Metric-Driven Referral Management
Several interesting and puzzling headlines have adorned the recent press related to consumer behavior when it comes to transparency and referral-based decision-making. On the one hand, insurance premiums and patients’ out-of-pocket healthcare costs appear to be on the rise1 , which would imply that consumers are more likely to seek out price transparency in order to get the best deals for their healthcare needs. And yet patients do not seem to act rationally when presented with opportunities to shop based on price2 , on average driving past 6 lower priced providers on their way to the provider that was recommended by their physician, with less than 1% of study subjects even utilizing available price transparency tools. The conclusion of the latter study is that the “influence of referring physicians is dramatically greater than the effect of patient cost-sharing.”
What if the same influence and consumer behavioral paradox extends to referrals and appointment lag times? How many patients shop for alternative providers after their PCP gives them the name of a recommended provider, even when it turns out, after the fact, that the recommended provider is not available for several weeks? In addition to the clinical risk and patient inconvenience of delayed care, the likelihood that the patient churns as a “no-show” has been shown to increase dramatically in proportion to the appointment lead time 3,4. To mitigate those risks, health systems can utilize the 6 essential Patient Access Metrics described in our recent white paper to assess optimization opportunities across their referral networks, and operationalize data-driven referral programs that put teeth behind those strategies.
Transparency Into Provider Network Composition and Available Appointment Capacity
If a health system assumes that patients will accept the recommendations made by their physicians, it is imperative that the referring provider offices are armed with tools to make optimal referral and appointment decisions based on the patient’s clinical needs and preferences. Research has shown that 72% of providers consistently refer to the same people in a given specialty, regardless of patient-relevant factors such as appointment availability, geographic proximity, insurance acceptance, diagnosis- or procedure- specific expertise5. Perhaps this is due to the fact that nearly half of referring providers state that they lack information about who is in their referral network, and more specifically, the clinical expertise, appointment availability, insurance acceptance rules, and practice locations of those colleagues. Programs that make this information accessible to office staff at the point-of-referral, combined with the ability to book appointments in real-time, can ensure that patients leave the office with a referral and appointment in hand to mitigate their risk of delayed care, no-shows, or outmigration.
Many health systems are also using provider network composition data combined with granular data on patient demand to identify where there may be gaps in their referral networks. For instance, how many requests for seizure-related services did we see through our call center and our website in the last month, and from which zip codes? And did we have sufficient provider supply in those areas to serve that demand? Network development teams can use those insights to inform recruiting, partnership, and acquisition opportunities.
Tracking Patient Demand Conversion and Referral Outmigration Rates
Transparency into patient demand conversion and referral outmigration rates at the individual practice level can help to identify hot spots for workflow and patient experience optimization. Many health systems are moving towards models that leverage central “mission control” dashboards and dedicated coordination teams for handling inbound referrals and appointment requests, regardless of source (e.g. in-network provider, out-of-network provider, consumer self-service, phone-based, etc.), such that they can gain insight into the full funnel of referral activity transpiring across their network. This enables organizations to gain visibility into referrals that may have historically fallen into the proverbial black hole, therefore resulting in non-conversion, or analyze process bottlenecks that could be offloaded from practices to ensure higher appointment conversion and referral retention rates.
These referral transparency efforts and process optimizations help mitigate the aforementioned delays and appointment lag time challenges that patients may be burdened with when they are left to their own devices to try and obtain a specialist appointment. When combined with the provider network composition analytics also mentioned above, organizations can assess whether instances of referral outmigration are warranted due to supply-side constraints, or whether referring practices might benefit from better insights into available in-network options.
Consumers may be slow to adopt self-service cost transparency and appointment availability search tools to verify or second-guess their physicians’ recommendations. In the meantime, why not put those tools in the hands of the referring providers themselves to ensure that they made solid recommendations to their patients to begin with, and utilize key metrics to measure impact and opportunity? In the long run, a holistic approach to referral management should ensure that the same level of visibility into the provider network and appointment inventory can be made available to all users across all channels (e.g. point-of-service, online, and phone-based), so that all parties involved in care decisions are armed with consistent and comprehensive information.
Julie Yoo is the co-founder and chief strategy officer of provider search and scheduling solutions company Kyruus.
1 https://www.healthaffairs.org/doi/abs/10.1377/hlthaff.2018.0188
2 http://www.nber.org/papers/w24869
3 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4370946/
4 https://www.ncbi.nlm.nih.gov/pubmed/28067678
5 Kyruus, 2018 Referral Trends Report.