In our ever-evolving healthcare system, there seems to be at least two constants, which is the complexity and state of our siloed data. Whether it be from an electronic health record (EHR), a health information exchange (HIE), or even claims data coming in from the different payers, the data is varied in formatting and in nomenclature. Raw data is far from unified, which makes it incredibly difficult for users to interpret. Since the start of the Affordable Care Act (ACA) and HITECH Act, there has been a bipartisan push for data transparency, but that need is multi-layered. Of course, decision-makers need all the data to make intelligent choices, but that data needs to be digestible. In turn that brings about the need for increased, sophisticated data management and data normalization by the data consumers, which are the healthcare systems.
This increased need to manage data, and transform it into something usable, won’t dissipate. In fact, the more risk contracts for value-based care health systems take on, the stronger the need for data management and normalization. If your organization is engaged in a number of contracts and payers, then chances are, if you aren’t validating your static reports, you have no way of ensuring that your performance is accurate. How are you changing processes to ensure you are on track to being successful within each of your contracts? Are you waiting until a quarterly report is provided, which is considered “stale data?” Then what happens if you have a dispute over performance? If you can’t validate the numbers, how can you be sure your performance is accurate as depicted by the payer data. The only way to keep up with the number of contracts and payers is to bring all of your data into one location and remove semantic, and measurement, ambiguity.
Knowing that, what does the landscape look like for healthcare system options? There tends to be quite a bit of variation. Some healthcare systems have invested lots of money into creating their own data warehouse with an entire team dedicated to the work on this particular project; other organizations may have a hybrid of vendors who help. Also, surprisingly, some organizations may still be using Microsoft Excel spreadsheets. Whichever direction these organizations choose to head in, there are some foundational elements that must exist in order to be successful:
- Expertise in Data Ingestion: Data ingestion is the process of obtaining, and importing, data into a database. The ingestion can occur either in real time or in batches. Given the diversity of the files that your organization will receive, the file formats will vary. When management of your value-based organization is stake, it is important to ensure that the data is ingested quickly, and the process has to be efficient.
- Validations: Data validation is the process of checking the accuracy and quality of the data before using it. You may think of this as data cleansing. As data comes in from different sources or payers, it is important to verify that the data you have ingested is accurate and complete to eliminate any data conflicts. This is often performed before the ETL (extract, transfer, load) process.
- Templatizing/Normalizing: Data normalization is the process of taking the data from the database and organizing it into tables in order to reduce duplication or data inconsistency. In the templating layer, we use Medicare ACO data as the gold standard. As you know, Medicare provides this data in a standardized fashion on a monthly basis. Commercial payers provide about 70-90% of the same data with some additional fields. Salient Healthcare identifies the overlapping fields and then uses them in the same fashion. Next, Salient identifies the missing data fields. In this step, Salient also identifies if other data fields can bridge the gap or develop proprietary logic to bridge this gap. Finally, Salient adds the new data fields. The new payer becomes part of the overall schema. The template will then be used for future clients. The template layer provides a systematic method of organizing the data.
- Preset UX/UI Interface: A preset interface allows you to compare the performance of your organization across multiple contracts. The Salient solution provides data visualizations for cross-cutting metrics to ensure patients receive consistent treatment and care. Our dashboards provide a series of pre-built analyses across five categories: finance, risk, quality, attribution, and utilization. The interface allows users to quickly compare performance across Key Performance Indicators by administrators or obtain actionable worklist by providers.
Value-based contract arrangements with multiple payers, such as Medicare, Medicaid, and commercial payers, means numerous data feeds and large complex sets of protected data. This leaves value-based care organizations with major hurdles in managing and centralizing the data from multiple payers and no ability to compare and protect access to all of it. As a result, organizations manage data across multiple systems, forcing them to miss opportunities to improve provider performance and to use the untimely, static reports, and data that come directly from the payers. Before your organization takes on too many contracts, it is important to identify how you will incorporate multiple payer data into one location.