Jun 29, 2020

Key AI trends digitally disrupting healthcare

AI
Machine Learning
Matt High
3 min
AI
We explore the key AI and machine learning trends disrupting the healthcare ecosystem...

According to Global Head of Artificial Intelligence and Automation at Infosys, John Gikopolous, the healthcare industry is ripe for disruption by new and innovative technologies such as AI and machine learning. He explained to us that there are four key trends that could drive this change. 

Intelligent Process Automation

“This is the central one,” says Gikopoulos. “Automation, RPA and IPA should really be running all of the various solutions in the background. From the payer perspective, it’s about collecting information, registering people, allowing one-point access to past information and so on; from the provider side, the entire experience in a hospital should be underpinned by automated processes. For pharma companies, for example, automation is used across all functions, from manufacturing and support, through IT, finance and even the running of clinical trials and R&D.”

The key advantage of RPA and IPA is the skipping of the human interface, according to Gikopoulos. Employing the technologies removes the delays or mistakes that humans make from the equation, thus making the entire value chain more efficient from end to end. 

Standardising data

“The biggest problem across the entire healthcare value chain is being able to call the same thing the same name at every stage of the process, so underlying or diagnosed conditions of a patient, the effect of different treatments - or a lack of treatment - has had in the past and so on,” Gikopoulos explains. 

“It drives every decision, from what active agents go into drugs and when to dispense then, through to clinical trials, how long people should be hospitalised and even what impact they may have on the greater public and the healthcare system. 

“And yet, it’s a question that just hasn’t been addressed adequately,” he continues. “Information comes from so many sources in healthcare that standardising that information is essential. Further, once you have that standardisation, then you can apply AI to identify the questions behind that information. 

Machine learning

Machine learning is used at the payer/patient or hospital/patient interface to analyse data, often provided by patients, and to provide informed reactions and decisions on that data. 

“When you start looking at the outcomes of treating patients in different ways depending on when they came in - what symptoms they have, what were their underlying conditions, demographic or social category, then you define a completely different decision tree compared to the static, traditional one that says the customer journey or the patient journey is greet at the reception, triage, send to doctor, have it diagnosed and then send to be treated,” notes Gikopoulos. 

He also cites the importance of machine learning in clinical trial efficiency, adding that it “could be grossly understated in certain cases”. These include, for example, identifying cause and effect in the use of different agents within drugs along the clinical trial value chain, which can work in a much faster and more targeted way with machine learning. 

Patient interfacing

According to Gikopoulos patient interfacing can “completely change the experience that we all have from communicating with this entire value chain”. Examples he cites include using remote channels to contact patients that need a certain treatment after discharge, or the use of telematics to remote diagnose patients. 

“Interfacing isn’t all about chatbots, avatars or cool looking bots that interact with you,” he says. “To a large extent, it’s more to do with the IoT and making us part of the connected world not just for the mundane and potentially also fun parts of our experience and existence, but also the more crucial areas like healthcare. 

"Imagine, for example, if all our machines, computers, phones and so on, instead of using their enhanced abilities to provide greater gameplay, screens, or sound quality, had actually focused on having the type of sensors that allow temperature or blood pressure to be taken, or a person's retina to be scanned for specific diseases. That kind of interface or interaction could, or might still, completely change everything."

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May 12, 2021

OMNI: First-ever platform to launch citizen RPA developers

RPA
Automation
Digitaltransformation
citizendeveloper
3 min
OMNI is empowering employees to become ‘citizen RPA developers’, democratising automation and other AI technologies

Robotic process automation (RPA) is the fastest growing segment of the enterprise software market due to its many benefits - from reducing manual errors to processing tasks faster. For businesses to truly benefit from this technology, RPA needs democratisation, and this is where citizen RPA development comes in. 

Gartner describes a citizen RPA developer as "a user who creates new business applications for consumption by others using development and runtime environments sanctioned by corporate IT.” This could be anyone using IT tools and technology, not limited to IT specialists. 

The work citizen RPA developers do spans from identifying automation opportunities to developing RPA architecture and solution proposals, focusing on scalability and extensibility. By deploying citizen RPA developers, organisations can enable enterprise automation and digital transformation on a much larger scale. 

This is particularly beneficial for businesses struggling to undertake digital transformation, as a citizen RPA development programme can help drive adoption of automation as a strategic growth driver at multiple levels. With increased adoption, the cost of digital transformation becomes lower, increasing RoI. 

Technology needs to be democratised – right from low-code and no-code platforms, business process modelling and identifying automation opportunities to decision-makers at all levels, creating a pool of early adopters. This group could comprise people across different functions, especially those who are aware of customer preferences, industry trends and end user experience.

But how can organisations harness the power of citizen RPA development? Step forward AiRo Digital Labs, a Chicago-headquartered global tech company. 

AiRo provides innovative digital and automation solutions for the healthcare, pharmaceutical and life sciences sectors. In 2021 they launched OMNI, a subscription-based, SaaS platform to help clients accelerate their citizen RPA developer program and build digital centres of excellence (COE) within their organisation. 

OMNI provides a personal RPA coach and virtual digital playground that helps enterprises rapidly build and scale automation, removing the risk of failure or talent gaps. The latter is key as research has shown that digitalisation is far more successful when championed by internal employees. 

This has the added bonus of empowering employees - who will self-learn technologies including robotic process automation (RPA), artificial intelligence, machine learning, chatbots, and natural language processing (NLP), reducing the lead time for new applications and technology, as well as reducing technical gaps, making up for skills shortages and enabling their business to respond faster to critical market challenges. The virtual sandbox within OMNI gives access to all the major intelligent automation platforms where citizen RPA developers can build DIY digital prototypes. Additionally, they can access more than 150 digital assets within OMNI marketplace. 

The platinum helpdesk of OMNI acts as your personal coach and is available 24 x 7 to address issues during the digital learning, prototype building, and digital governance journey.  

Another key benefit is that it enables digitalisation to be bespoke to each organisation, compared to off-the-shelves initiatives plugged into the enterprise. Individual organisation's objectives decide the scope and size of the process. 

As Gartner state, in today’s world of SaaS, cloud, low-code and “no-code” tools, everyone can be a developer. 

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