Jennifer Elston Lafata, PhD

 We describe online portal account adoption and feature access among subgroups of patients who traditionally have been disadvantaged or represent those with high healthcare needs.

 Retrospective cohort study of insured primary care patients 18 years and older (N = 20,282) receiving care from an integrated health system.

 Using data from an electronic health record repository, portal adoption was defined by 1 or more online sessions. Feature access (ie, messaging, appointment management, visit/admission summaries, and medical record access and management) was defined by user-initiated “clicks.” Multivariable regression methods were used to identify patient factors associated with portal adoption and feature access among adopters.

 One-third of patients were portal adopters, with African Americans (odds ratio [OR], 0.50; 95% CI, 0.46-0.56), Hispanics (OR, 0.63; 95% CI, 0.47-0.84), those 70 years and older (OR, 0.48; 95% CI, 0.44-0.52), and those preferring a language other than English (OR, 0.43; 95% CI, 0.31-0.59) less likely to be adopters. On the other hand, the likelihood of portal adoption increased with a higher number of comorbidities (OR, 1.04; 95% CI, 1.02-1.07). Among adopters, record access and management features (95.9%) were accessed most commonly. The majority of adopters also accessed appointment management (76.6%) and messaging (59.1%) features. Similar race and age disparities were found in feature access among adopters.

 The diversity of portal features accessed may bode well for the ability of portals to engage some patients, but without purposeful intervention, reliance on portals alone for patient engagement may exacerbate known social disparities—even among those with an activated portal account.

Healthcare organizations are increasingly using online portals to engage patients and enhance access to health information. Our findings illustrate that focusing on improving the diversity of the population that has an activated portal account alone may not be enough to prevent disparities. We found disparities not only in who adopted portal technology but also in which features were accessed by those with an activated portal account.

African Americans, Hispanics, those 70 years and older, and those preferring a non-English language were significantly less likely to be portal adopters.

Compared with white portal adopters, African American portal adopters were less likely to access each of the portal features evaluated.

Portal adopters who were 70 years and older were less likely to access most portal features, including those that were more interactive.

Since 2011, CMS has provided financial incentives to healthcare providers who implement and “meaningfully use” certified health information technology. Incentives to date have focused on providing patients the ability to view, download, and transfer data electronically. Although the incentives do not specify how organizations are to meet these criteria, most primary care practices and other healthcare delivery organizations have chosen to purchase electronic health records (EHRs) and accompanying patient portal software to meet these requirements.

 Neither how these online portals are being adopted nor their potential influence on known health disparities has been fully explored.

In most primary care practices, patient portals enable users to view personal health information, such as laboratory test results, discharge summaries, and notes from recent visits, thus fulfilling the CMS requirement to view, download, and transfer data electronically.

 In some practices, portal users can also schedule appointments and refill prescriptions or send other secure, unstructured messages to providers. Other practices enable users to record personal health information, such as allergies or preventive service use. Despite the array of functionalities that currently exist within commercially available patient portals, we know of just 2 studies that have examined which features adopters access once they have an activated portal account.

 Thus, although studies repeatedly have found traditionally vulnerable populations less likely to be portal adopters as defined by account activation,

 to our knowledge, just 1 prior study has reported findings specific to whether disparities exist in how those adopting portal technology engage with available health-related portal features once they have an activated account.

 It is important to understand whether disparities in health-related portal feature access exist among those who have an account, because portal technology is increasingly being used to target care delivery to those with specific chronic diseases.

We describe how a patient portal was initially used among an insured, sociodemographically diverse population receiving primary care within an integrated health system. This system was selected for its relatively early adoption of patient portal technology and for the comprehensiveness of portal features initially made available to patients. We report the extent to which members of traditionally disadvantaged subgroups of the population (ie, those from minority races, those for whom English is not a first language, and those of Hispanic ethnicity) and those known to have disproportionate health needs (ie, older patients and those with more comorbidities) had activated portal accounts (ie, were portal adopters). Among adopters, we further evaluate which health-related portal features were accessed. Of particular interest was the extent to which adopters accessed features that enable viewing test results and health history information versus accessing the more interactive features that allow adopters to manage and exchange health-related information. In doing so, we provide important information regarding the patient portal foundation upon which subsequent patient engagement capabilities are being built, and we identify subgroups at risk of being left behind as healthcare organizations increasingly rely on portal technology to enable patients to electronically view, download, and transmit their health information to a third party.

The study was conducted in an integrated health system serving metropolitan Detroit, Michigan. The health system’s institutional review board approved all aspects of the study protocol. In 2012, the health system purchased an enterprise license for EpicCare EMR, a commercial medical record software program, and its accompanying patient portal, MyChart.

 As originally implemented, patients seen in any of the health system’s 26 primary care clinics were able to use MyChart via a desktop computer, tablet, or mobile device to securely schedule appointments, receive appointment reminders, view test results, request prescription refills, view and manage information about their health, and send secure messages to care teams.

Portal implementation was a systemwide initiative, with patient- and clinician-targeted campaigns. Screen savers introducing MyChart were launched on examination room computers and other work&shy;stations. Posters were placed in clinic elevators, clinician work areas, examination rooms, and waiting rooms. Informational brochures were available in all clinics. Each component of the campaign instructed patients on how to request a MyChart activation code via the health system’s website or call center. Activation codes were also included on patient discharge summaries. All clinical staff received MyChart training, and front desk staff were instructed to inquire about patients’ account activation status. Work processes were adjusted to prioritize answering patient portal messages over telephone messages.

We identified patients 18 years or older enrolled in the health system’s affiliated health plan with at least 1 visit to their primary care physician between April 1, 2013, and March 31, 2014. Patients younger than 18 years, those without a primary care visit, and those with indeterminate portal activation status (eg, those with account information but no evidence of a session) were excluded.

Patient sociodemographic information, comorbidities, primary care visits, and portal activation information were obtained from the health system’s EHR data repository. These patient-level data were joined with clinic-level information available from health system administrative files.

“Portal adopters” activated their portal account and initiated at least 1 online session between December 1, 2012, and March 31, 2014. Among portal adopters, we examined which health-related portal features were accessed (ie, “clicked on”) between April 1, 2013, and March 31, 2014, in 4 categories: (1) messaging, (2) appointment management, (3) visit/admission summaries, and (4) medical record access and management. Adopters were considered to be users of the messaging feature if they (1) viewed electronic test result letters or other personal communications from their physician, (2) sent a message or viewed a response related to prescription refills, or (3) sent a message or viewed a response related to general medical advice. Those who reviewed their upcoming appointment information or accessed the appointment scheduling feature were considered to be users of the appointment management feature. Adopters who viewed outpatient visit summaries and/or inpatient admission summaries were deemed users of the visit/admission summaries feature. Finally, adopters who accessed the following features were considered to be users of the medical record access and management feature: test results, health summary, current health issues, medications, allergies, preventive care, medical history, immunizations, and/or home monitoring reports. We also constructed a binary variable (“interactive feature use”) that indicated whether the adopter had ever (vs never) clicked on a feature that enabled them to input information (eg, update health issues, update medications, update allergies, send a secure message to their doctor, or request a prescription renewal).

Of particular interest was whether portal adoption and feature access differed by patient race (African American vs white or other), ethnicity (Hispanic ethnicity vs not of Hispanic origin), or language preference (English vs other), or by health needs as defined by patient age or comorbidities. The latter was measured via the Deyo adaption of the Charlson Comorbidity Index (CCI),

a diagnosis-based measure of a patient’s case mix that has been shown to be associated with subsequent mortality and resource use.

We also obtained information on patient gender (female/male), marital status (currently married vs other), and patients’ realized access to office-based primary care. The last was measured by whether the patient had a health maintenance exam visit (ie, “checkup”) and by the total number of primary care office visits within the 12-month study period. In addition to the patient-level variables, we controlled for 3 clinic-level factors that may affect portal adoption and feature use: the number of primary care physicians practicing at the clinic (a proxy for clinic size), whether medical students and/or residents saw patients at the clinic (ie, designated teaching facility), and whether the clinic was located within the city of Detroit (urban) or a surrounding area (suburb).

A multivariable logistic regression model with random effects was fit using the PROC GLIMMIX procedure (SAS software, version 9.4 [SAS Institute Inc; Cary, North Carolina]) to test the role of the patient-level factors of interest in portal adoption status, controlling for other patient- and clinic-level factors. Patients were blocked by physician and nested within clinic, and the Laplace method was used for likelihood approximation. Among portal adopters, we report the percentage of patients accessing each of the 4 categories of portal features (messaging, appointment management, visit/admission summaries, and medical record access and management) as well as by whether or not an interactive feature was accessed. We used multivariable logistic regression to test the role of the same patient-level factors of interest in feature access, again controlling for other patient- and clinic-level factors. Because adopters’ feature access was not influenced by the clinic from which or physician from whom care was received (ie, interclass correlation coefficients <0.01), models were fit using PROC LOGISTIC (SAS version 9.4).

 describes the sociodemographic and other characteristics of the study population (N = 20,282). About one-third of study-eligible patients (32.6%) were portal adopters (n = 6621). The majority (92%) of adopters had at least 2 online sessions, and 86% had 3 or more sessions. Reflecting the relative newness of the portal’s availability, patients who were adopters had been using the portal for a mean (SD) of 8.9 (4.5) months, with 95% using their account for at least 1 month. Across the health system, patients’ portal adoption rates ranged from 6.8% to 64.9% among primary care clinics (interclass correlation, 0.067) with a mean (SD) of 32.7% (12.4%), and from 0% to 100% among primary care physicians (interclass correlation, 0.060) with a mean (SD) of 31.1% (20.2%). Prior to controlling for other factors, African Americans, those of Hispanic ethnicity, those preferring to use a non-English language, and those 70 years and older were substantially less likely to have an activated portal account (Table 1). It should be noted, however, that portal adoption among these vulnerable and relatively high—health-need populations varied substantially across physician panels and primary care clinics. For example, on some physician panels, as few as none (0.0%) of the African American patients had an activated portal account, whereas on others, all (100.0%) African American patients had a portal account. Similarly, at some clinics, as few as 4.5% of the African American patients receiving primary care from the clinic had an activated portal account, whereas at others, the percentage was as high as 52.9%. Similar variability at the physician panel and primary care clinic levels was seen for the other subgroups of interest.

Multivariable logistic regression model results comparing portal adopters with nonadopters are also presented in Table 1. After controlling for other factors, African American patients were half as likely as patients of other races to be portal adopters. Similarly, compared with patients aged 50 to 69 years, those 70 years and older were approximately half as likely to be portal adopters. Those of Hispanic ethnicity and with a preference for a non-English language were also significantly less likely to be portal adopters, whereas portal adoption was more likely among patients with higher CCI scores.

Most adopters accessed portal features related to medical record access and management (95.9%) (

), most often to obtain laboratory test results (91.7%). As depicted in Table 2, adopters also frequently accessed information on their current health issues (87.8%), medications (87.5%), immunizations (86.1%), allergies (86.0%), and health summaries (84.7%). The majority of adopters also accessed the appointment management features (76.6%), more frequently to review information on upcoming appointments (73.1%) than to schedule appointments (58.5%). Relatively fewer patients accessed the messaging features (59.1%), and within this category, access was evenly distributed across letters (35.5%), medical advice requests (31.7%), and medication renewal requests (31.3%). Fewer patients had accessed the visit/admission summary component (41.0%). Overall, 97.0% of portal adopters accessed at least 1 of the health-related features available within the portal, with 68.8% accessing at least 1 interactive feature.

 presents results from the 5 multivariable logistic regression models testing the association of the patient-level factors of interest with feature access. With the exception of accessing visit/admission summaries, patients of African American race were significantly and substantively less likely to have accessed each of the portal features. Similarly, with the exception of record access and management (which virtually all [95.9%] patients accessed), patients 70 years and older were also significantly and substantively less likely to have accessed each of the portal features. On the other hand, as a patient’s CCI score or number of primary care visits increased, so too did their likelihood of accessing the different portal features.

We found that approximately one-third of patients with a primary care visit were portal adopters, as defined by their having an activated portal account. Furthermore, the majority of portal adopters logged into their account multiple times and accessed a variety of features related to both viewing and inputting information. Together, these results illustrate the ability of patient portals to reach a large number of patients, particularly those who are already actively connected with a primary care provider.

Importantly, however, our results also underscore why efforts to enhance and ensure portal adoption as defined by account activation are likely to be insufficient: Our analyses point to disparities not only between those who activate and do not activate a portal account but also in how those with an active account access the features available within portals. Particularly concerning was the finding that African American and older patients were consistently least likely to have an activated portal account, and when they did, they were also least likely to access most portal features. The latter included being least likely to access the appointment management and messaging features—2 functionalities that are arguably critical to care access. The disparities in portal account activation that we observed are consistent with those now repeatedly found by others.

 The findings of disparities in portal adoption, combined with the additional disparities in feature access among portal adopters that we and another study have shown,

 highlight the importance of providing alternative channels for vulnerable and traditionally less engaged patient populations to view and share their health information. Such a need is echoed by findings from a recent qualitative study that highlight the basic computer-related and other barriers that vulnerable populations experience once logged into a portal

 and by a recent systematic review that highlights the role of health literacy in patients’ ability to accurately input and interpret information contained within portals.

We identified wide variability in portal account activation by primary care physician panel and clinic—both overall and among traditionally high-need and vulnerable populations. Understanding the factors that underlie such variability was beyond the scope of the present study, but evidence from elsewhere would point to a diverse array of multilevel factors that may include everything from patients’ health and technological literacy and internet access to the characteristics and supportiveness of physicians and other care team members to the programs and support offered within the broader community.

 Future studies should specifically explore the provider-, clinic-, system-, and community-level factors underlying this variability in portal use, as such studies may hold clues to innovative solutions for engaging and enabling the viewing and sharing of health information among hard-to-reach populations.

 What is clear is that there is likely not a universal patient portal solution for electronically capturing and sharing health information if the goal is the inclusion and engagement of all members of diverse primary care populations. Moving beyond this one-size-fits-all mentality is important in light of mounting evidence that patient adoption of and engagement with EHR-integrated portals can lead to improved outcomes among some patients, including treatment adherence and clinical outcomes among patients with diabetes and HIV.

Our results should be interpreted with a number of study limitations in mind. First, to determine the types of portal features used by patients, we measured user clicks on the links to access specific portal features. Thus, our results are based on the links that patients activated and not time spent per se. Capturing use in this way is consistent with other studies

 but may nonetheless result in overestimates of patient feature use because some clicks may have been due to curious exploration and not actual engaged use. Second, although we were able to control for some clinic-level factors, because of the nature of our study methods and data, we were unable to control for some potentially important patient-level factors, such as education and income. We were also unable to determine the reasons behind patients’ adoption or nonadoption of the portal and specific portal features accessed. Understanding the multilevel and modifiable factors that contribute to observed patterns of portal adoption and feature access is a fruitful avenue for future research. In addition, the context for this research was 1 integrated health system that serves an urban/suburban Midwestern locale. As such, findings may not reflect patterns of portal adoption and feature access that are faced over the long term or by health systems serving other types of populations or based in other locations.

Our results underscore the opportunities and challenges that patient portals present. On the one hand, when integrated within EHRs, portals represent an exciting new frontier for information exchange between healthcare organizations and patients. As such, portal technology may be able to support patients’ health-related decision making and adherence to evidence-based care (and thereby health outcomes) beyond what traditional office visits alone can accomplish. On the other hand, our findings caution that without purposeful planning and integration, reliance on patient portals alone for engaging patients outside of office visits could inadvertently serve to exacerbate existing racial and other social disparities. Our findings therefore highlight not only the need to continually assess the reach and impact of patient portals on diverse populations but also a need to consider alternative channels and mechanisms for sharing health information and engaging patients beyond the walls of traditional office visits.

The views expressed herein are solely those of the authors and do not necessarily represent the views of the Department of Veterans Affairs or the US Government.

 UNC Eshelman School of Pharmacy (JEL) and Lineberger Comprehensive Cancer Center (JEL), University of North Carolina, Chapel Hill, NC; Henry Ford Health System (JEL, DAS, MS), Detroit, MI; Department of Health Behavior and Policy, School of Medicine, Virginia Commonwealth University (CAM), Richmond, VA; School of Social Work, Michigan State University (DAS), East Lansing, MI; Center for the Study of Healthcare Innovation, Implementation & Policy, VA Greater Los Angeles Healthcare System (KD), Los Angeles, CA; Department of Epidemiology and Population Health, UM School of Public Health, University of Michigan (SMR), Ann Arbor, MI.

An early version of this manuscript was presented as an oral paper at the 14th International Conference on Communication in Healthcare, Heidelberg, Germany, September 2016.

 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 (JEL, KD, MS); acquisition of data (JEL, DAS, MS); analysis and interpretation of data (JEL, CAM, DAS, SMR); drafting of the manuscript (JEL, CAM, DAS, KD); critical revision of the manuscript for important intellectual content (JEL, DAS, KD, SMR, MS); statistical analysis (CAM, SMR); obtaining funding (JEL); administrative, technical, or logistic support (JEL); and supervision (JEL, MS).

 Jennifer Elston Lafata, PhD, UNC Eshelman School of Pharmacy, University of North Carolina, 301 Pharmacy Ln, 2214 Kerr Hall, CB #7573, Chapel Hill, NC 27599. Email: jel@mail.unc.edu.

1. Shay R. EHR adoption rates: 20 must-see stats. Practice Fusion website. practicefusion.com/blog/ehr-adoption-rates. Published March 1, 2017. Accessed November 16, 2016.

2. Monegain B. More than 80 percent of docs use EHRs. Healthcare IT News website. healthcareitnews.com/news/more-80-percent-docs-use-ehrs. Published September 18, 2015. Accessed November 16, 2016.

3. What is a patient portal? HealthIT.gov website. healthit.gov/providers-professionals/faqs/what-patient-portal. Updated September 29, 2017. Accessed April 4, 2017.

Articles

We found race and age disparities not only in who adopted patient portal technology but also in which features were accessed by those who were adopters.

Patients' Adoption of and Feature Access Within Electronic Patient Portals

Studies evaluating the impact of patient-centered medical homes (PCMHs) have produced mixed results. Missing from these studies has been a consideration of the effect of team functioning. We evaluated the relationships between team functioning and clinical quality, patient satisfaction, and online patient portal implementation among the PCMHs of an integrated health system.

Primary care physicians and nursing staff working within 26 PCMH-designated primary care clinics were asked to complete an online survey that included an item specific to team functioning.

 Survey data (response rate, 70%) were joined with information from electronic health records on portal activation, patient satisfaction surveys, and internal health system quality reports.&ensp;We report clinic-level correlations between  positive team functioning and clinical care quality measures, patient satisfaction, and patient portal activation.

On a scale of 1 to 5, where higher values represent more positive team functioning, ratings of clinic-level team culture ranged from 2.6 to 5.0 among nursing staff (mean = 3.6) and from 3.0 to 5.0 among physicians (mean = 4.1). Both nursing staff— and physician-reported team functioning was significantly associated (

 <.05) with patient satisfaction as measured by whether patients would recommend their physician and clinic to others. Although team functioning was also positively associated with other outcomes, no other associations reached statistical significance.

We found that positive team functioning is significantly associated with patient-reported satisfaction. Results from this study point to the importance of considering how physicians and other clinical staff within a PCMH perceive the team culture of their clinic and the potential impact of their team perceptions on patient satisfaction and other outcomes.

In response to healthcare reform in the United States, the patient-centered medical home (PCMH) has emerged as an innovative care model.

 Although the model is variable in structure,

 PCMHs are defined by a focus on care that is team-based, data-informed, and able to address the unique needs of patients across the care continuum, whether those needs are preventive, acute, or chronic. The medical home approach to care is defined by being comprehensive, patient-centered, coordinated, accessible, and committed to quality and safety. Providing such care requires teamwork and open and clear communication among a range of care providers, including physicians, physician assistants, nurses, pharmacists, nutritionists, educators, and care coordinators.

Recent systematic reviews suggest that PCMHs can decrease provider burnout and improve patient and staff experiences, as well as improve the delivery of preventive services and some aspects of chronic disease management, while reducing emergency department utilization.

 However, the evidence that PCMHs improve patient experiences and care quality has not been consistent.

 One of the factors that could underlie such mixed results may be differences in team functioning among different PCMHs.

The relationship between team functioning and organizational performance is well studied both within

 Team functioning may be particularly critical to the delivery of comprehensive primary care as envisioned within PCMHs, as a primary care practice’s organizational culture also impacts its ability to adapt to changing environmental pressures.

 In spite of the potential importance of team functioning to the success of PCMHs, differences in team functioning as reported by physicians and other clinical staff have not, to our knowledge, been explored as a potential factor associated with implementation success, patient satisfaction, or other clinical outcomes among PCMHs.

We evaluate the relationship between primary care physician and nursing staff perceptions of their clinic’s team functioning and measures of (1) preventive and chronic clinical quality, (2) patient satisfaction, and (3) the implementation success of an online patient portal among the 26 PCMH-designated primary care clinics of a large integrated delivery system in southeast Michigan.

Team effectiveness is commonly described based on input-process-output (IPO) models.

 IPO models explain the effects of organizational inputs, such as organizational culture and team composition, on team outputs, such as team performance and care outcomes.

, the IPO model for PCMHs is complex and includes many input, process, and outcome elements. Within PCMHs, interprofessional team functioning is thought to be a central aspect of effective work processes.

 For the purpose of this paper, we therefore focus on the functioning of the clinical team within PCMHs.

Humphrey and Aime defined teamwork as “assemblies of interdependent relations and activities organizing shifting sets or subsets of participants embedded in and relevant to wider resource and institutional environments.”

 This definition of teamwork is useful for describing teamwork in healthcare organizations because the team membership changes based on members’ work tasks, as well as across healthcare organizations. Several studies in the healthcare context have shown the positive effects of team functioning on care quality and safety,

 Team functioning is not only a key factor for PCMH implementation

 but could also serve as a mediating factor for the effectiveness of PCMHs.

 Using this theoretical framework, we assessed the relationships between team functioning and clinical quality, patient experience, and the implementation success of an online patient portal among 26 PCMH-designated primary care clinics.

Survey-eligible participants were the 189 family and general internal medicine physicians and 216 nursing employees (nurse managers and medical assistants) working in the 26 adult primary care clinics of the Henry Ford Health System in July 2014. The health system, located in southeast Michigan, serves Detroit and the surrounding suburbs. Each of the health systems’ adult primary care clinics was designated as a PCMH by the state of Michigan’s largest insurer, Blue Cross Blue Shield of Michigan. Because the health system uses 1 integrated electronic health record (EHR) system, which includes a centralized system of decision support, routine performance monitoring, and other clinical informatics features, in addition to a common staffing model and centralized services (eg, population care management), these care inputs are constant across each of the primary care clinics, enabling a focus on team functioning, which was expected to vary across primary care clinics. The study was approved under expedited review by the health system’s institutional review board.

In July 2014, eligible employees were emailed a link to an online survey that included items from the Clinician Staff Survey

 and demographic questions. Included within the Clinician Staff Survey is an item specifically designed to assess the respondent’s perceptions of their clinic’s team functioning. Study participants were provided with a $30 gift card to compensate them for their time. Survey responses were managed using REDCap electronic data capture tools.

 REDCap is a secure Web-based application designed to support data capture for research studies, providing (1) an intuitive interface for validated data entry, (2) audit trails for tracking data manipulation and export procedures, (3) automated export procedures for seamless data downloads to common statistical packages, and (4) procedures for importing data from external sources.

Administrative data were used to compile study outcomes. Specifically, health-system dashboard reports covering the 1-year period ending March 31, 2014, were used to ascertain measures of clinical care quality; patient satisfaction was assessed using the subset of health-system patient satisfaction surveys pertaining to primary care visits occurring in the 1-year period ending March 31, 2014; and data available within the EHR repository were used to determine portal account activation from portal introduction on December 1, 2012, until March 31, 2014.

 Team functioning was assessed via responses to the item, “People in this clinic operate as a real team.” Answer choices ranged from 1 to 5, where 1 indicated disagreement and 5 indicated agreement; thus, higher values represent more positive team functioning. Individual responses, stratified by physician and nursing staff, were summed for each clinic and divided by the corresponding number of responses to generate a mean team functioning score as perceived by physicians and nursing staff within each PCMH.

Clinical care quality was measured via commonly used evidence-based cancer screening and diabetes care management performance measures. For colorectal, breast, and cervical cancer screening measures, we report the proportion of eligible patients who were up to date with screening as recommended by the United States Preventive Services Task Force.

 Using the standards of medical care as established by the Healthcare Effectiveness Data and Information Set,

 we also used an available composite measure of diabetes care quality, the proportion of patients who met all 5 diabetes quality measures (those who received glycated hemoglobin [A1C] and low-density lipoprotein [LDL] cholesterol tests and achieved desired control levels [ie, A1C <8%, blood pressure <140/90 mm Hg, and LDL cholesterol

100 mg/dL) during the 1-year measurement period. Denominators for these metrics were age- and guideline-appropriate patients with an outpatient visit.

For patient satisfaction, we report the percentage of respondents endorsing the highest possible rating (“top-box scores”) from the 5-point Likert scale responses for the questions regarding how likely the patient respondent was to recommend the clinic and recommend their care provider to others. Finally, we measured patient portal implementation as the percentage of patients with a primary care visit with an activated portal account by March 31, 2014.

We used means (SDs) and percentages to describe physician and nursing staff participants, their perceptions of team functioning, clinical quality, patient satisfaction, and portal implementation success. Using the clinic/PCMH as the unit of analysis, we used Pearson correlation coefficients to assess the association between physician- and nursing staff—reported team functioning and each of the outcomes of interest.

A total of 119 physicians (63% response rate) and 165 nursing staff members (76% response rate) responded to the survey. Physician respondents were 59% female, 51% Caucasian, and 25% Asian/Pacific Islander. Nursing staff were primarily female (95%), 48% Caucasian, and 47% African American.

Mean clinic-level team functioning scores ranged from 3.0 to 5.0 for physicians (mean [SD] = 4.07 [0.51]) and 2.6 to 5.0 for nursing staff (mean [SD] = 3.67 [0.64]). The relationship between the clinic-level outcomes and positive team functioning as perceived by physicians and nursing staff is described in the 

. Positive team functioning as perceived by either physicians or nursing staff was not significantly associated with the measures of clinical care quality or portal implementation success. However, nursing staff—reported team functioning was significantly and positively correlated with patients’ reporting that they would recommend their clinic to others (

= .003) and that they would recommend their care provider to others (

= .006). Physician-reported team functioning was also significantly and positively correlated with these patient satisfaction measures, recommending their clinic (

Among 26 primary care clinics designated as PCMHs, we found that positive team functioning, as reported by both nursing staff and physicians, was significantly associated with patient-reported satisfaction as measured by the patient’s willingness to recommend their clinic and their care provider. Such patient satisfaction scores have long been monitored by healthcare administrators, but they have recently taken on new importance as patient-reported experiences are increasingly being tied to provider compensation.

 Although a study has recently linked interdisciplinary teamwork

 to patient experiences in the context of cancer care, less is known about how team functioning within a primary care clinic relates to patients’ experiences or care quality. Our results point to the likely importance of clinician and staff subjective experiences of working in a team to patient experiences. How team functioning directly or indirectly affects the implementation of key PCMH attributes and other dimensions of PCMH performance warrants additional study. This is especially true given that prior studies have noted that more successful implementation of PCMHs is also associated with higher patient satisfaction.

Notably, although we did find a positive association between physician and nursing staff perceptions of positive team functioning and measures of care quality or portal implementation success, these findings were not statistically significant. In contrast, others have found relationships between team functioning and quality-improvement initiatives in small primary care settings.

 It may be that in clinics like the ones studied here, in which there is strong decisional support and centralized population health infrastructure, relatively small differences in positive team functioning may be insufficient to alter the delivery of routine preventive and chronic care services. Whether the same is true among smaller practices, where care management support and other infrastructure may be minimal, is a question that warrants future study.

As Lemieux-Charles and McGuire point out, it is critical to understand how and under what conditions healthcare teams can be effective and address the problems that have limited healthcare organizations’ ability to provide high-quality care.

 Using the IPO model as a framework, this study has shown that team functioning may be an important factor to consider when studying the impact of PCMHs. In addition, team functioning may be an important mediating factor between the resources, or inputs, clinics are allocating to PCMHs and the ability of those clinics to successfully implement the key attributes of PCMHs, and ultimately to their ability to impact patient care experiences.

Given the potentially important role that front desk and other nonclinical staff play within PCMHs, their exclusion from the survey-eligible population is a limitation. In addition, the limited number of clinics (N = 26) precluded the ability to control for other factors that may impact positive team functioning or practice outcomes. Similarly, the relatively small sample of PCMHs likely limits the ability to detect statistically significant associations. In addition, we acknowledge that organizational characteristics such as team functioning are dynamic and thus difficult to classify based on a single cross-sectional survey.

In the context of PCMHs, it is critical to understand how a primary care clinic’s team functioning can influence not only how patients experience their care, but also how it can ultimately affect care quality and patient outcomes. Results from this study point to the importance of considering how physicians and other clinical staff within a PCMH perceive the team culture of their clinic and the potential impact of their team perceptions on patient satisfaction and other outcomes.

 School of Social Work, Michigan State University (DAS), East Lansing, MI; Department of Health Policy and Management, The Gillings School of Global Public Health (AAT), and Lineberger Comprehensive Cancer Center (JEL), and Eshelman School of Pharmacy, Institute for Healthcare Quality Improvement (JEL), University of North Carolina at Chapel Hill, Chapel Hill, NC; Department of Health Behavior and Policy, School of Medicine, Virginia Commonwealth University (CAM), Richmond, VA; Center for Health Policy and Health Services Research, Henry Ford Health System (DAS, JEL), Detroit, MI.

 The authors have no conflicts of interest to disclose.

 Concept and design (DAS, JEL); acquisition of data (DAS); analysis and interpretation of data (DAS, AAT, CAM, JEL); drafting of the manuscript (DAS, AAT, CAM, JEL); critical revision of the manuscript for important intellectual content (DAS, AAT, JEL); statistical analysis (AAT, CAM); obtaining funding (JEL); administrative, technical, or logistic support (JEL).

 Deirdre A. Shires, PhD, Michigan State University, School of Social Work, 655 Auditorium Rd, 122 Baker Hall, East Lansing, MI 48824. Email: shiresde@msu.edu.

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Physician- and nursing staff–reported team functioning was associated with patient satisfaction but not with clinical quality or patient portal implementation.

Team Functioning and Clinical Quality, Patient Satisfaction, and Patient Portal Implementation Among Patient-Centered Medical Homes

Periodic health examinations (PHEs) are the most common reason adults see primary care providers. It is unknown if PHEs serve as a “safe portal” for patients with mental health needs to initiate care. We examined how physician communication styles impact mental health service delivery in PHEs.

Retrospective observational study using audio-recordings of 255 PHEs with patients likely to need mental health care.

Mixed-methods examined the timing of a mental health discussion (MHD), its quality, and the relationship between MHD quality and physician practice styles. MHD quality was measured against evidence-based practices as a 3-level variable (evidence-based, perfunctory, or absent). Physician practice styles were measured by: visit length, verbal dominance, and elicitation of a patient’s agenda. A generalized ordered logit model was used.

 Many patients came with mental health concerns, as over 50% of the MHDs occurred in the first 5 minutes of the visit. One-third of the 255 patients had an evidence-based MHD, another third had a perfunctory MHD, and the remaining had no MHD. MHD quality was significantly associated with physician communication styles. Visits with physicians who tend to spend more time with patients, fully elicit patients’ agendas, and let patients talk (instead of being verbally dominant) were more likely to deliver evidence-based MHD.

If done well, PHEs could be a safe portal for patients to seek mental health care, but most PHEs fell short. Improving PHE quality may require reimbursement for longer visits and coaching for physicians to more fully elicit patients’ agendas and to listen more attentively. 

Periodic health exams (PHEs) could be a “safe portal” for patients with mental health needs to receive care. We found:

The quality of mental health discussions varied greatly in 255 audio-recorded PHEs in an 

integrated delivery organization: 1 of 3 was evidence-based, 1 of 3 was perfunctory, and 1 of 3 was nonexistent. 

Physicians who spend more time with patients, fully elicit patients’ agendas, and let patients talk were more likely to deliver evidence-based mental health care. 

Improving care quality may require reimbursement for longer visits, coaching for physicians to fully elicit patients’ agendas, and to listen more attentively. 

Routine assessment of mental health status should be reinforced.

The majority of patients with mental health concerns turn to their primary care physicians for help.

 Many rely on periodic health examinations (PHEs)—the most common reason for adults to visit physicians.

 Proponents of PHEs argue that, besides increasing the use of preventive services, PHEs bring patients into contact with their primary care physicians, and therefore, provide a route by which patients seek services that they feel uncomfortable disclosing to the scheduling staff. In fact, as many as 1 in 3 patients have undisclosed reasons for requesting a PHE.

 Mental health care needs may be an example of undisclosed reasons that prompt some patients to use the time-honored PHE tradition as a “safe portal” to seek mental health care.

Evidence-based mental health care improves patient outcomes not only through the reduction of mental illness symptoms, but also by improving physical and social functioning.

 Primary care physicians’ communication practices and skills have been documented to influence the quality of mental health service delivery in primary care visits.

 It has been shown that even when patients have an agenda, they commonly do not make it explicit in ambulatory encounters.

 Essential communication approaches that can be employed by the physician to ensure effective communication (eg, being open to patients’ agendas

), fully eliciting patients’ concerns and preferences,

 and co-creating the visit agenda with them,

 contribute to improving the overall quality of patient—provider communication.

In addition to fully eliciting patients’ agendas for the visit, physicians must give patients enough time to speak. It is not uncommon that some physicians dominate the conversation, in order to control the time used; however, it is necessary to balance the need to manage the conversation with the need to let the patient speak. Verbally dominant physicians disempower patients and impair patient engagement.

 It has been well-documented that patients were less satisfied with their physicians when physicians talked more and when patients perceived their physicians as domineering, and patients were ultimately less likely to sue physicians with low verbal dominance.

 The literature is relatively silent on how primary care physicians’ verbal dominance could affect the quality of discussions about mental health during PHEs.

Giving patients time to speak rather than dominating the conversation can affect the length of visits, however. Visit length has long been the subject of research and has been shown to be associated with the delivery of evidence-based preventive health services (both screening and counseling).

 It is not known if patients with mental health needs who see primary care providers (PCPs) who tend to provide longer visits with patients may be more likely to receive evidence-based mental health services. Mental health issues are perceived to be difficult and have been well-documented to receive suboptimal attention in ambulatory care settings.

 Furthermore, the provision of mental health screening and counseling during PHEs has received less attention than the delivery of evidence-based biomedical screening and lifestyle counseling during PHEs.

For PHEs to be valuable to patients with mental health needs, evidence-based mental health discussion is required. Figure 1 illustrates our hypothesis that 3 sets of predisposing physician and patient factors influence the quality of mental health discussion (MHD): evidence-based, perfunctory, or absent. The first group of factors consists of physician practice styles: how much time a physician usually provides in visits, a physician’s openness to the patient’s agenda, and a physician’s verbal dominance. The second set of factors relates to patients’ mental health status and if the patient is in an ongoing episode of care

 for a mental health issue. The third factor relates to a patient’s preparedness for the visit (ie, if a list has been prepared and brought to the visit for discussion with the physician). These tasks have also been shown to increase patient satisfaction and subsequent patient self-management.

Using qualitative and quantitative research methods, we investigated whether PHEs offer patients an opportunity to discuss mental health with physicians. We also measured the quality of MHD based on its concordance with evidence-based practices by coding audio recordings and transcripts of PHEs (described later). We further sought to operationalize measures of physician communication styles that could be associated with the delivery of evidence-based MHD and are also potentially malleable.

Patients with mental health needs were drawn from a sample of 484 participants, aged 50 years or older, in a study of preventive health discussions,

 which took place at an integrated health delivery system in Detroit, Michigan, between February 2007 and June 2009. Patients completed a brief telephone survey at recruitment, containing the Patient Health Questionnaire-2 (PHQ-2)

 and sociodemographic characteristics. Furthermore, each patient’s visit was observed and audio recorded by a research assistant. Additional details of the study have been reported elsewhere.

 The institutional review boards of relevant organizations approved the study. Informed consent of study participants conveyed that the study would examine patient—physician communication about preventive health issues. No specific mention of mental health focus was made.

The sample for the current study consisted of 56 PCPs and 255 patients identified as likely in need of mental health care if they met any of the following criteria: 1) scored ≥2 on the PHQ-2,

 2) filled or were prescribed a psychotropic medication, 3) had a mental health diagnosis (

International Classification of Diseases (ICD)-9

 codes 290, 293-302, 306-316) in the electronic health record (EHR), or 4) visited a behavioral health provider. The look back period was 12 months before the visit.

 Five researchers coded audio recordings and transcripts of the visits to capture topics within 7 major areas: biomedical (eg, high blood pressure), health behaviors (eg, smoking), mental health (eg, depression,), psychosocial (eg, family), physician—patient relationship (eg, physician availability), visit flow management (eg, agenda setting), and other (eg, small talk about weather). Topics were defined as issues that had at least 2 complete exchanges between patient and physician. The time spent on discussing each topic was also recorded for both the patient and the physician. This analytical approach has been described in detail and applied in previous research.

 Studying a visit as a conversational event enabled us to understand the relative time spent on each topic by the patient and the physician, and if and how one person dominated the conversation.

Scores from different raters were compared using intra-class correlations for numerical variables and percentage agreement for categorical variables. Intra-class correlations between raters and within the same rater ranged from 0.78 to 0.99.

The dependent variable was a 3-level variable for the quality of MHD. We defined MHD as any exchange about depression, general anxieties and worries, emotional distress, death, bereavement, grief, mourning, death of others, pain, suffering, concerns, and worries regarding one’s own physical condition, tests, treatments, procedures, or other mood disorders.

 Whether or not the MHD was evidence-based was determined by the degree of concordance with treatment guidelines,

 including if the physician assessed the patient’s mood using any item from the PHQ-9,

 made a mental health diagnosis, prescribed psychotropic medication, made a referral to a mental health specialist, or made a plan for active surveillance of mental health symptoms (Figure 1). Perfunctory discussions (eg, “Any anxiety or depression?”) followed immediately by a nonrelated statement or questions (eg, “Any vaginal spotting or bleeding?”) were coded as non—evidence-based. The value of the outcome measure is 0 for no MHD, 1 for a perfunctory MHD, and 2 for an evidence-based MHD.

Key explanatory variables included both measures of physician practice style and patient characteristics. Physician practice style measures included visit length, eliciting patient agendas, and verbal dominance gathered from their visits with other study patients to form exogenous measures of these constructs to the index visit. Visit length was measured by the face-to-face interaction time in minutes between patients and physicians from other visits. Eliciting patient agenda was defined as the proportion of visits in which the physician attempted to fully identify the patient’s agenda (5 on a scale of 1 to 5)

—there was unanimous agreement among the 5 coders for this variable. Verbal dominance was defined by the ratio of actual talk time by the physician divided by talk time by the patient. To account for physicians’ proclivity to provide evidence-based services, we also included a count of evidence-based preventive services.

Patient characteristics were obtained from the EHR, a pre-visit patient survey, and direct office visit observation. Patient health status included whether in an ongoing episode of care (EOC) for mental illness,

 PHQ-2 score, and if the patient had brought a list of issues to discuss.

For the quantitative analysis, we specified a generalized ordered logit model with partial proportional odds for the 3-level ordinal dependent variable of evidence-based MHD. Our model constrained the odds ratios to be proportional across these 3 levels for all variables except PHQ-2 score. Tests of the proportional odds assumption indicated it was reasonable for the remaining variables. Three groups of explanatory variables were included in the model, as illustrated in Figure 1. The first group included physician practice style factors, as measured in the other visits to the same physician among the study sample: a) average visit length, b) percent of visits in which they fully elicited the patient’s agenda, and c) verbal dominance.

The second group were patient factors: patient’s mental health needs (ie, self-reported depressive symptoms in the PHQ-2),

 anxiety (ie, self-reported anxiety attack in the previous 4 weeks), and whether the patient was in an ongoing episode of care for mental health.

 The third related to patient activation level (ie, whether the patient brought a list of issues to discuss with the physician). Finally, we controlled for patient demographics (ie, age, sex, race/ethnicity, and education), comorbidity (Charlson Comorbidity Index score

), and the number of evidence-based services delivered in the visit.

 Standard errors were clustered by physician. The statistical analyses were conducted using Stata 14 (STATA, College Station, Texas).

To provide some contextual information on how visits between physicians at different levels of verbal dominance might evolve differently, we selected a few visits with high versus low verbally dominant physicians seeing patients with similar PHQ-2 scores to examine the timing and quality of mental health discussion. We mapped the topics, sequentially, as they took place during the visit.

Visit, Patient, and Physician Characteristics

Table 1 summarizes the characteristics of the study sample: 255 patients likely in need of mental health care saw 53 physicians practicing in about 2 dozen primary care clinics. The majority of patients were white (66%) and female (63%), with an average age of 60. About 9% of patients reported having an anxiety attack in the 4 weeks prior to study recruitment, the average PHQ-2 score was 1.1 [standard deviation (SD) = 1.5], and 37% were in an ongoing episode of care for a mental health condition. Their Charlson Comorbidity Index scores averaged 0.8 (SD = 1.4); 11% of patients brought a written list. These did not vary by MHD levels.

The median visit length was 26 minutes (SD = 10.3). A third of visits (33%) contained no MHD, 33% had an evidence-based MHD, and 34% had a perfunctory MHD.

Among the physician factors, the average length of other nonindex visits was 27 minutes (SD = 8) and rose with the level of MHD, from absent to perfunctory to evidence-based: 25 (SD = 6), 28 (SD = 9), and 29 (SD = 8), respectively. The average percent of visits in which the physician fully elicited the patient’s agenda in other nonindex visits was 25% and increased with the level of MHD, from absent to perfunctory to evidence-based: 19, 27, and 29, respectively. The average physician verbal dominance in other visits was 3.2 (SD = 2.2) and declined in visits with absent, perfunctory, and evidence-based MHD: 3.8 (SD = 2.8), 3.2 (SD = 2.1), and 2.6 (SD = 1.2), respectively. Finally, the average number of evidence-based services delivered was 2.9 (SD = 1.5) and increased with the level of MHD from absent to perfunctory to evidence-based: 2.7 (SD = 1.4), 2.8 (SD = 1.4), and 3.2 (SD = 1.6), respectively.

Mental Health as a Reason for Some Patients to Schedule a PHE

Over 50% of MHDs occurred within the first 5 minutes of visit initiation (median = 4.9; SD = 9.1). Ninety percent of them occurred within the first 19 minutes of the visit, which is 7 minutes fewer than the median visit length. The median time of MHD initiation was 6.3 minutes for perfunctory versus 3.3 minutes for evidence-based discussions (

 <.05). Thus, evidence-based MHDs occurred 3 minutes earlier in the visit than perfunctory MHDs.

In addition to MHDs occurring in the first few minutes of the visit, the nature of the conversation also suggested that mental health might have been a reason that some patients scheduled a visit. For example, one patient said that she “might need kind of an antidepressant” 54 seconds into her visit. She then broke down in tears and told the doctor that her sister was recently diagnosed with lung cancer. Another patient started to cry before the physician closed the exam room door, saying, “And I knew as soon as I saw you I would start to cry.” She stated that she was stressed out at work, “wake[s] up in the middle of the night, worry, worry, worry,” and then said, at 1 minute 24 seconds, “And I want a happy pill. Is there such a thing?”

Factors Associated With the Quality of Mental Health Discussion

Among patients likely needing mental health care, two-thirds had no discussion or perfunctory discussion of mental health during periodic health exams.

Author | Jennifer Elston Lafata, PhD

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