Data analytics has become increasingly important in public health and health care, as it is used to identify treatments and processes that improve cost effectiveness and quality of care. These analyses, however, require the right tools and training. The complexity and volume of data, the need to share and coordinate between organizations, and the need for predictive analysis requires more advanced software and training. Unfortunately, this demand for resources poses a high barrier to not-for-profit and community-based health organizations (CBOs) operating in underserved communities and serving as the public health safetynet. Through a community-academic partnership, we identified the analytic professional development needs of community health analysts and effective strategies for delivering analytic training.

Partnership: The St. Louis Integrated Health Network (IHN) collaborates with community health centers, hospital systems, academic medical schools, public health departments and other safety net institutions to increase access to high-quality, affordable healthcare for all residents of Metropolitan St. Louis. IHN partnered with a local university’s department of health policy and data analytics to develop the Community Analytics Academy (CAA), a training collaborative involving public health, safetynet health care, and university partners, and designed to meet the need for data-informed decision-making among CBOs.

Statistical software such as SPSS, SAS, and Stata are often too costly for many non-profits to purchase, and Microsoft Excel doesn’t extend to the capabilities needed. The CAA chose to focus on R, a free open-source programming language and statistical computing environment with robust community support and supplemental software modules.

Results: Four weekly 3-hour in-person interactive sessions were offered. Participants (n=12) were data managers or data analysts working at CBOs including Federally Qualified Community Health Centers and local health departments.

Future recommendations include:

1. Offer an introductory R workshop annually with additional special topics workshops, online or in-person booster training, and trouble shooting.
2. Create coalition of CBO data managers and academic analytics programs to develop, maintain, and lead ongoing trainings.
3. Create training manuals based on CAA experiences, to disseminate innovation to and test replication by other public health and health care CBOs.