How RPA Is Remarkably Reducing Human Error in Medical Practices

In recent years, software automation has disrupted competition and innovation across sectors, including entertainment, retail, transportation, manufacturing, and more. And today, companies in highly regulated industries like finance and healthcare are starting to take notice and invest more heavily in automation.

The vast array of regulations that govern healthcare can overwhelm workers. As a result, healthcare professionals are increasingly faced with sky-high administrative workloads that threaten to hamper productivity and lead to worse patient outcomes. Of course, these administrative tasks are vital to a fully-functioning healthcare facility, but they're also incredibly time-consuming and error-sensitive.

At the same time, healthcare organizations are under more pressure than ever before to extract actionable insights from their data.

So, how do healthcare organizations navigate this increasingly demanding landscape? Robotic process automation (RPA) offers a better way forward.

Electronic Health Records (EHRs) Today

First, what are EHRs? In simple words, electronic health records are digital versions of a patient's medical history. They can include all important administrative and clinical data relevant to that person, including medications, past medical history, immunizations, lab data, radiology images, and more.

According to the Centers for Disease Control and Prevention (CDC), 89.9% of office-based physicians now use an EHR system. The widespread adoption of EHRs is largely down to how they can improve patient care by:

Reducing the incidences of errors by boosting the accuracy and clarity of medical records.
Making health data more available. This can reduce test duplication and avoid situations where gaps in a medical record lead to misdiagnosis.
Allowing patients to have access to their own medical records so they can make informed decisions about their care.
Companies can leverage an extensive repository of accurate health data for data analysis and insights.
Decreasing the administrative burden on healthcare professionals and freeing them up for more critical tasks.

EHRs have undoubtedly improved patient outcomes, but they don't come without challenges. Often, companies struggle to realize the full benefits of their EHR system, or they're held back by inefficient and antiquated technology. With this in mind, let's dive into the current drawbacks of EHR systems.

The Challenges of EHR Systems in the Modern Healthcare Landscape

The design, customization, and way healthcare practitioners use EHRs can lead to inefficiencies and workflow challenges and, ultimately, contribute to patient harm.

Let's start with data entry. A healthcare professional's work process may make it hard to enter accurate EHR data appropriately. For example, researchers in a study that analyzed 557 clinician reports found one case where a clinician chose the wrong frequency for a drug to be administered. Why? Because they didn't realize that the order of the options had changed in the EHR system. This is a perfect example of how systems designed to minimize human error can still fall victim to it.

Moreover, poorly designed interfaces can lead to data bottlenecks in EHR systems. EHRs are supposed to streamline workflow to increase accuracy and efficiency, but this doesn't always happen in practice. For example, the interface may be clunky and difficult to use because they were developed without consideration for how they will be used in real-world situations.

Data entry also suffers due to practitioners' heavy workload. For example, following an appointment with a patient, a practitioner may have to input large amounts of data into separate parts of the EHR system, which is time-intensive and frustrating. In addition, the practitioner may have another patient waiting outside and a pile of work already on their desk.

And critically, EHR systems are typically inflexible. For example, a 2007 study found that pediatricians reported an absence of pediatric functions in the EHR system they were required to use. Similarly, a 2011 study found fewer than 5% of anesthesia departments used an EHR system with anesthesia-specific functions.

You might be thinking, "well, that was a long time ago. EHR systems are probably much better today." And while you wouldn't be wrong for thinking this, you wouldn't be entirely correct either. While there have been significant advancements in EHR systems over the last decade, progress is slow and fraught with institutional and technical challenges.

Once implemented, EHR systems are challenging to replace. And this means that many healthcare facilities are stuck with antiquated and inflexible technology that they must continually manipulate to make it work for modern environments. At the same time, many practitioners find using these older systems complicated, further adding to the performance issues surrounding EHRs.

These challenges are significant today, but they also pose problems for the future of EHRs and Big Data. Healthcare organizations are under increased pressure to leverage health data to gain actionable insights. However, many healthcare organizations already face problems extracting EHR data for regulatory reports. Or in other words, if EHR systems are struggling to keep pace with today's environment, what does that mean for the future?

To recap, we can define the problems with EHR as follows:

Capturing clinical data for EHR systems increases the burden of day-to-day operations.
EHRs often have poor usability due to a disconnect from actual clinical workflows.
Accessing and extracting data for reporting is challenging.

Combating EHR Challenges With RPA

So, where does robotic process automation fit into this?

When faced with the growing list of EHR system challenges, healthcare organizations have two options - switch EHR systems or use RPA bots.

Switching EHR systems is prohibitively expensive, can cause disruptions to day-to-day operations, and is a giant time vampire. For example, while the EHR implementation timeline varies based on several factors, a general estimate is around nine months to one year for setting up a new EHR system. Naturally, this isn't an attractive idea for many organizations, but it's often necessary for organizations with ancient technology.

But the other option - fixing EHR system imperfections with RPA bots, is an attractive solution. It's far cheaper and can be done on a much shorter timeline. And equally important, with RPA, you can keep what works and fix what doesn't.

Before we dive a little deeper, let's look at some quickfire benefits of integrating RPA with an EHR system:

Organizations can keep their current system (no need for disruptive overhaul and costly and time-consuming user training).
It maintains the current workflow.
Low-risk implementation.
Employees have more time for more critical work.
Organizations can run processes outside of standard work hours.

So, in what specific ways can RPA improve EHR systems? Robotic process automation excels at rules-based, repetitive tasks typically performed by humans. These tasks are usually highly predictable and time-consuming but sensitive to errors when carried out by humans.

Here are some of the tasks robotic process automation can handle in a healthcare environment:

Data entry: RPA can capture and enter data into the relevant parts of the EHR system with no human involvement. For example, it can add information to patient records, add appointments to the EHR system, and more. This process can also assist with pre-authorization.
Automating the transfer of data between systems: EHR systems are often not fully-integrated with other critical systems within the healthcare IT environment. This can mean data must be taken from one system and manually entered into another. RPA can handle this entire process, extracting the necessary data and feeding it into the EHR system.
Data validation: Patient data often needs to be verified before filing insurance claims. Since manual verification is time-consuming, staff often become quickly overwhelmed, and errors become more common. These errors cause significant delays. RPA bots can validate this data against other records or by communicating with patients and only flag for human verification if there's a discrepancy.
Report management: RPA can assist in extracting necessary data for reports of billing payments.
Regulatory compliance: RPA empowers organizations to gain better control over their data, including where and how it's recorded. This means they're better prepared for unexpected external audits.

Critically, implementing a robotic process automation bot is far cheaper than hiring more full-time employees to handle the repetitive tasks required to keep the organization functioning.

For organizations that do need a new EHR system, RPA can help here too. Moving from one system to another requires considerable data migration, and RPA bots can assist in getting the data where it needs to be.

The Future of RPA in EHR Systems

Healthcare organizations are increasingly investing in Big Data analytics and AI to improve patient outcomes. Here, companies leverage data for a vast number of use cases. For example, AI can predict patient outcomes by tracking risk factors for disease. It can also perform analytics on staffing levels and admission patterns to determine when more staff will be needed. And it can provide critical insights into how the healthcare facility is performing, both financially and in terms of patient care.

However, excellent data analysis requires accurate and comprehensive data. Data is the lifeblood of AI - without it (and plenty of it), your results are meaningless. RPA is hugely important here because it helps collect and extract all the data needed to help AI systems thrive.

Wrapping Up

As a traditional and highly-regulated industry, healthcare often suffers from slow technological progress. EHR systems offer numerous benefits, but they're often fueled by outdated and clunky technology that stops healthcare organizations from reaching their full potential. RPA solves this problem by allowing systems to exchange data effortlessly and decreasing the administrative burden on healthcare practitioners.

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Published On:

October 30, 2024

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