Stuart R. Lipsitz, ScD

To compare geographic variation in healthcare spending and utilization between the Military Health System (MHS) and Medicare across hospital referral regions (HRRs).

 Data on age-, sex-, and race-adjusted Medicare per capita expenditure and utilization measures by HRR were obtained from the Dartmouth Atlas for 2007 to 2010. Similarly, adjusted data from 2007 and 2010 were obtained from the MHS Data Repository and patients assigned to HRRs. We compared high- and low-spending regions, and computed coefficient of variation (CoV) and correlation coefficients for healthcare spending, hospital inpatient days, hip surgery, and back surgery between MHS and Medicare patients.

 We found significant variation in spending and utilization across HRRs in both the MHS and Medicare. CoV for spending was higher in the MHS compared with Medicare, (0.24 vs 0.15, respectively) and CoV for inpatient days was 0.36 in the MHS versus 0.19 in Medicare. The CoV for back surgery was also greater in the MHS compared with Medicare (0.47 vs 0.29, respectively). Per capita Medicare spending per HRR was significantly correlated to adjusted MHS spending (r = 0.3; 

 <.0001). Correlation in inpatient days (r = 0.29; 

 <.0001) was also significant. Higher spending markets in both systems were not comparable; lower spending markets were located mostly in the Midwest.

 In comparing 2 systems with similar pricing schemes, differences in spending likely reflect variation in utilization and the influence of local provider culture.

		Geographic variation in healthcare spending and utilization within the Military Health System (MHS) is higher and significantly correlated with Medicare across hospital referral regions. 

		Results contrast the negative correlation in spending between Medicare and the commercial sector. 

		Higher variation in the MHS may reflect inherent variability in the health status of the MHS population. 

		Positive correlation between spending and certain measures of utilization may suggest a strong impact of local provider culture on utilization. 

		Our findings inform research that explores the impact of pricing schemes and local provider culture on unwarranted variation in utilization and spending.

Geographic variation in per capita healthcare spending is a well-documented phenomenon; however, its causes are less defined.

 Many have argued that such variation is unwarranted, especially in light of research showing that spending may be negatively correlated with quality of care and does not result in improved health outcomes or patient satisfaction.

A number of possible explanations for geographic variation have been studied. In a price-adjusted analysis of geographic variation in spending, Gottlieb et al found that administered prices set by Medicare in different regions accounted for only a small fraction of the variation observed. They attributed the high variation in spending to differences in utilization across regions.

Differences in the underlying health status of populations may be another justifiable cause of variation. Although some researchers did not find that higher-spending regions cared for sicker patients,

 others suggest that regions with sicker patients exhibit higher spending.

 Such approaches are limited; however, higher-spending regions may be more likely to perform tests that diagnose illnesses that would otherwise not have been diagnosed.

 Others believe that local culture is the predominant source of regional variation, perhaps due to a “specialist-oriented” practice pattern in high-spending regions,

 or hypothesize that regional differences result from increased utilization related to supply of physicians and hospital resources. For instance, physicians have been shown to modify admission and discharge of their patients based on the availability of intensive care unit beds by admitting patients with less-severe illnesses or extending length of stay when more beds are available.

Although the majority of geographic variation research has relied on Medicare data, 64.2% of the population is covered by private insurance compared with only 15.6% covered by Medicare.

 An avenue to better understanding the factors influencing geographic variation in healthcare spending is to compare these findings with other payers or providers. Such elucidation is important in defining policy and allocating national financial resources. If similar variation occurs in other health systems and in the same areas, it would suggest that a strong local provider culture may play a role in driving health spending, regardless of the payer. A few studies have examined variation in commercial healthcare systems to identify patterns and drivers of spending variation.

Chernew et al explored spending patterns by comparing Medicare spending with large commercial firm healthcare spending by hospital referral region (HRR).

 They found a higher degree of regional variation within the commercial insurance market compared with Medicare. Their results showed a negative correlation in per capita spending between payers both before and after adjusting for age and sex differences; however, their measure of utilization, inpatient days per capita, was significantly correlated due to the potential effect of local provider culture in both systems. In the US Department of Veterans Affairs (VA) healthcare system, the Congressional Budget Office initially found that geographic variation in per capita spending was lower compared with Medicare. However, while the variation in spending within the VA increased from 2001 to 2007, it fell in the Medicare program from 2001 to 2005.

 It appeared that the VA’s centrally funded system and systemwide evidence-based practice guidelines may not have been sufficient to minimize variation, which, again, was perhaps influenced by local practice patterns.

Another federal system worthy of analysis is the Military Health System (MHS). TRICARE, the healthcare benefit program for MHS beneficiaries, serves approximately 9.5 million active duty and retired military personnel and their dependents.

 Approximately 15% of beneficiaries are active duty service members; the remaining beneficiaries are predominantly spouses, children, and retirees.

 TRICARE is separate from the VA, which primarily caters to veterans; its beneficiaries are surprisingly comparable to the privately insured population in terms of sociodemographics. 

Our analysis focused on TRICARE Prime (a health maintenance organization [HMO]—like system) enrollees who were assigned a primary care manager to oversee their total care, making it more likely that complete episodes of care occurred within the purview of TRICARE. In addition, these beneficiaries had either very low or no out-of-pocket costs. Compared with the VA, whose beneficiaries receive care through a network of VA facilities, these enrollees can receive care in both the budget-based direct care system (in Military Treatment Facilities [MTFs]) and the fee-for-service (FFS) purchased care system (managed by civilian providers).

 Because Prime enrollees tend to be concentrated near military facilities, the percentage of direct care provision varies significantly. TRICARE therefore offers a unique perspective on variation given its centrally managed dual system of care and its use of administrative pricing in the community setting. Taken together, these characteristics suggest lower geographic variation in costs and utilization than for other payers, although local market factors and practice styles may still play a role. 

We had 2 analytic goals: 1) to investigate variation by HRR in the MHS and 2) to compare the variation in the MHS with Medicare as documented in the Dartmouth Atlas, which compiles data from the American Medical Association’s master file and the American Hospital Association’s annual survey.

 We analyzed per capita costs and 3 utilization measures—hospital inpatient days, back surgery, and hip replacement surgery&mdash;to explore the possible effects of discretion on variation. We hypothesized that, given the centralized system of care within the MHS, geographic variation in healthcare spending and utilization within the MHS would be lower compared with Medicare.

Data were obtained from 2 sources: Medicare age-, sex-, and race-adjusted Part A and Part B per capita spending and utilization for 306 HRRs were obtained from the Dartmouth Atlas for the years 2007 to 2010.

 These estimates represented 20% of the FFS population and relied on Medicare claim files.

 We included Medicare beneficiaries 65 years or older and total spending (including fees for physicians, hospital and skilled nursing facilities, outpatient facilities, home health agencies, hospice care, and durable medical equipment). Spending rates, as documented in the Dartmouth Atlas, were calculated from Medicare claims; patients enrolled in HMOs were excluded.

The total MHS per capita spending and utilization data for 2007 and 2010 were obtained from the MHS Data Repository. Spending data for purchased care, which used administered pricing similar to Medicare, were obtained from claims using variables representing total payments made. Direct care costs were patient-level cost allocations of the total costs of an MTF, based on clinic-reported workload and expenses. Total costs from the direct care sector and payments from the purchased care sector thus represent the overall cost to TRICARE. In 2016,approximately 4.9 million beneficiaries were enrolled in TRICARE prime, and of those, 32% (1.58 million) were on active duty.

 Beneficiaries included active duty military personnel, retirees, and dependents older than 18 years residing within the United States and enrolled in a TRICARE Prime region within the United States. Overseas enrollees, wards, foster children, step children, step parents, and former spouses were excluded from the analysis, in addition to beneficiaries with missing or inconsistent sponsor information (2.8% to 3% of enrollees), resulting in a study population of approximately 3.2 million. This study was approved by the Partners Institutional Review Board (Protocol #2011P002080).

Patients in the MHS were assigned to 1 of 306 HRRs based on residence zip codes, using the zip code crosswalk obtained from the Dartmouth Atlas.

 Adjustments for age, race, and gender within the MHS were performed using the indirect method according to the Dartmouth Atlas methodology.

 In this process, missing race was imputed for dependents using the sponsor’s race (40.3%), following the methods of Stewart et al.

Mean per capita spending and utilization rates were determined across all 4 years for each HRR in both healthcare systems. Coefficients of variation (CoVs) and interquartile ranges (IQRs) were calculated for spending and utilization measures. We calculated the correlation for MHS versus Medicare spending and utilization and examined high- and low-spending HRRs for each. The threshold for statistical significance was

<.05. We also examined market characteristics, including physician and hospital resources of the high- and low-spending MHS and Medicare HRRs for the year 2006. These were also obtained from the Dartmouth Atlas. 

The average number of enrollees per year was 3.2 and 5.1 million within the MHS and Medicare groups, respectively. The populations differed demographically. Approximately 34% of the Medicare cohort was 80 years or older, the average proportion of females was 57.6%, and the highest proportion of black beneficiaries was 6.3%. Within our MHS Prime population, 4.4% of beneficiaries were 65 years or older. Females represented 45% of beneficiaries, of whom approximately 17% were African American. Medicare patients were more geographically dispersed within the United States, with a few highly populated HRRs located on the East Coast. The MHS beneficiaries were mostly located in the Midwest and southern United States (

). Per capita Medicare spending per HRR from 2007 to 2010 averaged $8752 (range = $6300-$15,000). As expected, this was more than 3 times higher than the MHS, with an average per capita spending of $2504 (range = $1223-$5221). About utilization, Medicare beneficiaries averaged 1.62 hospital inpatient days per year, which is greater in frequency than that of MHS beneficiaries, at 0.49. In addition, Medicare patients underwent more hip and back surgeries per 1000 patients than MHS beneficiaries, as would be expected in an older population (

There was also significant variation in spending and utilization across HRRs in both the MHS and Medicare populations. The CoV for spending was higher in the MHS compared with Medicare (0.24 vs 0.15, respectively), and the IQR (75%/25%) was also higher in the MHS (1.41 vs 1.21, respectively) (

Similarly, measures of utilization showed greater variation within the MHS: the CoV for inpatient days was 0.36 in the MHS compared with 0.19 in Medicare; the CoV for back surgery was 0.47 versus 0.29, respectively; and the CoV for hip surgery was 0.69 versus 0.23.

Adjusted per capita Medicare spending per HRR was moderately, but significantly, correlated with adjusted MHS spending (

<.0001). Similarly, correlation in hospital inpatient days between both systems was significant (

<.0001). Back surgery was highly correlated between both systems (

<.0001); however, hip surgery was not significantly related. 

The highest spending Medicare markets were spread out geographically and included Miami, Florida, and McAllen, Texas (

). These markets did not rank highly in spending within the MHS. For instance, Manhattan was the third highest-spending HRR within Medicare, but ranked 276th (of 306 HRRs) within the MHS (Table 3). High-spending HRRs within the MHS included Washington, DC, and San Antonio, Texas—areas with a significant direct care footprint but lower Medicare rank (Table 3). Notably, there was greater similarity in low-spending HRRs. For instance, Dubuque, Iowa, ranked 305th in Medicare spending and 307th in the MHS. Lower-spending markets in both systems were located mostly in the Midwest (Table 3). 

An examination of healthcare resources between top- and bottom-spenders in both systems revealed consistent and anticipated patterns. In both systems, the top-spending HRRs were more heavily populated and had a comparable number of acute care beds per capita and a smaller ratio of primary care providers to specialty providers per capita. The relationship of this ratio between top and bottom spenders was consistent with the documented positive correlation between the number of specialists and more costly care (

Geographic variation in healthcare spending and utilization has gained a renewed focus in the climate of healthcare reform.

 In an attempt to elicit underlying causes for the unexplained variation, prior studies have compared Medicare spending with commercial healthcare markets

 Our study compared Medicare with the MHS, with a similar pricing scheme, in order to focus on the impact of utilization on healthcare costs.

Using age-, gender-, and race-adjusted data, we showed that variation of spending and utilization were higher in the MHS than Medicare. Higher variation in the MHS may have reflected inherent variability in the health status of the more demographically diverse MHS population. Additionally, the higher variation in the MHS may be partially explained by unobservable differences in patterns of care between the direct- and purchased-care sectors. In the Medicare cohort, the CoV for hip surgery was lower than that of back surgery, which is consistent with the fact that lower variation procedures typically tend to be nondiscretionary and physicians generally agree on the best treatment strategy.

 In the MHS population, however, the CoV for hip surgery was higher than that of back surgery, which is likely due in part to the very low rate of hip surgery in this population (approaching 0), which can drive up CoV values. 

Medicare sets prices for hospital reimbursement; however, in the commercial sector, prices are derived from negotiations between insurers and providers in a process influenced by competition between these 2 groups. Like Medicare, administrative pricing is also utilized in MHS for purchased care and is related by law.

 Therefore, price is less likely to explain the higher spending in Medicare compared with MHS, which is also supported by the higher degree of variation in utilization in both systems compared with the degree of variation in spending. Although the pricing schemes in the 2 systems are related, justifiable deviation from standardized indices is permitted.

 This may have resulted in subtle differences in how prices were set that may have allowed the MHS to take advantage of provider competition more effectively than Medicare and, consequently, reduced overall spending. These differences may stem from the difficulty in determining the appropriate price when actual costs are unknown, fluctuate over time, reflect discretionary provider culture, or are manipulated to benefit certain areas.

 For instance, if the MHS is more competitive in certain regions, the impact of provider market power on how prices are set in that region may be limited, potentially contributing to lower prices and decreased spending. 

The lack of positive correlation between commercial and Medicare spending across the United States has been partially attributed to administrative pricing by Medicare that is absent from the commercial healthcare market.

 This would imply that although variation in quantity of care is the main driver of spending in Medicare, variation in the privately insured market is largely driven by variation in provider prices, which is associated with hospital market power.

 We demonstrated a moderate direct correlation between Medicare and MHS spending after adjusting for age, gender, and race in both populations. This likely reflects similarity in pricing schemes between both systems. Likewise, utilization measures of inpatient days and back surgery were moderately significantly correlated; however, the hip surgery measure was not

The positive correlation between spending and certain measures of utilization may suggest the strong impact of local provider culture on utilization. Spending and hospital inpatient days have the same degree of correlation between both systems (0.3 and 0.29, respectively), likely reflecting the significant contribution of inpatient hospital care to total healthcare costs, which is supported by an intra-system assessment showing that correlation of spending and inpatient days within both systems was highly correlated (MHS: 0.5; Medicare: 0.6).

Despite moderate correlation in spending and utilization, we did not find a strong geographic pattern when we examined top-spending HRRs in Medicare and the MHS. McAllen, Texas—well known for high healthcare spending

—appeared as a top spender in our Medicare analysis. It ranked 141st of the 306 HRRs in the MHS. Similarly, although Takoma Park, Maryland, ranked high in the MHS population, it ranked 99th in Medicare. A simple explanation could be that high utilizers in each system tended to be located in different geographic areas and that local practice culture varied with the insurance market, forcing providers to adjust levels of utilization based on the predominant insurer in the region. Lower spending markets in both health systems appeared to be concentrated in the Midwest, suggesting that the local culture of utilization by providers had a larger impact in that region regardless of the insurer. The composition of the physician workforce may also impact the degree of spending, regardless of the insurer, as shown by the larger number of specialists located in top-spending MHS and Medicare markets compared with lower-spending markets. Although this finding was demonstrated previously in the Medicare population, the association between population concentration and quality of care delivery was less certain.

We showed higher variation within the MHS compared with Medicare, using HRRs as the unit of analysis. Although this examination of the MHS was novel, there were limitations to our study. Following the Dartmouth Atlas methodology, we did not adjust for health status, which may explain some of the variability in our study. Future studies should compare spending and utilization within the MHS to commercial healthcare, given the similar health profiles in these populations. We also did not assess price directly; however, TRICARE generally follows Medicare reimbursement levels and methods for purchased care.

 In addition, we did not assess market resources, such as physician and hospital capacity, which may impact administrative pricing, or study outcomes of care, which could justify higher spending in certain locations. Although we combined spending from the direct and purchased sectors of care within the MHS to calculate total costs, the individual costs for these 2 sectors were derived differently. Direct sector costs are patient-level cost allocations, and purchased sector costs are claims-based reimbursements. Thus, although purchased-care sector costs generally follow Medicare rules, direct-care sector costs do not, and this may impact comparisons of cost between the MHS and Medicare. Finally, we assessed differences in variation using correlation, preventing our ability to comment directly on causal relationships.

In comparing 2 systems with similar pricing schemes, differences in spending likely reflected variations in utilization and the influence of local provider culture. Our analysis found greater variation in the MHS compared with Medicare. It seems that unwarranted variation, largely attributable to utilization, is an issue deserving of further research to improve system performance through enhanced coordination and integration and to inform future managed care support contract negotiations. Research exploring the MHS compared with a more similar beneficiary demographic, such as the commercial insurance market, may be useful in this endeavor.

 Center for Surgery and Public Health, Department of Surgery, Brigham and Women’s Hospital (TA, SRL, LLN, JSW), Boston, MA; Uniformed Services University of the Health Sciences (LGK, SO), Washington, DC; The Dartmouth Institute (PG), Hanover, NH.

 Department of Defense, Uniformed Services University, Grant HU001-11-1-0023. The Henry Jackson Foundation grant funds this research.

 Dr Kimsey is an employee of the Department of Defense and is on active duty. 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. 

 Concept and design (TA, LGK, JSW, SRL); acquisition of data (TA, LGK, PG, JSW); analysis and interpretation of data (TA, LGK, LLN, PG, JSW, SRL); drafting of the manuscript (TA, LGK, JSW, SRL); critical revision of the manuscript for important intellectual content (TA, LGK, LLN, PG, SO, SRL); statistical analysis (TA, LGK, SRL); obtaining funding (LGK, LLN, SO); and supervision (JSW). 

 Taiwo Adesoye, MD, MPH, Brigham and Women’s Hospital, 1620 Tremont St, 4-020, Boston, MA 02120. E-mail: taiwo.adesoye@gmail.com. 

1. Skinner J, Fisher ES. Reflections on geographic variations in U.S. health care. The Dartmouth Atlas of Health Care website. http://www.dartmouthatlas.org/downloads/press/Skinner_Fisher_DA_05_10.pdf. Published March 31, 2010. Updated May 12, 2010. Accessed December 17, 2015. 

2. Fisher ES, Wennberg DE, Stukel TA, Gottlieb DJ, Lucas FL, Pinder EL. The implications of regional variations in Medicare spending. part 1: the content, quality, and accessibility of care. 

Articles

Geographic variation in healthcare spending and utilization within the Military Health System is higher and significantly correlated with Medicare across hospital referral regions.

Geographic Variation in Medicare and the Military Healthcare System

 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. 

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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. 

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

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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 examine changes in patient experience across key domains of the patient-centered medical home (PCMH) following practice transformation with Lean quality improvement methodology inclusive of payment reform.

Pre-intervention/post-intervention analysis of intervention with a comparison group, a quasi-experimental design. We surveyed patients following office visits at the intervention (n = 2502) and control (n = 1622) practices during the 15-month period before and 14-month period after PCMH Lean transformation (April-October 2009).

We measured and compared pre-intervention and post-intervention levels of patient satisfaction and other indicators of patient-centered care. Propensity weights adjusted for potential case-mix differences in intervention and control groups; propensity-adjusted proportions accounted for physician-level clustering.

More intervention patients were very satisfied with their care after the PCMH Lean intervention (68%) compared with pre-intervention (62%). Among control patients, there was no corresponding increase in satisfaction (63% very satisfied pre-intervention vs 64% very satisfied post-intervention). This comparison resulted in a statistical trend (

 = .10) toward greater overall satisfaction attributable to the intervention. Post-intervention, patients in the intervention practice consistently rated indicators of patient-centered care higher than patients in the control practice, particularly in the personal physician and communication domain. In this domain, intervention patients reported superior provider explanations, time spent, provider concern, and follow-up instructions compared with control participants, whereas control group ratings fell in the post-intervention period (

 In a pilot PCMH transformation including Lean enhancement with payment reform, patient experience was sustained or improved across key PCMH domains.

Using a quasi-experimental design, we evaluated patient experience following patient-centered medical home (PCMH) practice transformation with Lean enhancement coupled with payment reform.

		Compared with controls, intervention participants were more likely to report faster speed of registration (

 = .04) and provide higher ratings across the personal physician and communication domain (

		62% of intervention participants were very satisfied pre-intervention versus 68% post-intervention (

		Future studies are needed to determine if physicians, over time, will thrive under an adjusted salary-based reimbursement system in the PCMH.

The patient-centered medical home (PCMH) has emerged as a patient-focused, quality-driven, cost-effective model to revive primary care. A growing body of literature supports the notion that health systems emphasizing the role of primary care can achieve superior outcomes at a lower cost.

 The PCMH joint principles were developed into a set of best practices by the National Committee for Quality Assurance and have been used as the blueprint for organizational change for many recent PCMH demonstration projects. As preliminary results of PCMH demonstration projects emerge, successes have shown that the PCMH has the potential to reduce cost and improve quality,

 although transformation is often challenging

 and short-term declines in satisfaction may be observed following interventions to implement the PCMH model.

 Coupling practice transformation with payment reform may encourage more rapid adoption of the PCMH model, and both private and public payers are starting to invest heavily in such transformation pilots. Lean quality improvement methodology have been incorporated with some PCMH transformations to help facilitate PCMH practice improvements, but did not integrate changes to the current resource-based relative value scale.

 Therefore, if the PCMH model is to be widely utilized, it will be vital to demonstrate that its implementation with concurrent payment reform results in favorable outcomes with respect to quality and cost, as well as patients’ perceptions of care.

Overlooked for many years, patient experience has recently gained increasing prominence. The Affordable Care Act of 2010 calls for novel approaches to increase patient access and enhance patient-centered care in order to improve patient experience and outcomes.

In general, patient-centered care experiences and outcomes-oriented research are becoming increasingly recognized as a core element of quality healthcare delivery in the United States and other countries.

 In their pursuit of a 3-part aim to improve quality, cost, and patient experience, some health plans and health systems have begun to link measures of patient satisfaction to provider payment.

Few data have been available regarding the effect of PCMH transformation, with or without Lean quality improvement methodology, on patient experience in the setting of payment reform. To assess the impact on patient experience of transformation of care to a medical home linked with implementation of a new physician reimbursement scheme, we undertook a 3-year quasi-experimental study. We assessed patients’ experience with office visits at a clinic site where these interventions were implemented compared with a control site, both within a high-performing, large, multispecialty group practice. We evaluated the effect of PCMH transformation, including Lean quality improvement methodology, on patient experience with a widely used survey for measuring patients’ experience with outpatient care.

Following Cook and Campbell’s classification of quasi-experimental studies, we used an Untreated Control Group Design with Pretest and Posttest.

 In both the experimental and control groups, we conducted cross-sectional surveys of patients’ experiences  with care before and after the implementation of the PCMH model in the experimental site. We studied responses to a mail copy of the medical experience survey developed by Press Ganey (www.pressganey.com), a widely used, commercially available tool for measuring patients’ experience with outpatient care. All surveys were mailed 1 week after a patient’s encounter. Survey response rates across all clinic sites in the integrated system were 28.1% in 2008, 28.3% in 2009, and 36.6% in 2010. The study protocol was approved by the Partners Healthcare Institutional Review Board.

The study was conducted within Harvard Vanguard Medical Associates, a large multispecialty group of 14 practices in eastern Massachusetts, frequently recognized for achieving high scores in quality metrics.9 For this study, we included 2 of the 14 practices: 1 practice that underwent PCMH transformation with payment reform in 2009 and 1 practice,12 miles away with a similar demographic composition, that served as the control. Patients who had at least 1 internal medicine office visit from 2008 through 2010 were stratified by primary care provider. For each provider, surveys were mailed to 200 randomly selected patients, evenly distributed over the course of each of the 3 study years; each patient was surveyed only once per year. Between January 2008 and June 2009 (pre-intervention), a total of 2027 patients completed the survey: 1224 at the intervention site and 803 at the control site. Between July 2009 and December 2010 (post-intervention), we collected 2097 completed surveys: 1278 from the intervention site and 819 from the control site. Intervention and control participants were propensity weighted to adjust for possible case-mix differences between these 2 groups (see the Statistical Analysis section for more detail). 

The intervention was a pilot that started in April 2009 in the internal medicine department of 1 clinic site. It consisted of team restructuring in addition to a series of process improvements including use of Lean reengineering,

 centered around optimizing chronic disease management and reassigning tasks traditionally performed by a physician to medical assistants or nurses. For example, nurses were trained to use medication titration protocols for virtual management of many chronic diseases, shifting simple visits off provider schedules, and medical assistants took over many administrative duties traditionally completed by physicians. Components of the intervention are summarized in 

In a pilot patient-centered medical home transformation including Lean quality improvement methodology with payment reform, patient experience was sustained or improved across key domains.

Stuart R. Lipsitz, ScD

Articles

Geographic Variation in Medicare and the Military Healthcare System

Radical Prostatectomy Innovation and Outcomes at Military and Civilian Institutions

Patient-Centered Medical Home Transformation With Payment Reform: Patient Experience Outcomes

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