Kevin G. Volpp, MD, PhD

The United States is simultaneously moving toward value-based payments for populations and precision medicine for individuals. During this evolution, innovations in payment and delivery that enhance tailoring of treatments to individuals while improving the value of care are needed. We propose one such innovation that would allow physician payment and patient cost sharing to better reflect the value of care by allowing the appropriateness of a service for a given patient in a given clinical situation to play a more meaningful role in the design of such incentives. We introduce the idea of a payment modifier, based on indication and appropriateness, and discuss its advantages and challenges to implementation.

As the United States moves toward value-based payments for populations and precision medicine for individuals, innovations in payment and delivery that enhance tailoring of treatments to individuals while improving the value of care are needed.

Incorporating the appropriateness of a service into physician payment and patient cost sharing may encourage the delivery of higher-value care.

Building appropriateness into incentives has advantages, including reliance on evidence-based practice guidelines, coexistence within fee-for-service and prospective payment models, and maintaining physician and patient autonomy.

Incentives for appropriateness could be implemented using a payment modifier, which has advantages and limitations in the current healthcare system.

Alongside a shift toward value-based payments for populations, the US healthcare system is entering an era of precision medicine centered on tailoring treatments to individuals.

 Yet today, although value-based payment and insurance design encourage physicians and patients to think about cost and quality, neither payment systems nor benefit design explicitly take into account the appropriateness of a service for a given patient in a given clinical scenario.

Currently, a physician or hospital that delivers the same service to 2 patients in different clinical situations is reimbursed an identical amount. A coronary stent for a heart attack (on average, higher value) or stable angina (on average, lower value) garners the same payment, as does a magnetic resonance imaging scan for uncomplicated back pain (on average, lower value) or back pain with neurologic signs (on average, higher value). The patient also faces identical cost sharing across higher- and lower-value scenarios. However, the appropriateness of these services clearly varies by patient factors and the clinical situation.

Appropriateness is central to value. The benefit of a given service depends not only on its quality, but also on its clinical indication.

 Payment systems and benefit design could be improved by building appropriateness into physician and patient incentives, allowing them to complement population-level incentives, such as financial risk, that lack individual-level nuance. Within the existing payment and benefit design infrastructure, one potential approach is the incorporation of an “appropriateness modifier” that varies payment and cost sharing based on the clinical appropriateness of a service in a given situation.

For physician payment, an appropriateness modifier would adjust an existing fee upward when a service is delivered in a high-value situation or downward when it is delivered in a low-value situation. The indication can be based on patient characteristics, such as comorbidities or clinical presentation. The modifier could include only upward adjustments (analogous to “1-sided” population-based payment models without financial risk) or only downward adjustments (analogous to lowering fees for “never events”). Alternatively, the appropriateness modifier could include both upside and downside adjustments (analogous to “2-sided” alternative payment models with shared savings and shared risk). Because physicians tend to evaluate payment changes by their impact on income compared with a reference point, such as the prior year's income, 2-sided incentives may be necessary to promote higher-value decisions while encouraging buy-in.

For patients, cost sharing could be adjusted to mirror the incentives in physician payment. Co-payments could be lowered to not blunt demand for certain services in highly appropriate contexts (eg, interventions during urgent situations) or raised to discourage other services in less appropriate situations (eg, elective services that lack proven benefit relative to conservative management). Combining the appropriateness modifier for physician payments with an analogous clinically nuanced modifier for patient cost sharing could be synergistic in encouraging physicians and patients to make higher-value choices.

 Moreover, modifying the existing structure of co-payments using clinical appropriateness would be transparent and familiar to patients, although it would need to be implemented and communicated in a simple manner.

Because the disutility of a loss (lower fee or higher co-payment) is roughly twice the utility of an equal-size gain (higher fee or lower co-payment), the former is likely a more powerful tool for affecting behavior than the latter.

 However, even if total physician payments or patient cost sharing remained unchanged, patients would still benefit if the proportion of services delivered in appropriate scenarios increased. How large of a change in payment optimally balances budgetary effects with behavioral response is an empirical question requiring further evidence to elucidate.

As a starting point, an appropriateness modifier could be implemented among services for which clear differences in appropriateness are observable and supported by guidelines. This initial set of services may be small, given the lack of clinical detail in administrative data. However, the physician-driven Choosing Wisely recommendations offer candidate services.

 Examples of more appropriate and less appropriate situations, respectively, could include imaging for carotid artery stenosis in symptomatic versus asymptomatic patients, stents in unstable versus stable angina, imaging for headache or low back pain with versus without red flag signs (eg, neurologic deficits or fever), and conventional versus hypofractionated irradiation for certain malignancies where they have equal efficacy.

 Given that the value of a service is more likely to be a continuum rather than a binary state of high versus low, distinguishing highly appropriate and less appropriate situations for a particular service might capture a small subset of the situations in which it is delivered, leaving the remainder under status quo incentives.

Advantages of an Appropriateness Modifier

The appropriateness modifier would offer several advantages. First, the definition of “appropriateness” would derive from practice guidelines based on the clinical literature. It builds on the progress of value-based insurance design (which focuses on the average value of a service) by considering the situation-specific value of a service for a given individual. Unlike the blunt overarching nature of population-level financial risk, this clinically nuanced tool that reflects the scientific evidence generated by the physician community itself may more easily garner physician support.

Second, the appropriateness modifier can be built into fee-for-service without requiring downside risk, although it could be similarly implemented in the fee schedule underneath a risk contract. Unlike the new Merit-based Incentive Payment System under the Medicare Access and CHIP Reauthorization Act, which increases or decreases all of a physician’s fees by a certain percentage, the appropriateness modifier impacts only those services for which evidence and guidelines can distinguish higher- from lower-value scenarios.

Third, unlike bundled payments, which have an unintended consequence of rewarding bundles triggered in lower-risk patients or less appropriate situations, thus increasing revenue and raising quality scores that are easier to achieve with lower-risk patients,

 the appropriateness modifier directly focuses on the clinical indication and reduces the incentive to generate volume in less appropriate situations. At the same time, unlike some managed care techniques that seek to directly constrain utilization, the value modifier retains the patient’s and physician’s freedom of choice among treatment options—indeed, even the lower-value options&mdash;while allowing spending to reflect the value of the clinical situation.

Fourth, the appropriateness modifier would enable specialists to have greater control over value through their own decisions rather than relying on primary care physicians to drive value through referral decisions, a process that could create tension between colleagues. Because the modifier can live within and be synergistic with alternative payment models, aligning specialist incentives with primary care physician incentives may enhance behavior change. Furthermore, aligning physician incentives with patient incentives could facilitate shared decision making.

Lastly, incorporating the appropriateness modifier into physician fees could have beneficial spillover effects outside of physician services. These include spending on facility-based services (which generate separate facility fees) and on pharmaceuticals (which often have separate costs, such as the bonus Medicare pays to physicians prescribing chemotherapies).

Challenges of an Appropriateness Modifier

Implementing an appropriateness modifier involves some challenges. Because diagnosis codes help determine appropriateness, an incentive to “upcode” patients into highly appropriate situations (through, for instance, the addition of diagnosis codes) would exist. Although upcoding is similarly a concern under population-based payment models with risk-adjusted spending targets, it could be mitigated by the inclusion of clinical data in determining appropriateness—data that are less susceptible to subjectivity.

The appropriateness modifier may complicate decision making. Physicians frequently find prior authorization burdensome, and any additional documentation required to justify higher-value situations may seem similarly unappealing. However, if implemented as a type of decision support tool (eg, with electronic prompts at the point of order entry), payers or physicians could render this complexity an advantage and provide a compelling alternative to prior authorization or other blunt utilization management techniques.

Finally, the set of services for which appropriateness is clearly distinguishable using existing administrative data today is limited. Yet with greater detail in diagnosis codes through 

International Classification of Diseases, Tenth Revision

 and greater availability of clinical data through electronic health records, payers and physicians could likely expand the set of services for which appropriateness is discernible, potentially influencing clinical areas thus far untouched by value-based insurance design.

Moving American medicine toward value for populations, yet precision for individuals, will require innovations in payment and delivery. Incorporating a clinically nuanced measure of appropriateness into payment and benefit design could offer a meaningful next step.&ensp;

The authors acknowledge seminar participants at Harvard Medical School and the Harvard T.H. Chan School of Public Health for comments and feedback.

Department of Health Care Policy, Harvard Medical School (ZS), Boston, MA; Department of Medicine, Massachusetts General Hospital (ZS), Boston, MA; Department of Medical Ethics & Health Policy, Perelman School of Medicine (ASN, EJE, KGV), and Center for Health Incentives and Behavioral Economics, Leonard Davis Institute (ASN, EJE, KGV), University of Pennsylvania, Philadelphia, PA; Center for Health Equity Research and Promotion, Philadelphia Veterans Affairs Medical Center (ASN, KGV), Philadelphia, PA.

 Dr Song was supported by the Office of the Director, National Institutes of Health (NIH Director’s Early Independence Award No. 1DP5OD024564-01).

 Dr Navathe reports consultancies for Navvis Healthcare, Lynx Medical, and Indegene; grants received from Hawaii Medical Service Association and Oscar; and lecture fees received from Cleveland Clinic. Dr Emanuel is a board member of VillageMD, Abramson Cancer Center, and IMF External Science and Technology Advisory Group; is a member of the Peterson Center on Healthcare Advisory Board; and is employed by University of Pennsylvania and Oak HC/FT. He reports receiving lecture fees from Leigh Bureau, his speakers bureau, and royalties from book sales through publishers. Dr Volpp reports consultancies for VAL Health and CVS; research grants received from Hawaii Medical Service Association, CVS, Humana, Oscar, and Discovery/Vitality; and stock ownership in VAL Health. The remaining author reports no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

 Concept and design (ZS, ASN, EJE, KGV); acquisition of data (ZS, EJE); analysis and interpretation of data (ZS); drafting of the manuscript (ZS, ASN, EJE); critical revision of the manuscript for important intellectual content (ZS, ASN, EJE, KGV);  obtaining funding (ZS); and administrative, technical, or logistic support (KGV).

 Zirui Song, MD, PhD, Department of Health Care Policy, Harvard Medical School, 180A Longwood Ave, Boston, MA 02115. Email: zirui_song@post.harvard.edu.

1. Collins FS, Varmus H. A new initiative on precision medicine. 

. 2015;372(9):793-795. doi: 10.1056/NEJMp1500523.

2. Song Z, McMahon LF Jr. Rethinking physician payment. 

. 2015;30(2):152-154. doi: 10.1007/s11606-014-3035-2.

3. Kahneman D, Tversky A. Prospect theory: an analysis of decision under risk. 

. 1979;47(2):263-292. doi: 10.2307/1914185.

4. Bacher G, Zina A, Fendrick AM. Beyond SGR: aligning the peanut butter of payment reform with the jelly of consumer engagement. 

. April 22, 2015. healthaffairs.org/do/10.1377/hblog20150422.047207/full.

5. Loewenstein G, Friedman JY, McGill B, et al. Consumers’ misunderstanding of health insurance. 

. 2013;32(5):850-862. doi: 10.1016/j.jhealeco.2013.04.004.

6. Choosing Wisely: an initiative of the ABIM Foundation. Choosing Wisely website. choosingwisely.org. Published 2013. Accessed October 15, 2017.

7. Bekelman JE, Sylwestrzak G, Barron J, et al. Uptake and costs of hypofractionated vs conventional whole breast irradiation after breast conserving surgery in the United States, 2008-2013. 

. 2014;312(23):2542-2550. doi: 10.1001/jama.2014.16616.

8. Fisher ES. Medicare’s bundled payment program for joint replacement: promise and peril? 

. 2016;316(12):1262-1264. doi: 10.1001/jama.2016.12525.

9. Feldman LS, Shihab HM, Thiemann D, et al. Impact of providing fee data on laboratory test ordering: a controlled clinical trial. 

. 2013;173(10):903-908. doi: 10.1001/jamainternmed.2013.232.

Articles

Allowing the appropriateness of services to play a more meaningful role in the design of physician and patient incentives may improve the value of care.

Incorporating Value Into Physician Payment and Patient Cost Sharing

Using a large natural experiment among 39 employers, we examined the effect of adding financial incentives to workplace wellness programs. 

 The 39 study employers used the same national insurer to administer their wellness programs, allowing us to observe preventive and health-promoting behaviors before and after financial incentives were implemented. Fifteen treatment employers introduced financial incentives into their wellness programs over 3 years, providing variation in the start dates, whereas 24 employers did not introduce financial incentives. These incentives were attached to specific health actions, including annual preventive visits, biometric screening, and selected screening services for diabetes, heart disease, and cancer. 

 Using multivariate regression, we examined employees and their adult dependents who had insurance coverage for at least 12 months and were offered a wellness program. Outcomes include utilization of annual preventive visits, low-density lipoprotein cholesterol testing, fasting blood sugar (FBS) testing, and breast, cervical, and colon cancer screens.

 Financial incentives increased annual preventive visits by 7.7 percentage points, cholesterol testing by 7.9 percentage points, and FBS testing by 7.1 percentage points (

<.05 for each). Compared with baseline rates, these changes represent significant improvements of 21% to 29%. Increases for cancer screening were smaller: 2.7 percentage points for mammograms and 2.2 percentage points for colorectal cancer screening, which correspond to increases over baseline rates of 5.5% and 7.3%, respectively. We did not detect an impact on cervical cancer screening. 

The addition of financial incentives to wellness programs increases their impact on selected preventive care services. 

The addition of financial incentives to workplace wellness programs has a notable impact on whether individuals receive preventive care services. Modest financial incentive programs in workplace settings can be effective; however, individuals who did not receive services in the past year respond less than others. Because targeting financial incentives to selected subgroups is challenging within the Affordable Care Act framework, wellness programs may require additional outreach efforts.Several strategies have been suggested to promote use of preventive services, including expanded incentives and greater involvement of employers.

 Under the Affordable Care Act (ACA), new incentives were created to promote employer wellness programs and encourage employers and employees to support healthier workplaces. The law enables employers to offer financial rewards to employees who participate in wellness programs or meet certain health-related targets.

 Increasingly, employers offering wellness programs are incorporating these financial incentives with the belief that they will boost the impact of their programs; however, little is known about the effectiveness of these incentive programs. 

In 2015, 14% of all employers, half of employers with 200 or more employees, and two-thirds of employers with 1000 or more employees offered wellness programs.

 These programs include health screenings that collect self-reported health risk information and biometric data from an in-person health examination conducted by a medical professional.

 Many employers incorporate financial incentives into their wellness programs. Thirty-two percent of employers with biometric screening programs incorporate a financial incentive for employees who complete the biometric screens; among large employers (≥200 employees) with wellness programs, 56% do so.

The use of incentives in conjunction with wellness programs is largely driven by employers’ beliefs that programs with incentives are somewhat (64% of employers who use incentives) or very effective (27%). Incentives are disbursed in a variety of ways, including premium discounts, waivers of cost sharing, or additional covered benefits. In practice, typical incentives range from $150 to $500, while the average annual premium for single coverage is $6251.

Despite their widespread use, systematic evidence about the effectiveness of wellness programs with financial incentives is lacking. One literature review concluded that incentives were effective at increasing participation in self-reported health assessments, but was unable to assess their impact on the completion of preventive screenings.

 In randomized controlled settings, financial rewards not paired with wellness programs have been effective at increasing participation in health risk assessment completion,

 Randomized trials provide significant proof of concept that targeted financial incentives can be effective; however, as with all randomized trials in which participation requires an opt-in consent, it is difficult to assess generalizability, as many such trials only enroll 10% to 15% of potentially eligible individuals and participants likely differ from those who do not volunteer to participate on unmeasured characteristics, such as motivation.

The current study takes advantage of a sizeable natural experiment in which 39 large employers within the United States initially offered wellness programs without financial incentives. Over time, 15 of them added financial incentives and the remaining 24 did not. Financial incentives were attached to specific health actions, including annual preventive visits, biometric screening, and selected screening services for diabetes, heart disease, and cancer. Employees received personalized scorecards, both online and mailed to their home, to help them track their progress toward receiving incentives, which were awarded as premium reductions, cash, or gift cards. To our knowledge, ours is the largest study of wellness programs with financial incentives to date and includes over 1.4 million insured enrollees. A key strength of our design is the ability to examine pre-post effects of the introduction of incentives with a contemporaneous wellness program control group that did not introduce incentives. 

In addition, we considered whether the financial incentive effect is greatest for nonregular users of preventive services or for users who had received the service in the prior year. The financial incentive program is designed to be broad-based rather than targeted. Nonetheless, a program is more economically efficient if it entices new or nonregular users of prevention services rather than rewarding individuals who likely would have received the services in any case. 

The wellness program we studied allows enrollees to earn dollars or points for adopting better health behaviors. Enrollees are provided a personalized scorecard, which includes health actions that were completed, as well as those that were not, as an aid to maintain or improve their health behaviors. The points earned for various activities can be converted into cash rewards, premium reductions, or gift cards in a manner set by the employer. We examined clinical screening outcomes that can be measured via claims data. The incentive for any given clinical screening was the same for all covered individuals within an employer, but could vary across employers. Incentive amounts ranged from $0 to $80 for preventive visits, $0 to $100 for low-density lipoprotein cholesterol (LDL-C) tests, $0 to $100 for blood sugar ascertainment with glycated hemoglobin, and $0 to $75 for cancer screening tests. The maximum annual award that an individual could earn for receiving all of these services ranged from $250 to $900 across employers. By contrast, employers without financial incentives in their wellness program promoted and measured the same outcomes, but employees did not receive an explicit financial reward.

All employers used the same insurer to administer their wellness programs, allowing us to observe preventive and health promoting behaviors before and after financial incentives were implemented. The 15 treatment employers introduced financial incentives into their wellness programs between 2010 and 2012, providing variation in the start dates. Individuals’ outcomes were observed as long as they were covered by the insurer. In many cases, the insurer was the sole provider of coverage for the employers. The staggered implementation by employer is illustrated in the 

. Our data span January 2009 through December 2013. 

We used de-identified healthcare claims and health plan enrollment and wellness program data from the insurer. Outcome variables were obtained from claims data based on standard claims codes. These include utilization of annual preventive visits, LDL-C tests, fasting blood sugar tests, and breast, cervical, and colon cancer screens. Coding details are provided in 

]. We were able to determine chronic conditions from claims, but not body mass index (BMI). From enrollment data, we obtained individual age, gender, dates and type of coverage, and whether the insured member was an employee or adult dependent. Insurance enrollment data typically have limited demographic information. However, through a vendor, the insurer obtained imputed information on race, ethnicity, and education of covered members, all potentially important factors that could influence an individual’s propensity to seek preventive care. 

Our sample was restricted to adult (aged 18-64 years) employees who were covered by the insurer administering the wellness programs for at least 12 continuous months (1 full plan year). Spouses and domestic partners were included if they, in addition to the employee, were offered the wellness program. All sample members were included in models that examined annual preventive visits, LDL-C screening, and fasting blood sugar screening. Different subgroups were examined for each preventive screening (ie, women aged 40-64 years for breast cancer, women aged 18-64 years for cervical cancer, and men and women aged 50-64 years for colorectal cancer). Individuals older than 64 years were excluded because they were eligible for Medicare coverage. 

Our study employed a panel-data difference-in-differences (DID) design in which 39 employers offered wellness programs without financial incentives at baseline, 15 employers added financial incentives and the remaining 24 comparison employers did not.

 This is also commonly referred to as a stepped-wedge design. Because all employers used the same national insurer to administer their wellness programs, we observed preventive and health promoting behaviors before and after financial incentives were implemented based on common data collection. 

The effect of the incentive is estimated for each preventive service outcome using variations on equation 1, which reflects a staggered DID or stepped-wedge regression model. Equation: 

The observations are at the individual-year level, where 

 denotes the year. The dependent variable, 

, is an indicator variable that equals 1 if the relevant preventive service was received by individual 

represents a vector of individual covariates, including age; gender; imputed race, ethnicity, and education; whether the covered individual was an employee or dependent; whether the individual had asthma, coronary artery disease, congestive heart failure, chronic obstructive pulmonary disease, diabetes, or hypertension; and whether the individual was offered a high-deductible health plan with a health savings account or health reimbursement arrangement. Year and employer indicators are also included. Including employer indicators allows us to control for the average differences across employers in any time-invariant observable or unobservable employer-level predictors. The error term is represented by 

Our key independent variable is represented in equation 1 by “

 is a binary variable that takes the value 1 in any year that the individual is offered a wellness plan with incentives and 0 in all other years. Controlling for individual and other plan characteristics, the coefficient on this variable is interpreted as the average change across enrollees in employers with financial incentives relative to the average change for those with standard wellness programs. Standard errors were clustered by employer, and all reported 

values were for 2-tailed statistical tests.

We estimated multivariate linear probability models to isolate the degree to which offering a wellness program with financial incentives in a given year influenced the probability of an employee receiving a selected preventive visit, cancer screening test, or blood test in that year relative to employees of employers that offered wellness programs without financial incentives. We selected linear probability models because their coefficients can be interpreted as marginal effects of treatment and because our treatment variable was binary. 

In order to identify whether the program effect was greater for nonregular users of services versus users who had recently received the service, we repeated our models adding an indicator for receipt of the service in the prior year and its interaction with the incentive indicator. Although we used all years of data for each employer, we restricted the models to individuals present in the data for 24 continuous months. Introducing a lagged dependent variable (ie, prior receipt) could lead to bias if there is serial correlation in unobserved individual-level factors and it is greater or lesser at employers offering financial incentives.

We addressed observable differences between treatment and comparison employers with treatment-on-the-treated propensity score weighting. Our outcome models were weighted using inverse probability weights obtained from a series of propensity score models. The weighting models were estimated using boosted regression, as implemented in the “Toolkit for Weighting and Analysis of Nonequivalent Groups” package in R,

 which predicted the probability of being a treated observation in the year before those employers added financial incentives based on on age, gender, race and ethnicity, Census region, urban location, chronic conditions, and offer of high-deductible coverage with a health savings or health reimbursement account. Because the study used a DID comparison design, we weighted both treatment and control observations for each year to match the baseline year for the treatment group in order to balance the distribution of covariates both over time for each treatment group and between the treatment groups.

 This allowed us to control for any compositional changes over time in the treatment or comparison group as well as provide appropriate weights to observations in the control group. Separate weighting models were run for each treatment group by year combination. The high-deductible health plan variable was not balanced by weighting in all years and was removed from 6 of the 11 propensity score models, but was included in all outcome models. Weighting models were re-estimated for each of the cancer screening eligibility subgroups defined by age and gender. The propensity score weights were applied in calculating all reported results except sample sizes.

The characteristics of individuals in the treated employers were substantively similar to those in the control employers in the baseline year after weighting (

). Baseline rates for receiving a full biometric tests and breast cancer screening were 3 to 5 percentage points lower in the employers that did not introduce incentives. Thus, introducing financial incentives does not appear to be a selected response by employers to low baseline rates of targeted services. Covariate balance from propensity score weighting is typically measured by standardized mean differences (

). The balance was successful; among 365 comparisons, all were below 0.12 and only 4 were greater than 0.10.

Preventive Visits and Blood Tests for Disease Screening

Baseline rates for preventive visits were an average 36.1% in the treatment group. In years when employers offered a wellness program with financial incentives, members were 7.7 percentage points more likely to have wellness visits (

), a 21.3% increase. They were also 7.9 percentage points more likely to have cholesterol screenings and 7.1 percentage points more likely to have blood sugar tests for diabetes (

<.05). Results for the blood sugar and LDL-C tests were similar, and 95% of members who received the LDL-C test also received a blood sugar screening test. When considered as a set, the financial incentives resulted in 8.1% more individuals having all 3 biometric components (ie, a preventive visit and the 2 blood screenings) over a baseline rate of 19.0%, a 42.6% increase over baseline. Full regression results are shown in 

The wellness program with financial incentives was associated with smaller increases in cancer screening rates (Table 2). Financial incentives were associated with a 2.7 percentage-point increase in mammography (

<.05) and a 2.2 percentage-point increase in colorectal cancer screening (

<.01). Relative to baseline rates in the treatment group, these changes represent 5% to 7% improvements in screening rates. No differences were detected for cervical cancer screening rates over time in employers who offered incentives compared with those who did not.

Individuals who sought services in a given year were 17% to 30% more likely than others to receive them in the following year, controlling for other characteristics (

). Estimates of the incentive program’s effect by prior receipt of service are mixed, but 2 results emerge. The program had a significantly greater impact on receiving the full biometric screen for those who had previously had one, by 6.1 percentage points (

<.05). In contrast, for the cervical and breast cancer screening tests, the program had a greater effect on individuals who did not receive screening in the prior year, meaning that here the incentives had a stronger effect on nonregular service users. The effect of the incentive on nonregular users was 3.1% (

<.01) for breast cancer. We did not detect different impacts of incentives on colorectal cancer screening by prior year receipt. 

For all preventive tests, we found a strong association between having the test in the prior year and repeating it, independent of the incentive. Although federal screening recommendations do not support annual cancer screens for all individuals, we note that our data include a mix of individuals for whom an annual cancer screen would not be recommended and others who may have had positive results in the past and for whom annual exams are recommended.

Within a single national insurer, workplace wellness programs paired with financial incentives were associated with increased utilization of targeted preventive services relative to worksite wellness programs without incentives. Increases ranged from 3% to 42% over baseline rates. The largest impacts were seen for receiving a full biometric screen (ie, preventive visit and 2 lab tests), as our study found that adding financial incentives to wellness programs nearly doubled the number of individuals receiving a full biometric screening exam. These results represent the first national data on the impact of adding financial incentives to wellness programs affecting all employees within a set of employers. Although data from randomized trials show significant impacts of financial incentives on health behaviors for self-selected populations of participants, these data speak to the impact across entire employer populations of implementing wellness programs with financial incentives geared toward increasing prevention and screening.

Efficiency and equity are potential challenges in wellness programs with financial incentives. Rewarding individuals who would receive services regardless of a financial reward is not an efficient use of resources, yet under the ACA, wellness programs implemented in employer settings are required to apply to all “similarly situated” employees and therefore do not allow programs to target just those individuals who would otherwise be unlikely to get a given program. Given this, it is interesting that we found that the incentive effect was similar for individuals who did or did not receive preventive visits, screening blood tests, and colorectal cancer screens in the past year and that it was more effective for individuals receiving prior-year services for the full biometric test. Thus, the financial incentives were not more systematically effective at bringing in “new” or infrequent users than “prior” or more frequent users for these services. This was not the case for cervical and breast cancer, where those who had not received screening in the prior year were more likely to be impacted by the reward.

Our study has several limitations. First, we evaluated the incentive programs as they were implemented, which resulted in a limited range of incentive values. We cannot assess the impact of larger incentives. Second, we have taken several steps to mitigate potential selection bias on observed characteristics, yet we cannot rule out bias on unobserved characteristics that would occur if the trends in targeted service use systematically differed for the treatment versus comparison groups for reasons other than the addition of financial incentives. We were, however, able to rule out that employers with lower baseline use of targeted services were more likely to add financial incentives to their wellness programs. Third, because our analysis required at least 12 months of continuous enrollment, we lost members to attrition. If sicker employees were more likely to leave one set of employers than the other, this could bias our results. We addressed this issue partially through the use of propensity score weighting. Fourth, claims data have been found to underreport preventive services relative to medical records.

 Because our sample was composed of privately insured, continuously enrolled individuals, we believe this problem is attenuated. Moreover, any measurement error caused by our claims data should not differ systematically across the treatment and comparison employers or bias our results. Despite being the largest of its kind, the current study is limited to 15 intervention and 24 comparison employers, limiting the generalizability of the results. Finally, we were not able to assess impacts on health outcomes, as these were not included in the claims data.

We find that the addition of financial incentives to workplace wellness programs has a statistically significant impact on the receipt of targeted preventive care services. Although the magnitude of the incentives evaluated in this study were well below the federally determined maximum of 30% of premiums for outcomes-based incentives, the effectiveness of these programs signals that modest financial incentive programs in workplace settings can drive health behavior in desired directions. Because targeting financial incentives to particular groups, such as individuals who have not had preventive services in the past year, is challenging within the ACA framework, wellness programs may need to rely on other outreach efforts. Overall, our results highlight the potential promise of ACA-induced movement toward greater use of wellness programs in employment-based settings when they are paired with financial incentives.

The authors thank Tanya Lewis-Walls for her descriptions of the UnitedHealthcare United Personal Rewards program. Preliminary results were presented at the 2014 American Society of Health Economists meeting in Los Angeles, CA, and at the 2015 National Institutes of Health, Economics of Prevention Workshop in Bethesda, MD. Dr Cuellar (principal investigator) had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

 George Mason University (AC), Fairfax, VA; Carnegie Mellon University (AMH, HW), Pittsburgh, PA; Lehigh University (SR-S), Bethlehem, PA; University of Illinois at Chicago (ATL, AA); Independent consultant (MS), Chicago, IL; Perelman School of Medicine and the Wharton School, University of Pennsylvania (KGV), Philadelphia, PA; Philadelphia VA Medical Center (KGV), Philadelphia, PA.

 Funding for the evaluation was provided by Eunice Kennedy Shriver National Institute of Child Health and Human Development (5R01HD075118, Cuellar, PI). 

 Ms Shah is a former employee of UnitedHealthcare and owns stock in UnitedHealth Group. Dr Volpp is a principal in VAL Health, a behavioral economics consulting firm, and has also served as a consultant for CVS Health. He has also received research funding from CVS, Humana, Hawaii Medical Services Association, and the Vitality Institute. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Financial incentives created under the Affordable Care Act can help promote employer wellness programs and support preventive services utilization.

Boosting Workplace Wellness Programs With Financial Incentives

Clinical trials are increasingly testing the effectiveness of paying patients’ financial incentives for achieving desired clinical outcomes. Some researchers and providers are concerned that patient financial incentives will harm the doctor—patient relationship. How patients feel about these approaches, and these trials, is largely unknown. This study examined patients’ perceptions of a compound behavioral and financial incentive intervention used in a large multicenter trial to lower low-density lipoprotein cholesterol (LDL-C), including their perceptions of benefits and challenges and the study’s effect on patients’ relationship with their primary care physicians (PCPs). 

Semi-structured telephone interviews with patients post intervention. 

PCPs from 3 primary care practices in the northeastern United States were randomized to 1 of 4 arms: physician financial incentives, patient financial incentives, shared incentives between physicians and patients, and a control arm. Within each arm, 10 high, 10 medium, and 10 low performers in LDL-C reduction were interviewed. Interviews targeted reasons for enrolling in the study, the specific intervention elements that helped them reach the goal (incentives, engagement, monitoring), challenges faced in reducing cholesterol, and the impact of study participation on their relationship with their PCP.

Patients reported positive experiences with the study: 65% described personal changes to improve health and 61% reported increased awareness. Views about financial incentives varied: 71% clearly found them motivating and 36% claimed they made no difference. Patients noted that changing lifestyle (36%) and diet (65%) was difficult. Patients who substantially lowered their LDL-C revealed themes similar to those who did not. 

Overall, behavioral interventions with financial incentives appear to be socially acceptable to patients who participate in them. Both adherence monitoring and financial incentives were well received, with little effect on the physician-patient relationship.

Patients who enrolled in a trial to lower low-density lipoprotein cholesterol (LDL-C)—one that included financial incentives to patients or to physicians or were shared between patients and physicians&mdash;spoke positively of the multifaceted intervention. One-on-one interviews revealed:  

Wireless pillboxes for monitoring served as a reminder to take medications. 

Blood draws every 3 months were reinforcing and informative. 

Financial incentives were appreciated but not viewed as particularly motivating. 

Patients who substantially lowered their LDL-C revealed themes similar to those who did not. 

Financial incentives were rarely discussed; they did not disrupt the patient—physician relationship. 

Overall, financial incentives appear to be effective and socially acceptable.

Patient nonadherence to recommended treatment is a barrier to improving outcomes, but the use of financial incentives to overcome this barrier is increasing.

 Eighty-five percent of large employers use financial incentives to improve health behaviors in realms ranging from smoking and other addictions to weight control and medication adherence.

 In a recent randomized controlled trial focused on reducing low-density lipoprotein cholesterol (LDL-C) among patients with high cardiovascular risk or established cardiovascular disease, incentives based on principles of behavioral economics were used to motivate both patients and providers.

 In the parent study, 340 primary care physicians (PCPs) from 3 health systems and about 15,030 of their patients were enrolled into a 4-arm trial to test alternative incentive strategies to lower LDL-C. PCPs were randomized to 1 of 4 arms: physician financial incentives, patient financial incentives, shared incentives between physicians and patients, and a control arm. Progress was monitored through quarterly LDL checks and medication adherence via pill bottle opening. The primary outcome was LDL reduction over 12 months. The intervention was a compound of multiple behavioral economic strategies.

The main trial showed significant benefits for those in the shared incentive arm, but the effectiveness of the intervention ranged across individuals. The purpose of this current study was to investigate patients’ perceptions of the intervention and learn from their stories of how the intervention affected their behaviors. The specific objectives were to investigate patients’ perceptions of financial incentives and of multiple components of the behavioral intervention, including overall thoughts about benefits and challenges and how being in the study affected their relationship with their physician. We were particularly interested in how perspectives differed between patients who succeeded in lowering their LDL-C and those who did not. Secondarily, we examined if narratives about incentives differed among patients randomized to different arms of the study.

Overview/Summary of the Underlying Intervention

The protocol for the main trial is summarized briefly here.

 A total of 1503 patients with high cardiovascular risk or established cardiovascular disease were enrolled; they were nested within 340 PCPs from the University of Pennsylvania (Penn), Geisinger Health System (GHS), and Harvard Vanguard Medical Associates (HVMA). The study compared 4 approaches to reduce LDL-C: physician incentives, patient incentives, shared physician—patient incentives, or control (no financial incentive or regular feedback). For the patient and physician incentive arms, the maximum expected value of the incentive was $1024 per enrolled patient who met LDL-C goals. PCPs and patients in the shared arm shared those possible financial incentives. Patients also received surveys, quarterly LDL-C checks, and wireless pill bottles (Vitality GlowCaps) which were used to store their cholesterol-lowering medication and electronically monitor bottle openings.

In this study, semi-structured telephone interviews were conducted with patients post intervention from February 2014 through July 2014. The methods and script were approved by the University of Pennsylvania Institutional Review Board.

Purposive targeted sampling was used. Within each of the 4 arms, the goal was to interview 10 high, 10 medium, and 10 low performers in LDL-C reduction, stratified across the 3 study sites. Within each of the 12 cells, 5 patients were targeted from GHS, 4 from Penn, and 1 from HVMA, proportional to the total patient sample at each site. Patients were interviewed in multiple waves, formed as groups of patients completed their 15-month visits. The final number of interviews is shown in 

. For each wave of completed data, usually for 60 to 80 patients, the mean change in LDL-C (baseline to 12-month) was calculated, then sorted by site within arms. Within each arm, the distribution was inspected and the 4 to 5 patients with the most improved, most worsened, and least changed LDL-C were prioritized, choosing only 1 patient per clinical site per wave. Therefore, “high” and “low” do not have a constant cut-off. However, across the selection waves, median changes in LDL-C among targeted patients were similar.

The interview script was developed by a study team of health economists, physicians, and qualitative researchers. They drew on the literature and their expertise to develop conversational questions related to the study elements and the potential impact of behavioral interventions. Initial scripts were piloted and revised as necessary prior to the study and were constant for the duration of active interviewing. Revisions were based on queries about unclear questions, and the order of the questions was changed to improve flow. Pilot interviews are not included in the analyses. Key questions targeted reasons for enrolling in the study, parts of the study that helped them reach the goal (surveys, wireless pill bottle, cholesterol check every 3 months, and incentives, for patients in arms that received them), challenges faced in reducing cholesterol, and the effect of the study on their relationship with their doctor.

Two trained interviewers conducted the interviews. Recordings of the interviews were transcribed by an independent transcription agency. De-identified transcripts were imported into NVivo version 10 (QSR International Pty Ltd) for coding and analysis.

Four trained coders analyzed the data using elements from grounded theory. The team first identified broad themes based on the first 6 transcripts and then applied open codes to the transcripts. The initial set of codes was tested on each subsequent interview transcript to refine the codebook. Codes were revised iteratively by constant comparison between and within transcripts, and more content-driven axial codes were applied. A final coding scheme was established and applied to all transcripts. In a post hoc analysis, the comments about financial incentives were grouped by arm, blinded, and reviewed by the senior author, with a goal of understanding the main trial results. Inter-rater reliability was assessed on samples exceeding 20% of transcripts. The research team met weekly to resolve discrepancies via consensus.

In the following section, results are organized around the major research questions, examining perceived benefits and facilitators of study enrollment and components, barriers to study participation and meeting goals, and how study participation affected their relationships with their doctors. Results focus on themes mentioned by more than 20% of the sample. The median kappa was 0.92, with a range of 0.64 to 1.0.

A total of 110 of 120 planned interviews were completed (Table 1). They came from a targeted list of 163 patients, representing a minimum 67.5% response rate; 9 patients refused and the remainder were not reached prior to saturation. Patients providing interviews were demographically and clinically similar to those in the parent trial (

Prevalent themes are presented in italics, with quotations summarized in the 

). There were few differences between patients whose LDL-C improved greatly and those who did not improve. Similarly, meaningful differences among study arms were not prevalent. When substantial differences were discovered, they are noted at the end of each section.

Patients expressed a range of reasons for joining the study. First, the 

 of a doctor’s recommendation to join the study was a motivating factor for 40 (36%) patients. Second, 34 (31%) patients stated that they joined the study with the 

 to their high cholesterol. A third reason for joining was 

n = 30; 27%). Other relatively common motivators were 

(n = 27; 25%), including free and more frequent monitoring, and the 

 of helping with research (n = 25; 23%). The subgroup of interviewees whose LDL-C worsened gave more responses to this question, but the nature of what they said was not qualitatively different. The subgroup whose LDL-C improved often mentioned enrolling to facilitate healthy behaviors and to learn more about cholesterol issues.

Patients offered a wide range of responses to queries about the benefits of enrolling in the study. Seventy-one (65%) patients described 

 that affected the way they manage their health and their understanding of health-related topics. Commonly referenced topics were diet choice and exercise regimens. The study made patients think more about the foods that they were eating and the necessity of changing eating habits. These changes included reading labels before buying food, shopping for healthier food options, and portion control. The study also inspired patients to engage in more 

. Some learned that they were able to control their cholesterol with diet and exercise and others learned the necessity of medication in controlling their cholesterol.

Just over half (n = 61; 55%) of the patients spoke of appreciating 

. Several patients explained that being in the study made them more aware of the severity of their health condition; 34 (31%) specifically mentioned improved health. The subgroup of interviewees whose LDL-C got better more often gave examples of how they changed a health behavior and more frequently spoke of more consistent use of medication.

Benefits and Facilitators: Intervention Components

 More than half (n = 69; 63%) of the patients mentioned the benefits of receiving blood test results every 3 months; they 

, and tracking enabled them to make appropriate adjustments as well as understand how medication use affected their cholesterol levels. It was 

 to see how diet and exercise changes affected their cholesterol. They also appreciated the increased access to services (n = 48; 44%) and did not find the visits inconvenient (n = 47; 43%)

 Patients generally agreed that the wireless devices were a facilitator in lowering their cholesterol. The devices served as a 

to take medication (n = 39; 35%). Many thought the devices were easy to use and expected to continue to use them after the conclusion of the study. They liked being monitored (n = 33; 30%)

 Patients did not say much about the surveys. The most prevalent comment was about 

(n = 40; 36%). The surveys provided a friendly reminder of the appropriate steps that patients should be taking to keep their cholesterol under control.

 Seventy-five patients provided comments about financial incentives. Many (n = 53; 71%) described the 

 that the incentives had on their behavior, offering at least 4 different examples: 1) several thought it was good that the study provided them with 

 and have some extra money to spend on other things, 3) multiple patients stated that the incentives made the other inconvenient aspects of the study more 

, and 4) several patients felt the positive reinforcement 

 them to do the necessary things to lower their cholesterol and continue with the study. Most of the 42 (56%) who commented felt that the 

At the same time, 27 (38%) patients said that the incentives 

 on their performance in the study; positive results were what motivated them to keep up their efforts. They were involved in the study to get healthier and learn about themselves. The blinded review of the comments about incentives yielded nothing to distinguish among the treatment arms with patient incentives.

Only 11 (15%) patients recalled talking about the incentives with their doctors; these conversations were brief affirmations of their participation in the program and encouragement to continue with their progress. Overall, 38 (51%) patients made some commentary about physicians and incentives. Although there was no prevalent theme, the tone was generally indifferent (eg, it would not affect the patient’s own performance) or supportive (eg, they were happy for their doctors to receive payment as part of the study). Few feared that physician incentives would affect the physician’s impartiality.

There were no differences among subgroups of patients related to discussions about the 3-month visits, wireless medication devices, or the surveys. The subgroup of interviewees whose LDL-C improved more often spoke about the benefits of financial incentives.

 In contrast to the many experienced benefits, some barriers to improving cholesterol remained. The main downfall of 72 (65%) patients was their inability to stay away from food that is believed to contribute to higher LDL-C levels, and for others (n = 40; 36%), it was their inability to maintain a healthy lifestyle. A host of reasons were given, including

 who regularly ate or prepared unhealthful meals, 

. Comments about inadequate activity were often mentioned in the same context as 

. Multiple reasons were provided, often related to the weather, amount of time required for work, travel, or 

. Issues regarding the difficulties of maintaining a healthful diet and lifestyle were more prevalent among the subgroup whose LDL-C worsened over the study period.

 For more than half (n = 67; 61%) of the patients and for a number of different reasons, the wireless medication device used in this study (Vitality GlowCap) was seen as a nuisance, but usually temporarily. Patients on multiple medications did not like the necessity of having 

such as finding an appropriate outlet and connectivity difficulties; but for most, these were transitory issues amid an overall positive experience. The subgroup that did particularly well in improving its LDL-C more often noted issues with the wireless medication devices, but also that the issues were infrequent and/or solved.

 Numerous patients (n = 49; 45%) referenced regular medication use as a challenge. A significant part of the commentary was about 

, such as muscle weakness and pain and liver complications. Side effects, such as feelings of illness, muscle and joint pain, and “fuzzy memory,” were cited as reasons for dropping the medication altogether. Other patients 

 to take their medicine on a daily basis, and a significant number of patients stated they were 

 in taking any medication at all. The frequency and content of comments was similar between the high and low performers.

Only 1 patient specifically noted a change in the relationship between the patient and physician. However, 29 (26%) indicated that the study led to more conversations with their doctor—typically about the 

, especially if the labs demonstrated “poor results.” Some noted that they saw their physician more often due to participating in the study.

enrolled in the study because their doctor suggested

 it to them. Many of these were from the group who did well. Most of these comments noted that a brief conversation took place during the course of an office visit, in which the doctor brought up the study and recommended participation, and the patients subsequently agreed to join.

This qualitative process evaluation of a large randomized trial has 4 main findings. First, patients reported positive experiences with the study; they enrolled based on a genuine desire for a healthier life. The structure of the study was similarly favorable: blood draws every 3 months were informative, and the wireless medication devices—although they not without their glitches&mdash;provided valuable reminders to take one’s medication. Second, views about financial incentives were varied. Some clearly found them motivating, whereas others claimed they made no difference. Importantly, no patients found them negative. In general, discussion of the study with their physicians, including financial incentives, was not extensive. Third, patients noted that changing behavior and diet was difficult. Food preferences, habits, and social times interrupted plans to make positive changes. There was also ambivalence about taking medication, and for some, there was a clear desire to avoid medication. Fourth, patients who substantially lowered their LDL-C revealed themes similar to those who did not.

These findings contrast with some of the concerns the study team had when developing the study, as well as those voiced by others.

 In preparation for study launch, some team members and participating physicians wondered whether explicit financial incentives might adversely affect existing physician—patient relationships. Those concerns seem to have been unjustified. There was also concern that the wireless medication adherence devices would be seen as intrusive, but they were not. Most importantly, different experiences and feelings based on different intervention arms were anticipated; it seemed logical to think that whether a patient or their physician is being paid for outcomes might alter one’s sense of the process. In fact, no differences were found across arms. Nor were there systematic differences based on how well patients did in the intervention arm in terms of improving their LDL-C.

There is little previous research examining patient perceptions of financial incentives to improve their health. One other published paper found nearly equal proportions of a clinic population had favorable or unfavorable opinions of incentives, but the authors noted greater enthusiasm when incentives were framed as a reward rather than a penalty, when they were offered by health insurers as part of benefit design, and when the survey respondent personally had the condition for which the incentives were offered (in the case of smoking or obesity).

Patients who enrolled in a trial to lower low-density lipoprotein cholesterol spoke positively of the multifaceted intervention: pillbox monitoring and financial incentives were socially acceptable.

Kevin G. Volpp, MD, PhD

Articles

Incorporating Value Into Physician Payment and Patient Cost Sharing

Boosting Workplace Wellness Programs With Financial Incentives

Patients' Views of a Behavioral Intervention Including Financial Incentives

Synchronized Prescription Refills and Medication Adherence: A Retrospective Claims Analysis