Robotic Process Automation (RPA) is the technology that allows anyone today to configure software, a “robot”, to automate the actions of a human doing work with digital data. RPA robots can manipulate applications like humans. They interpret, trigger responses and communicate with other systems in order to automate a vast variety of repetitive tasks. Even better: an RPA software robot never sleeps, makes zero mistakes and costs a lot less than an employee.
How is RPA different from
other enterprise automation tools?
In contrast to other traditional IT solutions, RPA allows organizations to automate at a fraction of the cost and time. RPA is non-intrusive in nature and leverages the existing infrastructure without causing disruption to underlying systems--which would be difficult and costly to replace. With RPA, cost efficiency and compliance are no longer an operating cost, but a byproduct of the automation.
How does Robotic Process Automation work?
RPA robots are capable of mimicking many, if not most, human user actions. They download data from databases, run statistical scripts on that data, save that data into files, build tables and charts to display the data, build machine models from the data, write articles describing the results of their analyses and more.
"Data Factories" for Research or Operations in Healthcare
Build Data Lakes. Amplify Your Research Output.
The more data lakes you have, the more Real World Evidence (RWE) you can generate. Data is at the foundation of progress in healthcare. RPA allows healthcare systems to extract value from data lakes to drive research and operations. With its RPA pipelines, HealthLab’s can both help build new data lakes and the "data factories" to analyze them. Because the pipelines for these data factories are built with digital workers, the process is repeatable. This results in shortening the iteration and delivery time for everything, from small research studies to multi-center Real World Evidence (RWE) engines for the modern evidence based healthcare enterprise.
Share Your Platform, Not Your Data
Why share data when you don't have to? Research collaborations and innovation work can be challenging, and data often has to be shared. HealthLab's platform does not locally store any process-related data from software robots. The platform only maintains robot-related activity reports. The result is that the data and the analytics are all stored on your enterprise owned Cloud resources. In traditional partnership models, healthcare has to give away de-identified data. In this new model, enterprises simply share their analytic platform on which collaborative teams do their work with access only to data ponds designed for them.
Better Cost Management
A software robot’s expense is typically one-fifth the cost of a full-time healthcare staff member. RPA can deliver up to 47% overall savings, especially in healthcare areas requiring heavy data cycle iteration such as research or AI model development. With HealthLab's web accessible data pipeline, enterprises can significantly reduce operating costs for research and innovation. Robots can be deployed in Cloud from a single central server, making authoring automated data pipelines possible across multiple independent business units or in collaboration with third-party partners.
Automate deeply manual data processing operations across your entire organization and unleash your potential. Organize structured workflows and enjoy better data quality eliminating human error and focusing on higher priority, value-added initiatives. Say goodbye to costly implementations and do more things in less time.
Improve Quality Measures
Harness the power of RPA to efficiently prepare and process health care data for analysis to improve care outcomes. No longer confine yourself to simple key performance indicators. Deploy full statistically validated medical studies as a tool for tracking quality.
Amplify Real World
Evidence (RWE) Practice
Deploy APIs for knowledge derived from your "big data" and integrate them into the clinical workflow to empower evidence based practice.
Improve AI Model Power
Data factories can build and rebuild AI models as data lakes are refreshed and swapped with data from other healthcare areas. The result is that AI models can be trained and retrained for every area of the healthcare system, ensuring the best model is being used at every contact point in the healthcare system.
Looking for opportunities in automating discovery from "big data" in healthcare and
creating new healthcare services in the cloud? Contact our lab to learn more.