02 Dec Follow-up to Three Steps toward Discovering Big Data
By: Dan Reber
In my previous blog post, Three Steps toward Discovering Big Data, I began a discussion on the three significant steps, or transformations, I believe are required before healthcare data analysis can begin a significant evolution from crawling to walking.
The discussion began with a commentary on Step 1: Exception Reports. You can read up on the earlier part of the discussion HERE.
Now, I’d like to move forward to discussing Step 2: Analytics Department Setup and Step 3: Data Quality.
Step 2: Analytics Department Setup – Many HCOs have their IT department handle all reporting needs. This may work on the financial side (although I advise against it) but it will not work for clinical reporting and quality measures, as I believe the domain knowledge just isn’t there.
Here are three professional positions I believe are essential for a successful, and more importantly accurate, analytics department:
- Implementation Champion – This position is responsible for the overall direction and vision of the implementation. May oversee projects that have high visibility throughout the organization. The individual is usually the CMO/CMIO or Medical Director and must have the ability to change clinical procedures within the organization. This position can be broken down into Quality Champion and Process Champion.
- Implementation Director – This position is responsible for the day-to-day reporting and operations of the analytics department. The most efficient departments I’ve seen are those with a physician or RN, or both, to lead the operations.
- Business Analyst – This position is responsible for the prioritization of requests and management of larger projects. The individual best-suited for this position is an SME of the EHR, and is usually part of the EHR implementation team.
Step 3: Data Quality – I have seen many organizations disseminate reports before the data has even been validated. I am an avowed data geek and so I insist that data quality be top priority for any analytics department. Additionally, exception reports must not be confused with data validation. Exception reports do not take the place of validating the data, they simply show when a process is not being followed.
Here are a few essential steps to follow for better quality data:
- Data Comparison – Create an automated process that compares the data in canned reports in the host system, to data within the data warehouse. Only send alerts when the data does not match. Don’t be surprised, though, if the canned reports are incorrect. We have proven many to be inaccurate.
- Validation Contests – At the beginning of most analytics implementations, many users will say that the data is inaccurate or just doesn’t look right. Why not implement a contest to see who can find the most issues with the data and even offer a bonus to the top three. This will do two things – help locate data issues (and there will be some) and help the users trust the data once said issues are resolved.
- Spot Checks – Always, always, spot check on each and every report prior to sending out for the first time. Then continue with periodic spot checks on random reports going forward.
Data analysis in healthcare is challenging and when done incorrectly it will be inaccurate. My many years of experience within the industry have taught me that the above recommendations will provide a simpler process with more accurate results, trusted information, and most importantly – will result in better quality patient care.
Healthcare technology needs to first evolve and adapt so that the “big data” revolution can begin.
As always, feel free to reach out to me at firstname.lastname@example.org with any questions or comments.