Fundamentally, the use of an EMR system is to improve the quality of patient care, clinical outcomes, and overall efficiency. However, many hospitals and practices are not fully reaping these rewards, and in our experience, one of the contributing factors is under utilization of data or data integrity.
Clinicians, administrators and support staff are not always receiving the necessary data and reports from their EMR system. As a result, issues go unnoticed and can develop into a crisis, or decisions are made based on incorrect or lack of information. This damages the credibility of the EMR data, and undermines the perceived value of the EMR system.
To avoid this scenario and better leverage your EMR system investment, we've highlighted 5 best practices for optimizing data integrity and utilization:
1. Configure the EMR System to Meet Your Specific Data Needs
While vendors provide recommendations with respect to data capturing and reporting, ultimately the hospital or practice needs to determine its unique data requirements, and then configure the system accordingly. This may necessitate creating custom data elements and workflows, as well as the use of dashboards and customized reports to analyze your data in a meaningful way.
2. Customize the EMR System to Support Users
The EMR data and workflows need to be customized to fit the work practice of individual teams – and not the other way around. For example, some work groups may need a streamlined interface to carry out their tasks effectively and efficiently, while other team members may need to see extensive, detailed information at one time. These adjustments are not minor and should not be perceived as superficial. They support appropriate data usage and an enhanced user experience.
3. Implement Data Governance Policies
It is vital for practices to establish robust data integrity governance policies, and enforce them through audits and re-education. For example, EMR users should clearly understand the rules related to copying and pasting data, using abbreviations, data standardization, and so on. These policies should be reviewed regularly and updated accordingly, and include team member feedback.
4. Develop and Customize Templates
While templates can be quite helpful, they should not be assumed to be suitable or ready “out-of-the-box.” Implementation teams typically need to modify templates to capture all required information in a standardized and efficient manner. At the same time, it is important to recognize that templates may not be the best place to capture all required information – the use of notes and other tools may be necessary. Completeness of data should always be the top priority.
5. Detect and Resolve All Data Transfer Errors
EMR systems use extract-transform-load (ETL) workflows to collect, organize and analyze information from a variety of data sources. These workflows are very complex, and must run per strict performance requirements and rules in order ensure data integrity.
Given this, it's crucial to monitor and research any and all instances of ETL execution errors, to detect and resolve potential data integrity issues early. Otherwise, problems can go undetected and erupt into a full-blown crisis.