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Innovative Approach to Patient-Centered Care Coordination in Primary Care Practices
Robin Clarke, MD, MSHS; Nazleen Bharmal, MD, PhD; Paul Di Capua, MD, MBA; Chi-Hong Tseng, PhD; Carol M. Mangione, MD, MSPH; Brian Mittman, PhD; and Samuel A. Skootsky, MD
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Innovative Approach to Patient-Centered Care Coordination in Primary Care Practices

Robin Clarke, MD, MSHS; Nazleen Bharmal, MD, PhD; Paul Di Capua, MD, MBA; Chi-Hong Tseng, PhD; Carol M. Mangione, MD, MSPH; Brian Mittman, PhD; and Samuel A. Skootsky, MD
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.

RESULTS
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 (Table 1). 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 (Figure 2).
 
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 (Table 2). The patient characteristics in the intervention and control cohorts were similar (P >.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 (P <.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; P <.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.

DISCUSSION
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—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).
 
Limitations
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.
 
Implications
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.26 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.27

CONCLUSIONS
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.2 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.


Acknowledgments
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. 


Author Affiliations: 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.

Source of Funding: 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.

Author Disclosures: 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.

Authorship Information: 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).

Address correspondence to: 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.
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