The challenges in delivering healthcare data solutions that address complex healthcare business problems aren’t obvious when setting sail on an exciting new project. It’s focus a large percentage of your time searching for the latest sophisticated data crunching, machine learning, predictive data analytics tools around that will magically solve all of the problems without fully understanding what you are addressing.
There is a natural tendency to assume the obviousness of the problems and understate the complexity in dealing with them.
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First, It’s critical when embarking on any data solution that your first start with each individual problem you are trying to solve or answer and avoid the temptation in assuming that they are similar in makeup. Each discrete issue requires a scalpel, peeling back the layers, documenting what you see and not stopping until you get to the core.
Understanding the root cause for each discrete issue will lead you to an overall picture of what needs to be addressed and where you need to put your focus. You could be surprised on the types of root causes you may find.
Here are 7 critical challenges:
- The right data is not being collected to support the use case which may lead you enhance upstream workflow processes.
- Data is incomplete or quality is poor to adequately solve the use case, which may lead you improve system controls, and educate those inputting the data.
- Raw data requires enhancing to normalize or provide additional ontological context to support the use case.
- Data is not integrated between systems fully to support the use case.
- The business rules used to derive metrics and KPI’s could be calculated incorrectly.
- Data that is currently reporting is undocumented or understood to determine if it is working properly.
- Finally the assumptions in the information you would need to support the use case could be altogether incorrect!
That’s a lot of places that can cause an impediment in addressing a use case and you may find one, or all of these to some degree in a single use case, watering down the value of key metrics you may provide.
Yes, It can be quite complicated and overwhelming and it requires organizations to have resources from multiple support areas working together in a way they may currently not do.
However, you will find at the end of your analysis you can provide realistic expectations in what your data solution will be able to perform out of the gate but also provide a true appreciation of the complexity and scope of the issues to gain proper support going forward.