Data Storyteller

Having rapid access to data in health centers is critical to managing clinics, using operational data to improve care, and reporting quality measures. Quality, accessible data in health care can do more than feature in reports and accountability systems: effective communication of information can improve quality of care, alignment to evidence-based care principles, and data-informed decision making. Charts, graphs, maps, and other data visualizations play a key role in making information accessible. Visualizations make the patterns in large datasets rapidly apparent, showing when numbers are going up or down, performance compared to goals, comparison between patient groups, and more. The purpose of this resource is to provide a comprehensive guide to user-centered dashboard design for health center staff.

As healthcare becomes more data driven, health centers are aware that they need to build their “organizational muscle” for data analytics, but often may not know where to start. In this webinar the HITEQ Center introduced the Analytics Capability Assessment (ACA), a tool developed by the Center for Care Innovations that allows users to look critically at an organization’s analytics capability across three key domains: people, process, and technology. By determining their level of capability in each of these factors, organizations can develop an individualized roadmap to focus and prioritize capability-building efforts. The tool also helps to demystify some of the jargon that can often alienate non-technical staff. Methods, examples, and tips for deploying the ACA in health center organizations were also shared.

Risk stratification enables providers to identify the right level of care and services for distinct subgroups of patients. It is the process of assigning a risk status to a patient and then using this information to direct care and improve overall health outcomes. NACHC's Action Guide lays out 4 steps to get you started with risk stratification as well as key related concepts and considerations. 

HITEQ conducted an anonymous survey of population health analytic and data integration system needs and impressions among PCA/HCCNs in late 2016 and early 2017. The results of that survey, intended to help those looking to adopt similar systems, are laid out within. This includes ratings of key functionalities, discussion of most important features, and comments from those who have assessed and/ or implemented these tools.

Using predictive analytics in health care is an emerging field, especially for health centers. This tool will provide a brief explanation of the purpose of predictive analytics, the ingredients necessary to apply these methods, and ways that health centers are using this approach to improve results. The objective of this resource is to help health center leadership and staff understand how and when predictive analytics can help them, and to think about how predictive analytics might fit into their data-driven QI program.