 Limited data are available regarding the impact of the type of healthcare delivery system on technology diffusion and associated clinical outcomes. We assessed the adoption of minimally invasive radical prostatectomy (MIRP), a recent clinical innovation, and whether this adoption altered surgical morbidity for prostate cancer surgery.

 Retrospective review of administrative data from TRICARE, the healthcare program of the United States Military Health System. Surgery occurred at military hospitals, supported by federal appropriations, or civilian hospitals, supported by hospital revenue.

 We evaluated TRICARE beneficiaries with prostate cancer (

International Classification of Disease, 9th Revision, Clinical Modification

] code: 185) who received a radical prostatectomy (60.5) between 2005 and 2009. MIRP was identified based on minimally invasive surgery codes (54.21, 17.42). We assessed yearly MIRP utilization, 30-day postoperative complications (Clavien classification system), length of stay, blood transfusion, and long-term urinary incontinence and erectile dysfunction.

 A total of 3366 men underwent radical prostatectomy at military hospitals compared with 1716 at civilian hospitals, with minimal clinic-demographic differences. MIRP adoption was 30% greater at civilian hospitals. There were fewer blood transfusions (odds ratio, 0.44; 

<.0001) and shorter lengths of stay (incidence risk ratio, 0.85; 

<.0001) among civilian hospitals, while 30-day postoperative complications, as well as long-term urinary incontinence and erectile dysfunction rates, were comparable.

 Compared with military hospitals, civilian hospitals had a greater MIRP adoption during this timeframe, but had comparable surgical morbidity.

This is a retrospective review of administrative data from TRICARE, the healthcare program of the United States Military Health System. 

Compared with 1716 men in 767 civilian hospitals, 3366 men underwent radical prostatectomy in 36 military hospitals.

Minimally invasive radical prostatectomy adoption was 30% greater at civilian hospitals. 

The 30-day postoperative complications, as well as long-term urinary incontinence and erectile dysfunction rates, were comparable. 

Adoption of minimally invasive technology has not significantly improved surgical morbidity. 

This study suggests that adoption of technology may not always translate to an observable improvement in patient outcomes.

The rapid pace of clinical innovation—including the development of novel drugs, devices, diagnostic techniques, and surgical interventions&mdash;makes the practice of medicine in the United States dynamic. Hospitals and physicians constantly seek to offer new technologies that are effective and help speed recovery, thereby achieving the best possible results for patients. One example of clinical innovation is minimally invasive surgery (MIS), which is associated with fewer complications across a range of procedures.

 Recently, the introduction of robotic surgery has made MIS feasible for even complex surgical procedures such as radical prostatectomy (RP) for the management of prostate cancer in hopes of reducing its well-recognized surgical morbidity.

 Minimally invasive RP (MIRP) with laparoscopy was first reported in 1997

 and robotic MIRP with the da Vinci Surgical System (Intuitive Surgical, Inc; Sunnyvale, California) was introduced in 2001.

 MIRP has become the most common treatment for men with prostate cancer,

 with the latest 2013 estimates showing 85% of all RPs were performed using the robotic approach, with the remainder still being performed via the open radical prostatectomy (ORP) approach.

Prior investigations report that the widespread dissemination of clinical innovation, such as MIRP, in the United States has been associated with patient, surgeon, and hospital characteristics.

 These studies, however, did not directly examine the question of how the funding mechanism for healthcare services may have altered this adoption. In theory, hospitals that rely on revenue through reimbursement of services (ie, for-profit and nonprofit institutions) will preferentially adopt new clinical innovations at a faster rate because they attract patients, thereby maximizing hospital revenue.

In the current study, we analyzed a contemporary cohort of male enrollees in TRICARE, the healthcare program of the United States Department of Defense (DoD) Military Health System (MHS). These men all have prostate cancer and underwent RP at either civilian hospitals or military hospitals, which have substantially different models for healthcare delivery. We compared cohorts from these disparate healthcare environments to evaluate the hypothesis that civilian hospitals preferentially adopted MIRP compared with MHS hospitals, resulting in an improved surgical morbidity profile for patients with prostate cancer.

TRICARE is the healthcare program serving active-duty and retired service members and their dependents. This program provides 3 types of health plans: Standard (similar to traditional indemnity insurance), Extra (similar to a preferred provider organization), and Prime (similar to a health maintenance organization). Prime enrollees have a primary care manager (PCM) responsible for their care. TRICARE Plus provides enrollees 65 years or older with access to MHS facilities and benefits comparable to Prime, where available. Services provided by TRICARE are via the Direct Care System at MHS facilities where healthcare professionals are salaried, either as employees of the federal government or as hired contractors. There is also a Purchased Care System (PCS), which reimburses for services at civilian facilities, comprising for-profit and nonprofit institutions in which hospitals and surgeons maximize revenue by increasing the volume of care (ie, fee-for-service).

 Although all active-duty service members typically receive care at military treatment facilities, some TRICARE enrollees (eg, retirees or family members) may also receive care in the PCS, depending on where they reside.

We extracted data from October 1, 2005, to September 30, 2010, from the Military Health System Data Repository (MDR), an administrative database, for all TRICARE Prime and Plus beneficiaries within the United States. We focused on Prime and Plus beneficiaries for 2 reasons. First, the assignment of a PCM who oversees care suggests it is less likely that enrollees might receive care that is not performed within the purview of TRICARE and that care provided might be documented more completely. Second, although all TRICARE benefit plans are generous, Prime enrollees have either very low or no enrollment fees and co-payments, and no deductibles, minimizing the possibility that patient out-of-pocket costs might be a factor in treatment selection. We obtained approval from the institutional review boards of the Uniformed Services University of the Health Sciences and Brigham and Women’s Hospital and established a data use agreement with the Defense Health Agency Privacy Office.

We identified 28,998 men with a diagnosis of prostate cancer (

International Classification of Disease, 9th Revision, Clinical Modification 

] code: 185). Of these, 5082 underwent RP (

: 60.5). We examined outcomes describing RP morbidity, including surgical complications and blood transfusions, length of stay (LOS), anastomotic strictures, postoperative incontinence, postoperative erectile dysfunction, and the postoperative complication rates during the 30 days following surgery. Thirty-day surgical complications were determined based on the Clavien classification system, a validated approach for reporting complications

 We evaluated for any complication (Clavien grade ≥1), major complications (Clavien grades 3-5), and mortality (Clavien grade 5). Postoperative mortality was identified through disposition codes in the MDR. Postoperative blood transfusions were also assessed in the 30 days after surgery through a review of the administrative data.

LOS was calculated based on the days between the surgery and discharge from the hospital. The development of anastomotic strictures (

: 598.2, 598.8, 598.9) was captured from 31 to 365 days after surgery, thus limiting the years of analysis from fiscal year (FY) 2005 to FY 2009. Long-term functional status was estimated based on the use of surgical procedures to address incontinence and erectile dysfunction during the 18 months following surgery, thus excluding men undergoing surgery in the latter half of FY 2009 and in FY 2010. The codes used to capture incontinence and erectile dysfunction have been previously described.

We extracted demographic information pertinent to our study cohort, including patient age, race, and marital status, as well as clinical information, such as baseline incontinence and erectile dysfunction. For missing data, efforts were made to cross-reference multiple datasets to obtain accurate demographic information. We dichotomized the surgical approach as ORP (

: 60.5 + 54.21 and/or 17.42). To adjust for patient health status, we utilized the CMS-Hierarchical Condition Category (CMS-HCC) model, which uses demographics and diagnosis profiles captured for both inpatient and ambulatory encounters in order to adjust capitation payments for health expenditure risk.

 We used CMS-HCC both as a continuous and a categorical variable (tertiles).

We summarized patient and surgical characteristics for patients who underwent RP with descriptive statistics. Categorical variables were compared using Pearson’s χ

 test and continuous variables using the Mann-Whitney 

 test. We performed difference-in-differences (DID) estimation, with bootstrapping to calculate the 95% confidence interval (CI), to assess the effect of hospital revenue on adoption trends. To compare postoperative outcomes, we created unadjusted and adjusted regression models to evaluate the odds of postoperative complications, anastomotic strictures, incontinence, erectile dysfunction, and blood transfusion, as well as the incidence rate ratio for LOS. We accounted for clinically important confounders, including patient age, race, marital status, comorbidity (CMS-HCC), surgical approach (ORP vs MIRP), and baseline incontinence and erectile dysfunction. Additionally, we performed subgroup analysis on institutions where only MIRP was performed, examining differences in outcomes. Statistical analyses were performed using SAS version 9.3 (SAS Institute; Cary, North Carolina); all tests were 2-sided, and 

 <.05 was considered statistically significant.

In the study cohort, a total of 3366 men underwent radical prostatectomy in military hospitals compared with 1716 in civilian hospitals across the study period from 2005 to 2009. These surgeries occurred in 767 civilian and 36 military hospitals across the United States. Civilian hospitals were associated with an increased MIRP adoption by approximately 30% (DID estimation: 0.29; 95% CI, 0.19-0.39; 

) over the course of the study. The disparity in MIRP utilization in favor of civilian hospitals was present during the entire study, but substantially increased beginning in 2008.

There were minimal differences between the 2 groups in terms of baseline characteristics (

). Age and comorbidity status were similar between the 2 sectors, while there was a larger proportion of single men in civilian hospitals. There was a greater number of white, black, and Asian/Native American/other patients in the MHS (

<.0001), although the observed ethnic and racial differences may have been secondary to the larger number of unknown/missing data from civilian hospitals.

), the median LOS between the patient cohorts was similar, although both the unadjusted and adjusted analyses demonstrated a reduced LOS for civilian hospitals (incidence risk ratio, 0.85; 95% CI, 0.83-0.87; 

<.0001). Bivariate analyses showed that the shorter LOS for MIRP compared with ORP was more pronounced for civilian hospitals (MIRP: 1.74 days vs ORP: 2.52 days) than military hospitals (MIRP: 2.27 days vs ORP: 2.52 days). The proportion of patients receiving a blood transfusion within 30 days of the surgery was lower in the civilian hospitals (3.5% vs 8%; adjusted odds ratio, 0.44; 95% CI, 0.34-0.58; 

<.0001). When we stratified this further by type of surgery performed, we found that the lower blood transfusion rate for MIRP was true for civilian hospitals (MIRP: 2.0% vs ORP: 8.9%) but not for military hospitals (MIRP: 4.4% vs ORP: 4.4%).

In contrast, the observed 30-day postoperative complication rates were highly comparable on both the unadjusted and adjusted analyses. Although the 30-day mortality rate was lower for civilian hospitals (0.03% vs 0.12%), this did not achieve statistical significance. Similarly, we found that the development of an anastomotic stricture following surgery was lower among men in civilian hospitals (6.7% vs 9.1%), but our adjusted analysis failed to identify a statistically significant difference between men in the 2 cohorts. Assessment of long-term functional outcomes revealed that the utilization of surgery to address erectile dysfunction and incontinence was similar between men cared for in either healthcare environment. On subgroup analysis including only hospitals that performed MIRP, we also found no differences in terms of postoperative complications, incontinence, or erectile dysfunction (data not shown).

In this study of TRICARE beneficiaries with prostate cancer, we found a substantially greater diffusion of MIRP among civilian institutions compared with MHS institutions between FY 2005 and FY 2010, and this difference was most pronounced after 2008, coinciding with the widespread adoption of robotic MIRP in the United States.

 Despite the increasing disparity in MIRP diffusion, we observed similarity in surgical morbidity for these 2 healthcare environments based on postoperative complications and functional outcomes (ie, incontinence and erectile dysfunction). Our findings support the hypothesis that market forces may accelerate the adoption of clinical innovation, such as MIRP, among civilian hospitals, which rely heavily on generating revenue to deliver healthcare; however, the increased tendency to adopt MIRP was not associated with a meaningful improvement in surgical morbidity.

The strength of the current study is that we assessed men from a common insured population that were managed in 2 different healthcare environments distinguished, in large part, by the role of hospital revenue source in healthcare delivery. Because the delivery of health services at civilian institutions is reliant on generating sufficient revenue, it is feasible that the motivation for technology adoption stems from an aim to maximize revenue by attracting new patients or avoiding a loss of patients.

 In contrast to civilian hospitals, the MHS is supported by appropriations from the DoD. These budgetary decisions have an unusually broad scope of maintaining the health of all military personnel, their dependents, and retirees, as well as supporting military readiness, providing medical education, and improving public health. These competing priorities, coupled with the idiosyncrasies of federal appropriations laws and budgeting, may slow the adoption of technology within the MHS.

Prior population-based studies of nonfederal hospitals report that MIRP is associated with increased surgical volume. Chang et al demonstrated that surgeons, particularly high-volume surgeons, who adopted robotic MIRP experienced an increase in overall annual volume of RP.

 Similarly, Makarov et al reported that hospitals that acquired the robotic platform were associated with a significant increase in annual RP volume, while hospitals without the robotic platform experienced a loss in volume.

 Complementing the prior investigations, this study provides data that support the hypothesis that maximizing hospital revenue may have influenced the likelihood of adopting technology such as MIRP (Figure).

Despite the argument that hospital revenue drives technology dissemination, we also observed a substantial increase in the proportion of patients undergoing MIRP in the MHS during the final 2 years of the study. We speculated that this upward trend may be associated with a motivation to maintain parity with civilian hospitals

 and to keep TRICARE enrollees from seeking care at civilian hospitals to undergo MIRP. Surgeons at civilian hospitals may feel more of a need to learn new techniques to remain competitive for patient referrals in a “private” environment. In terms of reimbursement incentives at the physician level, a common misconception among nonurologists is that perhaps MIRP offers better reimbursement for the physician than ORP. However, this has not been found to be true; reimbursement to physicians for both ORP and MIRP has been found to be similar on a per-case basis.

Regardless of the true motivation for this increased utilization by military hospitals of MIRP in the latter years of our study, we concluded that the adoption of technology is influenced by a multitude of factors, among which generating revenue is likely to represent one of the most important issues for civilian hospitals. Although both civilian and military hospitals were observed to have an increasing trend in utilization of MIRP, the proportion in military hospitals was lower (Figure) compared with civilian hospitals across all years. We hypothesized that this could be due to certain military facilities having an overall lower caseload of RP, which does not justify the establishment of an MIS program, and which has substantial upfront investment and ongoing maintenance costs. Although this may appear to be a lack of technological diffusion in the military system, it might actually point to prudent adoption of technology in select facilities that have the necessary surgical volume to allow for cost-conscious and safe dissemination.

Our study design permitted an evaluation of the possible impact of MIRP adoption on patient outcomes. We found that civilian hospital procedures were associated with a decrease in blood transfusions and a slightly shorter LOS, which are commonly noted among patients undergoing MIS procedures.

 There was, however, no difference between procedures performed in civilian and military hospitals in the 30-day major complication rate (ie, Clavien grades 3-5) or in the long-term functional outcomes of postoperative incontinence and erectile function. The incidence of anastomotic strictures was also lower in civilian hospitals, but this did not achieve statistical significance in the adjusted analysis. Even with subgroup analysis focusing only on institutions offering MIRP, we did not detect any differences in outcomes between civilian and military hospitals, suggesting that our findings are not influenced by unmeasured factors associated with the availability of MIRP (eg, the availability of the robotic platform).

Our finding of relative equivalence in morbidity between healthcare systems, despite differences in the adoption of MIRP, is consistent with contemporary population-based studies that report mixed results regarding the perioperative outcomes of ORP and MIRP.

 There are, however, unmeasured factors, such as differences in the time for convalescence, total pain medication use, and patient satisfaction, which may preferentially favor MIRP given the decrease in surgical trauma associated with MIS.

 In our adjusted model, LOS and blood transfusion were slightly reduced for civilian hospitals even after adjusting for surgical approach (Table 2), possibly reflecting advantages at the civilian hospitals that are not quantified by the Clavien classification system for surgical complications or captured by assessment of functional outcomes.

Given the absence of a morbidity improvement for MIRP, we concluded that its rapid adoption among civilian hospitals was not based on data in the medical literature. In fact, well-executed studies regarding MIRP are only recently available.

 There are challenging obstacles for critical evaluation through a randomized controlled trial, most notably: 1) the difficulty in accruing patients willing to defer their choice for management to randomization, 2) the impracticality in blinding patients and surgeons, and 3) the challenges in controlling for surgeon learning curve and variations in technique.

 We surmised that the promise of new surgical innovation to improve outcomes is intensely attractive, particularly for a procedure like RP, which is associated with detrimental functional issues, such as urinary incontinence and erectile dysfunction, thereby creating pressure for surgeons and hospitals to explore new technologies. Consequently, despite the absence of Level I evidence demonstrating superiority or equivalence of MIRP over ORP, a large percentage of surgeons in the United States has now adopted robotic MIRP,

 and the majority of RP surgeries are now performed with the robotic platform.

Our findings must be interpreted within the context of several important limitations to our study design. First, we used administrative data from TRICARE (claims for reimbursement for civilian hospitals and utilization records for military hospitals), which is not designed for documenting a patient’s clinical course and outcome. It is possible that more granular clinical information might have altered the associations we report or revealed new relationships not identified in the current study. However, the TRICARE claims data share many similarities with Medicare claims data, which have been used extensively to evaluate surgical outcomes.

Second, we could not adjust for tumor stage and grade because these characteristics are not reliably captured with claims data; however, in men with prostate cancer, tumor characteristics are unlikely to influence the postoperative complications, LOS, and long-term functional outcomes. Third, to assess for postoperative urinary incontinence and erectile dysfunction, we considered surgical intervention as a proxy for functional status. Admittedly, we cannot exclude the possibility that there may be a disparity in the threshold to undergo those surgical interventions between civilian and military institutions.

Fourth, although we had hospital volume data for military hospitals, we did not have that same reliable data for civilian hospitals, precluding us from adjusting for surgical volume as a possible confounder, especially when this has been previously shown to affect postoperative outcomes.

 Lastly, we did not have reliable estimates for costs and revenues to comprehensively assess the differential amount of revenue generated by adoption of MIRP over ORP; this may have garnered additional insights regarding the potential financial incentives of adopting MIRP.

To our knowledge, this is the first study assessing the potential impact of the type of healthcare delivery system on the adoption of MIRP in the United States. Based on TRICARE enrollee data, we observed that adoption was more rapid among civilian hospitals, which are comprised of institutions heavily reliant on generating hospital revenue to deliver health services. Further analysis, however, showed that the increased use of MIRP did not translate into a marked improvement in outcomes, thus highlighting the potential dangers of embracing new clinical innovations prior to the availability of evidence showing benefit. Our findings are particularly relevant as the delivery of healthcare in the United States is undergoing dramatic change. Future work should focus on determining if the goals of cost containment and of revenue based on quality, the principles underlying accountable care organizations, promote or repress clinical innovation in the United States.

Center for Surgery and Public Health (JJL, JSW, WJ, NH, SRL, LLN, SLC), and Division of Urology (JJL, SLC), and Division of Vascular Surgery (LLN), Brigham and Women’s Hospital, Harvard Medical School, Boston, MA; Department of Preventive Medicine and Biostatistics, Uniformed Services University of the Health Services (LK, AH), Bethesda, MD; Department of Health Policy and Management, Milken Institute School of Public Health, The George Washington University (LHH), Washington, DC.

 Henry M. Jackson Foundation HU0001-11-1-0023 The Comparative Effectiveness and Provider Induced Demand Collaboration (EPIC): A Clinical and Economic Analysis of Variation in Healthcare.

 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.

 Concept and design (JJL, JSW, LK, LHH, WJ, NH, SRL, LLN, SLC); acquisition of data (LK, AH, LLN, SLC); analysis and interpretation of data (JJL, LK, AH, LHH, WJ, NH, SRL, LLN, SLC); drafting of the manuscript (JJL, JSW, AH); critical revision of the manuscript for important intellectual content (JJL, LK, LHH, WJ, NH, SRL, LLN, SLC); statistical analysis (JJL, WJ, NH, SRL, LLN, SLC); provision of patients or study materials (LK); obtaining funding (LLN, SLC); administrative, technical, or logistic support (AH, LLN, SLC); and supervision (JSW, LK, LHH, WJ, NH, SRL, LLN, SLC).

 Steven L. Chang, MD, MS, Brigham and Women’s Hospital, Division of Urology, 45 Francis St, Boston, MA 02115. E-mail: slchang@bwh.harvard.edu. 

1. Xu T, Hutfless SM, Cooper MA, Zhou M, Massie AB, Makary MA. Hospital cost implications of increased use of minimally invasive surgery. 

 2015;150(5):489-490. doi: 10.1001/jamasurg.2014.4052.

3. Sanda MG, Dunn RL, Michalski J, et al. Quality of life and satisfaction with outcome among prostate-cancer survivors. 

 2008;358(12):1250-1261. doi: 10.1056/NEJMoa074311.

4. Shore N. Management of early-stage prostate cancer. 

5. Schuessler WW, Schulam PG, Clayman RV, Kavoussi LR. Laparoscopic radical prostatectomy: initial short-term experience. 

 Laparoscopic radical prostatectomy with a remote controlled robot. 

7. Binder J, Kramer W. Robotically-assisted laparoscopic radical prostatectomy. 

 Perioperative outcomes of robot-assisted radical prostatectomy compared with open radical prostatectomy: results from the nationwide inpatient sample. 

 2012;61(4):679-685. doi: 10.1016/j.eururo.2011.12.027.

9. Gandaglia G, Sammon JD, Chang SL, et al

 Comparative effectiveness of robot-assisted and open radical prostatectomy in the postdissemination era. 

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Articles

CLINICAL

Minimally invasive radical prostatectomy was more commonly performed in civilian hospitals compared with military hospitals among TRICARE beneficiaries, with comparable postoperative outcomes.

Radical Prostatectomy Innovation and Outcomes at Military and Civilian Institutions

 To compare how premiums and expected out-of-pocket medical costs (OOPC) vary with the length of time Medicare Advantage (MA) beneficiaries have been enrolled in their plans.

 Descriptive and fixed effects regression analyses.

 Using linked administrative enrollment and plan data, we compared the costs of the MA plans that beneficiaries chose with the costs of other plans available to them. We show predicted values adjusted for age, gender, race/ethnicity, disability, individual health risk, presence of mental health diagnoses, health plan quality, relative size of the plan’s provider network, and the number of years continuously enrolled in the same plan. To further address the possibility of bias, we included county-level fixed effects and compared results to a beneficiary-level fixed effects model.

 We found average spending on premiums and OOPC in enrolled plans exceeded such costs in the lowest-cost plan by $697 in 2013. Beneficiaries who remained in their plans for 6 or more years were most at risk of spending these higher amounts, paying $786 more than they would have spent in the lowest-cost plan compared with $552 for beneficiaries in their first year of enrollment. For each year a beneficiary remained in their same plan, their additional spending in excess of the minimum cost choice increased by roughly $50.

 MA beneficiaries could reduce their exposure to healthcare spending by switching to plans with lower premiums, although there may well be rational reasons for paying costs in excess of those of the lowest-cost plan.

Our results suggest that beneficiaries may not be actively and regularly comparing plans, and thus are subject to a certain amount of inertia in their plan selections. 

On average, beneficiaries who have been enrolled in their plans for longer periods of time paid more for their plan compared with the lowest-cost option available to them. 

Medicare Advantage beneficiaries could reduce their exposure to healthcare spending by switching to plans with lower premiums. 

Changes to the Medicare choice environment that increase the salience of premiums or encourage beneficiaries to compare plans could reduce passive reenrollment and stimulate a more robust competitive environment for insurers.

The option to choose a private health insurance plan has existed for Medicare beneficiaries for several decades. More than 17 million beneficiaries (31%) were enrolled in Medicare Advantage (MA) plans in 2016—up from 6.8 million a decade earlier&mdash;and beneficiaries had an average of 19 plan choices in that year.

 When choosing a plan, beneficiaries face a variety of tradeoffs, including premiums versus expected out-of-pocket medical costs (OOPC), which have important consequences given the lower incomes and higher average medical spending of Medicare beneficiaries compared with the broader US population. Understanding how beneficiaries choose among their plan offerings and the consequences of those choices is important for both beneficiary well-being and the competitiveness of the MA market.

We sought to address several questions related to plan choice in the MA market. Compared with other plans available to beneficiaries, how much are they currently spending on premiums and cost sharing? How do plan costs compare for beneficiaries who have been enrolled in the same plan for different periods of time? Compared with lower-cost options available to beneficiaries, what is the dollar value of the foregone savings—if any&mdash;from remaining in the same plan over time?

When choosing an MA plan, there are a variety of plan features for beneficiaries to consider: 1) the premium—if any&mdash;to enroll in the plan, including premiums for Part D (prescription drug) coverage and any reductions to the standard Part B (physician and outpatient) premium; 2) coverage of services supplementing the traditional Medicare benefit (eg, dental or vision coverage); 3) which medical providers are in-network; 4) plan quality; 5) utilization management practices; and 6) cost-sharing features and anticipated spending on OOPC. Most beneficiaries have a fairly wide choice of MA plans, including both health maintenance organization (HMO) and preferred provider organization (PPO) offerings.

Although research shows consumers have difficulty selecting health insurance plans,

 the MA plan choice environment may be particularly confusing for consumers.

 First, Medicare beneficiaries, who are more likely to have cognitive limitations, can have difficulty assessing the various tradeoffs involved in deciding on a health plan.

 Second, because premiums are often used as an indication of quality and, in most counties, beneficiaries have access to multiple MA plans without an enrollee premium (aside from the Part B premium), they have to consider other attributes (eg, provider networks, cost sharing, covered benefits) that are more difficult to understand and compare.

 Third, many beneficiaries pay premiums through withholdings from their Social Security benefits, which may discourage beneficiaries from actively considering their choices even when premiums increase. Finally, as in other markets, beneficiaries who do not actively switch plans between years are automatically reenrolled in the same plan, potentially creating a bias toward their previously chosen option.

Although several previous studies have investigated decision making in the MA market, few have looked specifically at what beneficiaries pay for coverage and how that compares with other options. For example, one recent study found that MA enrollees were more likely to choose plans that reduced cost sharing than those that kept premiums low, but it did not quantify differences in plan costs or how those values changed when beneficiaries were enrolled in their same plan for longer periods of time.

 Studies have examined the appropriateness of beneficiary choices of standalone Medicare prescription drug plans. Among other things, this research has tried to assess whether beneficiaries are subject to inertia in their plan selections with some disagreement about the extent of the problem and how it has evolved.

 Comparatively little attention has been paid to these concepts in the market for MA plans, and few studies have investigated the financial impact of the plans MA beneficiaries chose.

Our analysis relied on a unique link between several administrative databases that CMS collects. Our key results were derived from a 2% random sample of the Medicare Beneficiary Summary File enrollment databases for 2013, although we also used historical data on beneficiary plan selections from as early as 2006 to measure tenure in MA plans.

 Additional details about the data are provided in the eAppendix (

We excluded beneficiaries who were not enrolled in both Part A and B of Medicare in January of each year, did not choose an MA plan with Part D coverage (MA-PD plans), were enrolled in a Special Needs Plan (SNP) or an employer plan, or were enrolled in Medicaid or the Part D Low-Income Subsidy program. From each beneficiary’s set of MA plan choices, we excluded SNPs, plans that were not an MA-PD, and employer-sponsored plans. Our final sample included 93,519 beneficiaries in 2013. All comparisons in the text are significant at the 0.1% level unless otherwise noted. Nominal dollar values are adjusted to 2013 US dollars using the Bureau of Labor Statistics’ Consumer Price Index US city average.

We compared each beneficiary’s choice of an MA-PD plan on 2 observable dimensions. First, we considered the total premium to enroll in the plan, including the Part B premium, any amounts the MA plan charges, and any premiums for Part D coverage. (Because Part B premiums can vary by income, we included the most common amount for a beneficiary with <$80,000 in income. Our measure incorporates any reductions to the Part B premium offered through MA plan rebates.)

Second, we analyzed expected OOPC, which is a CMS-derived actuarial estimate of average beneficiary spending for cost sharing for health services using a standardized sample of representative beneficiaries.

 CMS calculates OOPC values for each plan by mapping its cost-sharing features to the actual utilization patterns of the sample of beneficiaries. Five values of OOPC are calculated depending on a beneficiary’s self-reported health, and we imputed an average of these 5 values based on the probability that the beneficiary is in each of those health states. (The eAppendix provides more detail on the OOPC measure. eAppendix Table 1 shows that our descriptive results lie between the extremes of assuming all individuals are either in “excellent” or “good” self-reported health. Additionally, our main results in eAppendix Tables 2 and 3 are not sensitive to alternative imputation procedures, as shown in eAppendix Tables 4 and 5.)

CMS lists OOPC on the Medicare plan finder website, providing a prospective metric of spending in a plan that is potentially salient at the time of enrollment.

 Given that MA plans often use federal payments to cover services outside the fee-for-service (FFS) benefits package, OOPC is useful for identifying both variation in the breadth of coverage and differences in cost sharing between plans.

Our descriptive findings in the Table show enrollee premiums and OOPC for the plans beneficiaries chose and compare them to other plans they could have chosen. We compared the values for each beneficiary’s chosen plan with the values for: 1) the plan with the lowest enrollee premium, 2) the plan with the lowest OOPC, and 3) the minimum expected spending plan (MESP), or the plan with the lowest combined premiums plus OOPC. We also compared values for their chosen plan with the values for alternative choices within the same type of coverage, including HMO, PPO, and private FFS plans. Finally, we compared spending for a beneficiary’s plan to spending in alternative plans of the same type (HMO, PPO, private FFS, other) offered by the beneficiary’s current insurer. Although this comparison highlights how switching can reduce spending potentially without changing one’s provider network, it implicitly assumes beneficiaries would not switch insurers or plan types to save, and thus, could be considered an underestimate of foregone savings.

We assessed whether beneficiaries who remain in the same plan for longer periods of time pay larger premiums or receive less in expected benefits compared with those who switch plans. First, we compared the payments for beneficiaries who either newly enrolled in an MA plan in 2013 (“new enrollees”) or who switched plans between 2012 and 2013 (“switchers”) against those who remained in the same plan (“stayers”). Second, we examined payments by length of continuous enrollment in the same plan. For all regression results, we reported predicted differences in expected spending between the chosen plan and the MESP.

Using ordinary least squares models, we regressed differences in the cost of the chosen plan compared with the MESP on an identifier for switchers (Figure 1) or the number of years continuously enrolled in a plan (Figure 2). We controlled for a wide variety of individual characteristics that may be related to the decision to switch plans, including age, gender, race/ethnicity, individual-level health risk (using the CMS-derived Hierarchical Condition Category [HCC] risk score), and presence of mental health diagnoses. We also included indicator variables for the chosen MA plan’s star rating and provider network size compared with both the MESP and the median plan in the beneficiary’s county. The models included county-level fixed effects to control for unobserved aspects of the local MA market, such as geographic cost variation or the types of plans offered.

We limited our regression analyses to beneficiaries enrolled in Medicare for 6 or more years because newer beneficiaries may exhibit different underlying plan choice behavior, including frequent switching, as they learn about the market. We chose 6 years to reduce the likelihood that our key independent variable (ie, tenure in an MA plan, which has a maximum of 6 years) would be polluted by unobserved differences between new enrollees and older cohorts. (Our results were not sensitive to this specification.)

Because a spurious correlation could exist between beneficiary preferences for health plans and length of time enrolled in a plan, we compared our regression results to individual-level fixed effects models. This approach removed the effects of time-invariant preferences, such as risk aversion or taste for utilization management. As shown in eAppendix Tables 6 and 7, our results were insensitive to this alternative specification.

As shown in the Table, in 2013, average spending for MA beneficiaries was $1667 for premiums and $2078 in OOPC. On average, choosing the plan with the lowest premium would have reduced spending on premiums by 38% or $635 (from $1667 to $1032), and combined spending on premiums and OOPC would have been 12%, or $448, lower because higher OOPC offset lower premiums. Combined expenditures would have been 3%, or $112, higher in the plan with the lowest OOPC because of the higher premium for that plan. By definition, the MESP had the lowest combined premium and OOPC, which was 19%, or $697, lower than the costs beneficiaries paid on average in 2013.

We also compared beneficiary payments with the lowest premium and lowest OOPC plans of the same plan type as that which the beneficiary chose. The Table shows that beneficiaries could have selected the same type of plan and reduced their premium by 33%, or $552, (from $1667 to $1115) by selecting the plan with the lowest premium. Beneficiaries could have switched to the MESP among plans of the same type in 2013 and paid 15%, or $548, less on premiums plus expected cost sharing than they actually did. Among those with more than 1 plan available from their current insurer, they could have switched plans without switching insurers or their type of plan and reduced total costs by 8%, or $287.

Figure 3 shows that in 2013, 97% of MA beneficiaries had at least 1 plan available to them that would have lowered their total expected spending and about 87% had 3 or more plans. Additionally, 92% of beneficiaries had at least 1 plan of the same type with lower expected spending and 69% had at least 3 plans. Finally, 64% could have found a cheaper plan without switching their plan type or their insurer.

Figure 1 compares spending, adjusted for observable differences, for switchers and stayers between 2012 and 2013, and for new enrollees in 2013. Switchers, on average, narrowed the difference between the premium paid and the premium of the MESP (from $526 to $342), whereas stayers saw an increase (from $523 to $600). Although stayers and switchers reduced differences in OOPC, switchers reduced the difference in total costs by $195 ($739 in 2012 and $544 in 2013) compared with an increase of $58 (from $701 to $759) for stayers. Enrollees newly choosing an MA plan for the first time in 2013 spent $130 less on premiums than stayers and $96 less overall.

Figure 2 shows that the difference between expected spending in a beneficiary’s chosen plan compared with the MESP grew with the number of years continuously enrolled in the same plan. Whereas comparable beneficiaries who have been enrolled for longer periods do not seem to have larger expected OOPC, and that amount may even fall somewhat, they pay more for premiums with increasing tenure. Beneficiaries in their first year in a plan pay $552 more than the amount they would pay in the cheapest available plan for both premiums and cost sharing, whereas comparable beneficiaries with 6 or more years pay $786 more than the minimum plan. Increases in premiums charged for Parts A and B benefits primarily caused these differences rather than changes in OOPC or Part D premiums (not shown). For each year a beneficiary remains in their same plan, their additional spending in excess of the minimum cost choice increases by roughly $50.

Our findings across a range of models show that MA beneficiaries spend substantially more for premiums and expected OOPC than the lowest-cost plan in their area and the total amount increases as enrollees stay in their plan. The average amount in excess of the MESP of $697 constitutes more than 2 percentage points of the median income for MA beneficiaries in 2014 ($26,000). (Estimates were derived from the authors’ calculations of MA beneficiaries’ income using CMS administrative databases and survey data.) Because about half of MA beneficiaries remain in their plan over a given 5-year period, these annual amounts understate the total amount most beneficiaries could save over their average duration in a plan.

As tenure increased from 1 to 6 or more years, beneficiaries paid $339 more in premiums ($699 minus $361) compared with available lower-cost plans with only a $104 reduction in OOPC ($87 instead of $191). Plan networks and covered services do not typically change much from one year to the next, so it would be reasonable to expect increases in premiums in excess of lower-cost plans to be offset roughly in parallel by lower cost sharing. This finding suggests that many beneficiaries, and particularly those who have remained in their plan for several years, would benefit financially from switching to lower-premium plans.

The average amount by which beneficiaries spend more than they would if enrolled in the lowest-cost plan in their area is largely a result of their chosen plan’s premium rather than its OOPC. This may arise because CMS limits the annual amount by which plans can increase OOPC, which may cause plans to increase premiums instead. Nevertheless, our results are consistent with research suggesting that Medicare beneficiaries are generally inattentive to increases in plan premiums after their initial enrollment decision.

Although we controlled for a variety of demographic and health-related characteristics, we cannot draw firm conclusions regarding the appropriateness of beneficiaries’ choices because there may be unobserved components of beneficiary demand for plans. Beneficiaries who remain in the same plan may become accustomed to the features of their plan and be willing to pay to stay. Recognizing this limitation, we: 1) compared beneficiary spending within type of plan and against other plans offered by their current insurer, 2) controlled for expected healthcare utilization using the CMS-assigned HCC risk score, and 3) used an individual-level fixed effect model. Our multivariate results did not appreciably change under any of these specifications.

The absence of MA claims data prevents us from developing comparisons of what beneficiaries would have actually spent on cost sharing had they chosen differently. Healthcare needs differ greatly, and some beneficiaries may have chosen plans that reduced their own exposure to medical costs (eg, richer coverage for anticipated stays in skilled nursing facilities), even though they appeared to pay more for premiums or OOPC. Nevertheless, the CMS OOPC measure is advantageous because it provides the actual information regarding ex ante financial risk that the beneficiary had when choosing a plan.

Due to CMS regulations requiring plans to develop provider networks as of 2011, many private FFS plans exited the market around that time. Some beneficiaries in our sample may have been exposed to this policy change and chose new plans. Because individuals who switch would have shorter tenure, thus leading to lower payments on average, this would imply our estimates are lower bounds of the long-term effect of tenure on payments in excess of the MESP.

There are several implications of beneficiaries paying more for premiums rather than OOPC relative to the lowest-cost plan available to them. First, MA beneficiaries could reduce their exposure to healthcare spending by switching to plans with lower premiums, although there may well be rational reasons for paying these costs, which we cannot observe.

Second, our results are consistent with a plan choice environment where beneficiaries are passively reenrolling in their MA plans from one year to the next. In such a marketplace, it would be logical for MA insurers to attempt to either increase premiums or beneficiary cost sharing in the hopes that they could profit from inertial consumers, and we find some evidence that premiums increase the longer beneficiaries remain in their plans.

We found that the amount of money “left on the table” increases with length of enrollment in a plan, suggesting that beneficiaries may not be actively and regularly comparing plans, and thus, are subject to a certain amount of inertia in their plan selections. One potential consequence is a diminished incentive for insurers to compete for enrollees by reducing premiums and tailoring benefits to consumer preferences. Changes to the current Medicare choice environment that increase the salience of premiums or encourage beneficiaries to compare plans more actively could reduce the number who passively reenroll in their plans and stimulate a more robust competitive environment for insurers.&ensp;

The authors are grateful to Lyle Nelson from the Congressional Budget Office (CBO), Linda Bilheimer (formerly at CBO), Paul Masi from the Medicare Payment Advisory Commission (formerly at CBO), Thomas Selden and Samuel Zuvekas from the Agency for Healthcare Research and Quality (AHRQ), and Rick Kronick from the University of California at San Diego (formerly at AHRQ) for their valuable contributions and comments on earlier versions of the manuscript.

 Agency for Healthcare Research and Quality (PDJ), Rockville, MD; Congressional Budget Office (EM), Washington, DC.

 Concept and design (PDJ, EM); acquisition of data (PDJ, EM); analysis and interpretation of data (PDJ, EM); drafting of the manuscript (PDJ); critical revision of the manuscript for important intellectual content (PDJ); statistical analysis (PDJ, EM).

 Paul D. Jacobs, PhD, Agency for Healthcare Research and Quality, 5600 Fishers Ln, Mailstop 07W41A, Rockville, MD 20857. E-mail: paul.jacobs@ahrq.hhs.gov. 

1. Medicare Advantage. Kaiser Family Foundation website. http://kff.org/medicare/fact-sheet/medicare-advantage. Published May 11, 2016. Accessed May 11, 2017.

2. Medicare Advantage 2016 data spotlight: overview of plan changes. Kaiser Family Foundation website. http://kff.org/medicare/issue-brief/medicare-advantage-2016-data-spotlight-overview-of-plan-changes. Published December 3, 2015. Accessed May 11, 2017.

3. Sinaiko AD, Hirth RA. Consumers, health insurance and dominated choices. 

. 2011;30(2):450-457. doi: 10.1016/j.jhealeco.2010.12.008.

4. McWilliams JM, Afendulis CC, McGuire TG, Landon BE. Complex Medicare Advantage choices may overwhelm seniors—especially those with impaired decision making. 

. 2011;30(9):1786-1794. doi: 10.1377/hlthaff.2011.0132.

POLICY

Compared with lower-cost plans, Medicare Advantage enrollees pay more for their plans the longer they remain enrolled.

How Do Medicare Advantage Beneficiary Payments Vary With Tenure?

 Hospital Compare, a website maintained by CMS, allows comparisons of outcomes and processes of care but not of patient satisfaction for hospitals within the Veteran Affairs (VA) Healthcare System. Therefore, we sought to compare online hospital ratings between VA hospitals and their local affiliated hospitals. 

 We identified 39 VA hospitals and a non-VA affiliated hospital with at least 2 online Yelp ratings. We determined the difference in the mean rating (VA-affiliate rating) with weighting by the number of ratings for each hospital. We used multivariate regression to compare mean Yelp ratings between VA and non-VA affiliate hospitals, adjusting for hospital characteristics (bed size, teaching status, and accreditation). 

The mean patient rating for VA hospitals (± standard deviation) was higher (3.64 ± 1.0) than the rating for affiliated hospitals (3.09 ± 0.8; 

= .0036). There was no significant correlation in rating between a VA hospital and its affiliate (r = 0.07; 

= .59). After adjustment for hospital characteristics, the adjusted rating difference (VA-affiliate hospitals) was 0.65 (95% confidence interval, 0.18-1.12). 

VA hospitals had higher patient ratings than their non-VA affiliated hospitals, a finding not explained by bed size or teaching status.

Patient satisfaction is an important measure of clinical care but cannot be compared between Veterans Affairs (VA) and non-VA hospitals using government data (eg, Hospital Compare).

Online patient ratings of hospitals (eg, Yelp) have been shown to correlate strongly with more conventional measures of patient satisfaction, as well as patient outcome.

VA hospitals had significantly higher Yelp ratings than non—VA-affiliated hospitals suggesting better patient satisfaction at VA hospitals.

The results were not explained by differences in hospital characteristics, such as size and teaching status.

Recently, the Department of Veterans Affairs (VA) started the Choice Program (section 101 of the Veterans Access, Choice, and Accountability Act of 2014), which pays for veterans to receive care at non-VA hospitals if care cannot be provided in a timely manner (within 30 days) at a VA hospital or if the patient lives a significant distance (greater than 40 miles) from one.

 Hospital Compare, a website maintained by CMS, allows comparisons of outcome and processes of care for VA and non-VA hospitals.

 Patient satisfaction is reported on Hospital Compare for non-VA hospitals only. Currently, the only way for patients to compare VA and non-VA hospitals on patient experience is through private online customer rating services (eg, Yelp). 

A recent study found a high correlation between Yelp ratings and the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores as reported by Medicare on Hospital Compare for non-VA US hospitals.

 Another comparison of HCAHPS and Yelp ratings for non-VA US hospitals found that Yelp ratings covered the main domains of HCAHPS while providing information on additional domains of patient experience.

 These data suggest that Yelp may be a reasonable alternative for estimates of the patient experience at VA hospitals. 

A common alternative to a VA hospital is an affiliated university hospital. Over 90% of VA hospitals are affiliated with medical schools and many share trainees and physician staff with university hospitals. We sought to compare online hospital ratings between VA and local affiliated hospitals. This allowed us to compare the patient experience at VA and non-VA hospitals in the same region that often share trainees and physicians. 

We searched Yelp for hospital ratings for the 131 VA hospitals from March 2014 to June 2014 and recorded the Yelp ratings 1 (worst) to 5 (best). Ratings were excluded if the category was specific to a department (eg, cafeteria). We only included hospitals if there were 2 or more online ratings. Therefore, we attempted to identify the local university hospital affiliate of each of the VA hospitals. If there was more than 1 local affiliate, we used the affiliate with the greatest number of online reviews. For 1 VA hospital for which there was no local affiliate, we selected a random hospital within 5 miles that had online reviews. We determined hospital characteristics (bed size, membership in the Council on Teaching Hospitals [COTH], presence of an accredited graduate medical education program, and certification by The Joint Commission [TJC]) using data from the American Hospital Association. 

We determined the mean Yelp rating for each hospital.

Continuous variables of hospital characteristics were compared between VA and non-VA hospitals using 

 tests. Categorical variables of hospital characteristics were compared using χ

 tests. We first compared the mean Yelp ratings of the VA and affiliated hospitals using an unweighted paired 

 test. In a sensitivity analysis, comparisons of mean ratings were weighted by the number of ratings using analysis of variance. The Shapiro-Wilk test did not reject the assumption of normality for the rating distribution (

= .94). We then examined the correlation (clustering) of online ratings for the pairs of VA and non-VA hospitals. Finding negligible clustering (see results for correlation), we used ordinary least squares multivariate regression to compare mean Yelp ratings, adjusting for hospital characteristics (bed size, membership in COTH, presence of an accredited graduate medical education program, and certification by TJC). We repeated the analyses limiting the sample to those with 5 or more, 10 or more, and 15 or more individual ratings. A 2-sided 

 <.05 was considered statistically significant. All analyses were performed with Stata version 11.0 (Stata Corp; College Station, Texas). 

We identified 39 VA hospitals (30%) and 39 non—VA-affiliated hospitals with at least 2 Yelp ratings. Hospital characteristics are displayed in the 

. Non-VA hospitals were larger than VA hospitals. The vast majority of both VA and non-VA hospitals were affiliated with medical schools, although non-VA hospitals were more likely to be a member of COTH. Almost all used TJC for hospital accreditation. VA hospitals with less than 2 Yelp reviews (excluded from the study, n = 92) were smaller and less likely to be academic than those VA hospitals with 2 or more ratings (

The mean patient rating (± standard deviation) for VA hospitals was higher (3.64 ± 1.0) than the rating for affiliated hospitals (3.09 ± 0.75; 

 test-unweighted). The median difference between VA and affiliate ratings (VA — non-VA) was 0.5 (interquartile range, –0.5 to 1.5). After weighting for the number of reviews (analysis of variance), the mean patient ratings were slightly higher for both VA hospitals (3.70 ± 0.74) and affiliated hospitals (3.19 ± 0.54), although the difference remained similar and significant (

= .0025). This difference in online ratings persisted when the sample was limited to dyads of hospitals (VA and non-VA) with more than 2 reviews (

). There was no significant correlation in rating between a VA hospital and its affiliate (r = 0.07; 

= .59). After adjustment for hospital characteristics, the adjusted rating difference (VA-affiliate hospital) was 0.65 (95% confidence interval, 0.18-1.12). 

Patient satisfaction with care is increasingly recognized as important by payers, including CMS. Hospital ratings on the HCAPHS surveys are publically reported

 and used as part of pay-for-performance programs. Patient-initiated reviews have limitations, but a recent study found a strong correlation (r = 0.49; 

<.0001) between Yelp reviews and HCAHPS scores for non-VA US hospitals.

 At the time of the study (2011), 25% of all non-VA US hospitals had a Yelp review, and this percentage increased over time—this percentage is consistent with our ability to find reviews for 30% of VA hospitals. The mean score for all non-VA US hospitals was 3.3 in 2011, slightly higher than the non-VA hospital score in our study. 

Although Yelp reviews describe patient experience, they may also be a marker of patient outcome. Our study was not powered to detect differences in outcome; however, the Bardach study

 found that high Yelp ratings were significantly associated with lower 30-day all-cause mortality following an admission for myocardial infarction or pneumonia and lower 30-day all-cause readmissions following a discharge for myocardial infarction, heart failure, or pneumonia.

Our study is potentially limited by our comparison group, which included local university-affiliated hospitals that often share trainees and faculty with the VA hospital. Although veterans may often choose the local affiliate hospital to receive care, our results may not be applicable to VA comparisons with non-affiliated community hospitals. We excluded 70% of VA hospitals with fewer than 2 reviews and these were smaller and less academic than the included studies sites. A prior study found that smaller hospitals had higher patient satisfaction ratings than larger hospitals although there was no significant impact of teaching status on ratings.

 Differences in patients could explain the results. For example, we cannot know if veterans are more likely than nonveterans to rate all hospitals highly.

We found that VA hospitals have higher patient ratings than their affiliated hospitals, a finding not explained by bed size or teaching status.

 Veterans Affairs Palo Alto Healthcare System (PAH, AZ, NO, AS), Palo Alto, CA; Department of Medicine, Stanford University School of Medicine (PAH, LS)

US Department of Veterans Affairs HSR&D CHF 04-236.

 The hospital where Drs Heidenreich, Zapata, Oliva, and Sahay were employed was one of the study sites. 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.

The mean online patient rating for Veterans Affairs hospitals was higher (3.70 ± 1.3 out of 5) than the rating for affiliated hospitals (3.19 ± 1.3; P = .003).

June 2017

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