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The organization is broadening into a single assessment tool for varying regulations around the world.

A new report from the Duke-Margolis Center for Health Policy explores some of the policy changes that should be made to enable safer and more effective deployment of artificial intelligence in healthcare. As AI and machine learning become de facto ingredients in many key clinical technologies, a better understanding of how they can best be leveraged for optimal analytics and decision support is the goal of the study, "Current State and Near-Term Priorities for AI-Enabled Diagnostic Support Software in Health Care." WHY IT MATTERS The Duke report takes stock of the existing legal and regulatory landscape for algorithm-based CDS and diagnostic support software, and lays out some essential priorities to work toward in the years ahead to ensure safe deployment of AI in clinical settings. These aren't just theoretical concerns. AI and ML are making inroads all over healthcare, of course, and current legislation and regulatory policy – whether it's the massive 21st Century Cures Act or FDA's new updates to the Software Pre-Cert Pilot Program – are adequate but still not optimal for a future that promises to evolve at a dizzying pace. The Duke-Margolis paper, meant as a "resource for developers, regulators, clinicians, policy makers, and other stakeholders as they strive to effectively, ethically, and safely incorporate AI as a fundamental component in diagnostic error prevention and other types of CDS," looks at some of the major challenges and opportunities facing AI in the years ahead. Stakeholders like those listed about will need to grapple with big questions, more than a dozen researchers and authors write. Such as: Making a case for the value of more widespread adoption of these technologies. Such evidence would include how the software improves patient outcomes, boosts quality and lowers cost of care, gives clinicians relevant information in a manner they find "useful and trustworthy." Assessing the potential risk of using those products in clinical settings. "The degree to which a software product comes with information that explains how it works and the types of populations used to train the software will have significant impact on regulators’ and clinicians’ assessment of the risk to patients when clinicians use this software," said Duke researchers. "Product labeling may need to be reconsidered and the risks and benefits of continuous learning versus locked models must be discussed." Seeing to it that such systems are deployed in a way that's both flexible and ethical. More and more health systems will need to develop best practices that can mitigate any bias that could be introduced by the training data used to develop software, they explained. That's the only way to ensure that "data-driven AI methods do not perpetuate or exacerbate existing clinical biases." Also, these organizations will have to think hard about the data implications as the products scale up into settings that may be different from initial use cases. And, of course, "new paradigms are needed for how to best protect patient privacy," according to the report. THE LARGER TREND As the technological capabilities and clinical applications of AI-enabled decision support continue to expand, the Duke researchers said more regulatory clarity from agencies such as FDA, which has signaled an appetite for much wider approval of machine learning apps, is needed to protect patients from wanton use of the "black box" algorithms that many have warned about. In addition, there are other major areas that need ironing-out. Among them: proper allowances for patient privacy and data access, and the ability for these fast-emerging technologies demonstrate value and ROI for providers. In all of those, hospitals and health systems have an active role to play. Then there are all sorts of other technical questions that exist – but haven't necessarily been answered, certainly not on a consistent or widespread basis. Such as: how new approaches to labeling different software might improve understanding of its inner workings; how to weigh the relative risks and benefits of locked versus continuously learning models of AI; how to evaluate its performance over time most effectively; how to mitigate data bias; how to assess "algorithmic adaptability" and more. ON THE RECORD "AI is now poised to disrupt health care, with the potential to improve patient outcomes, reduce costs, and enhance work-life balance for health care providers, but a policy process is needed," said Greg Daniel, deputy director for policy at Duke-Margolis, in a statement. "Integrating AI into healthcare safely and effectively will need to be a careful process, requiring policymakers and stakeholders to strike a balance between the essential work of safeguarding patients while ensuring that innovators have access to the tools they need to succeed in making products that improve the public health," he said. "AI-enabled clinical decision support software has the potential help clinicians arrive at a correct diagnosis faster, while enhancing public health and improving clinical outcomes," added Christina Silcox, managing associate at Duke-Margolis and co-author of the report. "To realize AI’s potential in health care, the regulatory, legal, data, and adoption challenges that are slowing safe and effective innovation need to be addressed." Twitter: @MikeMiliardHITN Email the writer: mike.miliard@himssmedia.com Healthcare IT News is a publication of HIMSS Media.

The business of healthcare is changing. IT should have the confidence and ready state strategy to meet the needs of their population health and community outreach departments.

The AI vendor will be discussing these 2019 healthcare and technology trends with attendees at HIMSS19 in Orlando next month.

Sanofi, BNP Paribas and AXA are also among the groups calling for women innovators to develop tech tools that advance access to care.

The industry must first get ready for the AI era by building up skills in reading, working with, analysing and arguing with data – also known as data literacy.

Artificial intelligence (AI) has disrupted numerous industries in recent years, but for the technology to work effectively, the technology needs to be used right. For the healthcare sector, one of the end goals is to provide better patient outcomes, minimise human errors and alleviate some of the physical and mental burnout felt by healthcare practitioners as a result of the volume of admin work required. A study in the US found that for every hour that physicians spend providing direct clinical facetime to patients, almost two additional hours are spent on desk work. By utilising AI and analytics, this can be reduced, and by extension, so too will the rates of mental illness. For this to happen, however, the industry must first get ready for the AI era by building up skills in reading, working with, analysing and arguing with data – also known as data literacy. Data is the lifeblood of AI; which is what makes AI and analytics the ideal combination. Doctors are constantly receiving data from their patients, often pertaining to the symptoms of an illness or injury and how it can be treated. Healthcare professionals must develop their skills so as to confidently interpret this data and accurately input it into the system to fuel AI. Recent research by Qlik found that only 12 per cent of Australia’s healthcare professionals are data literate, behind the global average of 15 per cent. But, it is important to note that Australia has not been given an opportunity to skill up appropriately. In my time in the healthcare industry, I found that staff learn best when they are given a flexible learning environment to develop their skills in their own time and in a way that does not add to the pressure they are already under. This is why Qlik offers online learning via Qlik Continuous Classroom, where people can login and undertake modules that suit their learning needs at a time that is convenient for them. And for the more junior professionals entering the industry, these types of educational undertakings should be included as part of their structured learning to build the next generation of leaders that are data literate from the start. Embedding data analytics and AI within the healthcare industry is not as easy as just rolling out a software installation; it requires an entire strategy and cultural transformation. The new technology must be ingrained in the ways that doctors and physicians work. In addition to embedding analytics into the workflow, performance KPIs should be put in place to ensure that employees are working towards improving their data literacy and in turn, using it for data-based decision making. It’s also vital that the adoption of analytics is not just restricted to one level i.e. just surgeons, but is embraced by all levels, including admin staff, nurses, GPs, etc. It is the responsibility of the organisation as a whole to drive best practice procedures and provide feedback to ensure that everyone is getting the most out of the analytics platform. This can be done by continuously engaging users in conversation about their data and results, and presenting the results to their peers. MercyAscot in New Zealand has done this extremely well – using the Qlik platform to act on patient feedback quickly and making changes that cover all aspects of the hospital. The organisation recognised the need to more effectively leverage the significant volume of data collated over the years to improve the delivery of services to its patients. Rather than rely on static reports which were often time-delayed, MercyAscot created a system that allows staff to act more quickly on patient feedback to make quality improvements. In addition, connecting to multiple data sources and systems to see how various departments link together was paramount for it to drive optimum efficiencies holistically. MercyAscot’s staff are now interested in the data and openly discuss the numbers and improvements that it can make with using this data. “As an organisation, we were data rich, but information poor. We struggled to process our data fast enough to use it effectively across our business functions and knew this had to change,” MercyAscot Director of Medical Services Dr Lloyd McCann said previously. But it’s not just healthcare professionals that are skilling up to make the most of AI and analytics. The recently launched Data Literacy Project features organisations’ and industry professionals’ real-life stories of how analytics has made an impact on their day-to-day work. As part of this, an online tool was developed to allow organisations to discover their own Corporate Data Literacy score, against which companies can benchmark themselves. I encourage the healthcare industry to see where they stand on this scale and map out their future data literacy path based on individual scores. I hope to soon see a future where the healthcare sector is able to ensure that employees can make full use of the benefits of AI correctly and efficiently.   Charlie Farah is the Healthcare and Public Sector Director at Qlik APAC.

DeepQ, the healthcare division of HTC, yesterday announced a deployment of HTC VIVE™ hardware at Taipei Municipal Wan Fang Hospital to create the first multiuser patient education room in VR. Using VIVE Focus (a stand-alone VR headset from Vive) with the VR human patient education application, surgeons and families can join a shared VR world where surgeons can explain surgical procedures and educate patients. “Vive Focus can be used as a tool to break down barriers between doctors and their patients to improve care and drive education of patients to new levels,” said Edward Chang, President of HTC’s DeepQ division. “With Vive Focus, medical consultation can become mobile and more approachable to patients and doctors alike. We’re proud to work with Taipei Municipal Wan Fang Hospital to explore how VR can begin to change medicine.” “In the past, it has been difficult to educate patients on the impacts of a procedure or medical need. Through VR, physicians can now easily talk to patients about human organ structures and treatment plans in a shared environment,” said Kuan-Jen Bai, Dean of Taipei Municipal Wan Fang Hospital. A typical patient consultation today can involve human anatomy models, however, micro structures such as nerves, vessels, and lymph nodes are difficult to be displayed. Using VR, patients can more easily understand the impact of diagnosis and treatment plans alongside their physicians.  In the future, Wan Fang Hospital will also integrate the VR education platform with the Health Information System (HIS) system of the hospital's patient educational review system. After each VR patient education, the public, the family, and healthcare personnel’s review will be digitalised. According to a market forecast on VR in the healthcare market that was published in March 2018, it is predicted that VR technologies will enjoy a 54.5 percent compound annual growth rate in healthcare over the period of 2017-2023. Currently, most of the established VR offerings in healthcare often fall under a few major use cases, including education and training for physicians, and distraction therapy for inpatients and seniors. However, ever-advancing technology and growing acceptance by physicians and patients alike has many digital health researchers excited to investigate more novel clinical use cases and tackle the various hurdles that remain for VR.

Thanks to consumerism in the healthcare industry, providers are looking to improve patient experience and satisfaction in their care. HIMSS19 attendees who are focused on this topic for their organizations should check out these curated sessions and events.

The health IT vendor sees creative and cross-sector use of emerging technology among the big trends on the front burner at HIMSS19.