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Overview

Validating data from Health IT systems is the cornerstone of effective Health IT Enabled QI. Ensuring that Health IT-generated reports and data reflect an accurate picture of the care and outcomes of your population ensures that data is actionable for quality improvement, monitoring as well as many other purposes. This validation must be ongoing as system , provider, workflow, and other changes, can all impact accuracy of data. This section provides worksheets, guides, and tips for validating data.

Data Accuracy Resources
Azara DRVS Implementation Team Finds Enthusiastic Data Validators in Client Health Care for the Homeless

Azara DRVS Implementation Team Finds Enthusiastic Data Validators in Client Health Care for the Homeless

A Case Study from Health Care for the Homeless

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

In brief, Health Care for the Homeless (HCH), a Baltimore-based Federally Qualified Health Center, “went to extra lengths to ensure its Azara DRVS implementation included more than the usual amount of data validation, an examination of its data collection processes, a spirit of collaboration among a diverse staff and a willingness to accept an overarching truth: the effort to validate data properly will expose mistakes within the data, process deficiencies and other unforeseen challenges. But the reward for tackling these issues head-on is a health center with powerful, truly actionable data.”

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Acknowledgements

This resource collection was compiled by the HITEQ Center staff with guidance from HITEQ Advisory Committee members and collaborators of the HITEQ Center.

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Quadruple Aim

A Conceptual Framework

Improving the U.S. health care system requires four aims: improving the experience of care, improving the health of populations, reducing per capita costs and improving care team well-being. HITEQ Center resources seek to provide content and direction aligned with the goals of the Quadruple Aim

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