Data Storyteller

Many health centers struggle to reap the benefits of technological advancement and investments in health information technology (health IT), while others embrace them and reap rewards. Interoperability is one such example; requiring health centers assess systems, relationships, and implementation.

There are keys to successful interoperability implementation for which health centers must develop processes, stand up infrastructure (within the system, internally and externally, and organization), and then take action.

Process refers to structured processes, policies, and procedures within the health center.

Infrastructure refers to structural capacity and ability within the health center’s technology and staffing structure.

Action refers to full implementation to the point of active and ongoing use and engagement.

This scorecard encourages health centers to consider their processes, infrastructure, and action in a number of key areas. Each area key to interoperability are to be self-graded on a scale of 1 through 5, where 1 is poorly or not yet developed and 5 is well developed. Health centers can also use this to guide discussions and monitor progress over time.

This brief discusses the importance of integrated data, and provides examples of how other health centers have integrated and utilized oral health, colon cancer screening data, and other data within primary care.

As more health centers seek to break down siloes that can fragment patient care, collaboration with or integration of behavioral health care has been strengthened, although data integration remains difficult and privacy remains paramount. This brief discusses some of the approaches, successes, and challenges in integrating behavioral health data within primary care services.

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.