The Critical Role of Data Mapping in Integration Projects

Data mapping is a crucial aspect of successful healthcare integration projects. Data mapping refers to matching data elements between two or more healthcare systems to ensure seamless data exchange. With the increase in healthcare data, effective data mapping has become more critical than ever. There are multiple reasons:

• Healthcare data is becoming more complex
  

With the growing adoption of electronic health records (EHRs), there has been a significant increase in the volume and complexity of healthcare data. This data is often scattered across multiple systems and ensuring that it is accurate, complete and consistent can be challenging.

• The need for interoperability
  

Interoperability is becoming increasingly important as healthcare organizations need to be able to share patient data seamlessly between different systems to provide better patient care. Effective data mapping is critical to achieving interoperability between different healthcare IT systems.

• Compliance with regulations
  

Healthcare data is subject to several regulations, including HIPAA and GDPR. Compliance with these regulations requires healthcare organizations to ensure that patient data is protected and managed securely. Effective data mapping can help ensure that patient data is handled securely and complies with regulations.

• Improved patient care
  

Effective data mapping can help improve patient care by ensuring healthcare providers can access accurate and complete patient data. This can help providers make more informed decisions about patient care and improve patient outcomes.

Data Mapping Best Practices Help Ensure Systems Can Effectively
Work Together

Healthcare integration projects span the gamut from simple point-to-point integrations to complex HIE integrations with hundreds of endpoints. Data mapping plays a pivotal role in ensuring that the systems work together efficiently and effectively. Here are some best practices for data mapping that can help ensure a successful healthcare integration project.

• Understand the source systems

The first step in effective data mapping is understanding the source systems. This includes identifying the data elements within each system, their data formats and their respective meanings. A thorough understanding of the source systems can help prevent data loss, errors and inconsistencies during data mapping.

• Define the data mapping requirements
  

Defining the data mapping requirements is a critical step in the data mapping process. It involves identifying the data elements that need to be mapped between the source and target systems, their respective formats and the mapping rules that will be used to transform the data from one format to another.

Identifying the data elements that need to be mapped involves understanding the specific data requirements of the healthcare integration project. This includes identifying the types of data that need to be exchanged, such as patient demographics, clinical data and billing information.

Healthcare IT systems use different formats to store and exchange data, such as HL7, FHIR or DICOM. Understanding the formats of the data elements is essential to ensure that the data is mapped accurately. 

Defining the mapping rules involves specifying how the data elements will be transformed from one format to another. This can include rules for data translation, data conversion or data formatting. For example, if the source system uses a different code set than the target system, mapping rules may need to be established to convert the code set to the appropriate format.

By defining the data mapping requirements, healthcare organizations can ensure that data mapping is accurate and complete. It helps to ensure that all necessary data elements are mapped and the data is transformed correctly. Defining data mapping requirements can also help to streamline the data mapping process and reduce errors by establishing clear guidelines for data mapping.

• Use standard codes and formats
  

Using standard codes and formats can simplify data mapping and reduce errors. Standard codes and formats ensure that data is consistent and can be easily mapped between systems. Examples of standard codes and formats include SNOMED CT for clinical terminology and HL7 for message exchange.

• Validate data mapping
  

After data mapping is complete, it is essential to validate the mapping to ensure the data is mapped correctly. Validation can be done by comparing data before and after mapping and verifying that the data in the target system matches the data in the source system.

• Establish data governance

Establishing data governance is essential to ensure that data mapping is sustainable over time. Data governance involves defining data standards, policies and procedures for managing data across the healthcare organization. Data governance can help ensure that data is accurate, complete and consistent across all systems.

• Monitor and update data mapping
  

Data mapping is not a one-time event; it is an ongoing process. Monitoring data mapping is essential to identify any errors or inconsistencies and to make necessary updates. Regularly updating data mapping can ensure that data is mapped accurately and that the integration project is successful in the long run.

PilotFish Uniquely Suited to Facilitate Data Mapping and Utilize
Best Practices

Watch the video of our Data Mapper in Action!

The right tools can make Data Mapping easier, more accurate and efficient. PilotFish’s integration solution provides unique advantages relative to data mapping and following best practices for healthcare integration projects. These include:

• Configurability and flexibility
  

PilotFish’s highly configurable platform allows healthcare organizations to map data in a way that aligns with their unique data needs, business processes and workflows by providing a flexible and adaptable architecture that can be customized to meet their specific requirements.

PilotFish’s integration solution includes a wide range of configuration options that enable healthcare organizations to tailor the integration process to their unique needs. This includes options for defining data mapping requirements, data validation rules, data transformation rules and error handling rules. These configuration options allow healthcare organizations to specify precisely how data is mapped between different systems, ensuring that data is mapped in a way that aligns with their unique data needs.

• Built-in data mapping tools
  

PilotFish’s integration solution comes with built-in data mapping tools that can simplify the data mapping process. The PilotFish data mapper consists of an exclusive graphical 3-pane drag&drop data mapper featuring a graphical interface for visually mapping data elements and support for standard coding systems such as HL7, FHIR and DICOM. The data mapper also generates W3C-compliant XSLT. Developers who choose to work in code view can easily toggle between the graphical and code views. Non-technical users can utilize a palette of XSLT structures and functions to perform any function that can be accomplished in the code view. The ease of use of PilotFish’s graphical data mapper allows organizations to utilize their IT resources better and use Business Analysts for much of their data mapping requirements.

• Automated testing and validation

 PilotFish’s integration solution includes automated testing and validation tools that can help identify errors and inconsistencies in data mapping. These tools can compare the data before and after mapping and verify that the data in the target system matches the data in the source system. The automated testing and validation tools can also provide feedback on any errors or inconsistencies in the mapping process, allowing healthcare organizations to address these issues quickly and efficiently.

PilotFish’s integration solution takes automated testing and validation to the next level by offering a simulation mode. This allows healthcare organizations to test the data mapping process in a simulated environment, reducing the risk of data loss or corruption during the testing phase.

• Support for data governance
  

PilotFish’s integration solution provides support for data governance by allowing healthcare organizations to establish data standards, policies and procedures for managing data across the organization. This includes support for standard coding systems, such as HL7, FHIR, and DICOM which can help ensure data consistency across all systems.  

 Data governance can help establish data standards and data definitions, which can help reduce ambiguity and ensure that all data is managed in a consistent manner. It can also help define policies and procedures for data management, which can help ensure that data is handled securely and in compliance with healthcare regulations. PilotFish also includes support for data validation and error handling, which can help ensure that data is accurate and complete.

• Integration with multiple systems
  

PilotFish enables integration with multiple systems through its flexible and adaptable architecture, which allows it to connect with a wide range of healthcare IT systems. PilotFish’s integration solution uses a combination of pre-built connectors, support for virtually any standard data format and mapping tools for transforming data for data exchange with other systems or databases.

PilotFish’s pre-built Listener and Transport connectors facilitate connectivity with a wide range of healthcare IT systems – including EHRs, HIEs, medical devices and other healthcare data sources. These connectors allow PilotFish to quickly and easily connect with multiple systems without extensive coding or development. The PilotFish solution is also highly extensible via an Open API. This facilitates adding custom Processors to massage the data as well as new listener and transport adaptors for more connectivity options.