Feedback Research Institute (FRI)

The Feedback Research Institute (FRI), includes the Practice to Research Acceleration Center (PRAC), Social Impact Artificial Intelligence Lab (SIAIL) Platform and the Research and Evaluation Center for Advancing Practice (RECAP), is mission-driven to revolutionize human services through a data-driven measurement feedback system that will disrupt five decades of stagnation and accelerate the development of service excellence. FRI has a giveaway business model for under-resourced non-profit service providers. We have controlled our software costs by partnering with major software companies’ social good programs, which have agreed to supply software, that is used extensively in the corporate world, to us at no cost with use with non-profits. FRI has been operating as a group of volunteers for over a year, but this is not a sustainable model.  We require financial support to further our mission.  First, we will financially support the team of volunteers in their efforts.  Second, we are developing a new software AI powered platform that will require outside technical support.  However, until we generate income from our sales to well-resourced non-profits, government agencies and for-profit service providers, we will need start-up funds to continue work with the under-resourced community services.

About me

My colleagues recently honored me with a Festschrift journal issue celebrating my contributions to mental health (Google “Bickman Festschrift”). They identified three themes: Improving Precision and Use of Service Data to Guide Policy and Practice; Implementation and Dissemination and Preparing for Innovation: Design, context, and policy.  Two articles present more details: One that described me as “an Innovator and Disruptor” in its title and a second that, I authored, described my 50‑year journey from a basic science researcher to an applied researcher. The journey continues with my moving from an applied researcher to leading an innovative organization focused on the direct application of digital technology to improve human services.

Our organization

We grew from just two individuals in one year to now 10 by members by attracting new mission driven members. We are diverse in professional backgrounds, training, and demographics but joined by a common goal and passion for social good.

Our goal 

To revolutionize human services by providing and supporting the implementation of existing software and a FRI designed artificial intelligent measurement and feedback platform, designed to improve the precision, efficiency, and effectiveness of human services, especially at no cost for under-resourced community service providers.

The Problem

Human services cost billions of dollars and include mental health and welfare services. Millions of clients are served, but there are persistent problems with access, productivity, and improving client outcomes.  Previous reforms (e.g., evidence-based treatments) suffer from lack of widespread adoption, low engagement, and diminished effectiveness in the real-world settings.  Moreover, the problems of health equity endure in poor communities. 

Addressing the problem

We will first introduce off-the shelf digital technology that that are in widespread use in the business sector to ameliorate many of these problems. This will improve efficiencies, business process fidelity and advance commitment to the use technology. Simultaneously, we will develop an AI powered platform that will use a common data model to advance the digital technology.  It will include a precision treatment delivery measurement-feedback system to optimize client outcomes. 

Our Organization 

 We are a hybrid 501(c)(3) (Feedback Research Institute) with a corresponding Benefit Corporation (Precision Human Services). We have three major components of our organization that are detailed in our web site.  Our external advisory board is chaired by Professor Michael Lindsey Director of NYU’s McSilver Institute for Poverty Policy and Research.

Human services deal with some of the most complex problems facing society including mental illness, addiction, and child welfare. There is no other sector that faces greater societal demands but with such limited funding, technology, and personnel resources. The effectiveness of even the best-in-class evidence-based practices did not achieve their potential effects because they required modern technology that is now available to us.  Previous innovations lacked the technology to enhance treatment in two ways: (1) they were unable to personalize or provide precision client-based treatments and (2) they did not have the technology for treatment self-improvement through AI.

Our unique solution is to introduce a measurement feedback system (MFS) with digital process mining and AI for human services. Our MFS offers objective, fact-based insights, data contemporaneous with treatment, that help organizations audit, analyze, and improve their existing business processes by answering both compliance-related and performance-related questions. This technology is an essential part of enabling users to measure their efficiency and effectiveness by fully understanding how their processes operate and measure their achievements before initiating change. They will then have the capacity to invest in innovative technology to achieve service excellence.

AI-based applications hold great promise to improve services. Our team with over 300 years of combined work in human services, digital technology, and data science, recognizes the importance of supportive implementation. Thus, we have developed an approach that focuses on bringing together the relevant parties in implementing AI-enhanced digital service. FRI has the capacity to deliver software applications that can optimize services in two steps.   

Step 1: We will use an existing measurement feedback system with off the shelf digital process mining and simple AI. We focus on keeping the introduction to technology simple and frictionless.  This approach will develop the provider’s data capacity and desire to rely on objective, fact-based insights that have helped them audit, analyze, and improve existing processes.  

Step 2: We will add predictive analytics for client outcome optimization to personalize treatment and add AI driven predictive analytics to tailor feedback to client and clinicians’ needs. This application will be able to improve with continuous learning.  

At this point in our development we cannot prove we are having an impact but we are utilizing software that have demonstrated effectiveness in the business world and an AI approach that has shown impact in diverse health care fields.