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The American Journal of Managed Care December 2017
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Chronic Disease Outcomes From Primary Care Population Health Program Implementation
Jeffrey M. Ashburner, PhD, MPH; Daniel M. Horn, MD; Sandra M. O’Keefe, MPH; Adrian H. Zai, MD, PhD; Yuchiao Chang, PhD; Neil W. Wagle, MD, MBA; and Steven J. Atlas, MD, MPH
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Chronic Disease Outcomes From Primary Care Population Health Program Implementation

Jeffrey M. Ashburner, PhD, MPH; Daniel M. Horn, MD; Sandra M. O’Keefe, MPH; Adrian H. Zai, MD, PhD; Yuchiao Chang, PhD; Neil W. Wagle, MD, MBA; and Steven J. Atlas, MD, MPH
Patients in practices with central population health coordinators had greater improvement in short-term chronic disease outcome measures compared with patients in practices without central support.

At baseline, among 160,123 patients in the primary care network, there were 12,316 patients with diabetes (4206 in PHC and 8110 in non-PHC practices); 12,591 patients with CVD (4027 in PHC and 8529 in non-PHC practices); and 41,184 patients with HTN (14,461 in PHC and 26,723 in non-PHC practices) present at both baseline and follow-up. For each condition, patients from PHC practices were younger, more likely to be female, more likely to be white and less likely to be Hispanic, more likely to speak English, more likely to have commercial insurance and less likely to have Medicare, more likely to be connected to a specific physician, and more often seen in a community health center (Table 2). Patients eligible for breast, cervical, and colorectal cancer screening demonstrated similar differences (eAppendix Table 2).

Primary Outcomes for Chronic Disease Populations

Performance on outcome measures increased more over the 6-month follow-up period for patients in PHC practices compared with patients in non-PHC practices for all measures (Table 3). Among patients in PHC practices, all baseline measures were lower compared with patients in non-PHC practices, but at the end of follow-up, outcome measures were higher for 3 of 5 chronic disease measures (diabetes BP, diabetes A1C, and CVD LDL-C). The largest percentage point (PP) differences in the change in proportion of patients at goal over follow-up between PHC and non-PHC practices were among patients with diabetes (LDL-C, 9.1 PP PHC vs 4.5 PP non-PHC change; BP, 2.8 PP vs –2.0 PP change; A1C, 6.0 PP vs 1.3 PP change). The difference-in-differences was also significantly greater in PHC practices for LDL-C goal attainment in patients with CVD (9.0 PP vs 5.7 PP) and for BP goal attainment in patients with HTN (3.7 PP vs 1.4 PP). All differences in performance among patients in PHC and non-PHC practices persisted after adjustment for baseline characteristics (P <.001). 

Change in Process Measures for Chronic Disease Populations

Patients in PHC practices also had greater improvement in process measures compared with those in non-PHC practices (Table 3). Among patients with diabetes, the increase in the proportion of patients at goal for the process measures for LDL-C (4.4 PP vs 0.6 PP), BP (3.2 PP vs ­–2.2 PP), and A1C (4.0 PP vs –1.3 PP) was greater in PHC practices. Among patients with CVD, the increase in patients at goal for the LDL-C process measure (5.6 PP vs 2.5 PP) was greater in PHC practices. Among patients with HTN, the increase in patients at goal for the BP process measure (3.9 PP vs 1.1 PP) was greater in PHC practices. All differences remained significant after adjusting for baseline patient characteristics (P <.001). 

Change in Process Measures for Cancer Screening

In contrast to outcomes for chronic disease measures on which the central PHCs focused their work, differences in cancer screening (breast, cervical, colorectal) measures were similar in PHC and non-PHC practices (Table 3). These screening rates increased slightly in both PHC and non-PHC practices, with small differences in the magnitude of PP increase favoring non-PHC practices (breast, 0.5 PP vs 0.9 PP; cervical, 0.6 PP vs 1.5 PP; colorectal, 1.3 PP vs 1.7 PP). 

Factors That Accounted for Changes in Outcomes Over Time

To assess what accounted for greater changes in outcomes in PHC compared with non-PHC practices, we examined numerator factors contributing to goal attainment (Table 4). PHC practices were more successful in increasing the proportion of patients reaching the targeted clinical value for LDL-C, BP, and A1C outcomes. 


We developed and implemented a PHM program for chronic disease management using a health IT tool within a large diverse primary care network. We compared outcomes among practices that were assigned central personnel to support administrative tasks with practices that used local staff with central training and support only. Over the first 6 months of the program, practices, regardless of whether they were assigned a central PHC or not, experienced improvements in most process and outcome measures for diabetes, CVD, and HTN. However, practices with PHCs achieved larger increases in quality than practices that did not receive this support. Patients in PHC practices were more likely to be at clinical goal, not just to have had the tests performed. The PHCs did not focus on cancer screening, and during the same time period there were similar changes in cancer screening rates between PHC and non-PHC practices.

Prior study findings have demonstrated that chronic disease management programs can improve outcomes of care for patients with diabetes,6,9,22-24 CVD,25 and HTN,26-28 but few studies have evaluated PHM programs in routine clinical practice. Research studies involve additional personnel and support that are often not available to practices. Prior studies have evaluated the impact of pharmacy-led programs,9,25,28 nurse- or case manager–led programs,22,27,29 and population-level clinical registries6 to improve outcomes for a single chronic condition. Our study presents the results of a natural experiment following implementation of a PHM program in routine practice for multiple chronic diseases. 

Our study examined the first 6 months after implementing a chronic disease PHM program and showed increases in quality of care across all practices and for each chronic disease. We believe these positive results reflect a combination of functioning teams within practices, a sophisticated registry tool, and financial incentives supporting clinically meaningful outcomes. However, we do not know about possible comparable changes in quality in the time period prior to implementing this program. Thus, we cannot say with certainty that the increases seen during the 6-month period following implementation of this program were due solely to the program itself. It is likely that the large increases in most outcomes for the chronic disease measures were partially due to the intervention. 

Practices with central PHCs demonstrated larger improvements in chronic disease outcome measures than practices without PHCs. These differences were impressive, especially given the small changes in preventive cancer screening, not a focus of this intervention, during the same time period. These results support investing in central organizational infrastructure with personnel who can receive specialized training and develop focused expertise in population-based chronic disease management. How best to optimize the roles and functions for those involved in PHM remains to be determined.30 Practice personnel primarily focus on visit-based care, but the non–visit-based functions of central PHCs could be performed by practice staff as envisioned for PCMHs.12 However, a central organizational structure may promote the transfer of optimal workflow among heterogeneous practices, and non–visit-based activities in addition to the constant stream of outpatient office visits may overwhelm stressed primary care teams.31,32 


The main limitation of this study is that practices were not randomly assigned to receive a central PHC. Rather, we nonrandomly allocated our PHCs based on practice type, size, location, and willingness to include a PHC in their workflow. Differences in patient populations or practice personnel and their willingness to implement PHM could account for the larger increases in PHC practices we observed for process and outcome measures. However, we adjusted for patient and practice characteristics, including PCMH recognition status, in our multivariable models and all differences persisted. If the differences were due to motivation in practice personnel between PHC and non-PHC sites, we also would have expected to see larger differences in breast, cervical, and colorectal cancer screening rates even though the PHCs did not focus on these registries. However, differences in cancer screening were similar (range of PP differences, 0.4-0.9 favoring non-PHC practices). 

Because intervention practices were provided more personnel to improve quality of care, it is possible that the greater improvement observed in PHC practices was due to the additional personnel, rather than the centralized organization and use of these resources. We focused our analyses on patients present in each disease registry at both baseline and follow-up, which did not include patients newly diagnosed or who left our network during follow-up. In sensitivity analyses that included all patients, the difference-in-differences between PHC and non-PHC practices were similar to our primary analyses (eAppendix Table 1a and 1b). PHC practices had lower performance at baseline and, therefore, more room for improvement. However, not only did PHC practices have improvements of a larger magnitude, but they actually surpassed performance in non-PHC practices for most measures. Lastly, beginning in January 2015, the PHC program was expanded to all primary care practices in our network, so this evaluation focused on the initial 6-month pilot period. Additional follow-up will assess expanding the program to non-PHC practices and the ability to sustain outcomes over time.


Interest in PHM activities has been spurred by new financial models using value-based payment, advances in health IT, and reorganizing delivery of primary care to support highly functioning teams.33-35 Prior to implementing our PHM chronic disease program, our network had made progress through implementing a novel IT tool5 and helping practices achieve PCMH recognition.35 As part of this program’s implementation, we changed traditional Healthcare Effectiveness Data and Information Set metrics used for pay-for-performance contracts to a model that was all-payer and all-patient, and incorporated clinically relevant metrics (such as giving credit for HTN control for patients already on 3 separate medicine classes regardless of blood pressure targets) as defined by the registry tool itself. These incentives were tied to individual practice PCP and staff incentives using existing monies associated with contractual insurer agreements. 

Our study results demonstrated that a PHM program using a health IT tool improved process and outcome measures for patients with diabetes, CVD, and HTN over short-term follow-up. Further, utilizing central PHCs who worked closely with practice personnel led to greater improvement in outcome measures in those practices’ patients compared with patients in practices not assigned central coordinators. This supports the use of central personnel working with practice-based staff on PHM programs, but longer-term follow-up is needed to assess outcomes over time. New funding mechanisms are needed to support such practice- and network-based efforts to improve population-based chronic disease management.

Author Affiliations: Division of General Internal Medicine (JMA, DMH, YC, SJA), and Massachusetts General Physicians Organization (DMH, SMO, SJA), and Laboratory of Computer Science (AHZ), Massachusetts General Hospital, Boston, MA; Harvard Medical School (JMA, DMH, AHZ, YC, NWW, SJA), Boston, MA; Brigham and Women’s Hospital (NWW), Boston, MA; Partners Healthcare (NWW), Boston, MA.

Source of Funding: Funding for this program, including the IT tool and personnel, was provided by the Massachusetts General Physicians Organization and Partners HealthCare. The funders were involved in creating and implementing the program, but the authors were responsible for design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, and approval of the manuscript without funder approval.

Author Disclosures: Dr Zai is a consultant of SRG Technology (SRGT), of which he owns stock options. SRGT licensed TopCare from the Massachusetts General Hospital Laboratory of Computer Science, where Dr Zai is employed, and he receives royalties from TopCare sales. Dr Atlas has a royalty agreement with SRGT but has received no payments. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. 

Authorship Information: Concept and design (JMA, DMH, SMO, AHZ, NWW, SJA); acquisition of data (JMA, YC, NWW, SJA); analysis and interpretation of data (JMA, DMH, YC, NWW, SJA); drafting of the manuscript (JMA); critical revision of the manuscript for important intellectual content (JMA, DMH, SMO, AHZ, YC, SJA); statistical analysis (JMA, YC); provision of patients or study materials (SMO); obtaining funding (SJA); administrative, technical, or logistic support (DMH, SMO, AHZ, NWW); and supervision (DMH, SJA). 

Address Correspondence to: Steven J. Atlas, MD, MPH, Massachusetts General Hospital, 50 Staniford St, Boston, MA 02114. E-mail: 

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