Using Technology to Do More with Less

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March 24, 2022

1 – 2 PM EST

Virtual Session


What’s the session all about?


Endless data is collected by public and private agencies that serve youth within the child welfare, behavioral health, and juvenile justice systems. These agencies vary in their capability to collect and use this data to inform decision-making and are often challenged in this effort due to staffing shortages, growing demands for data, or duplication of efforts. This webinar will highlight how some agencies are now utilizing the benefits of artificial intelligence and machine learning to operationalize historical data to predict outcomes and inform decision-making through FirstMatch®, a predictive analytics software tool that helps organizations collect, organize, manage, visualize, and analyze data for decision-making. Webinar attendees will have the opportunity to participate in a live Q/A session.

Speaker Information

Shawn Peck - Vice President, FirstMatch

Shawn Peck, Vice President

Adelphoi Innovative Solutions

Shawn oversees administration of all FirstMatch services. Shawn previously served as SPEP Project Manager at the Evidence-Based Prevention and Intervention Support (EPIS) Center at Penn State University, and was responsible for engaging probation departments across the state to apply the findings of the Standardized Program Evaluation Protocol (SPEP™), a scoring system used to estimate the impact of juvenile justice programs on reducing recidivism. Shawn holds a Master of Public Administration from Penn State University and a Bachelor of Science in Biblical Studies from East Coast Bible College.


Stacey Wellman, Implementation Specialist

FirstMatch / Adelphoi Innovative Solutions

Stacey oversees the planning, design, and implementation of business operations at FirstMatch. She brings 15 years of experience in juvenile justice as a juvenile probation officer with Franklin County and as a service provider with Abraxas Youth and Family Services. She holds certifications including Standardized Program Evaluation Protocol (SPEP™) Level 1 Specialist, Youth Level of Service (YLS) Master Trainer, Effective Practices in Community Supervision (EPICS) Coach, Motivational Interviewing (MI) Coach, Trauma-Informed Criminal Justice System Instructor, Aggression Replacement Training (ART®) Agency Trainer, and Girls Circle Facilitator.

Stacey holds a Master’s Degree in Administration of Justice from Shippensburg University and a Bachelor’s Degree in Criminal Justice from Lock Haven University.

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FirstMatch is a state of the art admissions software that uses predictive analytics to help decision makers select the right treatment program for a child, increasing likelihood of success.

FirstMatch was developed as a better way to recommend a treatment for a child in the child welfare, juvenile justice or behavioral health systems.  By using data to make treatment decisions, youth spend less time cycling in an out of programs and more time with their families, leading to reduced trauma, and better outcomes.

Right now, youth are matched with a treatment program based upon a combination of factors such as cost, space in the program, proximity to home, past experiences, relationships with referral source, macro level outcomes, contract status, and anecdotal information.

When a child’s clinical information is entered into FirstMatch, the tool uses a provider’s historical outcomes to recommend the best program for that particular child. Counties and providers use the data to make an informed choice.

Currently, 66% of youth in congregate placement have already been in one or more placements and 44% have been in two or more previous placements.  It’s not uncommon for youth to be in 10 or more prior residential placements, with the average being 4.4.  In addition, 88% of these youth have been in one or more previous in-home programs.

This problem is not unique to youth in residential placements. Right now, 64% of youth in in-home programs have been enrolled in at least on other in-home program.

Machine learning  is a type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.

FirstMatch use machine learning to comparespecificpredictive factors of a youth referral to the historical outcomes achieved by unique programs for other youth that presented with the same or similar predictive factors (and combinations of these various factors).  The program then uses this data to recommend the most appropriate treatment program for the youth.

The tool increases its effectiveness by using an agency’s unique outputs and results to continuouslylearn what to look for in terms of patterns and trends that are undetectable by human analysis.

Once a youth’s data is entered into FirstMatch, the tool makes a program recommendation for the child.  FirstMatch also produces a report that reveals the likelihood that a youth will achieve the desired outcome within a particular program, including recidivism, re-entry, remain-out-of-care and program completion rates, among others.

FirstMatch leads to improved outcomes for systems, organizations, and youth.  So far, we have seen a decrease in negative incidents, higher program completion rates, and cost savings from youth being placed in fewer programs.

Through comparative graphs, the dashboard displays the tool’s predictions to actual outcomes, as well as referral source trends and referral status. The dashboard can be customizable to meet an agency’s specific needs.

FirstMatch offers many benefits to organizations.  Agencies can have confidence that by using data to drive decisions, they are either accepting the right youth into their programs. When kids are in the right program for them, programs have less negative incidents associated with inappropriate placements, reduced staff turnover, and better outcomes for kids and families.  The FirstMatch tool also provides a dashboard that allows for real-time admissions referral management. 

Matching a youth to program based upon data means youth receive the help they need from one right program, rather than cycling through four or five wrong ones.  Less cycling means less trauma, better outcomes, and more success for kids.

By using FirstMatch, systems can make a more informed decision for a child’s treatment, significantly increasing the chance of a successful outcome.  This tool also provides another layer of quality assurance within agencies by helping to identify anychanges in programming that may lead to a decrease in desired outcomes.  FirstMatch also offers transparency with placing agencies in the acceptance and rejection of youth.

The amount of time necessary for implementation varies, depending on an organization’s capacity.  The average implementation takes about 4-6 months to build the tool and about another 8-12 months to train the tool.

FirstMatch is HIPAA compliant and the data is considered to be Protected Health Information (PHI).  Organization’s control access to their data through a cloud based server that they own.

Please visit the website at We’ll be happy to provide a demo upon request.  You can also contact us by calling 724-331-1767 or emailing