Childhood Obesity Preventer

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.

Health Catalyst published the inaugural version of the Healthcare Analytics Adoption Model in 2012, a proposed framework to measure the adoption and meaningful use of data warehouses and analytics in healthcare in ways similar to the well-known HIMSS Analytics EMRAM model. A second version of the Health Analytics Adoption Model was released in 2013.

This can be a helpful framework for understanding what is infrastructure and processes are needed to use data successfully to manage the health of your population and engage in alternative payment arrangements that continue to gain traction. 

This paper is designed to help you with your PHM analytics buying decision.  It outlines general criteria to help you assess a clinical analytics vendor; discusses the technology and change management an effective analytics solution should support; and introduces a Healthcare Analytics Adoption Model that will help you analyze vendors and evaluate your own plans for analytics adoption.

Data visualizations in many forms can be incredibly valuable in helping health center staff and leadership move from a passive understanding of the data to active support of health IT enabled, data driven quality improvement approaches. While introducing visualizations can create immediate value and understanding, ensuring that their full value is realized requires that stakeholders be fully engaged and understand how visualizations (such as dashboards) can support decision making.

The Center for Care Innovations (CCI) developed this tool to address a potential gap around defining and assessing analytics capability in health centers, as well as to provide education on some of the complexity and nuance of working with data and building a data-driven culture.

Healthcare organizations are flooding with data. Health centers have a wealth of data about their patients and their community. It is essential that these organizations  build a strong foundation of people, processes and technology to leverage that data to improve care and better serve the underserved.

Overvew: This case study looks at data validation and its role in implementing new analytics systems, such as Azara DRVS.

Creator: Health Care for the Homeless, Inc., courtesy of Chuck Amos, Director of Performance Improvement and HITEQ Advisory Committee Member