How can you improve data quality in EHR analysis?

1 Define data quality goals

Before you start analyzing EHR data, you need to define your data quality goals and criteria. Data quality goals are the standards and expectations that you set for your data, based on your analysis objectives and requirements. Data quality criteria are the measurable attributes that you use to assess and monitor your data quality, such as accuracy, completeness, consistency, timeliness, and validity. By defining your data quality goals and criteria, you can identify the data sources, variables, and indicators that are relevant and reliable for your analysis, and avoid unnecessary or irrelevant data.

2 Clean and validate data

Once you have defined your data quality goals and criteria, you need to clean and validate your data. Cleaning data involves identifying and correcting errors, inconsistencies, outliers, and missing values in your data. Validating data involves verifying and confirming that your data meets your data quality criteria and standards. You can use various methods and tools to clean and validate your data, such as data profiling, data cleansing, data transformation, data auditing, and data quality rules. For example, you can use data profiling to examine the structure, content, and quality of your data, and identify any anomalies or issues. You can use data cleansing to remove or replace any incorrect, incomplete, or duplicate data. You can use data transformation to convert or normalize your data into a consistent and compatible format. You can use data auditing to check and monitor the quality of your data throughout the analysis process. You can use data quality rules to define and enforce the rules and constraints that your data must follow, such as data types, formats, ranges, and relationships.

3 Integrate and standardize data

Another important step to improve data quality in EHR analysis is to integrate and standardize your data. Integrating data involves combining and consolidating data from different sources, systems, and formats into a unified and coherent dataset. Standardizing data involves applying and adhering to common and consistent definitions, terminologies, codes, and classifications for your data. By integrating and standardizing your data, you can enhance the completeness, consistency, and comparability of your data, and reduce the ambiguity and variability of your data. You can use various methods and tools to integrate and standardize your data, such as data mapping, data matching, data merging, data linkage, and data harmonization. For example, you can use data mapping to identify and align the data elements and attributes from different sources that correspond to each other. You can use data matching to find and link the records that refer to the same entity or concept, such as patients, providers, or diagnoses. You can use data merging to combine and reconcile the matched records into a single record. You can use data linkage to establish and maintain the relationships and associations between different records and datasets. You can use data harmonization to apply and adopt common and consistent standards and vocabularies for your data, such as SNOMED CT, ICD-10, LOINC, or HL7.

4 Document and communicate data

The final step to improve data quality in EHR analysis is to document and communicate your data. Documenting data involves creating and maintaining a clear and comprehensive record of your data sources, methods, processes, results, and quality issues. Communicating data involves sharing and disseminating your data and analysis findings with your stakeholders, users, and audiences, in an accurate, transparent, and understandable manner. By documenting and communicating your data, you can increase the trustworthiness, credibility, and usability of your data, and facilitate the feedback, collaboration, and learning from your data. You can use various methods and tools to document and communicate your data, such as data dictionaries, metadata, data quality reports, data visualization, and data storytelling. For example, you can use data dictionaries to describe the meaning, structure, and characteristics of your data elements and attributes. You can use metadata to provide additional information about your data sources, methods, processes, and quality issues. You can use data quality reports to summarize and highlight the status, performance, and improvement of your data quality. You can use data visualization to present and explore your data and analysis findings in a graphical and interactive way. You can use data storytelling to explain and interpret your data and analysis findings in a narrative and engaging way.