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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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Rachel O. Reid, MD, MS; Brendan Rabideau, BA; and Neeraj Sood, PhD
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David Cutler, PhD; Michael Ciarametaro, MBA; Genia Long, MPP; Noam Kirson, PhD; and Robert Dubois, MD, PhD
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Michelle P. Lin, MD, MPH; Bonnie B. Blanchfield, ScD, CPA; Rose M. Kakoza, MD, MPH; Vineeta Vaidya, MS; Christin Price, MD; Joshua S. Goldner, MD; Michelle Higgins, PA-C; Elisabeth Lessenich, MD, MPH; Karl Laskowski, MD, MBA; and Jeremiah D. Schuur, MD, MHS
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Qian Shi, PhD, MPH; Thomas J. Yan, MS; Peter Lee, BS; Paul Murphree, MD, MHA; Xiaojing Yuan, MPH; Hui Shao, PhD, MHA; William H. Bestermann, MD; Selina Loupe, BS; Dawn Cantrell, BA; David Carmouche, MD; John Strapp, BA; and Lizheng Shi, PhD, MSPharm
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Stanley E. Waintraub, MD; Donna McNamara, MD; Deena Mary Atieh Graham, MD; Andrew L. Pecora, MD; John Min, BS; Tommy Wu, BA; Hyun Gi Noh, MSC; Jacqueline Connors, RN, OCN; Ruth Pe Benito, MPH, BS; Kelly Choi, MD; Eric Schultz, BS; and Stuart L. Goldberg, MD
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Rami J. Hosein, MD, MPH; Joan C. Lo, MD; Bruce Ettinger, MD; Bonnie H. Li, MS; Fang Niu, MS; Rita L. Hui, PharmD, MS; and Annette L. Adams, PhD, 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.

Objectives: We implemented a health information technology–enabled population health management program for chronic disease management in academic hospital–affiliated primary care practices, then compared quality-of-care outcome measures among practices assigned a central population health coordinator (PHC) and those not assigned a PHC.

Study Design: Quasi-experimental.

Methods: Central PHCs were nonrandomly assigned to 8 of 18 practices. They met with physicians, managed lists of patients not at goal in chronic disease registries, and performed administrative tasks. In non-PHC practices, existing staff remained responsible for these tasks. The primary outcome was difference-in-differences over the 6-month follow-up period between PHC and non-PHC practices for outcome measures for diabetes (low-density lipoprotein cholesterol [LDL-C], glycated hemoglobin [A1C], and blood pressure [BP] goal attainment), cardiovascular disease (LDL-C goal attainment), and hypertension (BP goal attainment). Secondary outcomes included process measures only (obtaining LDL-C, A1C, and BP readings) and cancer screening test completion. 

Results: The difference in the percentage point (PP) increase in outcome measures over follow-up was greater in PHC practices than non-PHC practices for all measures among patients with diabetes (LDL-C, 4.6 PP; A1C, 4.8 PP; BP, 4.7 PP), cardiovascular disease (LDL-C, 3.3 PP), and hypertension (BP, 2.3 PP) (adjusted P all <.001). Changes in cancer screening outcomes, which were not a focus of PHC efforts, were similar between PHC and non-PHC practices. 

Conclusions: Use of central PHCs led to greater improvement in short-term chronic disease outcome measures compared with patients in practices not assigned a central PHC.

Am J Manag Care. 2017;23(12):728-735
Takeaway Points
A population health management program using a health information technology tool can significantly improve process and outcome measures for patients with diabetes, cardiovascular disease, and hypertension. 
  • Utilizing central population health coordinators who work closely with practice personnel can lead to greater improvement in outcome measures. 
  • Our results support the use of central personnel working with practice-based staff on population health management programs, but longer-term follow-up is needed.
Provisions of the Affordable Care Act support population health management (PHM) activities as part of fostering accountable care organizations (ACOs).1 PHM activities are intended to shift the focus from individuals seeking care in face-to-face visits using fee-for-service payments to managing a panel of patients who seek care in a practice network using value-based payment or global budgeting models.2 Advances in health information technology (IT) have increased the feasibility of population-level oversight of all patients in a network.3-5 However, few studies have evaluated the impact of implementing a PHM program as part of routine care within primary care networks.6-9 

The patient-centered medical home (PCMH) care model supports primary care physician (PCP)-led teams managing the preventive care and chronic disease management needs of patients.10-12 This team-based model is intended to help PCPs manage competing time demands during office visits and helps ensure timely intervention when goals are not being met.12 Adoption of clinical registries, promoted by Stage 3 Meaningful Use in the Health Information Technology for Economic and Clinical Health Act, seeks to use active surveillance to identify patients with measurable gaps in care to improve quality and/or safety of care.13 These PHM activities often use nonphysician team members with established workflows for visit- and non–visit-based outreach.14 

Although primary care practices represent a natural focus for such PHM work, especially when coupled with face-to-face visits, it is uncertain whether non­–visit-based activities should reside solely within practices or whether this role may be appropriate for central coordination within an ACO’s primary care network. To investigate these issues, we developed and implemented a PHM program for chronic disease management utilizing an established health IT clinical registry within a large heterogeneous primary care practice network. Some practices were assigned a central resource, referred to as population health coordinators (PHCs), to take on these PHM activities. Other practices were asked to assign practice staff to these tasks. We evaluated quality-of-care process and outcome measures over the first 6 months of the chronic disease management program. We hypothesized that practices assigned a central PHC would have greater performance increases in quality measures compared with practices that were not assigned a PHC. 


Study Setting and Design

The study took place in the Massachusetts General Hospital Primary Care Practice-Based Research Network, consisting of 18 primary care practices. All practices in the network use electronic health records (EHRs) and have utilized a PHM health IT tool, TopCare (SRG Technology),15 for preventive cancer screening since 2011.In 2014, this PHM tool was expanded to include registries for patients with diabetes, cardiovascular disease (CVD), and hypertension (HTN). The tool identified network patients, assigned them to chronic disease registries, and tracked goal attainment in near real time. We developed a program for chronic disease management using central PHCs assigned to specific practices. We conducted a quasi-experimental evaluation of the program and compared quality-of-care process and outcome measures over the first 6 months (July 1, 2014, to December 31, 2014) of the PHM program for patients with diabetes, CVD, and HTN in practices assigned a central PHC (n = 8) or not (n = 10). Because all practices were already using the PHM tool for preventive cancer screening without central PHC input and this did not change during the study period, we also examined cancer screening outcomes as a way to control for the nonrandom assignment of the central PHC personnel. All practices also had the same financial incentives based on performance as defined by the patients meeting criteria for each registry. 

Network and Chronic Disease Registry Participants

A validated automated algorithm was modified to be used in near real time to identify potential eligible adult (≥18 years) patients who had at least 1 visit to a study practice within the prior 3 years at baseline or had a visit during the 6-month follow-up period and were connected with a specific network physician or practice.16,17 Patients were considered to have diagnosed diabetes (type 1 or type 2) using a previously validated algorithm.6 Patients with CVD, including coronary artery disease, peripheral vascular disease, and cerebrovascular disease, were identified using EHR problem and procedure list terms and procedure codes for interventions, and patients with HTN were identified utilizing billing diagnosis codes and EHR problem list terms.18 These algorithms were internally validated based upon blinded chart review of randomly selected patients (sensitivity and specificity >90%). Patients eligible for breast, cervical, and colorectal cancer screening were women aged 50 to 74 years without bilateral mastectomy, women aged 21 to 64 years without total hysterectomy, and men or women aged 52 to 75 years without total colectomy, respectively.5 We excluded patients who switched between PHC and non-PHC practices during the follow-up period. 

PHM Program With Central PHCs

PHM leaders hired and nonrandomly allocated 4 PHCs to work with 8 practices as part of a pilot project to centralize PHM efforts to improve quality of care. PHCs came from a variety of backgrounds in healthcare delivery and were selected based upon their having excellent communication skills and an ability to learn new electronic systems. The network did not have sufficient resources to implement a PHC in all 18 network practices. Among practices that expressed an interest in the program, PHCs were assigned so that practices reflected network diversity with regard to size, type (hospital-based, community-based, or community health center), and baseline quality scores. These decisions were made with a goal of equitably distributing available PHC resources within the network to get practice leader support and to maximize the impact of the program for practices with and without PHCs. 

A training curriculum was developed for PHCs that included clinical instruction focused on chronic disease management and preventive health as well as the basics of health coaching and motivational interviewing. Additionally, PHCs were trained on optimal use of the clinical registry tool and the EHR and participated in a customized process improvement curriculum focused on process mapping and Plan-Do-Study-Act cycles.19 PHCs shadowed clinical and nonclinical personnel to understand practice workflow. They executed administrative tasks, including appointment scheduling, ordering overdue laboratory testing, performing chart reviews, and obtaining home blood pressure (BP) values and outside tests or laboratory results. In addition, PHCs regularly met (“huddled”) with physicians to review those patients who required clinical intervention to develop an action plan that could have included such elements as a call from a registered nurse to review medication compliance or titrate a statin or antihypertensive, initiation of home BP monitoring, scheduling an office visit to change treatment, or referring the patient to a diabetes specialist or nutritionist.

PHM Program Without Central PHC Support

The remaining 10 network practices not assigned a PHC were provided training and support in use of the PHM IT tool. The staff in these practices remained responsible for managing administrative tasks. 

Covariates and Process and Outcome Measures

Patient characteristics and laboratory, BP, and cancer screening data were obtained from an electronic central data repository.20 Each registry tracked process metrics, including obtaining tests or readings, and outcome metrics, such as goal attainment. 

Criteria for process and outcome measures are described in Table 1. Patients passed a process measure by obtaining a test in a given time period or if a clinical exception was entered in the PHM tool and EHR. For colorectal cancer screening, home fecal occult blood testing (FOBT) was an option for patients who refused other methods. However, because optical screening was the network’s preferred approach and documentation of FOBT results in the EHR was poor, FOBT was not included in outcome assessment. Patients were at goal for outcome measures if they passed the process measure and either met a target laboratory or BP value or were on maximal medical therapy. HTN BP criteria are in accordance with Eighth Joint National Committee guidelines.21 

The primary outcomes for this study were the difference-in-differences over the 6-month follow-up period between PHC and non-PHC practices for outcome measures for diabetes (low-density lipoprotein cholesterol [LDL-C], glycated hemoglobin [A1C], and BP goal attainment), CVD (LDL-C goal attainment), and HTN (BP goal attainment). As secondary outcomes, we examined difference-in-differences for chronic disease process measures (obtaining LDL-C, A1C, and BP readings) and test completion for breast, cervical, and colorectal cancer screenings. Additionally, we evaluated numerator factors (process/outcome at goal, on maximal medical therapy, clinical exceptions) that accounted for changes in our primary outcomes. Primary and secondary outcomes focused on patients who were in a registry at both baseline and follow-up time periods. We also performed sensitivity analyses that included all patients, even if present at only 1 time period (eAppendix Table 1a and 1b [eAppendices available at]).

Statistical Analysis

We compared baseline patient and physician/practice characteristics between the PHC and non-PHC groups using χ2 or t tests. For primary and secondary outcomes, we controlled for these characteristics (age, gender, language, race, insurance, practice type seen in [ie, community health center or not], practice PCMH recognition status, and patient–physician continuity)17 using a logistic regression model with a time-by-PHC-practice interaction term and accounting for clustering among patients using the general estimating equations approach (PROC GENMOD, SAS version 9.4, SAS Institute; Cary, North Carolina). The Partners Institutional Review Board approved the use of data collected as part of routine care with a waiver of informed consent.

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