More Data, More Value?

We’ve entered a new data-rich era of digital health. Thanks to sources like electronic health records, personal fitness apps, and wearables, there’s a lot of available data out there. This influx of information should allow us to better understand our health and what we can do about it. But simply having a lot of data isn’t enough. Providers need to be aware of what information is available, what it means, and what to do with it.

In fact, survey results show that 39% of clinicians reported that the patient data that is available to them isn’t actionable, and 33% said the patient data available to them isn’t relevant.

Here’s an all-too-common scenario:

Greg comes into the ER with chest pain and is diagnosed with an acute myocardial infarction, or a heart attack. He is rushed to the cath lab where he has two stents placed and is started on several medications, some of which he will have to take for life. Greg had no idea he was at risk for a heart attack. He felt fine up until the day he went to the ER. Heart disease is the number one killer in the United States and is largely preventable. Was Greg's case preventable or predictable given that he had no symptoms?

It turns out Greg had a chest x-ray performed 4 months ago when he went to urgent care for a cough. The chest x-ray did not show pneumonia, but the radiologist did note that there was coronary artery calcification, or plaque build up in the blood vessels of Greg's heart. Because the chest x-ray was ordered by the urgent care doctor, it wasn't routed to Greg's primary care provider so his physician didn't know to follow up on it.

Greg has only been to his primary care physician once in the last year, where his blood pressure was borderline high, but in the past few months he went to urgent care, his podiatrist, and his urologist who all individually noted high blood pressure. 

Greg also recently got his labs drawn to apply for life insurance, where his cholesterol levels were high. Because his primary care provider was not the ordering physician, they were not notified of the results.

As you can see, because Greg's healthcare data lives in so many different places, locked away in different EMRs, no single provider had all of the information they needed in order to understand and address Greg's risk of heart disease. When you put everything together, however- the elevated blood pressure, high cholesterol, and plaque build up in his blood vessels - it's pretty clear that Greg was at high risk of developing heart disease. So, how can we get the right information in front of providers to prevent situations like this? 

Challenge 1: Data lives in so many different places

The first challenge is consolidating patient data from multiple sources. Siloed data leads to gaps in proper patient care. Providers need all of the information to be able to make informed decisions at the point of care. Luckily, this is where Particle comes in. Particle’s API plugs into Carequality, CommonWell, and eHealthExchange plus additional clinical data sources. Overall, Particle’s API has a 90%+ market-leading hit rate. This allows providers to obtain all of their patients’ medical histories, aggregated from different care sites across the entire country. 

Challenge 2: More data doesn’t equal more value

The second challenge is knowing what to do with this data - how do we make data actionable? Healthcare organizations and providers deal with a high potential for distraction when trying to isolate critical pieces of information from massive amounts of data. To name a few, complex data formats and limited resources are common obstacles to usable data. However, if there was a way for doctors to get the right data at the right time, we could see more accurate diagnoses, more preventative treatments, and better outcomes - especially for those with chronic medical conditions. But, the question remains: How can physicians get the data they need to actually improve patient care?

A Condition-Oriented Solution

Particle recently launched FOCUS - a suite of curated datasets across seven specialties that give providers access to the data they need in order to drive effective patient care. Also, this data is delivered in a simplified format that’s clean and easy to work with. Well-formatted data can fuel a number of powerful clinical support tools within existing workflows, including interactive graphs or charts that make it much easier for a provider to absorb information and use it appropriately. Let’s walk through how these datasets were developed. 

FOCUS datasets are built on a clinical understanding of how each disease is diagnosed, tracked, and managed across patient populations. First, data is refined to four categories: diagnoses, labs, procedures, and medications. Each of these categories corresponds to a unique set of medical codes: ICD-10 for diagnoses, LOINC for labs, CPT/HCPCS for procedures, and RxNorm for medications. Next, we leveraged both clinical and medical coding expertise to define an extensive list of codes specific to each specialty across all four databases. Essentially, we highlighted the data that matters the most.

For a better understanding of how these medical codes helped identify important patient data, let’s review what information is captured within each database:

• Healthcare Common Procedure Coding System (HCPCS) + Current Procedural Terminology (CPT): The Healthcare Common Procedure Coding System consists of two levels. Level I is the CPT coding system, which describes medical, surgical, and diagnostic procedures or services performed by physicians and other healthcare professionals. Each CPT code has five digits. Level II includes alphanumeric codes used to report supplies, equipment, and devices provided to patients. 
• International Classification of Diseases, Tenth Revision (ICD-10): ICD-10 is a code system used by healthcare professionals to classify and code all diagnoses, symptoms, and procedures.
• Logical Observation Identifiers Names and Codes (LOINC): The LOINC code system is used to electronically transmit a vast amount of health data, primarily including clinical laboratory test orders and results.
• RxNorm: RxNorm provides a set of codes for clinical drugs, which are the combination of active ingredients, dose form, and strength of a drug.
  

Our goal was to develop code sets that were easy to use, but also comprehensive enough to be useful.  If you are interested in learning more about what information is included within each dataset, a code summary is available for download here. 

We are continuing on our mission of enabling simple and secure access to actionable healthcare data. Please get in touch with us to learn more about how Particle can enable your organization to treat complex conditions with the best data, served exactly how you need it.

You can also check out our webinar, Integrating clinical data into your workflows with Particle FOCUS, to learn more!