EHR Data Mapping: Solving Complexity in Multi-System Environments

How many errors are there in an EHR on average?

In a test of 401 electronic health documents using HL7 validation, almost 86.3% of the documents contained an average of 4.9 errors per document, according to the National Library of Medicine. Furthermore, the majority of these errors were data completeness and syntax-related issues.

These documents generate an average of 53.1 data-quality alerts per document. This shows how delicate the clinical-data mapping process is. Interestingly, all these systems claimed they have standard-compliant exchanges.

This is the reason why studies show that more than 50% of EHR development projects have failed. And some of the major reasons cited for this were inaccurate or mis-mapped patient histories.

Upon digging deeper, the problem that linked all these errors together was that the EHR systems simply fail to communicate with each other. As their data language was the major barrier that simply made their language gibberish to each other.

So, as a provider, you might be finding a solution for this, right?

Well, there is, in the form of EHR data mapping. For your EHR system, it acts as a translation layer for interoperability, giving your systems the ability to understand data stored in other systems and make sense of it.

And this is the reason why data mapping has become a cornerstone in enabling scalable EMR data integration. Moreover, with the rise of AI-driven systems in healthcare, data mapping is today used for solving complexities in multi-system environments.

So, without further ado, let’s understand the intricacies of data mapping in healthcare and how it can be beneficial for your healthcare practice.

What is EHR Data Mapping in Healthcare?

Let’s start with the basics for some of the readers who might have been new to healthcare IT terminologies. So, in simple terms, EHR data mapping is the process of aligning, translating, and connecting data elements between different healthcare systems so that they understand the data and make use of it.

Now, in the EHR integration process, some of these terminologies might appear to be of similar meaning. However, there can be a striking difference between them. Refer to this table for a better understanding:

But here you might ask, how is clinical data mapping and interoperability interconnected? Well, in the clinical context, it helps healthcare organizations retain the true meaning of the clinical data across systems.

Data mapping ensures that accurate patient records are maintained across systems. The aftermath of this can transform your practice by allowing you to make safe clinical decisions, removing duplicated or inconsistent data, and most importantly, supporting care continuity across providers.

The Use of AI in EHR Data Mapping

Traditionally, the process of data mapping has been manual, and the chances of making an error were quite high. Here, AI-driven approaches can automate important aspects such as automated field matching, semantic mapping, and normalization of data so that your system can understand and make use of it. On top of that, a few of the practices are even using it to detect duplicate records and remove them from the records.

How EHR Data Mapping Works in Healthcare Systems

If you go by the definition, then you know that data mapping is a process. However, what is that process, and how does EHR data mapping work in healthcare systems?

Well, let’s find out.

So, to understand the process better, think of EHR data mapping as a layered process so that both the structure and meaning of the data are preserved across systems.

Starting with the first step of the process:

• Structural Mapping: This type of mapping is simple, where you match a field from one system to another. For instance, it handles differences in field names, data formats, or data types. This way, it forms the foundation of data mapping, as it gives the system the ability to place the data correctly in the relevant place.

• Semantic Mapping: This mapping, on the other hand, refers to retrieving the meaning of the data across systems. It aligns clinical concepts from two systems, and some of the standardized terminologies used in this are SNOMED CT, ICD-10 codes, LOINC, etc. This mapping ensures that clinical accuracy is maintained across records.

In simpler terms, structural mapping ensures where the data goes, whereas semantic mapping ensures what the data means.

EHR Data Mapping Process: End-to-End Workflow

The EHR data mapping process consists of five major steps. Here is a brief blog that will help you understand what activities are involved in each step and what outcome is achieved from each process:

EHR Data Mapping for Multi-System Environments

I know that I have been saying that data mapping is the process of matching the data fields from one system to another. But in real-world settings, do you really think that data flows between just two systems?

You see, your data flows from multiple EHRs, lab systems, imaging systems, RPM devices, billing platforms, and whatnot. Now, the challenge is that the same patient might be present in multiple systems, or the data in one of those systems might be incomplete, or the information might be stored in a different style. This slight difference can have a huge impact on your practice, right?

That is why EHR data mapping becomes more complex, especially in multi-system environments. The problem we discussed is the major obstacle in multi-system environments.

In such cases, your data mapping must identify such information stored in slightly different ways and merge all these records without losing accuracy.

Furthermore, it should be able to handle different coding systems. In simple words, it should translate codes across systems, preserve their meaning, and ensure consistency in reporting and decision-making.

• The Problem with Unit & Format Inconsistencies

Different systems store the same data in different formats or units. One of the most common examples of this can be seen in dates; while a system might store it in DD/MM/YYY, the other one might store it in MM/DD/YYYY.

Such inconsistencies can create mapping issues that can be critical or change the meaning of the data. And the best way to overcome this is by normalizing the data before starting the mapping process.

• AI-Driven Normalization & Conflict Resolution

Traditional approaches have struggled with managing multi-system complexity due to various reasons like scalability, variations, and continuous flow of data. Here, normalizing this data can be quite critical and at times even becomes time sensitive. In such cases, you can use AI to handle this complexity and intelligently normalize data based on units, formats, etc.

Moreover, it can even resolve the cross-system entity resolution and recover the semantic conflict, restoring the accurate meaning of the data stored in different systems.

Challenges in EHR Data Mapping & How to Solve Them

I might brag on and on about the challenges that you might face with EHR data mapping, but they would often go over your head. So let’s understand them better with their real-world impact and how you can solve them. Refer to the table below:

Ensuring Data Accuracy & Integrity

The success of your data mapping efforts relies directly on data accuracy and integrity. Given the impact it can have on your practice, it is important for you to ensure data accuracy and integrity. Here are some of the best ways in which you can ensure that.

• Source-to-Target Validation: Validate the source of the data from which you are fetching and while doing so, also validate the target, so that it can reach its right system.

• Design Lineage & Audit Trails: Ensure the data you are receiving into your system can be tracked right to its original source. And along with that, conduct audit trails so that you know what is happening in your system.

• Error Detection & Correction: During all these processes, you are bound to encounter many errors. While you address them, correct them then and there so that the next processes are streamlined and accurate.

• Compliance Considerations: Like EHR development, data mapping also has compliance considerations that you must adhere to. These compliances might be specific to data mapping, but play a huge role in normalization and cleansing.

These are some of the ways in which you can ensure data accuracy and integrity. Furthermore, you can also use machine learning models for continuous validation of data and ensure that the data quality is always accurate.

Best Practices for Scalable EHR Data Mapping

If you have made it this far, then you must know some of the best practices for scalable EHR data mapping.

• Standardized Data Formats & Coding Systems: The one thing that every page about EHR data mapping will say to you is to use standardized data formats and coding systems. This is because it makes your system understand data better.

• Design for Multi-System Environments: When data mapping, design the module for a multi-system environment. This is one of the best practices because in healthcare, data hardly flows through just two systems.

• Use ETL/ELT Strategically with Data Mapping Pipelines: Depending on infrastructure, data characteristics, and compliance, use the Extract, Transform, Load or Extract, Load, Transform.

• Implement Governance & Monitoring: Have a mechanism in place that allows you to monitor data mapping pipelines and ensure that the performance is top-notch.

Frequently Asked Questions