Impact of Consumer-Directed Health Plans on Low-Value Healthcare

The American Journal of Managed CareDecember 2017
Volume 23
Issue 12

Switching to a consumer-directed health plan is associated with reduced overall outpatient spending, but not with reduced spending on low-value healthcare services.


Objectives: To assess the impact of consumer-directed health plan (CDHP) enrollment on low-value healthcare spending.

Study Design: We performed a quasi-experimental analysis using insurance claims data from 376,091 patients aged 18 to 63 years continuously enrolled in a plan from a large national commercial insurer from 2011 to 2013. We measured spending on 26 low-value healthcare services that offer unclear or no clinical benefit.

Methods: Employing a difference-in-differences approach, we compared the change in spending on low-value services for patients switching from a traditional health plan to a CDHP with the change in spending on low-value services for matched patients remaining in a traditional plan.

Results: Switching to a CDHP was associated with a $231.60 reduction in annual outpatient spending (95% CI, —$341.65 to –$121.53); however, no significant reductions were observed in annual spending on the 26 low-value services (­–$3.64; 95% CI, –$9.60 to $2.31) or on these low-value services relative to overall outpatient spending (–$7.86 per $10,000 in outpatient spending; 95% CI, –$18.43 to $2.72). Similarly, a small reduction was noted for low-value spending on imaging (–$1.76; 95% CI, –$3.39 to –$0.14), but not relative to overall imaging spending, and no significant reductions were noted in low-value laboratory spending.

Conclusions: CDHPs in their current form may represent too blunt an instrument to specifically curtail low-value healthcare spending.

Am J Manag Care. 2017;23(12):741-748Takeaway Points

  • Consistent with prior study findings, switching from a traditional plan to a consumer-directed health plan (CDHP) was associated with reduced overall outpatient spending.
  • However, switching to a CDHP did not reduce spending on low-value healthcare services that offer unclear or no clinical benefit and represent a significant source of waste.
  • This pattern was consistent for laboratory services, imaging services, and services both more and less sensitive to patient preferences.
  • CDHPs may encourage patients to curb spending indiscriminately rather than specifically reducing low-value services; more targeted consumer incentives in CDHPs may be necessary to reduce this source of waste.

Low-value healthcare services are medical tests and procedures that provide unclear or no clinical benefit to patients, but still expose them to both risk and expense. Despite evidence of their lack of clinical benefit to patients, these unnecessary services remain frequently ordered and contribute substantially toward wasteful spending within the US healthcare system.1-4 Reducing the use of low-value services offers an opportunity to decrease wasteful spending while improving access and quality. One influential effort to reduce low-value services is the American Board of Internal Medicine Foundation’s Choosing Wisely campaign. This initiative, which assembled recommendations from 75 physician and professional societies on low-value services to avoid in their specialty, has garnered support and partnership from patient and payer organizations alike.5-7

An emerging body of research has begun to measure low-value services in the US healthcare system. Some study results have demonstrated that the volume of low-value services delivered to Medicare patients varies across regions and physician organizations.8-11 Another recent study's results demonstrated regional variation among commercially insured patients and that patients from more advantaged groups (ie, white, higher-income) receive more low-value services.12

In a related trend, consumer-directed health plans (CDHPs) are growing in popularity. These plans combine high deductibles with tax-sheltered health savings accounts (HSAs) that allow patients to pay out-of-pocket costs using pretax dollars. This benefit structure results in greater cost sharing for patients, which is intended to spur value-conscious care choices and reduce wasteful spending. In the employer-sponsored insurance market, CDHP enrollment increased from 4% to 29% over the last decade.13 In the individual market, nearly 90% of Affordable Care Act Marketplace enrollees are in CDHPs.14 Prior research has shown that CDHPs do reduce overall healthcare spending.15-17 If CDHPs encourage more value-conscious choices, then these spending reductions should be concentrated among low-value services that offer unclear or no clinical benefit. However, the effects of CDHPs on low-value services have not been studied. In this study, we assessed the impact of enrolling in a CDHP on low-value healthcare service spending.


Study Design

In this quasi-experimental analysis, we used a difference-in-differences (DID) approach to compare the change in patients’ spending after switching to a CDHP from a traditional plan with that of matched patients who remained in a traditional plan.


We used a 25% random sample of 2011 to 2013 Optum Clinformatics Datamart insurance claims for UnitedHealthcare-affiliated commercial plan members across all 50 states. To enable comparisons across patients and geographic areas, Optum standardizes allowed payments in their data as follows: facility outpatient charges are priced at a percent of the submitted charge, professional services are priced at approximately 130% of Medicare fee-for-service pricing for the relative value units (RVUs) assigned to the service, and ancillary services are priced at approximately 120% of the Medicare pricing for the RVUs assigned to the service.

Patient demographic data included age, sex, race, household income, and geographic region via census divisions. Race and household income were estimated by Optum via proprietary algorithms using residential address and other personal information. Health plan information included plan type and whether the plan included CDHP features. We measured comorbidity as the count of diagnoses contributing to the Charlson Comorbidity Index using 2011 claims.18

Inclusion Criteria

We included patients aged 18 to 63 years in 2012 who were continuously enrolled from 2011 to 2013. We excluded patients without complete sociodemographic information, those who were enrolled in a CDHP before 2013, and those enrolled in health maintenance organization and exclusive provider organization plans, as these plan types only rarely offered CDHP options.


We compared 2 groups of patients. The first group comprised patients who switched from a traditional plan to a CDHP between 2012 and 2013; the second included patients who remained in a traditional plan. To reduce the impact of selection bias, we matched the traditional-plan patients to the CDHP patients on observable characteristics (ie, age, sex, race, household income, census division, comorbidity, and 2012 health plan type). To do so, we employed exact matching, which is more stringent and robust than propensity score methods.19 First, we identified patients in the traditional-plan group who exactly matched patients in the CDHP group based on the observable patient characteristics described above. We allowed more than 1 patient in the traditional-plan group to match each patient in the CDHP group. Then, we excluded patients within each group who did not have at least 1 patient who was an exact match in the other group. Finally, to account for one-to-many matching, we weighted the patients within the traditional-plan group so that their distribution of characteristics was the same as the CDHP group.

Measuring of Low-Value Service Spending

We employed 26 previously published measures of low-value services, focusing on services delivered in the outpatient setting, where the impact of CDHPs on consumer behavior is greatest (Table 1).8,9,12,20,21 These measures are based on Choosing Wisely recommendations, expert consensus, or literature evidence. Detailed specifications are provided in eAppendix Table 1 (eAppendices available at

We measured spending for instances of low-value services using 3 approaches. First, for most low-value services, we simply used the cost from the service’s claim as the spending for that service. Second, for low-value services for which there are predictable related services that co-occur (eg, venipuncture for a blood test), we also included the cost for any claims for a narrow set of related services that occurred on the same day in the spending for that low-value service. We applied this approach to the following measures: homocysteine testing in cardiovascular disease, parathyroid hormone testing for stage I-III chronic kidney disease, hypercoagulability testing for venous thromboembolism, preoperative chest radiography, preoperative pulmonary function testing, stress testing in stable coronary artery disease, and inferior vena cava filters to prevent pulmonary embolism. (Specifications for the co-occurring services are provided in eAppendix Table 1.) Finally, for complex services where the true cost of the service included a wider array of co-occurring related services, we summed outpatient costs for the entire day of the low-value service. We applied this approach to the following low-value services: renal artery angioplasty or stent, arthroscopic surgery for knee osteoarthritis, spinal injection for lower back pain, and vertebroplasty or kyphoplasty for osteoporotic vertebral fractures.

After measuring spending for each instance of a low-value service, we summed each patient’s annual spending for each low-value service. Then, we summed each patient’s annual spending across all low-value and all outpatient services. To reduce the impact of spending outliers on our analyses, we winsorized annual spending for each low-value service and for overall outpatient spending by imputing the spending amounts at the 5th and 95th percentiles for any patients whose spending fell outside these percentiles.

We used these spending calculations to assess 3 spending outcomes: 1) annual outpatient spending overall, 2) annual low-value spending (ie, spending on the 26 low-value service measures), and 3) annual low-value spending per $10,000 in overall outpatient spending. In essence, this proportional outcome allowed us to analyze low-value spending controlling for overall spending.

Regression Analyses

Employing a DID approach to estimate spending, our regression models included a variable identifying patients in the CDHP group, a variable identifying the year after the switch, and an interaction term between these variables that assessed the association between CDHP enrollment and spending. This approach accounts for both spending trends over time and any observed or unobserved differences between the CDHP and traditional-plan groups that were stable over time. We used 2-part models because of the frequency of patients with zero spending. In these models, the first part (a probit model) estimated the probability of any spending and the second part (a generalized linear model with a γ-distribution and a log link function) estimated the amount of spending for those patients who had any spending.22 Our models adjusted for patient and plan characteristics, including age, sex, race, household income, census division, comorbidity, and plan type. We present our results as average marginal effects, or the average change in spending attributable to switching from a traditional plan to a CDHP.

To address whether CDHP effects differed by service type, we repeated these analyses limited to laboratory (Current Procedural Technology [CPT] codes 80000-89999) or imaging (CPT codes 70000-79999) spending. Although a physician or provider is the one ultimately ordering the low-value services, some services are more likely to be subject to patient demand or preferences than others. Therefore, we repeated these analyses for 8 services deemed more sensitive to patient preferences (sinus CT for uncomplicated acute rhinosinusitis, head imaging for syncope, head imaging for uncomplicated headache, back imaging for patients with nonspecific low back pain, imaging for diagnosis of plantar fasciitis, stress testing for stable coronary artery disease, arthroscopic surgery for knee osteoarthritis, and spinal injections for lower back pain) versus the remaining 18 services.

The University of Southern California Institutional Review Board exempted this study. We used SAS version 9.2 (SAS Institute; Cary, North Carolina) for descriptive analyses and STATA (StataCorp LP; College Station, Texas) for regression analyses.


Study Cohort and Matching

A total of 11,149 CDHP patients and 408,019 traditional-plan patients met inclusion criteria. Of these, 11,075 (99.3%) CDHP patients and 365,016 (89.5%) traditional-plan patients had at least 1 exact match in the other group. After weighting, the groups were exactly matched on patient characteristics and had similar 2012 spending (Table 2).

Effect of CDHP Enrollment on Low-Value Spending

We found that between 2012 and 2013, overall outpatient spending decreased by $100.93 for CDHP enrollees but increased by $130.67 for traditional-plan patients; accordingly, switching to a CDHP was associated with a $231.60 (95% CI, —$341.65 to –$121.53) reduction in annual outpatient spending. Low-value spending decreased by $7.93 for CDHP patients and by $4.29 for traditional-plan patients, resulting in no significant association between switching to a CDHP and low-value spending (–$3.64; 95% CI, –$9.60 to $2.31). Finally, low-value spending per $10,000 in overall outpatient spending decreased by $15.54 for CDHP patients and by $7.68 for traditional-plan patients, again resulting in no significant association between switching to a CDHP and relative low-value spending (–$7.86 per $10,000 in overall outpatient spending; 95% CI, –$18.43 to $2.72) (Table 3).

Among analyses restricted to imaging, we observed a similar association between switching to a CDHP and reduced spending on outpatient imaging overall (—$22.17; 95% CI, –$38.60 to –$5.74). We also observed a small association between switching to a CDHP and reduced low-value outpatient imaging spending (–$1.76; 95% CI, –$3.39 to –$0.14), but no difference in low-value imaging spending relative to outpatient imaging spending overall (–$50.63 per $10,000 in outpatient imaging spending overall; 95% CI, –$119.22 to $17.96). Among analyses restricted to laboratory services, we again observed an association between switching to a CDHP and reduced outpatient laboratory spending overall (­–$13.44; 95% CI, –$22.59 to –$4.28), but no differences for low-value laboratory spending in general (–$0.19; 95% CI, –$0.56 to $0.19) or relative to outpatient laboratory spending overall (–$3.90 per $10,000 in outpatient laboratory spending overall; 95% CI, –$26.39 to $18.58) (Table 4).

Stratifying services by their sensitivity to patient preferences, we observed no association between switching to a CDHP and spending on low-value services more sensitive to patient preferences, in general (—$2.56; 95% CI, –$8.51 to $3.39) or relative to overall outpatient spending (–$6.94 per $10,000 in outpatient spending overall; 95% CI, –$16.00 to $2.13). The same was true for those low-value services less sensitive to patient preferences, both in general (–$0.87; 95% CI, –$2.22 to $0.47) or relative to overall outpatient spending (–$3.06 per $10,000 in outpatient spending overall; 95% CI, –$8.16 to $2.04) (Table 4).

The results of unadjusted analyses are qualitatively similar and are available in eAppendix Table 2.

Sensitivity Analyses

To ensure that our approach to spending outliers did not affect our conclusions, we repeated our main regression analyses without winsorization and found the results to be similar (eAppendix Table 3).

Patients who are planning to switch to a CDHP might try to obtain extra medical services immediately before their switch in anticipation of higher cost sharing after. Indeed, we observed that CDHP patients’ overall outpatient spending increased in the last 3 months before their switch compared with traditional-plan patients, suggestive of this anticipatory spending (eAppendix Figure 1). This was not true for low-value spending, however. This pattern could cause selection bias in our analyses, attributing savings to CDHPs that are only detected due to this anticipatory spending. To address this concern, we repeated our analyses including spending in the last 3 months of 2012 in our postswitch measurement period and found that this did not meaningfully change our results (eAppendix Table 4).

If patients who switched into a CDHP already had declining spending before their switch, this could also cause selection bias in our analyses, inappropriately attributing savings to CDHPs that would have occurred even without a change in coverage. To address this concern, we compared trends in monthly spending for CDHP and traditional-plan patients in the 2 years before the switch and found similar spending trends between the 2 groups (eAppendix Figure 1).


Switching to a CDHP is associated with decreased outpatient spending overall, but no change in spending on 26 common low-value services. This pattern of decreased overall spending, but not low-

value spending, was paralleled among imaging and laboratory services and services both more and less sensitive to patient preferences.

It was not possible for us to know patients’ reasons for switching to a CDHP. Accordingly, we cannot know whether patients decided to switch to these plans with lower premiums and higher cost sharing because they anticipated low medical spending in the coming year or because of some other reason unrelated to their healthcare needs (eg, their employer changed their plan offerings). This raises concerns that patients who switch to CDHPs might have different spending patterns than those who do not, which could create selection bias in our analyses. This has been observed in prior studies of CDHPs.23-27 To minimize the impact of selection bias, we used stringent exact matching to ensure that patients in the traditional-plan group were as comparable as possible with those in the CDHP group on characteristics we could directly observe. We also used a DID approach, in which each group was compared with itself over time, to account for the influence of any confounders that we could not observe that were stable over time. We also performed sensitivity analyses to address whether there were differences in the CDHP group’s spending over time that could account for our results. Although we did observe an anticipatory increase in spending immediately before a switch to a CDHP, accounting for this pattern did not materially change our results. Moreover, monthly spending trends in the preswitch period were parallel for the CDHP and traditional-plan groups, which further mitigates concerns about selection bias. If our analyses were impacted by selection bias, it would result in our attributing a difference in low-value spending to CDHP enrollment that was actually due to this bias. For example, if patients who became more cost-conscious over time switched to CDHPs, our analyses would find less low-value spending after the switch, even if CDHPs actually had no effect on low-value spending. Despite this possibility, we found no association between low-value spending and CDHP enrollment, suggesting that CDHP enrollment likely does not affect low-value services.

Additionally, the modest reduction in overall outpatient spending associated with CDHP enrollment we found is comparable with that seen in prior research. Haviland and colleagues found a $114 reduction per patient in outpatient spending in the first year that companies began to include CDHPs in their plan offerings.15 Buntin and colleagues found a $45 monthly reduction per family in outpatient spending among those who enrolled in a CDHP compared with those not offered these plans.16

Prior research dating to the RAND Health Insurance Experiment shows that plans with greater cost sharing, like CDHPs, produce reductions in spending on healthcare, both needed and not.28,29 CDHPs have shown mixed effects or modest reductions on receipt of high-value care (ie, preventive or chronic disease services and adherence or continuation of chronic medications), particularly among more vulnerable populations.16,17,27,30-37 Additionally, CDHP patients have shown limited understanding or ability to act upon the increased cost sharing or other features of their plan’s benefit design through price shopping.17,38-40 Our finding of no reduction in low-value service spending adds an additional dimension to the evidence that patients may not discriminate well between high- and low-value services when responding to increased cost sharing.

Some point to value-based insurance design (VBID), which offers lower cost sharing for high-value services and higher cost sharing for low-value services, as a more targeted alternative to CDHPs to steer patients toward value-conscious care.41-43 In several settings in the employer-sponsored market, VBID has resulted in increased quality and medication adherence, but not necessarily cost savings.44 The Center for Medicare and Medicaid Innovation is currently testing VBID in Medicare Advantage in multiple states.45 VBID may offer a more nuanced mechanism than CDHPs to spur value-based behavior, but cost savings are unproven and patients face similar challenges in understanding benefit design features.

Alternatively, the lack of effect of CDHP enrollment on even those low-value services more sensitive to patient preferences and demand may support the argument that the most effective locus to spur value-conscious decisions may not be patients, but providers. Price transparency does not consistently result in patient price shopping, even for those in CDHPs.40,46 However, payment arrangements that give providers “skin in the game,” like Blue Cross Blue Shield of Massachusetts’ Alternative Quality Contract, have achieved cost savings by steering patients toward lower-priced services.47 Additionally, use of low-value services appears to vary substantially among provider organizations.10 This suggests that providers can influence demand for value-conscious care and that appropriately targeted provider incentives have potential to reduce wasteful low-value spending. More research is needed to understand how provider and group characteristics influence delivery of low-value services.


Our study has several limitations. We cannot observe benefit package details (ie, employers’ HSA contributions, deductible levels), but the effect of CDHP enrollment on spending could vary with benefit generosity.15,16,48 Also, although the 26 low-value services assessed are common, represent professional consensus, and encompass many service types and clinical areas, they are inherently limited in scope. The impact of CDHP enrollment on other low-value services may differ. Additionally, we observe only 1 year after patients’ switch. Patients may take time to adapt to CDHPs’ cost sharing to specifically reduce low-value spending. However, prior research has shown CDHPs’ largest outpatient spending effects to occur in the first year.15,37,49 Finally, our data are derived from a single insurer, which may limit generalizability; however, this insurer spans many markets nationally.


Switching to a CDHP was associated with reduced overall outpatient spending, but not with reduced spending on low-value services in particular. As CDHP enrollment continues to grow, our findings suggest that their broadly increased overall cost sharing may encourage patients to cut spending indiscriminately, rather than to specifically reduce low-value care. Modification of the consumer incentives in CDHPs, more targeted VBIDs, or efforts focused on providers, rather than patients, may be necessary to expressly reduce wasteful spending. 


Mr Rabideau and Dr Sood had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.Author Affiliations: RAND Corporation (ROR), Boston, MA; Division of General Internal Medicine and Primary Care, Brigham and Women’s Hospital (ROR), Boston, MA; Harvard Medical School (ROR), Boston, MA; Leonard D. Schaeffer Center for Health Policy and Economics (BR, NS), and Department of Health Policy and Management, Sol Price School of Public Policy (NS), University of Southern California, Los Angeles, CA.

Source of Funding: Leonard D. Schaeffer RAND-USC Initiative in Health Policy and Economics and the National Institute for Health Care Management.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (ROR, NS); acquisition of data (NS); analysis and interpretation of data (ROR, BR, NS); drafting of the manuscript (ROR, NS); critical revision of the manuscript for important intellectual content (ROR, NS); statistical analysis (ROR, BR, NS); provision of patients or study materials (NS); obtaining funding (ROR, NS); administrative, technical, or logistic support (BR); and supervision (NS).

Address Correspondence to: Neeraj Sood, PhD, University of Southern California, Verna and Peter Dauterive Hall 210, 635 Downey Way, Los Angeles, CA 90089. E-mail: REFERENCES

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