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Table of Contents

What is the Health Data Life Cycle?

Data here, data there, data going everywhere!

The winding path towards value-based care has placed even more emphasis on the need for quality data. Not only to have the data, but to use the data to improve the quality of care and cost. 

It's safe to say there's a veritable ocean of data flooding healthcare providers, and that ocean is only getting deeper.

The often-perplexing and overwhelming process from finding the data to using the data has several steps, each with it's own challenges. We've developed a seven-stage health data life cyle to break them down.    

The health data life cycle explains how data that is ARTA - accurate, reliable, timely, and actionable -  helps hospitals and clinicians improve quality of care, the patient experience, and your financial bottom line. 

Our new eBook, 7 Stages of the Health Data Life Cycle, spells out the basics and details about each stage: Find the data; capture the data; normalize the data; aggregate the data; report the data; understand the data; and act on the data.

Each stage is vital individually and as a whole. Understanding this will help you avoid data that seems to show "smoking babies" or "pregnant men." Capture the data; normalize the data; aggregate the data; report the data; understand the data; and act on the data.

 

Primaris 7 Stages of Health Data ebook

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1. Find the Data

Where is it located?

Make sure to obtain ALL the data. That means clinical notes, electronic health records that might be in disparate systems, medication orders, and the rest. That also means knowing all the places to locate the data. Paper charts? EHRs? Quite possibly multiple EHRs? Claims systems? Knowing all the different locations you need to get the data from can sometimes be one of the biggest challenges and lead to missing data. Omitting any portion of the patient record could mean inaccurate reporting and loss of reimbursement opportunities. 

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Primaris Health Data Life Cycle Part 1 Video

2. Capture the Data

How do you get the data? 

Once you know where the data is, the next step is getting it from the source system into a common system for analytics. You need to understand how to get into the various systems used, as well as ensure all software is able to integrate and is reliable. Later steps won't be possible without proper attention to collecting the data from all identified sources.

Don't forget there is structured and unstructured data. Check boxes and diagnosis codes can be captured electronically; however, a qualitative clinician note will need to be abstracted manually.

Learn more with our blog and video:

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3. Normalize the Data

'John Doe' or 'Doe, John?'

Primaris Risks of Poor Data Infographic

It's complicated. And it's simple. Are units listed in metrics? Last name first or first name last? Normalization ensures that data is more than just a number or a note but meaningful data that form the basis for action. Different EHRs and systems will have different ways of recording information.

Take the time to make sure reporting formats are consistent and used in the same way. Missing or extraneous data can create a cascade of consequences, such as the provider's reimbursements. Beyond missing data, normalization is also critical for accurate and reliable data.

4. Aggregate the Data

Who's in the pool?

This step is critical to value-based care because it consolidates the data from individual patients to groups or pools of patients. Here's where accuracy is of utmost importance, because this step will put patients in the proper stratification and lead to comprehensive population health strategies. 

Aggregating the data is necessary before healthcare providers can analyze the overall impact and performance of the whole pool. It is crucial for you to select the correct groups, use the correct tools, track down any missing data, and check for errors. Double and triple check this step!

Learn more with our blog and video:

Primaris Quality Talk Healthcare PodcastQuality Talk Podcast
What is the health data life cycle, why is it important in our new, value-based care environment, and why is it imperative that each step of the data lifecycle lead to the ultimate goal? Better patient care. Episode 38, The 7 Stages of the Health Data Life Cycle, answers those questions. Listen here

 

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5. Report the Data

Are you measuring up?

Reporting data to registries and CMS is nothing new, but there is definitely a stronger demand to do so. This will only continue to progress as we move towards value-based care.

Be sure the staff member or outside partner handling your data abstraction is absolutely up-to-date on quality measure specifications and performs some version of a cross-check process for accuracy. Reporting accurate, reliable, timely, actionable data is key to quality health outcomes. Not to mention, reporting is increasingly tied to financial results.

Learn more with our blog and video:

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6. Understand the Data

What are your conclusions?

Primaris ARTA-How Healthy is Your Healthcare Data

Identify from the start the team or individuals you will rely on for analyzing and drawing conclusions from the data. An outside partner with expertise in this task might also be valuable to your organization.

What was effective? What was not? Where can you improve and have the most affect for investment and resources, while also improving patient outcomes? When healthcare organizations have data they can understand, a root cause analysis is an ideal way to achieve sometimes conflicting goals of quality and cost. 

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7. Act Upon the Data

Are you ready?

There are other crucial facets of the data life cycle that we don't get into, such as managing and maintaining the data, but the paramount function of data is using it to improve clinical processes and outcomes, the patient experience, and the financial bottom line. 

How is your data being used to manage the quality and cost of care? Set smart goals that are measurable in order to track your progress as the data life cycle creates opportunities to improve patient care and the organization's bottom line. Make the most of your data abstraction efforts and use the data to drive quality improvement.

The final stage in the data life cycle is certainly the most important; the life cycle comes full circle. 

Learn more with our blog and video:

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Primaris Healthcare Data Lifeline

The Life Cycle Comes Full Circle

How Healthy is Your Data?

In the age of value-based care, healthy data is the key. Understanding the different stages that healthcare data goes through is vital for transforming clinical practices, complying with a new array of quality improvement requirements and, ultimately, affecting the way and amounts that physicians get paid for providing care.

Review all that you've learned by checking out our wrap-up blog and video:

How would you like a print document of the information shared on this page? Here's our tipsheet, 7 Tips for the Health Data Life Cycle", for a recap on our breakout of each stage. 

Before you go—check out these infographics showcasing the importance of healthy healthcare data and the risks of using data that is "not-so-healthy.":

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Are your data demands pulling you under?

Many providers are drowning in their data and struggling to keep up with quality reporting demands. Grab on; we can help! Have you considered outsourcing your abstraction needs? Download our e-book, "The 5 P's of Abstraction Outsourcing", to learn more about how letting our team of experts handle your abstraction can lead to quality reporting success for you and your team.  

Download today!

“Primaris’ partnership with us is characterized by their collaborative approach, constant communication and attention to detail… Having Primaris on-board is almost like having our own abstraction department.”

–Northern Hospital of Surry County

“Working with Primaris has brought several positive outcomes to our facility. Their partnership affords us time to focus on implementing new plans and procedures to ultimately provide outstanding patient care. The impact Primaris has made is evident when comparing our national scores against those of other organizations.”  

-Rainy Lake Medical Center