IRB indicates institutional review board; PC, primary care; PCP, primary care practitioners; PI,
principal investigator; R&D, research and development.aNonphysician
participants included 7 registered nurses, 4 nurse practitioners, 4 registered nurse case managers,
1 physician assistant, and 1 pharmacist.bNonphysician participants included 10
licensed practical nurses, 9 registered nurses, 3 medical support assistants, 2 registered nurse
care coordinators, and 1 pharmacist.cNonphysician
participant.dTwo additional nonphysicians left the practice-level incentives group
during the intervention. One new nonphysician was enrolled.eSeven nonphysicians
left the physician- and practice-level incentives group during the intervention. Two new
nonphysicians were enrolled.fOnly data from physicians were used to assess both
physician-level and practice-level performances; each physician participant received ≥2
instances of the intervention.
Data are based on review of the electronic medical records of 40 randomly selected patients per
physician.aNineteen physicians participated in the baseline period; 760 patient
records reviewed. Eighteen physicians participated in the final intervention period; 720 patient
records reviewed. Thirteen physicians participated in the post-washout period; 520 patient records
reviewed.bEighteen physicians participated in the baseline period; 720 patient
records reviewed. Nineteen physicians participated in the final intervention period; 760 patient
records reviewed. Seventeen physicians participated in the post-washout period; 680 patient records
reviewed.cSeventeen physicians participated in the baseline period; 680 patient
records reviewed. Fifteen physicians participated in the final intervention period; 600 patient
records reviewed. Eleven physicians participated in the post-washout period; 440 patient records
reviewed.dEighteen physicians participated in the baseline period; 720 patient
records reviewed. Nineteen physicians participated in the final intervention period; 760 patient
records reviewed. Fourteen physicians participated in the post-washout period; 560 patient records
eTable. Longitudinal Modeling Results of the Effect of the Intervention on Implementing
Antihypertensive Medication Change
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Petersen LA, Simpson K, Pietz K, et al. Effects of Individual Physician-Level and Practice-Level Financial Incentives on
Hypertension Care: A Randomized Trial. JAMA. 2013;310(10):1042–1050. doi:10.1001/jama.2013.276303
Copyright 2013 American Medical Association. All Rights Reserved. Applicable
FARS/DFARS Restrictions Apply to Government Use.
Pay for performance is intended to align incentives to promote high-quality care, but results
have been contradictory.
To test the effect of explicit financial incentives to reward guideline-recommended hypertension
Design, Setting, and Participants
Cluster randomized trial of 12 Veterans Affairs outpatient clinics with 5 performance periods and
a 12-month washout that enrolled 83 primary care physicians and 42 nonphysician personnel (eg,
Physician-level (individual) incentives, practice-level incentives, both, or none. Intervention
participants received up to 5 payments every 4 months; all participants could access feedback
Main Outcomes and Measures
Among a random sample, number of patients achieving guideline-recommended blood pressure
thresholds or receiving an appropriate response to uncontrolled blood pressure, number of patients
prescribed guideline-recommended medications, and number who developed hypotension.
Mean (SD) total payments over the study were $4270 ($459), $2672 ($153), and $1648 ($248) for the
combined, individual, and practice-level interventions, respectively. The unadjusted baseline and
final percentages and the adjusted absolute change over the study in patients meeting the combined
blood pressure/appropriate response measure were 75% to 84% and 8.84% (95% CI, 4.20% to 11.80%) for
the individual group, 80% to 85% and 3.70% (95% CI, 0.24% to 7.68%) for the practice-level group,
79% to 88% and 5.54% (95% CI, 1.92% to 9.52%) for the combined group, and 86% to 86% and 0.47% (95%
CI, −3.12% to 4.04%) for the control group. The adjusted absolute estimated difference in the
change between the proportion of patients with blood pressure control/appropriate response for
individual incentive and control groups was 8.36% (95% CI, 2.40% to 13.00%; P=.005). The other
incentive groups did not show a significant change compared with controls for this outcome. For
medications, the unadjusted baseline and final percentages and the adjusted absolute change were 61%
to 73% and 9.07% (95% CI, 4.52% to 13.44%), 56% to 65% and 4.98% (95% CI, 0.64% to 10.08%), 65% to
80% and 7.26% (95% CI, 2.92% to 12.48%), and 63% to 72% and 4.35% (95% CI, −0.28% to 9.28%),
respectively. These changes in the use of guideline-recommended medications were not significant in
any of the incentive groups compared with controls, nor was the incidence of hypotension. The effect
of the incentive was not sustained after a washout.
Conclusions and Relevance
Individual financial incentives, but not practice-level or combined incentives, resulted in
greater blood pressure control or appropriate response to uncontrolled blood pressure; none of the
incentives resulted in greater use of guideline-recommended medications or increased incidence of
hypotension compared with controls. Further research is needed on the factors that contributed to
clinicaltrials.gov Identifier: NCT00302718
As part of the Affordable Care Act, the US government has introduced pay for performance to all
hospitals paid by Medicare nationwide.1 The New York City
Health and Hospitals Corporation recently announced a performance pay plan for physicians.2 These and other value-based purchasing systems are intended to
align incentives to promote high-quality health care.3
Evaluations of the effectiveness of pay-for-performance programs directed at hospitals have shown
contradictory results.3-5 The Premier
Hospital Quality Incentive Demonstration showed an increase of up to 4.1 percentage points in
process-quality measures during the first 2 years,6 but
these modest gains were not sustained.7 Moreover,
risk-adjusted mortality in the same program showed no improvement.8
In contrast, recent studies assessing outcomes of hospital pay-for-performance programs
implemented in the United Kingdom on a wider scale, with larger bonuses and with different
approaches to quality improvement, showed clinically significant mortality reductions.9 Enhancing the face validity of these findings, these reductions
were concentrated among hospitals that also showed the best process-measure performance.
Evaluations of incentives targeted at individual physicians and physician practice teams (ie,
clinicians, nurses, and support staff who deliver health care) also show variability.3-5 A Cochrane review of incentives to
improve the quality of primary care found that 6 of 7 eligible studies showed a statistically
significant positive effect, but the authors encouraged caution in interpreting findings because of
design limitations and generalizability concerns.4 Thus,
many questions about pay for performance are unanswered.4,5,10
Given the implementation of the patient-centered medical home and models of accountable
care,11 the effects of financial arrangements that reward
health care practice teams will become more interesting to payers and policy makers.12 We are not aware of other multisite randomized trials of pay
for performance directed at both physicians and practice teams. Therefore, we designed a cluster
randomized controlled trial to test the effect of explicit financial incentives to individual
physicians and practice teams for the delivery of guideline-recommended care for hypertension in the
primary care setting.
Characteristics of the study hospitals and detailed trial methods were published elsewhere.13 Research assistants at the Houston coordinating center enrolled
a minimum of 5 full-time primary care physicians from 12 hospital-based primary care clinics in 5
Veterans Affairs (VA) Networks. Then, the clinics were randomized to 1 of 4 study groups: (1)
physician-level (individual) incentives; (2) practice-level incentives; (3) physician-level plus
practice-level (combined) incentives; and (4) no incentives (control). We cluster-randomized by
hospital to avoid contamination of the intervention; all participants at a hospital belonged to the
same intervention group.14 Randomization was constrained on
teaching status, geographic and clinic location, and participation in the Antihypertensive and
Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT).15 A data analyst assigned a uniform random number to each of the possible
allocations using SAS version 9.1.3 (SAS Institute) and selected the one with the highest random
number. At the 6 hospitals randomized to receive a practice-level incentive, physicians could invite
up to 15 nonphysician colleagues (eg, nurses, pharmacists) to participate as part of their practice
team caring for hypertensive patients on their panel. Also, to meet the physician recruitment goal
of 7 per site (to account for attrition), we enrolled additional eligible physicians after
randomization. The study was approved by the institutional review boards of all participating
institutions.16 All participants provided written informed
Participants attended webinars beginning in February 2008 that reviewed the guidelines from the
“Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and
Treatment of High Blood Pressure (JNC 7).”17
Participants were also informed of their study group assignments, study measures, and financial
The intervention phase included a 4-month performance baseline period (August-November 2007) and
4 consecutive 4-month periods starting in April 2008. After the end of each period, medical record
abstractors at the Houston coordinating center who were blinded to the study aims and study group
assignments collected data from electronic medical records for 40 patients with hypertension
randomly selected from each physician’s panel. Participants who were absent for 4 or more
weeks during a given 4-month performance period were not eligible to earn rewards for that period.
Practices could replace nonphysician participants who withdrew during the study. Intervention group
participants received up to 5 incentive payments in their paychecks approximately every 4 months and
were notified each time a payment was posted.
Customized audit and feedback reports detailing performance for each period and the next
period’s performance goals were posted to the study’s secure website. After the final
feedback report in April 2010, we followed up participants for a 12-month washout period to
determine whether the effects of the intervention were sustained after the incentive ended. We then
collected data for a 4-month post-washout performance period, May through August 2011.
Participants earned incentives for achieving JNC 7 guideline–recommended blood pressure
thresholds or appropriately responding to uncontrolled blood pressure (eg, lifestyle recommendation
for stage 1 hypertension or guideline-recommended medication adjustment), prescribing
guideline-recommended antihypertensive medications, or both. The directors of the participating
hospital regions contributed $250 000 for the incentives. Results from simulations using pilot
data and accounting for estimated improvement rates showed a maximum per-record reward of $18.20,
$9.10 for each successful measure.
For the practice-level payments, the aggregated earnings of the physician participants were
equally distributed between the physician and nonphysician participants in the practice team.
Physicians in the combined incentive group received their individual-level performance payment plus
their practice-level share. For example, there are 7 physicians and 3 nurses at a site receiving the
combined incentive. If we assume each physician meets both study measures for all 40 records
reviewed ($9.10 per measure × 2 measures × 40 records × 7 physicians) in this
hypothetical case, the practice’s total incentive earning is $5096. For the 10-member practice
team, the per-member share is $509.60. Each nurse would receive $509.60, and each physician would
receive $1237.60 ($509.60 team share plus $728 for a perfect individual performance).
We adapted an algorithm by Donner and Klar18 to determine
study power. As is standard in such trials, the participants were contained within the units of
randomization (clusters). In this study, the participants were primary care personnel and the
clusters were VA hospitals. We used estimates of the intraclass cluster correlation of 0.39 for
appropriate medication and 0.14 for blood pressure control obtained from pilot data. Equal cluster
sizes were assumed to obtain a minimum cluster size. The Donner and Klar algorithm calculates a
minimum cluster size and number of clusters using an iterative algorithm based on the noncentral
t-distribution. Application of the algorithm yielded a result of 3 clusters per group and 5
physicians per cluster with a minimum of 40 patients per physician with a 95% significance level and
80% power to detect a difference of 17 percentage points between an intervention and control group
for appropriate medication and 15 percentage points for blood pressure control. A target minimum of
7 physicians per cluster was used to account for attrition.19
To evaluate the adequacy of randomization, we tested for differences in physician characteristics
across the groups using χ2 tests for binomial variables (or Fisher exact test when
cell sizes were <5) and the Kruskal-Wallis rank test for continuous variables. We performed a
repeated-measures longitudinal analysis using the hospital as a random effect. The unit of analysis
was the physician. The analysis approach was intention to treat; physician performance was evaluated
according to the group to which his or her hospital was randomly assigned. We evaluated the effect
of incentive type for each outcome: (1) each incentive group vs the control group, (2)
individual-level incentive groups vs the control group, and (3) practice-level incentive groups vs
the control group. The goal of the analysis was to evaluate the rate of change in the proportion of
patients who met the study measures over time for the intervention group physicians.20 Models were developed independently for each incentive type and
each study measure. Using an approach described by Cheng and colleagues,21 we constructed a maximal model using scientifically relevant covariates
selected a priori and then performed backward elimination to delete variables with
P ≥ .05, arriving at our final model. The maximal model allowed
us to evaluate both the covariance structure and the list of covariates for inclusion in the final
model. Maximal model covariates included characteristics of the hospital where the physician
practiced (teaching hospital, ALLHAT site, northern vs southern region of the United States),
physician demographic and practice characteristics (sex, race, and years practicing since completing
residency), characteristics of the physicians’ patients sampled for analysis (mean age,
percentage male, percentage black, and percentage diabetic), and whether the physician had reached
the ceiling value for the study measure in the baseline period. Also, the models included the
covariates of time, the effect of the intervention, and the rate of change of the effect of the
intervention over time. In addition to the primary study measures, we evaluated whether the
physician made any medication adjustment (ie, not solely guideline-appropriate changes).
Because the patients for analysis were randomly selected from a physician’s panel at each
time point, time-dependent covariates were used. A ceiling value calculated using the Achievable
Benchmarks of Care methodology22 was included in the maximal
models because we expected performance to “top off” at some value before reaching 100%.
Ceiling values were 95.0% for blood pressure control or appropriate response to uncontrolled blood
pressure and 78.3% for guideline-recommended medications. We also determined whether any
site-to-site variation existed and included the facility (cluster) as a random effect as necessary.
We evaluated final models using a 2-sided t statistic with 95% significance and
adjusted for multiple comparisons using the Benjamini-Yekutiele method.23 As a check against overfitting, we conducted a bootstrap analysis in which each
physician’s patients were resampled and the analysis was repeated 1000 times.
We tracked participants’ engagement with the study website. We compared viewing of the
baseline or the first intervention period’s audit and feedback report between intervention and
control group participants using Fisher exact test.
To evaluate performance following the washout, we performed a linear analysis with clustering by
hospital with the post-washout performance rate as the dependent variable and the final intervention
performance rate as a covariate. We evaluated the effect of each type of incentive and developed the
models independently using backward elimination.
Using data from automated processing of structured fields from electronic health records, we
evaluated the incidence of hypotension among all patients with hypertension who had at least 1
primary care encounter between February and May 2009. We looked 4 months from the patient’s
encounter to identify low systolic blood pressure readings (defined as an outpatient systolic blood
pressure <90 mm Hg), an outpatient diagnosis of hypotension, or both. All statistical analyses
were performed using SAS software version 9.3.
Between February 2007 and April 2008, 83 VA primary care physicians and 42 nonphysician members
of practice teams (eg, nurses, pharmacists) were enrolled from 12 study sites (Figure 1). Feedback reports displaying participants’ baseline
performances were provided starting in October 2008. Eight participants withdrew before the first
period’s feedback report and payment; 7 left the primary care setting or their facility, and 1
withdrew for a personal reason. After participants received intervention components for the first
performance period, an additional physician left the primary care setting. Nine nonphysicians (eg,
nurses, pharmacists) from 4 sites left during the intervention period and 3 new nonphysicians (eg,
nurses, pharmacists) were enrolled. The 77 physicians who contributed at least 2 periods of
performance data were included in the analysis. We completed post-washout data collection in April
2012 for the 55 physicians who remained enrolled by the end of the washout period.
Among physicians who participated in all 5 performance periods, the mean (SD) total payment for
physicians over the course of the study was $4270 ($459) in the combined group, $2672 ($153) in the
individual group, and $1648 ($248) in the practice group. There were no significant differences in
the distributions of physician sex, race, years practicing since completing residency, or patient
characteristics (Table 1). There were significant
differences across groups in characteristics of the hospitals where the participants worked,
including whether they were teaching hospitals (P < .001), whether
they were ALLHAT sites (P < .001), and whether they were in the
southern or northern United States (P = .04).
In unadjusted analyses, the percentage of patients either with controlled hypertension or
receiving an appropriate response increased for each incentive group between baseline and the final
performance period, 75% to 84% in the individual group, 80% to 85% in the practice group, and 79% to
88% in the combined group. Performance did not change in the control group, 86% (Figure 2A). The adjusted estimated absolute change over the
study of the patients meeting the combined blood pressure or appropriate response measure was 8.84%
(95% CI, 4.20% to 11.80%) for the individual group, 3.70% (95% CI, 0.24% to 7.68%) for the practice
group, 5.54% (95% CI, 1.92% to 9.52%) for the combined group, and 0.47% (95% CI, −3.12% to
4.04%) for the control group. The adjusted estimated absolute difference over the study in the
change between the proportion of the physician’s patients achieving blood pressure control or
receiving an appropriate response for the individual incentive group and the controls was 8.36% (95%
CI, 2.40% to 13.00%; P = .005; Table 2). Thus, a typical study physician in the individual group with a panel size of 1000
patients with hypertension would be expected to have about 84 additional patients achieving blood
pressure control or receiving an appropriate response after 1 year of exposure to the intervention.
After accounting for multiple comparisons, this remained significant at the .05 level. Significance
was confirmed by the bootstrap analysis. Site-to-site variation did not have a significant effect on
the modeling results.
Over the course of the trial, unadjusted guideline-recommended medication management increased by
the final intervention period compared with baseline in all study groups: 61% to 73% in the
individual group, 56% to 65% in the practice group, 65% to 80% in the combined group, and 63% to 72%
in the control group (Figure 2B). For the individual,
practice, and combined incentive groups and the control group, the adjusted estimated absolute
change over the study of the physicians’ patients meeting the measure was 9.07% (95% CI, 4.52%
to 13.44%), 4.98% (95% CI, 0.64% to 10.08%), 7.26% (95% CI, 2.92% to 12.48%), and 4.35% (95% CI,
−0.28% to 9.28%), respectively. Although the use of guideline-recommended medication increased
significantly over the course of the study in the intervention groups, there was no significant
change compared with controls (Table 2). In adjusted
post hoc analyses assessing any medication adjustment (either to start a medication, add a
medication, or make a dose adjustment) over the course of the study for the individual incentive
group and control group, the absolute difference was 15.36% (95% CI, 0.20% to 28.41%;
P = .05). For those in the combined incentive group, the difference was
14.80% (95% CI, 0.00% to 27.11%; P = .07) (see eTable in the Supplement).
Far more intervention than control group participants viewed their feedback reports on the
website (66 [67%] vs 5 [25%] respectively; P = .001), suggesting that
participants were aware of the relationship between performance and rewards.
In a model adjusted for geographic region, physician race, and comparison of performance in the
final intervention period to the post-washout performance period, there was a significant reduction
in performance in the combined measure of blood pressure control or appropriate response to
uncontrolled blood pressure in each intervention group compared with controls (Table 3). Therefore, the effect of the intervention
declined significantly after the incentive was withdrawn.
In post hoc analysis, patients cared for by intervention group participants were no more likely
than controls to have hypotension (164 [1.2%] vs 54 [1.4%] patients, respectively;
P = .18).
In this cluster randomized trial, we evaluated the effectiveness of pay for performance in
primary care settings for a common chronic condition. We tested incentives targeted at individual
physicians or health care practice teams and a combined incentive to both the individual physician
and team. We found that physicians who were randomized to the individual incentive group were more
likely than controls to improve their treatment of hypertension as measured by achievement of blood
pressure control or appropriate response to uncontrolled blood pressure. Thus, a typical study
physician in the individual group with a panel size of 1000 patients with hypertension would be
expected to have about 84 additional patients achieving blood pressure control or receiving an
appropriate response after 1 year of exposure to the intervention. Although the use of
guideline-recommended medications increased significantly over the course of the study in the
intervention groups, there was no significant change compared with controls. We also showed that
those in the individual incentive group were more likely to make antihypertensive medication
adjustments in response to uncontrolled blood pressures. Enhancing the face validity of our
findings, participants in intervention groups were far more likely than controls to sign into a
secure website and view their performance reports. And similar to a report from Kaiser Permanente,
we found that the effect of the intervention was not sustained after the incentive was
withdrawn.24 Although concerns about overtreatment have been
cited in criticisms of pay-for-performance programs, we did not find a higher incidence of
hypotension in the panels of physicians randomized to the incentive groups.
Some might consider the magnitude of the incentives small. The mean individual incentive earnings
over the study represented approximately 1.6% of a physician’s salary, assuming a mean salary
of $168 000.25 However, the budget that VA
administrative leaders allocated for the incentives was a reflection of their
“real-world” constraints, enhancing the generalizability of our findings. Also, the
final amounts of the incentives were similar to those recently announced by the New York City Health
and Hospitals Corporation for primary care physicians for 13 measures, rather than a single,
performance measure,2 meaning that the incentive we used for
a single condition (hypertension) was proportionately greater.
What aspects of the design and implementation of our study may have contributed to our findings?
First, our measures are meaningful process measures to clinicians. Second, we measured and rewarded
actions mostly under the control of physicians and their practice teams.26 Because blood pressure is not completely under a clinician’s control, we
rewarded a combined measure of blood pressure control or an appropriate response to an uncontrolled
blood pressure (a so-called “tightly linked” measure).27,28 Third, responding to an abnormal blood pressure is a discrete
task, as opposed to complex problem-solving, such as diagnosing the etiology of abdominal pain.
Fourth, we rewarded participants for their absolute rather than relative performance, avoiding a
tournament or competition; participants received a prespecified financial incentive each time they
met a performance measure.29,30 These aspects of
our study enhanced the salience of the incentive rewards.
In addition, we combined clear audit and feedback with an incentive.13 Monetary incentives might amplify the positive effects of performance feedback
reports.31 Bandura’s self-efficacy theory states that
incentives work by piquing an individual’s interest in a task, leading to greater effort at
performing the task and ultimately to an increased sense of self-efficacy.32 We found that intervention group participants were much more likely than
control participants to view their feedback reports, suggesting that those who received financial
incentives demonstrated greater interest in their performance than those who received audit and
feedback alone.33 This suggests that incentive-based
interventions and audit and feedback interventions could be synergistic. The goal of the incentive
is not to coerce individuals into performing the requested task but to increase their interest in
their performance of the task, overcoming clinical inertia.34
Unexpectedly, performance gains did not hold after a 12-month washout period, during which we
avoided prompting or interacting with participants. Although performance did not decline to
preintervention levels, the decline was significant. While we speculate that the cessation of
performance feedback information may have contributed to the performance decline, further research
should elucidate why this phenomenon occurred.
The VA system has a nationwide quality monitoring and assessment program for primary care and
chronic conditions and a culture of performance improvement. As reported by Sutton et al,9 the cultural context of the performance rewards may be a
significant contributor to their effectiveness. Although these contextual factors may have enhanced
the likelihood that our intervention was effective, the high baseline performance of VA health care
physicians with blood pressure control rates of approximately 75%35 may have created a “ceiling effect,” whereby gains in performance
were more difficult to achieve than they might be in the non-VA setting. Therefore, the improvements
might have been greater in a system where baseline performance was lower.
Given that health care organizations are restructuring to implement the patient-centered medical
home,11 we assessed the effect of rewards to health care
practice teams. We hypothesized a priori that incentives to practice teams would be effective, but
we did not find significant effects of either the practice team or the combined incentives. Our
interviews with participants suggested that the integrity of the team construct may have been
impaired at some sites, perhaps dampening the effectiveness of the practice-level as well as the
combined incentives. At 2 of the practice-level incentive sites, participants noted that they did
not know who else was in their practice. At 1 site, nonphysician practice team members were moved to
different clinic locations, preventing them from working as a team. Participants also noted the
importance of a team in improving hypertension management yet confirmed that the team structure they
were under was rudimentary (prior to the implementation of the VA Patient-Centered Medical Home
known as the Patient Aligned Care Team [PACT]).36 Thus, it
is possible that, had the PACT structure already been in place, the practice-level and combined
incentives would have had a greater effect.
Despite concerns that baseline performance and team cohesion might have dampened the effect of
the interventions, several aspects of the VA health care delivery system made this an ideal setting
to test the effectiveness of the incentives. First, because the VA uses a single payment approach,
we eliminated the problem of multiple payers or varying performance measures diluting the effect of
the incentive or the performance targets. Second, VA physicians are salaried, ensuring that the
rewards were a clear addition to their expected pay. And while the VA is a uniquely well-suited
laboratory for this study, because the structure of the VA health care system is similar to other
large delivery systems such as Kaiser Permanente and the Department of Defense, our findings are
generalizable. Although VA enrollees are overwhelmingly male, there is little reason to believe that
would have systematically biased the study findings.
Hypertension is a common chronic condition, affecting approximately 70% of those 65 years and
older,37 requiring careful follow-up, adjustments to
medication and lifestyle, effective patient-doctor communication, and treatment plan adherence.
Inadequate blood pressure control results in excess cases of coronary artery disease, congestive
heart failure, renal insufficiency, peripheral arterial disease, and stroke.17 Even small reductions in blood pressure translate into significant reduction in
risk of morbidity and mortality17 and in system-wide
costs.38 This trial addresses the needs of policy makers and
payers for information about a clinically relevant payment intervention in routine practice.
Payment-system interventions are attractive because of their potential scale and reach. However,
payment-system interventions are only one piece of the solution to improve management of chronic
diseases such as hypertension. More resource-intensive, tailored, patient-level self-management
strategies may be needed to truly affect patient outcomes.
Individual financial incentives, but not practice-level or combined incentives, resulted in
greater blood pressure control or appropriate response to uncontrolled blood pressure. None of the
incentives resulted in greater use of guideline-recommended medications compared with controls.
Further research is needed to understand the factors that contributed to these findings.
Corresponding Author: Laura A. Petersen, MD, MPH, Health
Services Research and Development (152), Michael E. DeBakey Veterans Affairs Medical Center, 2002
Holcombe Blvd, Houston, TX 77030 (firstname.lastname@example.org).
Author Contributions: Dr Petersen had full
access to all of the data in the study and takes responsibility for the integrity of the data and
the accuracy of the data analysis.
Study concept and design: Petersen, Simpson, Urech, Hysong, Profit, Conrad,
Acquisition of data: Petersen, Simpson, Woodard.
Analysis and interpretation of data: Petersen, Simpson, Pietz, Profit, Dudley,
Drafting of the manuscript: Petersen, Simpson, Urech.
Critical revision of the manuscript for important intellectual content:
Petersen, Simpson, Pietz, Urech, Hysong, Profit, Conrad, Dudley, Woodard.
Statistical analysis: Petersen, Simpson, Pietz, Dudley.
Obtained funding: Petersen.
Administrative, technical, or material support: Petersen, Simpson, Urech,
Study supervision: Petersen, Woodard.
Conflict of Interest Disclosures: All authors
have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and
none were reported.
Funding/Support: This work is supported in part by
the Veterans Affairs (VA) Health Services Research & Development (HSR&D)
Investigator-Initiated Research (IIR) program (04-349; Principal Investigator [PI] Laura A.
Petersen, MD, MPH), a National Institutes of Health grant (RO1 HL079173-01; PI Laura A. Petersen,
MD, MPH), the American Recovery and Reinvestment Act of 2010 (National Heart, Lung, and Blood
Institute [NHLBI] 1R01HL079173-S2), and the Houston VA HSR&D Center of Excellence HFP90-020; PI
Laura A. Petersen, MD, MPH). Dr Petersen was a recipient of the American Heart Association
Established Investigator Award (grant 0540043N) and was a Robert Wood Johnson Foundation Generalist
Physician Faculty Scholar (grant 045444) at the time that this study was planned and funded. Dr
Hysong was a recipient of an NHLBI Investigator Research Supplement to Promote Diversity in
Health-Related Research (1R01HL079173-S1) during the early stages of the study and is currently a VA
HSR&D Career Development Awardee (CDA 07-0181). Dr Profit’s contribution was supported in
part by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (1 K23
HD056298-01; PI Jochen Profit, MD, MPH). Dr Conrad is an awardee of the Robert Wood Johnson
Foundation Health Care Financing and Organization Program (grant 63214). Dr Dudley is a Robert Wood
Johnson Investigator Awardee in Health Policy.
Role of the Sponsor: The funding sources had no role in the design and conduct of
the study; collection, management, analysis, and interpretation of the data; preparation, review, or
approval of the manuscript; and decision to submit the manuscript for publication.
Oversight Committee: Barry R. Davis, University of Texas School of Public Health,
Houston; Joseph Francis, Department of Veterans Affairs, Washington, DC; Gabriel Habib, Michael E.
DeBakey Veterans Affairs Medical Center, Houston; Jeffrey Murawsky, VA Great Lakes Health Care
System, Westchester, Illinois; Robert A. Petzel, Department of Veterans Affairs, Washington, DC;
Brian Reed, Baylor College of Medicine, Houston.
VA Investigators: Jan Basile, Rose Birkmeier, Warren D. Blackburn Jr, Patricia
Cioffi, Peter Friedmann, Saib Gappy, Stephen A. Geraci, Nicholas Haddad, Kent A. Kirchner, James
Levenson, Steve Orwig, Alan Pawlow, Adam Powell, Shakaib Rehman, Amy W. Smith, Jessie M. Spencer,
Vidya Sridharan, Donald Weinshenker.
VA Primary Care Physician Participants: Vijaya Ajjagottu, Jeffrey Austerlitz, Peter
Baren, Andree Burnett, Eugene Constantinou, Donald Curran, Raminder Dhadli, Sean Ercan-Fang, Harold
Fain, Swapna Gupta, Helen Han, Shireen Haque, Judith Hildebrand, Claudine Johnson, Yelena
Kamenker-Orlov, Thomas Kumenda, Fengqi Liu, George Malatinszky, Minnie Martin, Nicholas Masozera,
Ramon Matawaran, Raghuram Matta, Ivan Monserrate, Praveena Mungara, Kimberly Olson, Tuukka Ostenso,
Radha Rao, Simona Retter, Terrence Shaneyfelt, Lubna Sheikh, Patricia Sullwold, Vanisree Suverna,
Adeyinka Taiwo, Oanh Thai, Ishita Thakar, Rachel Wilson Peery.
Disclaimer: The views expressed are solely of the authors and do not necessarily
represent those of the authors’ institutions.
Additional Contributions: We thank the following members of the study coordinating
center at the Michael E. DeBakey VA Medical Center Health Services Research and Development Center
of Excellence: Supicha S. Chitwood, MPH, for coordinating the incentive payments; Mark Kuebeler, MS,
for managing the data; Meghan Z. Lutschg, BA, for coordinating data collection; and Richard SoRelle,
BS, for managing the institutional review board requirements across the study sites. We also
acknowledge the contributions of the trial’s oversight committee, the VA study site
investigators, and the VA primary care physician participants. None of the individuals mentioned
received compensation for their contributions besides their salaries.
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