescribing habits. Of

particular concern to me are the perceived influences pharmaceutical sales representatives

and insurance companies have on a clinician’s prescribing habits. Many of these non-


clinical factors have caused a great amount of ethical debate as people question if these

factors unfairly skew a clinician’s prescribing habits and have a negative effect on our

health care system. For my master project I want to find out how clinicians perceive the

factors that influence their prescribing habits and if they believe these factors impede

them from giving the type of drug therapy that would be of greatest benefit to a patient.

By doing a literature review on this topic I have identified several prominent factors that

past studies have shown to influence prescribing habits. These factors are:

Evidence based medicine Patient pressure (expectations, drug seeking behavior) Pharmaceutical industries influence (samples, gift, direct to consumer

advertisements) Clinical experience Peer influence Insurance formularies (copay) Clinical specialty

In the following paragraphs I will outline what some of the past studies have shown on

how these factors influence the prescribing habits of clinicians.

Background

Evidence-Based Medicine

Evidence based medicine is when clinicians look at all the available medical

studies and literature that pertains to an individual patient or a group of patients and use

this information to help them properly diagnose illnesses, choose the best testing plan and

to select the best treatments and methods of disease prevention. It involves combining the

best research evidence with the patient's values to make decisions about medical care

writes Janet Torpy (2005) in JAMA. In the same article, Torpy (2005) states that

evidence-based medicine has helped to reduce mortality from heart attacks and improved

care for persons with diabetes. In an editorial Simon R Maxwell (2005) wrote about the


increasing emphasis being put on evidence based medicine when he stated “recently

doctors could prescribe medicines without worrying that their choices might be judged

against evidence accumulated in the world's literature. Now, prescribers are increasingly

expected to back up their decisions with evidence” (p 247). A study that shows evidence

based-medicine influences prescribing habits was published in the Journal of the

American Board of Family Medicine. In this study researchers evaluated the incidence of

new prescriptions written by generalists and specialists belonging to the same HMO

before and after publication of a study concerning these medications (Calvo, Cecilia, and

Rubinstein 2002). The data of this study showed the proportion of new prescriptions for

these medications changed between a 6-month period before publication and the 6-month

period after publication of the study concerning these medications (Calvo et, al. 2002) .

Based on the data, collected the researchers concluded that the “change in the

prescription patterns of the physicians showed a clear temporal association with the

publication of new evidence” (p. 461). Similar results were also obtained in a study that

investigated the effect that the Scnainavian Simvastatin Survival Study had on the rate of

lipid lowering agents prescribed in a population of patients who had suffered an acute

MI. This study recommended the use of a statin after a heart attack (Jackevicius,

Anderson, Leiter, & TU, 2001). The researchers found that there was a steady increase in

the overall rate of statin use before the publication of Scandinavian Simvastatin Survival

Study (4S), but that the rate of statin use increased significantly after the publication of

4S. These findings led the authors to conclude that “It is possible to shift practice if the

evidence of benefit is strong, the intervention is easy to implement and the intervention is

marketed aggressively” (p187). Many other studies have shown a correlation between an


increase in the amount of prescriptions for a certain drug and a study or guideline

becoming public knowledge (Torpy, 2005; Maxwell, 2005; Calvo et al., 2002;Jackevicius

et al., 2001; Ross & Macleod, 2005). Conversely, another study questioned the influence

of evidence-based medicine on prescribing habits. According to this study, entitled

Failure of Evidence-based Medicine in the Treatment of Hypertension in Older Patients,

evidence based medicine has little effect on how hypertensive medications are prescribed

(Knight, Glynn, & Levin, 2000). In this study the researchers examined if clinicians were

following the JNC VI evidence-base recommendation that first line treatment for

hypertension should be Beta-blockers and thizade diuretics. After examining prescribing

patterns, Knight and colleagues (2000) found that Thiazide use decline relative to

calcium channel blockers after the JNC VI recommendation was made . Based on these

results, the researchers concluded that in older patients clinicians did not follow evidence

based guidelines in the treatment of hypertension and that more of an effort had to be

made to encourage evidence-driven prescribing practices (Knight et. al ). A similar study

conducted by Michael Fischer and Jerry Avorn (2004) examined the same question and

found, after reviewing 815,316 regiments for the treatment of hypertension, that 40% did

not follow evidence based guides. According to Fisher and Avorn (2004), if evidence-

based guidelines were followed in this group of patients 11.6 million dollars could have

been saved. All these studies show that the exact influence of evidence-base medicine on

prescribing habits is unknown and its influence varies from clinician to clinician.

Patient Desire

In the past the patient-clinician relationship was unilateral with the patient

accepting and not questioning the treatment regiment prescribed by the doctor. With


healthcare consistently evolving, this is no longer the case as clinicians and patients both

participate in the process of forming a treatment regiment. This bilateral relationship

between clinicians and patients gives patients a greater say in their care. Along with

changes in the patient-clinician relationship, patients also have the opportunity to acquire

a great amount of medical information on various injuries, disorders, sicknesses and

medications because of the internet and other media outlets. Due to the progressive

changes in the patient-clinician relationship and the patients increase awareness of

available medications, more clinicians have found themselves pressured to prescribe a

certain medication by patients. This has led to patients’ expectations having an influence

on a clinician’s prescribing patterns. A study by Kravitz, Epstien and Fieldman (2005)l.

investigated this influence on prescribing by using standardized patients who portrayed

major depression or adjustment disorder. These patients presented themselves to 152

family medicine or general internist offices. The researchers found that if the

standardized patient made a brand specific request (Paxil), 53% of the time they were

prescribed a medication while if they made a general request, 76% of the time they were

prescribed an antidepressant and those that made no request only received a medication

31% of the time (Kravitz et al., 2005). A similar study published in the British Medical

Journal demonstrated the effectiveness of a patient’s request on a clinician’s prescribing

tendencies. In the study Mintzes et al. (2002) surveyed a total of 1431 patients attending

physicians’ offices in Sacramento and Vancouver. After controlling for various factors,

the researchers examined the influence of requests on the probability that a patient

received a new prescription and found patients who requested a prescription were more

likely to receive one than those that did not (139/175 v 329/1256). All these results led


Mintzes et al. (2003) to conclude that patients’ requests for medicine are a powerful

driver of prescribing decisions. A different designed study that supports the findings of

the last two studies consisted of clinical pharmacists’ interviews of 110 physicians who

were part of a medical school-based prescribing improvement program. These physicians

were identified from state Medicaid prescribing records as “moderate to high

prescribers”. During the interviews, these physicians’ motives for prescribing were

discussed and 51 out of 110 (or 46% ) stated that patients’ demands made them prescribe

a certain medication. This was the most common answer given in the study ( Schwartz,

Soumerai & Avorn, 1989) Unfortunately drug-seeking behavior leads to patients

applying pressure on clinicians which ultimately influences prescribing habits. In an

article reviewing opioid therapy for patients that are chronically ill or have abused opioid

drugs the authors outline how these types of patients can influence prescribing habits

when the authors wrote: “Pain management in this type of patient is very complex and

time-consuming so physicians who do not have the resources or time to deal with this

type of patient tend to bypass principles outlined in the guidelines and comply with

patients’ demands for increased opioid doses” (Ballantyne & Mao, 2003 p.1950). All of

these studies show that a patient’s request and expectation for a prescription medication

can influence clinicians prescribing habits.

Clinical experience

Clinicians are no different than everyone else as they are influenced by past

experiences and their age. Whether these experiences are bad or good, they shape the way

a clinician prescribes medicine. A good example of clinical experience influencing

prescribing behavior is a study that examined the prescribing tendencies of physicians


caring for patients with stable angina. The Beauliu et al. (2001) found that older

physicians were significantly less likely to prescribe aspirin (odds ratio for physician in

practice for > 20 years compared to those in practice < 10 years 0.58) for this condition

(p. 301). A similar study by Willison, Soumerai, and Palmer (2000) found that

physicians’ adoption of thrombolytic therapy for acute myocardial infarction (AMI) was

associated with several characteristics. One of these characteristics was age. Another

study that showed age and clinical experience effect prescribing habits described the

professional characteristics of doctors who prescribe appropriate medicine to nursing

home residents. (the criteria for appropriate medicine was determined by a expert panel)

After conducting their study Beers et al. (1993) found that older doctors belonged to the

quartile for prescribing inappropriate medication. These studies agreed with a systemic

review of articles published in the Annuals of Medicine entitled The Relationship

between Clinical Experience and the Quality of Health Care. After reviewing 62 articles

on the subject, Choudhry, Fletcher and Soumerai (2005) stated in their discussion “that

older physicians seem less likely to adopt newly proven therapies and may be less

receptive to new standards of care”. Contrary to these studies that showed an increase in

clinical experience and age led to inappropriate drug therapy Riy-Byrne et. al (2005)

found that length of time in practice did not predict appropriate prescribing. The

importance of a good first impression was shown in a study by Jones et al. (2001) as it

concluded early clinical experience of using a drug seems to strongly influence if a

clinician uses this drug in the future. A final piece of evidence that shows the effect of

clinical experience was when Klaschik and Clemens (2007) acknowledged that their

experience affects the way they prescribe medicine to the chronically ill. It was their


experience that the standard practice of using transdermal fentayl was not efficacious in

relieving pain in this patient population. This clinical experience prompted them to do a

study comparing the effectiveness of transdermal fentanyl and oral morphine for pain

relief in the chronically ill (Klaschik and Clemens, 2007). All these findings are evidence

that clinical experience helps form prescribing habits.

Clinical Specialty

Many studies have shown that an important influence on prescribing habits is the

clinician’s specialty. For instance, consultants tend only to prescribe new drugs within

their specialty (Jones et al., 2001) An example of this is Family Practice Physicians and

other non-Internists are much less likely than cardiologists to prescribe beta-adrenergic

blocking agents (Fehrenbach, Budnitz, Gazmararian,& Krumholz, 2001) Specialist

prescribing habits are also associated with more appropriate therapy. This is shown by an

audit of prescriptions for elderly patients by Anderson, Beers, and Kerluke, (1997). They

concluded that “physicians without specialty certification were more likely to prescribe

potentially inappropriate drugs” (Anderson, Beers & Kerluke, 1997).

Peer Influence

Peers influence prescribing habits as many clinicians rely on the knowledge and

approval of their peers before prescribing a certain drug. An article published in the New

England Journal of Medicine that assessed the relationship that clinicians have with the

pharmaceutical industry suggested that the industry focus marketing efforts on physicians

who are perceived as influencing the prescribing behaviors of others (Campbell et al.,

2007). According to one study by Ross and Macleod (2005) clinical practices with a

higher number of practitioners are more likely to prescribe medication that follows


evidence based recommendations because of the peer pressure. Another study John

Sbarbaro (2001) found one of the more effective ways to changing a physicians behavior

was by using peer review. This finding and many others demonstrate the effect that peers

can have on a clinician’s prescribing habits.

Pharmaceutical Industry’s Influence

Much of the research that has been done on the factors that influence prescribing

habits has centered around two types of influences-the pharmaceutical industry’s

influence and the insurance companies’ influence. Many people believe that these

industries unfairly use factors that skew a clinician’s judgment into prescribing a drug

that is not in the patient’s best interest. Due to this belief these, industry’s practices have

been subjected to intense public scrutiny.

In 1997, the FDA changed its’ guidelines for the broadcast of direct to consumer

advertisements. Prior to 1997, the FDA allowed prescription drugs to be advertised on

TV but rules and regulations for this type of advertising made it very difficult for the

pharmaceutical companies to make effective commercials. The changes in the guidelines

by the FDA made it easier to make good effective advertisements and, consequently,

more direct to consumer advertisements were made. (Rosenthal, Berndt, Donohue, Frank

and Epstein, 2002) The great amount of direct to consumer advertisements has become

very controversial as some people question the ethnicity and believe it can lead to

overuse of prescription medications because it encourages patients to pressure their

doctors to prescribe a certain drug (Donohue, Cevasco, and Rosenthal, 2007; Weissman

et al, 2004). Other people view the effects of direct to consumer advertisements

positively as nearly half of the respondents to one survey by Lyles ( 2002) believed that


such advertisements helped them make better decisions about their health. More than

60% who had seen a physician during the previous 3 months believed that the

advertisements helped them to have better discussions with their doctors concerning their

health. The same survey also found that consumers that saw an advertisement for a

particular medication that they were taking felt better about the drug’s safety (Lyles,

2002). No matter how direct to consumer advertisements are perceived there is no

denying that these advertisements effect prescribing habits. Recently the newspaper USA

Today published the results of a survey conducted by the paper, the Kasier Family

Foundation and the Harvard School of Public Health. It showed that “Prescription-drug

ads prompt nearly one third of Americans to ask their doctors about an advertised

medicine and 82% of those who ask say their physicians recommended a prescription”.

(Appleby, 2008 p. 4D) Several other studies have shown that direct to the consumer

advertisements are an effective technique that influences prescribing habits. For example,

a survey of 648 physicians showed when a DTCA (direct to consumer advertised) drug

was prescribed, only “46% of the physicians surveyed thought it was the most effective

drug compared with 48% who felt it was as effective as other drugs but prescribed the

DTCA drug in order to accommodate the patient’s request. The remaining 5% thought

that other drug or treatment options may have been more effective for the patient’s

condition but wanted to accommodate the patient request” (Mintzes et al., 2002 p. 279).

Another survey by Lipsky and Taylor (1997) reported that “71 % of family physicians

believe that direct to consumer advertising pressures physicians into prescribing drugs

that they would not ordinarily prescribe”. (p. 498) All these studies findings clearly show

the influence that direct to consumer advertisement has on prescribing habits.


Direct to consumer advertisement is one way the pharmaceutical industry tries to

influence prescribing habits, but in 2005, it only made up “14% of total promotional

expenditures” (Donohue, Cevasco, and Rosenthal,. 2007 p. 674). The great majority of

the pharmaceutical industries promotional budget is spent on marketing their products to

clinicians as demonstrated by the industry spending more than 7 billon dollars which is

“approximately 8,400 to 15,400 dollars per doctor per year” (Blumenthal, 2004 p. 1885).

The reason for the pharmaceutical industry aggressively marketing their product to

clinicians is best described by Glickman, Bruce, Caro, and Avorn (1994) in an article

they wrote on the subject. In the article they describe the purchase of prescription drugs

as economically unique because they are a "directed" demand. Clinicians direct the

purchase of pharmaceutical’s products through drug selection and determination of

appropriateness of the product. Essentially, physicians influence the market by acting as

"gatekeepers" who direct prescription drug use decisions. Due to the unique and pivotal

role clinicians have in the economic success of pharmaceutical companies, the industry

uses many different tactics to influence prescribing habits (Glickman, Bruce, Caro, and

Avorn, 1994). At the core of its’ promotional campaign, the pharmaceutical industry has

“spent 5 billion dollars to create an army of nearly 90,000 sales representatives that

interact directly with clinicians” (Wazana, 2000). These representatives use gifts, free

meals, travel, educational programs and product samples to try to sell their product.

(Glickman, Bruce, Caro, and Avorn,1994; Wazana, 2000) This relationship between

doctors and the pharmaceutical industry is extensive as a National Survey of Physician-

Industry relationship by Campbell ed. al (2007) reported. The survey’s results showed

that “94% of physicians had some type of relationship with the pharmaceutical industry


and most of the relationship (83%) involved receiving food in their workplace or

receiving drug samples (78%)”(p. 1742). Other forms of the relationship that were

reported were “35% of physicians received reimbursement for costs associated with

professional meetings or continuing education and 28% of physicians reported receiving

payments for consulting, giving lectures or enrolling patient in trials” Campbel et. al.,

2007 p. 1743). According to one article these techniques do influence prescribing habits

because of the social science concept of “self serving bias” (Dana and Loewenstein 2003

p. 252). This is human beings inability to make objective decisions when one of the

choices will benefit them. (Dana and Loewenstein, 2003). This is shown by reviews of

the literature that confirmed a direct relationship between the frequency of contact with

reps and the likelihood that physicians will behave in ways favorable to the

pharmaceutical industry (Wazana, 2000; “What impact does pharmaceutical”, 2008). An

example of the pharmaceutical industries influence is a retrospective study by Spingarn,

Berlin, and Strom (1996) that tracked house staff who attended a grand rounds given by a

pharmaceutical company speaker and found them more likely to prescribe that company's

drug as a treatment than did their colleagues .Wanzana’s (2000) review of articles written

on the subject found that pharmaceutical sponsored CME events and a clinician

accepting funding for travel or lodging for these educational symposia were associated

with increase prescription rates of the sponsor’s medication. Another study reviewed,

showed the effects free samples have on prescribing as “FPs who distribute free samples

are more likely to prescribe those medications than their counterparts who do” (Symm et

al., 2006 p. 448). A study by Adair and Holmgren found that free samples same effect on

prescribing habits: “Medical residents prescribed more advertised drugs and fewer over-


the-counter medications if they had access to prescription drug samples than residents

without access” (cited in Levin, 2005 p. 28) Due to the great amount of media attention

this relationship between clinicians and the pharmaceutical industry has received, the

subject is widely studied. As a consequence, these studies and papers are just a few

examples of the many writings on the topic.

The last way the pharmaceutical industry affects the prescribing habits of

clinicians is by setting the price of medications it produces. In the United States prices for

brand-name prescription drugs are 35 to 55 percent higher than in other industrialized

countries as the US is one of the only developed nation that does not regulate the cost of

medication (cited in Frank, 2004). As a result the pharmaceutical companies charge high

prices of brand name medications. Due the high cost of medications many clinicians

consider this a factor when they prescribe certain types of pharmocotherapy. An example

of this is a study by Reichert, Simion and Halm (2000) that surveyed how physicians

measure their attitudes about prescribing and their knowledge of the cost of medications.

The survey revealed that “Eighty-eight percent of physicians felt the cost of medicines

was an important consideration in a prescribing decision, and 71% were willing to

sacrifice some degree of efficacy to make drugs more affordable for their patients” (p.

2799) These results also show that a percentage of clinicians believe the cost of medicine

inhibits them from giving prescription drugs they believe is in the patients best interest.

(Reichert, Simion and Halm, 2000). Another study by Pham, Alexander, and O’Malley

reveled similar results as 78 % of the physicians they surveyed routinely considered out

of pocket cost before prescribing medications for a patient (2007).


Insurance companies

The cost of medicine is an issue of national concern as the increasing cost of

prescription drugs has received a great amount of attention. The US, unlike most other

developed countries, has no governmental body that regulates the price of drugs or the

profits of pharmaceutical companies. International price comparisons often show the US

citizens pay more than anyone else for drugs. Due to the lack of government regulation,

insurance companies, Medicaid, and Medicare all use drug formularies to curve the cost

of expensive drugs (Altman & Thomas 2002). These drug formularies consist of

multitiered copayment systems that motivate patients and clinicians to use medically

equivalent generic substitutes. The most common tier system is a three-tier formulary, as

in 2002, “57% of the workers in the US who had drug benefits were enrolled in plans

with a three-tier formulary” (Kaiser Family Foundation and Health Research and

Educational Trust [KFFHRET],2002). It is setup in a way that encourages clinicians and

patients to pick the drugs that insurances/HMOs prefer. The first tier consists of generic

drugs that require the lowest copay. The second-tier is composed of drugs that are brand

name drugs that are preferred by the insurance/HMO. These drugs require a higher copay

than the first-tier medications. The third-tier is made up of brand name drugs that are not

preferred by the insurance company. Therefore, they require the highest copay

([KFFHRET],2002). In order to increase compliance with a drug regiment, clinicians

must consider the cost of medications and their copayment as one study shows that two

thirds of older adults planned to underuse their medications because of cost (Piette,

Heisler, and Wagner, 2004). A similar constructed study by Soumerai et al. (2006)

concluded that “Medicare part D patients have a high rate of cost related nonadherence


and this was particularly a problem among those in poor health, multiple morbidities and

limited drug coverage” (p. 1834). Based on these results, the authors urged clinicians to

always be aware of the cost of medication and the type of insurance the patient has in

order to increase compliance (Soumerai et al., 2006 p. 1835) In a study that examined the

effects a tier based formulary has on antihypertensive drug selection and spending it was

found that patients in a tiered cost-sharing plan might be using less costly drug classes

rather than the ACE inhibitors and ARB which are considered first line therapy for

hypertension (Kamal-Bahl & Briesacher, 2004) . This finding is troubling, as it shows

that formularies and copayments might have restrict clinicians from delivering optimal

therapy. These findings show that insurance companies and cost of medication do

influence prescribing patterns but a study by Ernst et al. (2000) yielded different results.

In their study on the subject, Ernst et al. (2000) found that family physicians do not

know the cost of common prescription drugs and thus do not consider this factor when

they prescribe medicine. Another way insurance companies can dictate the prescribing

habits of clinicians is by using prior authorization. Insurance companies to help control

the cost of medicine implanted this process. It involves a clinician getting authorization

from an insurance company before prescribing a certain drug. Sometimes this request is

denied as Hamel and Epstien (2004) write that prior authorization programs “have the

potential to reduce patient’s access to beneficial drugs, especially when requirement for

documentation are onerous and the appeals process is restrictive.” (p. 2156) All these

studies mentioned above suggest that insurance formularies and prior authorization

programs of effect prescribing habits but the extent of these factors’ influence varies

among clinicians.


Method

Survey

Using a web based survey design, this investigation will examine the factors that

influence prescribing habits and how important these factors are to clinicians when they

are making prescribing decisions. Using a Likert scale the respondents will be asked to

assess the extent of influence the following factors have on their prescribing habits:

Evidence based medicine Patient pressure/desire (expectation) Pharmaceutical industry’s influence (samples, gifts, direct to consumer

advertising) Clinical experience Peer influence Insurance policies (Prior authorization, formularies, etc.) Clinical specialty

A four-point scale was chosen as it adequately covers the range of responses that a

potential candidate filling out the survey would require. This number of points also will

not result in “hair-splitting” that only serves to fragment the data and the time required to

view the range of responses is manageable (HOW… to develop an effective survey

response, 2000). The four points are:

No influence, Minimal influence, Influence Great influence.

A Likert scale is ideal to answer this question because it allows those surveyed to rate

how much weight they attach to each factor. Another advantage of a Likert scale is it

will make it easy to quantify the responses for statistical measurement (Blessing J. D.,

2006).


To further assess clinicians’ opinions on the factors that influence prescribing, the

respondents will also to be directly asked which factor from above list has the greatest

influence and which factor has the least influence on their prescribing habits.

Another question asked to the respondents doing this survey, is if any of the

above mention factors that influence prescribing habits hinder a clinician from giving the

type of care they believe would be in a patient’s best interest. In order to do this the

following question was asked “Please mark the factors, if any, that impede you from

giving your patients the pharmacological therapy that would be in the patient's best

interest.”. The clinicians completing the survey can pick from a list of the above mention

factors as well as “none of these factors impede me from prescribing medication that I

believe would be in my patient’s best interest”. Respondents many also choose more than

one factor to answer the question. Both these options are given to avoid bias.

The survey is set up on the internet and is voluntary. An internet survey was

chosen because this method of collecting survey information generally has a higher rate

of response compared to other forms of survey designs (Blessing J. 2006). Other

advantages to an internet based survey is it gives access to a broader potential pool of

candidates, is economical, the data is immediately available and you do not have the risk

of losing paper surveys that were filled out (Blessing J. 2006). The website Survey

Monkey® will be used to accomplish this task. This website will be used to design the

survey, make the survey accessible to the sample pool via a link, collect the accumulated

data and do basic statistical analysis.


Sample

The potential sample will be all the members of the Monroe County Medical

Society who receive their internet newsletter. The organization is composed of

approximately 1,700 physicians. Of the 1,700 members, 1,200 of these members are

actively practicing medicine in Monroe and its’ surrounding counties. The Nurse

Practitioner Association-Greater Rochester Chapter has also agreed to post a link to my

study on their website as well as to notify their members of the survey in a newsletter that

the organization circulates. The third and final group that will make up my sample is the

physician assistant that are members of WYNPAA (Western New York Physician

Assistant Association). This organization will inform its members of the survey via email

and a link. All these organization’s members practice medicine in Western New York,

have members practicing in all specialty areas and in many different clinical settings

which eliminates many of the variables that could lead to inaccurate comparison between

the three types of clinicians. The recruitment email sent to these organizations’ members,

will outline the goals of the survey, the reason for conducting the survey and a link to the

survey. It will also explain that no personal identifying information is asked and the

respondents are anonymous. In order to achieve the greatest amount of participants the

recruitment letter will also invite the recipients of the letter to encourage colleagues to

partake in the survey. The letter will also outline that the survey will only take

approximately five minutes to complete and by filling out the survey the receipts will be

giving informed constant. Copies of the three different recruitment emails that will be

sent are appendix A,B, and C.


Number of participants

To increase the amount of respondents to the survey a convenience sample will be

used. This approach only samples those that are available and willing to participate in the

survey. According to the U.S. Department of Labor Bureau of Labor Statistics and the

AANP facts and information sheet, there are currently 663,000 physicians, 66,000 PAs,

23,500 NP’s practicing medicine in the US. When these numbers are totaled, the number

of prescribing clinicians in the United States is 752,500 (“U.S. Department of Labor,

Occupational Outlook Handbook”, 2007a, “U.S. Department of Labor, Occupational

Outlook Handbook” 2007b, “Nurse Practitioner s Fact Sheet” 2000). In order for the

results of my survey to approximate the feelings of all prescribing clinicians in the US to

a 95% confidence level in a confidence interval of + 10, the sample size would have be

93 clinicians. To obtain the same confidence level in a +5 confidence interval the sample

size would have to be 289 clinicians (“The Survey System” 2007). The following is the

calculation for finding sample size:

Ss = Z2*(p) *(1-p)

C2

Z= 1.96 which is a 95% confidence level

P= percentage for picking a choice as decimal (.25 because the amount of max.

variability, can pick 1 out of 4 possible answers)

C= confidence interval expressed as decimal (+10=.10)

Ss= predictive sample size to have a confidence level of 95% within a +10 of

median in a population of 762,500 clinicians in the US


If the confidence interval is + 10 the sample size needed to achieve 95%

confidence level is 93

If the confidence interval is + 5 the sample size needed to achieve 95%

confidence level is 289

Due to these factors the study will be available online for one week after the

recruitment emails are sent with greater than 100 responses the goal. If this goal is met

the results would have a 95 % confidence level within + 10 confidence interval for

predicting the general response of all clinicians to the survey’s questions.

Duration of time to collect responses

In order to meet the goal of 100 participants the survey will be available online

for one week after the recruitment emails are sent. If this goal is not met another email

will be sent reminding people about the survey and the survey will be available for

another week. At this time period the survey will be closed and the data will be analyzed.

Statistical analysis

As stated above, the advantage of the Likert scale is it allows researchers to

quantify the participant’s responses. In order to do this, a numerical value is assigned to

each possible response. For the Likert scale used in this survey the numerical value will

directly correlate with the influence; the greater the influence the greater the numerical

value.

No influence =0 Minimal influence =1 Influence = 2 Greatly influence = 3


These values than can be compiled and the factors that have the greatest influence on

prescribing habits will have the highest mean values (total Likert score/number of

clinicians). The Likert scale data can also be analyzed for the median. These scores than

can be broken down into various subgroups and used to make bar graphs comparing how

clinicians with different demographical characteristics answered the questions. The

demographics that will be compared are:

The different specialty MD vs. PA vs. NP Clinicians that practice medicine inpatient vs those that practice outpatient Patient insurance: insured vs uninsured patient population Years of experience practicing medicine. Gender of the clinician

The questions that do not involve a Likert scale will be elevated for the most prominent

answer. Professional demographical information can than be used to form subgroups to

compare how these questions were answered.

All statistical analysis and graphical representation of the data will be done using

Survey Monkey® and Microsoft Excel®

Distribution of the results

A copy of the survey’s results as well as the final paper will be given to each of

the organizations whose members participated in the survey. These organizations than

can make the results of the survey and the final paper available to the participates.


Results

General

There were 201 clinicians that participated in the survey. Of these 201 clinicians

that participated 99 (49.3 %) were MDs, 68 (33.8%) were PAs, and 34 (16.9%) were NPs

(figure 1). The range of clinical experience was from 0 to greater than 50 years. The

majority of the participants surveyed (67.8%) had 0-20 years of clinical experience.

More female clinicians filled out the survey than males (female 58.2% > male 41.8).

These clinicians primarily treat patients with private insurance but clinicians that

primarily treat patients with Medicaid, Medicare and no insurance were also represented

in the survey. The setting that these participants work in is predominately outpatient as

for every one participate that practices medicine in an inpatient setting there is

approximately four participants that work in an outpatient setting (36 vs. 165). Baseline

demographical characteristics of those surveyed are listed in Table 1. Those that were

surveyed represent all types of medical specialties. Figure 1 is a graphical representation

of the number of participants per specialty.

The factor that the clinicians surveyed gave the highest average Likert score too

was clinical experience. Clinical experience’s Likert score was 3.71. The second highest

average Likert score was given to the factor of evidence-based medicine. Its’ Likert score

was 3.54. Insurance policies and clinical specialty also received Likert scores greater then

three. The lowest Likert score was 2.12 which was given to the pharmaceutical industries

influence. Peer influence and patient preference were factors that also had average Likert


scores that were less than three. Figure 2 is a graphical summarization of the average

Likert scores from all 201 participants.

When asked directly to pick the one factor that has the most influence on their

prescribing habits, 85 (42.3 %) participants choose clinical experience. This was the most

popular answer. Evidence based medicine was the second most picked answer as 83

(41.3%) of the participants picked this factor. 17 (8.5%) clinicians picked clinical

specialty as the factor that had the greatest influence on their prescribing habits. The

factor patient preference was not chosen by a single participate as the factor that has the

most influence on their prescribing. Figure 3 is a graphical representation of the results

from this question “Which factor has the greatest influence on your prescribing habits?”

The pharmaceutical industries influence was the factor that was picked by the

most participants as the answer to the question “Which factor has the least influence on

your prescribing habits.” 117 (58.8%) of the those surveyed picked this factor. The

second most common factor picked was the pharmaceutical industry’s influence as 31

(15.5%) of the participants chose this factor. Clinical experience was not chosen by

anyone surveyed and evidence based medicine was only chosen by one participate

making these factors the least prominent factors picked by those answering this question.

Clinical specialty, patient preference, peer influence and insurance policies were factors

that were picked by some of the clinicians taking part in the survey but these factors were

neither the least common nor the most common answers to the question. Figure 4 is a

graphical representation of the results gathered concerning the question “Which factor

has the least amount of influence on your prescribing habits”


The question “which factor if any impedes you from giving your patients the

pharmacological therapy that would be in the patient’s best interest” was responded to by

197 of the 201 clinicians that fill out the survey. The most popular answer to this question

was insurance companies’ policies as 151 (76.6%) of the participants chose this answer.

Patient preference was chosen by 39 participants (19.8) as a factor that inhibits

practitioners from prescribing certain therapies. This factor was the second most popular

pick. Thirty one clinicians (15.7% ) filling out the survey believe that none of factors that

were listed restricted their prescribing habits. The pharmaceutical industries influence

also had greater than 30 participants choose it as a factor that restricts their prescribing

habits. Evidence based medicine, clinical experience and peer influences were all factors

that three people filling out the survey thought inhibited their ability to prescribe

pharmacological therapy. Only two people surveyed believed that clinical specialty

restricted their prescribing habits. This was the least prominent factor picked. Figure 5 is

a bar graph that shows how many clinicians partaking in the survey believe a certain

factor inhibits their prescribing habits.

Demographical Characteristics Results

Type of clinician

When the answers to the survey questions are separated and tabulated based on if

the participate is a MD, PA or NP, the results are found to be similar between the three

groups of clinicians. (When the data is broken down according to the demographical

characteristic, clinician type, similar results are found.) MDs, PAs and NPs all gave the

highest Likert scores to clinical experience and evidence based medicine. Based on the

MDs responses the average Likert score was 3.73 for clinical experience and 3.64 for


evidence based medicine. PAs’ responses yielded similar average Likert scores for these

two factors. PAs gave clinical experience a Likert score of 3.69 and 3.49. NPs’ responses

to the Likert scale questions resulted in an average score of 3.68 for both these factors

(clinical experience and evidence based medicine). Pharmaceutical industries influence

received the lowest average Likert scale from all three types of clinicians. Figure 6 is a

graphical representation of the average Likert scores based on the MDs’, PAs’, and NPs’

responses to the survey questions.

When asked to pick the one factor that has the greatest influence on prescribing

habits, evidence based medicine was the factor picked the most by NPs and MDs. The

factor that was picked most by PAs was clinical experience. Figure 7 is composed of pie

charts that show the percentage of MDs, PAs, and NPs that picked a certain factor as the

factor that has the greatest influence on their prescribing habits.

The pharmaceutical industries influence was the factor picked the most by all

three types of clinicians to answer the question “Which factor has the least amount of

influence in shaping your prescribing habits” .

Greater than 80% of NPs surveyed believe that insurance companies impede their

prescribing habits. The percentage of MDs and PAs that believe this is slightly less as

76.3 % of the MDs and 73.5 % of the PAs surveyed think that insurance companies

inhibit their prescribing. This factor was the most picked factor among PAs, MDs, and

NPs as the answer to the question which factor(s) impede you from giving your patients

the pharmacological therapy that is in the patient’s best interest. Figure 8 is a graphical

representation of the results to this question.

Clinical experience


When the data from the survey is broken down and analyzed based on groups

formed by clinical experience, the Likert scores were similar for each factor. The factors

with the highest Likert scores for all the age groups were evidence based medicine and

clinical experience. All the age groups also gave their lowest Likert scale score to the

pharmaceutical industries influence. Figure 9 is a graphical representation of the average

Likert scores that the different clinical experience groups gave to each factor.

When asked to pick the factor that has the most influence on their prescribing

habits, 46.3 % of clinicians with 0-10 years of clinical experience chose the factor of

clinical experience. This factor was the most popular choice for clinicians with 0-10 years

of clinical experience. The most popular answer to this question for all the other age

groups was evidence based medicine. Figure 10 shows the percentage of participants

within a certain range of clinical experience that pick one factor as having the most

influence on their prescribing habits.

More than 40% of the clinicians surveyed in each group -except for the those

clinicians with greater than 50 years of experience- believed that the pharmaceutical

industries influence had the least amount of influence on their prescribing decisions. This

factor was the most popular choice for all the groups with less than 50 years of

experience as the answer to the question “Which factor has the least amount of influence

on your prescribing habits?”. There were three clinicians with greater than 50 years of

experience that filled out the survey and each of these participants picked a different

factor that had the least amount of influence on their prescribing habits. The factors

chosen by these three clinicians were evidence based medicine, patient expectations, and

pharmaceutical industry’s influence. Figure 11 is a graph that shows the percentage of


participants within a certain range of clinical experience that pick one factor as having the

least amount of influence on their prescribing habits.

Insurance companies’ policies was the factor that was chosen by the highest

percentage of participants in each group when those surveyed were asked to pick the

factors that impede their prescribing habits. The factor that had the second highest

percentage of participants per grouping pick it varied. In the groups with less than 30

years of clinical experience the factor that was the second most popular choice was the

pharmaceutical industry’s influence. For clinicians with more than 30 years of clinical

experience the second most popular choice was “none of the factors listed affected the

way they prescribed medications”. Figure 12 is clinicians with a certain amount of

clinical experience that believe a factor impedes their prescribing habits.

Gender

When the responses to the survey are separated by gender, clinical experience is

the factor given the highest Likert score. Males gave this factor a Likert score of 3.68

while females gave it a Likert score of 3.73. Both genders gave the second highest Likert

score to evidence based medicine. The pharmaceutical industry’s influence was given the

lowest Likert score by both genders. Females gave this factor an average Likert score of

2.25 while males gave it a lower score of 1.94.Figure 13 shows the average Likert scores

that the different genders assessed each factor surveyed.

When asked to pick the one factor that had the greatest influence on their

prescribing habits, 52.4 % of males picked evidence based medicine while 40.5 % of

males chose clinical experience. The most popular choice to answer this question by

females was clinical experience. 43.6 % of the females surveyed picked this factor.


Evidence based medicine was the second most popular choice among females as 33.3 %

of females surveyed picked this factor. Figure 14 shows the percentage of males and

females that picked a certain factor as having the most influence on their prescribing

habits.

The most prominent factor picked by both genders to answer the question “What

factor has the least amount of influence on prescribing habits?” was the pharmaceutical

industry’s influence. 57.1 % of males and 59.1 % of females responding to the survey

thought this was true. The only factor that was not picked to answer this question by both

genders was clinical experience. Figure 15 shows the percentage of males and females

that picked a certain factor as having the least amount of influence on their prescribing

habits.

The percentage of male participants that believed a certain factor impeded their

prescribing decisions ranged from 1.2 % to73.2 %. Clinical specialty was the factor that

1.2% of the male participants believed inhibited their prescribing decisions while 73.2%

of males surveyed believed insurance companies’ policies inhibited their prescribing

decisions. The percentage of female participants that thought a factor inhibited their

prescribing decision ranged from 0.9 % (Clinical specialty, patient preference) to 79.1 %

(insurance policies). Similar percentages of males and females believed that none of the

factors inhibited their prescribing decisions. 15.9% of males and 15.7% of females

thought this. Figure 16 shows the percentage of males and females that believe a certain

factor impedes their prescribing decisions.

Primary Insurance of patients


The participants were broken down into four groups based on the type of

insurance (Medicaid, Medicare, private insurance, and no insurance) that was carried by

most of their patients and Likert scores were computed for each factor. The Likert scores

ranged from 1.67 – 3.81. Clinical experience was the factor that received the highest

Likert score in all four groups. The second highest Likert score was given to evidence

based medicine in all four groups. All four groups also had the same factor receive the

lowest Likert score. This factor was the pharmaceutical industry’s influence. Figure 17

shows the average Likert scores that were given by 4 groups of participants that were

formed by the type of insurance their patients carry (Medicaid, Medicare, private

insurance or no insurance).

When asked to pick the single factor that had the greatest amount of influence on

their prescribing habits, the most popular choice among participants whose patient

population mainly carries Medicaid was clinical experience. For participants whose

patient population mainly carries no insurance the prominent chose was evidence based

medicine. Participants that care for people with private insurance did not overwhelming

pick one factor as 43.2% picked evidence based medicine and 42.4 % picked clinical

experience as the answer to this question. Similar results were also evident in the group

of clinicians who mostly care for patients that have Medicare as 40.6 % chose evidence

based medicine and 43.8% chose clinical experience. Patient preference was the only

factor that was not picked by any of the participants as an answer to this question. Figure

18 shows the percentage of participants that care for patients with a type of insurance that

picked one factor as having the most influence on their prescribing habits.


The highest percentage of participants whose patient population mainly carry

Medicare, Medicaid, and private insurance, picked the pharmaceutical industry’s

influence as the factor with the least amount of influence on their prescribing habits. The

most prominent answer given by clinicians who predominately take care of patients with

no insurance to answer this question (What factor has the least amount of influence on

your prescribing decisions?) was patient preference. 50% of those surveyed who take

care of patients with no insurance believed patient preference was the factor that had the

least amount of influence on their prescribing decisions. Evidence based medicine and

clinical experience were two factors that were not chosen by any of the participants to

answer this question. Figure 19 shows the percentage of participants that care for

patients with a type of insurance that picked one factor as having the least amount of

influence on their prescribing habits.

The percentage of clinicians in each group (primarily treat patients with Medicaid,

Medicare, private insurance, or no insurance) that believe a certain factor impedes them

from making prescribing decisions ranged from 0% to 79.3%. Over 70 % of the

clinicians who predominantly care for patients with Medicaid, Medicare, and private

insurance believe that the insurance industries policies impede their prescribing habits.

The most popular choice among clinicians surveyed who care for patients with no

insurance was the answer of “none” of these factors inhibit prescribing decisions. All the

factors listed that could impede prescribing decisions were at least picked by one of the

participants that care for people with private insurance. Figure 20 shows the percentage

of participants that care for patients with a type of insurance that believe a factor impedes

their prescribing decisions.


Clinical setting: outpatient vs. inpatient

After separating the participants responses based on the clinical setting that they

work in, the Likert scores were similar for all the factors assessed. The factor with

highest average Likert score for both groups was clinical experience. Participants that

work in an inpatient environment gave this factor an average Likert score of 3.69 while

those that work in an outpatient setting gave this factor an average Lickert score of 3.71.

The lowest average Likert score for both groups was given to the pharmaceutical

industry’s influence on prescribing habits. Figure 21 is a bar showing the different

average Likert scores given to each factor by inpatient and outpatient clinicians.

When asked what one factor had the greatest influence on their prescribing habits

participants that practice in an inpatient setting and outpatient setting mostly picked

clinical experience and evidence based medicine. Of the clinicians surveyed that practice

medicine in an inpatient environment, 41.7 % picked evidence based medicine as the

factor that had the greatest influence on shaping their prescribing habits while 33.3 %

thought clinical experience was this factor. From the clinicians that practice medicine in

an outpatient setting, 41.2 % picked evidence based medicine while 44.2 % choose

clinical experience to answer this question. Patient preference and the pharmaceutical

industries influence were two factors that were not picked to answer this question by

participants that practice medicine in an outpatient setting. Participants that practice

medicine in an inpatient setting did not pick patient preference and peer influence as

answers to this question. Figure 22 is a bar graph that shows the percentage of clinicians

who practice in either an inpatient or outpatient setting that chose a factor as having the

most influence on their prescribing habits.


When asked to pick the one factor that had the least amount of influence on their

prescribing decisions, over 50% of the participants in both groups, choice the

pharmaceutical industries influence. This was the most popular chose to answer this

question among participants that practice medicine in an inpatient setting and those that

practice in an outpatient setting. Figure 23 is a bar graph that shows the percentage of

clinicians who practice in an inpatient and outpatient that chose a factor as having the

least amount of influence on their prescribing habits.

The percentage of participants that work in an outpatient setting that believe a

certain factor inhibits their ability to prescribe medicine ranges from 0.6 % to 78.9 %.

The most popular factor picked that impedes prescribing among participants that work in

an outpatient setting was the insurance industry’s policies. 14.3% of clinicians surveyed

that practice in an outpatient setting believe that no factor assessed inhibits them from

prescribing medicine that would be in the patient’s best interest. The percentage of

participants that treat patients in an inpatient setting that believe a certain factor inhibits

their ability to prescribe medicine ranges from 2.8% to 66.7%. The factor that 66.7 % of

those that were surveyed who work in an inpatient setting believe inhibits their ability to

prescribe medicine is the insurance industry’s policies. Of those that practice medicine in

an inpatient setting, 22.2 % believe that none of the factors assessed inhibit their ability to

prescribe medications. Figure 24 is a bar graph that shows the percentage of participants

that work in either an inpatient or outpatient clinical setting who believes a factor

impedes their ability to prescribe medicine. Most influential prescribing specialty

insurance


Discussion

This study evaluated the extent of influence that clinicians believe certain factors

have on their prescribing habits. By surveying local clinicians that have the ability to

prescribe medicine with a Likert scale I was able to gain insight into how these clinicians

make prescribing decisions. The study showed that all the factors assessed do have some

influence on prescribing decisions as no factor received an average Likert score of zero.

The lowest average Likert score was 2.12 and it was given to the pharmaceutical

industries influence. This factor was also the most popular choice to the question “Which

factor has the least amount of influence on your prescribing habits” further supporting the

minimal amount of influence the pharmaceutical industry has on a clinician’s use of

medication. The pharmaceutical industry’s influence on clinicians is very controversial

and a topic of public interest. As explained earlier, the reason this industry tries to

influence clinicians’ choices concerning medications is that providers act as

“gatekeepers” that direct the use and purchase of prescription drugs (Glickman et al.).

This makes the pharmaceutical industry’s profits dependent on how clinicians view a

particular drug. Due to this relationship, many people believe that pharmaceutical

companies use their sales representatives, advertisements and other forms of marketing to

pressure clinicians to prescribe their medications. According to the results of this survey,

clinicians acknowledge this industry’s influence but believe it to be minimal. The

minimal amount of influence that clinicians attach to the pharmaceutical industry’s

efforts to manipulate their prescribing decisions shows that local people should not be

concerned about the relationship a clinician has with this industry.


The factors receiving the highest average Likert scores based on the responds to

the survey were clinical experience and evidence based medicine. Clinical experience

received the highest average Likert score of 3.71 while evidence based medicine received

a slightly lower score of 3.59. The results of the question “Which factor has the greatest

influence on your prescribing habits” showed similar results as evidence base medicine

was picked by 83 clinicians and clinical experience was picked by 85 clinicians. It was

hypothesized that these two factors would have the highest average Likert scores before

the participants were surveyed. The great amount of influence clinicians attach to these

factors is probably due to how they are educated. Medicine is a science that is based on

research and the evidence. Throughout their training and education clinicians are taught

how to think scientifically. The importance of clinical trials to determine the

effectiveness of a drug is repeatedly emphasized to them. Before a drug is approved by

the FDA it must survive numerous clinical trails that assess the risk and benefits of the

drug. Clinical journals are consistently publishing research articles on how a certain drug

performs within a define population of patients. It is interesting, however, that clinical

experience had a higher Likert score than evidence based medicine and was picked by

more clinicians as the factor with the greatest influence on their prescribing habits. These

results show the limitations of evidence based medicine as Monico, Moore and Calise

pointed out that many questions and situations in medicine are without controlled clinical

trails. When these questions and situations arise, clinicians must rely on past experiences

to help guide and/or treat their patients. A simple example of this is every patient is that

unique and no one is sure how a person will react to a medication even if a clinical trial

has shown that a person can benefit from taking the drug. Another reason why clinical


experience might be viewed as more influential than evidence based medicine is the

massive amount of journal articles a clinician would have to read to be aware of all the

new research that is being done. In today’s healthcare environment this task would be

impossible as it would demand too much of a clinician’s time.

The insurance companies’ policies received an average score of 3.04 on a four

point Likert scale as a factor that influences prescribing. Of great concern is that 76.6 %

of the clinicians surveyed thought that insurance companies’ policies impeded their

ability to prescribe medications that they believe would be in the patient’s best interest.

This result is most likely due to insurance companies use of a tier system which

encourages clinicians to prescribe medications preferred by these companies. Through

my own clinical experiences I have often overheard clinicians voicing their frustration

about this system and its ability to manipulate how they prescribe medicines. Only 15.7

% of those surveyed believe that none of the factors listed affect the way they prescribe

medicine.

Since 84.7 % of the participants think that there are factors that inhibit them from

giving a patient the pharmacotherapy that they believe is necessary, follow up studies

need to done. These studies should evaluate if these factors that inhibit clinicians from

prescribing certain medications lead to a patient’s health being adversely affected. For

example, a study could be designed to compare adverse outcomes between similar groups

of patients. One of these groups’ provider must practice following the rules and

regulations that an insurance company enforces and the other group’s provider does not

have to follow these rules and regulations. The patient populations than can be evaluated

for specific outcomes. One such study has already been done. Published in the New


England Journal of Medicine a study entitled Cardiovascular Outcomes after a Change in

Prescription Policy for Clopidogrel concluded that “removal of a prior authorization

program led to improvement in timely access to clopidogrel for coronary stenting and

improved cardiovascular outcomes” (Jackevicius, et. al., 2008, p.1806) . If more studies

like this one show that the factors that clinicians believe impede their prescribing

decisions adversely affect a patient’s health, various reforms must be taken to eliminate

these factors from the practice of medicine.

A growing problem in modern day medicine is the overuse of antibiotics which

has created resistance bacteria. Many people feel that this problem has been created by

providers giving into patients’ request for antibiotics when they are not needed. An

example of this line of thinking is Richard Colgan and John Powers who wrote “Patients

want antibiotics and physicians continue to prescribe them in situations where antibiotics

may be withheld for many reasons.” (2004, p. 1003) In the same article Colgan and

Powers also wrote “physicians often feel compelled to prescribe an antibiotic to satisfy

patient demands.” (p. 1004)) According to the average Likert scores found by

conducting this survey, patient preference has a minimal amount of influence on

clinicians’ prescribing decisions. This contradicts Colgan and Powers statement on the

cause of antibiotic resistance.

When the participants’ responses to the Likert scale and the survey questions

were grouped by demographical characteristics and these subgroups’ results compared

there was no obvious differences found. The demographical characteristics that were

studied for their affects on the survey’s results were: gender, clinical setting a participant

works in, clinical experience, type of clinician and primary insurance that the


participants’ patients carry. The results showed that for all the demographical groups

studied, evidence based medicine and clinical experience were the factors that had the

most influence on prescribing habits while the pharmaceutical industry’s affect on

prescribing was found to be the least influential factor. Insurance companies’ policies

was the most popular choice of the different demographic groups as a factor that inhibits

prescribing. Based on these results one could conclude that the different demographical

characteristics have no affect on prescribing habits which is not what was hypothesized.

The demographical characteristics having no affect on prescribing habits is most likely

due to all clinicians being educated and trained the same way.

Areas of Strength

One area of strength for this study is the group of participants. 201 clinicians took

part in the survey, this meets my goal of greater than 100 participants. With greater than

100 clinicians taking part in the survey the results approximates the feelings of all

prescribing clinicians in the US to a 95% confidence level with a confidence interval of +

10 . This group of 201 participants was composed of a wide range of clinicians

representing many different personal and professional characteristics. The results of this

survey are given more creditability because of this diversity. The diversity of the

participants also allowed me to compare if certain demographical characteristics affect

the way clinicians view their prescribing habits.

Another area of strength of this survey is that it uses a Likert scale to evaluate the

extent of influence various factors have on prescribing habits. Many studies in the past

have evaluated if a particular factor affects prescribing habits but this study is unique


because by using a Likert scale I was able to evaluate the extent of influence a certain

factor has on prescribing habits.

Weaknesses and limitation of study

One shortcoming of this study is that t only evaluates the extent of influence that

clinicians perceive certain factors have on prescribing. This study is limited because it

does not assess how clinicians actually practice medicine. How a clinician practices

medicine might be totally different than how a clinician perceives they practice medicine.

For instance, a clinician might undervalue the pharmaceutical industries influence on

prescribing because this industry’s influence is frowned upon by people that practice

medicine. As a consequence of this attitude people might unconsciously devalue this

industry’s influence.

Another weakness of this survey is the factors that were listed to be assessed do

not stand on their own. Many of the factors that were listed in the survey that affect

prescribing habits are associated with another factor. For instance, much of the evidence

based research that evaluates a drugs safety, efficiency, etc. is sponsored by

pharmaceutical companies. Another example of this is direct to consumer advertising.

Drug advertisements on TV, in magazines and newspapers might provoke a patient to

demand that a clinician prescribes a medication. Due to this someone filling out the

survey might find this overlap confusing.

The study is also limited by the sample population being composed of clinicians

who all belong to three medical organizations. This could skew the data because

members of an organization usually have common beliefs and characteristics, which can

lead to an organization’s views being over represented in this study. All the medical


organizations from which the sample population is obtained from do promote different

forms of continuing education. This could lead to evidence based medicine being deemed

more influential than it actually is.

Areas of uncertainty and future area of research

As stated earlier, this study is limited because it is based on a survey that

evaluates how clinicians perceive a certain factor influences their prescribing habits. It is

not known if the way a clinician perceives a factor influences their prescribing habits is

actually the amount of influence a factor has on how they practice medicine. Future

studies could also be done to evaluate if these factors assessed by the survey have an

adverse or beneficial affect on patient care.

Conclusion

This study utilized a survey composed of a Likert scale and questions about

prescribing habits to assess the way 201 clinicians in the western New York area view the

influences certain factors have on their prescribing habits. The factors that were assessed

were evidence based medicine, clinical specialty, clinical experience, the pharmaceutical

industry’s influence, patient preference and insurance companies’ policies. The results

show that the two factors that have the most influence on prescribing habits are clinical

experience and evidence based medicine while the factor with the least amount of

influence was the pharmaceutical industry’s influence. Insurance companies’ policies

were viewed by greater than 75% of the participants surveyed as a factor that impedes

them from giving a patient the pharmacotherapy that is in the patient’s best interest.

These results were the same when the participants’ responses were grouped based on

demographical characteristics and analyzed.


Reference:

Anderson GM, Beers MH, and Kerluke K. (1997) Auditing prescription practice using explicit criteria and computerized drug benefit claims data. J Eval Clin Pract, 3, 283-94.

Appleby J. (2008, March 4) As dry ads surge, more get Rx’s filled USA Today p. 4D

Altman S. and Thomas C., (2002) Controlling spending for prescription drugs. New Engl J Med, 346, 855–856

Ballantyne J. and Mao J. (2003) Opioid therarpy for chronic pain. N. Engl. J. Med 349, 1943-1953

Beaulieu, D, Balis R., Jacques A, Battista R, Lebeau R and Brophy, J (2003) Are patients surffering from stable angina receiving optimal medical treatment? QJM 94, 301-308

Beers MH, Fingold SF, Ouslander JG, Reuben DB, Morgenstern H, and Beck JC (1993) Characteristics and quality of prescribing by doctors practicing in nursing homes. J Am Geriatr Soc, 41, 802-807

Blumenthal, D (2004) Doctors and Drug Companies. N. Engl. J. Med, 351, 1885-1890

Breen, K., (2004) The medical professions and the pharmaceutical industry: when will we open out eyes? MJA, 180, 409-410

Campbell EG, Gruen RL, Mountford J, Miller LG, Cleary PD, Blumenthal D. (2007) A national survey of physician-industry relationships. N Engl J Med, 356, 1742-50

Calvo, C. and Rubinstein, A. (2002) Influence of New Evidence on Prescription Patterns, Journal of American Board of Family Medicine 15, 457-462

Colgan R and Powers J (2000) Appropriate Antimicrobial Prescribing: Approaches that Limit Antibiotic Resistance AAFP, 65, 999-1004

Choudhry NK, Fletcher RH,, and Soumerai, SB. (2005) . Systematic review the relationship between clinical experience and quality of health care. Ann Intern Med, 260-73

Dana J, and Loewenstein G. (2003) A social science perspective on gifts to physicians from industry. JAMA, 290, 252-255

Daidoff ,F, Haynes, B., Sackett, D., and Smith R., (1995) Evidence based medicine BMJ 310, 1085-1086.


Donohue, J. M., Cevasco, M., Rosenthal, M. B. (2007). A Decade of Direct-to-Consumer Advertising of Prescription Drugs.. N. Engl. J Med, 357, 673-681

Ellner, A. (2003). Rethinking prescribing in the United States. BMJ, 327, 1397-1400

Ernst M, Kelly M, Hoehns J, et al. (2000) Prescription medication costs. Arch Fam Med., 9, 1002-1007.

Fehrenbach SN, Budnitz DS, Gazmararian JA and Krumholz HM. (2001) Physician characteristics and the initiation of beta-adrenergic blocking agent therapy after acute myocardial infarction in a managed care population. Am J Manag Care, 7, 717-23)

Fischer, M and Avon, J ( 2004) Economic implication of evidence based prescribing for hypertension: Can better care cost les. JAMA, 291, 1850-1855

Frank R., (2004), Prescription-Drug Price.s N. Engl. J Med. 351, 1375-1377

Glickman L, Bruce EA, Caro FG, Avorn J. (1994) Physicians' knowledge of drug costs for the elderly. J Am Geriatr Soc. 42, 992-996

Greenberg Raymond et. al (2005) Medical Epidemology (4th edition): New York Lange Medical Books McGraw Hill Companies

Hamel M. and Epstein A. (2004) Prior-Authorization Programs for Controlling Drug Spending, N. Engl. J. Med. 351, 2156-2157

Heffler S., Levit K, and Smith S. (2001) Health spending growth up in 1999: faster growth expected in the future. Healt Aff., 20, 193-203.

Hillman, J, Zuckerman , I , and Lee , E, (2004) The impact of the Women Health Initiative on Hormone Replacement Therapy in a Madicaid Program. Journal of Women’s Health, 13, 986-992

How… to develop an effective survey response. ( Oct 9, 2000) Total Communication Measurement. Retrieved on internet May 8, 2008, from http://searh.ebscohost .com/loginaspx?direct=true&db=buh&AN=3714070&site=ehost=live

Hsu et al. (2006). Unintended Consequences of Caps on Medicare Drug Benefits. N. Engl. J. Med. 354, 2349-2359.

Jackevicius, Cynthia et. al. ( 2008) Cardiovascular Outcomes after a Change in Prescription policy for Clopidogrel. N. Eng. J. Med. 359. 1802-1809.


Jackevicius CA, AndersonG, Leiter L., Tu JV. (2001) Use of Statin in patients after acute myocardial infraction: Does evidence change practice? Arch Intern. Med, 161, 183-188

Jaffe, J. (August 2003) Evidence-Based medicine: implication for Consumer-Driven Health Benefits 24(8) Retrieved June 1, 2008 from http://www.ebri.org/pdf /notespdf/0803notes.pdf

Jones et al, (2001) Prescribing new drugs: qualitative study of influences on consultants and general practitioners. BMJ, 323-328.

Kaiser Family Foundation and Health Research and Educational Trust.(2003) Employer health benefits: 2002 summary of findings. Retrieved April 2 http://www.kff.org/insurance/3252-index.cfm

Kamal-Bahl S, Briesacher B. (2004) How do incentive-based formularies influence drug selection and spending for hypertension? Health Aff ., 23, 227-236.

Klaschik and Clemens ( 2007) Clinical experience with transdermal and orally administered opioids in palliative care patients--a retrospective study. Jpn J Clin Oncol, 4, 302-9.

Knight E, Glynn R, Levin R. et al (2000) Failure of evidence-based medicine in the treatment of hypertension in older patients. J Gen Intern Med., 15, 702-709

Kravitz R, Epstein R, Fieldman M, et al. (2005) Influence of Patient request for direct to consumer advertised antidepressants JAMA, 29, 1995-2002

Levin, A. (2005) Sample availability may affect residents’ medication choice. American Psychiatric Association, [40], 17, 28

Lipsky M, and Taylor C. (1997) The opinions and experiences of family physicians regarding direct-to-consumer advertising. J Fam Pract, 45, 495-499

Lyles A. (2002) Direct marketing of pharmaceuticals to consumers. Ann Rev Public Health, 23, 73-91

Maxwell SRJ. (2005) Evidence based prescribing BMJ, 331 247-248.

Mintez B, Barer M, Kravitz et al. (2002) Influence of direct to consumer pharmaceutical advertising and patients' requests on prescribing decisions: two site cross sectional survey BMJ. 324, 278-279.

Monico, E, Moore, C., and Calise (2005). The impact of evidence based medicine and evolving technology on the standard of care in emergence medicine. The Internet Journal of Law. 3(2) Retrieved June 2, 2008

http://www.kff.org/insurance/3252-index.cfm

http://www.ebri.or/


http://www.ispub.com/ostia/index .php?xmlFilePath=journals/ijlhe/vol3n2/evidence.xml#e30

Nurse Practitioner s Fact Sheet, AANP, Retrieved April 19, 2008 from

http://www.aanp.org/

Pancholi M., Stagnitti M. ( July, 2004) Outpatient Prescription Medicines: A comparison of Expenditures by Household-Reported Condition, 1987 and 2001. Agency for Healthcare Research and Quality Accessed March 30, 2008, from http://www.meps.ahrq.gov/mepsweb/data_files/publications/st43/stat43.pdf

Piette J, Heisler M, and Wagner T (2004). Cost-related medication underuse: do patients with chronic illnesses tell their doctors? Arch Intern Med, 164, 1749-55

Pham, H., Alexander, G.. and O’Malley (2007) Physician consideration of patients’ out-of pocket costs in making common clinical decisions. Arch Intern. Med 167,7, 663-668

Reichert, S., Simon, T., and Halm, E., (2000) Physicians’ attitudes about prescribing and knowledge of the cost of common medication. Arch Intern. Med. 160, 2799-2803.

Review: What impact does pharmaceutical promotion have on behavior (June 2, 2003) Drug Promotion Database Retrieved April 4 2008 from http://www.drugp romo.info/read-reviews.asp?id=4

Rosenthal M, Berndt E, Donohue J, Frank R, and Epstein A. (2002) Promotion of prescription drugs to consumers. N Engl J Med, 346, 498-505.

Ross S, and Macleod M,(2005). Antihypertensive drugs prescribing Grampain. Br J Clin Pharmacology, 60, 300-305

Roy-Byrne P, Russo J, Dugdale DC, Lessler D, Cowley D, and Katon W (2002) Undertreatment if of panic disorder in primary care: Role of patient and physician characteristics. J Am Board Fam. Pract., 15 443-50

Sbarbaro J, (2001) Supplement Article: Can we influence prescribing patterns? Clinical Infectious Diseases i-e56

Schwartz R, Soumerai S, and Avorn J.(1989) Physician motivation for nonscientific drug prescribing. Soc. Sci Med, 28, 577-582

Soumerai SB, Pierre-Jacques M, Zhang F, et al. (2006) Cost-related medication nonadherence among elderly and disabled Medicare beneficiaries: a national survey 1 year before the Medicare drug benefit. Arch Intern Med, 166, 1829–35

http://www.drugp/

http://www.meps.ahrq.gov/mepsweb/data_files/publications/st43/stat43.pdf

http://www.ispub.com/ostia/index


Spingarn R, Berlin J and Strom B. (1996) When pharmaceutical manufacturers' employees present grand rounds, what do residents remember? Acad Med., 71, 86-88.

Symm et al.(2006) Effects of using free sample medication on the prescribing practices of Family Physicians. J. Am board Fam. Med, 19, 443-449

The Survey System: sample size formulas (2007). Creative Research System. Retrieved April 19, 2008, from http://www.surveysystem.com/ssformu.htm

Torpy, J. (2005) Evidence Based Medicine, JAMA 296, 1192.

U.S. Department of Labor, Occupational Outlook Handbook, 2008-09 Edition Physician Assistants. (December 18, 2007 ) Bureau of Labor Statistics,. Retrieved April 19, 2008 from http://www.bls.gov/oco/ocos081.htm.

U.S. Department of Labor, Occupational Outlook Handbook, 2008-09 Edition, Physicians and Surgeons, (December 18, 2007 ) Bureau of Labor Statistics. Retrieved April 19, 2008 from http://www.bls.gov/oco/ocos074.htm

Wazana A. (2000) Physicians and the pharmaceutical industry: is a gift ever just a gift? JAMA, 283, 373-380

Weissman J, Blumenthal D, Silk A, et al (2004) Physicians report on patient encounters involving direct-to-consumer advertising.[Electronic verion] Health Aff., Retrieved March 28 from doi:10.1377/hlthaff.w4.219.

Willison DJ, Soumerai SB, and Palmer RH (2000) Association of physician and hospital volume with use of aspirin and reperfusion therapy in acute myocardial infarction. Med Care., 38,1092-102.

http://www.surveysystem.com/ssformu.htm


Appendix A

Recruitment email to members of Monroe County Medical Society

Dear Clinicians:

I am writing you in hopes that you will help me with my master by participating my study. The purpose of this study is to measure how certain factors affect the way you make prescribing decisions. I am inviting you to participate in this survey because of your experience and ability to prescribe prescription medication. This research project has been approved by the D’youville college’s IRB and Monroe County Medical Society. This survey is Web-based. If you choose to participate, please click on the following link:

http://www.surveymonkey.com/s.aspx?sm=D7io24b1yh6trdn48j4iMg_3d_3d

Completion of the survey will take approximately 5 minutes. You may choose not to participate in this survey. The survey will be open for approximately a week. Depending on the initial response to the survey a second email might be sent reminding you of the survey and the survey will remain open for an extended length of time. No personal identifying information is asked and all information is confidential. There are no known psychological or physical risks from being in this study and you will not benefit from it personally. However, I hope that information gained in this study will benefit all healthcare personal.

If you know of any colleagues that would like to participate in this survey please forward the link to them.

If you have any questions about the research study, please contact Darren Schutt at [email protected]

This research project has been approved by the D’youville college IRB and Monroe County Medical Society.

Sincerely,

Darren Schutt

mailto:[email protected]


Appendix B

Recruitment email to members of Nurse Practitioner Association Greater Rochester Chapter

Dear Clinicians:

I am writing you in hopes that you will help me with my master by participating my study. The purpose of this study is to measure how certain factors affect the way you make prescribing decisions. I am inviting you to participate in this survey because of your experience and ability to prescribe prescription medication. This research project has been approved by the D’youville college’s IRB and the board of Nurse Practitioner Association Greater Rochester Chapter. This survey is Web-based. If you choose to participate, please click on the following link:





This research project has been approved by the D’youville college’s IRB and Nurse Practitioner Association Greater Rochester Chapter.

Sincerely,

Darren Schutt



Appendix C

Recruitment email to members of WNYPAA

Dear Members of WNYPAA:

I am writing you in hope that you will help me with my master by participating in my study. Most of you already know about the survey as do to miscommunication a recruitment email was already sent out to members of your organization. Unfortunately, for those of you that have already filled out the survey I could not use your original responses for the study because IRB did not give my project formal approval. Instead, your original participation was used to evaluate the quality of the survey and identify portion of the survey that were confusing. Due to your participation I have made some changes to survey to make it clearer and easier to fill out. Now that I have IRB’s formal approval of the study would you please take five minutes to fill out this online survey. I am sorry for the inconvenience and thank you for your participation.

For those of you who do not know about the project I am inviting you to participate in this survey because of your experience and ability to prescribe medication. The purpose of this study is to measure how certain factors affect the way you make prescribing decisions. This research project has been approved by the D’youville college IRB and WNYPAA. This survey is Web-based. If you choose to participate, please click on the following link:





This research project has been approved by the D’youville college’s IRB and WNYPAA.

Sincerely, Darren Schutt



Table 1

Demographical characteristics of participants Table 1

The type of clinician PA MD NP

Number of participants 68 99 34

The gender of the participants male female

Number of participants 84 117

Clinical setting Outpatient Inpatient

Number of participants 165 36

Number of years practicing 0-10 years 11-20 years 21-30 years 31-40 years 41-50 years greater 50 years

Number of participants 82 56 33 24 3 3

Primary insurance carried by participates’ patient population Medicare Medicaid Private insurance No insurance

Number of participants 32 30 132 6


Figure Captions

Figure 1. Number of participants per specialty.

Figure 2 Average Likert score for each factor based on all participants responses

Figure 3 Results from the survey question asking clinicians to pick the factor that has the

most influence on prescribing

Figure 4 Results for the survey question asking clinicians to pick the factor hat has the

least amount influence on their prescribing habits

Figure 5 Results from the survey question asking clinicians to pick the factors if any

impede them from giving patients the pharmacological therapy that would be in the

patient’s best interest.

Figure 6 Average Likert scores from based on MDs, NPs, PAs’ responses to survey

questions

Figure 7 Pie charts representing the percentage of MDs, PAs, and NPs that picked a

certain factor as the factor that has the most influence on their prescribing habits

Figure 8 Bar graph showing the percentage of MDs, PAs, and NPs that picked a certain

factor that impedes them from giving a patient the pharmacological therapy that they

believe is in the patient’s best interest.

Figure 9 Graphical representation of the average Likert scores that the different clinical

experience groups gave to each factor

Figure 10 The percentage of participants within a certain range of clinical experience that

pick one factor as having the most influence on their prescribing habits.

Figure 11 The percentage of participants within a range of clinical experience that pick

one factor as having the least amount of influence on their prescribing habits.


Figure 12 The percentage of participants within a range of clinical experience that

believe certain factor impedes their ability to prescribe medicine

Figure 13 Average Likert scores that the different genders assessed to each factor

surveyed

Figure 14 Bar graph showing the percentage of males and females that picked a certain

factor as having the most influence on their prescribing habits.

Figure 15 Bar graph showing the percentage of males and females that picked a certain

factor as having the least amount of influence on their prescribing habits.

Figure 16 Bar graph showing the percentage of males and females that believe a certain

factor impedes their prescribing decisions.

Figure 17 Bar graph showing the average Likert scores that given by 4 groups of

clinicians that were form by the type of insurance their patients have (Medicaid,

Medicare, private insurance, or no insurance) participants

Figure 18 Bar graph showing the percentage of participants that care for patients with a

type of insurance that picked one factor as having the most influence on their prescribing

habits.

Figure 19 Bar graph showing the percentage of participants that care for patients with a

type of insurance that picked one factor as having the least amount of influence on their

prescribing habits.

Figure 20 Bar graph showing the percentage of participants that care for patient with a

type of insurance that believe a factor impedes their prescribing decisions.

Figure 21 Bar graph showing the different average Likert scores given to each factor by

inpatient and outpatient clinicians


Figure 22 Bar graph that shows the percentage of clinicians who practice in an inpatient

and outpatient that chose a factor as having the most influence on their prescribing habits.

Figure 23 a bar graph that shows the percentage of clinicians who practice in an inpatient

and outpatient that chose a factor as having the least amount of influence on their

prescribing habits.

Figure 24 is a bar graph that shows the percentage of participants that work in an

inpatient and outpatient clinical setting believe a factor impedes their ability to prescribe

medicine.


Figure 1

clinical specialty

2

13

31

24

68

2

8

15

3

15

12

1

5 53

5

1

5

1 12

33

0

5

10

15

20

25

30

35

anes

tesiolog

ist

orthop

edics

family

med

icine

intern

al m

edicine

hosp

italis

t

emerge

ncy med

icine

neur

olog

ist

cadiolog

ist

surgery

urolog

y

OB/G

YN

pediatric

endo

crinolog

y

psyc

hiatry

gero

atric

s

allergist

derm

atolog

y

neph

rology

onco

logy

opthha

lomolog

y

radiolog

ist

infection dise

ase

othe

r

clinical specialty

numbe

rof p

artic

pant

s


Figure 2

Likert scoring

3.59

2.7

2.12

3.71

2.65

3.04 3.14

0

0.5

1

1.5

2

2.5

3

3.5

4Evide

nce

Bas

edm

edicin

e

Pat

ient

pre

fere

nce

phar

mac

eutic

alin

dust

ries

clin

ical

exp

erie

nce

peer

influ

ence

insu

ranc

e po

licie

s

clin

ical

spe

cial

ty

Factors

Ave

rage

Liker

t


Figure 3

Which factor has the most influence on shaping prescribing habits

83

0 1

85

411

17

0

10

20

30

40

50

60

70

80

90

Evide

nce

Bas

ed m

edicine

Pat

ient

pre

fere

nce

phar

mac

eutic

al in

dust

ries

clinical e

xper

ienc

e

peer

influ

ence

insu

ranc

e po

licies

clinical s

pecialty

factor

num

ber p

artic

ipan

ts

Percentage of particpants

42%

0%

0%

43%

5%8%

2%

Evidence Basedmedicine

Patient preference

pharmaceuticalindustries

clinical experience

peer influence

insurance policies

clinical specialty


Figure 4

Response to which factor has the least amount influence on your prescribing habits

1

31

117

0

2315 13

0

20

40

60

80

100

120

140

factor

# of

par

ticip

ants

1% 15%

58%

0%

11%

8%

7%

Evidence Based medicine Patient preference pharmaceutical industries clinical experience peer influence insurance policies clinical specialty


Figure 5

Factors that restrict prescribing

31

3

3927

3 3

151

20

20

40

60

80

100

120

140

160

factors

# nu

mbe

r of p

artic

ipan

ts


Figure 6

Type of clinician

0

0.5

1

1.5

2

2.5

3

3.5

4

PA MD NP

Type of clinician

Likert Scoring average

Evidence Based medicine

Patient preference

pharmaceutical industries ,

clinical experience

peer influence

insurance policies

Clinical specialty


Figure 7

PA

28%

0%

2%

54%

3%

4%

9%Evidence Basedmedicine

Patientpreference

pharmaceuticalindustries

clinicalexperience

peer influence

insurancepolicies

clinical specialty

MD

51%

0%0%

41%

4%2% 2%

NP

40%

0%0%21%

0%

18%

21%

What Factor Has the Greatest Influence on Prescribing Habits?


Figure 8

Factors that clinicians believe inhibit them from giving patients the therapy they believe is necassary

0

10

20

30

40

50

60

70

80

90

100

PA MD NP

Type of clinician

Per

ceta

ge o

f clin

icia

ns s

urve

yed

None

Evidence based medicine

Patient preference

pharmaceutical industries

clinical experience

peer influence

insurance policies

clinical specialty


Figure 9

Years praciticing medicine

0

0.5

1

1.5

2

2.5

3

3.5

40-

10 yea

rs

11-2

0 ye

ars

21-

30 yea

rs

31-4

0 ye

ars

41-5

0 ye

ars

grea

ter 5

0ye

ars

Years practicing medicine

Ave

rage

Liker

t Sco

res

evidence basedmedicine patient preference

pharmaceuticalindustries clinical experience

peer influence

insurance policies

clinical specialty


Figure 10

Most influential factor on prescribing decision vs. clinical experience

0

10

20

30

40

50

60

70

80

90

100

0-10 years 11-20 years 21-30 years 31-40 years 41-50 years greater 50years

Years of clinical experience

% of p

artic

ipan

ts w

ith a certain amou

t of c

linical

expe

rinec

e

evidence based medicine Patient preference pharmaceutical industries clinical experience peer influence insurance policies clinical specialty


Figure 11

Factor with the least amount influence on prescribing habits vs. clincial expeience

0

10

20

30

40

50

60

70

80

90

100


Years of clinical experience

% of c

linicains

with

a certain amou

nt of c

lincial

expe

rienc

e

evidence based medicine patient preference pharmaceutical industries clinical experience peer influence insurance policies clinical specialty


Figure 12

Factors that impede prescribing vs. clinical experience

0

10

20

30

40

50

60

70

80

90

100


Clinical experience

% of c

linicians

that believe

a certia

n factor im

pede

s pres

cribing

none evidence based medicine patient preference pharmaceutical industries clinical experience peer influence insurance policies clinical speciailty


Figure 13

Average Likert score by gender

3.62 3.57

2.67 2.73

1.94

2.25

3.68 3.73

2.552.73

2.93.153.05

3.21

0

0.5

1

1.5

2

2.5

3

3.5

4

male female

Gender

Ave

rage

Likert s

corin

g

evidence basedmedicine patient preference


peer influence

insurance policies

clinical specialty


Figure 14

% of females and males that picked a factor that has the most influence on their prescribing

52.4

33.3

0 00 0.9

40.543.6

1.2 2.62.47.7

3.6

12

0

10

20

30

40

50

60

70

80

90

100

male female

Gender

% of p

artic

ipan

ts th

at picke

d a ce

rtain fa

ctor as ha

ving

the

mos

t influen

ce on pres

cribing



Figure 15

% of males and females that picked a certain factor as having the least amount of influence on prescribing

1.2 0

13.117.2

57.159.2

0 0

9.512.913.1

3.46 6.9

0

10

20

30

40

50

60

70

80

90

100

male female

Gender

% partic

ipan

ts th

at picke

d a ce

rtain fa

ctor as ha

ving

the leas

t am

ount of influen

ce on pres

cribing



Figure 16

% of males and females that believe a certain factor impedes their prescribing decisions

15.9 15.7

2.4 0.9

20.7 19.118.3

10.4

3.79

1.2 1.7

73.2

79.1

1.2 0.90

10

20

30

40

50

60

70

80

90

100

male female

Gender

% of p

articipan

ts th

at believe

a certain fa

ctor im

pede

s pres

cribing

None evidence based medicine patient preference pharmaceutical industries clinical experience peer influence insurance policies clinical specialty


Figure 17

Type of insurance affect on Likert scoring

0

0.5

1

1.5

2

2.5

3

3.5

4

Primary insurance of patients

Like

rt sc

orin

g av

erag

e

Evidence basedmedicine Patient preference


peer influence

insurance policies

clinical specialty


Figure 18

Patients insurance affect on the % of participants that pick one factor as having the most influence on prescribing

0

10

20

30

40

50

60

70

80

90

100

Medicare Medicaid Private insurance No insurance

Patients' insurance type

% of p

artic

ipan

ts th

at picke

d on

e factor

as ha

ving

th

e mos

t influen

ce on pr

escribing

Evidence based medicine Patient preference pharmaceutical industries clinical experience peer influence insurance policies clinical specialty


Figure 19

Factors with least amount of influence on prescribing vs. patients' insurance

0

10

20

30

40

50

60

70

80

90

100



% of p

articipan

ts th

at pick a factor as ha

ving

the leas

t am

ount of influen

ce on pres

cribing



Figure 20

Percentage of participants that care for patient with a certain type of insurance that believe a factor impedes their prescribing

0

10

20

30

40

50

60

70

80

90

100



% partic

ipan

ts th

at believe

a fa

ctor im

pede

s pres

cribing

None



peer influence

insurance policies

clinical specialty


Figure 21

Inpatient vs. outpatient Lickert scores

0

0.5

1

1.5

2

2.5

3

3.5

4

Outpatient Inpatient

Clinical setting

Ave

rage

Licke

rt sc

ore



peer influence

insurance policies

clinical specialty


Figure 22

Most influential factor on perscribing for inpaitent and outpatient clinicians

0

10

20

30

40

50

60

70

80

90

100


clinical setting

% of c

lincian

s that pick factor as mos

t influen

tial



Figure 23

Least influential factor on prescribing for inpatient and outpatient clinicians

0

10

20

30

40

50

60

70

80

90

100


clinical setting

% of p

artic

ipan

ts th

at pick factor as leas

t influen

tial

Evidence based medicine

Patient preference

pharmaceutical industries

clinical experience

peer influence

insurance policies

clinical specialty


Figure 24

% of inpatient and outpatient clinicians who believe a factor impedes perscribing

0

10

20

30

40

50

60

70

80

90

100


clinical setting

% of c

linicians

that believe

a fa

ctor

impe

des pr

escribing

None Evidence based medicine Patient preference pharmaceutical industries clinical experience peer influence insurance policies clinical specialty

running head: factors that influence - the npa master.doc · web viewthe factors that influence the...

Documents