Getting the right clinical data has always been a journey and with the current regulatory changes and complexity, it feels like a never-ending marathon.

The data acquisition journey for most health plans looks like this:

1. Find the data -> No standard way, bespoke solutions needed
2. Get the data and process-> Ingest and transform the data that exists in multiple formats 
3. Determine gaps in the data -> Find that the data isn’t usable and determine the data gaps
4. Remediate the data -> Remediate the bad data that was brought it
5. Get more data -> Get more data and go back to step 1

The problem for health plans is spending all this effort on getting the data without knowing ahead of time: Is this data worth the effort? Is this data usable? Is it fit for purpose? Plans work to get the data before they even know the value of it. 

It’s a common challenge – health plans spend more time chasing data than using it.  Plans get the data and it’s not complete, it’s missing key data, it’s not the right format, it doesn’t have the code-sets needed. In short – the data isn’t useful.  The kicker is now you need more – more data sources, more vendors help, more transformations, more data enrichment, more managed services…which means more time and more money.

On this data journey, when you thought you were at the finish line of a sprint, you are at the start of a marathon and it can feel like you are running in place.

How to shorten the data acquisition journey

How can health plan leaders adjust their journey to find the data that is fit for purpose and worth the effort before even starting? In the current environment of scarce resources and shrinking budgets, this adjustment is necessary for survival.  Imagine a journey that is only 2 steps:

1. Find the right and trusted data
2. Use the data 

Without the needless effort and wasted resources chasing the low value, low trust data, the journey is shortened allowing health plan leaders to focus their effort on using the data to create value.

Data quality assessment is the key

How does a plan “find the right and trusted data” to answer the fundamental question: Is this patient data fit for purpose? Is this data fit for YOUR purpose?

The PIQI Framework is  an emerging standard methodology that enables you to assess the data before you try to use it. It provides a framework that  tells you the quality of the patient message based on the evaluation criteria. 

As an HL-7 emerging standard, the PIQI framework provides a common language for data quality assessment so health plans can communicate with data partners about what needs to be fixed before using and paying for using the data.

PIQI Framework for data assessment

PIQI Framework enables  health plans to objectively evaluate the overall quality of messages and can be used to pinpoint where that quality falls short.. Plans can use it to describe what is important to them in data quality, and to assess if the data meets their criteria. Plans can know exactly what is wrong with the data and if it is fixable before they touch  the data.

The data can be measured against dimensions like accuracy, availability, conformance, and plausibility. These dimensions tell plans exactly what the data quality issue is. This allows plans to communicate precisely what is wrong with the data and importantly how the data partner can fix the data to make it usable for you. 

This is all before you take a single step in trying to use the data.  It consolidates steps 1 – 4 in the typical health plan data acquisition journey into one step: “Find the right and trusted data”.

The core principles that enable this are:

1. A simple data model 
2. A taxonomy for classifying the issues
3. A modular and sharable assessment approach
4. An adaptable user-configurable implementation

With these core concepts, a powerful and adaptable data quality assessment tool is ready to tackle a diverse range of patient data quality assessments.

Getting the right data doesn’t need to be a marathon

Using the PIQI Framework means your data journey doesn’t need to be a marathon.  It provides clarity around the data quality and usability to allow for fixes before purchasing or even trying to use the data.  Ultimately this saves the plan resources and leads to better quality outcomes.  In a time when plans are being asked to do more with less, it allows them to focus on using the data, not chasing it.

In our next article we will explore how PIQI is adaptable to your needs, and how you can use to make sure your data partners meet your data quality needs