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Impact of Consumer-Directed Health Plans on Low-Value Healthcare
Rachel O. Reid, MD, MS; Brendan Rabideau, BA; and Neeraj Sood, PhD
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Impact of Consumer-Directed Health Plans on Low-Value Healthcare

Rachel O. Reid, MD, MS; Brendan Rabideau, BA; and Neeraj Sood, PhD
Switching to a consumer-directed health plan is associated with reduced overall outpatient spending, but not with reduced spending on low-value healthcare services.
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).

DISCUSSION

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.

Limitations

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.

CONCLUSIONS

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. 

Acknowledgments
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: nsood@healthpolicy.usc.edu. 
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