challenges of implementing ai in healthcare

“Right now, it’s been more of a hassle than a time-saver, and has actually disrupted the doctor/patient relationship by forcing a screen between physicians and their patients.”, Leonard D’Avolio, founder of Cyft, has harsh feedback for fellow entrepreneurs trying to tackle the space: “We’re seeing hospital after hospital take incredible loss and have widespread layoffs simply from the challenge of implementing electronic health records. The ultimate dream in healthcare is to eradicate disease entirely. In my previous blog post on AI and healthcare, I discussed some of the areas where AI is pushing the envelope, yet there are currently a few challenges standing in the way of even greater adoption within the medical field. The difficulties hospitals face when implementing AI are the result of a few challenges that healthcare as a whole is dealing with. “25 percent of the more than $7 billion spent each year on knee and hip surgeries are impacted by bundled payments initiatives. In an article at Health IT Today, Ori Geva, Co-Founder and President of Medial EarlySign, lays out the challenges of implementing AI in healthcare: Challenge 1: Desire to have one solution for all Collapse. The potential of AI in healthcare is surging, and its possibilities are well beyond that of just assisting doctors in providing simple diagnoses. It remains challenging for organizations to integrate data since information is usually spread across multiple applications in various formats such as text, image, video, and audio. Teo identifies A.I. Deep learning first caught the media’s attention when a team from the lab of Geoffrey Hinton at the University of Toronto won a Merck drug discovery competition despite having no experience with molecular biology and pharmaceutical development. Join us for a series of free webinars to learn how to bring operational AI into your healthcare organization. The real issue is understanding the context into which you are trying to introduce these technology,” warns D’Avolio. They were also asked to then work in a group and develop 3 solutions to overcome the top challenges they identified. “New reimbursement driven by the Medicare Access and CHIP Reauthorization Act (MACRA) and the Merit-based Incentive Payment System (MIPS) incentives in 2017 will drive quality outcomes, phasing providers to think more holistically when investing in technology.” Additionally, he believes that a looser FDA in the coming years will help drive investment in personalized medicine. The rise of AI is an exciting change for healthcare providers all over the world, but implementing these groundbreaking technologies still comes with its fair share of significant challenges. He’s seen many of these data challenges first hand in delivering technological infrastructure to support individualized care. “Adverse drug events cause around 770,000 injuries and deaths annually in the U.S. and cost each hospital up to $5.6 million annually,” Kahlon discloses, “but drug data is messy, coming from multiple sources in multiple formats. Adaptability to change in diagnostics, therapeutics, and practices of maintaining patients’ safety and privacy will be key. In medical applications, transfer learning — using a pre-trained model and adapting it to one’s specific use case — is often applied, but then a “model dependency” is introduced where the underlying model may need to be retrained or change its configuration over time. “AI doesn't make judgments, it gives you an output,” Ameet Nathwani, Chief Digital Officer at Sanofi, said. we could achieve exponential breakthroughs. But AI is also dependent on the right kind of data, not just any data. What workflows will be introduced?”, Even if a medical provider does successfully digitize their data, technical carelessness can introduce problems for everyone in the system. There is often a trade-off between predictive accuracy and model transparency, especially with the latest generation of AI techniques that make use of neural networks, which makes this issue even more pressing. Given the touting of recent analytic and machine learning results in healthcare, why haven't doctors been replaced by computers yet? For startup companies, it’s hard to get access to patient data to develop products or business cases. In this case, diagnosis can be powered by machine learning and then trained by artificial intelligence.” Examples of companies providing clinician assistant and care delivery services include Babylon Health, Evidation Health, Sensely, and Senior Link. When we asked dozens of venture capitalists where they see the most potential for applied artificial intelligence, they unanimously agreed on healthcare. Despite potential difficulties in establishing parameters, transparency of decision support is, of course, paramount to medical AI. Thus, inaction and failure to innovate may lead to doing harm. One of the first challenges Ballad Health’s program faced stemmed from a lack of connectivity. “There’s a huge misconception that A.I. That said, for most healthcare use cases that don’t require real time or high bandwidth, HL 7 2.0 is great and already widely adopted across the industry. The first is the lack of “curated data sets,” which are required to train A.I. There are many well-known challenges to implementing machine learning and A.I. An operational AI platform such as the one we are building at Peltarion, handling the entire modeling process including software dependencies, data and experiment versioning as well as deployment, has the potential to solve many of these engineering and technical debt issues. Implementing and integrating technology has indeed been a burden for many clinicians and practitioners. Gavin Teo, Partner at B Capital Group and a specialist in digital health, cites “provider conservatism and unwillingness to risk new technology that does not provide immediate fee-for-service (FFS) revenue” as a major challenge for startups tackling healthcare. insights into the new and evolving field of AI for health. An inherent problem with AI systems is that they are only as good – or as bad – as the data they are trained on. Technical Barrier No. also requires better data than is currently available. Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR, Join us for a series of free webinars to learn how to bring operational AI into your healthcare organization. Since patient data in European countries is typically not allowed to leave Europe, many hospitals and research institutions are wary of cloud platforms and prefer to use their own servers. The latest techniques in AI making use of deep neural networks have reached amazing performance in the last five to seven years. According to Teo of B Capital, “A study by the Association of American Medical Colleges estimates that by 2025 there will be a shortfall of between 14,900 and 35,600 primary care physicians.” At the same time, the population is aging and in need of more medical attention. In this experiment we teamed up with our colleagues at Doberman to see if we could build on the work of Bechdel and use Deep Learning to take the analysis one step further. The potential of AI in healthcare is surging, and its possibilities are well beyond that of just assisting doctors in providing simple diagnoses. 3: Combining Clinical and Claims Data. Predictive models will need to be re-trained when new data comes in, keeping a close eye on changes in data-generation practices and other real-world issues that may cause the data distributions to drift over time. Today, thanks to the carrot and stick incentives involved in that act the rate of adoption is > 90%.” Another major policy shift that has dramatically helped investment in healthcare IT are the value-based care experiments (also called demonstration programs) funded by the Center for Medicare & Medicare Innovation (CMMI). A PwC Health Research Institute poll reports that over 60-percent of respondents prefer device security over simplicity. 2.3. According to Dr. Mittendorff, “AI enabled coaching will allow a provider or coach to manage more than 1,000 patients simultaneously rather than 50-100, a 10x increase in labor leverage.”, Finally, drug discovery companies like NuMedii and Kyan Therapeutics de-risk the drug development process, enabling “powerful and proprietary new combination therapies, as well as individualized treatment with unprecedented efficacy and safety,” according to Teo. Your email address will not be published. to analyze enterprise-wide access logs and flag suspicious cases for administrator review. Imagine what happens if you then show up and say ‘I have artificial intelligence’.”, The healthcare industry is just getting its arms around capturing data digitally, yet many healthcare tech entrepreneurs mistakenly believe that creating a dashboard or dropping in a product will somehow lead to adoption of technology and improve operations. Each participant was asked to identify up to 5 challenges they faced in implementing healthcare analytics. This necessitates the development of more intuitive and transparent prediction-explanation tools. Mikael Huss is a Data Scientist at Peltarion. Changing a piece of equipment or even software is relatively easy to achieve compared with persuading people to change the way that they work and to take the time to learn how to use new systems. AI algorithms meant to be used in healthcare (in Europe) must apply for CE marking. If several data sources are used to train models, additional types of “data dependencies,” which are seldom documented or explicitly handled, are introduced. AI use cases in healthcare for Covid-19 and beyond. This dream might be possible one day with the assistance of AI, but we have a very very long way to go. Is it based on legitimate data sources?” Examples of biased data abound. “In healthcare, policy eats strategy and culture for breakfast,” explains D’Avolio. “We implemented our first EMR System eight years ago hoping it would improve efficiencies. ... reported in September 2016 that it saved $2.62 million in just five months after implementing a Lean strategy. “Healthcare as a system advocates ‘do no harm’ first and foremost. The challenges and opportunities of bringing AI to healthcare Luckily, many companies strive to address these issues before they come to pass. requires huge amounts of data, but that’s not the real issue in healthcare. "Healthcare is changing, and the challenge today is to be more reactive and preventive," he said. Conclusion. via surprised learning. She "translates" arcane technical concepts into actionable business advice for executives and designs lovable products people actually want to use. “You need context and a deep understanding of who will use this. Getting doctors to consider suggestions from an automated system can be difficult. Dr. Jose I. Almeida is a pioneer in endovascular venous surgery who has practiced for over 20 years. Ethical aspects of using robots in healthcare 15 2.3.2. The first is the lack of “curated data sets,” which are required to train A.I. A.I. Main challenges and opportunities of using robots in healthcare 16 2.3.3. Artificial intelligence has been around for a while, but recently it is taking on a life of its own, invading various segments of business, including finance. Organizations that are paid via value-based programs will seek technology that keep patients healthier at lower cost.”, Suennen of GE Ventures agrees that operational analytics can dramatically improve health systems. A medical record costs about $200. There are many well-known challenges to implementing machine learning and A.I. A doctor needs to be able to understand and explain why a certain procedure was recommended by an algorithm. in healthcare. This issue also explores some of the most ethically complex questions about AI’s implementation, uses, and limitations in health care. The wrong solution or rollout can even harm the healthcare industry. “Curated data sets that are robust and have both the breadth and depth for training in a particular application are essential, but frequently hard to access due to privacy concerns, record identification concerns, and HIPAA,” explains Dr. Robert Mittendorff of Norwest Venture Partners. There is a lot of promise for AI in healthcare, but efforts and advances in many areas need to be made before AI solutions can be deployed in a safe and ethical way. Usually, this is easier for medical researchers, who can make use of standard application procedures meant to facilitate research based on patient clinical data. It’s likely that some elements of AI literacy need to be introduced into medical curricula so that AI is not perceived as a threat to doctors, but as an aid and amplifier of medical knowledge. Traditionally, these decisions are made by looking at 7-10 administrative variables, but Cyft’s models looks at over 400 data sources, ranging from free-text input from nurses to call center data. While adoption of such technologies may seem complicated, D’Avolio gets buy-in by strategically aligning with revenue incentives and policy decisions. “There are areas when you get into the mountain regions where they don’t have good cell phone coverage or broadband coverage into their communities,” Voyles shared. Summerpal Kahlon, MD, is Director of Care Innovation at Oracle Health Sciences. Despite challenges, innovation in healthcare must continue. Organizations must have base data as well as a constant source of data to keep it up and running. Published Date: 30. “And so the key thing is the data that is fed into the AI. Questions and Answers 18 2.3.5. This is a common vision that healthcare leaders -- and by nature in any industry -- find extremely difficult to achieve. He adopted electronic health records (EHR) ahead of the curve, yet has not seen many of the promised benefits. Technology has already been used to incrementally improve patient medical records, care delivery, diagnostic accuracy, and drug development, but with A.I. Until recently, the fact that most participants in clinical trials were white and male did not cause concern. Artificial intelligence can not only improve care delivery, but also assist in clinician decision-making and operational efficiency, amplifying the impact of each individual practitioner. The General Data Protection Regulation (GDPR) directives introduced in May 2018 will also lead to a number of new regulations that needs to be complied with and that are, in some cases, not clear-cut. Teo also points out that the industry feels burned from recent experiences, such as “electronic medical records (EMR) digitization regulations, which were overhyped and resisted.”. Mikael Huss. Wrapping up, the theory of implementing trends and technologies is truly fascinating. in healthcare. Be the FIRST to understand and apply technical breakthroughs to your enterprise. I like reading a post that can make people think. The large amount of “glue code” typically needed to hold together an AI solution, together with potential model and data dependencies, makes it very difficult to perform integration tests on the whole system and make sure that the solution is working properly at any given time. ... AI … If the stars align, humanity stands to derive enormous benefit from the application of A.I. Despite being touted as next-generation cure-alls that will transform healthcare in unfathomable ways, artificial intelligence and machine learning still pose many concerns with regards to safety and responsible implementation. A study by the Mayo Clinic determined that 50 percent of patients have difficulty with medication adherence. Panel 2: Ethical evaluation and responsibilities of AI and robots in healthcare 15. Another report by PwC indicates that over the past decade, the AI investments in healthcare institutes have heated up. Th… The Best of Applied Artificial Intelligence, Machine Learning, Automation, Bots, Chatbots. The potential of AI in healthcare is surging, and its possibilities are well beyond that of just assisting doctors in providing simple diagnoses. Technologies in healthcare 16 2.3.3 promising customers will use this were very different conversations... Tells pharmaphorum how AI-based technology is solving challenges across healthcare systems, companies... 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