Thomas R. Graf, MD

WE ARE IN a unique time in healthcare. Although there are periodic crises of cost in medicine, this time we have a convergence on quality that has produced a consensus around 3 things: quality measures, the ability to measure quality in a granular fashion, and the desire for patient-oriented outcomes. This convergence has created a sharper focus on accountability for quality. Combining these new foci has resulted in a drive to improve the value proposition of medicine in America.

Currently we spend more than $3 trillion annually

 on care that is highly variable in terms of quality, clinical outcomes, and cost; diferences in spending can be 10-fold or greater.

Given that we are paying a signifcant premium for healthcare relative to other industrialized nations, there should be a more consistently excellent product.

So, as healthcare providers and healthcare payers are under intense scrutiny to reliably deliver measurably better health, they are looking to all elements of the health supply chain to deliver that value.

At the same time, the cost to bring a therapy through the FDA-approval process keeps rising, and pressure from generics and alternative medications is growing. Increased sophistication of the mechanisms of action of these new drugs, along with expectations from venture capital frms and shareholders, has pushed the overall cost of pharmaceuticals past what we spend on physicians in the United States. Today, pharmaceutical spending is second only to the spend on hospitals.

This has led to potential innovation in the pricing and positioning of new million-dollar therapies, as the drive to deliver true value for patients/plan members intensifes.

For many years, payers and, to some extent, providers have been shifting from traditional fee-for-service care toward models that reward results. Tentative steps such as paying for process metric achievement for primary care physicians to more quality gated gainsharing models have given way to far more performance risk sharing, where providers are focused on total medical expense budgets or partial capitation agreements. Some states, such as California and Maryland, have even more advanced models. Despite these advances, the share of healthcare spending tied to these new models remains small and the pressure to develop ways to drive quality improvement as a cost-saving measure continues to mount.

Additionally, as new and very expensive therapies are developed, the desire to ensure efcacy at these prices is even greater. The need to show that a medication can change a disease trajectory and afect an outcome that patients, providers, payers, and employers (or whoever the ultimate payers is) care about is paramount.

This begs the question: what does each group care about? The simple answer is that each stakeholder cares about many things and many concerns overlap

 care about the impact on their lives and functional status. They care what a drug costs them out of pocket, and they care somewhat about the overall cost.

 have similar concerns, but they focus more on cost than individual responsibility.

 care about tho same outcomes as patients and employers, but with a greater focus on medical quality in addition to impact, and are interested in intermediate outcome measures far more than patients.

 care about medical quality and increasingly about total cost, but they also worry about the customer service experience of members and how drug costs afect employers.

The other important consideration is the time horizon of the impact. For patients, it is their lifetime; for employers, the duration of employment; for payers, the period of coverage; and for providers, the length of time they spend caring for each patient. These diferent time horizons create additional challenges in thinking through how to create a successful value equation.

The ideal is a program that improves medical quality in a way that is visible and important to patients and lowers the total cost of care in a year or less (for Medicaid, perhaps in a month or less). This is very hard to create. Readmission reductions programs are a great example: They have a big impact and quickly and defnitively impact cost. Medical quality is often more about the reliable delivery of the best care.

This creates a real challenge for pharma. While most drugs take considerable time to afect outcomes that matter to patients and translate into cost reductions, the impact on medical quality may come quickly. In diabetes, for example, although many medications have been shown to reduce glycated hemoglobin levels within several months, which makes doctors happy, they take far longer to impact heart attack rates, which is a measure that patients can appreciate. Insurers, meanwhile, often don’t see value until an avoided heart attack translates into lower costs for patients—who are not admitted for the heart attacks they don’t have. And the employer won’t see the impact until enough employees avoid heart attacks to actually bring down future rate increases. This is a signifcant issue now that some drugs cost hundreds, thousands, or tens of thousands of dollars per month.

For patients with diabetes, thus far, the proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor, evolocumab (Repatha) has been shown to reduce cardiovascular (CV) events (eg, hospitalization, heart attack and stroke), which is an incredibly important outcome for a patient. This happens in a clinically relevant time horizon (median 26 months), which is important for patients and their employers and from a public policy standpoint. Politicians have chosen to weigh in on mandating coverage of even marginally efective treatments (Ornish intensive cardiac rehab for Medicare for instance) and a case could be made that the evidence is powerful. However, insurers will be challenged by creating appropriate rules so that this medication is used only for those patients who will truly beneft because of implementation issues.

Clearly, reducing CV events will be important for both quality and cost. However, since many patients are currently well treated with much less expensive medications, and many more could be if they were maximally managed, there is a risk that patients will receive these $14,000-per-year drugs when they really do not need them.

If patients are exposed to side efects and complications of PCSK9 inhibitors, this would impact the cost savings they create. Additionally, all cause morbidities and mortality and cost need to be considered. Reducing CV complications alone is not enough.

An analysis of the total medical expense would be helpful and key to the real value equation. Understanding the quality improvements, both on CV outcomes and overall health, is the frst step. We need to know mortality impact, morbidity impact, and utilization impact to measure the positive effects. We also need to know the full cost, not just of the medication, but of managing the complications, the natural course of progression, and the cost of monitoring. From an employer and patient perspec tive, the lost work time and other “life” efects are very important but largely invisible to the insurer and to the physician.

There’s also the challenge of defning a realistic comparator. Total medical expense, a likely best candidate, is often harder to determine than it would first appear—and absent a solid comparator&mdash;the relative impact is impossible to judge. Pharma has ofered several interesting ideas for how this might be managed:

 We are now well past the era of using volume discounts to help control costs. The goal today is controlling the total cost of care, especially pharmaceutical costs, which have outpaced the overall medical spend. Some have looked at rebates or refunds if certain complications occur, such as heart attacks.

 The challenge is that these practices do not necessarily support the optimal use of the medication, that they encourage widespread marketing to the lowest-risk patients to ensure complications do not occur too frequently. This also does not get at the underlying issue: the therapy may simply cost too much. Offering a 30% reduction for certain patients on a drug that is 50% overvalued does not control costs.

 The idea of not charging patients who do not respond to a treatment is another approach. This model is uniquely suited to the high-cost specifc-use medications being introduced in cancer care; it would fail for other drug classes where determining response is more difficult. To truly develop comprehensive accountability, a program that includes annual total cost of care and trend impact is needed. The challenge here is that there are many factors outside of a specifc disease and certainly beyond a drug’s impact that afect the total cost of care. Many pharmaceutical companies have developed or partnered with others to provide wrap-around services to improve adherence and modify lifestyle—offering stress reduction treatments or help with exercise and diet to support improved outcomes and reduced healthcare utilization. However, these services may interfere with similar programs at the health plan or provider level. Also, these programs are either not explicitly tied to cost or only focus on the cost of the specifc diagnosis or disease state. This makes perfect sense from a pharmaceutical and provider perspective. However, from the payer, employer, and patient perspectives, programs that reduce CV costs and utilization but raise costs in other areas are not helpful. Proving causality or even an indirect relationship between the two is even harder.

For this approach, a collaboration between physicians, other providers, and drug manufacturers would help with the all-encompassing nature of total medical expenses. However, this only becomes practical for companies that have medications for multiple disease states and systems that have large numbers of patients. For insurers, this works only if the same pharma—provider coalitions care for a signifcant number of their members. The practical application of these global innovations is challenging.

What can we really do? How can we move forward? It is clear that there is no silver bullet to improving quality while driving down cost in healthcare; in the pharmaceutical arena, as well. A tailored approach blending all the ideas will be necessary. We also need to look for related areas in which we can eliminate non—value-added costs from the system. Examples include site-of-service issues, where the infusion location often translates into a 2- to 10-fold cost difference, or cost-plus-percentage markups for medications that work well when medications are priced in the hundreds of dollars but fail when prices reach $500,000. These are simple changes that can impact cost that do not really create accountability but do rationalize the overall pharmaceutical spend.

Creating a direct link between the impact of the proper use of the medication and the corresponding price paid is critical to the long-term success of healthcare. The keys are:

Reaching agreement among payers, providers, and pharma on a process for measuring the direct and long-term impacts, quality, utilization, and cost of new therapies.

Agreeing on a process to ensure selection of the optimal patients for each new therapy, along with a mechanism to create accountability for patient selection.

For many, the idea of a rebate for treatment failures or complications makes sense; for others, the idea of overall cost of care for a specifc disease category on an annual basis works; for still others, By working through these areas and tailoring the methodology to the disease state and specifc medication profle, we can best make lasting progress to drive quality improvement to reduce total cost of care rather than just cutting a few dollars today.

Dr Graf is vice president and chief medical officer, Horizon Blue Cross Blue Shield of New Jersey. 

1. National Health Expenditure fact sheet. Centers for Medicare & Medicaid Services website. cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/nationalhealthexpenddata/nhe-fact-sheet.html. Updated June 14, 2017. Accessed November 24, 2017.

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. 2016;22(2):102-109. doi: 10.18553/jmcp.2016.22.2.102.

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4. Glied S, Ma S, Solís-Román C. Where the money goes: the evolving expenses of the US health care system. 

. 2016;35(7):1197-1203. doi: 10.1377/hlthaff.2015.1356.

5. Keshavan M. We may soon have our first $1 million drug. Who will pay for it? And how? 

. October 13, 2017. statnews.com/2017/10/13/gene-therapy-pricing/. Accessed November 24, 2017.

6. Blumenthal D, Abrams MK. Tailoring complex care management for high-need, high-cost patients. 

. 2016;316(16):1657-1658. doi: 10.1001/jama.2016.12388.

7. Davidson SM, Shwartz M, Stafford RS. The feasibility and value of new measures showing patterns of quality for patients with three chronic conditions.

. 2008;31(1):37-51. doi: 10.1097/01.JAC.0000304096.59205.e0.

8. Blumenthal D. Value-based drug pricing: watch out for side effects. The Commonwealth Fund website. commonwealthfund.org/publications/blog/2016/jul/value-based-drug-pricing. Published July 21, 2016. Accessed October 16, 2017.

9. Silow-Carroll S, Alteras T. Value-driven health care purchasing: four states that are ahead of the curve. The Commonwealth Fund website. commonwealthfund.org/publications/fund-reports/2007/aug/value-driven-health-care-purchasing--four-states-that-are-ahead-of-the-curve. Published August 1, 2007. Accessed October 16, 2017.

10. Mittler JN, O’Hora JL, Harvey JB, Press MJ, Volpp KG, Scanlon DP. Turning readmission reduction policies into results: some lessons from a multistate initiative to reduce readmissions. 

. 2013;16(4):255-260. doi: 10.1089/pop.2012.0087.

11. Sabatine MS, Giugliano RP, Keech AC, et al; FOURIER Steering Committee and Investigators. Evolocumab and clinical outcomes in patients with cardiovascular disease. 

. 2017;376(18):1713-1722. doi: 10.1056/NEJMoa1615664.

12. Hernandez I. Outcomes-based pricing for PCSK9 inhibitors. 

. ajmc.com/contributor/inmaculada-hernandez-pharmd/2017/09/outcomes-based-pricing-for-pcsk9-inhibitors. Published September 24, 2017. Accessed November 24, 2017.

13. Carroll J. Heart attack while on Repatha? Amgen will give you your money back. 

. March 20, 2017. endpts.com/heart-attack-while-on-repatha-amgen-will-give-you-your-money-back/. Accessed November 24, 2017.

Articles

A chief medical officer for a major payer outlines the challenges making sure that certain high-cost therapies are directed to the patients who need them.

Moving Pharma Contracting Into the Era of Accountability

 To estimate long-term cost savings associated with patients’ exposure to an all-or-none bundle of measures for primary care management of diabetes.

 In 2006, Geisinger’s primary care clinics implemented an all-or-none diabetes system of care (DSC). Claims data from Geisinger Health Plan were used to identify those who met Healthcare Effectiveness Data and Information Set criteria for diabetes and had 2 or more diabetes-related encounters on different dates before 2006. A cohort of 1875 members exposed to the DSC was then compared against a propensity score matched non-DSC comparison cohort from January 1, 2006, through December 31, 2013.

A set of generalized linear models with log link and gamma distribution was estimated. The key explanatory variable was each member’s bundle exposure measured in months. The dependent variables were inpatient and outpatient facility costs, professional cost, and total medical cost excluding prescription drugs measured on a per-member-per-month basis.

Over the study period, the total medical cost saving associated with DSC exposure was approximately 6.9% (

 <.05). The main source of the saving was reductions in inpatient facility cost, which showed approximately 28.7% savings (

<.01) over the study period. During the first year of the DSC exposure, however, there were significant increases in outpatient (13%; 

A system of care with an all-or-none bundled measure used in primary care for patients with diabetes may reduce long-term cost of care while improving health outcomes.

Despite the availability of evidence-based clinical guidelines, unwarranted variation in diabetes care remains.

Since 2006, Geisinger’s primary care clinics have implemented an all-or-none diabetes bundled system of care. A recent study has shown that the intervention was associated with improved health outcomes. This study examines its impact on cost of care.

The first year of the system exposure was associated with higher outpatient and professional costs. Subsequent years of exposure were associated with lower total cost, driven by reductions in inpatient costs. 

Standardization of care process may simultaneously improve patient health outcomes while reducing cost.

Despite the existence and availability of effective clinical guidelines for treating diabetes,

 wide variability in the treatment patterns of patients with diabetes remains,

 resulting in adverse health outcomes and incurring avoidable care and cost.

 Reducing unjustified and nonpatient-centered variations in care, therefore, via a comprehensively redesigned system of care tuned to deliver all of the care needed to every patient at every encounter, can lead to both improved patient health outcomes and avoid expensive “downstream” care.

An increased focus on standardization is likely to increase the reliability of care delivery. One such effort to standardize care is Geisinger’s diabetes system of care (DSC). Geisinger has redesigned its care system to allow physicians to focus on “physician work” (ie, complex medical decision making, and patient relationships and leading staff members functioning in a top-of-license team). This physician-directed, team-delivered care is facilitated and enhanced by hard-wired technology accelerators available in primary care clinics.

 The care team is a standard office complement involving physicians, advanced practitioners, and front-office staff. Staffing ratios are approximately 2.25 nonproviders to 1 physician or advanced practitioner. This system of care allows the team to focus on an all-or-none bundle that consists of quantifiable measures of care based on commonly accepted clinical elements and intermediate outcome targets (summarized in 

) that can be easily implemented during routine primary care visits and are associated with improved outcomes for the patients.

The DSC is a practice-level intervention that changes how care is delivered to all patients with diabetes treated within a primary care practice site. Thus, in this study, all primary care physicians and healthcare providers are employed by Geisinger, are practicing in one of the primary care sites owned by Geisinger, and are subject to the DSC. Operationally, the DSC specifies delegated accountable responsibilities for each team member, with the goal to develop work flows that are measurable and reliable—not dependent on the diligence of individual providers. To this end, DSC emphasizes automation of routine care processes (eg, a provider decision support system built directly into patients’ electronic health records [EHRs], automatically generated patient report cards [to be shared with patients during office visits], and automatic updates to the patient registry).

The DSC also includes monthly feedback, active physician leadership, and administrative leadership management of performance and incentive payments to the primary care team, based on the proportion of the team’s population of patients with diabetes who achieved all of the process and intermediate outcome measures. Furthermore, these feedback reports are made available to all other physicians/teams within Geisinger to encourage improvements among lower-performing teams. One study

 has shown that during the 3-year period following the first implementation of the DSC in January of 2006, patients with diabetes whose physician-led care teams had implemented the DSC experienced lower rates of myocardial infarction, stroke, and retinopathy. For more detailed descriptions of the DSC program elements and their implementation, refer to previously published studies.

In this study, we built upon this previous work and sought to quantify the impact of these improved health outcomes by estimating any cost saving associated with patients’ exposure to the DSC. More specifically, we tested the following 2 hypotheses: 1) during the first year of the DSC exposure, the cost of care will be higher for the DSC-exposed patients than it is for the non—DSC-exposed patients, due to increased adherence to the clinical guidelines (eg, frequent testing, aggressive medication optimization); and 2) in subsequent years, cost of care for the DSC-exposed patients will be lower. In testing these hypotheses, we separately examined the association between the DSC and each of the main cost components—namely, inpatient, outpatient, and professional costs&mdash;and sought to determine the main sources of savings.

Claims data from Geisinger Health Plan (GHP) covering the period from January 1, 2005, through December 31, 2013, were obtained. We used claims data rather than encounter-based EHR data because claims data provide the comprehensive summary of all types of care used by patients during the coverage period, whereas the EHR data may omit certain types of care (eg, emergency department visits, inpatient visits) if they occurred outside of the Geisinger Health System. Moreover, claims data provided “allowed” amounts (ie, health plan payments to providers plus member out-of-pocket expenses via co-payments, coinsurance, and deductibles). For the purposes of this study, we defined "cost of care" as the allowed amounts incurred by each member in each month of the study period. To the extent that the allowed amounts represent the “price” of the care rendered by the provider for the member, we believe the allowed amounts serve as a reasonable proxy for the true cost of care.

The date of the first full implementation of the DSC across all Geisinger primary care clinics was January 1, 2006. The study population was, therefore, confined to those who had GHP coverage for at least 6 months before and after January 1, 2006, to ensure that care experiences both before and after the DSC implementation were captured. To define the diabetes patient population, the Healthcare Effectiveness Data and Information Set criteria were used.

 The study population was then further restricted to those 18 years or older between 2006 and 2013 and who had at least 2 claims with an 

International Classification of Diseases, Ninth Revision, Clinical Modification

 code for diabetes (250.x) on different dates before 2006 to ensure that the study sample consisted only of those who were eligible for the DSC intervention. Within the study population, GHP members were grouped into either the intervention group (defined as those who received primary care from a physician office that used the DSC as of January 2006) or the comparison group (defined as those whose physician office did not).

Over the study period, some members moved in and out of the sample depending on their GHP enrollment in each year. Also, some members switched between the intervention group and the comparison group. In cases where a member had switched from a primary care practice using the DSC to one not using it, such members were excluded from the analysis because they were assumed to have been contaminated by the DSC intervention. In other cases where a member had switched from a practice not using the DSC to one using it, the month that switching occurred was considered as the first month that he or she was exposed to the DSC intervention.

A logistic regression model was developed to estimate a propensity score for each member, in which member age, gender, and Charlson comorbidity index score

 (as of December 2005) were included as covariates. These pre-DSC period data were obtained from the members’ GHP claims data for calendar year 2005. We then used the nearest-neighbor propensity score—matching method with a caliper of 0.2 (± 0.2 of the standard deviation of the propensity score) to find 1-to-1 matched cohorts. The final sample included 1875 members in the DSC intervention cohort and the same number of members in the comparison cohort. Also, there were 454 members who could not be matched to the intervention cohort and, therefore, were not included in the analysis. These members tended to be younger, have fewer chronic conditions, and were more likely to be male than the matched cohorts. See the 

) for a comparison across these 3 groups using the available pre-DSC data (2005). Moreover, there were 59 primary care practices that had adopted the DSC and 564 that were never represented in the final sample. The latter number was higher than the former because the comparison cohort was drawn mostly from smaller independent practices not owned by Geisinger.

A set of generalized linear models with log link and gamma distribution was estimated. In all models, the key explanatory variable was each member’s length of exposure to the DSC at a given month during the study period, measured in terms of the number of months during which the member had received care from any of the primary care practices using the DSC since January 2006. Other covariates included in the model were member age, gender, case management status, plan types (Medicare health maintenance organization [HMO]/preferred provider organization [PPO], commercial HMO/PPO), comorbidities (chronic kidney disease, end-stage renal disease, asthma, congestive heart failure, chronic obstructive pulmonary disease, coronary artery disease, hypertension, cancer, and depression), GHP prescription drug coverage status (ie, whether the member has drug coverage through GHP or not), and year and month indicator variables.

Case management in this context refers to GHP’s nurse-based management of a high-risk patient population, which may confound the DSC effect because some of the members in the sample were also enrolled in the case management program. The drug coverage status variable accounts for the fact that not all members in the sample obtained drug coverage through GHP. To the extent that access and utilization of prescription drugs affect utilization of other medical care, the DSC effect may also be confounded by the drug coverage status. Additionally, another potential confounder is the impact of Geisinger’s advanced medical home model known as ProvenHealth Navigator (PHN), which has been shown to be associated with lower cost of care.

 To capture this confounding medical home effect, the regression model also includes a PHN exposure variable, as previously defined and used in the analysis by Maeng and colleagues.

The dependent variables were inpatient and outpatient facility costs, professional cost, and total medical cost (excluding prescription drugs), measured on a per-member-per-month (PMPM) basis. Cost of care in this case was defined as the “allowed” amount (ie, the sum of GHP’s payment to providers plus member out-of-pocket cost sharing). Prescription drug costs were not separately considered because, as noted above, not all members in the sample obtained drug coverage through the GHP; therefore, drug cost data were incomplete. A small positive value (0.0001) was added to all the dependent variables to ensure that the 0 values were not dropped from the analysis due to the log-link function.

To translate the regression model coefficients into actual dollar amounts, regression-adjusted mean PMPM costs were calculated based on the actual data (ie, “observed” cost). Then, the DSC exposure variable was set to 0 for all members in the intervention cohort and the corresponding regression-adjusted mean PMPM costs were again calculated (ie, “expected” cost). The difference between the observed and expected costs, therefore, represented the cost impact of the DSC. Standard errors around the estimated values were adjusted to reflect the fact that the members were clustered within primary practices. Thus, clustered standard errors were obtained and used to perform statistical significance tests.

 Furthermore, to isolate the impact of the DSC exposure on cost, the year and month indicator variables were set to 0 to remove the effects of secular trends (eg, inflation) and seasonality. The full regression outputs are shown in the eAppendix.

 highlights the post DSC intervention (2006-2013) descriptive statistics of the analytic sample. Note that the unit of analysis is member-month; as such, the sample size corresponds to the number of member-month combinations in the data, rather than the number of unique members. Table 2 suggests that the distributions of age, gender, and plan type are similar between the diabetes bundle exposure and nonexposure member-month observations. However, the prevalence of some of the comorbid conditions, particularly chronic kidney disease (8.4% vs 21.9%) and depression (5.9% vs 11.4%), appears to be higher among the DSC-exposed member-month observations. Furthermore, the proportion of the member-month observations that were enrolled in case management (10.2% vs 29.3%) and the mean exposure to medical homes, were also higher (1.2 months vs 21.7 months) among the DSC-exposed observations. Reflecting these differences, the unadjusted average PMPM costs also appear to be higher among the DSC-exposed observations.

shows the regression-adjusted mean total medical PMPM costs of care. The estimated total medical cost savings associated with any DSC exposure during the study period (ie, months of DSC exposure >0) was $47 PMPM or 6.9% (

 <.05). In particular, there were statistically significant cost savings during the third ($73 PMPM; 12.5%), fifth ($124 PMPM; 18.8%), and sixth ($104 PMPM; 14.7%) years.

 breaks down the total medical cost estimates shown in Table 3 in terms of its main components—specifically, inpatient, outpatient, and professional costs. The most significant source of the savings appears to be the inpatient costs, which largely follow the same pattern as that of the total medical cost shown in Table 3. Table 4 also suggests that during the first year of the DSC exposure, there were statistically significant increases in the outpatient ($20 PMPM; 13%; 

The DSC is designed to create an environment in which every care provider team member can help the patient succeed in achieving reliably better care. Clearly, the increased monitoring that this engenders raises short-term costs; additionally, more reliable eye screenings, immunizations, and laboratory work, as well as support for medication adherence, all increase the immediate cost of care. Not only did the greater focus on process measures increase the initial cost, but the focus on the intermediate outcome measures of control of blood pressure, blood sugar, and blood cholesterol is also likely to have increased the short-term costs. The increased monitoring may have also led to accelerated care, which would have increased costs for medications and led to earlier disease identification. This, and the necessary management of emerging complications, led to higher costs at the outset of the program, but also earlier in the course of disease. This is supported by the increased professional costs as closer monitoring of the diabetic population created more frequent visits and increased the use of more office-based interventions.

The focused attention on reliably achieving all recommended testing and treatment across the whole population, however, rapidly led to a reduction in the total cost of care in the long run. Early clinical impact is critical to driving down the longer-term total cost of care, particularly in light of the increased short-term costs. The reduction in true patient-centered outcomes, such as the reduction in heart attack and stroke, are necessary to create population-level savings. The distribution of the cost savings demonstrated in this study supports this. The most significant reductions occurred, as expected, in the inpatient costs to offset the elevated early professional costs.

The limitations of this study include reliance on retrospective administrative data that lack clinical variables useful for more sophisticated risk adjustment. Another important limitation is the fact that the cost savings represent only the “gross” savings, or the return portion of the return-on-investment (ROI) quotient. That is, the savings shown above do not take into account all the expenses related to the implementation of the DSC, which would include the incentive payments to physicians associated with their all-or-none DSC bundle achievements, the expenses associated with the EHR support and data reporting, additional staff time and resources needed for provider training, patient outreach and engagement, etc. Due to the inherent difficulty of measuring and quantifying all such expenses in dollar terms, we are unable to reliably capture the ROI in this analysis. Furthermore, the DSC may also be associated with higher drug spending due to increased drug adherence. Although drug cost was not explicitly examined in this study, it is an area of future study.

Given the relatively high age of the cohorts (72 years old), some proportions of the cohorts are presumed to have deceased during the study period. To the extent that the DSC is associated with improved patient outcome as shown in the previous study,

 it is reasonable to expect that the DSC cohort might have survived longer than the non-DSC cohort. Given the substantial cost associated with end-of-life care, the fact that the DSC cohort might have survived longer implies higher cost of care for the DSC cohort unrelated to their DSC exposure, which would lead to smaller estimated savings. The reduced magnitude of the cost savings at >72 months of the DSC exposure (shown in Tables 3 and 4) is suggestive of this phenomenon. Thus, our results may be considered to be conservative estimates, reflecting the higher costs associated with end-of-life care. Because our claims data do not accurately capture patient death, however, we are unable to test this hypothesis. The impact of DSC on mortality is an area of future research.

The creation of reliable systems to consistently deliver all the needed care for maximizing the control of chronic disease to reduce downstream complications will be a critical element for health systems as they react to broader financial responsibility for health outcomes and the healthcare process. The DSC program described in this study provides a paradigm that can be adopted in wider settings to achieve that aim. Moreover, the fact that the DSC explicitly ties physician compensation to achievement of the all-or-none bundle is a significant move away from traditional fee-for-service toward a sustainable payment system that aligns patient outcomes with physician and team financial incentives. As discussed elsewhere,

 up to 20% of the total cash compensation for Geisinger-employed primary care physicians is based on the discrete performance metrics of the DSC and similar chronic and preventive systems of care. This system-based and population-focused compensation arrangement is a departure not only from the traditional fee-for-service model, but also from the now-popular pay-for-performance schemes that typically rely on the diligence and hard work of individual physicians rather than a reengineered system.

The findings of this study support the notion that higher quality in healthcare can coexist with and be responsible for the total cost of care savings. Ensuring the success of DSC in larger contexts will require retooling both the organizational structures and the electronic systems that support and facilitate the model implementation, as well as restructuring the physical assets of medicine. For example, availability of nearby ambulatory care centers with enhanced EHR-capabilities that directly connect patients to their care providers would enhance the effectiveness of the system of care model by encouraging greater patient engagement in the decision-making process and, thereby, achieve more reliable management of their chronic conditions.

The authors wish to acknowledge James M. Pitcavage, MSPH, whose contribution as the project manager greatly facilitated this study. 

Geisinger Center for Health Research (DDM), Danville, PA; Sutter Health (XY), Walnut Creek, CA; Geisinger Clinic (TRG), Danville, PA; xG Health Solutions (GDS), Columbia, MD.

All authors were employees of Geisinger Health System at the time this study was conducted. This work was done as part of their employment with Geisinger Health System. Dr Maeng had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. 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 (DDM, XY); acquisition of data (DDM); analysis and interpretation of data (DDM, XY, TRG); drafting of the manuscript (DDM, XY, TRG); critical revision of the manuscript for important intellectual content (TRG, GDS); statistical analysis (DDM, XY); provision of patients or study materials (DDM, XY); obtaining funding (TRG, GDS); administrative, technical, or logistic support (TRG, GDS); and supervision (GDS).

Daniel D. Maeng, PhD, Geisinger Center for Health Research, 100 N Academy Ave, MC 44-00, Danville, PA 17822. E-mail: ddmaeng@geisinger.edu.

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Geisinger's all-or-none diabetes bundled system of care implemented in routine primary care settings was associated with sustained long-term total cost savings.

Value of Primary Care Diabetes Management: Long-Term Cost Impacts

To determine whether a system of care with an all-or-none bundled measure for primary-care management of diabetes mellitus reduced the risk of microvascular and macrovascular complications compared with usual care.

A parallel pre-post observational design was used. In 2006, a system of care for diabetes was implemented for some members of the Geisinger Health Plan. A total of 4095 primary-care patients were in the Diabetes System of Care group (DS) and compared with a propensity score—matched cohort of 4095 primary care patients not in the system of care (non-Diabetes System of Care [NDS]).

Cumulative hazard rate was measured over a 3-year period for retinopathy, amputation, stroke, and myocardial infarction (MI).

The adjusted hazard ratios (HRs) for MI (HR, 0.77; 95% CI, 0.65- 0.90), stroke (HR, 0.79; CI, 0.65-0.97), and retinopathy (HR, 0.81; CI, 0.68-0.97) were all significantly lower among DS patients. The adjusted HR for major amputations (HR, 1.32; CI, 0.45-3.85) did not differ between groups, but only 17 major amputations occurred during the follow-up period. The necessary number of patients to treat in order to prevent 1 event over 3 years was 82 for MI, 178 for stroke, and 151 for retinopathy.

A system of care with an all-or-none bundled measure used in primary care for patients with diabetes may reduce the risk of MI, stroke, and retinopathy over a 3-year period.

A diabetes system of care using an all-or-none bundled measure can reduce the microvascular and macrovascular complications of diabetes in only 3 years.

		A system of care for diabetes can reduce myocardial infarction, stroke, and retinopathy in only 3 years.

		Translation into a workable care protocol in primary care is challenging. Workflows were developed that were measurable, scalable, reliable, and not dependent on the diligence of individual providers.

		Payers and health systems can innovate and invest together in the infrastructure to build similar systems of care, knowing that the benefit for their patients with diabetes will be prompt.

The prevalence of diabetes mellitus in the United States increased 40% between 2007 and 2010, to 11.9% of the population aged 20 years and older.

 Patients with diabetes are at increased risk for microvascular complications including retinopathy, nephropathy and amputation, and macrovascular complications including stroke and myocardial infarction (MI). Glycemic control has dramatically improved microvascular outcomes in patients with type 1 diabetes

 but has not consistently been associated with improved macrovascular outcomes, such as cardiovascular disease (CVD). Recent studies show that intensive glucose control is associated with increased risk or no benefit.

In contrast, lipid and blood pressure (BP) control have resulted in clear reductions in CVD in persons with diabetes

 Moreover, the combined control of these risk factors reduced the risk of CVD outcomes.

Notably, the time to onset of risk reduction can be relatively short. For example, smoking cessation reduces coronary events within 6 months for patients at high risk.

In spite of the evidence, translation of standards of care into a workable care protocol in primary care is challenging. The American Diabetes Association (ADA) has promulgated detailed standards of care for improving patient outcomes, including recommendations for medical care from physician-coordinated teams.

 However, providing state-of-the- art guideline care is rarely achieved.

 Although there is evidence that the use of an electronic health record (EHR) may improve process-of-care measures and intermediate outcome measures for diabetes

 even when guideline care is provided, it is unclear whether the benefits of reduced risk accrue and how much time is required before risk reduction is measurable.

A team-based model was designed to more consistently provide evidence-based care to patients with diabetes. This system of care used a 9-component bundle of diabetes measures, a workflow redesign, EHR tools, and financial incentives to improve the consistency and reliability of care, as previously reported.

A key aspect of this program is an all-or-none measure that determines physician incentives. We describe the impact of this system of care on risk of microvascular complications of retinopathy and amputation and macrovascular complications of stroke and MI over the first 3 years of implementation.

This research study was approved by the Geisinger Institutional Review Board. The evaluation of the diabetes system of care was completed among members of the Geisinger Health Plan (GHP). Approximately 45% of GHP members receive their care from Geisinger primary care physicians. GHP also contracts with non-Geisinger physicians who provide primary care to the other 55% of members. The diabetes system of care was implemented in all 38 Geisinger primary care sites, referred to hereafter as Diabetes System (DS). DS patients were compared with propensity score—matched primary care patients from other clinics in the same region, referred to hereafter as non-Diabetes System (NDS). One key part of the DS is that it was implemented in an integrated healthcare system. This allowed for many advantages in implementation, such as a shared EHR system across all providers, ease of reporting to all providers without conflict, and an equitable application of physician incentives to all providers. The NDS system had many nonintegrated care characteristics, yet had numerous diabetes improvement programs in effect during the study period that are discussed further in the discussion section.

The DS was designed by a multispecialty, multidisciplinary work group. The ADA Guidelines for the Standard of Medical Care for Diabetes were used as the basis for the system of care.17 The team model included an allor- none measure with physician incentives awarded only when a patient fulfilled all 9 components of the bundle (

). Previous research has examined compliance rates within the DS program and found significant increases in all measures of diabetes care within the first 12 months.

Beginning in January 2006, the all-or-none bundle was routinely measured in all adults with diabetes at Geisinger primary care sites. DS included delegated accountable responsibilities for each team member—physician, advanced practitioner, nurse, case manager, front office staff, and call center staff. Workflows were developed to be measurable, scalable, reliable, and not dependent on the diligence of individual providers. DS included EHR decision support, autogenerated patient report cards, and automated updating of a patient registry. During the office visit, licensed practical nurses and medical assistants were presented with decision support for all process-related diabetes care and had standing orders for influenza and pneumococcal immunizations, diabetes lab work, eye exam referrals, and diabetic foot screenings. Providers are presented with decision support for only those elements of diabetes bundle care that require complex medical decision making, including glycated hemoglobin (A1C), low-density lipoprotein cholesterol (LDL), and BP management. Patient report cards were generated during the office visit to be shared later with the patient. The individual metrics and all-or-none bundle results for each clinic team and for each individual provider were reported monthly. Each team had access to the individual reports and team reports of all other physicians/ teams within Geisinger.

Analysis of outcomes was based on claims data obtained from GHP. As previously noted, GHP members in the DS were compared with those in the NDS. The analysis was confined to GHP members because medical claims data on all eligible diabetes patients could be used to identify relevant documentation of micro- and macrovascular events. Analysis was limited to the period of services provided and paid by GHP from January 1, 2004, to December 31, 2008, and was confined to patients who were aged 40 years or older with a diabetes diagnosis. The 2 years of observations from 2004 to 2005 were used to establish baseline event rates in the two patient populations before the DS was implemented.

During the study period, individuals were included as diabetes cases if they met Healthcare Effectiveness Data and Information Set (HEDIS) criteria

 and had 4 or more encounters with a diabetes 

International Classification of Diseases, Ninth Revision

) code on different dates during the study period. The earliest encounter with a diabetes 

 code was taken as the diabetes baseline date. Patients had to have at least 180 days of follow-up after their diabetes baseline date to contribute to the analysis.

We identified all inpatient and outpatient claims during the 5-year study period related to the following 

 codes 89.5x, 89.6x, 89.7x, and 99.7x); MI (

 codes 430, 431.xx-438.xx). The following numbers of cases were identified: 2518 retinopathy; 138 amputation; 3824 MI; and 2112 stroke. Incident microvascular or macrovascular events were identified as the earliest documentation of 1 of the above codes if there was also at least 1 year of follow-up prior to the first documentation of the claims event. This “clean-out period” restriction was imposed to eliminate prevalent events among those who became new members of GHP. In sensitivity analysis, we also used a 2-year clean-out period. The findings were essentially the same. Using the 1-year criteria, we identified 519 retinopathy, 17 amputation, 714 MI, and 445 stroke incident events.

Chronic kidney disease, 1 of 3 serious microvascular diseases associated with diabetes, was not evaluated as an outcome because it is not reliably reported in insurance claims data unless a patient has end-stage disease.

Because patients were not randomized, we used a 2-step method to protect against inferential threats attributable to baseline differences in risk factors. First, logistic regression was used to calculate a propensity score

 for each eligible diabetes patient to balance disease burden for members of the DS and NDS groups at the beginning of intervention (January 1, 2006), which was denoted as the index date. If patients had pure postintervention exposure, then the index date was set to be the same as the baseline date. Claims data from the date of enrollment to the index date were used to derive the propensity score based on patient gender and age at index date, as well as claims documentation of comorbidities (mild liver disease, peripheral vascular disease, heart failure, cerebrovascular accident, pulmonary disease, severe liver disease, renal disease, malignant cancer, cardiovascular disease, and dialysis) up to the index date. The propensity score was used to match DS and to NDS diabetes patients (± 0.2 of SD of the propensity score, an optimal caliper usually used in propensity score—matching studies

) and to balance both groups on future risk of events of interest. Second, to account for differences in person-time of observation in the postsystem-of-care periods, we calculated the probability of being “exposed” to the post-system period. To this end, each patient’s age at diabetes baseline date, duration of person-time in the preintervention period, and comorbidities documented before the index dates were included in the conditional logistic regression model. Conditional logistic regression was used to control for matched pair variation. A score obtained for each subject represented the likelihood of postsystem-of-careexposure.

Person-time was excluded until a patient reached 40 years of age. Cumulative risk was estimated using the weighted Cox Proportional Hazards regression model, which combined a traditional Cox Proportional Hazard regression model with inverse probability weighting (IPW). The inverse of the previously described postsystem exposure score was used as a weight, along with age at the index date and gender. A patient was censored from observation if they discontinued as a GHP member, died as a GHP member, or incurred their first unique major microvascular or macrovascular event as previously described. The C statistic was used to measure discriminatory capability of the model and calculated as described by Pencina and colleague’s method.

 The cumulative hazard of each outcome was predicted by the model. Statistical analyses were performed using SAS version 9.2 (SAS Institute Inc, Cary, North Carolina) for the time-to-event analysis and relative risk comparison.

The number of patients needed to treat (NNT) was calculated based on risk reduction, which was estimated from the above Cox proportional hazard model; that is, the 3-year survival rate was estimated from the model for DS and NDS group, respectively, and the difference represented the 3-year risk reduction. The NNT is the inverse of the risk reduction.

The initial study cohort of 16,086 GHP members met the criteria for diabetes diagnosis. (

). After applying exclusion criteria (<180-day follow-up, age <40, only preintervention exposure or prevalent outcome), the sample was reduced by 3421 of the 8355 patients in the DS group clinics and by 3634 of the 7734 in the NDS group clinics. After applying propensity score—matching criteria, 4095 DS subjects were matched to 4095 NDS subjects. Before matching (

), DS group members were significantly older, had proportionately more of their person- time in the postintervention period, and had a longer follow-up period than NDS members. After matching, DS and NDS members still differed on duration of follow-up time in the postintervention period. The 2 groups were similar on the risk score derived at baseline and up to the time of the index date. The adjusted hazard ratio (

) for MI was 0.77 (95% CI, 0.65-0.90); for stroke, 0.79 (95% CI, 0.65-0.97); and for retinopathy, 0.81 (95% CI, 0.68- 0.97). The risk of the macrovascular and microvascular events were significantly lower among DS patients compared with NDS patients (Figure 2 includes cumulative hazard plots). These hazard plots show that the reduction in macrovascular outcomes (MI and stroke) is most pronounced after year 1 of the DS, and that the microvascular outcome retinopathy is evident after approximately 18 months. The risk of developing retinopathy, MI, and stroke among DS patients was significantly less than that of NDS patients in the 3-year follow-up period. The NNT to prevent 1 event over 3 years was 82 (95% CI, 37-133) for MI; 178 (95% CI, 57-681) for stroke; and 151 (95% CI, 47- 512) for retinopathy.

This analysis of claims data from a propensity-matched observational design shows a statistically significant lower risk of macrovascular and microvascular disease end points in the first 3 years of a diabetes system of care that included an all-or-none bundled measure compared with primary care without this intervention. The impact was substantial, with only 82 patients needed to treat to prevent 1 MI event, 178 to prevent 1 stroke, and 151 to prevent 1 case of retinopathy. Perhaps the most notable finding is the apparent early impact of the care model. The findings suggest an impact in the first 3 years with the possibility that a reduction in risk began to emerge after the first year. This finding is consistent with prior randomized controlled trials indicating that reduction in risk of cardiovascular outcomes can be achieved.

 However, the early impact of an all-ornone diabetes system of care on microvascular and macrovascular conditions has not been previously described.

The finding of a reduction of MI, stroke, and retinopathy in only 3 years through a risk factor intervention is supported by prior studies on individual risk factors such as smoking cessation, BP control, and influenza immunization. 

 Multiple trials have also shown statistically significant improvements in microvascular and macrovascular outcomes in 3 to 5 years of follow-up in programs designed to reduce cardiovascular risk.

This study showed improvement in a shorter period of followup. Observing statistically significant differences strongly depends on sample size, and showing statistically significant differences does not indicate when the benefits of an intervention first emerge. These previous trials were designed to show that the intervention had an effect, not to determine the earliest point at which the effect occurred. The current observational study included multiple simultaneous interventions which could have amplified the early benefits in risk reduction when compared with other studies using a single intervention.

A limitation of this study is that the DS group intervention did not occur in isolation.After the initiation of the DS, other performance improvement projects were initiated, including reporting of diabetic foot examination rates, diabetic eye examination rates, aspirin use, and angiotensin- converting enzyme inhibitor/angiotensin II receptor blocker use. However, it is unlikely that these other initiatives contributed to the early separation in risk observed in the DS clinics compared with the NDS clinics. The NDS sites also had other ongoing care improvements sponsored by the health plan. These included Web-based registry tools and outreach programs based on HEDIS measures for diabetes, which could have resulted in improved diabetes outcomes, but in a less substantial manner than in the DS sites. The DS intervention included many interventions, including physician and staff monetary incentives. These were used at the DS sites to focus attention and promote team attainment of goals. NDS sites also received a Physician Quality Summary bonus from GHP, but the monetary incentives were less than what was provided the DS sites and not related to an all-or-none improvement. The extent to which these varying monetary incentives contributed to the success of the intervention was not studied.

Finally, patients in the DS care arm were more likely to be cared for in practices that are a part of an integrated health system and in sites that have an EHR. While we used propensity score matching at the time of the index date to balance the “intervention” and control arms, there is the possibility that the separation of the time-toevent curves for micro- and macrovascular disease risk was explained by unmeasured confounding attributable to a host of factors that are simply correlated with the effectiveness of delivering the diabetes care.

This diabetes system of care had multiple interventions, and the impact of each will require further research. Determining the best combination of bundled measures, work flow redesign, financial incentives, and the relative impact of each aspect of the system of care are ongoing.

 Analysis of the impact of the system of care on overall mortality and total cost of care is planned.

Prior evidence indicates that it is possible to improve process of care measures for diabetes in clinical practice, 

 and document small to modest improvements in glycemic control.

 There is a single report of reduced adverse events (amputations, retinopathy, and MI) using a diabetes disease management program.

 If substantiated, our findings that a DS reduces the microvascular and macrovascular complications of diabetes in only 3 years have potentially important implications for patients, providers, and payers. 

Geisinger Health System, Danville, PA (FJB, TRG, TA, DED, SBP, GDS); Sutter Health, Sacramento, CA (XY, WFS, JP).

Using a system for primary care management of patients with diabetes may reduce the risk of myocardial infarction, stroke, and retinopathy over a 3-year period.

Primary Care Diabetes Bundle Management: 3-Year Outcomes for Microvascular and Macrovascular Events

 To estimate cost savings associated with ProvenHealth Navigator (PHN), which is an advanced model of patient-centered medical homes (PCMHs) developed by Geisinger Health System, and determine whether those savings increase over time.

A retrospective claims data analysis of 43 primary care clinics that were converted into PHN sites between 2006 and 2010. The study population included Geisinger Health Plan’s Medicare Advantage plan enrollees who were 65 years or older treated in these clinics (26,303 unique members).

Two patient-level multivariate regression models (with and without interaction effects between prescription drug coverage and PHN exposure) with member fixed effects were used to estimate the effect of members’ exposure to PHN on per-member per-month total cost, controlling for member risk, seasonality, yearly trend, and a set of baseline clinic characteristics.

 In both models, a longer period of PHN exposure was signifi cantly associated with a lower total cost. The total cumulative cost savings over the study period was 7.1% (95% confi dence interval [CI] 2.6-11.6) using the model with the prescription drug coverage interaction effects and 4.3% (95% CI 0.4-8.3) using the model without the interaction effects. Corresponding return on investment was 1.7 (95% CI 0.3-3.0) and 1.0 (95% Cl —0.1 to 2.0), respectively.

 Our fi nding suggests that PCMHs can lead to significant and sustainable cost savings over time.

 Geisinger Health Systems has implemented its own version of patient-centered medical homes called ProvenHealth Navigator (PHN) since 2006. Our results demonstrate that the longer the member has been exposed to PHN sites, the lower the cost of care. The return on investment (ROI) has not yet signifi cantly exceeded the break-even point. However, because the magnitude of savings from PHN depends on the length of members’ exposure to PHN, it remains to be seen whether the cumulative ROI can eventually exceed the break-even point.

 Cost savings are achievable by redesigning primary care, but it takes time to reap the benefits.

At this time of national focus on the need to simultaneously improve quality and reduce cost, novel solutions are needed. The fragmented healthcare delivery system further adds to the challenge.

 The key stakeholders—physicians, patients, health insurers, and hospital administrators, as well as the federal government&mdash;now recognize the need for healthcare redesign to increase the quality of care while containing the cost of care.

In particular, reorienting the current healthcare delivery system to one in which greater emphasis is given to more effective primary care is likely to be an important solution to the problem.

Recently, patient-centered medical homes (PCMHs) have gained attention as a way to re-engineer the care-delivery process. In the broadest sense of the term, PCMHs refer to “provision of comprehensive primary care services that facilitates communication and shared decision-making between the patient, his/her primary care providers, other providers, and the patient’s family.”

 There is early but growing evidence that PCMHs have the potential to improve care for individuals, improve health of populations, and slow the growth in costs of healthcare by reviving primary care and bringing the patient and family to the center of the care-delivery system

The ultimate goal of PCMHs is to improve patient outcomes via redesigned primary care. The preventive aspect of primary care enhances the value proposition of PCMHs as a long-term intervention. Such an emphasis on primary care, therefore, implies that PCMHs may have the desirable effect of reducing cost of care. The early experiences of the PCMH demonstrations strongly suggest this.

 In this study, we explicitly explore this possibility by evaluating the effect of ProvenHealth Navigator (PHN), an advanced model of PCMHs developed and implemented by Geisinger Health System (Geisinger), since 2006 on cost of care.

PHN consists of the following 5 core program components: (1) patientcentered primary care, (2) integrated population management, (3) medical “neighborhood”

 (ie, alignment of key community partners, such as home health agencies, skilled nursing facilities, outpatient/ancillary services, hospital facilities, and community pharmacies), (4) comprehensive quality improvement, and (5) value-based reimbursement redesign that includes a quality, outcome-based pay-for-performance program. More detailed descriptions of how the PHN intervention was designed and implemented are published elsewhere.

PHN is designed to move resources further “upstream” in the primary care setting to reduce “downstream” costs from the highest acuity settings. In this system, primary care is considered upstream in the sense that it serves as the starting point in the chain of care delivery. In general, care becomes more expensive as patients move downstream to specialty and inpatient care. Therefore, successful upstream efforts are expected to result in reductions of inpatient care—related costs due to fewer uncontrolled exacerbations of chronic diseases and more effective care transitions that prevent hospital readmissions and unnecessary duplication of services.

A PHN site refers to one of the primary care clinics that has been designated as such by Geisinger and has undergone significant changes in its management and operations in accordance with the PHN practice redesign. PHN was implemented in several phases over the 5-year period from late 2006 through 2010. At its core, the PHN intervention can potentially reduce cost over time while improving quality via:

• Prevention: redesigned primary care using automation to leverage resources and enhance reliability for early detection and interventions prior to exacerbations and complications

• Chronic disease optimization: redesigned primary care using a high-touch, high-technology approach to manage emerging exacerbations and to minimize complications

• Comprehensive care management: health-plan— trained, practice-embedded “concentrated care–RNs” who serve as nurse case managers focused on proactive identification and management of the more medically complex patients

This analysis focuses on individual members’ exposure to PHN by measuring how long a member has been exposed to a PHN site and examining whether a prolonged exposure to PHN is associated with a lower cost of care over time. It is expected that the longer the patient has been exposed to a PHN site, the greater the cost savings.

ProvenHealth Navigator, Geisinger's version of advanced patient-centered medical homes, is associated with significant savings in total cost of care over time.

Thomas R. Graf, MD

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