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A new strategic partnership is seeing SAS Analytics combining with Microsoft’s Azure to open up new possibilities in healthcare.

If there鈥檚 one positive to come from the COVID-19 pandemic, it is the acceleration of digital technologies and working practices throughout every industry sector across the globe, and none more so than the healthcare sector. plans have had to be put into place within days or weeks instead of years.

But having the hardware and software in place is only part of the story. The most important component of any digital system is the data that resides within it, and the purposes to which that data is put.

鈥淢eeting the challenges of a global pandemic means that every healthcare stakeholder 鈥� from patients and providers to insurers and pharmaceuticals 鈥� must share data from disparate geographies, scenarios, and populations in the effort to help understand, treat, and eventually eradicate COVID-19,鈥� said Dr Mark Lambrecht, Director of the Global Health and Life Sciences Practice at SAS.

鈥淣o doubt, the processes built today 鈥� as well as the key lessons learned and preparations made 鈥� will define the future of healthcare.鈥�

Some of those lessons and preparations will emerge from a which aims to accelerate healthcare innovation through and computing.

Transforming our understanding

Although massive efforts are underway to connect healthcare data that comes in every form, standard, and quality imaginable, 鈥淕aining insights from the intersection of patient observations and clinical trials, for instance, can feel Sisyphean 鈥� yet that ability will likely define the future of healthcare,鈥� said Lambrecht.

One standout example of success is the , which is already demonstrating the value and possibilities of connecting patient data. This community-based, genetics study uses SAS machine learning and artificial intelligence to improve population health in Nevada.

鈥淏y gathering information from citizens who enrol in the program, geneticists can identify predispositions for certain diseases, or alert asthma patients when they travel to a part of the state with poor air quality,鈥� said Lambrecht.

鈥淭o amplify these applications of data analytics and AI on a global scale requires massive compute power and a secure infrastructure to share and control patient data,鈥� he added.

鈥淭hat鈥檚 what excites me about our new partnership with Microsoft and the combined power of SAS analytics running on Azure.鈥�

This idea is supported by Heather Cartwright, who leads a team on new cloud and AI technologies for health data at Microsoft. She says that unsustainable workloads in the healthcare space have acted as an impetus for adoption of the cloud.

鈥淭here is an overwhelming increase in the types of data care teams need to manage. As the number of inputs clinicians use to treat patients grows, we need to leverage different tools for health data,鈥� said Cartwright.

鈥淐loud technology provides the scale which is urgently needed to manage health data workloads, but just as important, it enables machine learning with that data,鈥� she added.

鈥淗ealth leaders understand how that will transform our understanding of human health and how we deliver care in the future. So healthcare is finally saying, 鈥極kay we need to go to the cloud, and we need to know how.鈥欌��

But sometimes it鈥檚 not easy, says Lambrecht. 鈥淎s the leader of SAS鈥� scientific response to COVID-19, I can testify to the difficulty of bringing observational patient data derived from healthcare claims, healthcare registries, clinics, and all types of patient interactions together for analysis,鈥� he said.

鈥淭o lead the way forward, healthcare organisations need a comprehensive enterprise cloud strategy and an analytics strategy that drives insight from real-world data.鈥�

Lambrecht says that Microsoft and SAS are 鈥渃ommitted to meeting healthcare organisations where they are,鈥� with cloud-based solutions that are ready to run on day one, but can also scale as organisations grow.

A good example of this is , a leader in both technology and clinical care. Among the first organisations running SAS analytics natively on Azure, Mercy boasts a virtual health division and an analytics culture that helps it bring information together about COVID-19 patients and rapidly package that data to make it available to other health organisations working on innovative therapies.

The safe and secure cloud

Although the healthcare sector traditionally takes a conservative approach to innovation, it needs to be able to scale and drive insights from different types of data sources. SAS AI and analytics provides that scalability.

As Cartwright puts it, 鈥淲hen you鈥檙e innovating, trust is essential. We want to make sure that health systems maintain control over their data when they move it to the cloud, that they can define database access and bring their own identity.鈥�

鈥淲e make sure these security measures are in place so our customers can trust that their data is in the right foundation, because that frees them to really focus on innovation.鈥�

Flexibility is important too, which is why Azure Synapse provides the ability to work in whichever environment healthcare professionals are already comfortable. 鈥淪cientists shouldn鈥檛 have to learn a new language in order to work with a different data set,鈥� said Cartwright.

Critical to that flexibility are the feedback loops and machine learning that enables dynamic decision-making at every level of healthcare.

鈥淚t is so important to bring the front lines of healthcare into that machine learning process,鈥� said Cartwright. 鈥淔eedback loops are essential to make models better鈥� refining, expanding even, or identifying new algorithms we need to develop.鈥�

鈥淪AS and Microsoft are building solutions that physicians can trust,鈥� added Lambrecht. 鈥淲e鈥檙e rapidly creating simpler interfaces that do not hide the analytical complexity or the data complexity, but still allow decision makers to make the right decision, to extract insights that correctly steer how they need to run their organisation.鈥�

For Cartwright, transparency in AI development is key.

鈥淧eople using data models should be able to go deeper and understand what is happening in those models, what the inputs are for those models and the parameters, so that they can have trust in it,鈥� she said. 鈥淎nd then we can continue to validate and make sure that they are working at the right levels.鈥�

In other words, acceleration shouldn鈥檛 come at the cost of proven, hierarchical data validation processes.

Oncology is a good example. Without substituting the expertise of the physicians, SAS鈥� algorithms and models helped automate the read-out of metastatic liver lesions due to chemotherapy treatment by rapid calculation of various metrics like volume or surface of the lesions.

The algorithms didn鈥檛 hide the complexity of the analytics, but they did provide enormous support for oncologists who would otherwise spend a lot of time on error-prone tasks.

Which just goes to show that if healthcare organisations have their data in the cloud, with analytics engines running and data science teams working closely together, they will have a 鈥榬eadiness machine鈥� to make decisions in a crisis, says Lambrecht.

鈥淚鈥檓 thrilled to work with Heather as SAS and Microsoft build that secure and powerful readiness machine together,鈥� he said.

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