Yuchiao Chang, PhD

 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.

 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. 

 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 

 all <.001). Changes in cancer screening outcomes, which were not a focus of PHC efforts, were similar between PHC and non-PHC practices. 

 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.

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).

 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.

 Advances in health information technology (IT) have increased the feasibility of population-level oversight of all patients in a network.

 However, few studies have evaluated the impact of implementing a PHM program as part of routine care within primary care networks.

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.

 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.

 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.

 These PHM activities often use nonphysician team members with established workflows for visit- and non—visit-based outreach.

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&shy;—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. 

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),

 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.

 Patients were considered to have diagnosed diabetes (type 1 or type 2) using a previously validated algorithm.

 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.

 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.

 We excluded patients who switched between PHC and non-PHC practices during the follow-up period. 

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.

 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.

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.

 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 

. 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.

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 (

We compared baseline patient and physician/practice characteristics between the PHC and non-PHC groups using χ

 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)

 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.

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 (

). Patients eligible for breast, cervical, and colorectal cancer screening demonstrated similar differences (

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 (

). 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 (

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 &shy;—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 (

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 (

). 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,

 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,

 and population-level clinical registries

 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.

 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.

 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.

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.

 Prior to implementing our PHM chronic disease program, our network had made progress through implementing a novel IT tool

 and helping practices achieve PCMH recognition.

 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.

 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.

 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.

Articles

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.

Chronic Disease Outcomes From Primary Care Population Health Program Implementation

To evaluate the impact on smoking status documentation of a payer-sponsored pay-for-performance (P4P) incentive that targeted a minority of an integrated healthcare delivery system’s patients.

Three commercial insurers simultaneously adopted P4P incentives to document  smoking status of their members with 3 chronic diseases. The healthcare system responded by adding a smoking status reminder to all patients’electronic health records (EHRs). We measured change in smoking status documentation before (2008-2009) and after (2010-2011) P4P implementation by patient P4P eligibility.

The P4P-eligible patients were compared primarily with a subset of non—P4P-eligible patients who resembled P4P-eligible patients and also with all non–P4P-eligible patients. Multivariate models adjusted for patient and provider characteristics and accounted for provider-level clustering and preimplementation trends.

Documentation increased from 48% of 207,471 patients before P4P to 71% of 227,574 patients after P4P. Improvement  from 56% to 83% occurred among P4P-eligible patients (adjusted odds ratio [AOR], 3.6; 95% confidence interval [CI], 2.9-4.5) and from 56% to 80% among the comparable subset of non—P4P-eligible patients (AOR, 3.0; 95% CI, 2.3-3.9). The difference in improvement between groups was significant (AOR, 1.3; 95% CI, 1.1-1.4; P = .009).

A P4P incentive targeting a minority of a healthcare system’s patients stimulated adoption of a systemwide EHR reminder and improved smoking status documentation overall. Combining a P4P incentive with an EHR reminder might help healthcare systems improve treatment delivery for smokers and meet “meaningful use” standards for EHRs.

Routine documentation of smoking status improves clinicians’ delivery of tobacco treatment and is mandated in “meaningful use” standards for electronic health records butcan be challenging to accomplish.

		A payer-sponsored pay-for-performance incentive prompted systemwide action by the healthcare system, resulting in improved documentation among both targeted and nontargeted patients.

		The effect was greatest among targeted patients, suggesting the financial incentive added value as well as prompting the reminder.

		Combining a performance incentive with an electronic reminder could help healthcare systems to improve treatment for tobacco use and enable population health interventions for smokers.

Smoking kills more than 440,000 people in the United States annually and remains the leading preventable cause of death.

Despite the availability of effective treatments for tobacco dependence, physicians assess smoking at only 63% of visits and  offer counseling to only 21% of smokers.

 Identification and documentation of smoking status are the first steps to addressing tobacco dependence and have been shown to increase physicians’ delivery of treatment.

 Clinical practice guidelines for treating tobacco dependence recognize the importance of documentation and call for routine smoking status screening and documentation by healthcare systems. The US government’s “meaningful use” electronic health record (EHR) incentive program requires smoking status identification in a coded field.

 Yet achieving a high rate of smoking status documentation is a challenge for many healthcare systems.5

Efforts to improve smoking status documentation have included electronic or paper-based reminders, performance feedback, and a simple vital sign stamp.

 Pay-for-performance (P4P) incentives have been used in some systems to promote delivery of guideline-based tobacco treatment, or as a component of broader quality improvement efforts.

 These studies have examined performance incentives for various guideline-based treatment activities, including smoking status documentation, documentation of physician-delivered counseling, referral to telephone counseling, and payments to providers for patients’ tobacco abstinence. Prior studies of P4P for smoking status documentation were limited to US physician groups or healthcare delivery  systems outside the United States.

 Relatively little is known about the effects of P4P programs in large, multipayer, integrated US healthcare delivery systems in which P4P incentives do not apply to all patients in the system. A payer-sponsored P4P incentive in a multipayer system may only be effective for the patient population to whom it applies. However, practice changes stimulated by a targeted P4P measure can have broader benefits that improve documentation and treatment delivery for targeted and non-targeted patients.

A P4P incentive that was introduced in 2010 in a large multipayer healthcare delivery system rewarded practices for documenting the smoking status of patients with specific commercial insurers and 3 chronic diseases. The organization’s multiple payers and diverse provider groups with varied practice styles and patient populations make standardized quality improvement efforts especially challenging. Our objective was to study the effect of a targeted, payer-sponsored P4P incentive payment on smoking status documentation across the healthcare system.

Partners HealthCare Inc is a large integrated healthcare delivery system in eastern Massachusetts whose provider network, Partners Community HealthCare, Inc (PCHI), represents more than 5000 primary care and specialist physicians, and works with multiple payers including commercial insurers, Medicare, and Medicaid.

 All PCHIaffiliated practices use one of several different EHRs. This study was restricted to the practices using the system’s predominant EHR, the Longitudinal Medical Record, a locally developed system used by 63% of PCHI practices that was first implemented in 1998. Active providers have been using the EHR for a median of 3.5 years (interquartile range, 1.4-6.7 years). It includes progress notes, laboratory results, medication lists, electronic prescribing, and a variety of clinical reminders.

Since 2001, PCHI has negotiated P4P contracts with 3 large, commercial, not-for-profit insurers who collectively cover the majority of commercially insured patients.

 Starting on January 1, 2010, the 3 commercial insurers contracted separately with PCHI to pay practices for achieving a target smoking status documentation rate among a group of highrisk patients. The incentive was implemented using a withheld amount that was returned to practices meeting the prespecified target. Selected targets must meet agreed-upon standards of care that are easy to measure, likely to improve quality of care, and involve enough patients for statistical reliability.

 The amount of payment to practices depended on revenue from P4P-eligible patients and ranged from 3% to 4.8% of practice revenue from the participating insurers for all P4P measures, and $3.8 million was at risk in PCHI for the smoking status documentation measure. Based on the preincentive documentation and the national guideline recommending that patients be screened at every visit, the target documentation rate was set at 80%.

Documentation was measured among eligible patients over a 2-year span (January 1, 2010, to December 31, 2011), with payment at the end ofthe first year for progress toward the goal and at the end of the second year for reaching the goal.

18 years old) who made a visit to a PCHI outpatient practice during the measurement period, were insured by 1 of 3 participating commercial insurers, and had a high risk chronic condition (hypertension, diabetes, or coronary heart disease). The eligible visit could have been to any PCHI specialist or primary care practice in academic-affiliated or community-based practices.

To help practices reach the 80% target, PCHI added an organizationwide clinical decision support tool consisting of a clinical reminder to document smoking status in all patients’ EHRs. The nontargeted EHR reminder was implemented concurrently with the P4P program on January 1, 2010. The EHR-based reminder was designed so that clicking the reminder linked to the coded field for smoking status documentation. Documentation could also be accessed in the EHR through a vital signs entry screen or a health monitoring grid that tracks preventive care and chronic disease management.

To measure the effects of the P4P program on prevalent smoking status documentation, we conducted an observational study before and after P4P implementation. We compared smoking status documentation between the group of high-risk patients who were targeted by the P4P incentive and (1) all non—P4P-eligible patients and (2) a subset of non– P4P-eligible patients who most resembled the P4P-eligible group by having commercial insurance and the same targeted diagnoses as P4P-eligible patients. The study was approved by Partners HealthCare System’s Institutional Review Board.

We used data from the EHR to estimate the effect of the P4P incentive on smoking status documentation. We identified adult patients (

18 years old) who had an office visit with a PCHI provider before (2008-2009) or after (2010-2011) the P4P incentive. We extracted patient data including smoking status, age at the visit, sex, race/ethnicity and primary language entered at registration, insurance, primary care proWe identified the 3 high-risk, chronic conditions included in the P4P incentive. Patients were designated as having the chronic condition if the diagnosis was entered into the EHR before the qualifying visit for that period. Hypertension was defined as a coded entry on the problem list, with the last systolic blood pressure greater than 135 mm Hg or the last diastolic blood pressure greater than 85 mm Hg. Coronary heart disease was based on coded problem list entries of coronary arteriosclerosis, angioplasty, stent placement, coronary artery bypass graft, or myocardial infarction. Diabetes was based on a coded problem list entry of diabetes or a glycated hemoglobin value greater than 7.0%.

We included provider-level demographic data (age and sex) for the primary care provider designated in patients’ EHR registration at the start of the study year. Provider-level data were taken from a master provider list maintained by PCHI. Some provider-level data were missing; for these, we included a dummy variable for missing data.

Smoking status documentation is designated in a structured field in the EHR as “active smoker,” “past smoker,” and “never smoker.” To measure the change in documentation before and after implementation of the P4P incentive, we calculated prevalent documentation of smoking status on December 31, 2009, and December 31, 2011. To match the 2-year duration of the P4P program, we pooled patients with at least 1 visit in the 2 years before (2008-2009) and the 2 years after (2010-2011) the P4P implementation. We hypothesized that documentation prevalence would not be significantly different from year to year before P4P implementation, and confirmed this hypothesis prior to pooling.

We calculated the unadjusted prevalence of smoking status documentation before and after the P4P incentive among P4P-eligible patients (as defined above), among all non—P4P-eligible patients, and among a subset of the non– P4P-eligible patients who were comparable to the eligiblepatients in having had a visit to a PCHI provider and a qualifying  high-risk chronic condition, but a nonparticipating commercial insurer. We measured the prevalence of documentation among providers stratified by the proportion of P4P-eligible patients seen.

We used multivariable logistic regression modeling to test the hypothesis that P4P measures plus the EHR reminder increased rates of smoking status documentation after the contract was introduced on January 1, 2010. We compared the P4P-eligible patients with the comparable subset of non—P4P-eligible patients before and after P4P implementation using first-order interaction terms. We adjusted for patient age, sex, race/ethnicity, language, and number of visits per year, and provider age and sex. To account for clustering, we chose to cluster by provider rather than practice as the higher-level unit of analysis because changing provider behaviors was the primary aim of the P4P and the EHR reminder. We accounted for clustering using generalized estimating equation techniques.

We investigated practice-level effects by calculating the proportion of visits to each practice by patients with P4P insurance. We found no association between clinics’ proportion of P4P-insured visits and patient eligibility, so we did not include this in multivariable analyses. We calculated adjusted odds ratios (AORs) comparing smoking status documentation before and after P4P implementation among the P4P-eligible group and the non—P4Peligible comparison group. The first-order interaction terms of time (before and after) by P4P eligibility were used to test whether patients targeted by the P4P incentive had higher rates of documentation than those not targeted. We tested the difference in change in documentation by eligibility. Analyses were performed with SAS software, version 9.3 (SAS Institute Inc, Cary, North Carolina).

From 2008 to 2009, 207,741 adults had 1 or more visits to PCHI outpatient providers who used the predominant EHR system for their records. Among these, 5671 (3%) were eligible for the P4P incentive and 1209 (1%) were in the similar but non—P4P-eligible control group (

). The P4P-eligible and similar but non—P4P-eligible patients together were seen by 807 providers, who individually saw between 1 and 191 of the patients with 1 of the 3 chronic diseases and commercial insurance. In 2010 and 2011, after the P4P introduction, 227,574 adults visited outpatient clinics using the EHR; 10,236 (4%) were P4P eligible and 2120 (1%) were in the non–P4P-eligible comparison group (Figure 1).

Compared with all non-P4P patients, P4P-eligible patients were older and more likely to be male, black, and English speaking, and to make more visits (

). Providers for P4P-eligible patients were more likely to be female and were older. The subset of non—P4P-eligible patients were slightly older (55 vs 54 years) but were otherwise similar to P4P-eligible patients.

Overall, smoking status documentation increased each year among all patients seen during the study period, from 47% in 2008 and 49% in 2009 to 63% in 2010 after the P4P intervention and 74% in 2011. The relative increase in documentation from 2008 to 2011 was the largest among never smokers, with documentation increasing by 26% in this group compared with

an 18% increase in past smokers and a 14% increase in active smokers. By eligibility, the increase in documentation was greater among P4P-eligible patients than among all non—P4P-eligible patients or the subset of non–P4P-eligible patients who were comparable to the P4P-eligible patients (

). When patients were pooled into 2-year periods before and after the P4P intervention, documentation increased among the P4P-eligible patients from 56% before (2008-2009) to 83% after P4P implementation (2010-2011) (

). The increase was similar among the subset of comparable but non—P4P-eligible patients (56% to 80%) and smaller among all non–P4P-eligible patients (48% to 71%).

The mean documentation by provider among those who saw no P4P-eligible patients increased from 30% before P4P implementation (median 20%; range 0%-100%) to 59% after P4P implementation (median 66%; range 0%-100%). Among providers who saw any P4P-eligible patients, the change in mean documentation increased by the proportion of P4P-eligibile patients seen (lowest tertile: 55% before [median 67%; range 1%-100%] to 68% after [median 77%; range 15%-100%]; middle tertile: 52% before [median 50%; range 0%-100%] to 70% after [median 82%; range 13%-100%]; highest tertile: 54% before [median 59%; range 0%-100%] to 74% after [median 84%; range 17-100%]).

In multivariable logistic regression models, both P4Peligible and the similar but non—P4P-eligible patients were more likely to have a documented smoking status after the P4P was implemented. We compared smoking status documentation before and after the P4P was implemented among P4P-eligible patients (AOR 3.6; 95% confidence interval [CI], 2.9-4.5; 

<.001) and the similar subset of non-P4P patients (AOR 3.0; 95% CI, 2.3-3.9; 

 <.001) (Table 3). Before the P4P intervention, there was no difference in documentation between the P4P-eligible patients and the similar subset of non—P4P-eligible patients (AOR1.0; 95% CI, 1.0-1.1; 

= .45). After the P4P intervention, documentation was higher among the P4P-eligible patients compared with the subset of non—P4P-eligible patients (AOR 1.3; 95% CI, 1.1-1.4; 

 = .009). The differencein- differences between P4P-eligible and non—P4P-eligible was significant (

 <.001). Other characteristics that were independently associated with an increase in  moking status documentation were older age, female sex, black or Hispanic race, non—English speaking, having a younger primary care physician, and having a female primary care physician.

A targeted P4P incentive from insurers that stimulated adoption of a systemwide EHR reminder significantly improved smoking status documentation in a large multipayer integrated healthcare delivery system. Although the P4P incentive applied to only a minority of patients in the system, smoking status documentation rates increased among all patients.

Among patients targeted by the P4P incentive, the improvement in documentation could be attributable to the performance incentive, the EHR reminder, or both. That the effect was slight but statistically significant among P4Ptargeted patients compared with nontargeted patients suggests that the financial incentive added to the effect of the EHR reminder alone. Our findings are consistent with prior work in single-payer systems or physician group practices that demonstrated the effectiveness of performance incentives for improving smoking status documentation.

 In our own system, a decision support program for chronic disease management had the greatest improvement in measures that were also incentivized with a P4P contract.

 Our data suggest that combining a P4P incentive with an EHR reminder may be an effective way for integrated healthcare delivery systems to promote provider behavior change and reach meaningful use goals for smoking status documentation. However, the reminders may account for a large share of documentation improvement. A healthcare system could achieve improvement in documentation with a reminder alone.

A unique feature of this study was the opportunity to observe the effect of a P4P incentive on patients in the same practices who were not targeted by the incentive. The increase in smoking status documentation among these patients could be attributed to the EHR reminder that was implemented for all patients. Had the EHR reminder not been implemented, providers might still have responded to the targeted P4P incentive by altering their smoking status documentation practice for all patients, rather than trying to figure out which patients were eligible. While we cannot determine whether this “spillover” effect occurred in this observational study, we suspect this behavior would occur if providers believe a performance target is important.

In general, P4P programs that promote documentation improve process measures but have not demonstrated effects on quality of care or patient outcomes.

 Screening patients for tobacco use has been associated with increased treatment delivery in some settings, but increased cessation rates havenot been demonstrated.

 However, the evidence base linking brief clinician interventions with smoking cessation outcomes is strong.

 Furthermore, smoking status documentation may have additional benefits in that it enables healthcare systems to implement chronic disease management tools to help smokers achieve cessation outside the clinical encounter.

We acknowledge several limitations to this study. First, we cannot exclude secular trends as an explanation for the increase in documentation in 2010. Notably, meaningful use standards for EHRs were announced in 2010,

 which created an incentive for healthcare systems to improve documentation. During the time period of this study, our healthcare system was still engaged in planning activities and had not yet implemented interventions to meet the standard. Second, unmeasured confounders may be associated with specific insurers. Insurers might be targeting their members who smoke in other ways that prompt patients to discuss smoking with their provider, or insurers might be contacting high-volume providers to promote assessment of smoking status. Third, we used EHR data to identify patient diagnoses, whereas the P4P incentive was administered with claims data. Accurate diagnoses depend on providers maintaining up-to-date problem lists and may be underreported in our data. We chose to use EHR data, which were readily available and reflected the information available for clinicians in deciding whether to screen patients. If patients with comorbidity were identified in administrative claims but not in our EHR data, they would have been included in our non—P4P-eligible group. This would have made our groups more similar and biased our results toward no effect. A fourth limitation is that the P4P incentive mandated that smoking status be documented at the visit or at some earlier time. It was unknown whether smoking status entries from the past were still accurate. The increase in smoking status documentation also identified more never smokers than current smokers. While the utility of documenting smoking status lies in the system’s ability to identify a population of smokers who can be targeted for treatment and care management, increasing overall documentation increases accuracy of smoking prevalence estimates and encourages healthcare system action and investment.

In conclusion, a limited P4P incentive targeting only a minority of a healthcare system’s patients stimulated the system to adopt a universal EHR reminder to prompt smoking status documentation. The combination improved smoking status documentation among all patients, although improvement was greater among patients targeted by the P4P incentive than among those who were not. It appears that the P4P incentive added value beyond its prompting of a systemwide EHR reminder. These findings suggest that combining a P4P incentive with an EHR reminder could help healthcare systems striving for meaningful use goals to improve treatment for tobacco use and enable population health interventions for their patients who smoke.

 From Division of General Medicine (GRK, NAR), Department of Epidemiology (YC), Tobacco Research and Treatment Center (JHKK, NAR), Department of Medicine, Massachusetts General Hospital, Boston, MA; Harvard Medical School (GRK, YC, JAL, JSE, NAR), Boston, MA; High Performance Medicine (JHKK), Clinical Informatics Research and Development (JSE), Partners HealthCare, Boston, MA; Division of General Medicine and Primary Care (JAL, JSE), Brigham and Women’s Hospital, Boston, MA; Mongan Institute for Health Policy (NAR), Boston, MA

A payer-sponsored pay-for-performance incentive for smoking status documentation prompted implementation of a systemwide electronic reminder and improved documentation among all healthcare system patients.

Healthcare System Effects of Pay-for-Performance for Smoking Status Documentation

To assess the ability of a health information technology system to facilitate population- based breast cancer screening.

 Cohort study with 2-year follow-up after a 1-year cluster randomized trial.

 Study population was women 42 to 69 years old receiving care within a 12-practice primary care network. The management informatics system (1) identified women overdue for mammograms, (2) connected them to primary care providers using a web-based tool, (3) created automatically generated outreach letters for patients specified by providers, (4) monitored for subsequent mammography scheduling and completion, and (5) provided practice delegates with a list of women remaining unscreened for reminder phone calls. Eligible women overdue for a mammogram during a 1-year study period included those overdue at study start (prevalent cohort) and those who became overdue during follow-up (incident cohort). The main outcome measure was mammography completion rates over 3 years.

 Among 32,688 eligible women, 9795 (30%) were overdue for screening (4487 intervention, 5308 control). Intervention patients were somewhat younger, more likely to be non-Hispanic white, and more likely to have health insurance compared with control patients. Adjusted completion rates in the prevalent cohort (n = 6697) were significantly higher among intervention patients after 3 years (51.7% vs 45.8%; 

 = .002). For patients in the incident cohort (n = 3098), adjusted completion rates after 2 years were 53.8% versus 48.7%, respectively (

Population-based informatics systems can enable sustained increases in mammography screening rates beyond rates seen with office-based visit reminders.

A health information technology system was designed to facilitate population-based breast cancer screening among overdue women in a primary care network.

		 Among overdue patients at the start of the study, mammography completion rates were higher among intervention patients (adjusted 3-year rates 51.7% vs 45.8%; 

		 Among patients who became overdue during the study, a similar increase in mammography completion rates was seen (adjusted 2-year rates 53.8% vs 48.7%; 

		Population-based screening that supplements office-based reminders can increase mammography rates in overdue women.

The US healthcare system is dramatically expanding the use of health information technology as a way to improve the quality and efficiency of care.

 In primary care networks, population- based surveillance is being used to identify specific individuals for prevention or disease management interventions. To date most interventions have focused on the use of electronic health records to facilitate care during office-based visits or inpatient hospital admissions.

A novel informatics system to facilitate population-based preventive cancer screening was developed and implemented within a large primary care network.

 Breast cancer screening was chosen because it is the most common cancer in women and the second-most-common cause of cancer-related death,

 because there is scientific evidence supporting screening to decrease breast cancer mortality,

and because many women are not being regularly screened despite broad consensus about the value of screening, especially for postmenopausal women.

 The study’s goal was to increase breast cancer screening rates by identifying eligible women overdue for a mammogram and allowing primary care providers to use an informatics tool to quickly review overdue patients and initiate outreach for those selected for contact. The system then automatically mailed reminder letters to the selected patients, tracked mammogram ordering and completion, and facilitated the scheduling of reminder phone calls by practice delegates for women remaining unscreened.

Previous results demonstrate that among women overdue for screening at the start of the study period (prevalent cohort), this system increased breast cancer screening rates over 1 year of follow-up.

 Outcomes for women who became overdue during the 1-year intervention period (incident cohort), representing those just becoming overdue after prior testing or newly eligible for screening based on age criteria, have not been previously reported. Because this incident cohort represents the ongoing population for reminder systems, the current report compares results in incident and prevalent cohorts and assesses the durability of the 1-time intervention benefit over a 3-year period.

The informatics system used in this study, the controlled, cluster randomized trial method, and the primary outcome results over 1 year among individuals who were overdue for screening at the study start are described elsewhere.

A total of 12 primary care practices were allocated to intervention (n = 6) or usual care (n = 6) control groups after stratifying by practice type, the number of eligible patients, baseline mammography rates, and unaffiliated outside facility screening rates. Providers could not be blinded to group assignment. The study was approved by the institutional review board at Massachusetts General Hospital (MGH).

The study population consisted of 163,028 individuals seen in the Massachusetts General Primary Care Practice- Based Research Network during the 3 years ending December 31, 2006. All patients were linked to either a specific primary care physician (PCP) or (for patients who could not be linked to a specific physician) to the primary care practice where they received most of their care, using a previously validated algorithm.

 This linking ensured that the review of women overdue for breast cancer screening was by the PCPs or practices most directly responsible for each patient’s care.

Eligible study subjects were women 42 to 69 years of age who had no record of mammography in the prior 2 years. This group included women who were overdue as of the intervention start date (March 20, 2007; prevalent cohort) or became overdue during the first year of follow-up (March 20, 2007-March 19, 2008; incident cohort). Patients were excluded if their listed PCP was outside of the MGH network, they had previously undergone bilateral mastectomy, or they had died. All practices used electronic health records that provided visit-based cancer screening reminders.

The informatics tool was implemented in the 6 intervention practices on March 20, 2007, and remained available to providers through March 19, 2010. During the intervention year (through March 19, 2008), providers received reminders to use the tool. After this 1-year period, providers could still use the tool, but they received no additional reminders and the original patient registry was not updated. For intervention providers, the informatics tool consisted of a web page listing their eligible patients linked to the network’s electronic health record.

Separate list views were visible for PCPs for their own patients and for practice-designated population managers (nurses, medical assistants, or nonclinical staff) for patients in each practice not linked to a specific PCP. Physicians and population managers received 3 e-mail reminders (start date, 3 months, 8 months) with a direct link to the population screening web page during the intervention year. A mailed reminder with step-by-step instructions was sent to physicians not yet using the system after 2 months. The web page could also be accessed directly from the hospital’s intranet and included (1) a list of overdue patients, (2) clinically relevant decision support information to help determine whether or not to initiate patient contact, (3) an actionable component to initiate or defer the mammography screening process. If a provider initiated patient contact, a centralized process was started with a letter. Providers could also defer screening (eg, if the patient had previously declined screening after a discussion or had screening done elsewhere) and remove a patient from their list for the remainder of the study. Electronically signed patient letters were sent centrally and included information about the value of screening and how to schedule a mammogram.

 Physicians and population managers were linked with a practice-specific delegate (nonclinical staff or medical assistant) who used his or her own version of the informatics tool to facilitate tracking and scheduling of patients needing contact. Practice delegates were responsible for contacting patients who did not schedule screening on their own. When speaking with patients, delegates could schedule a mammogram by directly accessing the hospital’s radiology ordering system using the informatics tool.

Patient characteristics, mammography reports, and dates of completion were obtained from an electronic clinical and billing data repository.15 Physician characteristics were obtained from the hospital registrar.

The primary outcome was mammography completion rates among patients overdue for screening at the start of the study (prevalent cohort) and among women newly overdue for mammography (incident cohort) during the first study year, comparing intervention and control practices.

The maximum length of follow-up was 3 years for those in the prevadlent cohort and at least 2 years among those in the incident cohort.

A mammogram was considered to have been completed if there was an electronic report for an imaging test at a network-affiliated institution or if a mammogram was listed in billing data for the patient. Secondary outcomes included time to mammography completion among all overdue patients (prevalent and incident cohorts), censored by cancer diagnosis, death, or end of follow-up. New cancer diagnoses using Partners Healthcare. Cancer Registry data were compared among intervention and control groups.

Baseline patient and physician/practice characteristics were compared between intervention and control groups and between prevalent and incident cohorts using 2-sample 

tests, as appropriate. For the primary outcome, adjusted mammography completion rates and 95% confidence intervals were calculated for both the prevalent and incident cohorts at 1, 2, and 3 years of follow-up using Cox proportional hazard models with the robust sandwich covariance matrix estimate to account for clustering while adjusting for potential confounders (PROC PHREG in SAS version 9.2; SAS Institute Inc, Cary, North Carolina). In these models, physicians were considered as the unit of cluster for physician-connected patients and the population manager was considered the unit of cluster for practice-connected patients. To control for differences in patient and practice characteristics among intervention and control practices, patient age, race/ethnicity, insurance status, English language proficiency, practice type (health center vs non—health center), and number of months since last practice visit were included in the models as covariates. All adjusted rates were calculated by holding these covariates at the population mean levels. Unadjusted time to screening completion survival distributions were depicted with Kaplan-Meier curves and compared using a log-rank test. Adjusted hazard ratios comparing intervention with control practices for the entire follow-up period were also reported from the Cox proportional hazards models. The percentages of patients with new cancer diagnoses were compared between intervention and control groups using 

There were 64 eligible physicians and 6 practice population managers in the 6 intervention practices and 74 eligible physicians in the 6 control practices. Among intervention providers, 65 of 70 (92.9%) used the system. There were no significant differences between intervention and control practice physicians with regard to age (47.4 vs 46.9 years; 

 = .78), years since medical school graduation (19.9 vs 19.2;

 = .67), or sex (48.4% vs 51.4% were women; 

 = .86). Two practices in each arm were community health centers. Screening rates at baseline were similar in intervention and control practice groups (79.5% vs 79.3%; P = .73). 

 depicts practice randomization and follow-up.

Among 32,688 eligible women, 9795 (30%) were overdue for screening during the 1-year study period, including 4487 patients in intervention practices and 5308 patients in control practices (

). Intervention and control patients were equally likely to be connected to a specific physician (58.9% vs 58.8%) and overdue for screening at the start of the study (prevalent cohort, 67.9% vs 68.8%). Intervention patients were slightly younger, and more likely to speak English, to be non-Hispanic white, to have health insurance, and to have their last clinic visit further in the past than control patients. Compared with patients who were overdue at the start of the study (prevalent cohort), patients who became overdue during 1-year follow-up (incident cohort) were more likely to be connected to a physician (67.6% vs 54.8%; 

<.001), to have commercial health insurance (70.9% vs 61.9%;

 <.001), and to have been seen more recently for a practice visit (mean 8.6 [8.6 standard deviation (SD)] months vs 13.8 [8.1 SD] months; 

Providers took action on 3415 of 4487 (76.1%) intervention patients; 2865 (63.9%) were contacted by letter and 550 (12.3%) were deferred. The most common reasons for deferral included test completion at an outside facility (312 [56.7%]) and patient refusal (89 [16.2%]). Among 3045 intervention patients overdue at baseline (prevalent cohort), action was taken on 2629 (86.3%) patients; 2212 (72.6%) were contacted by letter and 417 (13.7%) were deferred. Among 1442 intervention patients who became overdue during the 1-year follow-up period (incident cohort), action was taken on 786 (54.5%) patients; 653 (45.3%) were contacted by letter and 133 (9.2%) were deferred.

Mammography Screening Rates Over Time Percentage of Overdue Population Screened Over 3-Year Follow-up.

 The percentage of patients in the intervention and control groups who completed screening over the 3-year follow-up period was analyzed separately for prevalent and incident cohorts. Among patients overdue at baseline (prevalent cohort), adjusted completion rates were significantly higher among patients in the intervention group compared with the control group at 1 year (30.1% vs 26.0%; 

). Among patients becoming overdue during the first year (incident cohort), adjusted completion rates were higher but of borderline statistical significance in the intervention group compared with the control group at 1 year (39.8% vs 35.5%; 

A health information technology system designed to facilitate population-based breast cancer screening increased mammography rates in overdue women beyond rates achieved with office-based reminders alone.

Yuchiao Chang, PhD

Articles

Chronic Disease Outcomes From Primary Care Population Health Program Implementation

Healthcare System Effects of Pay-for-Performance for Smoking Status Documentation

Population-Based Breast Cancer Screening in a Primary Care Network