ARTICLE IN PRESS

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Social Science & Medicine 62 (2006) 422–432

www.elsevier.com/locate/socscimed

Studying physician effects on patient outcomes:Physician interactional style and performance on

quality of care indicators

Peter Franks�, Anthony F Jerant, Kevin Fiscella, Cleveland G Shields,Daniel J Tancredi, Ronald M Epstein

Department of Family and Community Medicine, Universtiy of California Davis, UC Davis Medical Center,

2329 ACC, Sacramento, CA 95817, USA

Available online 1 July 2005

Abstract

Many prior studies which suggest a relationship between physician interactional style and patient outcomes may have

been confounded by relying solely on patient reports, examining very few patients per physician, or not demonstrating

evidence of a physician effect on the outcomes. We examined whether physician interactional style, measured both by

patient report and objective encounter ratings, is related to performance on quality of care indicators. We also tested for

the presence of physician effects on the performance indicators. Using data on 100 US primary care physician (PCP)

claims data on 1,21,606 of their managed care patients, survey data on 4746 of their visiting patients, and audiotaped

encounters of 2 standardized patients with each physician, we examined the relationships between claims-based quality

of care indicators and both survey-derived patient perceptions of their physicians and objective ratings of interactional

style in the audiotaped standardized patient encounters. Multi-level models examined whether physician effects

(variance components) on care indicators were mediated by patient perceptions or objective ratings of interactional

style. We found significant physician effects associated with glycohemoglobin and cholesterol testing. There was also a

clinically significant association between better patient perceptions of their physicians and more glycohemoglobin

testing. Multi-level analyses revealed, however, that the physician effect on glycohemoglobin testing was not mediated

by patient perceived physician interaction style. In conclusion, similar to prior studies, we found evidence of an

apparent relationship between patient perceptions of their physician and patient outcomes. However, the apparent

relationships found in this study between patient perceptions of their physicians and patient care processes do not

reflect physician style, but presumably reflect unmeasured patient confounding. Multi-level modeling may contribute to

better understanding of the relationships between physician style and patient outcomes.

r 2005 Elsevier Ltd. All rights reserved.

Keywords: USA; Provider-patient communication; Quality of care; Multi-level analysis; Adherence

e front matter r 2005 Elsevier Ltd. All rights reserve

cscimed.2005.05.027

ing author. Tel.: +1916 734 5494.

ess: pfranks@ucdavis.edu (P. Franks).

Introdction

Studies have suggested that physicians exhibit char-

acteristic interaction styles with patients that are related

to patient care. Among several physician interaction

styles (Bertakis, Roter, & Putnam, 1991; Flocke, Miller,

d.

ARTICLE IN PRESSP. Franks et al. / Social Science & Medicine 62 (2006) 422–432 423

& Crabtree, 2002; Kaplan, Greenfield, Gandek, Rogers,

& Ware, 1996), patient-centered communication has

generated a growing body of research (Stewart et al.,

2000) and has influenced public policy (Committee on

Quality Health Care in America, Institute of Medicine,

2001; Gerteis, Edgman-Levitan, Daley, & Delbanco,

2002). This influence stems from findings in both

observational and randomized controlled studies that

physician interactional style is associated with a variety

of patient outcomes including adherence, satisfaction,

trust, health status change, and symptom resolution.

Despite these studies, however, uncertainties about the

role of physician communication style in patient out-

comes remain (Mead & Bower, 2002; Michie, Miles, &

Weinman, 2003).

Three kinds of studies have been used to explore the

relationships between physician interactional style and

patient outcomes: observational studies using either

objective recordings of physician–patient communica-

tion or patient reports of physician style, and rando-

mized trials aimed at increasing the patient-centeredness

of physicians. Randomized trials aimed at improving

patient outcomes by changing physician interaction

styles provide the most robust test of the impact on

outcomes. While physician interventions show little

effect on health outcomes, some (but not all) trials have

showed effects on satisfaction and adherence (Griffin et

al., 2004). Some studies are confounded by concurrent

patient interventions (Kinmonth, Woodcock, Griffin,

Spiegal, & Campbell, 1998) while others fail to account

for the fact that physicians’ patient panels are different

(clustered within practices).

More common are observational studies examining

the influence of physician style, typically using either

objective ratings of recorded encounters or patient

surveys. Patient survey measure studies have suggested

that physician style, manifest as a more participatory

style, obtaining agreement on treatment, or autonomy

supportiveness, has been associated with greater im-

provements in back pain, headache resolution, diabetes

control, health status, compliance and satisfaction

(Adams, Smith, & Ruffin, 2001; Little et al., 2001;

Meredith, Orlando, Humphrey, Camp, & Sherbourne,

2001; Roberts, 2002; Thom, Ribisl, Stewart, & Luke,

1999). Findings for more immediate outcomes, such as

satisfaction and adherence, are more robustly observed

than are more distal outcomes (Lewin, Skea, Entwistle,

Zwarenstein, & Dick, 2001; Mead & Bower, 2002;

Michie et al., 2003). However, unmeasured patient

factors may alter patients’ reporting of physician style

and confound interpretation of these studies. For

example, certain patient personality characteristics have

been associated with better perceived outcomes (Duber-

stein et al., 2003; Meeks & Murrell, 2001) and these

characteristics may also be associated with more positive

assessments of the patients’ physicians; consequently,

there may be an apparent association between physician

style and outcomes.

Studies using objective ratings of clinical encounters

circumvent this problem, but are subject to other

limitations. Most of these studies observe very few

patient encounters per physician, often only one. The

outcomes are typically measured only in the patients

observed in the encounters. Thus, it is difficult to

discern whether the observed encounter behavior and

the patient outcome reflect: (a) a patient effect result-

ing in both the behavior observed in the encounter and

the subsequent patient outcome; or (b) a physician

(style) effect on both the encounter and subsequent

outcome.

To address some of these limitations, we assessed

the effects of physician interactional style on patients

from two perspectives: patient reports and objective

ratings of audio-recorded standardized patient encoun-

ters. We also used multi-level modeling to examine

whether the relationship between the apparent physician

effects on patient care processes are mediated by

patient reports or objective ratings of physician interac-

tion style.

In view of the current interest in patient-centered care,

our examination of physician interactional style focused

on patient-centered communication. Based in prior

literature (Mead & Bower, 2002; Michie et al., 2003),

we hypothesized that a more patient-centered style—

which includes physician efforts to ‘find common

ground’ about illnesses, treatments and preventive

guidelines—would lead to improved performance on

process of care indicators.

To avoid confounding by assessing adherence based

on patient self-report, we used performance on claims-

based patient care indicators that were available to us

from a large managed care organization (MCO). While

prior studies have not claimed a physician effect on these

specific processes of care, studies of effects of patient–

physician communication have demonstrated effects on

outcomes (such as diabetes) that depend on adherence to

testing and treatment guidelines. Those studies suggest

that a physician interaction style that results in increased

patient self-efficacy, trust, mutual understanding and

participation in care would affect process outcomes such

as regular testing of glycohemoglobin. We chose the

process of care indicators commonly used by managed

care organizations to monitor the quality of care. Some

of these measures depend more on physician–patient

interactions (such as ordering glycohemoglobin) and

were hypothesized to be more related to physician

interactional style than others less dependent on

physician–patient interactions (such as mammography,

where reminders are often generated independent of

physician input). Our goals in reporting these findings

are not to prove or disprove any single relationship

between interactional style and performance on a

ARTICLE IN PRESSP. Franks et al. / Social Science & Medicine 62 (2006) 422–432424

particular process of care indicator. Rather, we want to

highlight a potential threat to the validity of practice-

based health services research that proposes relation-

ships between physician-specific mediating variables and

clinical outcomes, and illustrate a robust approach to

examining the problem.

Most studies examining the relationship between

physician style and patient outcomes have used simple

linear or logistic regression. Multi-level modeling offers

one means for untangling physician and patient

influences on the patient–physician relationship by

allowing the partitioning of variance into separate

(physician level and patient level) components (Snijders

& Bosker, 1999). The analysis proceeds as follows:

in a model excluding the putative mediators of the

physician style, the contribution of the physician

variance component to the total variance in the outcome

of interest is assessed. Demonstrating a significant

physician variance component provides evidence that

physicians differ from one another in the measured

domain. The analysis is then repeated including the

putative mediator (the physician style measure); media-

tion is suggested by attenuation in the physician

variance component. Thus, two conditions provide

necessary evidence of a relationship between physician

style and patient outcome: (1) a significant contribu-

tion of the physician variance component to the

total variance associated with the outcome in question;

and (2) demonstration (by attenuation of that physician

variance component) that the outcome is mediated by

either the survey or observational ratings of the

physician.

Methods

Three data sources were linked together by 100

primary care physicians (PCP): (1) claims data from

patients assigned to each PCP who were enrolled in a

managed care organization, yielded the claims-based

process of care measures; (2) transcripts of two

encounters of standardized patients with each PCP,

yielded the objective rating of physician interactional

style; and (3) patient surveys, completed by about 50

patients of each PCP, provided measures of patient

perceptions of the physicians’ interactional style.

Physician sample

In late 1999, we identified 594 PCP in active clinical

practice within 45min drive of Rochester belonging to a

large managed care organization serving the 8-county

Rochester, NY region (population 1.1 million). To

achieve stable measures of performance indicators, only

the 506 physicians who had more than 100 patients in

the MCO were eligible; thus, enrolled physicians,

compared with non-enrolled physicians, had larger

practices. We also over sampled family physicians to

allow for comparisons between family physicians and

internists (reported elsewhere). A maximum of 2

physicians per practice were recruited to avoid clustering

effects and minimize physician detection of standardized

patients. The remaining 297 eligible physicians were

recruited by 12 physician-recruiters in random order

until a total of 100 physicians were recruited. Physicians

gave informed consent to participate in a study of

‘‘patient care and outcomes’’. They agreed to have 2

unannounced covertly audio-recorded standardized pa-

tient visits over the subsequent 12 months (2000–2001).

Physicians were reimbursed $100 for each standardized

patient visit; $100 was also provided to the office staff

for their assistance. The study received institutional

review board approval.

Claims data

The health care organization provided claims data for

1996–1999. Key dependent variables are described

below. Covariates included age, gender, patient Zip

code based socio-economic status, and case-mix based

on patient Ambulatory Diagnostic Group (Weiner,

Starfield, Steinwachs, & Mumford, 1991) codes accu-

mulated during each year.

Patient care process indicators

We used quality of care indicators that could be

reliably derived from the managed care claims from

1996–1999; one year was used as the interval for each

indicator. Women were classified as to whether or not

they had a Pap test (based on Current Procedural

Terminology, 4th edition code submitted by the

cytopathology laboratory), and women over 40 were

coded as to whether or not they had a mammogram

(based on the code submitted by the performing

radiology office). Patients were defined as diabetic if

they had received the diagnosis on at least two

occasions. Diabetic patients were classified as to whether

or not they had received a test for glycohemoglobin or

cholesterol (office-based), and whether or not they had

received an eye exam. Following Weissman and Epstein

(Weissman, Gastonis, & Epstein, 1992), we classified

hospitalizations as potentially avoidable or not avoid-

able. Six medical conditions met criteria as avoidable

hospitalization conditions possibly benefiting from

quality primary care: angina, congestive heart failure,

hypertension, asthma, chronic obstructive pulmonary

disease, and diabetes mellitus (Bindman et al., 1995;

Weissman et al., 1992). Patients were classified as to

whether or not they were admitted for an avoidable

hospitalization condition.

ARTICLE IN PRESSP. Franks et al. / Social Science & Medicine 62 (2006) 422–432 425

Standardized patient encounter data

We developed standardized patient roles to provide

physicians with identical clinical scenarios and to avoid

several problems with real patients. For example,

standardized patients can be introduced in to the

practice incognito, whereas in real patient encounters

such blinding is impossible; physicians and patients may

modify their behavior when they know that they are

being observed. A physician community advisory panel

suggested that use of more than 2 standardized patients

would likely compromise physician recruitment. During

2001–2002, each physician had visits from 2 standar-

dized patients, portraying one of two roles. The two

roles were selected to contrast physicians’ responses to

patients with straightforward symptoms with those who

present ambiguous or medically unexplained symptoms.

The heartburn role involved nocturnal chest pain

exacerbated by food and partially relieved by antacids,

with minimal emotional distress. The ambiguous symp-

toms role involved multiple symptoms, poorly charac-

terized chest pain and moderate emotional distress

characteristic of multi-somatoform disorder (Kroenke

et al., 1997). In pilot testing, the roles were calibrated to

avoid prompting referral to an emergency department or

administration of medications in the physician’s office.

Each physician saw 1 male and 1 female standardized

patient, and 1 of each illness condition. The first

standardized patient visit was randomized by illness

condition and gender.

Encounters were recorded and analyzed using the

Measure of Patient-Centered Communication (MPCC)

(Brown, Stewart, & Ryan, 2001). The MPCC has

demonstrated adequate reliability (inter-rater reliability

reported as 0.80–0.83) and validity (Brown et al., 2001).

It measures three aspects (of components) of physician

communication, and is unique in that it is theoretically

linked to a model of patient-centered communication.

For Component 1 (‘‘exploring both the disease and the

illness experience’’), the coder notes patient statements

about symptoms, ideas, expectations, feelings and the

effect of the symptoms on functioning. Component 2

(‘‘understanding the whole person’’) measures the degree

to which the physician explores family, social, and

occupational issues. Component 3 (‘‘finding common

ground’’) measures the degree to which the physician

arrives at a common understanding with the patient

about the nature of the problem and its management.

We trained 2 coders to score the recordings using the

MPCC; 10% of recordings were dual-coded. The total

MPCC score represents the mean of the 3 component

scores. Scores range from 0 (not patient centered) to 1

(very patient centered). Our reliability data as well as

means and standard deviations of the scores were

virtually identical to those reported by the developers.

The correlation of MPCC scores between the two cases

was 0.39 (p ¼ 0:0001). We used the mean MPCC score

for the two cases in our analyses. The reliability of this

mean score, as a measure of the consistency of the

physician’s interpersonal style, was is 0.56 (using the

Spearman–Brown prophecy formula). Because of logis-

tic problems we were able to obtain useable recordings

on only 96 of the enrolled physicians.

Patient survey data

During 2001–2002, about 50 consecutive patients age

18–65 were approached by a research assistant in each of

the physician’s waiting room to complete a survey prior

to their office visit. The survey included 4 patient

perception of physician scales: the 5-item Health care

climate questionnaire (HCCQ autonomy scale) (Wil-

liams, Freedman, & Deci, 1998), which measures

autonomy supportiveness and patient involvement in

decision-making; two subscales from the Primary Care

Assessment Survey (PCAS) (Safran, Kosinski, et al.,

1998), the 4-item knowledge subscale (PCAS-K herein-

after referred to as knowledge scale) and the 8-item trust

subscale (PCAS-T trust scale); and satisfaction, using a

6-option Likert scale question (‘‘All things considered,

how satisfied are you with your regular doctor?’’) (Ware,

Snyder, Wright, & Davies, 1984). The survey also

included questions on demographics (age, gender, race/

ethnicity, years of schooling), health status (a checklist

of chronic disease conditions, the Medical Outcomes

Study Short Form 12) and the duration, in years, of the

relationship with the physician.

Analyses

Data were analyzed using STATA (Version 8.2,

StataCorp, College Station, TX) and analyses adjusted

for the nesting of patient observations within physicians.

First, we derived a mean patient perception of physician

score (based on patient responses to the survey) for each

physician. High inter-correlations among the scales

(satisfaction, trust, autonomy, knowledge), all exceeding

0.63, and concern about Type I errors arising from

analyzing the 4 scales separately, suggested the use of

factor analysis to reduce the number of variables

(Harman, 1976). Principal component factor analysis

applied to the 4 scales produced a single factor

accounting for 75% of the variance in the individual

scales; no other factors emerged. The individual scales

all loaded highly (0.83–0.86) on the factor. Cronbach’s afor a single scale, using the 4 scales as items on that

scale, was 0.88. Taken together, these findings suggest

the presence of a single underlying factor, which we

termed the ‘‘satisfaction/trust/autonomy/knowledge

[STAK]’’ score, accounting for much of the variation

in responses observed with the 4 scales. The intraclass

correlation coefficient of the STAK scores observed

ARTICLE IN PRESSP. Franks et al. / Social Science & Medicine 62 (2006) 422–432426

among the patients clustered within each physician was

0.10 (95% confidence interval (CI) ¼ 0.07–0.13), provid-

ing evidence that there were significant differences

between the physicians in their patients’ perceptions of

them. With 47 patients per physician, the reliability of

the mean STAK score is 0.84 (95% CI ¼ 0.79–0.88)

(Snijders & Bosker, 1999). Reported analyses use

STAK, though analyses using the individual scales also

produced similar results.

We adjusted the STAK scores for patient factors

(demographics, health status, and relationship length)

unlikely to be related to physician style. Patient-level

linear regression analyses were conducted with the

STAK score as the dependent variable, and the patient

demographics, health status, and relationship duration

as independent variables. The mean residual (difference

between observed and predicted STAK score) across all

patients nested within each physician was used as the

adjusted STAK score for the physician. We also

conducted analyses using unadjusted mean STAK scores

with similar results. We report results based on the

adjusted scores, since these are less confounded by

patient factors unrelated to the physician.

Next, we conducted a series of analyses to examine the

main questions of the study. These analyses were

conducted at the patient level (this time using the claims

data), as random effects logistic regression models

allowing for the nesting of patient within physician

(included as a random effect). The dichotomous depen-

dent variables were the prevention measures (mammo-

gram, Pap testing), the diabetes management measures

(eye examination, glycohemoglobin and cholesterol

testing), and the avoidable hospitalization measure.

The key independent variable was either the mean

MPCC (derived from the coding of the standardized

patient visits) or adjusted mean STAK score (from the

survey data). Analyses adjusted for patient age, age

squared (to examine non-linear effects associated with

age), gender, Zip code-based socioeconomic status

(using the Zip code of the patient’s address) (Franks et

al., 2003), year (1996–1999), year of enrollment (1–4),

total years of enrollment (1–4), a dummy variable for

each Ambulatory Diagnostic Group, and physician

specialty (family practice or internal medicine). Each

patient appears in the analysis once. Thus, 1 year of the

possible 4 years each patient was enrolled was randomly

selected for inclusion in the analyses.

We report whether the key independent variable

(MPCC [physician N ¼ 96] or STAK [physician

N ¼ 100]) exhibited any significant relationships with

the dependent variables. We also examined the physician

variance component of each analysis. We report the

proportion of total variance contributed by the physi-

cian variance component (rho, or the intraclass correla-

tion coefficient), with the key independent variable

(MPCC or STAK) omitted, as evidence for any

physician effect on the outcome. When relevant, we

report the percentage reduction in the physician variance

component observed with the key independent variable

included as evidence that MPCC or STAK mediates the

physician effect (Snijders & Bosker, 1999).

Results

Of the 297 physicians identified for recruitment, 109

(37%) refused to participate and 14 were ineligible. The

claims data revealed that the socio-demographic, utiliza-

tion and clinical characteristics of MCO patients in study

and non-study practices were similar (Table 1), though,

by design, enrolled physicians were more likely to be

family physicians and had more managed care organiza-

tion patients in their practices. A total of 4746 patients

(96% of those approached) completed the survey.

Patients in the office survey were modally female, white,

had at least some college education, and at least a 5-year

relationship with their physician (Table 2).

Table 3 shows the adjusted relationships between

patient perceptions (STAK) and objective ratings

(MPCC) and the care indicators. Three care indicators

(mammogram, Pap test, and glycohemoglobin test)

showed statistically significant adjusted associations with

patient perceptions (Table 3). Two, mammograms and

glycohemoglobin testing, were in the expected direction

of better care indicators associated with better patient

perceptions. One, Pap test, was in the opposite direction:

better perceptions were associated with a lower like-

lihood of an annual Pap test. Only for glycohemoglobin

testing were the changes clinically important (defined as a

change in risk of more than 10%(Maldonado & Green-

land, 1993). A 1 standard deviation increase in STAK

was associate with an 18% increase in the odds of getting

a glycohemoglobin test. One care indicator, mammo-

graphy, was statistically significantly associated with

MPCC score. A 1 standard deviation increase in MPCC

score was associated with a 2% increase in the odds of

getting a mammogram (p ¼ 0:05).Glycohemoglobin and cholesterol testing revealed sta-

tistically significant adjusted physician effects (Table 4).

When either the STAK or MPCC variables were included,

there was evidence of both significant physician and

patient perception (STAK) effects for glycohemoglobin

testing only. The physician variance component with

STAK excluded was 0.244 (95% CI ¼ 0.147, 0.404) and

0.238 (95% CI ¼ 0.142, 0.397) with STAK included, a 1%

non-statistically significant reduction in variance.

Discussion

Using standard regression methods similar to those

used in prior studies examining the relationship between

ARTICLE IN PRESSP. Franks et al. / Social Science & Medicine 62 (2006) 422–432 427

physician interactional style and patient outcomes, we

replicated prior research showing that patient ratings of

their physicians are associated with valued outcomes

(Adams et al., 2001; Kinmonth et al., 1998; Lewin et al.,

2001; Mead & Bower, 2002; Michie et al., 2003; Safran,

Taira, et al., 1998; Stewart et al., 2000; Thom et al.,

1999; von Korff, Barlow, Cherkin, & Deyo, 1994). In

our case, these outcomes were increased performance on

some recommended care indicators. The apparent

robustness of this finding is strengthened by the use of

outcome measures independent of patient report. How-

ever, the principal finding was that, although glycohe-

moglobin testing exhibited both a physician effect and

an apparent relationship to STAK, the patient percep-

tion measure did not mediate or explain the physician

effect. We discuss the implications of this finding after

reviewing other results that assist in its interpretation.

(1)

Tab

Cha

Pat

Num

Age

Sex

Inco

Hig

Any

Pati

Yea

AD

Avo

Ann

W

W

D

D

D

Per

D

In

T

N

Phy

S

G

A

S

R

N

Not

cod

We observed significant physician effects on diabetes

management indicators (cholesterol and glycohemo-

le 1

racteristics of managed care patients and physicians in study and non

No

ient characteristics

ber 48

(SD) 41

, % female 52

me, median (SD)* 36

h school graduates, % (SD)* 63

visit to a physician, % 82

ents referred, % 25

rs enrolled in the MCO, mean (SD) 3.0

Gs, mean (SD) 2.9

idable hospitalizations, % 0.1

ual performance indicators, %

omen with Pap tests 51

omen 440 with mammograms 46

iabetics with eye examinations 46

iabetics with glycohemoglobin tests 66

iabetics with cholesterol tests 54

patient costs in 1996 dollars, mean, median (SD)

iagnostic testing 19

patient 26

otal costs 95

umber 59

sician and practice characteristics

pecialty, % family practice 24

ender, % female —

ge, mean (standard deviation) —

olo Practitioner, % yes —

ural Practitioner, % yes —

umber of patients, mean (SD) 81

es: SD ¼ standard deviation; ADGs ¼ Ambulatory Diagnostic Grou

e linked to 1990 Census data. Information based on 1996–1999 claim

globin testing), but not on other care indicators.

Ordering these tests is directly under physician

control, so a physician effect on their utilization

has face validity. By contrast, completion of eye

examinations and mammography typically require

intermediate scheduling steps, often including spe-

cialist involvement and reminders; thus it is perhaps

not surprising that we found no PCP effect on these

indicators. A similar lack of effect on ‘‘multi-step’’

testing has been found in intervention studies to

increase uptake of cancer screening, in which multi-

factorial interventions targeting physicians, patients,

and processes of care are more effective than

physician interventions alone (Jepson et al., 2000;

Stone et al., 2002).

(2)

We found no clinically important associations

between the objective rating of physician style

(MPCC) and any process of care outcome. Weak

associations between the MPCC and mammo-

graphy were accompanied by an absence of a

-study physician practices

n-study Study

3,094 121,606

.1 (11.2) 41.0 (11.0)

.7 53.9

,874 (10,160) 37,830 (10,683)

.8 (7.9) 64.8 (7.8)

.5 83.1

.6 25.7

7 (1.12) 3.07 (1.12)

9 (2.67) 3.02 (2.67)

6 0.12

.8 52.5

.2 46.2

.4 47.0

.1 67.0

.5 54.7

6, 23.6 (513) 189,19.6 (521)

4, 0 (4038) 254, 0 (4702)

0, 228 (4914) 938, 228 (5493)

4 100

47

23

45 (8)

24

32

3 (776) 1218 (758)

ps. * ¼ socio-economic variables derived from patient Zip

s data.

ARTICLE IN PRESS

Table 2

Characteristics of patients in survey in terms of number (%)

unless otherwise indicated

Characteristic Value No. (%) of

subjects

missing data

for category

Total 4746 (100) n/a

Age, mean (SD) 44.9 (12.1) 66 (1.4)

Gender 41 (0.9)

Female 2955 (62.3)

Male 1750 (36.9)

Race/ethnicity 34 (0.7)

African American 499 (10.5)

Hispanic 109 (2.3)

Other 110 (2.3)

White 3994 (84.2)

Education 21 (0.4)

o 12 years 337 (7.1)

12th grade 1370 (28.9)

1–3 years college 1490 (31.4)

4 years college 828 (17.4)

Graduate school 700 (14.7)

Length patient-doctor

relationship

12 (0.3)

o 1 year 360 (7.6)

1–3 years 1035 (21.8)

3–5 years 814 (17.2)

45 years 2525 (53.2)

Number of medical conditions,

mean (sd)

2.3 (1.5) 0 (0)

MCS-12, M (sd) 48.8 (10.5) 288 (6.1)

PCS-12, M (sd) 46.0 (11.1) 288 (6.1)

Somatization scale, M (sd) 7.6 (6.5) 22 (0.5)

HCCQ, M (sd) 18.0 (3.1) 13 (0.3)

PCAS–K, M (sd) 15.1 (4.3) 12 (0.3)

PCAS–T, M (sd) 34.1 (4.0) 2 (0.0)

Satisfaction, M (sd) 5.2 (0.8) 17 (0.4)

Notes: Survey data was only available for patients of 96 of the

study physicians.

Table 3

Adjusted relationships of patient perceptions and observed physician inte

Care measure (N) STAK

AOR (95% CI)

Mammogram (34,283) 1.03 (1.00, 1.05)

Pap test (65,644) 0.97 (0.96, 0.99)

Diabetic patients (4155)

Eye examination 1.08 (0.99, 1.19)

Glycohemoglobin 1.18 (1.04, 1.34)

Cholesterol 1.04 (0.93, 1.17)

Avoidable hospitalization 0.98 (0.81, 1.18)

Notes: N ¼ sample size for indicators. For avoidable hospitalization, full s

ratio (95% confidence interval). The AOR indicates the effect on the dep

patient perception or observed communication style measure, adjusted fo

Tab

Fra

adju

com

Car

Pap

Ma

Dia

E

G

C

Avo

Not

P. Franks et al. / Social Science & Medicine 62 (2006) 422–432428

significant physician variance component for mam-

mography.

(3)

We found a clinically important association between

patient perceptions (STAK) and glycohemoglobin

(but not cholesterol) testing. This finding might

suggest that a better perceived physician style

increases glycohemoglobin testing. However, as

noted below, other analyses make this explanation

unlikely. More likely is that patients, because of their

characteristics (e.g. higher conscientiousness or self-

efficacy), both comply with glycohemoglobin testing

and rate their physicians more highly. Reported

associations between adherence and patient-reported

trust and knowledge (both part of STAK) (Safran,

Kosinski, et al., 1998; Safran,Taira, et al., 1998) are

consistent with this explanation. Such patients may

also be likely to cluster by physician (Federman et

al., 2001; Greenfield, Kaplan, Kahn, Ninomiya, &

Griffith, 2002).

(4)

Our principal finding, the physician effect on

glycohemoglobin testing, was not mediated by

raction style with care indicators

MPCC

p AOR (95% CI) p

0.03 1.02 (1.00, 1.05) 0.05

0.01 1.00 (0.98, 1.02) 0.73

0.09 0.97 (0.89, 1.06) 0.52

0.01 0.99 (0.87, 1.12) 0.83

0.45 1.04 (0.94, 1.16) 0.44

0.80 1.12 (0.93, 1.34) 0.23

ample of 121,606 was used. AOR (95% CI) ¼ adjusted odds

endent variable of a one standard deviation change in the

r covariates.

le 4

ction of variance (rho) due to physician component in

sted models excluding patient perception and observed

munication style measures

e indicator Rho (95% CI) p

test 0.00 (0, 0) 1.0

mmogram 0.00 (0, 0) 1.0

betic patients

ye examination 0.00 (0, 0) 1.0

lycohemoglobin 0.07 (0.04, 0.11) o0.001

holesterol 0.04 (0.03, 0.07) o0.001

idable hospitalization 0.02 (0, 0.38) 0.27

e: p value is of the likelihood ratio test of rho ¼ 0.

ARTICLE IN PRESSP. Franks et al. / Social Science & Medicine 62 (2006) 422–432 429

patient perceived physician interaction style

(STAK). We noted a small (1%) change in the size

of the physician effect on glycohemoglobin testing

when STAK was included or excluded from the

analytic models, with large overlaps in confidence

intervals. Taken as a whole, these findings suggest

that the observed relationship between STAK and

glycohemoglobin testing does not represent an effect

on glycohemoglobin testing attributable to aspects

of physician interactional style we measured. In

other words, the physician effect on glycohemoglo-

bin testing is not mediated by STAK, but by

unmeasured variables.

We found little evidence to suggest that physicians’

patient-centeredness substantively affects process of care

indicators. This negative finding is consistent with the

results of intervention studies aimed at changing

physician communication (Griffin et al., 2004). That

same review showed that patient activation was a more

effective strategy for improving patient outcomes.

A number of previous studies have reported associa-

tions between patient-reported assessments of their

primary care and patient-reported outcomes. Most of

these examined adherence and satisfaction (Lewin et al.,

2001; Mead & Bower, 2002; Michie et al., 2003),

although a few examined health status (Adams et al.,

2001; (Safran, Kosinski, et al., 1998; Safran,Taira, et al.,

1998), quality of life (Kinmonth et al., 1998), continuity

of care with primary provider (Thom et al., 1999), back

pain symptoms and function, (von Korff et al., 1994),

and glycohemoglobin and lipid levels in diabetics

(Kinmonth et al., 1998). Other studies have reported

associations between objectively assessed physician

interaction style and these outcomes (Mead & Bower,

2002; Michie et al., 2003; Lewin et al., 2001). These

associations have been interpreted to mean that ‘‘better’’

physician interaction style—either perceived or objec-

tively rated—is causally related to improved patient

outcomes.

Our use of more robust methods could explain why

our findings contrast with prior studies. We examined

the relationships between physician interaction style and

patient outcomes from two perspectives (patient percep-

tion and objective ratings) rather than the single

perspective used in most studies. We also assessed

claims-based process of care outcomes rather than

patient reports considered in most studies. Finally, use

of multi-level modeling allowed us to more carefully

examine the relationships among patient and physician

factors.

Studies linking ratings of specific encounters to

outcomes for the same patients may have been

confounded by patient effects on physician interactional

behavior (Bertakis et al., 1991; Hall, Irish, Roter,

Ehrlich, & Miller, 1994; Roter et al., 1998; Stewart

et al., 2000). It is also possible that effects of a given

physician style can be observed only in the context of

interactions between physicians with that style and

specific patients (Inui & Carter, 1985; Plsek, 2001).

A study demonstrating that congruency of beliefs

between physician and patient resulted in higher patient

trust and endorsement of their physicians than when

beliefs were incongruent provides support for this

idea (Krupat, Bell, Kravitz, Thom, & Azari, 2001).

It is consistent with the patient-centered approach

that good communication requires input from both

parties. Thus, measuring physician style alone may

be insufficient to explain variations in care (Epstein

et al., 2005).

Studies employing patient reports may reflect con-

founding by unmeasured patient characteristics affecting

both patients’ perceptions of their physicians and care

outcomes. For example, in one study of patients with

diabetes, greater active coping behaviors were associated

with increased patient satisfaction and also predicted

glycosylated hemoglobin level (Rose, Fliege, Hildeb-

randt, Schirop, & Klapp, 2002).

Given that the physician effect we observed on

glycohemoglobin testing was not mediated by STAK

ratings, how can the relationship be explained? The

physician effect may reflect a physician style unrelated to

the measured interaction style. Several physician style

classifications have been described (Benbassat, Pilpel, &

Schor, 2001; Bertakis et al., 1991; Flocke et al., 2002;

Kaplan et al., 1996; Kinnersley, Stott, Peters, & Harvey,

1999), some reflecting physician(Benbassat et al., 2001)

and practice environment characteristics(Kaplan et al.,

1996) that may not be manifest in interaction styles

captured here, and perhaps physician attributes valued

by patients but not measured. It is possible that

physician interaction style does mediate the physician

effect on glycohemoglobin testing, but that neither

STAK nor MPCC were sufficiently sensitive to detect

it. Finally, the observed relationship could reflect

confounding by unmeasured patient factors that are

also clustered by physician.

Our study has several limitations. Willingness to

participate in the kind of intrusive study we conducted

may select physicians with a relatively narrow spectrum

of physician styles. While we have some limited

information suggesting the claims data of enrolled and

non-enrolled physicians were similar, our results cannot

be generalized to other physicians. It should be noted

though, that these problems of representativeness plague

all studies of physician–patient communication. We

observed apparent relationships between patient out-

comes and physician style similar to that reported in

many of those studies. It was only when we examined

the relationships more carefully using multi-level ana-

lyses we found that the measures of physician style did

not mediate the patient outcomes.

ARTICLE IN PRESSP. Franks et al. / Social Science & Medicine 62 (2006) 422–432430

The patient survey measures of trust, knowledge,

satisfaction and autonomy support can be understood

both as measures of patient characteristics (e.g.,

tendency to trust), physician characteristics (e.g., trust-

worthiness) and relationship characteristics (e.g., shared

meaning and values). Our analyses can thus only capture

the physician’s contribution to these characteristics, and

may be confounded by patient–physician self-selection

and accommodation.

Absence of an observed physician effect on the

majority of outcomes could represent insufficient power

to detect small effects (Greenfield et al., 2002; Hofer et

al., 1999). However, the small confidence intervals

around the negative effects observed in this study

suggest that adequate power was not a clinically

significant problem.

Other limitations relate to our study measures: use of

claims data limited the ‘‘outcome’’ measures that could

be examined reliably. Physician effects and physician

interactional style may influence variables not measured

in this study. Studies examining these outcomes are

needed using methods that address the potential

confounding suggested here. Finally, while the reliability

of the STAK factor was adequate, the modest reliability

of the MPCC may have precluded detecting effects,

especially since the spectrum of physician patient-

centeredness styles observed may have been narrow.

While the MPCC is the only extant reliable measure of

patient centeredness, it addresses only three domains of

the construct.

We based our hypotheses on studies of adherence to

medication, which may not generalize to the outcomes

measured in our study. This may be because disease-

related outcomes found in some studies (Greenfield,

Kaplan, Ware, Yano, & Frank, 1988) may not be

mediated by compliance with prevention and disease

management guidelines.

Using more than 2 standardized patients per physi-

cian would have been desirable; based on the reliability

observed, 5 standardized patients would have yielded an

acceptable reliability of 0.76. However, the community

physician advisory board for this study advised us that

more than 2 standardized patients would likely make

recruitment very difficult.

Finally, this study and all those that we have cited

were conducted in primary care settings in Northern

Europe, North America or Australia, making general-

izability to other socio-cultural environments question-

able.

Despite these limitations, our findings suggest caution

in interpreting some studies reporting a relationship

between measures of physician interactional style or

characteristic and patient outcomes. Given that the

Institute of Medicine (Committee on Quality Health

Care in America, Institute of Medicine, 2001) and

accreditation organizations (Batalden, Leach, Swing,

Dreyfus, & Dreyfus, 2002) have endorsed the idea of

encouraging physicians to employ more patient-centered

interaction styles, careful research is needed to tease out

what components of patient-centered communication

contribute to improved outcomes. Alternative study

methodologies and analytic approaches, including the

multi-level analyses and standardized patients used here,

will be necessary to tease out the components of

physician behaviors that contribute to better patient

outcomes. Randomized controlled trials will be needed

to determine whether those behaviors can be encour-

aged. It is possible that interventions to change

physician interaction styles might benefit domains other

than those studied here, such as trust, understanding

and satisfaction. Randomized trials should determine

which means should be used to improve the processes of

care we studied. These might include changes in practice

organization, financial incentives, health systems inno-

vations, training patients to increase their involvement

in care, as well as interventions to change physician

interactional style.

Acknowledgements

Funded, in part, by grants from the Agency for

Healthcare Research and Quality, R01 HS10610 and

RO1 HS09963. We would also like to thank Paul

Duberstein for his help in developing the psychological

framework of the model, and Sean Meldrum for

assisting with the data analysis.

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