# Community-engaged Systems for Population Health Improvement: A Novel Approach to Improve Diabetes Outcomes in Rural Communities

## TL;DR

This paper introduces a community-driven approach to improve diabetes outcomes in rural areas by using local data and rapid prototyping.

## Contribution

The novel contribution is a decentralized, data-driven process for creating sustainable diabetes prevention and care solutions tailored to rural communities.

## Key findings

- Baseline data show that two-thirds of the population in both communities are at risk for prediabetes.
- Only 35% and 12% of those at risk have been screened in the two communities.
- Community coalitions focused on improving screening rates after analyzing baseline data.

## Abstract

Approaches to prevent and manage diabetes at a community population level are hindered because current strategies are not aligned with the structure and function of a community system. We describe a community-driven process based on local data and rapid prototyping as an alternative approach to create diabetes prevention and care management solutions appropriate for each community. We report on the process and provide baseline data for a 3-year case study initiative to improve diabetes outcomes in two rural Nebraska communities.

We developed an iterative design process based on the assumption that decentralized decision-making using local data feedback and monitoring will lead to the innovation of local sustainable solutions. Coalitions act as community innovation hubs and meet monthly to work through a facilitated design process. Six core diabetes measures will be tracked over the course of the project using the electronic health record from community clinics as a proxy for the entire community.

Baseline data indicate two-thirds of the population in both communities are at risk for prediabetes based on age and body mass index. However, only a fraction (35% and 12%) of those at risk have been screened. This information led both coalitions to focus on improving screening rates in their communities.

In order to move a complex system towards an optimal state (e.g., improved diabetes outcomes), stakeholders must have access to continuous feedback of accurate, pertinent information in order to make informed decisions. Conventional approaches of implementing evidence-based interventions do not facilitate this process.