Artificial Intelligence

At the HIMSS Asia Pac 19 Conference in Bangkok, Thailand in early October, Omar Sunna, Director, Global Product Management, GE Healthcare, shared his insights on some of the data and workflow-related challenges currently faced by healthcare providers and how some of GE’s solutions and applications can help tackle or address some of these issues. Consuming the right amount of algorithm that creates desired levels of outcomes Omar said that one of the big challenges for healthcare providers is knowing which algorithm is the most appropriate for the required workloads. Additionally, there is no single vendor that is going to provide all the algorithms and the challenge is to create an ecosystem that allows the provider to work on the algorithms regardless of vendors/developers that can be infused into the workflow. “We see this as a tremendous opportunity and a challenge today. It is an opportunity for us to partner with customers and solve this issue in a way that is compatible for the workflow, in a way that controls the costs, increases productivity and promote the right levels of access for the patients. Above all, empower for improved outcomes and higher quality care through leveraging intelligent applications and devices,” he explained. One of the solutions that can tackle this challenge is GE’s Edison AI platform, which focuses on two things. The first is to break the silos of data within the healthcare organisation, to orchestrate the data within the organisation and create a foundation for aggregating the data, making sure that they can look at the patient holistically. The second is to create an ecosystem so that GE developed algorithms as well as ones developed by partners of third parties (such as startups) are able to integrate it into medical devices and leverage Edison's data lake, so the algorithm continues to grow, even though it was developed by a third party company.  “Continuous learning is a big focus because the more you feed the algorithm with data, the more precise and accurate it gets, and that’s the goal with Edison.” A potential implementation of AI-embedded workflow One for the opportunities that Omar sees in which AI can help in improving workflows is in the area of radiology, such as to notify the radiologist that there is potentially an urgent need that needs to be done. There are also opportunities to show preliminary findings to the radiologist so that he or she is able to interpret it, review it, look at patient-specific trends based on prior imaging and make the determination of the right diagnosis for that patient in the most efficient manner.  He provided an example of the workflow orchestration maturity model of an imaging interpretation which can be divided into three steps: 1) How to determine the right imaging exam 2) The diagnosis and treatment plan 3) The study interpretation and reporting In his experience, most of the customers have a fragmented and ad hoc approach and there is an opportunity for quality improvement in terms of the pre-scan algorithm and which protocols are used but most of that are dictated by their jobs in a manual process. “There’s an opportunity where the data can be leveraged so you need multiple sources of data. Today, a lot of healthcare providers are struggling with being able to access that data from multiple sources.” Data and data management There is a data explosion in healthcare – data within the hospital setting in growing at 48% per year and data aggregation is growing as well. From a healthcare technology provider perspective, Omar sees great opportunity to provide solutions for GE’s customers. Within GE’s enterprise solutions business unit, its software applications are divided into three categories: solving for diagnostic speed and accuracy, fostering collaboration outside of the hospital network and focusing on productivity around the health system. In terms of solving for diagnostic speed and accuracy, one of the software applications and tools is the Centricity Cardio Enterprise, which helps the cardiologist aggregate all the data that is relevant for the patient. The cardiologist can access the ECG, the echo, the cardiac CT and arrive at a diagnosis in a fast time with a higher level of competence. For fostering collaboration outside the hospital network, such as consulting with specialists and sub-specialists, there needs to be a way to share reports and imaging with them easily and in a secure manner and this is where solutions such as GE’s vendor neutral archive (VNA) and Centricity 360 come into play. Lastly, GE’s analytics solutions that are embedded within the workflow that provides the right level of insights can help improve efficiency of clinicians, help them make the right decisions and lead to an increase in productivity. Omar believes that the vendor neutrality of GE solutions brings about several benefits. This means that GE’s VNA can adhere to industry standards such HL7 which has the capability of FHIR APIs and the ability to leverage IHE-XDS standards.  “Another aspect of neutrality is being able to leverage the IT ecosystem. Being able to reside in the customer's own hardware, being able to leverage on-prem storage provided by the hospital and being able to open it up to different vendors, as long as we validate the standards of that technology. We also leverage cloud-based storage. All of that is key for us to be able to build scale,” Omar concluded.

The more than two-dozen participants chosen to move on to Stage 1 of the challenge include Accenture, Geisinger, IBM, Mayo Clinic, Merck, Northrop Grumman and others.

The paper, which looked at the development, procurement and use of data-driven technologies for healthcare, was published earlier this week.

The Houston health system is enlisting technology and services help from Virtusa, Cardinal Health and Amazon Web Services to explore ways cloud-based AI can help manage diabetes and subarachnoid hemorrhage.

Hal Wolf, president and CEO of HIMSS, says using AI infused into clinical decision support can provide healthcare professionals and patients/consumers with the right information pathways and treatment plans to enable better care outcomes.

Gyant CEO Pascal Zuta describes how his company's AI platform can help physicians perform tasks only they can do, thus improving workflow.

Real-world evidence drawn from unstructured clinical notes was more accurate in algorithmic prediction of coronary artery disease than structured data, a new study in JAMIA shows.

In most industries, change or evolution isn’t a gradual process. You might have years and years of equilibrium, with little improvements here and there, and then all of a sudden a massive technology and/or business model change takes us to a new level. For example, the taxis of 1950 looked a lot like the taxis of 2010. Then along came ride-sharing apps. Now, as we look forward to self-driving cars, I would say that the “rent-a-ride” market is in the middle of an explosion of rapid change – how it will end, we don’t know. Looking at how technology has changed the healthcare industry, I would argue that we have already had one explosion of rapid change, and we are in the middle of a second one now.  When my career started, we wrote billing systems – systems with structured data, typed in on numeric keypads, by dedicated users of the computer. This was the first generation of success in health IT, and it digitised the administrative parts of healthcare. Then a number of technology breakthroughs happened – standardising on SQL databases, the storage and viewing of text and images, and perhaps most importantly, the graphical user interface. This allowed non-dedicated users, like physicians, nurses and patients, to use software to help them with their work and lives. This ushered in the next couple of decades where we built and deployed electronic medical records (EMRs). Now health IT is in the middle of a second explosion of rapid change. This disruption to healthcare offers the promise of even bigger benefits – with improved outcomes and reduced costs – than we have seen with digitisation to date. If you look at the disruptive technologies of today: cloud, artificial intelligence (AI), wearables, imaging/text analytics, advanced interoperability and ubiquitous user experience (UX), we have the raw ingredients for this next massive change. And if you examine that list, not one of those technologies has reached its full potential yet. They are all showing promise, but most importantly, they are incredibly promising as we explore the various use cases where they can help. The history of InterSystems has mirrored this trajectory. Our initial database technology enabled the major billing systems of the original health IT market. A couple of decades ago, we launched our Caché product line and enabled the major EMRs in the market today, including Epic, and our own EMR TrakCare. Last year, in line with this current explosion of rapid change, we launched our next platform, InterSystems IRIS for Health, to bring capabilities for this current explosion of rapid change – cloud, AI, API interoperability, analytics – to our customers. And some of our customers are already embracing these capabilities to change the way they support the healthcare industry. Earlier this year, MediWay Technology, one of the largest healthcare software companies in China, deployed its new iMedical Cloud healthcare IT ecosystem platform using IRIS for Health. This allows MediWay’s iMedical healthcare information system (HIS) to support all stakeholders in the Chinese healthcare system – including government, healthcare organisations, and consumers – in an environment where medical reform policies are driving increased collaboration, information sharing and use of big data. Two iMedical Cloud applications, hosted on Tencent Cloud, were launched – transactional cloud collaboration (Cloud HIS) and data cloud collaboration (HealthChain). These can be used within a region for real-time healthcare information exchange, centralised management control and unified allocation of resources. Built and deployed on a unified data platform, iMedical Cloud supports the vastly increased data volumes required with features like sharding. This distributes data across a number of cloud-based servers to provide flexible, inexpensive performance scaling. On the other side of the world in New York, HBI Solutions is using machine learning and predictive analytics to convert vast amounts of data into knowledge that can be applied in real time to decision support. Healthcare providers see not only their patients’ pasts, but also their likely futures. HBI’s Spotlight Data Solution identifies elements, or “features”, in health records that could indicate future problems.  What’s exciting about being in the middle of a period of rapid change is you don’t know how it will turn out. What is quite clear, based on the enabling technologies of this era, is that we are moving closer and closer to the patient, and closer and closer to being helpful to the clinicians and not just an administrative sidekick. Health IT has had a long and illustrious history digitising healthcare. But now we are about to create an even better future. I can’t wait to see what we build together. Are you looking for someone who you can talk to about your own rapid changes? Take the next step here.  About the Author As Vice President, InterSystems HealthShare, Don Woodlock oversees a family of products created to empower the transformation of health and care through comprehensive, shared health information. In his role, Woodlock is responsible for advancing the HealthShare vision to meet the challenges associated with delivering improved quality, accessibility and efficiency across the health and care industries.

Promising AI tools can enable physicians to perform more effectively across the work that they do, says Dr. Jesse Ehrenfeld, board of trustees chair for the American Medical Association.

Genomic medicine researchers at the laboratory have been using artificial intelligence, developed as part of Microsoft's Project Hanover, to help manage the vast amount of research data needed to power its precision oncology initiatives.