Data Collection in Healthcare: Where Are We Now?
September 23rd, 2020
Data collection in healthcare is beneficial to all stakeholders. Challenges and opportunities remain, and all players need to work together to evolve.
Healthcare was certainly not the first vertical to embrace big data. Now, it’s a critical part of operations. From decision support systems that help providers make more accurate care decisions to payers that aggregate claims data to understand drivers behind poor health, data touches every part of the ecosystem. However, that doesn’t mean data collection in healthcare is seamless and without issue.
Many challenges and opportunities exist in the collection of healthcare data. How can those in the field overcome the challenges and leverage the opportunities?
Why Is Data Collection in Healthcare Important?
Data is the fuel in any industry—it powers the present, deciphers the past, and can help predict the future. Healthcare is no different. Both providers, payers, and regulatory bodies understand the value of data. It can improve patient care, reduce costs, streamline processes, combat fraud, and lead to insights in a public health crisis.
Think of the role healthcare data is playing in COVID-19. The number of cases and deaths is helping predict what areas are more at risk. Contact tracing is revealing how the virus spreads. It’s an invaluable resource for all those on the front lines of the pandemic.
What Are the Opportunities in Healthcare Data Collection?
Beyond the criticality of data in for public health emergencies like the pandemic, there are, of course, thousands of other opportunities. Here are some of the ways healthcare big data is changing care.
More data leads to better decisions for care. Analytics and modeling take data to a new realm of predictive opportunities. The more providers know about a disease, in general, the more informed they can be when working with patients on treatments.
Additionally, when providers with the same patient share data, they can improve continuity of care. They have a big picture of all the patient’s ailments, not just the ones they treat.
Wearables to Monitor and Diagnose
Wearable devices aren’t just for counting steps. Healthcare has become a boom for wearables to monitor conditions and diagnose. Data collection from them feeds into predictive modeling, and providers can use it to make a precise diagnosis of a disease or monitor a chronic condition like diabetes. Data like this can enhance treatment plans and log a patient’s progress.
Many providers now have portals for patients to access their health information. When patients are able to view this information, it helps them be more involved in their health. They may also be more likely to continue follow up visits and be more adherent to their medication regimen.
What Are the Challenges of Healthcare Big Data?
Healthcare data collection is complicated. Most of the challenges revolve around compliance, interoperability, poor data quality, and inefficient processes.
All collection of PHI (protected healthcare information) must be compliant with HIPAA. In most scenarios, this requires data encryption at collection, transit, and rest. How you collect healthcare data, no matter why must follow regulatory guidelines. While HIPAA has been a law for over two decades, the digitation of the healthcare space is still an ongoing process. It’s imperative that you design collection processes that ensure security and privacy.
Interoperability: Data Sharing and Exchange Have Issues
Healthcare interoperability is one of the most significant challenges in the field. All patient data isn’t just in one system. There are EHRs, pharmacy software systems, decision support systems, and many other health information systems (HIS). What you collect in one platform may also need to be shareable to others. Aggregation efforts are often problematic. That’s because data sharing isn’t always a priority.
Beyond sharing between internal systems, you may also need to provide data to other providers, payers, or regulatory bodies. The new interoperability rule seeks to untangle all these problems, but it’s not going to be easy with complete standardization.
Poor Data Quality
Accuracy of healthcare data is another challenge. Inaccuracy happens for several reasons. Some common causes are patient data isn’t up to date, human errors, and miscoding. If you’re going to leverage data, it must be quality data. Otherwise, it’s useless. Every healthcare entity should establish and maintain data governance and quality rules.
Inefficient Collection Processes
Did you know that paper is still a big part of the healthcare ecosystem? Every day, patients walk into providers and fill out paperwork about their health history, insurance, and other PHI. The information on the paper then must go into a digital form, either by manual entry of some type of scanning. These processes are not practical, and they can be risky.
How Are You Handling Healthcare Data Collection?
You probably have processes in place, but you know there are gaps, keeping you from resolving challenges and tapping into opportunities. However, you don’t have to navigate this road alone. As healthcare data management experts, we have solutions to help you with data collection, sharing, conversions, analytics, and more.
Check out how we support all healthcare players with their data.