Referral Management in Children’s Hospitals: Using Data to Drive Change

Posted By: Katharine McQueen Industry,

The Patient Access Collaborative dives deeper into referral management for children’s hospitals, examining the data surrounding a key performance indicator of improving access.  The referral conversion rate is calculated as “the proportion as expressed in percent, of inbound internal and external referrals that are converted to scheduled appointments.” Essentially, when a patient is referred to a specialist, excluding lab and imaging, the referral conversion rate is the calculation that determines how many of those referrals evolve into a booked appointment. 

The Department of Pediatrics at the University of Utah sits as a top-five performer for patient access in children’s hospitals, as measured by the referral conversion rate. The Patient Access Collaborative had the pleasure of speaking with Lorie Lepley, Manager of Referral Management, and David Meyers, Director of Ambulatory Operations, both with Pediatrics Subspecialty Care at the University of Utah Health, as they discussed the importance of using data to help drive change in referral management. 

Looking at the science to measure success, Meyers described how they best utilize the referral conversion rate to increase access. Meyers emphasized, “focusing on the metric to make improvements has been quite a journey… and one that is very important to [us].” In 2015, the team began tracking referrals and placing a higher rate of importance on following the life cycle of referrals, to provide a more solid picture of where improvements in access could be implemented.   

Meyers, who oversees ambulatory operations, is intent on keeping the total percentage of converted referrals above 60%. Not only does he focus on the referral getting scheduled, but he and his team hone in on the “appointment completed” status.  He noted, “when percentages indicate referrals are 60% or below, the team examines the cause and what the life cycle of the referral looks like to increase outcomes.” Although 74% of all referrals disposition into a scheduled appointment, with a turnaround time of eight days, access can get pushed out, as specialties can take longer. The delays contribute to lower percentages of appointments completed. 

Lepley, who is responsible for managing the team, explained, “the process for managing large new patient volume and referrals requires a great deal of staffing and screening, so patients can be seen in a timelier manner.” Lepley identified four protocols to best manage referrals. These include: (1) ensuring the records are promptly received after appointments are scheduled to provide a smooth transition for the referring providers, (2) scheduling monthly meetings to review the referrals and records, (3) measuring the data to initiate necessary changes and (4) providing multiple pathways for receiving referrals, for example, fax, email, multiple EMRs, website appointment requests, and over the phone.  

Meyers discussed the team recognized changes were necessary to improve scheduling and implemented a more expeditious lag time of 21 days from the previous 47-day window. He stated, “the 21-day timeframe is a median access lag time used to measure when a family is contacted by phone to schedule an appointment, to the date they are seen. The data is presented to the divisions so they can see where improvements can be made.” Monitoring and measuring the data allows the department to recommend adjustments to the availability to appointments where access is challenged. Meyers emphasized the overarching goal is 85% patients scheduled, with a lag time of 21 days or less.   

The data highlights two areas where improvements can be made: time to “hold” and time to “accept.” Time to “hold,” measures how long a referral is delayed and considers the time necessary for insurance to review and approve the referral. Time to “accept,” measures the timeframe to make a first contact. The objective is to keep the lag time from “hold” status to “accept” status at a minimum. To achieve the minimum lag time, Lepley suggested, “the team aims for two contact attempts, phone call and text, within five business days.” Each division is equipped with referral timelines to assess the information and initiate optimal appointment outcomes. Every referral should include: (a.) time to accepted, the average time to secure a first contact (b.) how much time the referral spends in a hold status (c.) how much time it took to get to scheduled. Lepley tracks referrals that have aged at least six months to identify opportunities to improve. 

Challenges include managing timeframes for specialty divisions, like cardiology, with limited access and availability, and some referrals are re directed to subset specialties or different clinics. To combat obstacles, “the team must maintain consistent communication to ensure the referral status in each file is up to date for staff members, to expedite scheduling and book appointments,” Lepley explained. When a referral is not met, she concluded, “they prefer to close the loop by sending an outcome notification to the provider.” Currently, 26% of patient referrals fall into the closed loop category.   

The team at University of Utah is cultivating the extraordinary, by weaving together protocols, people, and technology to create exceptional referral management workflow. Next steps are leveraging e-consults and streamlining pathways for providers to submit records for expedited review. 

Lessons Learned: 

  1. Reviewing every referral slowed the number of patients that could be seen and highlighted where limited access made sense.
  2. A separate, distinct team was needed to manage referrals.
  3. Forced pathways to singular referral forms or platforms were not satisfactory to referral sources.
  4. Referral team cannot overcommit on contact attempts.