Posts Tagged: data management

What Is Healthcare Data Management?

Posted by & filed under Healthcare.

what is healthcare data management

The question, what is healthcare data management, doesn’t have a simple answer. It’s complex and includes many different nuances and challenges. The entire healthcare ecosystem is part of the answer. Simply put, it’s how a healthcare organization manages its data.

That management of data includes keeping it private and observing compliance mandates. It also deals with the data’s accessibility, portability, and interoperability. The amount of healthcare data is increasing every minute. Its value to the delivery of care, reducing costs, and improving public health is immense. However, the true potential of healthcare data remains untapped. 

In this post, we’ll look at the ways that we address, what is healthcare data management. 

Data Management Includes Moving It

Data movement in healthcare includes many subsets. There are data conversions and data sharing. In a healthcare data conversion, you move patient data from one software platform to another. It sounds easy, but it’s rather complex. 

To migrate data successfully, you must:

  • Follow compliance guidelines.
  • Ensure field matching is accurate.
  • Include a robust QA process.
  • Move structured and unstructured data.

Those are the pillars of successful healthcare data migrations. Because there is no industry standard for EHRs or pharmacy management solutions, you can’t just copy and paste or use a simple program. It’s a process that requires expertise, testing, and quality controls to deliver the most accurate outcome.

Data sharing is also critical in data moving. Internal and external systems need to exchange data in a secure and consistent process. Data sharing is a data management pain point for many, but it’s necessary to support decision-making and have a holistic view of public health. 

Another aspect of sharing is patient access through portals. Most EHRs have this feature and share data automatically, but there may be additional information that patients should be able to retrieve. For example, lab reports are sometimes in other systems, requiring the portal to acquire that data from its host. 

Data Management Requires Storing It

Storing data is another segment of healthcare data management. First, you should have secure cloud backups of all your applications and data. This function is imperative for strong cybersecurity practices.

The other element of storing data is archiving it. Healthcare data archiving enables you to move data from active or legacy programs into a centralized repository. It’s then accessible, secure, and searchable. In addition, you’ll be able to meet medical record retention guidelines by archiving. 

Data Management Involves Analyzing It

Healthcare data has little value without analysis. By leveraging technology platforms, you can derive critical insights from your data. You can begin to answer micro and macro questions based on the patterns and trends in your data.

Data analytics will likely be the most critical aspect of healthcare data management. It has the potential to improve care delivery and be predictive for both public outcomes and individual patients. Further, it could reduce costs, helps organizations maximize revenue, and more business-focused needs.

What Is Healthcare Data Management? It’s How You Use It 

Healthcare data management means many things. What it means to your organization is how you use it. Your data must be accessible, portable, and interoperable to have healthy data operations. When you don’t have those functions, it derails your capabilities. We’re experts in all areas and are a healthcare data partner of choice for many organizations. Explore our healthcare data management solutions today.

Healthcare NLP and Unlocking Unstructured Data Potential

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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

Posted by & filed under 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.