Data continues to be a significant sore spot for the healthcare industry, with increasing security breaches, cumbersome systems, and data redundancies undermining the quality of care delivered.
Adding to the pressure, the US Department of Health and Human Services (HSS) is set to introduce more stringent regulations around interoperability and handling of electronic health records (EHRs), with transparency a top priority.
However, it’s clear that technology has played a crucial role in streamlining and organizing information-sharing in the industry, which is a significant advantage when outstanding services heavily rely on speed and accuracy.
Healthcare organizations have been turning to emerging technologies to alleviate growing pressures, which could possibly save them $360 billion annually. In fact, 85% of companies are investing or planning to invest in AI to streamline operations and reduce delays in patient care. Technology is cited as a top strategic priority in healthcare for 56% of companies versus 34% in 2022, according to insights from Bain & Company and KLAS Research.
Yet there are a number of factors healthcare providers should be mindful of when looking to deploy advanced technology, especially considering that AI solutions are only as good as the information used to train them.
Let’s take a look at the biggest data pain points in healthcare and technology’s role in alleviating them.
Enormous Amounts of Data
It’s no secret that healthcare organizations have to deal with a massive amount of data, and it’s only growing in size: By next year, healthcare data is expected to hit 10 trillion gigabytes.
The sheer volume of data that needs to be stored is a driving force behind cloud storage popularity, although this isn’t a problem-free answer, especially when it comes to security and interoperability. That’s why 69% of healthcare organizations prefer localized cloud storage (i.e., private clouds on-premises).
However, this can easily become challenging to manage for a number of reasons. In particular, this huge amount of data has to be stored for years in order to be HHS-compliant.
AI is helping providers tackle this challenge by automating processes that are otherwise resource-exhaustive in terms of manpower and time. There are a plethora of solutions on the market designed to ease data management, whether that’s in the form of tracking patient data via machine learning integrations with big data analytics or utilizing generative AI to speed up diagnostics.
For AI to do its job well, organizations must ensure they’re keeping their digital ecosystems as interoperable as possible to minimize disruptions in data exchanges that have devastating repercussions for their patients’ well-being.
Moreover, it’s crucial that these solutions are scalable according to an organization’s fluctuating needs in terms of performance and processing capabilities. Upgrading and replacing solutions because they fail to scale is a time-consuming and expensive process that few healthcare providers can afford. That’s because it means further training, realigning processes, and ensuring interoperability hasn’t been compromised with the introduction of a new technology.
Data Redundancies
With all that data to manage and track, it’s no surprise that things slip through the cracks, and in an industry where lives are on the line, data redundancies are a worst-case scenario that only serves to undermine the quality of patient care. Shockingly, 24% of patient records are duplicates, and this challenge is worsened when consolidating information across multiple electronic medical records (EMR).
AI has a big role to play in handling data redundancies, helping companies streamline operations and minimize data errors. Automation solutions are especially useful in this context, speeding up data entry processes in Health Information Management Systems (HIMS), lowering the risk of human error in creating and maintaining more accurate EHRs, and slashing risks of duplicated or incorrect information.
However, these solutions aren’t always flawless, and organizations need to prioritize fault tolerance when integrating them into their systems. It’s vital to have certain measures in place so that when a component fails, the software can continue functioning properly.
Key mechanisms of fault tolerance include guaranteed delivery of data and information in instances of system failure, data backup and recovery, load balancing across multiple workflows, and redundancy management.
This essentially ensures that the wheels keep turning until a system administrator is available to manually address the problem and prevent disruptions from bringing the entire system to a screeching halt. Fault tolerance is a great feature to look out for when selecting a solution, so it can help narrow down the product search for healthcare organizations.
Additionally, it’s crucial for organizations to make sure they’ve got the right framework in place for redundancy and error occurrences. That’s where data modeling comes in as it helps organizations map out requirements and data processes to maximize success.
A word of caution though: building the best data models entails analyzing all the optional information derived from pre-existing data. That’s because this enables the accurate identification of a patient and delivers timely and relevant information about them for swift, insight-driven intervention. An added bonus of data modeling is that it’s easier to pinpoint APIs and curate these for automatically filtering and addressing redundancies like data duplications.
Fragmented and Siloed Data
We know there are a lot of moving parts in data management, but compound this with the high-paced nature of healthcare and it’s easily a recipe for disaster. Data silos are among the most dangerous blind spots in this industry, and in life-or-death situations where practitioners aren’t able to access a complete picture of a patient’s record, the consequences are beyond catastrophic.
While AI and technology are helping organizations manage and process data, integrating a bunch of APIs and new software isn’t always smooth sailing, particularly if it requires outsourcing help whenever a new change or update is made. Interoperability and usability are at the crux of maximizing technology’s role in healthcare data handling and should be prioritized by organizations.
Most platforms are developer-centric, involving high levels of coding with complex tools that are beyond most people’s skill sets. This limits the changes that can be made within a system and means that every time an organization wants to make an update, they have to outsource a trained developer.
That’s a significant headache for people operating in an industry that really can’t sacrifice more time and energy to needlessly complicated processes. Technology should facilitate instant action, not hinder it, which is why healthcare providers and organizations need to opt for solutions that can be rapidly and seamlessly integrated into their existing digital ecosystem.
What to Look for in a Solution
Opt for platforms that can be templatized so they can be imported and implemented easily without having to build and write complex code from scratch, like Enterprise Integration Platform as a Service (EiPaaS) solutions. Specifically, these services use drag-and-drop features that are user-friendly so that changes can be made without the need to code.
This means that because they’re so easy to use, they democratize access for continuous efficiency so team members from across departments can implement changes without fear of causing massive disruptions.
Another vital consideration is auditing, which helps providers ensure they’re maintaining accountability and consistently connecting the dots so data doesn’t go missing. Actions like tracking transactions, logging data transformations, documenting system interactions, monitoring security controls, measuring performance, and flagging failure points should be non-negotiable for tackling these data challenges.
In fact, audit trails serve to set organizations up for continuous success in data management. Not only do they strengthen the safety of a system to ensure better data handling, but they are also valuable for enhancing business logic so operations and process workflows are as airtight as possible.
Audit trails also empower teams to be as proactive and alert as possible and to keep abreast of data in terms of where it comes from, when it was logged, and where it is sent. This bolsters the bottom line of accountability in the entire processing stage to minimize the risk of errors in data handling as much as possible.
The best healthcare solutions are designed to cover all bases in data management, so no stone is left unturned. AI isn’t perfect, but keeping these risks and opportunities in mind will help providers make the most of it in the healthcare landscape.
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