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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
A Practical Guide to Data Analysis in Health Centers
Maddie Bishop-Harris
/ Categories: Health IT Enabled QI, Accessing Data for QI, Foundation for HIT Enabled QI, Improving Performance, Communicating with Data, Validating Data Accuracy, Health IT & QI Workforce, Onboarding, Recruitment, Professional Development

A Practical Guide to Data Analysis in Health Centers

HITEQ Center, August 2025

This practical, introductory guide to data analysis features Ana, a nurse who is transitioning into a clinical informatics role by learning data analysis in a health center setting.

The guide: outlines foundational data concepts, including the three main categories of healthcare data: clinical, operational, and financial; details how data is collected, organized, and stored; provides an overview of various data analysis tools like Python, R, and SQL; explains how to clean and transform raw data; and concludes with examples of how data can be used for things like quality improvement and payment management.

Readers can download the three practice data sets provided to follow along with Ana throughout the guide.

Download the guide, along with three practice data sets, in the Documents to Download Section below.

Previous Article The Importance of Data Creators
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Tags: data specialistData ReportingData TeamData AnalyticsData Visualizationdatadata typesData AccessData Aggregationdata analysisdata dashboardsdata-driven decision makingData and storytellingdata literacydata privacyData vizPythonStructured Query Language (SQL)R StudioRData FormsData Structuredata cleaning

Documents to download

Related Resources

Acknowledgements

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