Healthcare has a long history of complex data integrations and lack of visibility to make actionable decisions. To better increase visibility into your data, you can provide standardized integration of disparate systems, customized workflows to validate this data, and reporting to make it actionable in real-time for your business.
To clean laboratory data there are several key insights that organizations should keep in mind.
One of the biggest challenges in data integration is understanding all the various sources of data and then figuring out how to make them work together. It is important to have a clear idea of what you hope to achieve from data integration and to define these requirements upfront. This will help to ensure that the process is smooth and well-designed.
2 - Implement a Data Governance Program early on
A data governance program is essential for ensuring that data integration is successful. This program will help to define who has access to which data, establish standards for data quality, and create a process for managing changes to the data. By implementing a data governance program early on, you can avoid many of the common problems that occur during data integration projects.
A centralized data hub can be extremely helpful in data integration projects. This hub will act as a central point of access for all the data, making it easier to find and use. It also provides a single location for monitoring the health of the data.
In technical terms, you can also think of an Enterprise Service Bus (ESB) that will sit in the middle of all these healthcare data lakes and act as a translation layer for all incoming and outgoing HL7 messages.
When selecting tools to support data integration, it is important to make sure that they are specifically designed for use in laboratories. This will ensure that they are able to handle the unique challenges of laboratory data, including variability in formats and the need to manage complex reference ranges.
Integration tools that are specifically designed for laboratories also help to ensure that they can be used with a variety of different systems, including EHRs, LISs, billing systems and others.
When considering how to improve data integrations within the laboratory, it is important to have a good understanding of what integration tools are available. The tools that are right for each situation will vary according to the desired type of integration and other factors, such as whether there is a need for specific data mapping capabilities or other needs.
A big challenge in data integration is that of missing data, where there are holes in the available information. This can occur for a variety of reasons, and it often makes it difficult to see the full picture of what is happening with a patient or for billing purposes.
It is important to understand all potential data fields that are necessary to successfully process a claim or to provide an accurate history of a patient's lab tests. It is equally important to consider the potential sources for this data and where gaps might occur.
When there are large, unanticipated gaps in the available information, it can help to develop strategies for filling these with values, such as a configurable order workflow system.
Another important consideration for data integration is the new trend toward digital orders. Digital ordering systems allow for a quick and easy way to order lab tests and can help to reduce errors that occur with paper-based orders.
While the process of signing into a LIS or EHR may be cumbersome, it remains a simple process when compared to submitting orders for lab tests through paper-based systems. This can be especially beneficial when it comes to turnaround times, where digital orders are typically processed more quickly than their paper counterparts.
Integrating data from disparate sources can be tricky, especially if the data is not formatted in a consistent way. It is important to have a process for validating the data before it is integrated into your system. This can help to ensure that the data is accurate and reliable.
8 - Use reporting to make data actionable.
Once the data has been integrated, it is important to use reporting tools to make it actionable. This will allow you to track key performance indicators and make decisions based on real-time data.
Healthcare data is some of the most complex and sensitive data around. To ensure that this data is used to its fullest potential, healthcare organizations need to focus on the above points. By doing so, they will be able to make better decisions about patient care and reimbursement. Have you implemented any of these strategies in your own organization?