Posts By: Beth Osborne

Healthcare NLP and Unlocking Unstructured Data Potential

healthcare NLP

Data is everywhere in healthcare. It is the fuel for how the industry is evolving, growing, and delivering care. Data management is complex in the field for many reasons. Unstructured data is one of the most challenging aspects. However, healthcare NLP (natural language processing) is an excellent tool to help unlock the potential of this information.

How Does Healthcare NLP Work?

NLP uses AI to extract unstructured data from EHRs or other HIS (health information systems). In addition to extraction, EHRs are using NLP to provide speech-recognition capabilities during patient visits. There’s no lack of EHR dissatisfaction from clinicians. An AMA (American Medical Association) survey found nearly half of users have some point of frustration with their EHRs.

With the right technology, NLP can improve interaction with EHRs and deliver insights from this mountain of unstructured data.

How Can NLP Deliver Insights Related to Unstructured Data?

By using NLP, you can aggregate unstructured data from multiple sources for analysis. NLP converts the text to structured data, which healthcare systems can use to:

  • Classify patients
  • Extricate insights
  • Summarize information

When you can do this, healthcare NLP can provide opportunities in several areas.

EHR Data Is More Usable

EHRs typically arrange information by patient visit, which isn’t the most feasible way to find what you need. Instead, NLP makes things like history more visible, and that can assist a provider in care plans.

You can isolate by specific words and builds a model of the use of that descriptor. This data would have been buried otherwise. As a result, the clinician could have spent excess time on searching or may have missed a diagnosis.

Predictive Analytics for Population Health

NLP enables predictive analytics that can help improve population health challenges. Since it can analyze unstructured data from multiple sources, NLP is a great tool for predictive analytics at scale. This process is the source of many studies and could be a gamechanger in SDOH (social determinants of health) and healthcare hurdles around COVID-19.

Access to More Data

One of the most exciting aspects of NLP in healthcare is that excerpt capabilities. The biggest barrier to healthcare data management in many ways is accessibility. Most analytics tools have limitations on what they can obtain and process. NLP can do much more such as extracting data from pathology reports, so that clinicians can discover answers to complex questions around diseases.

NLP Only Works with Quality Data

The problems with NLP are like any other data problems. The issue is quality—garbage in, garbage out, as they say. If your EHR or HIS has inaccurate, incomplete, and inconsistent data capture, NLP isn’t a magical tool that can make something out of nothing. To employ NLP and derive benefits, the quality of data matters.

The Best Use Cases of NLP in Healthcare

NLP still has challenges to work out before it can reach its potential with unstructured data. In addition to use in predictive analytics and quality improvement, it’s also effective for decision support. Such a scenario could be flagging patients in an EHR with a family history of a specific diseases. Flagging could initiate communication to those patients of possible risks and what screenings they should undergo.

The future trajectory includes much more such as inferring meanings to add context to patient records and semantics. Semantics are hard for NLP but coming along. The improved technology will be able to decipher subjects and objects.

NLP Healthcare: Technology to Fuel Data-Driven Decisions

The use of NLP in healthcare will grow, as the industry generates more data each day. That data is critical in care delivery, cost reduction, forecasting, and more. It could possibly become a key feature in an EHR or HIS, and its value could revolutionize decisions about proactive healthcare and treatment plans for patients.

Unique Data Management Needs for Chain Pharmacy

The world of chain pharmacy data management is complicated. It’s much different from independent pharmacies. That’s because chain pharmacies have multiple locations, often across numerous states. They also are more likely to acquire other pharmacies, meaning they have transition needs. So, what are these unique data management needs?

They Have More Data and Require Sharing Across Locations

The sheer amount of data is one big difference for chain pharmacy. The largest chains in the country have thousands of locations. That data also must be shareable. If a patient has a prescription at one location but moves or needs a refill while away from home, they need to be able to do this at any site. It’s a lot of moving pieces that require standardization. 

More Patient Records Means Lots of Data to Store

The pharmacy management systems for chains are always expanding. However, not all this data is active. Even though it’s not active, you still have to retain it for periods of time per state laws. Instead of overburdening systems, many choose to archive pharmacy records on a cloud-based platform.

Doing so makes your software less bulky, but you can still access it. Further, anyone within your network can when you select a web-based option. So, if pharmacy A needs records for a patient from pharmacy B, it’s easy to find and doesn’t require lots of legwork. 

Chain Pharmacies Are Acquirers

The pharmacy industry experiences acquisitions and closures every year. Chain pharmacies are top acquirers. They may either keep the location but rebrand or close it and just buy the patient data. In either case, they need a variety of pharmacy transition services, including:

  • Data conversions from the existing pharmacy system to their system.
  • Data archiving of older patient records that they don’t want to migrate but still must keep to adhere to record retention requirements.
  • Print and mail services to inform patients of the change, rebrand locations, or provide signage announcing the pharmacy moved. 

Custom Data Needs Are Common

This subset of pharmacy can also have specialized data needs. Those look different for every pharmacy but could include analytics, reporting, PHI data transfers, data cleaning, data aggregation, and data warehousing.

For these customized processes, you may look to firms that have deep experience working with pharmacy data. Your internal IT teams may not have the capacity or expertise, and that’s where a partner like InfoWerks makes achieving data-related goals pain-free. 

The Chain Pharmacy Data Experts

InfoWerks has over two decades of pharmacy data experience. We’ve completed tens of thousands of projects and archived billions of records. We are the partner of choice for the biggest chains in the country, and we’re always adapting to changing needs of the industry. To learn more about our solutions, contact our pharmacy experts today

Immunization Interoperability: Barriers, Challenges, and Opportunities

immunization interoperability

The U.S. and the entire world are in the middle of the most extensive mass vaccination effort ever. The pandemic and inoculation are also creating lots of data. That’s data that needs to be shareable and exchangeable. In other words, immunization interoperability is paramount right now. The problem is that healthcare interoperability, in general, has a myriad of challenges and barriers.

The U.S. healthcare system is not a national one. That’s a big part of the problem. There’s no consistency around data or how to share it. Additionally, providers all use different EHRs, some that can exchange information easily and others that cannot.

Immunization interoperability issues aren’t new. The pandemic just shined a brighter light on them, like with so many others.

The U.S. Has 61 Immunization Information Systems

Did you know that the U.S. and its territories have 61 independent immunization information systems (IIS)? These align with different city and state health departments, and the CDC has governance

In 1998, there was a movement toward a national immunization registry, but it failed to gain traction. Instead, the IIS were established. While they do follow national standards, the operation is at the city and state level. Further, states also dictated immunization requirements for their citizens. 

How Immunization Interoperability Works Now

While it would seem that data is living in silos, there are current processes in place. Providers enter immunization information of patients into their EHR and send it to the IIS. In turn, some public health programs can access the information. IIS also sends information back to the EHR to support forecasts. 

Unfortunately, due to the lack of standardization around healthcare interoperability and data, it’s not effective across the board. 

The data exchange becomes complicated because there are inconsistencies in data formats, standards, and access. Different types of providers perform inoculations, such as primary care physicians, pharmacists, and hospital clinicians. 

What’s the Answer for Immunization Interoperability?

There’s no one answer or one pathway. Immunizations are just one small part of the bigger picture of healthcare interoperability. We’re just all more aware of immunizations right now and the discussions around digital vaccine cards or some other method of tracking and validating vaccines. 

Standardization of data formats is necessary, so that requires agreement with regulatory bodies and EHR companies. Simplifying the IIS seems like a smart option, as well. But, should there be only one? That’s probably not going to happen, just like there’s not a national patient identifier. The U.S. doesn’t have a national healthcare system. Each state has different laws and rules, and healthcare is, in most cases, a private business.

Investment in public health technology infrastructure is another must-have. The CDC is on board with this with its Data Modernization Initiative.

Data Must Be Interoperable to Be Actionable

The bottom line is that healthcare data must be interoperable to be actionable. If this information is only available in a limited capacity, it can’t serve the individual patient or the greater public. We’re extreme advocates for removing obstacles around data sharing. Data is the fuel for every industry’s future, and healthcare is no exception.