The Role of Intelligent Automation in the Future of Healthcare

In today’s world, we are no strangers to the idea of automation in many aspects of our daily lives. However, the global COVID-19 crisis served as a dire wake-up call to those businesses that had failed to digitize and automate prior to 2020, choosing instead to rely on legacy practices.

On the path to recovery from this black swan, we are seeing an unprecedented acceleration of digital automation – specifically, Intelligent Automation (IA) - driving the new expectations of the post-pandemic era. Across this new landscape, the embrace of digital processes and enhancements such as IA will, in many ways, determine what businesses succeed and what companies are left behind.

IA refers to the application of?Artificial Intelligence?(AI) and related new technologies, including Computer Vision, Cognitive Automation, Machine Learning, and Robotic Process Automation (RPA). To accelerate digital transformation across businesses, IA can be broken into three integrated components: AI, business process management (workflow automation), and RPA. In layman’s terms, IA is essentially RPA with a brain.

When industries leverage IA, they can operate with more agility and consistency across their processes. In the realm of healthcare specifically, this is paving the way to meaningful innovations in operational efficiency and value-based patient care.

Intelligent Automation in Healthcare

Healthcare systems around the world are notoriously over-leveraged and under-served, a reality that could no longer be ignored at the peak of the COVID-19 pandemic. Despite the incredible medical breakthroughs and innovations that have revolutionized patient care and disease management in recent times, many back-office processes utilized by healthcare providers remain outdated and ill-equipped to meet modern healthcare demands. Moreover, the number of people who require medical assistance is on the rise, along with the associated healthcare costs. In response to increased demand, the healthcare industry must leverage technology and automation to make medical care accessible to the masses.

With an influx of operational challenges and repetitive administrative tasks that incur costs and act as a barrier to the accessibility of high-quality patient care, the healthcare industry finds itself primed for innovation at the hands of Robotic Process Automation (RPA). RPA technology (RPA robots) can mimic “almost any predictable human interaction,” meaning they can replicate many of the behaviours and tasks that would normally require human intervention (i.e. managing patient data, filling in forms, etc.) while maintaining the same HIPAA compliance their human counterparts are required to follow. To this effect, the implementation of RPA can effectively streamline inventory management, data collection, the creation of electronic healthcare records (patient data), appointment scheduling, healthcare regulation compliance, and billing and claims processing. The application of this technology does not only streamline processes and enhance efficiency, it can?reportedly?increase savings up to 50%. These cost-saving benefits are especially notable when looking at health insurance claims as RPA solutions can auto-adjust claims at the cost of $1, compared to the $4 cost associated with human intervention. A?recent publication?by Alsbridge notes, “considering that more than 3 billion healthcare claims are filed each year, applying RPA to extend auto-adjudication rates by an additional 10% to 12% can generate savings well in excess of $1 billion.”

AI-empowered automation is hardly a novel concept, but in healthcare, automated chatbot technology and AI-assisted charge capture can transform the experience of both the patient and the healthcare provider. While an AI-powered chatbot offers 24/7 personalized service and data capture, AI-assisted charge capture helps healthcare providers optimize their revenue and reduce or eliminate missing or delayed payments.

From a patient-care perspective, we see a wealth of opportunity. With the enhanced organizational efficiency and reduced costs associated with RPA and AI, more resources, time, and coverage can be awarded to patients who need hands-on medical care. Other possible outcomes include reduced patient wait times, enhanced value-based patient care, increased appointment turnout and fewer canceled appointments and the elimination of human error in the digitization of patient records. Finally, patients can expect easier, single-point access to their information and medical history, all within the same platform they use to schedule and pay for appointments.

Starting and Scaling IA: Potential Pain Points and Barriers to Adoption

Starting an IA pilot requires some momentum, specifically, commitment and buy-in from leadership and intentional planning. Often, healthcare providers will partner with third-party service providers, such as IA and RPA vendors, to provide guidance and best practices that will help ensure the long-term success of the pilot or project based on key success metrics.

For an example IA pilot project, we can look to the Hospital Referral Triage Automation initiative rolled out in 2017 and 2018 at Western General in Edinburgh. The project consisted of two RPA robots and two AI models to address extended wait times experienced by patients requiring a gastroenterology referral. One RPA robot pulled medical records for a patient within the pilot, while an NLP model was used to comprehend GP referrals to a gastroenterology service. The first classification model inferred urgency status from the referral, while the second classification model inferred clinical outcome. Finally, the second robot booked appointments based on urgency status and predicted outcomes. Starting this year, when the process went live for 50% of their target cohort after two years of the pilot period, the referral turnaround has reduced around 15% against their average wait targets.

It is important to note that IA capabilities do not sit comfortably within IT or any single department or business unit when deploying a project of this nature. Automating more processes on a project-to-project, team-to-team basis will typically fail to deliver this technology’s full potential and value. Moreover, integrations with RPA are typically more brittle than integrations via APIs. Participating healthcare providers should decompose process steps carefully, regularly consult IA experts, observe and consult with staff performing work, and consistently communicate the goals of an automation program within the organization. With this in mind, these pilot projects should be led by a Center of Excellence (CoE) comprised of leaders, experts, and, often, a CoE owner who is a senior business stakeholder with senior decision-making authority. The CoE will establish guidelines and best practices, automation strategy, technology adoption, performance standards and provide development and support to ongoing IA initiatives based on measurements and learned optimizations. Through these efforts, the CoE can demonstrate to business leaders the value of the automation approach and transition an AI pilot project to a company-wide initiative that gathers, assesses, prioritizes IA opportunities, and aligns stakeholders.

IA is a long-term consideration for any healthcare provider, and the associated outcomes are truly transformative for both the patient and the service provider. A more automated healthcare approach empowers better, more accessible patient care in a manner that reduces cost and favors operational efficacy. This transformation frees up clinicians to focus on what matters most: the well-being of every patient. In this regard, intelligent automation is positioned to revolutionize healthcare in the very near future.