Reimagining health care with hyperautomation

Hyperautomation has numerous advantages, including enterprise agility, enhanced efficiency and identification of automation candidates.


The COVID-19 crisis has drastically exacerbated the manpower and efficiency problems in health care. Health systems have struggled to meet the increasing demands for faster and more reliable data processing from various internal and external sources, and to offer enhanced health outcomes.

A recent study stated that the US health care industry collectively spends $2.1 billion annually on poorly performed and error-prone manual tasks on provider data management alone. Similarly, in India, 5.2 million medical errors are happening annually.

This can be attributed to the acute shortage of qualified medical doctors, nurse practitioners and infrastructure. With only 0.76 doctors and 2.09 nurses per 1,000 population, it becomes even more critical for health care delivery to be efficient, focused on health care decision-making, and without manual administrative tasks.

The global health care sector is constantly pursuing new avenues and pushing its boundaries to improve and provide more effective and tangible offerings to its users. Here, automation technologies such as Robotic Process Automation (RPA) have the potential to improve business agility and scale up operational efficiencies.

However, RPA is not a standalone solution for addressing the changing dynamics of the health care ecosystem.

Intelligent automation (IA), or “cognitive automation,” adds a layer to RPA by incorporating technologies such as artificial intelligence (AI), machine learning (ML), as well as natural language processing, structured data interaction, and smart document processing.

IA leverages AI and brings in analysis, reasoning, judgment, and decision-making into automation for higher-order tasks and helps address the limitation of RPA in automating the processing of unstructured data.

With a keen focus on patient-centric care, organizations are rapidly adopting digital platforms and solutions within workflows and processes. This includes automating series of related tasks while directing human workflow, thus giving rise to the concept of hyperautomation.

From automation to hyperautomation

Gartner defines hyperautomation as the use of “advanced technologies, including AI and ML, to increasingly automate processes and augment humans.

Hyperautomation extends across a range of tools that can be automated, but also refers to the sophistication of the automation (i.e., discover, analyze, design, automate, measure, monitor, and reassess).

Simply put, it is a combination of automation tools and complementary technologies used to carry out various levels of work. It has the potential to enable end-to-end automation of the entire ecosystem.

Hyperautomation uses several complementary technologies: if there is a limitation in the application of one technology, it is addressed or overcome using a complimentary technology in combination.

Interestingly, it is expected that by 2024, most organizations will be able to lower their operational costs by 30% by combining hyperautomation with redesigned operational processes. So, while RPA forms the scaffolding for Hyperautomation, this advanced form of automation technology showcases numerous advantages, including:

Enterprise agility: Hyperautomation offers quicker and deeper insights for more accurate decision-making. Also, organizations looking for competitive advantage need not wait for months to successfully roll out the new process.

Faster identification of right automation candidates: Using advanced analytics and process mining tools, organizations can identify the right processes, or parts of processes, that would benefit from automation with minimal effort and no distraction to business users.

Augments process efficiencies: Hyperautomation aids in identifying areas for improvement that can enhance user experience. Further, over time, the data that is generated can be analyzed to identify further process improvements, leading to higher operational efficacies.

While there are not many disadvantages with the technology, vulnerability of data and data security on the cloud are always areas of concern.

Role of hyperautomation in health care

Hyperautomation has enormous potential for delivering reimagined health care solutions. Here’s how it can enable such differentiated solutions:

AI-assisted diagnostics: Hyperautomation combined with technologies such as computer vision, ML and smart workflows can provide quick and precise reports. For example, computer vision and ML-based solutions interpret radiology scans to diagnose a medical condition.

Thereafter, the automated workflow can prioritize secondary reads and allocate cases based on specialty, departments, reading capacity, etc. Post validations, the diagnosis reports can be automatically sent back to the physician and integrated with electronic medical records (EMRs).

Front-end revenue cycle management: In this area, technologies like conversational AI, RPA, process mining, and data analytics can come together to deliver better experience and efficiency. For example, AI-enabled virtual agents can be used to take requests for scheduling/rescheduling appointments.

In addition, while collecting eligibility information during the appointment and patient registration process, RPA can be leveraged to identify missing eligibility data and a Natural Language Processing enabled voice agent can be utilized to connect with the insurance company to collect missing eligibility information.

Also, process mining and data analytics can be applied to identify opportunities to improve patient registration and the insurance verification process, and to reduce dependency on RPA and virtual agent solutions over time.

Remote monitoring: Remote monitoring is fast gaining popularity, especially in today’s situation where physical distancing and lockdowns restrict physical movement to health care facilities. Technologies like Internet of Things (IoT), wearables, digital health assistants, and data analytics come into play here.

Health care providers can use sensors and wearables to monitor parameters like blood pressure and glucose level of high-risk patients with either chronic conditions or who are undergoing post-acute care. Digital health assistants can drive adherence to treatment recommendations while data analytics can drive alerts and provide recommendations for patient engagement plans to manage personalized care.

A three-step plan to achieve hyperautomation

As organizations look at adopting hyperautomation, they need to approach it step-by-step in order to ensure maximum benefits.

Step 1: Establish a clear-cut and well-defined automation strategy that accounts for business as well as technology teams.

Step 2: Ensure that investments in process automation align with the expected results.

Step 3: Ensure availability of the complete digital toolbox to meet evolving business requirements.

There is a growing trend where hyperautomation capabilities are expected to turn mainstream across various business operations and not remain limited to niche or highly specialized use cases.

As the pandemic continues to fuel the pace of transformation in the industry, not only will hyperautomation smooth the process but also positively impact patient care and health outcomes.

It would also create new opportunities for organizations to deliver achievable business value through huge operational improvements and return on investments, thus creating a competitive edge for themselves in the ever-evolving market.

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