Every person deserves to live a healthy life. Yet many individuals and families do not have access to the care they need due to social, economic, and environmental factors. Our vision is for IRIS to be part of collective community efforts to address systematic inequities in access to services.
Equity Driven Implementation
The need for a shared referral tool is often identified by community service providers who recognize that navigating systems of care is complex, particularly for people who have been disadvantaged by policies and systems. Through the IRIS implementation approach, community partners collectively envision the future they want and design a shared referral process to move towards that goal. Data collected in IRIS and analyzed by network partners serve as indicators of improvements on this path, while also identifying persistent gaps. Specifically, community-designed referral fields collected about each person referred can help community partners better understand existing inequities in access to services. This information is critical to designing solutions that begin to address barriers to care.
Designing an approach for using data to improve equitable access to services is part of implementation. Necessary discussions among network partners include:
- What is the shared purpose of our collaborative approach? Why does it matter to individuals and families?
- Individuals and families living in the same community have different experiences navigating systems of care. What voices are missing from our vision and how can they be heard? The National Implementation Research Network (NIRN) Stakeholder Engagement Guide can be used to help identify the strengths and gaps in representation among those at the table.
- Based on the experiences of individuals in our community, what inequities in access to services must be addressed to achieve our vision?
- What data must be collected in IRIS to ensure these inequities, and the impact of solutions, can be measured? What data collected outside of IRIS will be used?
- How will the data be analyzed and shared? How will those who are most impacted by network decisions be involved? How will the community collectively understand strengths and gaps?
Referral Data Collection
IRIS offers communities the agency to determine what demographic information they collect from each individual referred. Priority measures identified through implementation should be used to configure specific referral fields to gather this information at the time of each referral. Depending on the community vision and identified needs, data collected from clients to understand access to services might include race and ethnicity, gender, primary language, income, insurance status, etc.
Referral fields should be designed to standardize data collection in the priority areas. For example, if the community intends to understand access to services based on the primary language of individuals referred, a referral field should capture the specific languages that would be valuable to understanding access and designing meaningful solutions. Individual referral fields can be set as mandatory, meaning a response is required before the referral can be set, or left as optional.
Referral Data Analysis
Data analysis should focus on community vision and priorities, enabling informed decision-making within the network. Community-designed referral information fields are collected alongside standardized information about each referral (partners involved in the referral, length of the referral process, outcome, etc.). Disaggregating demographic data collected through referral information fields and stratifying that data with the information IRIS collects about the referral process allows IRIS networks to answer key questions about how people access services in the community. Specifically, analyzing referrals by organization/service type, outcome, or response type in the context of demographic information can help networks understand:
- Who is being referred to services within the network? Who is not?
- Who is accessing which services?
- How do referral response times differ by organizations and individuals referred?
- How do referral outcomes differ by organization and individuals referred?
Reviewing data in this way allows community partners to further understand access to services and examine the gaps between the intentions of network partners and impact.
Considerations for Impact
Collecting specific demographic data allows partners to understand existing inequities and measure the impact of community-designed solutions. However, communities should be mindful of responsible data use when including client demographics and other potentially sensitive information in referral information fields. Best practices include:
- Consider the impact of choosing mandatory fields versus fields where clients can choose which information is shared and stored in IRIS.
- Enable front line staff to feel comfortable asking for information from clients and obtaining active consent. Be transparent about how demographic information will be used and help staff share that messaging with clients during the referral process.
- Establish guidelines and clear processes for how the data will be used to examine and address inequities. Seek to engage those who are impacted by the inequities the network aims to improve through participation in both the analysis of data and solution design.
- Commit to transparency with IRIS partners, clients, and the community. What was learned through available data? What changes have been made?
Using this approach, IRIS networks can thoughtfully leverage referral data to make meaningful changes to improve access to services.
Further Reading
Resources the IRIS team has found helpful on this topic include:
- Equitable Implementation at Work from the Stanford Social Innovation Review
- Investing in Data Capacity for Community Change from the Urban Institute
- Racial Equity Data Road Map from the Massachusetts Department of Public Health
- Stakeholder Engagement Guide from the National Implementation Research Network
Download: Using IRIS Data to Improve Equitable Access to Services
