RPA and the rise of intelligent automation in healthcare
Andrew Pellegrino, Director of Intelligent Automation at DataRobot, analyzes RPA and the rise of intelligent automation in healthcare
Digital transformation has been cited as a top trend in healthcare and smart automation can be a part of it.
The Robotic Process Automation (RPA) market is booming. It gives companies a tremendous opportunity to automate manual, time-consuming, repetitive, and transactional processes. RPA can help improve process quality, speed and productivity and integrate legacy systems. This is becoming increasingly important in the current climate as companies try to accelerate digital transformation projects.
It is clear, however, that while RPA has the potential to be a very valuable tool, the common barriers to its success are the complexity of the business, subjective decisions, and unstructured data. RPA can only automate simple tasks. It requires processes to follow finite, predefined rules with structured data.
The key to starting digital optimization projects is connecting the head (artificial intelligence and machine learning) to the hands (RPA). I’m talking about the convergence of RPA with AI and ML to create intelligent automation that has the potential to massively expand the scope of knowledge work that was previously considered too complex for automation, and which requires human intervention, to make predictions. With intelligent automation, AI and ML automate decision making and RPA automates the manual next steps within the process.
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How? At a high level, machine learning can be broken down into two main components. The first part contains training models for historical data to make predictions. This includes collecting and preparing the data – often the most time consuming step in machine learning – and concluding with a training dataset that is labeled and ready to be modeled. Next, models are built using algorithms for different types of data problems, i.e. classification, regression, binary. Once the model has been created and deployed to production, the next component of machine learning begins – evaluating invisible data against the created models. This is the step where RPA can ask the machine learning model what to do next, with the model providing a predictive decision for RPA to proceed without human intervention.
Interestingly, IDC identified digital transformation as the top trend in the life science and healthcare industries this year. So it’s not surprising that this industry’s interest in automation use cases has grown, where adding AI and ML with RPA could add value to the entire ecosystem. The goal is to create a scalable digital workforce that can run processes that do not require human intervention and achieve a return on investment in less than 12 months.
Of course, the key organizational benefit of using intelligent automation to remove human labor from everyday tasks, in this case, is that healthcare professionals can focus on higher value, human-led decisions, diagnoses, and treatments. A better patient experience and better outcomes can be achieved by optimizing patient engagement and giving clinicians faster access to more information, which in turn enables them to provide targeted and tailored care.
Better visibility of data in real time is also used by pharmaceutical companies and medical device manufacturers to, for example, eliminate potential compliance concerns by reducing fraud and error rates and increasing accuracy, security and protection. This is particularly the case in the life science industry.
Smart automation is used to accelerate drug discovery, vaccine development, and clinical trials by automating processes related to documentation and regulatory oversight. Eliminating bottlenecks is proving to be key to addressing some of the challenges posed by the pandemic, particularly in terms of test kit deployment and fast-track analysis.
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The ability to standardize data, use larger data sets, remove bias, and train algorithms more efficiently, for example to identify which compounds are more effective or worth moving through the drug discovery process faster, delivers results faster and almost does it possible to do the work in advance. This in itself suggests that the evaluation, results, opportunity for approval and efficacy at the drug discovery stage could occur alongside clinical development, regulation and document processing, potentially leading to virtual clinical trials.
By introducing greater automation in laboratories, data can also be reintegrated into production and other data lakes in order to make trends more visible, enable faster and more scaled production and create more agile supply chains, which have important requirements especially at this point in time.
For example, forecasting production demand is a key use case. Predicting where there may be an increase in demand due to external effects such as an increase in the flu or an increase in Covid-19 or a possible change in the population can increase demand. Likewise, to be able to monitor and track pharmacovigilance quality issues and complaint handling – see trends related to regulatory filings or complaints, monitor trends earlier, update field teams to address issues (e.g. related to on samples and shipments) can proactively manage days instead of weeks – can contribute to increased sales.
Fortunately, intelligent automation enables the life science and healthcare industries to manage and integrate legacy systems and take advantage of digital transformation without upgrading software, developing APIs, or a new system in weeks instead of months, or in some cases years to create.
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Data can be collected from multiple sources and must be cleaned up and prepared before modeling begins. Instead of being locked in an ivory tower, AI and RPA are democratized through intelligent automation. People can get direct access to data science and use the information themselves instead of waiting for the same information to be retrieved by a group isolated in another location.
The ability for the life science and healthcare industries to leverage these AI, ML, and RPA tools and techniques to aid AI-driven decisions and achieve ROI in a short period of time is increasingly becoming a practical reality.
The convergence of RPA and AI and ML is the next step on the road to intelligent automation. Organizations are solving, and not stopping, data-driven machine learning use cases like patient readmission, workforce forecasting, medication compliance, and reducing patient stay. Instead, they use the predictions to add new RPA automations that were previously not suitable for solving more critical use cases and use several intelligent automation components together. It is of course an exciting time to be in this industry and to drive real change over the years.