Robotic Process Automation (RPA) in the Healthcare Industry

November 27, 2020

Robotic process automation (RPA) is a technology that automates business operations based on the concept of artificial intelligence (AI) workers or software robots.

RPA software mimics the virtual human worker and performs activities and tasks that are repetitive. This reduces the involvement of humans in the process, improves efficiency, reduces costs, and saves time.

Organizations in the healthcare industry are using RPA in patients scheduling, insurance claims management, improvement of healthcare cycle, and provision of optimal care, as we will discuss in this article.

For a more in-depth look at robotic process automation, its features, and benefits, refer to this article.

An overview of RPA in healthcare

The healthcare sector in any country is a complex yet critical system to the well-being of the citizens. It’s comprised of medical equipment, health insurance, clinical trials, and more.

Managing information related to clinical applications, third-party portals, scheduling applications, human resource applications, enterprise resource planning (EPR), and radiography information systems is often an uphill task.

The integration across these systems is equally challenging and labor-intensive. It requires patients, insurance companies, doctors, and many other stakeholders to ensure seamless care delivery.

Maintaining a balance between the rising number of individuals seeking care and paperwork involved in hospital processes calls for a more efficient and accurate back-office process.

A back office in a healthcare institution includes the administration and the support staff. The back-office staff work on regulatory compliance, record maintenance, and provide IT, settlement, clearance services.

Advanced automatic solutions like robotic process automation help healthcare organizations lower operational costs, increase operational efficiency, and reduce human error in data processing. RPA systems can process information in a record time and address challenges resulting from human error.

RPA use cases in the healthcare industry

Patients scheduling

Medical appointment scheduling platforms have improved the healthcare experience for patients and healthcare providers. They simplify booking a doctor appointments and maximizing the usage of the available resources.

But, doctors still use different electronic medical record systems (EMRs) to schedule patient appointments.

Synchronization of real-time data to show appointment availability is challenging with the EMRs systems. Doctors are forced to manually feed appointment data into multiple systems for patients to access accurate information about scheduled appointments and available time slots.

This way, doctors find themselves spending too much time on routine work and insufficient time on patient diagnosis and treatment.

Robotic process automation bots are trained to schedule patient appointments based on doctor availability, location, and diagnosis. Other factors to consider in patient scheduling include insurance information and the needs of the patient. RPA streamlines front-office support and automates data collection and processing.

Once a patient books an appointment, the robot places a schedule in the database and removes that particular appointment slot. The patient gets the appointment details through e-mail. Robots also send appointment reminders to patients through e-mails and messages and optimize patient scheduling.

Insurance claims management

Insurance claims processing includes many administrative, customer service, and managerial functions. These functions involve information-intensive manual tasks intended to protect the insurance company against errors or fraud.

Despite these efforts, American customers lose at least $80 billion to insurance fraud yearly. The emerging trend of claims processing with RPA in the insurance business is not surprising.

RPA bots ensure faster data processing while avoiding errors. These bots are programmed to collect relevant insurance claims data and make it available to employees handling claims registration. The process is much faster when compared to manual handling of data by employees.

These bots monitor the entire claims process and help avoid delays. Importantly, they help identify compliance-related exceptions while avoiding the non-compliance of regulations.

RPA streamlines the insurance claims management process and improves efficiency.

Healthcare cycle

Healthcare institutions are leveraging RPA systems to facilitate big data analysis. Analyzing big data by human hands is increasingly difficult and time-consuming.

Big data analysis using RPA is faster, higher quality, and with fewer errors. With robots and automated software, there are no distractions in data analysis.

With proper data analytics, doctors perform continuous monitoring of patient records effectively. This ensures a more accurate diagnosis and data-driven treatment strategies.

The automation of medical records contributes to a reduction in complications and deaths. This will be due to a better care cycle resulting from improved data analytics.

Essentially, RPA software records and monitors large volumes of data. Clinics and hospitals extract and analyze this data to generate analytics. They then apply these insights to different treatment and diagnosis methods.

Optimal care delivery

RPA helps achieve optimal care delivery by replacing manual inputs with digital input systems. RPA automates data extraction processes, medical records analysis, and detection of both structured and unstructured data. It then compares this information with historical health records to achieve the best diagnosis.

Predictive analytic techniques are applied to medical cases to give solutions, care alerts, and warnings. Furthermore, predictive analytics helps detect early signs of patients deteriorating health in the general ward and the ICU.

These techniques are equally important in identifying at-risk patients under home care to prevent hospital re-admissions. The application of predictive analytics helps in the early identification of medical equipment maintenance needs to avoid downtime.

Analytical techniques that doctors and medical researchers use in their day-to-day work include Natural Language Processing (NLP),machine learning, evidence-based medicine, and big data.

How RPA ensures patient data privacy

The automation of hospital processes and operations using RPA makes use of sensitive patient data. Ensuring the security and privacy of this information is a top concern of healthcare organizations.

An RPA solution built with security in mind ensures medical data security and helps protect patient privacy. RPA software bots offer role-based access as a solution to data privacy. With this approach, only the correct people can access the private information of patients.

Patient data is handled by many hands, each wanting access to unique information. A physician’s demand for information varies from that of the management. The doctor looks at health records, whereas the management looks at financial and other logistical records.

RPA helps control patient data access while ensuring that each stakeholder only gets access to specifically relevant data. An RPA solution also automatically keeps a detailed audit history of data access.

A healthcare organization can spot the issues or improper data usage in case of a data breach and make available data access records for an audit.

Conclusion

The advancement in artificial intelligence and robotic process automation has started many innovations and transformed healthcare delivery. As a result, the world is enjoying a healthy ecosystem with superior care delivery service that saves lives.


Peer Review Contributions by: Lalithnarayan C


About the author

Eric Kahuha

Eric is a data scientist interested in using scientific methods, algorithms, and processes to extract insights from both structural and unstructured data. Enjoys converting raw data into meaningful information and contributing to data science topical issues.

This article was contributed by a student member of Section's Engineering Education Program. Please report any errors or innaccuracies to enged@section.io.