Welcome back! So you have:
- Obtained executive buy-in
- Inventoried your systems
- Identified system constraints
- Identified organization constraints
Now it’s time to decide on your first data normalization project. In this blog post I will provide insights into how to choose a project that you can complete successfully.
Even though you now have executive buy-in, it is key to demonstrate the value of that first project as quickly as possible. That’s the focus of the next step in the data normalization lifecycle: conducting an impact analysis to prioritize your data normalization to-do list.
Typically a healthcare organization has already identified a set of candidates—this is usually the catalyst for formalizing a project, and establishing resources and timetables. However, conducting a wider impact analysis is critical to prioritizing where to get started. That discovery phase will cover where an organization’s data resides, who owns it, and which users and departments will benefit. This project list and accompanying insight feed directly into the impact analysis stage of a project.
The normalization projects an organization identifies will include projects of varying value, impact, and degrees of difficulty. An impact analysis takes those factors into account and can help identify a starter project. The team backing a project needs an easy win to demonstrate the viability of the investment and create momentum for more ambitious projects down the road. Rapidly demonstrating value to the executive team is important for securing their continuing support. Executive buy-in, as noted in a previous blog in this series, is the first phase of a normalization project.
In addition to proving the value of normalization, the first project also lets the project team cut its teeth on normalizing data. The team will acquire an understanding of what is involved in managing such an effort.
The main considerations for selecting the first normalization activity are (1) the project’s overall value to, or impact on, the healthcare organization, and (2) the ease (or difficulty) of project execution. That latter insight should stem from the health system’s constraint identification phase. A project that has critical limitations in terms of money, time, resources, or technical issues won’t make a good first project, even if that project promises a high payoff.
A healthcare organization’s impact analysis should also take into account the potential for cultural change. Data normalization will likely introduce new requirements for managing the integrity of the data. For example, the process for creating new code for a particular decision-support rule may have been highly informal when a healthcare organization used its own local terminology. But when implementing a new process around data normalization, that organization will typically have to formalize a governance structure to ensure that new code conforms to a new set of requirements. Clinicians and informaticists will need to understand and adopt this process via targeted training programs to ensure organizations don’t end up right where back they started.
System impacts should also be considered. This means that, if you decide to normalize a system, you need to determine whether that data is exported or sent to any other systems after it is used. This could mean that the data feeding into a data warehouse adheres to whatever set of standards are decided upon. Systems that typically feed other systems include, on the provider side, electronic health record (EHR) systems, admission, discharge and transfer (ADT) systems, and other clinical information systems. On the payer side, relevant entry systems could include claims systems.
Finally, the normalization project team must look into the impact on users and customers of the healthcare organization's data. Users should be made aware that a change in data representation is coming, and of what that means for them. In an example in a previous blog, the health system instituting a standardized identification scheme for each of its locations or services lines will need to communicate that to all relevant parties. Customers receiving data from the health system will need to know how each location or service line will be represented. I was on one project where they wanted to normalize almost every field in their EMRs—this would have taken years to accomplish given the number of fields and amount of disparate data. Once they understood how big an effort it was going to be, they decided to go with just a couple of fields that would make some key reporting possible and get them their early win.
Brace For Impact
An impact analysis will help you prioritize your data normalization projects and enable you to select the first initiative. Choosing a starter project that can deliver value in a short time is critical to keeping the management team on board and to maintaining employee morale.
Are you ready to zero in on your first normalization project? Leave your comments below.
Read Previous Data Normalization blog: Identify Constraints that May Impact Your Data Normalization Plan: