Samuel A. Skootsky, MD

Care coordination programs are frequently implemented in the redesign of primary care systems, focused on improving patient outcomes and reducing utilization. However, redesign can be disruptive, affect patient experiences, and undermine elements in the patient-centered medical home, such as team-based care.

Case-controlled study with difference-in-differences (DID) and cross-sectional analyses.

The phased implementation of a care coordination program permitted evaluation of a natural experiment to compare measures of patient experience and teamwork in practices with and without care coordinators. Patient experience scores were compared before and after the introduction of care coordinators, using DID analyses. Cross-sectional data were used to compare teamwork, based on the relational coordination survey, and physician-perceived barriers to coordinated care between clinics with and without care coordinators.

We evaluated survey responses from 459 staff and physicians and 13,441 patients in 26 primary care practices. Practices with care coordinators did not have significantly different relational coordination scores compared with practices without care coordinators, and physicians in these practices did not report reduced barriers to coordinated care. After implementation of the program, patients in practices with care coordinators reported a more positive experience with staff over time (DID, 2.6 percentage points; 

 A flexible program that incorporates care coordinators into the existing care team was minimally disruptive to existing team dynamics, and the embedded care coordinators were associated with a small increase in patient ratings that reflected a more positive experience with staff.

A care coordinator program was implemented as part of primary care redesign. This study examined the intervention’s effects on patients’ experiences and team dynamics.

Teamwork scores in practices with care coordinators were not significantly different than scores in practices without care coordinators.

Overall patient experience with staff in practices with care coordinators improved significantly after program implementation, although the care coordinators worked with only a fraction of patients.

Our findings suggest that embedded care coordinators improved the primary care experience for patients; they integrated themselves in the care teams without disruption of the existing team’s function.

Healthcare systems transitioning toward value-based models of healthcare delivery often focus on redesigning primary care as a means of achieving better access and quality at a lower cost.

 The patient-centered medical home, a common model that aims to revamp the role of primary care, emphasizes population health and integration across the care continuum.

 Coordination of care is one of the key tenets of the patient-centered medical home, and practices adopting the model frequently implement a care coordination program as part of their practice transformation. These programs, which typically empanel high-risk patients based on prior utilization or comorbidities,

 can result in significant reductions in acute care utilization.

The health system for the University of California, Los Angeles (UCLA Health) implemented an innovative care coordination program as part of its primary care redesign. The intervention embeds nonlicensed care coordinators (CCs) in primary care practices to work with physicians in real time. Program details including coordinators’ backgrounds, the program’s training processes, and operations are described elsewhere.

 Although other components of the redesign focused on technology or ancillary services across all practices, the introduction of the care coordination program was the most significant and potentially disruptive change in the operations of the existing primary care system, and it was applied differentially to practices over time. The CCs focus on patients discharged from an acute care episode, high-complexity patients, and patients referred by physicians, totaling approximately 10% of patients in a primary care physicians’ panel. The primary purpose of the CCs was to improve care and reduce utilization for the most complex patients. Practices in the health system with CCs have been shown to be associated with a 20% reduction in emergency department visits compared with practices without CCs in the health system.

However, practice redesign is disruptive, and the mixed results of primary care transformation efforts

 As a result, it is important to understand how CCs and other “transformative” interventions impact patients’ experiences and the overall structure and function of the team.

Team-based care, another central tenet of the medical home, facilitates primary care’s expanded role as a medical home.

 At a minimum, practice redesign should support the team’s ability to provide cohesive care. Relational coordination has emerged as a dominant method of measuring how various actors in a healthcare setting work together

 and focuses on the relationships between team participants fulfilling specified roles.

Practice redesign efforts must also consider the impact on patients’ experiences as a health-related outcome. Furthermore, patient experience scores are a common feature of quality metrics used in various accountable care payment models, directly affecting the redesign’s return on investment. The scores also indirectly reflect the success of primary care redesign: patients with better experiences are more adherent to physician recommendations, more loyal to their physicians, and more engaged in their care, all desirable outcomes in value-based models of care.

 Finally, publicly reported patient experience scores can affect a health system’s reputation.

In this study, we sought to expand the understanding of CC interventions by evaluating the downstream effects on patients and other members of the care team. Because this program affected only a small number of patients, we hypothesized that the overall set of patients in practices with CCs would report no change in access to care, care coordination, and their overall rating of their experience compared with patients in practices without CCs. Regarding team dynamics, we hypothesized that CCs would not change the relational coordination within a practice and that physicians in practices with CCs would perceive a lower burden of coordinating care for their patients.

A phased implementation of the UCLA Health Care Coordinator Program within UCLA Health allowed for a natural experiment comparing practices with CCs already embedded (CC practices) with the practices that had not yet introduced CCs (non-CC practices). Because the health system continuously collects patient experience data for all practices, we used a difference-in-differences (DID) approach to compare preintervention and postintervention patient experience between the 2 cohorts. Data on relational coordination and physician-perceived barriers to care were collected only in the postintervention period and were compared between CC practices and non-CC practices.

Description of Health System and Primary Care Practices

UCLA Health is an urban academic system that had 26 primary care practices caring for adult patients at the time of the study encompassing internal medicine, family medicine, and geriatrics. Primary care practices within UCLA Health are primarily traditional community-based practices with full time physicians; 4 practices also have trainees. The first CCs were introduced into 5 practices in May 2012, and another 9 were introduced in November 2012; half of all practices had CCs by December 2012. The other half of primary care practices incorporated CCs starting in March 2014. The rollout of the primary care redesign was set up to represent the wide range of practices within the UCLA Health System: practices belonged to 1 of 4 “pods” corresponding to the management structure of the ambulatory care network. Each pod had semi-autonomous management. The first practices to receive CCs were selected by UCLA Health System leadership to encompass a representative sample from each pod and to include sites with, and without, trainees. 

The CCs were embedded members of the primary care team, working with all physicians within their assigned practice, and were tasked with addressing nonmedical barriers to care. Our prior work showed that the majority of the work of the CCs related to transitions of care, including such physician-directed tasks as scheduling appointments, providing self-management support services, and monitoring and following up with patients between visits.

 As a result, a CC served as a dependable point person for a subset of patients in order to complete tasks that were typically done by a variety of receptionists and clerks, or not performed at all. 

The health system used the Clinician and Group Survey by Consumer Assessment of Healthcare Providers and Systems 12-Month Survey to measure and track patient experience.

 The survey evaluated 6 categories of each patient’s experience, detailed in 

). Four categories used multiple questions to form composite measures: quality of patient—doctor interaction, access to care, care coordination, and courteous and helpful office staff (defined as clerks and receptionists). The last 2 categories covered 2 global rating questions: 1) overall rating of the doctor and 2) whether the respondent would be willing to recommend the doctor to their family and friends.

Patient experience data were split into 2 periods: the preintervention period ranged from May 2011 through February 2012 and the postintervention period ranged from May 2013 through February 2014. Because the last CC in the intervention group was introduced in December 2012, this allowed a minimum of 4 months for CCs to become established team members of their primary care practice. Two CC practices were excluded because they were in a transition period between CCs at the time of the study, and the newly hired CCs had not had time to form relationships with their colleagues.

Relational coordination and physician burden surveys.

Relational coordination is a tool initially developed in the airline industry

 that was, subsequently validated in multiple healthcare settings.

 It is defined as a mutually reinforcing web of communication and relationships carried out for the purpose of task integration.

 In primary care, higher relational coordination has been associated with higher-quality chronic care delivery,

and improvement in relational coordination predicts improvements in chronic care delivery.

The measure is computed by having respondents rate the quality of working relationships with their colleagues based on 3 relational dimensions (shared goals, shared knowledge, and mutual respect) and 4 dimensions of communication: frequency, accuracy, timeliness, and whether the communication seeks to solve problems or assign blame (see 

In order to assess physician-perceived burdens of coordinating care, we asked primary care physicians within the health system and not directly involved in this research to describe the tasks that do not require clinical training or licensure but are needed for patients to receive well-coordinated care. We overlaid these responses with tasks documented by CCs as part of their workflow to identify areas in which CCs might reduce the physician burden of care coordination. Five questions assessed physicians’ perceived burden of nonmedical care coordination tasks; each was scored on a 5-point scale where 5 was the lowest perceived burden.

The survey was pilot tested in a non—primary care practice. All staff and physicians in non-CC practices were instructed to fill out the survey between January 2014 and April 2014 (prior to adding a CC). Staff and physicians in CC practices completed their surveys between May 2014 and August 2014. The project received institutional review board exemption as a quality improvement initiative.

For the patient experience survey data, χ

 tests were used to evaluate differences in categorical variables between patients in the CC practices and non-CC practices. The Wilcoxon Rank Sum test was used to evaluate differences in the number of staff, number of MDs, and percent of full-time physicians between CC and non-CC practices. Because some physicians did not practice full time, potentially affecting their coordination with other team members, we determined the mean “percent clinical time” for physicians in a practice. This was defined as the sum of clinical half-days worked by all physicians in the practice divided by a total possible 10 clinical half-days per week (eg, if a practice had 2 physicians and 1 worked 3 half-days and the other worked 8 half-days, the practice’s percent clinical time would be 11/20, or 55%).

The pre- and post differences in the 6 patient experience outcomes between the CC practices and non-CC practices were compared using a linear mixed-effects model with fixed effects for the presence of a CC, time period (pre- or post), and CC by time interaction, and a random clinic effect. Models were adjusted by age group, race/ethnicity, gender, self-health rating, and education level; whether the clinic was a training site; total number of clinic staff; and average percent of full-time physicians at each clinic. Estimates were produced using modeled contrasts. 

 and Fisher exact tests were used to evaluate differences in the respondent characteristics between CC practices and non-CC practices, as appropriate. Based on summary statistics, physician age was collapsed into 3 categories (aged <35 years, 35-54 years, or >55 years) and physician work experience was collapsed into 3 categories (<5 years, 5-10 years, >10 years).

A composite measure of relational coordination was computed as the mean of the individual relational coordination survey questions. Composite relational coordination was compared between CC practices and non-CC practices using a linear mixed-effects model with a practice-level random effect and a fixed effect for the presence of a CC. Models were adjusted by job role, gender, age, experience working at the site, and total number of employees (including physicians) at that site. 

Differences in scores of the barriers to coordinated care between the CC practices and non-CC practices were compared using a linear mixed-effects model, with a clinic random effect and a clinic having a CC as the fixed effect. Models were adjusted for physician age, gender, number of years working at the clinic, number of years of total experience; number of doctors at the clinic, total staff at the clinic, and average percent of physician clinical time. SAS version 9.4 (SAS Institute; Cary, North Carolina) was used for all statistical analyses.

Patient, Physician, and Practice Characteristics 

Of 13,441 patient experience surveys, 40% were from patients surveyed during the baseline (preintervention) period, and the remainder were after the introduction of CCs (postintervention period) (

). Out of the entire group, most were female (64%) and predominantly white (66%), and 12% reported a Hispanic or Latino ethnicity. Patient characteristics between the pre- and postintervention cohorts did not differ significantly in self-reported overall health rating, or proportion with hypertension (43%), diabetes (13%), or depression (19%). Several variables (age, gender, race, and education) were statistically different between these relatively large cohorts, but absolute differences between the temporal cohorts were small.

The 12 CC practices and the 12 non-CC practices were similar across the variables examined, with the exception of the number of physicians (

). Physicians were about equally split by gender, age range, and work experience, although experience at their practice site differed between cohorts (Table 2).

After adjusting patient experience scores for patient- and clinic-level factors, there was a decline in all 6 scales of the patient experience for the non-CC practices between the preintervention and postintervention periods, whereas change over time was mixed in the CC practices. Although changes in scores were small in absolute terms, some were statistically significant. For example, among non-CC practices, patient ratings of access decreased by 1.6 points (

 = .03) and coordinated care decreased by 2.7 points (

 = .002); for the CC practices, ratings of coordinated care decreased by 2.2 points (

). DID analysis was not statistically significant. Patient perception of staff, which asks patients about experience with clerks and receptionists, increased by 1.9 points in CC practices (

 = .0005), and there was a statistically significant interaction between CC and time, with a net adjusted difference of 2.6 percentage points regarding experience with clerks and receptionists favoring CC practices compared with non-CC practices (

Response rates were 86% for nonphysicians (327 of 382) and 75% for physicians (132 of 177), for an overall response rate of 82%. Relational coordination scores, measured on a 1-to-5 scale, ranged from 3.8 in the lowest-scoring practice to 4.5 in the highest-scoring practice (mean = 4.1; standard deviation [SD] = 0.20). After adjusting for respondent- and clinic-level factors, relational coordination was not significantly different in practices with a CC compared with those without a CC (mean scores = 4.06 and 4.00, respectively; 

 = .39). Subset analysis of physician responses using the same method, and further adjusting for years of work experience, found that physicians in practices with CCs reported a small but significantly higher relational coordination (difference = 0.27; 

Physicians working with CCs did not report a different level of burden concerning tasks associated with coordinating care compared with physicians without CCs (

). Physicians reported the greatest burden being filling out forms, and 4 of the 5 measures had group estimates below 3.0 for both CC- and non-CC practices (Table 4).

This study evaluated the effects of a care coordination program beyond its effect on utilization. In addition to reducing acute care utilization, the CC intervention we studied also preserved patient experience amid a downward trend within the health system. The lack of statistically significant association in relational coordination with the addition of CCs suggests that intercalation of a CC into primary care practices does not produce a disruptive effect on teamwork. Practice redesign is a long, difficult process, and these secondary outcomes ensured attention to the broader impact of the transformation beyond utilization.

The lack of a statistically significant change in relational coordination may have reflected the design of the UCLA Health Care Coordinator Program as a flexible intervention to improve the care of the most complex patients. Although one may have expected improvement in relational coordination with the addition of the CC, interrupted teamwork was also possible. Disruptions to workflow and team dynamics can be barriers to primary care practice transformation.

 These findings suggest that the integration of the CCs into the existing primary care team was minimally disruptive to the existing team’s function. Furthermore, a baseline measure of relational coordination can serve as a benchmark to monitor continuous efforts to improve team-based care, a frequently cited goal of the medical home, but a component rarely measured directly.

The positive association between CC practices and patient ratings of experience with office staff has several possible explanations. First, patients may have perceived the CCs as part of the staff in their medical home. The integration of the CCs in the medical home team was an intentional goal of the program’s design, and built on prior research supporting the effectiveness of embedded CCs.

 Second, CCs worked on nonmedical tasks for patients, the completion of which was often communicated to patients by clerks and receptionists, who remained the patient’s touchpoint to the practice. Finally, the CCs focused on complex nonmedical tasks that may have otherwise fallen to the clerks and receptionists, thereby unburdening them to provide a better patient experience for the full complement of their patients. These hypotheses require further evaluation to understand the link between the observed improvement in patient ratings of staff and the implementation of CCs.

Overall, in the studied health system, patient-reported access and care coordination decreased by a small, but statistically significant, amount over time. The cause of this trend is unclear, but may have reflected difficulties experienced at the time in which the health system implemented an electronic health record system,, which occurred during the observed period.

Finally, the low scores in all measures of physician-perceived burden of care coordination underscored the well-established difficulties in the daily work of primary care physicians.

 This survey did not find a reduction in perceived barriers in practices with CCs compared with those without, but CCs only worked with a minority of each physician’s patient panel, and these questions may not have adequately captured the impact of CCs on physician practice patterns. More research is needed to identify practice design features that improve physician-perceived burdens of care coordination.

This study has several limitations. Only 1 health system was evaluated, although 12 practices had CCs and 12 practices did not. The relational coordination and primary care burdens surveys were conducted only in a cross-sectional fashion, limiting inference of causality, but generating hypotheses for future research (eg, potential interactions between practice size and adding a CC). Furthermore, the power to detect changes in relational coordination (beta = 0.8 to detect a difference of 0.07 points in mean relational coordination at alpha = 0.05) and the physician-perceived burden of care coordination (beta = 0.8 to detect a difference of 0.50 points for all of the questions at alpha = 0.05) limited our ability to state with certainty that teamwork or physician-perceived barriers of care coordination were not improved in the CC practices compared with non-CC practices. 

Inertia and the barriers to change too often stymie primary care redesign. On the other hand, piecemeal solutions to individual problems without regard to the preexisting structure and function of practice are not sustainable, and can ultimately result in a chimeric system that is confusing for patients, complicated for physicians and staff, and costly to the system as a whole.

 Although this care coordination program primarily sought to reduce utilization, primary care redesign must maintain a broader vision of population health through team-based care. The results of this study suggest that the flexible design of this program is minimally disruptive to overall teamwork and modestly improved the patient experience with staff for patients in CC practices. The findings support a sustainable transformation of primary care and an empirical benchmark for measuring improvement into the future.

David Geffen School of Medicine, UCLA (PD, RC, CHT, HW, SS, NW); Los Angeles, CA; UCLA Fielding School of Public Health (RS) Los Angeles, CA; Center for Health Policy and the Center for Primary Care and Outcomes Research, Stanford University School of Medicine (KM); Stanford, CA.

Dr Di Capua received support from HRSA Institutional National Research Service Award (NRSA) T32-HP-19001.

Drs Wenger, Clarke, and Skootsky were employees of the UCLA Medical Group during the time of this study. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. 

 Concept and design (RC, PD, HW, SS, RS, NW); acquisition of data (RC, PD, RS); analysis and interpretation of data (RC, PD, HW, KM, CHT, SS, NW); drafting of the manuscript (PD RS, NW); critical revision of the manuscript for important intellectual content (RC, PD, KM, CHT, SS); statistical analysis (PD); provision of patients or study materials (PD, HW, CHT); administrative, technical, or logistic support (PD, KM, RS); supervision (SS). 

Paul Di Capua, MD, MBA, MSHPM, 1500 San Remo Ave, Ste 360, Coral Gables, FL 33143. E-mail: dr.dicapua@gmail.com.

Articles

A primary care redesign program embedding care coordinators into practices slightly improves the patient experience and does not disrupt team dynamics.

The Effect of Implementing a Care Coordination Program on Team Dynamics and the Patient Experience

Although care coordination is an essential component of the patient-centered medical home structure, current case manager models have limited usefulness to population health because they typically serve a small group of patients defined based on disease or utilization. Our objective was to support our health system’s population health by implementing and evaluating a program that embedded nonlicensed coordinators within our primary care practices to support physicians in executing care plans and communicating with patients.

 Matched case-control differences-in-differences.

Comprehensive care coordinators (CCC) were introduced into 14 of the system’s 28 practice sites in 2 waves. After a structured training program, CCCs identified, engaged, and intervened among patients within the practice in conjunction with practice primary care providers. We counted and broadly coded CCC activities that were documented in the intervention database. We examined the impact of CCC intervention on emergency department (ED) utilization at the practice level using a negative binomial multivariate regression model controlling for age, gender, and medical complexity.

 CCCs touched 10,500 unique patients over a 1-year period. CCC interventions included execution of care (38%), coordination of transitions (32%), self-management support/link to community resources (15%), monitor and follow-up (10%), and patient assessment (1%). The CCC intervention group had a 20% greater reduction in its prepost ED visit rate compared with the control group (

 Our CCC intervention demonstrated a significant reduction in ED visits by focusing on the centrality of the primary care provider and practice. Our model may serve as a cost-effective and scalable alternative for care coordination in primary care.

	We implemented and evaluated a program that augmented the primary care practice’s role as the medical home by embedding nonlicensed personnel to coordinate care. Our comprehensive care coordinators (CCCs) were trained within our system, were co-located with the primary care physicians, served patients from all payers, and received support from centrally based licensed personnel. 

		In 1 year, CCCs touched nearly 14,000 unique patients, primarily executing the physician’s plan of care or coordinating transitions. 

		We found a 20% year-over-year greater reduction in emergency department visits among the patient population attributed to the 14 practices with CCCs compared with 14 practices without.

The passage of the Affordable Care Act (ACA) reinforced primary care practice redesign as the main element for providing optimal population health.

 This redesign takes many forms, but the term “patient-centered medical home” (PCMH) has come to describe the ideal practice.

 The PCMH is central to healthcare reform, with national organizations (eg, National Committee for Quality Assurance, URAC) having certified thousands of practices as PCMHs and some state programs providing financial rewards for acquiring certifications.

 However, the last decade of experience demonstrates that PCMH transformation is difficult, disruptive, and expensive.

 Although PCMH demonstrations have shown improved outcomes, real-world applications of PCMH practice redesign have inconsistently improved quality and utilization metrics.

 Our University of California at Los Angeles health system (UCLA Health), consisting of over 28 primary care practice sites, developed a transformation model to implement practice redesign swiftly and broadly across our network. Our approach included aspects from many PCMH domains, centering on an innovative approach for coordinating patient care.

Care coordination is a core component of the PCMH model

 and was one of the “7 Joint Principles” promulgated by the primary care societies.

 Most of the literature on PCMH care coordination describes programs oriented around patients who are high-risk utilizers, have specific medical conditions, or are discharged from the hospital.

 These programs are frequently delivered from an external administrative unit separated from the primary care ambulatory practice, such as by the patient’s health plan, a health maintenance organization (HMO), or an intensive ambulatory practice.

 Care coordination models without a tight linkage to primary care did not meet our health system’s priorities of providing population-based care management that strengthens the patient—primary care physician (PCP) relationship.

In our PCMH model, a comprehensive care coordinator (CCC) is embedded in each practice with the flexible job role of providing additional support to any patient who needs it within the practice’s panel. Some have proposed this role as part of the PCMH model, and we innovatively filled this role with nonlicensed staff instead of case managers, social workers, or counselors.

 These CCCs extend PCPs’ reach by addressing barriers to coordinated care through short- and long-term relationships with patients. Our CCCs act more as patient navigators than health coaches, as the emerging literature differentiates the roles of nonlicensed personnel.

 In contrast to typical navigators, CCCs perform this function as part of the care team by reviewing the electronic medical record (EMR) system and executing care plans. Our care coordination model prioritizes higher-need patients with unmet medical or social needs, but moves away from empaneling patients solely by disease or risk of future utilization. Whereas UCLA Health has population-based capitation and risk-sharing contracts, many patients are in traditional fee-for-service plans and the CCCs support patients irrespective of insurance type.

In this paper, we describe the structure of our care coordination program, including the CCC’s training, typical work flow, and number and types of interventions delivered over 1 year. We hypothesized that providing care coordination through embedded, nonlicensed personnel to a broad and heterogeneous group of patients would allow our practices to deliver more complete primary care. We tested this hypothesis by examining emergency department (ED) utilization between practices with and without a CCC.

We embedded 1 CCC per practice in 14 of the 28 primary care sites within UCLA Health. CCCs were not required to have specific healthcare training or licensure; instead, we sought prior experience functioning in complex organizations, acting independently to solve problems, and participating within busy teams. Many had been medical assistants, military medic or corpsman, emergency medical technicians, or community health workers.

Because they did not have formal licensure and training, 2 types of support were provided to promote effective and high-quality patient care. First, the CCCs completed approximately 40 hours of initial training under a case manager and licensed social workers. The curriculum included topics on problem solving, patient engagement, post acute—care planning, socio-behavioral risk assessment, physician communication, community resources, and health plan navigation. This introduction was reinforced through new CCCs shadowing a veteran for 2 weeks and then through case-based problem solving at biweekly, 2-hour CCC conferences. The second source of support for CCCs was a centralized team of more highly trained personnel that consisted of an RN program director, a nurse case manager and a licensed clinical social worker. These individuals were available telephonically to answer complex questions, provide consultation for complex case management needs, or to perform medical interventions, such as home visits, that require licensure and training. We had a ratio of approximately 1 licensed personnel to 7 CCCs.

CCCs identified patients in need of coordination (patient identification), engaged in outreach to patients (patient engagement), and performed 1 or more interventions usually during a time frame of the next several days to weeks depending on the need (

). While all patients within the practice’s panel qualified for a CCC intervention, 3 categories of patients were identified to receive targeted efforts: patients recently in an acute care setting (ED or hospital), patients with high utilization rates or high-risk scores, and patients directly referred by the primary care provider. CCCs identified patients in these categories through regular automated reports and registries, structured huddles with the care team, or informal communication with PCPs. Each practice integrated its CCC into the PCMH team in different ways, but at minimum, the main touch point was a CCC—PCP daily huddle with CCCs followed by coordination with the practice’s front- and back-office staff (electronically or in-person) to define each patient’s needs. CCCs reached out to patients directly, either by phone or in person, reviewed the patient’s medical record, and performed an informal assessment of barriers to care. Based on this assessment, CCCs performed 1 or more interventions in the following categories: coordination of transitions, execution of care plan, monitoring and follow-up between visits, linkage to community resources, and/or patient assessment. The decision to “discharge” a patient from the CCC’s list was made by the PCP–CCC dyad, not by centrally defined criteria. CCCs documented in an internally developed online Patient Care Coordination System (PCCS); initially, PCCS was not linked to UCLA’s EMR, but subsequently, copies of PCCS documents were automatically ported over.

Among the primary care practices within UCLA Health, most are traditional community-based practices with full-time clinicians; only 3 are academic with trainees. These practices include family medicine, internal medicine, internal medicine-pediatric, and geriatric physicians. The number of PCPs in these practices ranged from 3 to 11; however, to ensure co-location, 1 CCC was embedded in each practice. CCCs were introduced into the intervention sites in 2 waves: wave 1 consisted of 5 practices starting in May 2012 and wave 2 consisted of 9 practices starting in November 2012. The first wave was considered a 6-month pilot phase that helped shape the implementation process and the CCC work flow for the second wave. The control sites were the remaining 14 practices, which did not receive a CCC.

We tabulated the total number of CCC “touches” and unique patients touched in the PCCS documentation system from May 2012 to July 2013. A CCC touch was defined as an encounter that a) contributed to the development and/or implementation of a plan of care for a patient or family and b) was documented in PCCS. A coding system to categorize CCC touches was adapted from a systematic review of care coordination published by the Agency for Healthcare Research & Quality,

 literature on case management, discussions with CCCs, and an exploratory review of the PCCS database. Touches were then classified into 5 broad categories based on manual review of the first full year of PCCS records for the wave-1 practices. Five coders, in total, coded all touches with a high inter-rater reliability (κ = 0.89).

We evaluated the impact of the CCC intervention on ED utilization at the practice level. Although CCCs were available to all patients, we restricted the population used for the evaluation to patients for whom we had full data capture of ED visits: patients in the HMO insurance plan delegated to our medical group. ED visits were identified through encounters at our hospitals’ EDs or through paid claims for visits at external facilities. A visit-based attribution rule was used to assign a patient to a PCP, so only patients with at least 1 PCP visit during either the pre- or post time periods were included.

We limited our evaluation to wave-2 practices compared with the 14 control practices that did not have a CCC. The wave-1 practices were not included on account of several differences from the other practices: wave 1’s mean age was statistically higher as it included our only geriatrics practice, the time period for wave 1 was different, and wave 1 was seen as a pilot. ED visit rates were calculated for the intervention and control sites based on the 12 months prior to and after the introduction of CCCs into the practices. A negative binomial multivariate regression model was used to test the effect of being in a CCC practice on ED utilization. The models controlled for baseline ED visit rate, age, gender, and risk adjustment factor score (RAF)—a payment modifier used by CMS and a marker of medical complexity. Significance was based on 

 <.05. We also calculated the cost savings for payers accounted by any averted ED visits.

We received UCLA Institutional Review Board exemption for this quality improvement study.

Overall, 105,840 patients from all payers were attributed to the wave-1 and wave-2 practices and were therefore eligible to receive interventions from the CCCs. At the time of the analysis (18 months and 12 months, respectively, after wave 1 and wave 2 were implemented), the 14 CCCs had touched 10,522 unique patients (approximately 10% of those eligible). Therefore, each CCC was, on average, intervening on 53 new patients per month. For approximately one-third of patients, the CCC completed the intervention in a single day by assisting with a care transition or a PCP’s plan. For patients engaged by the CCC for 2 or more days, the median number of days touched per patient was 3 (interquartile range [IQR] = 2-9). These engagements lasted for a median of 85 days (IQR = 12-261). The vast majority of identified issues were handled within the office by the nonlicensed CCC, the PCP, and practice staff; only 316 patients (3% of the total) received higher-level care from the centralized nurse case manager or licensed clinical social worker (

). The mean age of patients touched by CCCs was 59 years, and 61% were female. The mean RAF for these patients was 0.99, which is very similar to the average of 1.0 that CMS sets for the full Medicare population. This indicates that those touched were more medically complex than expected for a population of mixed Medicare and commercial insurance. Twenty-one percent had an ED visit in the prior 12 months.

We manually reviewed the 8036 CCC encounter records PCCS database contained over a 1-year period among the 5 primary care practices in wave 1. The coders categorized these CCC interventions with the following breakdown: 37% execution of care, 32% coordination of transitions, 15% self-management support/link to community resources, 10% monitor and follow-up, 5% unclassified, and 1% patient assessment (

For the evaluation, 25,356 unique patients met the inclusion criteria for the wave 2 intervention cohort practices and 18,077 did in the control practices (

). The patient characteristics in the intervention and control cohorts were similar (

 >.05). The preintervention ED visit rate was 131 per 1000 patient-years for the intervention group and 148 per 1000 patient-years in the control group. Post intervention ED visit rates over the 12 months after introduction of the CCC was 118 per 1000 patient-years in the intervention group and 139 per 1000 patient-years in the control group. The negative binomial regression coefficient for the intervention (with the control as the reference) was —0.22 (

 <.0001). This indicates that after adjusting for age, gender, and medical complexity, the intervention group had a 20% greater reduction in its prepost ED visit rate compared with the control group. We excluded wave 1 in this regression analysis, but in a sensitivity analysis where we combined wave 1 with wave 2, we found a lower but still significant 12% decrease in ED utilization (β = —0.13; 

 <.001) compared with the control practices.

In absolute terms, this 20% reduction across the 25,356 patients in wave 2 meant a reduction of 646 ED visits over 12 months compared with usual care (ie, that delivered by the controls). At an estimated cost to payers of $2000 per ED visit, in isolation, this is an estimated reduction in total cost of care of $1.4 million. The costs of those personnel dedicated to the program, including salary and benefits for the 14 CCCs and the 2 clinical advisors (but not inclusive of the time from medical directors and other support staff), was approximately $950,000 over that same 12-month period.

Our health system implemented primary care practice redesign as part of a comprehensive transition to providing population healthcare. In order to be successful, the redesign needed to touch many patients across the system, could not be disruptive to ongoing practice operations, and had to be affordable during a time when advanced primary care is not fully reimbursed.

To achieve these goals, we designed a PCMH model that enhanced the PCP—patient relationship by extending each practice’s ability to support patients leading up to, following up from, and between physician visits. The activities of the CCCs catalogue the core patient- and physician-centric needs that were not fully met by the traditional primary care practice model within our health system, such as a reliable channel of communication to the PCP, help with navigating the health system or health plan, or assistance with accessing available community resources. Many of these interventions were completed within several days or weeks. In confirmation of how this model differed from prototypical care management programs targeted to certain subsets of higher-need patients, our nonlicensed CCCs touched nearly 1000 patients each—fully 10% of each practices’ panels&mdash;and only 3% of these patients needed the more complex type of care management offered by licensed personnel. This tiered allocation of personnel types allowed us to meet the needs of our practices and patients with the appropriate person and, in so doing, kept implementation costs low enough to spread the new services broadly across our system. Through this experience, we believe that the optimal ratio is 1 CCC per 4 full-time PCPs (or per approximately 8000 adult patients).

Our study had several important limitations. The analysis was conducted at the practice, not patient, level. However, practice-level rates of ED visits are an important indicator of a practice’s ability to manage disease progression and provide accessible clinical services. Next, although the intervention applied to all insurance types, in order to meet the imperative of complete data capture, we restricted the analysis to the HMO population delegated to our medical group. Because HMO populations have lower acute facility utilization at baseline, if anything, analyzing this group introduced a conservative bias for detecting a significant effect. Additionally, while the intervention demonstrated significant reduction in ED visits for 1 year, additional analyses are needed to determine whether the intervention has sustained or compounded improvement over time. Lastly, although we found a significant result within 1 institution, this may not be generalizable to other health systems.

Although our study did not systematically define the possible mechanisms that drove the decrease in ED use, conversation with several CCCs and clinical advisors identified 3 possible explanations that we will examine in future work. First, the CCCs developed relationships with patients and served as a channel of communication to PCPs, which patients used instead of going to the ED. Second, CCCs supported PCPs in delivering complex care (eg, arranging for home intravenous antibiotic medication) to patients who would previously have been sent to the ED. Third, CCCs became skilled in identifying and overcoming the nonmedical obstacles of a large and complex health system, which increased patients’ follow-up with services ordered by the PCP.

Our PCMH program was scalable and easily adopted by a large number of practices within a short period. In contrast to other PCMH implementations, little external facilitation was necessary to achieve the successful adoption of the CCC into the care team.

 The redesign program was led by a centralized team that handled CCC hiring and training, while regular meetings with practice leaders allowed for local adaptation of the model. An internal survey of 52 physicians in the intervention sites (48% response rate) showed positive responses to this approach, with 94% responding that the program was effective and 80% that their patients were overwhelmingly enthusiastic about the augmented service. As opposed to this practice redesign being disruptive to their care, the PCPs reported that the CCCs saved them an average of 30 minutes per day.

The ACA rewards health systems for providing comprehensive population management and for reducing the population’s total cost of care. Coordinating care is a central competency of organizations that succeed as accountable care organizations.

 However, many popular care coordination solutions require wholesale change of care delivery processes and can weaken the patient—physician relationship at the heart of patient-centered care.

We developed and tested a care coordination program that enhanced the centrality of the PCP, was implemented widely across a health system and population, and used cost-effective allocation of resources. The program demonstrated a significant reduction in ED utilization, which resulted in a savings just within our HMO population that more than offset the cost of the program over the same time period. When extrapolating the savings to the all-payer population that the program served and to potentially averted hospitalizations, the program is likely highly beneficial to payers—and to our health system for those insurance groups where we have developed shared savings contracts. Given the results of our program, we have expanded CCCs into our remaining primary care practices. We plan future studies including a formal cost-effectiveness analysis and evaluations of effects on other outcomes, including patient experience and acute hospitalizations.

The authors thank Renee Sednew for delivering project management, Don Mogill for maintaining the PCCS database, Andrew Hackbarth for data collection, and Indu Gupta, Brian Doyle, Asad Malim, and Aliza Ali for assisting in developing and coding the CCC classification scheme. A poster presentation of partial analysis was presented at the Society of General Internal Medicine, March 2014, San Diego, CA. 

 Division of General Internal Medicine & Health Services Research, UCLA Faculty Practice Group & Medical Group (RC, PDC, CHT, CMM, SAS) Los Angeles, CA; Department of Medicine, David Geffen School of Medicine at UCLA (RC, CHT, CMM, SAS), Los Angeles, CA; RAND Corporation (NB), Arlington, VA; Department of Health Policy and Management, UCLA Fielding School of Public Health (CMM), Los Angeles, CA; VA Center for Implementation Practice and Research (BM), Los Angeles, CA; Kaiser Permanente Southern California (BM), Los Angeles, CA.

 Initial funding for the Clinical Care Coordinator positions was through the Delivery System Reform Incentive Payments (DSRIP)/California’s Section 1115 Medicaid Demonstration Waiver. Support was received from the University of California at Los Angeles (UCLA), under a National Institutes of Health (NIH)/NIA Grant, and from a NIH/National Center for Advancing Translational Sciences UCLA Clinical and Translational Science Institute Grant. Another author was supported by HRSA Institutional National Research Service Award.

 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. The work describes care improvement within UCLA Health, which benefits through reputation and shared savings from accountable care organization payments, in theory.

 Concept and design (RC, NB, PDC, CMM, BM, SAS); acquisition of data (RC, PDC, BM); analysis and interpretation of data (RC, NB, PDC, CHT, CMM, BM); drafting of the manuscript (RC, NB, PDC, CHT, SAS); critical revision of the manuscript for important intellectual content (RC, NB, CMM, BM, SAS); statistical analysis (RC, CHT); obtaining funding (BM, SAS); administrative, technical, or logistic support (RC, SAS); and supervision (RC, BM, SAS).

 Robin Clarke, MD, MSHS, Medical Director for Quality, UCLA Faculty Practice Group, Assistant Clinical Professor, Division of General Internal Medicine, 10945 Le Conte Ave, Ste 1401, Los Angeles, CA 90024. E-mail: RClarke@mednet.ucla.edu.

Description of a program embedding nonlicensed care coordinators in primary care practices including training, interventions, and the effect of the program on emergency department visits.

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