Data Mapping Complexities in Multi-System EHR Integrations


Data-Mapping-Complexities-in-Multi-System-EHR-Integrations-1-1024x538 Data Mapping Complexities in Multi-System EHR Integrations

You probably have heard ‘Data Mapping’ somewhere if you ever wondered about IT or how computers work, right?

If not, then let me put you in a scenario that you might have faced. So, supposedly, you are running a healthcare practice and rely on your custom healthcare software for carrying out your day-to-day care activities, from care delivery to claims submission, etc. 

In this setting, imagine you are consulting a patient, and while talking to them, you’re also scanning their Electronic Health Records to know their medical history. But as soon as you try to access their records, you’re shown irrelevant data, or the data simply doesn’t show up. Frustrating, isn’t it? 

Well, you’re not alone; there are as many people and providers just like you. Giving you a sneak peek into some of the details, 33% of all data-related issues are caused by incorrect data types. Along with that, some of the intricate stats reveal that data cleaning, ingestion, and integration collectively represent 90% of the errors.

And did you know the connecting link between all of them? Yes you’ve guessed it right, its data mapping.

Furthermore, with many healthcare practices coming forward to build custom healthcare software, they obviously need EHR integration to be a part of the connected healthcare ecosystem. And data mapping being one of the most crucial aspects of EHR integration, addressing its complexities is necessary.

On that note, let’s explore the data mapping complexities in multi-system EHR integration and what can be done to overcome those to be a part of a well-connected healthcare ecosystem. So, without further ado, let’s get started!

Field-Level Mapping Challenges in Healthcare Systems

To peep you into field-level data mapping, it is basically a process of connecting individual data fields in one data source to the corresponding data fields in another data source. In this context, a source can be considered as a different or disparate system.

Having said that, let’s see the challenges that arise in this. The very first challenge that you’re most likely to encounter is the structural difference between EHR systems. You see, different systems use different variations in data models and database schemas, which makes it difficult for the system to locate and draw parallels with the data fields. Furthermore, different approaches for storing the same clinical concepts adds more to the complexity.

That is why during EHR field mapping, required and optional fields across systems must be identified and mapped according to your need and requirements.

  • Naming Convention Inconsistencies

Another healthcare data mapping challenge that you’ll have to deal with is different field names for identical concepts. For example, a system might have sorted patients under ‘Patient Names’ data field and the other as ‘Subject Name’, such practices confuses the system, making it difficult if not impossible to locate the correct data fields.

The same goes with abbreviations and acronyms variations that people use. And if you’re using an off-the-shelf software then the version-specific terminology can be another challenge that you need to overcome.

  • Field Formatting & Validation Differences

When it comes to data, everything matters and that is why field formatting and validation differences become so important. You see, the difference between date and time format can even make data mapping different. And the same goes of unit of measurement discrepancies and the data validation rule inconsistencies.

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Medical Terminology & Code System Mapping

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There can be an entire article on multiplicity of healthcare code systems. Keeping it to a minimum and explaining its context for healthcare data mapping let’s simplify:

There are codes for different purposes in the digital healthcare landscape. For instance, the diagnostic codes that are used for medical terminology mapping vary from healthcare system to system and the system they prioritize from ICD-10-CM, ICD-9, SNOMED CT, etc.

The same goes for procedure codes which vary from CPT, ICD-10-PCS, HCPCS, etc. However, the standard coding standards followed in medication coding and laboratory coding are fairly simple as RxNorm, NDC are for medication and LOINC for laboratory codes.

In this case, understanding these codes and drawing parallels becomes essential for your successful data mapping venture.

  • Version Control Challenges

The coding system in healthcare is ever evolving just like the regulatory landscape, and this gives the healthcare organizations enough liberty to use the version that they want. Now, using different coding systems is not a problem, but when two systems use two different coding systems, then integrating them is even more challenging.

Moreover, with annual updates and transition timing updating every year, it becomes even more difficult for the system. And in case you update them, then backward compatibility issues is something that you have to manually draw parallel connections with.

  • Local & Custom Code Integration

There is a high chance that you might have proprietary code for easy use in your practice, without any standard equivalents in the terminology. Furthermore, there is also the possibility of local modification of original codes with addition of qualifiers, or alter display texts to suit your needs creating validation challenges.

Additionally, integrating with codes that are custom or unique to a specific hospital or organization can make effective data mapping a hard task. So, it is also important to consider these factors for integration local and custom codes makes the data mapping much easier.

Semantic Interoperability: Beyond Simple Field Mapping

Healthcare data mapping is not just matching point A with point B, it’s also about preserving the context that gives medical information its meaning. However, this is not so easy to do as there are chances of losing the clinical context during the transfer.

The first reason this can happen is the small differences or nuances between how various systems record the severity of a symptom. One system might use mild, moderate, or severe, while another uses numerical grading from 1 to 10.

Moreover, systems usually speak different languages, with some using detailed clinical terminologies, whereas others use simplified terms. For example, a problem list might explicitly say Type 2 Diabetes, and another may say the patient is on metformin.

  • Relationship and Hierarchy Mapping Issues

Clinical data is often interconnected and progresses based on the previous events or steps taken. So, maintaining the relationship between these steps and data points is crucial for getting a complete picture of patient health.

For this, establishing a parent-child relationship between data helps significantly. Here, the main event, such as an encounter, is marked as a parent, and all subsequent events, like tests, are marked as a child. However, maintaining this challenging and flat list of diagnoses wouldn’t capture this hierarchical structure.

Moreover, the sequence and timing of clinical events are also important, and mapping systems need to accurately record the timing and order of events. Clinical data is often recorded in complex structures like encounter summaries with multiple sections. Ensuring their internal relationships are preserved is a technical challenge, along with mapping these nested elements.

  • Handling System-Specific Features

As your organization might have a unique workflow, like this, each organization has its own proprietary workflows that generate unique data elements. This creates a hurdle as mapping these unique data elements to a common standardization can be difficult without significant customization.

In addition, the variety of templates, forms, and documentation approaches creates additional translation challenges. True semantic interoperability requires understanding not just the data itself, but the clinical meaning behind it.

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Data Transformation and Business Rules Complexity

Data-Transformation-and-Business-Rules-Complexity-1-1024x576 Data Mapping Complexities in Multi-System EHR Integrations

Ever tried communicating with systems with completely different values for the same thing you are sharing? It becomes a translation challenge, one system uses “Male” and “Female”, and the other uses simple M and F.

This does not end here, as there are also differences in option selection, that is, binary and multi-value selection. A system might represent a patient condition, such as allergies, as a single multi-field; on the other hand, a second system might use yes/no fields. 

Another variation comes in the form of a scale and scoring system, where in one the scale is from 1-5, but in another the scale starts from 0-10. This is why the transformation requires understanding the underlying meaning and potentially using normalization and categorization logic.

  • Complex Transformation Requirements

Transforming the data goes way beyond the simple healthcare data mapping, as some data elements need to be calculated, combined, or completely changed. Moreover, healthcare data is governed by various rules; for instance, a medication might be prescribed only if a specific diagnosis is present. So, mapping needs to account for all these rules and translate these conditional rules to ensure data integrity and to take appropriate actions.

Oftentimes, the meaning of one data point is dependent on multiple data values. Mapping requires considering these interdependencies to maintain the clinical significance of data.

  • Handling Missing, Default, and Null Values

One of the underestimated challenges is managing the data that is not even present. For example, different systems may treat missing data as unknown, while others treat it as not applicable. Furthermore, when a piece of data is not provided, systems might use the set values; however, if these values differ, it could fill the system with incorrect data. 

This is why handling all the missing data mapping processes needs a robust mechanism that handles incomplete data through error reporting, using placeholder values, or specific reporting rules.

Testing and Validation of Data Mappings

The complexity of healthcare data mapping and transformation makes it crucial to have robust testing and validation strategies. There are several approaches that help in this; the first is edge case identification and testing. Only testing typical data is not enough; testing unusual data formats or combinations of data that are not common but possible.

Performance and accuracy may drop with an increase in the volume of the data or data with significant variability. So testing with realistic, large, and diverse datasets helps uncover mapping issues that might surface under a huge data load.

Data mapping is just a step in a larger clinical workflow, and this is why validation needs to go beyond individual data points. It should seamlessly integrate into the system’s processes and give the expected outcomes.

  • Data Quality Verification Method

Maintaining the same quality of data during the transfer and transformation is important. Here, using statistical analysis helps you tremendously, as it compares the source and target by using their statistical properties like mean and standard deviation. 

Next, for data types like images and documents, visual inspection by the subject matter experts can reveal some subtle mistakes. However, automating the process with validation rules can save your time and energy, along with improving the accuracy of validation. These rules can highlight anomalies, and then you can check and correct the data.

  • Iterative Refinement Processes

Clinicians and administrators are the first to identify issues with mapped data, so collecting their feedback and acting upon it can enhance the process. After the deployment of mapping, ensuring that the data flow and quality in the production environment remain the same is important. Doing this makes the early detection of issues possible. 

However, doing the testing and validation is not a one-time process; you need to do it continuously while improving it based on user feedback and fast experimentation. With this, the process does not remain stagnant and becomes refined and adapted to evolving needs.

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Tools and Methodologies for Healthcare Data Mapping

Tools-and-Methodologies-for-Healthcare-Data-Mapping-1024x576 Data Mapping Complexities in Multi-System EHR Integrations

When you want an effective healthcare data mapping, only having the procedure is not enough; you also need tools and methodologies. There are some methodologies that help maintain data consistency; one such method is specifying the structure of source as well as target data elements. It also specifies the transformation rules and business logic for future maintainability and troubleshooting.

Another method is to have a visual representation of data mappings, typically with graphic interfaces. This improves understanding and collaboration among the technical and clinical members by illustrating data flows and transformations.

Lastly, centralizing repositories for storing and managing metadata and the mappings is vital for version control, governance, and impact analysis when changes occur in the source or target system.

  •  Technology Solution

When you are doing multi-system integration or connecting disparate systems, having interface engines and a mapping tool helps tremendously. These specialized tools include graphical mapping interfaces, transformation libraries, and monitoring capabilities, which make building, deploying, and mapping data between disparate systems easy.

As for medical terminology mapping, terminology servers are used. These servers facilitate accurate mapping codes among the different coding systems, ensuring consistent meaning. Moreover, Natural Language Processing (NLP) effectively converts unstructured data into structured data, making mapping these data elements easy.

  • Governance and Oversight

To maintain accountability, consistency, and adherence to standards, establishing clear data governance policies is crucial. This framework must define the roles, responsibilities, and approval processes for mapping activities. Alongside this, involving clinicians and other experts in the process helps make the mapping process accurate and reflect the exact meaning of clinical context in the real world.

The data standards and healthcare systems are always changing, so a robust change management process is quite important. This helps you manage updates, communicate changes effectively, and minimize the disruption in the clinical process.

Conclusion

Successful EHR integration depends on a thorough data mapping process that connects technical and clinical domains. Moreover, the organizations that approach mapping with dedicated expertise, clear governance, and robust validation processes achieve more reliable and accurate outcomes.

The healthcare systems are continuously evolving, so effective healthcare data mapping is not a one-time project. This is why partnering with experienced integration specialists gives you the needed guidance, ensuring smooth data flows and regulatory compliance.

Thinkitive is one such integration expert in the market that can make your healthcare integration a success with effective and efficient data mapping. Click here to take a look at our projects and book a meeting.

Frequently Asked Questions

1. How much time should we allocate for data mapping in our integration project timeline?

Allocate 15-30% of your integration project timeline for data mapping. This accounts for data profiling, rule definition, transformation, and thorough testing. Factors like data complexity, volume, quality, and automation tools will heavily influence the actual duration.

2. Who should be involved in healthcare data mapping decisions?

Healthcare data mapping decisions should involve a multidisciplinary team. This includes healthcare professionals like clinicians, nurses, IT specialists, data architects, compliance officers, and data governance leads. Their combined expertise ensures accuracy, regulatory adherence, and usability for improved patient care and operational efficiency.

3. What are the most common data mapping errors in healthcare integration projects?

In healthcare, data mapping errors often stem from inconsistent data formats, a lack of standardization across systems like different EHRs, missing or incomplete data, and difficulty in aligning diverse terminologies, such as medical codes. This can lead to inaccurate patient records and flawed decision-making.

4. How do we handle fields that don’t have direct equivalents between systems?

Handling fields without direct equivalents is tricky. We often create a holding pen for data, mapping it to a generic field or using a custom extension. Sometimes, we combine multiple source fields into one target, or split a single source field into several, depending on the data’s purpose in the new system. It’s all about understanding what the data means and how it’ll be used.

5. What tools can assist with healthcare data mapping projects?

For healthcare data mapping, useful tools range from powerful ETL platforms like Informatica, Talend, and Astera, which handle complex transformations, to specialized integration engines like Mirth Connect, Cloverleaf, and Corepoint that excel with healthcare standards like HL7 and FHIR. These tools help streamline the challenging process of connecting disparate healthcare systems and ensuring data quality.

6. How do regulatory requirements impact healthcare data mapping?

Regulatory requirements like HIPAA and GDPR are huge in healthcare data mapping. They demand strict standards for patient privacy, security, and data accuracy, ensuring information is consistently structured and protected across systems. It’s all about keeping patient data safe and sound.

7. How do we maintain data mappings over time as systems are upgraded?

We usually tackle this by having a golden record of mappings, using version control for changes, and automated testing to catch any breaks during upgrades. Think of it like constantly updating a master dictionary everyone agrees on.

8. What metrics should we track to ensure our data mappings are functioning correctly?

To ensure data mappings work correctly, track:

  • Accuracy: How often the mapped data matches the source.
  • Completeness: Are all required fields populated?
  • Consistency: Is the data uniform across systems?
  • Validity: Does data adhere to defined rules and formats?
  • Timeliness: Is the data current and available when needed?
9. How do FHIR-based integrations differ from traditional HL7 interfaces in terms of data mapping?

FHIR uses modern, flexible resources like Patient, Observation that map data to common web formats, JSON/XML, making integrations simpler and more adaptable. Traditional HL7 often uses rigid, custom-coded messages, requiring more complex, point-to-point mapping for each system.

10. What is the role of artificial intelligence in healthcare data mapping?

AI is a game-changer in healthcare data mapping! It automates the tedious task of connecting disparate data sources, ensuring information flows smoothly between systems like EHRs. This means cleaner, more accurate data for better patient care, streamlined operations, and faster insights, ultimately improving overall health outcomes.

Shubham Sawant

Business Analyst

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