A key challenge to making full use of your Patient Satisfaction Survey Data is learning how to integrate it with your EHR data. Having an understanding of a patient’s associated encounters can provide valuable context on the results you receive on a Patient Satisfaction Survey. The challenge oftentimes is having the right data to link these together.

Here are 5 strategies to enable integration and fully leverage EHR and Patient Satisfaction Survey Data.

1. Use MDM to match patient information
   • Use the patient submitted information to match the patient record in the EHR
2. Ensure pass-through of encounter information to your survey provider
   • If the encounter is captured as part of the Patient Survey, then ensure that you receive this your survey provider for integration with clinical records
3. Create a complete patient record from the data warehouse
   • This aggregates the encounter(s) relevant information for the patient to provide context to the survey responses
4. Group surveys by service location and practitioner
   • Using the integrated EHR data, then group survey results by location and practitioner to determine trends
5. Use integrated data to drive change
   • Identify leading indicators to unfavorable patient satisfaction scores and then use these to drive change in the organization

Using patient survey results in conjunction with EHR records can remove operational blind spots that can exist when using Patient Survey Data solely. When fully integrated, you can move on to identify trends. This then should be used to promote change and improve patient experience and well-being.

What are the business rules that companies are using for claims processing?

This is a common question when it comes to performing analysis on claims data and is becoming increasingly important with regulations on billing transparency on the way. The logic used for claims processing is typically embedded in code and is not transparent to those reporting on claims data.

Complex rules surround provider contracts, billing service levels, clinical codes, and member coverage. These rules change frequently, making it increasingly difficult to reconcile billing all the way through claims adjudication. As a result, it is critical to utilize key data governance practices in the 5 following ways:

1. Create a business glossary and context diagram
   • Identifies and defines a business description of each discrete business term used in claims processing as well as their relationships
2. Develop a data dictionary
   • Defines each of the tables and fields used in the database to support the data elements in claims processing
3. Define relationships between the business glossary and data dictionary
   • Shows how business terms map onto the tables and fields in the database
4. Generate data lineage
   • Shows where and how code is using the tables and fields, defined in the data dictionary, in the entire claims processing flow
5. Reveal the data governance metadata to end-users in a meaningful way

The objective is to enable you to understand how and why the data changes across each step of the claims process. This will provide deeper insight into billing and collections errors, revenue cycle management, and proactively monitor the entire claims process.

There are numerous obstacles in making healthcare data useful for downstream analysis for decision making. There may be no bigger challenge than taking data that has been captured from disparate healthcare EMR systems and cleansing and normalizing them into something uniform for consumption. This is a result of the way that data is captured in EMR systems.

It is important to understand why data could be non-standard to determine what to do about it.

• The source allows free form text to be entered such as a patients address
• Source allows fields to be optionally populated
• Source captures data at different granularities across the workflow such as an ordered medication vs the administered medication vs medication on patient med list
• Multiple sources will have different internal codification of values such as gender
• Sources has note or comment fields that are by its nature non standard

Each distinct challenge could require a different approach to determine how to best address them.

But before you set off on looking for tools to fix the problem, its vital to come up with a methodology to understand the scope and a prioritization.

1. Identify the key data fields that are used to drive business decisions and rank them by business value
2. Profile these key data fields to understand the characteristics of the distinct data that is populated. What % is non standard? What % can easily be standardized?
3. Determine the complexity and effort to standardize each of the key data fields.
4. Prioritize a backlog based off the business value of the data and the effort to standardize.

The key is accepting that it is an ongoing to effort to data standardization and you want to bite chunks off at a time. Not all data has equal business value and need to determine where best to use your resources. There are tools that will make addressing these challenges easier but you will need to understand the problems and have a sober awareness of the areas that will be challenging. The goal should be to improve the usefulness and not looking to achieve perfection.

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.

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:

1. The right data is not being collected to support the use case which may lead you enhance upstream workflow processes.
2. 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.
3. Raw data requires enhancing to normalize or provide additional ontological context to support the use case.
4. Data is not integrated between systems fully to support the use case.
5. The business rules used to derive metrics and KPI’s could be calculated incorrectly.
6. Data that is currently reporting is undocumented or understood to determine if it is working properly.
7. 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.