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Overcoming Challenges Aggregating Clinical and Administrative Data

The Right Tools Are Key to Harnessing Data Analytics

The proliferation of Accountable Care Organizations (ACOs) and the pay-for-performance model has created an intense demand for systems that can effectively harness various sources of billing and clinical data to provide data analytics that yield insights into population health and provider quality.  Solution Providers that provide services around reporting for analytics, population health and quality have a virtually unlimited demand for their services. However, the value of their services can only be unleashed when their systems can acquire the required data and are able to digest that data efficiently as to adequately provide insight. At the end of the day, these solutions are only as powerful as the quality of the data they can effectively parse and aggregate.

Guidelines in Choosing the Right Tools

The right tools are what make overcoming the inherent challenges of parsing and aggregating data possible.. These four guidelines can’t be ignored in selecting the right tools to unleash the value of data analytics.

  1. The first requirement is that you need tools that allow you the flexibility to easily read in data from a limitless number of input sources and formats. At a minimum, you should be able to read in:
    • Standards such as HL7, CDA, or ANSI X.12 EDI
    • Custom XML or JSON APIs
    • Old fashioned batch files in fixed-width or delimited formats
    • Print reports, CSVs, or spreadsheets
    • Direct database access via SQL
  2. Once the data gets read in, you’ll need to parse it. Can your tool handle the inherent variability in healthcare standards and data? Does your tool work with non-standards compliant data? Can it parse non-standard HL7 and EDI?
  3. Next, you’ll need to utilize a universal adapter. This helps with acquiring, cleansing and normalizing the data from any number of data sources before it can be securely transmitted to your system into a consistent format. By utilizing a common model approach, you can eliminate the proliferation of point-to-point interfaces and the hand-coding used. These point-to-point interfaces are not only brittle, but also extremely difficult to maintain. Using a common model approach, whether it is one-to-many, many-to-one or many-tomany, dramatically reduces the number of interfaces required as well as the ongoing maintenance.
  4. Within healthcare, some data is file based and some arrives via real-time messaging. Almost always, that data contains PHI and must be transmitted over a HIPAA-compliant, encrypted channel. (it is safest to assume that it must, in all cases.) Flexibility is key here in handling communication protocols. The right tools can support a wide range of protocols. Once the data gets read in, you’ll need to parse it. Can your tool handle the inherent variability in healthcare standards and data? Does your tool work with non-standards compliant data? Can it parse non-standard HL7 and EDI?

By starting your data integration aggregation process with the right tools in place, you can harness the power of data analytics. Use the above guidelines to make sure your selection is right.

If you need help, call PilotFish. We have been helping organizations aggregate and integrate data for over 14 years – so we know a thing or two about it. We’ve got the tools that are proven to get the job done. We’re happy to do a POC to prove out your use case so that you know that you have made the right decision. Call me directly at 813.864.8662 for how we might assist you.

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Monika Vainius

Written by: Monika Vainius

Executive Vice President of Applied PilotFish Healthcare Integration. Monika has extensive experience with systems interoperability. She combines this experience with her professional passion for healthcare and healthcare technology to comment on current healthcare and IT news. Website

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