Referral data metrics: what they are, what exists, and why they are important

Navigation systems are increasingly utilizing data to improve their practices. From the microcosmic measurement of client demand in each zip code to the macrocosmic measurement of whether navigation systems improve the environment around them: data can drive the growth, improvement, and success of care. Still, navigation systems are far from fully utilizing the potential of the data all around them. This blog will demonstrate the critical role of data and data metrics in three network designs—demanded directories, healthcare developers, and system integrators.

Demanded directories, healthcare developers, and system integrators collect and analyze data as part of their network functionalities. This data collection can significantly improve client outcomes when directing learning communities, training, strategic plans, and budgetary analyses. In other words, the data collected by these networks can serve as valuable information that can guide the improvement of the client-to-care process.

Each of these three network designs collects different types of data, and each type of data is uniquely useful. In her publication “Mapping the Navigation Systems of Pennsylvania: Opportunities for The Future,” NNSI director Michelle Shumate identifies five types of data collected by these networks and the best emerging practices to utilize these data within the larger referral landscape. The five specified types of data are quality control metrics, demand metrics, supply metrics, referral metrics, and system impact.


Metric Network Type Definition Example
Quality Control Metrics Demanded directories Measurement of the quality of interaction between the client and the navigator Number of calls, types of questions asked, whether appropriate referrals were made
Demand Metrics Demanded directories, healthcare developers, system integrators Measurement of whether resources available are sufficient to match client demand Number of clients that have requested services
Supply Metrics Demanded directories, healthcare developers, system integrators Measurement of the availability and capacity of providers to offer services in an area Number of organizations providing services, information of the outcome of services
Referral Metrics Healthcare developers, system integrators Measurement of the overall quality of a service episode through accuracy, efficiency, and service-episode outcome Number of rejections a service episode receives before a client receives services, time client waits between a request for help and subsequent steps, whether the client receives services
System Impact Metrics All navigation types Measurement of whether navigation systems result in better outcomes than not having navigation systems Tracking level of need in particular zip-codes, tracking emergency department usage, missed visits, Medicare and Medicaid costs, patient self-reported health outcomes before and after navigation, homelessness rates 
  1. Quality Control Metrics

Answers the question: Are navigators appropriately and effectively conducting their role in the help-seeking process? 

Comparatively, demanded directories collect and utilize quality control metrics most frequently. Demanded directories do this by recording and reviewing call center calls, which allows for interactions between navigators and clients to be analyzed. Metrics such as the number of calls, the types of questions, and the success of the referral allow demanded directories to evaluate navigators on a large scale and not be replaced by different system metrics. The quality of the interactions between navigators and clients is critical in the help-seeking process, so the accessibility of quality control metrics plays a key role in improving the help-seeking process. 

  1. Demand Metrics

Answers the question: What are the demands and needs of clients, and are there sufficient enough resources to fulfill those demands and needs?

 Demanded directories, healthcare developers, and system integrators all collect demand metrics. Demand metrics refer to the number of clients requesting services. This metric is distinguished by zip code, client demographics, and client eligibility for benefits. This type of metric allows communities to determine whether they have the resources to match client needs. Additionally, they can indicate abnormalities, such as frequent unmet needs in areas with ample services, which can signify a problem at the provider level. The referral platform can then adequately address these provider problems.

  1. Supply Metrics

Answers the question: Is the availability and capacity of service providers capable of meeting the needs of clients?

Supply metrics are the mirror image of demand metrics. Supply metrics refer to the ability and the capacity of providers to offer services in an area. Demanded directories and healthcare developers operate under an open network to identify all service providers in the area. This keeps track of all organizations providing services at the time of the most recent update of their respective resource directories. Alternatively, system integrators provide real-time provider capacity data for fewer organizations. This is due to system integrators’ smaller network size. System integrators attain complete information about referral outcomes through tracking referrals through closed-loop systems. This information involves whether the provider accepted the referral and the time taken to receive care. These metrics help system designers and human navigators to redistribute and reallocate referrals across the system.

  1. Referral Metrics

Answers the question: How effective, from start to finish, was the service provided in terms of accuracy, efficiency, and outcome? 

Due to their closed-loop referral structure, healthcare developers and system integrators can gather referral metrics. This metric tracks the status of different types of referrals. In the chain of events of the help-seeking process, referral metrics describe the referral’s accuracy, efficacy, and service-episode outcome. Accuracy refers to the number of rejections a service before a client receives services. This reflects the quality of the referral. Efficiency refers to the amount of time a client waits between requesting services and the remainder of the help-seeking process. Service type can dictate efficiency because some services take longer to initiate and complete. Service-episode outcome refers to whether a client receives services as opposed to some other alternative. This information can be especially valuable when combined with demand metrics because it demonstrates which clients are more likely to persist and receive services in referral networks.

  1. System Impact

Answers the question: Do navigation systems actually result in better outcomes than not having navigation systems?

System impact refers to the more macro metric of tracking whether navigation systems result in better outcomes than not having navigation systems. The academic research on this metric is sparse, and time and resources must be allocated to broadening this metrics’ visibility. Understanding this metric could be the key to unlocking even more efficient and beneficial care to those seeking help. Some examples of current efforts to quantify the success of navigation systems include healthcare developers’ tracking of emergency department usage, missed visits, and Medicare and Medicaid costs to determine the cost of care and healthcare use; providers tracking client process against client self-identified goals through patient surveys showing client success rate and client satisfaction; tracking whether individuals using navigation systems experience the same frequency of Child Protective Services involvement or homelessness than those who do not receive services; and tracking the level of need in a particular zip code to determine if referrals improve community-level health. 

Overall, the five metrics noted above constantly educate the practices of referral landscapes and act as helpful indicators of what is happening, what is not happening, and what needs to be done in the future to optimize the success of such landscapes.