 In contrast to chronic heart failure (CHF), measures of quality of care for chronic obstructive pulmonary disease (COPD) are poor. Our objective was to examine differences in organizational structure available to support quality of care for patients with CHF and COPD.

 We performed 2 nationwide surveys exploring organizational structure for the management of CHF and COPD. We surveyed the chief of medicine and the chief of cardiology and pulmonary medicine at 120 Veterans Affairs facilities in the United States.

Analogous questions about organizational structure that enhanced adherence to guideline-based care were compared between CHF and COPD surveys.

We found large and notable differences in the organizational structure for disease management, with systematically less attention given to COPD than CHF. These differences were evident in multiple processes of care. Key differences included fewer facilities: having COPD clinics than CHF clinics (12.7% vs 50.8%;

 <.01), relating performance measures with COPD providers than CHF providers (17.1% vs 70%; 

 <.01), and having home monitoring programs for COPD than for CHF (50.5% vs 87.4%; 

 Despite the growing burden of COPD, less organizational structure existed for COPD than CHF. Lack of organizational structure for COPD likely impedes an organization’s abilities to encourage high-quality care and avoid recently implemented hospital readmission penalties. Our results suggest the need to develop a systematic approach for healthcare systems to provide essential organizational structure based on the burden of disease in the population.

	Using 2 nationwide surveys to assess the organizational structure available for chronic disease management at Veterans Affairs facilities, we found systematically less attention given to management of chronic obstructive pulmonary disease (COPD) than to chronic heart failure (CHF). 

		We identified key differences in the management of patients with COPD and CHF. 

		Recognizing differences in organization structure for disease management may help health systems to prioritize quality improvement efforts for patients with COPD. 

		Our study highlights how disparities in quality improvement can develop in the absence of a systematic approach based on the burden of disease.

Broad organizational efforts have led to implementation and dissemination of programs that drive quality and measurement of performance within healthcare organizations. These performance measures have guided a higher quality of care for patients with numerous chronic conditions, including chronic heart failure (CHF).

 Over the past 30 years, greater adoption of organizational structure for the management of CHF has been associated with a decline in CHF-related mortality.

 During this same period, mortality rates due to chronic obstructive pulmonary disease (COPD) have been steadily increasing, with COPD becoming the third most common cause of death.

Between 2007 and 2011, the United States’ prevalence of CHF and COPD were 5.1 million and 12.7 million cases, accounting for $34.4 billion and $49.9 billion in annual healthcare spending, respectively.

 Within Veterans Affairs (VA), more patients have a diagnosis of COPD than CHF and there is higher total spending on COPD.

 However, organizational programs promoting guideline concordance for management of COPD, analogous to those for CHF, have not been systematically implemented.

 Recent studies in the United Kingdom have shown an unacceptable variation in the organization and delivery of care for patients with COPD.

 Contrasting how facilities structure their clinical practices and programs in managing CHF versus COPD could shed light on future directions to improve care for COPD at the organizational level. Applying the lessons learned from CHF could ensure a consistent and homogenous approach to conditions that typically do not receive as much attention as CHF.

We compared the organizational structures for CHF and COPD patients within VA facilities across the United States. We focused on differences in number and type of clinicians, specialty clinics, performance measures, and discharge practices. Identifying these differences in clinical practices and organizational structure could highlight methods for improving quality of care for COPD and pinpoint areas where clinical resources for COPD are insufficient.

We performed a nationwide, cross-sectional study of VA facilities to evaluate the clinical practices and organizational structure of COPD and CHF care. We surveyed chiefs of medicine, as well as cardiology and pulmonary medicine, of VA facilities with acute inpatient units. Respective CHF and COPD surveys asked about clinical support that enhanced adherence to guideline-based care. The CHF survey was developed by the CHF QUality Enhance Research Initiative (QUERI), a VA-based effort to improve the quality of evidence-based care. The COPD survey was developed by this study team to mirror questions in the CHF survey and target evidence-based practices identified from the National Quality Forum and the Agency for Health Research and Quality clearinghouse.

 Responses were compared between analogous questions from the 2 surveys.

The VA Puget Sound Health Care System Institutional Review Board approved the study.

The CHF QUERI developed the CHF survey to evaluate processes of care, clinical practices, and discharge practices at VA facilities (see 

]). The CHF survey included 19 questions and was expected to take 5 to 10 minutes to complete. Survey questions elicited information on the number of cardiologists, standardized heart failure programs, standardized computer order sets for CHF, and compliance with CHF guidelines. Questions relating to CHF practices and guidelines were developed based on existing recommendations for the treatment of heart failure.

 The CHF survey was conducted in 2008 by VA Patient Care Services at all 144 VA facilities, with a response rate of 100%.

We developed the COPD survey to parallel questions in the CHF survey, assessing organizational factors related to the management of COPD (see 

). Analogous questions asked about the number of pulmonologists, standardized COPD programs, standardized computer order sets for COPD, compliance with COPD guidelines, and COPD discharge practices. Questions relating to COPD practice and guidelines were based on current recommendations for the treatment of COPD.

 The COPD survey was administered in 2011 to 122 VA facilities with acute inpatient units (120 of which were also sent the CHF survey); 111 facilities responded (91%). Although the 2 surveys were conducted in 2 different time periods, the VA organization structures remained stable without major policy changes for COPD during that time.

The final study sample included 120 VA facilities with acute inpatient units that were sent both CHF and COPD facility surveys. For facilities that had responses from both the chief of medicine and the chief of cardiology and pulmonary medicine, we used the responses from the chief of cardiology and pulmonary medicine in our study because they would have more detailed knowledge of clinical practices as the leader of the department.

We conducted a descriptive analysis to compare survey responses for analogous questions from the CHF and COPD surveys using a 2-sample 

 test of proportions. None of the survey questions had more than 2 missing responses, and missing responses were excluded from the analysis. All statistical analyses were performed using Stata version 14.1 (StataCorp, College Station, Texas).

There were notable differences in organizational structure for managing CHF and COPD reported by the facility leaders (

). The majority of facilities had staffed pulmonologists and cardiologists (91% vs 93.3%; 

 = .51), and there was no significant difference between the proportion of pulmonologists specializing in COPD care and that of cardiologists specializing in CHF care (32.4% vs 39.2%; 

 = .28). However, there were significantly fewer facilities that reported having COPD clinics than having CHF clinics (12.7% vs 50.8%; 

For programs that were typically led by specialty services, there were no significant differences between management of COPD and CHF for most standardized practices and protocols in the outpatient setting. Facilities reported similar proportions of having protocols for outpatient management (27.0% vs 18.5%; 

 = .12), patient self-management (23.4% vs 25.2%; 

 = 0.75), and patient education programs (68.5% vs 67.5%; 

 = .87) for COPD and CHF, respectively. Compared with CHF outpatient disease management, COPD outpatient management included slightly fewer facilities that reported having a disease specific exercise program (29.7% vs 39.2%; 

 = .13) and less access to specialized pharmacists (45.9% vs 53.8%; 

A significantly lower proportion of facilities reported having COPD home monitoring programs than CHF home monitoring programs (50.5% vs 87.4%; 

 <.01). Among facilities that did have home monitoring programs, fewer telemedicine devices were used for monitoring data transmission for COPD than CHF (75% vs 88.5%; 

 = .03). Among facilities with home monitoring programs, COPD management also had significantly lower use of nurses for adjusting medications (10.7% vs. 31.7%; 

 <.01) and relied more heavily on physicians (46.4% vs 31.7%; 

There were no significant differences in reporting the use of standardized order sets for disease exacerbations (COPD: 29.7% vs CHF: 25%; 

 = .42), as well as inpatients routinely seen by a specialist (physicians, nurse practitioners, or nurses) during an inpatient stay (20.7% for COPD patients vs 25% for CHF patients; 

Although the majority of facilities reported routinely sharing disease-related performance measures with providers for CHF, less than one-fifth of hospitals reported sharing COPD-related performance measures (70% vs 17.1%; 

 <.01). Overall, facilities were more likely to share CHF-specific performance reminders than COPD-related performance reminders with providers. The majority of facilities reported sharing CHF-related performance reminders with providers, including 94.3% for angiotensin-converting enzyme (ACE) inhibitors, 78.4% for beta blockers, and 85.1% for ejection fraction (EF) (

). Provider feedback for COPD management was much lower, and included 64% of hospitals providing feedback on appropriate use of oxygen therapy, 57.7% on smoking cessation counseling, 34.2% on spirometry assessment, and 16.2% on steroids for exacerbations.

Although a similar proportion of facilities reported assessing any quality measure prior to discharge for COPD and CHF patients (95.5% vs 95.8%; 

 = .90), there were notable differences in the specific quality measures that were assessed (

). The majority of facilities reported assessing specific CHF measures, including EF assessment (83.3%), ACE inhibitors for (90%), beta blockers (80%), and spironolactone (51.7%). Assessing specific COPD measures occurred in 30% of facilities for prior confirmation of COPD by spirometry, 74.2% for assessment of smoking status, 70% for providing smoking cessation intervention, and 69.2% for oxygen for room air saturations of less than 88%.

In comparison to CHF, we found consistent and large systematic differences in the organizational structure available to support the high-quality management of COPD. These differences were evident across processes of care, including clinic structure, type of provider involved in patient care, home monitoring programs, provider feedback on performance measures, and quality measures assessed during hospitalization. A comparative lack of organizational structure for COPD likely contributes to documented concerns about quality of care for patients with COPD.

 The VA has hundreds of measures of quality, though there are no measures specifically adopted for COPD. Our findings identify a gap in disease-specific focus that may help prioritize quality improvement efforts at a system level for patients with COPD.

The lack of organizational structure that we observe may be due to a lack of research to identify appropriate process measures for COPD. COPD is ranked as the most underfunded condition relative to disease-specific mortality.

 National Institutes of Health (NIH) funding for COPD research has not grown in many years and is still only 6% of the funding available for heart disease.

 Nevertheless, over the past 30 years, mortality due to COPD has doubled, whereas death due to heart disease has decreased by more than half.

 This decline in the burden of cardiovascular disease followed substantial increases in research funding.

 To better allocate research spending, the Institute of Medicine in the United States has recommended that the NIH explicitly associate the amount of research funding with the burden of disease—an approach that is relevant to how health systems prioritize clinical quality efforts.

Despite the relative lack of research funding for COPD, there is a body of literature that supports the implementation of organizational structure for COPD management. Systematic reviews of integrated disease management programs for COPD have demonstrated many parallels to CHF programs, with reduced hospital admissions, improved quality of life, and improved exercise capacity.

 Similarly, COPD and CHF studies on exercise programs have demonstrated better patient outcomes with participation in pulmonary and cardiac rehabilitation.

 Although there are still many deficiencies and controversies in COPD research, improving the organizational structure of disease management can lead to meaningful improvements in patient outcomes.

In our study, we found significantly less organizational structure available, even among COPD interventions that have been well studied. There was less feedback for guideline-based performance measures and fewer quality measures assessed prior to hospital discharge for COPD than for CHF. Previous studies have demonstrated that awareness of COPD guidelines and adherence to guideline recommendations is low.

 Among 69,820 patients hospitalized in 360 different hospitals for acute COPD exacerbations, only 33% received appropriate guideline-based care.

 Similarly, only 46% of ambulatory patients received appropriate chronic COPD management.

 In contrast, previous studies have estimated that as many as 72% of ambulatory patients receive appropriate chronic CHF management.

 Developing and aligning quality and performance measures to widely accepted guidelines has driven the implementation of these practices into care for multiple clinical conditions, and would likely improve the quality of care for COPD.

Patients who receive healthcare at the VA tend to receive higher quality of care compared with patients who receive healthcare elsewhere.

 Much of this difference is seen in areas where the VA has adopted performance measures for disease management. The VA has instituted several performance measures for CHF and has developed the CHF QUERI to identify and apply best practices for CHF care.

 Despite more VA patients being affected by COPD than CHF, no similar programs exist to improve quality for COPD.

 The specific payment metrics that tend to focus on heart disease outcomes in the private sector do not affect the VA healthcare system, allowing for a broader approach to improving health quality.

We have shown that in a large, nationwide healthcare system, there is a relative deficiency of organizational structure available for the management of COPD patients. Nevertheless, new policies have recently been implemented that impact COPD care and have focused attention to the disease. CMS has expanded the Hospital Readmission Reduction Program to include COPD.

 Hospitals that have high risk—adjusted 30-day readmission rates are subject to a penalty with reduced reimbursement for Medicare patients. This penalty is being implemented without evidence demonstrating that readmissions are a function of the quality of COPD care, preventable, or lead to poor health outcomes.

 Despite the lack of a clear path to reduce readmissions, healthcare systems may attempt to develop and adopt organizational structure and practices for COPD management to offset the potential financial penalties.

 Adoption of carefully thought out processes that emphasize a culture of quality may improve outcomes, even with evidence to suggest that the individual processes may not be effective.

We had a number of potential limitations that may affect the inferences we can draw from this study. We performed surveys that could be affected by response, recall, and social desirability bias, and we were unable to ascertain whether responses to the survey reflected true practice. In addition, the COPD survey was conducted several years after the CHF survey and we cannot account for any secular changes in practice during this period or between the time of the COPD survey and this publication. However, there was no systemwide intervention targeting COPD management in the VA during this period, and it is unlikely that organizational structure for COPD changed significantly. We believe that if a bias does exist, it likely reflects a greater discrepancy for organizational structure if both surveys had been taken in 2008. Although previous studies have found benefits from increased organizational structure, we were not able to assess whether individual practices were effective at improving outcomes. The time from initial surveys to publication may also limit the application of these results as increasing attention on COPD outcomes may have resulted in more organizational structures for COPD care. Lastly, the study setting was limited exclusively to VA facilities; therefore, our findings may not directly generalize to other healthcare systems.

This study also had important strengths. First, we engaged national VA clinical leaders to disseminate our surveys, which may have contributed to our high response rates and minimal missing data. Second, we surveyed all facilities, thereby minimizing any opportunity to introduce bias by the type of facility or the regional variations in care. Third, we addressed questions that were based on the National Quality Forum or other developed standards, enhancing the ability to utilize in real-world settings.

In our national comparative study on the organizational structure available for treatment of COPD and CHF, we found less organizational structure available for COPD than for CHF management, which highlights the value placed on health systems for developing these structures for COPD. We suspect that these deficiencies exist throughout various health systems, in part because of the paucity of research and quality improvement efforts that currently exist for COPD. Our study highlights how disparities in quality measurement can develop in the absence of a systematic approach to identify health conditions that warrant closer monitoring. With continuous emphasis placed on value of care, accountable care organizations and other integrated healthcare settings will need to develop processes to address conditions that lead to poor patient outcomes and significant financial risk.

 VA Connecticut Health Care System, Department of Veterans Affairs (STR, LAB), West Haven, CT; Section of Pulmonary and Critical Care, Department of Internal Medicine, Yale University (STR), New Haven, CT; Health Services Research and Development, VA Puget Sound Health Care System, Department of Veterans Affairs (C-FL, ESW, PLH, DHA), Seattle, WA; Department of Health Services (C-FL, ESW, PLH) and Divisions of Pulmonary and Critical Care, Department of Medicine (DHA), University of Washington, Seattle, WA; Health Research and Policy, VA Palo Alto Health Care System, Department of Veterans Affairs (PH), Palo Alto, CA; Department of Medicine, University of Connecticut Health Center (LAB), Farmington, CT.

 Funding for this research was provided by a Veterans Affairs clinical research grant IIR-09-354. The views expressed here are those of the authors and do not necessarily reflect the position or policy of the Department of Veterans Affairs.

 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 (STR, C-FL, LAB, ESW, DHA); acquisition of data (STR, C-FL, DHA, PH); analysis and interpretation of data (STR, C-FL, ESW, PLH); drafting of the manuscript (STR, C-FL, LAB, DHA, PLH, PH); critical revision of the manuscript for important intellectual content (STR, C-FL, LAB, DHA, PLH, PH); statistical analysis (STR, C-FL, ESW, PLH); provision of patients or study materials (STR); obtaining funding (C-FL, DHA, PH); administrative, technical, or logistic support (C-FL, PH, DHA); and supervision (C-FL, PH, DHA).

 Dr David Au, MD, 1100 Olive Way, Ste 1400, Seattle, WA 98104-3801. E-mail: David.Au@va.gov.

1. Fonarow GC, Abraham WT, Albert NM, et al; OPTIMIZE-HF Investigators and Hospitals. Influence of a performance-improvement initiative on quality of care for patients hospitalized with heart failure: results of the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients With Heart Failure (OPTIMIZE-HF). 

Articles

WEB EXCLUSIVE

In a nationwide cross-sectional comparison of organizational structure for chronic disease management, less attention was given to chronic obstructive pulmonary disease than chronic heart failure.

Organizational Structure for Chronic Heart Failure and Chronic Obstructive Pulmonary Disease

 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.

1. Klein R. Hyperglycemia and microvascular and macrovascular disease in diabetes.

. 1995;18(2):258-268. doi:10.2337/diacare.18.2.258.

2. UK Prospective Diabetes Study Group. Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. 

. 1998;317(7160):703-713. doi:http://dx.doi.org/10.1136/bmj.317.7160.703.

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

This study assessed the clinical and budgetary impacts of human papillomavirus (HPV) primary screening with HPV16/18 genotyping, in contrast to current cervical cancer screening strategies. 

A decision-tree framework and Markov model were used to model clinical and cost implications of screening and diagnosis of disease. 

A model was developed to compare the annual clinical and budgetary impact of HPV screening with genotyping versus cytology, and co-testing with and without genotyping. Epidemiology and test performance inputs are from the literature and the Addressing THE Need for Advanced HPV Diagnostics (ATHENA) trial. Costs are from a US payer perspective. Clinical impact was measured as the resulting incidence of cervical cancer, and budget impact is reported as annual cost per screened woman. The model considered the impact of patient noncompliance (loss to follow-up) at both the initial screen and re-test. 

Cytology was found to be inferior to both co-testing and HPV primary screening. Co-testing was inferior to co-testing with genotyping. Co-testing with genotyping every 3 years (incidence = 5.5 per 100,000 women; annual investment = $61) or 5 years (incidence = 7.4 per 100,000 women; annual investment = $37) was slightly more effective, but more costly than HPV primary screening every 3 years (incidence = 6.2 per 100,000 women; annual investment = $48) or 5 years (incidence = 8.1 per 100,000 women; annual investment = $30). Genotyping strategies were relatively stable to the effects of patient noncompliance. 

Primary HPV screening with genotyping represents a sensible combination of clinical effectiveness and costs, while reducing the risks associated with patient noncompliance.

	Human papillomavirus (HPV) screening with genotyping represents a sensible combination of clinical effectiveness and costs.

		Recent FDA approval and an interim clinical guidance have resulted in HPV testing as an option for primary screening of cervical cancer.

		HPV screening with genotyping every 3 years leads to a lower incidence of cervical cancer than either of the 2 current guideline-recommended strategies—cytology every 3 years or co-testing every 5 years&mdash;with 6.2 of cervical cancer cases per 100,000 women versus 11.7 and 7.4, respectively. There is also lower cost per disease detected ($32,123 vs $36,876 and $36,196, respectively).

		Incorporating genotyping into screening is especially important as the screening interval increases or when patient compliance is a concern. 

In 2014, a human papillomavirus (HPV) test that detects high-risk types and individual genotypes HPV 16 and 18 utilizing amplification of target DNA (the cobas HPV Test) was approved by the FDA for primary screening in cervical cancer. HPV types 16 and 18 have been found to cause more than 70% of cervical cancers

; women who are positive for HPV 16 and/or 18 are at an increased risk of high-grade cervical intraepithelial neoplasia (CIN), even if they have normal cytology.

 CIN is a dysplastic change beginning at the squamocolumnar junction in the uterine cervix that may be a precursor of cervical cancer: grade 1 (CIN1), mild dysplasia involving the lower one-third or less of the epithelial thickness; grade 2 (CIN2), moderate dysplasia with one-third to two-thirds involvement; grade 3 (CIN3), severe dysplasia or carcinoma in situ, with two-thirds to full-thickness involvement. Targeting detection of these high-risk HPV types allows clinicians to properly manage patients at highest risk for developing cervical cancer.

In 2015, a panel represented by multiple societies issued new interim guidance recommending HPV primary screening as an alternative to current cytology-based screening strategies.

 This provides clinicians and patients with another option for routine screening—options which now include cytology alone, cytology in conjunction with HPV testing (co-testing) with or without genotyping, or HPV primary screening with genotyping.

 Likewise, payers now have the opportunity to consider an expanded range of screening options.

This study was undertaken to estimate, from a US payer perspective, the near-term clinical and budgetary impacts of adopting HPV primary screening with HPV 16/18 genotyping compared with current cervical cancer screening strategies derived from established clinical guidelines.

A decision-tree framework was used to model the screening and diagnosis of disease ≥CIN2; a Markov transition model was constructed to simulate the natural history of HPV, CIN, and cervical cancer. Women enter the decision tree with the probability of initial disease representative of a US cervical cancer-screened population of individuals 30 years or older (mean age = 45 years).

The model compares the screening strategies currently recommended by the United States Preventive Services Task Force (USPSTF)/American Cancer Society (ACS) for women aged 30 to 65 years with strategies that incorporate HPV screening with genotyping to identify high-risk strains 16 and 18, resulting in comparison of 7 screening strategies in total. The screening strategies include: 1) cytology every 3 years, 2) co-testing every 3 years, 3) co-testing every 5 years, 4) co-testing with genotyping every 3 years, 5) co-testing with genotyping every 5 years, 6) HPV primary screening with genotyping every 3 years, and 7) HPV primary screening with genotyping every 5 years.

 The decision-tree diagrams are represented in 

. A diagnosis of ≥CIN2 incurs treatment cost and exits the model.

The screening algorithms are described as follows:

Cytology is the primary screening method. Women with indeterminate cytology results—referred to as atypical squamous cells of undetermined significance (ASC-US)&mdash;are triaged using HPV testing. A positive HPV result or cytology worse than ASC-US leads to colposcopy. Women with negative results return for routine cervical cancer screening in 3 years (see Figure 1A).

The USPSTF/ACS recommend a second screening strategy of co-testing with cytology and HPV, which allows extension of the screening interval from 3 to 5 years for women negative on both tests. Colposcopy is indicated in women with cytology results of ASC-US/HPV positive, or cytology worse than ASC-US, regardless of HPV result. Women with normal cytology but who are HPV positive return for follow-up co-testing in 12 months. Although 5-year screening intervals are recommended for women negative on both tests, in practice, a 3-year interval is frequently used. Both intervals were modeled (see Figure 1B).

Co-testing with genotyping every 3 or 5 years. 

Another option for women with co-testing results of normal cytology but who are HPV positive is to genotype for HPV 16/18. Women testing positive for 16/18 are sent to colposcopy, whereas women positive for HPV but negative for 16/18 repeat co-testing in 12 months. All other co-testing results are managed the same way as for co-testing without genotyping (see Figure 1B).

HPV primary screening every 3 or 5 years. 

This strategy utilizes HPV with genotyping as the primary screening modality. Women who are HPV negative return for routine screening in 3 or 5 years. Women who are HPV 16/18 positive are referred for immediate colposcopy. HPV positive women who are HPV 16/18 negative have cytology performed on the residual sample. A cytology result of ASC-US or worse leads to immediate colposcopy, whereas normal results from cytology return women for follow-up testing in 12 months (see Figure 1C).

Consistent with published US rates, the model assumes a 75% probability of compliance with follow-up testing and routine screening intervals.

 Similarly, patients lost to follow-up at the time of re-test are assumed to have a 75% probability of returning to routine screening at the next interval. In the interim, patients with HPV infection/CIN may persist, progress, or regress from one stage to another.

The progression and regression of HPV and CIN were modeled using a Markov state transition model with a 1-month cycle, which captures the probability of a screened population of individuals 30 years or older, transitioning to a more or less advanced stage of CIN or HPV infection. Women enter the Markov model following results of the initial screen in 1 of the following 8 health states: well and HPV negative, non-16/18 HPV positive, 16/18 HPV positive, CIN1, CIN2, CIN3, invasive cervical cancer (ICC), or death.

 shows the graphical representation of the Markov model. We assume only CIN3 may directly progress to ICC. Patients face a probability of death from ICC; however, death from other causes is not considered.

The model was used to assess the impact of the screening strategies over 2 screening cycles (2x interval length). The results of the model were then annualized to arrive at a 1-year time horizon that reports the expected annual incidence of cervical cancer and average annual cost of screening women 30 years or older (see eAppendix [

] for calculation). Annual outcomes were reported in order to normalize results across screening strategies with different interval lengths and to present the data on a basis that is easier for payers to compare. The model uses probabilities instead of a cohort approach to allow each payer to assess the impact on their population by multiplying the annual per-screened-woman outcomes by their relevant member population. The costs are reported annually and are assumed to be applicable in the short term (6-10 years) as the basis of the calculation is 2 screening cycles. The results assume that as long as the national population of screened women 30 and older are representative of a health plan’s population

 the entry/exit of individual members should not impact the overall results, allowing the results to be representative of individual health plans.

Epidemiological and test performance inputs were taken from the Addressing THE Need for Advanced HPV Diagnostics (ATHENA) trial and are based on women 30 years or older (mean age = 44.7 ± 10.1 years). The ATHENA trial has been described elsewhere.

Briefly, as a prospective cohort study which enrolled 47,000 women undergoing cervical cancer screening in the United States, it is the largest cervical cancer screening registrational trial to evaluate HPV testing.

Data used for the natural history of cervical cancer were taken from US and international studies. Clinical inputs are shown in 

Where multiple sources existed, inputs were based on a weighted average, with results from studies with larger populations weighted more heavily than studies with smaller populations.

Costs include all screening costs in addition to costs for the diagnosis and treatment of CIN and ICC. Costs for screening, diagnosis, and treatment of CIN are taken from the US Medicare fee schedule.

 Cost for HPV testing was based on the cobas HPV Test, which includes simultaneous testing for strains 16/18, and therefore, no additional cost was assumed for genotyping. Direct costs for treating ICC were taken from published US studies and assume the average cost of treatment and follow-up across all stages of cervical cancer.

 (Cost inputs are available in eAppendix 2 [Table]). All costs were adjusted to 2014 US dollars.

A 1-way sensitivity analysis and probabilistic sensitivity analysis (PSA) were undertaken to assess the impact of parameter uncertainty on modeled results. Clinical inputs were varied across the ranges reported in the literature and assumed a beta distribution, while costs were varied by ±50% and assumed a gamma distribution. The correlation between sensitivity and specificity was controlled using the diagnostic odds ratio.

 The PSA followed a standard Monte Carlo approach based on 5000 randomly generated simulations of parameter values.

When assessing the costs and effectiveness of each strategy relative to alternatives, screening with cytology alone results in an increase in the incidence of cancer and higher mortality due to missed cancers than any other strategy, and at a cost higher than that of strategies incorporating a 5-year interval. We can thus consider cytology to be inferior to alternatives with 5-year screening intervals since it is both less effective and more expensive.

Of the remaining strategies, co-testing every 3 or 5 years without genotyping has similar costs as co-testing every 3 or 5 years with genotyping, but results in more cancer. Consequently, we can consider the co-testing with genotyping strategies to be superior to co-testing without genotyping. Thus, the strategies that utilize genotyping represent a desired combination of improving screening effectiveness while reducing cost. For instance, HPV primary screening at 5 years, when compared with the current guideline-recommended strategies of: 1) primary cytology every 3 years; and 2) co-testing without genotyping every 5 years, leads to reduced cervical cancer incidence and 27% and 19% reductions in cost, respectively.

Of all strategies modeled, the one that incorporates co-testing with genotyping and HPV primary screening at 3-year intervals results in the lowest annual incidence of cervical cancer (5.5 and 6.2 per 100,000 women, respectively). However, such strategies may require an increase in overall financial investment.

The number needed to screen to avert 1 case of ICC was calculated as the inverse of the absolute risk reduction from modeled screening strategies compared with the current US incidence of cervical cancer for screened women 30 years or older (8.0 per 100,000).

 As compared with today’s environment of mixed methodologies for cervical cancer screening, co-testing with genotyping and HPV primary screening at 3-year intervals result in the lowest numbers needed to screen to detect 1 cancer at 40,000 and 55,556, respectively. Results are shown in 

To assess the impact of loss to follow-up on the performance of the screening algorithms, we compared the linear relationships between compliance and disease incidence for all strategies. The comparison indicates that co-testing every 5 years is most sensitive to noncompliance (slope coefficient = 0.467, where a steeper slope indicates higher sensitivity to noncompliance), followed by co-testing every 3 years (slope coefficient = 0.400). Genotyping strategies are relatively stable to the effect of noncompliance (slope coefficients range between 0.227 for co-testing with genotyping every 3 years to 0.300 for HPV primary screening every 5 years). This suggests that strategies incorporating genotyping may mitigate the effect of noncompliance through early detection of the highest-risk patients at the initial visit.

Full results of the 1-way sensitivity and PSA are available in eAppendix 4 and 5, respectively. The 1-way sensitivity analysis, comparing HPV primary screening at 3 years with the alternative strategies, reveals that the costs of HPV screening and cytology as well as the prevalence of HPV had the largest impact on the incremental cost per patient. When comparing a 3- versus 5-year time horizon, the same parameters were impactful; the additional cost of office visits had the largest impact on the cost difference.

. The analysis revealed that HPV primary screening at 3 years is likely to reduce the annual incidence of ICC compared with the other guideline-endorsed strategies of cytology every 3 years and co-testing with or without genotyping every 5 years (100%, 98%, and 75% probability that HPV primary screening will reduce the incidence of ICC versus comparator, respectively), but may increase costs at shortened intervals. The results of the PSA suggest considerable uncertainty regarding effectiveness; this is due to the small population of true positives, which impacts the precision of sensitivity in screening studies.

When evaluating new strategies for screening, it is critical to consider the clinical benefits that can be achieved with a screening change versus the incremental costs of that change. HPV primary screening every 3 years has the second lowest incidence of cancer and related mortality, yet at a substantially lower cost per screened woman compared with the most effective strategy, co-testing with genotyping every 3 years ($48 vs $61). This represents an opportunity to improve clinical outcomes while balancing resource allocation.

This study assesses the clinical and economic implications from a payer perspective of human papillomavirus genotyping for cervical cancer screening in comparison with existing practices.

March 2016

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