Human bodies are incredible machines, made up of many systems that perform billions of important and complex processes throughout our lives. Capturing and tracking the clinical data associated with a human body – even just the essential vitals – at timely intervals outside of a health system creates challenges. And managing data becomes even more difficult when you include other relevant data like demographic, geographic, financial, or other more emergent social determinants of health – like access to education, food or clean water.
Because healthcare requires, uses, and possesses enormous amounts of data – housed in a growing number of different software applications – organizations have made significant investments in patient portals, EMRs, revenue cycle softwares, and more. But this proliferation of battleship EMRs and third-party enabling technology has unintended negative impact to the mission of delivering care.
The phrase “death by a thousand clicks” describes the burnout that this new complexity is causing patients, their families, and healthcare workers alike. Yet, despite the complexity of the data and the interdependencies of the various systems and applications across this ecosystem, these healthcare investments carry vast promise. Each individual piece of software has the potential to improve patient care, reduce errors and even find insights that make healthcare better. Yet the myriad issues seem to distill into one fundamental challenge – a lack of interoperability. The “moonshot” to solve the interoperability challenge – the creation of a unified and ubiquitous symphony of health data – is proving elusive. Dynamic and emerging technology capabilities, system issues, and misaligned incentives are proving cumbersome to manage effectively, and adding a human layer to drive the interoperability required for exceptional care is incredibly expensive. Is AI the single integrator the industry needs?
The healthcare problem is a lack of interoperability
So, why is interoperability important? A lack of interoperability means that none of the programs “talk” to each other. Instead, employees spend countless hours copying and pasting information, running queries, and entering data.
If we could improve healthcare interoperability, the benefits would run through the entire system, impacting every provider, patient and payer. Employees would spend less time on manual, mundane tasks and more time solving problems and working with patients. Fewer errors would be made, improving patient care and hospital financial performance. Healthcare organizations’ back-end operations would run seamlessly, enabling better coordinated front-end care. In many cases, purely administrative back-end functions could be eliminated altogether, allowing organizations to route more spend into direct care, patient engagement, and caregiver well-being. Everyone knows that true interoperability in healthcare is a worthy goal; it’s been on people’s minds since the American Recovery and Reinvestment Act (ARRA) outlined expectations for the electronic exchange of health data in 2009. But over a decade later, while electronic storage of data has increased dramatically, the sharing has not.
We need a new way of thinking about interoperability – artificial intelligence
Current methods of interoperability rely on APIs and HL7 feeds, custom programming, and multiple consultants – and all of this effort still isn’t giving healthcare organizations and workers what they need. We need an entirely new way of thinking about interoperability.
When people think about artificial intelligence, interoperability isn’t usually what they think of first. Robotic surgeries, drug research, or personalized care plans typically come to mind. While these applications are certainly exciting and important, healthcare AI today can have the largest impact by acting as an integrator for the administrative workflows that underlie our care system. By applying the power of artificial intelligence, healthcare can have a single integrator that works across systems to integrate data and provide interoperability. This is the holy grail of healthcare – complete interoperability between all systems and information shared between each of the silos that make up a hospital – effortlessly. And with the power of AI, the future of interoperability is closer than we think.
Olive uses artificial intelligence to work directly in UIs – not APIs
Olive doesn’t use standalone (and in some cases brute force) APIs and HL7 feeds. Instead, Olive uses AI capabilities, including advanced computer vision (CV) and robotic process automation (RPA), to directly utilize the system’s user interfaces (UIs) and make thoughtful, individualized decisions and actions. Olive mimics the actions a human user would take on an application to pull and enter data from one system to another. This allows Olive to integrate data from any source, meaning she is platform agnostic. It does not matter whether you use Cerner or Epic, SAP or Infor, Wavemark or Par Excellence, Olive can easily work with all your existing systems. Because Olive Works mimics human actions, all she needs is a username to access each program – just like a Human.
By breaking down the silos that separate our data and our workflows, Olive can automate increasingly complex workflows and find insights hidden in the data you are already collecting. For example, Olive recently partnered with Tufts Medical Center to use Olive Works to streamline their COVID-19 testing operations. Patients looking for a test enter their information on their website, which is integrated into their Salesforce platform. Olive automatically copies the data from Salesforce to their EMR, Invision. Olive also integrates with their patient platform, Medumo, for texting patients about their testing appointment. Olive also takes testing history and symptom data and pre-populates a clinical assessment so that clinical staff have information when the patient presents for a test.
This process has saved Tufts 86% of testing time due to reduction in manual data entry, helping expand their testing capacity while also providing more accurate, timely patient information to doctors and nurses on the front line.
Olive’s AI as a Service model gives you a single partner for interoperability
Your health system likely has just the right amount of third-party software platforms and vendors and, in some cases, perhaps slightly more than necessary. What you need now is a single partner that can bring your programs together to automate workflows and find insights hidden in the data you already have. Help your existing investments perform at the “top of their license” by supplying them with the most accurate, most timely information through an improved interoperability layer using AI.
With Olive’s AI as a Service model, healthcare organizations can tap into the power of intelligent automation without wasting time and money learning to be experts in AI. Olive has scoped over 1,500 processes throughout the health system, from the revenue cycle to the pharmacy. So instead of healthcare needing new integrations or new custom feeds to automate another process, you can simply ask Olive to take on more work. When you don’t have to start from scratch for each integration, you can quickly scale AI and gain the tremendous benefits AI has to offer.
Plus, unlike traditional automation tools or RPA solutions, Olive’s advanced AI as a Service solution continues to work and complete her job even when other software changes occur. Instead of simple, coordinate-based screen-scraping, Olive uses her advanced artificial intelligence capabilities to accurately interact with other platforms. This means when other platforms update, no code adjustments are needed to update Olive, unlike using APIs. Olive preemptively detects changes across the entire Olive network, prompting the Olive support team to update Olive so she can continue to complete her tasks. In the as-a-service model, this support is evergreen. You always get Olive’s best as part of your Olive license.
For more information on AI as a Service compared to other AI purchasing models, check out our white paper, Investing for Enterprise Impact: A Healthcare Executive’s Guide to AI Workforce Models.