Data continues to be a big sore spot for the healthcare industry, with increasing security breaches, cumbersome systems, and data redundancies undermining the standard of care delivered.
Adding to the pressure, the US Department of Health and Human Services (HSS) is about to introduce more stringent regulations around interoperability and handling of electronic health records (EHRs), with transparency a top priority.
Nevertheless, it’s clear that technology has played an important role in streamlining and organizing information-sharing within the industry, which is a big advantage when outstanding services heavily depend 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 reality, 85% of firms are investing or planning to speculate in AI to streamline operations and reduce delays in patient care. Technology is cited as a top strategic priority in healthcare for 56% of firms versus 34% in 2022, in line with insights from Bain & Company and KLAS Research.
Yet there are numerous aspects healthcare providers ought to be mindful of when seeking to deploy advanced technology, especially considering that AI solutions are only nearly as good as the data used to coach them.
Let’s take a have a look at the largest data pain points in healthcare and technology’s role in alleviating them.
Enormous Amounts of Data
It’s no secret that healthcare organizations should take care of an enormous amount of information, and it’s only growing in size: By next 12 months, healthcare data is anticipated to hit 10 trillion gigabytes.
The sheer volume of information that should be stored is a driving force behind cloud storage popularity, although this isn’t a problem-free answer, especially with regards to security and interoperability. That’s why 69% of healthcare organizations prefer localized cloud storage (i.e., private clouds on-premises).
Nevertheless, this could easily develop into difficult to administer for numerous reasons. Specifically, this huge amount of information needs to be stored for years to be able to be HHS-compliant.
AI helps providers tackle this challenge by automating processes which might be otherwise resource-exhaustive when it comes to manpower and time. There are a plethora of solutions in the marketplace designed to ease data management, whether that’s in the shape of tracking patient data via machine learning integrations with big data analytics or utilizing generative AI to hurry up diagnostics.
For AI to do its job well, organizations must ensure they’re keeping their digital ecosystems as interoperable as possible to reduce disruptions in data exchanges which have devastating repercussions for his or her patients’ well-being.
Furthermore, it’s crucial that these solutions are scalable in line with a company’s fluctuating needs when it comes to 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 since it means further training, realigning processes, and ensuring interoperability hasn’t been compromised with the introduction of a brand new technology.
Data Redundancies
With all that data to administer and track, it’s no surprise that things slip through the cracks, and in an industry where lives are on the road, data redundancies are a worst-case scenario that only serves to undermine the standard 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 giant role to play in handling data redundancies, helping firms streamline operations and minimize data errors. Automation solutions are especially useful on this context, speeding up data entry processes in Health Information Management Systems (HIMS), lowering the danger of human error in creating and maintaining more accurate EHRs, and slashing risks of duplicated or misinformation.
Nevertheless, these solutions aren’t at all times flawless, and organizations have to prioritize fault tolerance when integrating them into their systems. It’s vital to have certain measures in place in order that when a component fails, the software can proceed functioning properly.
Key mechanisms of fault tolerance include guaranteed delivery of information and knowledge 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 offered to manually address the issue and forestall disruptions from bringing the complete system to a screeching halt. Fault tolerance is an awesome feature to look out for when choosing an answer, so it may well help narrow down the product seek for healthcare organizations.
Moreover, it’s crucial for organizations to make certain they’ve got the proper framework in place for redundancy and error occurrences. That’s where data modeling is available in because it helps organizations map out requirements and data processes to maximise success.
A word of caution though: constructing the most effective data models entails analyzing all of the optional information derived from pre-existing data. That’s because this permits the accurate identification of a patient and delivers timely and relevant details about them for swift, insight-driven intervention. An added bonus of information modeling is that it’s easier to pinpoint APIs and curate these for mechanically filtering and addressing redundancies like data duplications.
Fragmented and Siloed Data
We all know there are plenty 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 amongst probably the most dangerous blind spots on this industry, and in life-or-death situations where practitioners aren’t in a position to access a whole picture of a patient’s record, the implications are beyond catastrophic.
While AI and technology are helping organizations manage and process data, integrating a bunch of APIs and recent software isn’t at all times smooth sailing, particularly if it requires outsourcing help every time a brand new change or update is made. Interoperability and value are on the crux of maximizing technology’s role in healthcare data handling and ought to be prioritized by organizations.
Most platforms are developer-centric, involving high levels of coding with complex tools which might be beyond most individuals’s skill sets. This limits the changes that could be made inside a system and signifies that each time a company desires to make an update, they should outsource a trained developer.
That’s a big headache for people operating in an industry that basically can’t sacrifice more time and energy to needlessly complicated processes. Technology should facilitate easy motion, not hinder it, which is why healthcare providers and organizations have to go for solutions that could be rapidly and seamlessly integrated into their existing digital ecosystem.
What to Search for in a Solution
Go for platforms that could be templatized so that they could be imported and implemented easily without having to construct and write complex code from scratch, like Enterprise Integration Platform as a Service (EiPaaS) solutions. Specifically, these services use drag-and-drop features which might be user-friendly in order that changes could be made without the necessity to code.
Because of this because they’re really easy to make use of, they democratize access for continuous efficiency so team members from across departments can implement changes without fear of causing massive disruptions.
One other 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 ought to be non-negotiable for tackling these data challenges.
In reality, audit trails serve to set organizations up for continuous success in data management. Not only do they strengthen the security of a system to make sure higher data handling, but also they are useful 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 maintain abreast of information when it comes to where it comes from, when it was logged, and where it is distributed. This bolsters the underside line of accountability in the complete processing stage to reduce the danger of errors in data handling as much as possible.
One of 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 take advantage of it within the healthcare landscape.
