How to Put Some Science Behind the Art of Physician Referrals

Medicine is both an art and a science. Some physicians would contend that science—specifically data science—has overtaken medicine and limited their ability to make decisions based on informed intuition and personal experience.

Medicine is both an art and a science. Some physicians would contend that science—specifically data science—has overtaken medicine and limited their ability to make decisions based on informed intuition and personal experience.

One area, however, that still seems to be firmly rooted in the art of medicine is physician referrals. Patients are referred by their primary care physicians to a specialist, or from one specialist to another, based on several factors: often relationships, subjective reviews, or the reputation of the referral physician, but not necessarily based on outcomes data of the physician’s care quality, costs, and availability.

This tendency to refer based on the referring physician’s experience or relationship instead of data is exemplified in quotes from a recent qualitative study where researchers interviewed physicians about their referral patterns. A cardiac surgeon observed “90% or 80% (of my incoming referrals) [are] from a cardiologist who’s seen me in a meeting, or heard of me, or something like that.” Another surgeon remarked, “I think I’ve built it [my reputation] based on patients I’ve operated on, who’ve gone back to their cardiologists or PCPs and have said nice things about me.”

While there is nothing inherently wrong with referring patients to physicians based on an impressive conference presentation, feedback from patients, or reputation in the community, due diligence should still be applied. The greater availability of data science tools means this investigation into the experience of a referral physician and their patient outcomes can be conducted quickly. Such due diligence does not replace the physician’s judgment based on their experience and assessment of the patient, but rather adds a layer of certainty to an important clinical decision.

The Risks of Traditional Referral Patterns

Although more than 39% of healthcare payments are still fee-for-service, the remaining portion across commercial, Traditional Medicare, Medicare Advantage, and Medicaid payers is tied in some way to provider cost, quality, or both. Nearly two-thirds of hospitals plan to enter or expand their value-based payment program participation, such as through a bundled payment arrangement or accountable care organization (ACO) structure.

Relying on traditional physician referral patterns means that both physicians and hospitals could be taking a greater financial risk with the value-based portion of their compensation. Under some bundled payment arrangements, such as the Medicare Shared Savings Plan, the ACO is responsible for all costs attributed to their enrolled patients. If they refer a patient to a specialist with historically higher costs and poorer outcomes, they risk increasing costs for that patient, which affect their end-of-year expenditures. Worse yet, they risk the patient experiencing a poor outcome.

By investigating the referral physician’s performance, which should include an analysis of a broad set of cost, quality, outcome, and other data, providers can make a more fully informed decision and build a reliable network of high-quality referrals. Building a network should not fully disregard the referring provider’s experience or the patients’ experience with that referral physician, but data analysis offers a perspective that enables an optimal outcome with lower costs.

Maintaining Network Integrity

Other types of networks also need to be considered in the referral decision; namely, health system and health plan preferred networks. With the rise of value-based care, controlling network leakage—when patients seek care outside of the health system, ACO, or integrated network—has become a new organizational priority. A recent report shows 96% of health system executives say they are trying to stem patient leakage, but less than one-third of executives who have a plan to retain more patients believe they have the right tools to achieve that goal.

This emphasis on maintaining network integrity is understandable given the average out-of-network referral equates to $5,000 in lost downstream revenue. If a primary care physician refers only ten patients per month outside of the preferred network, the health system risks losing $600,000 per year—and that is just one referring physician.

The patient also financially suffers if they are referred outside of their health plan’s network. They pay higher out-of-pocket rates, which is likely to decrease their satisfaction and increase the risk of a missed appointment. Medicare beneficiaries, for example, can pay between 118% to over 1000% more for care from out-of-network specialists.

Although physicians are, of course, permitted to refer to any physician they choose based on their assessments of the patient’s needs and goals, staying within a preferred network—be it a health system, ACO, or health plan—should be considered.

Data-Driven Referral Decisions

Other than network affiliation, numerous data sources are available to guide referral decisions. Claims data is the primary indicator of cost. A health system or ACO, however, may only have claims data for their physicians, which means it may be necessary to partner with a third party to fill gaps in commercial claims. A data partner can also supply years of quality data for a referral network, based on Healthcare Effectiveness Data and Information Set (HEDIS) and other measures, which are crucial for gauging the historic performance of the referral.

Medical history and social determinants of health (SDoH) factors, such as the primary language of the patient and their access to transportation, should also weigh into the decision. Likewise, proximity to the patient’s home location is a crucial consideration that can greatly improve the likelihood of the patient following through with an appointment.

Population health management tools exist to analyze these data points and more to deliver a numerical score for each physician in the referral network based on the unique needs and circumstances of the patient.

Closing the Loop

To further reduce the burden on the physician, there are other tools involved in the referral, such as preauthorization, scheduling, and patient records-sharing. A recent report shows fewer than 35% of specialists report receiving the patient’s medical history from the referring primary care physician, which can be forwarded automatically at referral acceptance.

Communication with the patient is also crucial to ensure that the referral appointment is completed. Phone calls, text messages, and mailers that remind patients of the appointment can help close the referral loop. Automated confirmation of appointment completion from the referral physician’s office is also a crucial step to enable a positive outcome.

Utilization of such data-driven due diligence and automated tools, combined with the physician’s judgment and experience, can result in more successful and comprehensive referrals and outcomes. As such, health systems, ACOs, and other integrated organizations that want to strengthen network integrity and improve value-based revenue performance should evaluate their current referral process and consider incorporating a data-driven strategy to optimize their results.

Matt Cheatham is Lightbeam’s Referral Management Operations Director.