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The American Journal of Managed Care November 2016
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Referrals and the PCMH: How Well Do We Know Our Neighborhood?
Andrew Schreiner, MD; Patrick Mauldin, PhD, Jingwen Zhang, MS; Justin Marsden, BS; and William Moran, MD, MS
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Referrals and the PCMH: How Well Do We Know Our Neighborhood?

Andrew Schreiner, MD; Patrick Mauldin, PhD, Jingwen Zhang, MS; Justin Marsden, BS; and William Moran, MD, MS
A descriptive analysis of specialty referral patterns in an academic, internal medicine patient-centered medical home (PCMH).
In discussing referral patterns in the contexts of setting, provider, and patient, the Figure provides an intersection between all 3 elements. In this Figure, a marked gap emerges in the referral rates of patients at the highest risk of utilization. Residents refer quintile 5 patients more often than their faculty counterparts. However, as the risk of utilization falls, the referral rates begin to converge, with no statistical difference in the rate between the 2 clinics. Additionally, the faculty rate of referral decreases from quintile 4 to quintile 5 patients. Multiple explanations may contribute to this change in referral rate across the continuum of utilization risk. First, faculty physicians may exhibit more comfort with patients at high risk of utilization. This comfort may decrease their tendency to order specialty referrals, ancillary services, or high-cost imaging procedures. Next, patients in the faculty clinic—as a result of longer-term continuity and ongoing therapeutic relationships—already receive specialty care for their conditions, which may limit the number of new referrals originated during the study period.  This could be better assessed with a longer study period or a longitudinal study of these patients in the faculty clinic.

Value may also play a role in the decision to refer patients at highest risk of utilization. Faculty may make a conscious effort to improve the quality-to-cost quotient by requesting specialty consultation and high-cost testing less often for patients with a higher propensity to utilize healthcare resources. More importantly, patients in long-term therapeutic relationships with their faculty provider may not pursue or desire more testing, consultations, or additional services. A more direct question with feedback from patients and faculty providers may better address this issue. Fluctuation in referral rate along the continuum of utilization risk may provide an opportunity to further explore the intersections of patient, provider, and practice factors that impact referral patterns.

One persisting question revolves around ordering recommended screening procedures.  Referrals for recommended screening procedures would appropriately increase referrals for those at lower risk of healthcare utilization compared with those in the highest quintile who may not benefit from these screening measures. Increasing preventive measures in those who might benefit, while limiting such testing in those with potentially more comorbidities, may improve value.16 This study did not include mammograms or bone density testing on account of lower cost. However, such information is needed to illustrate the full impact of screening measures on referral rates.

Although referrals contribute to clinical volume, healthcare cost, and care fragmentation, they do provide an essential role in the care of patients. At this point, the ideal number of referrals is not clear. If healthcare systems continue efforts to improve care coordination and multi-specialty system integration, the risks of increased cost and care fragmentation may lessen. Referrals’ contribution to quality, safety, and meaningful clinical outcomes requires further assessment. Health services researchers will need to address the utility of referrals in the setting of need for individual patients, providers, and healthcare systems.


Our study has several limitations. This project was conducted in a single academic medical center and done so over a relatively brief study period (6 months). There was also no adjustment for social determinants, which likely have an impact on patient-level factors contributing to referral decisions. Social determinants impact access to care, the need for services, the volume of issues facing the primary care physician, and the transfer of information. With regard to access, social determinants may limit the testing or consultations available to patients, impact the adherence to scheduled appointments, or affect the ability to follow up on incomplete referral processes. Social determinants play a role in the volume of issues encountered in the primary care setting, which may lead to differing referral patterns in an effort to fit time constraints while managing multiple problems. Additionally, they play a role in the transfer of information. With current barriers to care coordination including fragmentation and lack of system integration, the patient often serves as the primary vehicle for information exchange between providers. Social determinants impact the ability of patients to participate in this role. Also of note, there is the potential for a first-order interaction when looking at demographic data—in particular, when looking at race and payer system.

Another limitation includes the inherent demographic differences between faculty and resident patients, limiting the ability to fully isolate the impact of clinician experience on referral patterns. Residents see more high-risk patients, and the patients are more often impoverished and reliant on public payer systems for healthcare. Future studies will need to address experience within the context of more similar patient populations to fully understand the impact on referral patterns. A multi-level model may have better assessed the role of clustering; however, this type of modeling was not feasible due to the structure of our data. Despite assignment to individual resident providers and empanelment in the resident physician practice, patients did not always see their assigned resident. On account of resident scheduling and clinic availability, assigned patients would often see other resident providers, which made it difficult to account for patients seen per provider and to appropriately assess clustering. However, this is a scenario that does not play out on the faculty side, where patients saw their assigned faculty provider (advanced practice providers also involved) almost every visit. Finally, the data collected is dependent on the veracity of the EHR.


Setting, provider, and patient factors all can play a role in referral patterns. In looking at referral rates in the setting of risk of healthcare utilization, residents refer high-risk patients more often than their faculty counterparts. This difference in rate of referral narrows as we move down the spectrum of risk. Understanding the factors that influence these disparities in referral rates may aid patients, clinicians, healthcare systems, and policy makers in identifying opportunities to improve the referral process. Efforts to optimize care coordination and the utilization of specialty physicians, diagnostic testing, and ancillary services could enhance healthcare quality and reduce cost in the future.

Author Affiliations: Medical University of South Carolina (AS, PM, JZ, JM, WM), Charleston, SC. 
Source of Funding: None.
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. 
Authorship Information: Concept and design (JM, PM, WM, AS, JZ); acquisition of data (JM, PM, WM, AS, JZ); analysis and interpretation of data (JM, PM, WM, AS, JZ); drafting of the manuscript (JM, PM, WM, AS, JZ); critical revision of the manuscript for important intellectual content (JM, PM, WM, AS, JZ); statistical analysis (JM, PM, WM, AS, JZ); and supervision (PM, WM). 
Address Correspondence to: Andrew Schreiner, MD, 135 Rutledge Ave, Ste 1240, Charleston, SC 29425. E-mail:

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