• AI
  • AI and machine learning need data storage resources

    Many things come in bundles. Amit Ray, author of Mindfulness Meditation for Corporate Leadership and Management says “As more and more artificial intelligence is entering into the world, more and more emotional intelligence must enter into leadership.” It’s not enough. A report by Source Media, sponsored by Pure Storage says powerful, advanced computing and storage capacity and capabilities are needed too.

    It recognises AI’s “vast” potential. Currently, some radiology departments use it effectively to improve workloads. Progress across other clinical activities depends on extra computing and storage power for two activities, training and clinical use.

    When researchers deliver AI and machine learning techniques to clinical practice and healthcare, solutions need huge amounts of data for training models, including labelling data. It’s especially important for neural networks. These are hardware and software patterned on the way neurons work in human brains. They’re deep learning technologies often focusing on solving complex signal processing or pattern recognition problems.

    If storage’s inadequate, it can’t keep up with the workload. The result’s diminished AI. Healthcare’s typical eHealth investment model’s to buy enough computing storage infrastructure as a minimum requirement, then expand it a few years after it’s clogged up. Eventually, it’s replaced with modern solutions after a period of obsolescence.

    This doesn’t fit AI and machine learning. It has to match the computer power and storage capacity needed as AI and machine learning expands. Developers and healthcare organisations can then move beyond exploring AI’s potential and bring into full use. The, patients benefit.

    While assembling the resources needed for AI and machine learning’s challenging for Africa’ health systems, the infrastructure requirements add to the constraints. Before venturing into the AI space, it’s essential to contemplate and deal with the whole resource requirements and their affordability. 
  • Experts offer their different views on London hospital’s AI

    The AI project announcement by a major London hospital’s attracted a wide span of opinions and ideas. Building on the plan reported in eHNA, the Times has several letters on the initiative. 

    Prof Sir Robert Lechler, President of the Academy of Medical Science emphasised the requirement to have the basics in place to realise the benefits and more healthcare to lead toe fourth industrial revolution. It includes sufficient resources for collaboration with industry, academia and regulators.  He sets the goal of people’s good mental and physical health.

    Hilary Evans, CEO Alzheimer’s Research UK, sees AI as an opportunity to revolutionise dementia research. Her goal is to improve early detection and diagnosis of the progressive disease. 

    Another prof, Harold Thimbleby from Swansea University has a different view. He says more AI’s a simple theory and that much of it data is flawed. Instead, fixing bad ICT would be more cost-effective, offer increased medical value and extend across more health conditions. The effect can be dramatic. 

    Nicola Perrin, Head of Understanding Patient Data, says AI success relies in patients having confidence in how their data’s used. A constructive dialogue’s needed with the public, She evokes the alarms raised from the Facebook and Cambridge Analytic controversy.

    J Merrion Thomas, a surgeon, says the money for AI would be better spent  on earlier benefits, such as highlighting known risk factors and early diagnosis. These will save lives immediately, rather than wait for AI’s benefits.

    These wide-ranging comments are just as relevant for all types of eHealth. They illustrate the engagement and commitment challenges of eHealth’s numerous stakeholders and provide valuable lessons for Africa’s health systems. eHealth never goes ahead in a straight line.

     

  • Is AI set to take off in a London hospital?

    As marches go, AI in healthcare’s still in its early stages. It may be that it’s about to make a big leap forward. The UK’s Guardian newspaper  reports that University College London Hospitals (UCLH) and the Alan Turing Institute has agreed a three-year partnership to realise AI’s benefits to healthcare on an “unprecedented scale.” Planned projects include using AI to: 

    Improve UCLH’s A&E department’s performance, currently below 77% of patients needed urgent care treated within four hours, well below the standard set for England and stuck at 2010 levelsAnalyse CT scans of 25,000 former smokers recruited as part of a research projectAutomate cervical smear tests assessments. 

    A challenge is avoiding learned helplessness. It’s where health professionals become too reliant on automated instructions and abandon common sense. AI’s algorithms might be correct 99.999% times, but are rarely 100% reliable.

    Another’s sustaining rigorous data governance standards, especially privacy and confidentiality. The plan’s to apply algorithms to UCLH’s servers to avoid breaches. Private companies won’t have access. 

    A previous AI project in Engalnd’s NHS was a collaboration between London’s Royal Free Hospital and Google’s DeepMind. The Royal Free accidently gave Google access to 1.6 million records of identifiable patients.

    Alan Turing was an English computer scientist, mathematician, logician, cryptanalyst. He was highly influential in developing theoretical computer science, formalising concepts of algorithm and computation using the Turing machine. In the 1940s at Bletchley Park, he was instrumental in developing the Bombe machine to crack enemy’s complex and rapidly changing Enigma code.

     

  • AI is also attractive for cyber-criminals

    As healthcare increases investment on eHealth projects and services, there should be synchronous investment in security measures.  In 2017, 25% of all data breaches were related to the healthcare industry.  This is because cyber-criminals have been working to make their attacks more advanced to easily target connected devices, cloud, and multi-cloud environments.  These advanced cyber-attacks are even able to evade detection by most legacy security solutions in place. 

    Advancements are aided by adopting AI and machine learning to carry out complex attacks at a rapid pace. Botnets such as Reaper have been made more sophisticated, enabling them to target multiple vulnerabilities at once.  Others, such as polymorphic malware allows for hundreds of variations of a threat to be created for different purposes in a matter of hours. 

    To address these challenges, Fortinet has recently released a few product enhancements that will tip the scales back in the favour of the healthcare industry;

    Fort iOS 6.0 – provides an integrated security architecture that spans the distributed networkFortiGuard AI – is an AI solution that is able to address automated attacksThreat Intelligence Services (TIS) - provides visibility into network activity and metrics to give healthcare security teams an understanding of their threat landscape 

    It has become inexpensive for criminals to mount attacks on healthcare data, but increasingly expensive for their targets. One key to the healthcare security transformation is flipping this paradigm.

  • Philips introduces AI tools for healthcare efficiency

    HIMSS Conference and Exhibition is synonymous for sharing new innovations and showcasing next world technology.  At HIMMS18 in Las Vegas, Phillips announced the launch of a new set of tools that supports the progressive adoption of analytics and artificial intelligence (AI) in key healthcare domains.

    Their HealthSuite Insights gives data scientists, software developers, clinicians and healthcare providers access to advanced analytic resources to compile and analyse healthcare data.  It also offers tools and technologies to build, maintain, deploy and scale AI-based solutions. 

    AI-based solutions have great potential to improve patient outcomes and healthcare efficiency. However, developing and deploying AI solutions for healthcare use cases can be time consuming, resource intensive and expensive.  Philips’ Insights Marketplace can help with this.  The Insights Marketplace will provide the healthcare industry’s first ecosystem where curated AI assets from Philips and others are readily available for license. 

    Philip’s HealthSuite Insights and Insights Marketplace may help accelerate Africa’s eHealth development. African countries are increasingly aware of the necessity of technology in improving the performance of healthcare.  Some parts of Africa have already started integrating artificial intelligence in their healthcare systems.

  • Facebook’s using AI to prevent suicides

    According to the World Health Organisation (WHO), a suicide occurs every 40 seconds globally.  Social, psychological, cultural and other factors can interact to lead a person to suicidal behavior.  Facebook believes that they are uniquely positioned to help combat suicides amongst adolescents and its users.

    They’re using AI and smart algorithms to detect suicidal tendencies and patterns.  The AI software scans users’ messages and posts for signs of suicide, such as asking someone if they are troubled.  Facebook already has tools in place for people to report concerns about friend's who may be considering self-harm, but the new AI software can speed the process and even detect signs people may overlook. 

    Posts that are flagged as worrisome are communicated to first-responders.  It’s also dedicating more human moderators to suicide prevention, training them to deal with the cases 24/7. They have partnered with organisations like Save.org, National Suicide Prevention Lifeline and Forefront from to provide resources to at-risk users and their networks. 

    Ubiquitous technologies often come with unrealised responsibilities.  Facebook’s demonstrating they're willing to take on these responsibilities and use their platform for greater social and health benefits.

  • Will robots be cooking on gas in hospital kitchens?

    Inpatients need nutritious meals as part of their care plans. This puts hospital catering services as an important part of healthcare teams. While robots in clinical activity have received considerable attention, their opportunities in hospital catering hasn’t. Flippy might change that.

    A report  in Tech Crunch says Miso Robotics is rolling out a robotic kitchen assistant. It’s called Flippy. It’s first job’s flipping burgers. Already, it’s a bit of a celebrity, with a YouTube and Vimeo performances. 

    While burgers may not be the ideal meal for inpatients, Cali Burger makes and sells burgers in twelve countries and found Flippy its first job. It doesn’t look like a chef.

    It’s a small, wheeled cart with a six-axis robotic arm and  a sensor bar. It takes data from thermal sensors, 3D sensors and several cameras to assess its environment. Digital systems send tickets from the counter to the kitchen as Flippy’s orders.

    Then, it picks up unwrapped burgers, puts onto a hot grill, tracks their cooking time and temperature, then alerts chefs when to apply cheese or other toppings. When that’s done, Flippy plates the burgers.

     but doesn’t wrap them or add finishing touches like lettuce, tomatoes, avocado or a restaurant’s signature sauce.

    Momentum Machines makes kitchen robots too. Flippy’s different. It relies on  AI software and machine learning, so it learns to make new foods, adapting to a restaurant’s seasonal menu changes. This might be the potential for Flippy’s descendants to take on more sophisticated jobs in hospital kitchens. Let’s hope they’re not wayward offspring called Floppy.

  • Need a Big Data and AI overview; this’s it

    It seems that Big Data isn’t big after all. David Stephenson, in his book Big Data Demystified, published by Pearson, says “Big” significantly understates the volume and differences to conventional data. Understanding it needs to be in its context of AI and Machine Learning (ML). 

    He ranges over Big Data’s:

    UsefulnessEcosystemStrategyImplementationTechnology selectionTeam buildingGovernance and legal compliance.

    Case studies bring each of these into practical environments. While Stephenson’s keen on Big Data, his book’s not an exhortation to rush into initiatives. Instead, his “Keep in mind” boxes are valuable switches from his commentary that provide realistic insights for policy makers, strategists, executives, managers, practitioners, health workers and students.

    It’s clearly written and offers new, late and in between comers to Big Data many very valuable insights and case studies. Examples are his analyses of Big Data’ infrastructure requirements and its 3Vs, Volume, Velocity and Variety. His concept of a “data lake” draws a vivid perspective of Big Data’s difference to databases 

    He includes a salutary lesson. Many Big Data projects “Die on the launch pad because of inadequate preparation, internal resistance or poor programme management.” His case study was a $62m crash.

    As Africa’s health systems move towards more Big Data opportunities, Big Data Demystified will help to set scenarios that lie ahead. Investment in new skills is part of it.

  • eHealth's 'good to great' formula offers success for 2018

    Amit Ahlawat in his book, “Seven Ways to Sustained Happiness”, says, “New doors open up; we stop looking back, enjoy the present and start planning and prioritising for the future in an optimal and optimistic manner." Similarly, as the doors of 2018 have swung open, eHealth must look forward, carrying with it the wins and lessons from 2017 to plan for an optimistic future. So, what does this future look like?  More importantly, what are Africa’s  eHealth priorities in 2018?

    2017 left us with a whirlwind of eHealth innovation, some big wins and some great lessons. Over the past few days, every noteworthy eHealth blogger, author and fund have written about their insights for 2018. As a young voice in this industry, I’d like to share my eHealth predictions for the year ahead. 

    My infatuation with analytics leads me to my first prediction; 2017’s curiosity with BDdata will result in greater investment in analysing data and making it more useful in 2018. eHNA’s published several articles over the last two years around the need for predictive analytics and the applications of Machine Learning (ML) in Africa’s healthcare. Micromarket Monitor predicts a Compound Annual Growth Rate (CAGR) of over 28% in predictive analytics investment in the Middle East and Africa by 2019.  Growth will be driven by the high penetration of new technologies in eHealth, rapidly increasing eHealth start-ups in Africa and the deluge of data they generate.

    Next, the rise in mHealth applications will swing more users towards Bring Your Own Devices (BYOD). While  it’s been a hot topic in 2017, Africa’s eHealth seems unconvinced by it. An eHNA article reported that over 90% of healthcare workers own a smart device. Barring security concerns, mHealth’s growing use in clinical decision support and healthcare delivery will propel government and organisations towards developing BYOD strategies. 

    Unsuspectingly, gamification may grab lots of attention this year. As healthcare moves away from a reactive to a proactive response, gamification may provide a large helping-hand in behaviour modification and awareness. It’s already created a sensation with Pokemon Go. Research suggests it improves physical and mental health.

    There’ll be many more predictions and events for Africa’s eHealth in 2018. The success of these will be underpinned by prioritising and investing in:

    Developing eHealth leadershipChange managementRisk managementCyber-security. 

    eHealth needs a unique type of leader with the right eHealth perspective, insight and skills to identify and maximise Africa’s eHealth opportunities. Without this, opportunities may not be seized. Acfee feels strongly about this and has put together a number of resources to develop eHealth leaders and champions.

    Change management’s vital for eHealth transformation. It helps stakeholders understand, commit to, accept and embrace the changes that eHealth brings with it. Prosci reports that projects with excellent change management are six times more likely to meet their objectives than projects with poor change management.

    Lastly, no endeavour is without risk. England’s WannaCry crisis and spambot Onliner are proof that eHealth and innovation will attract a fair amount of risk. 2017’s frenzy around cyber-security has taught us some valuable lessons. Lessons that need to carried into this year and strongly embedded into risk management protocols. For preparedness is no luxury, but a cost to eHealth’s progression and efforts.

    I look upon 2018 with great zeal and zest for the infinite opportunities that lie ahead. 2017 has shown that Africa has a promising eHealth future ahead of us, and the contributions you make as innovators, collaborators and visionaries can only strengthen it. I wish you all a prosperous new year and hope that you will remain in our readership as we unfold 2018’s innovations and breakthroughs.

  • What were the top ICT stories in 2017?

    Now 2017’s history, the significant ICT themes can be seen. A retrospective by Health IT Analytics found the top ten from its posts. They’re Big Data, Fast Healthcare Interoperability Resources ( FHIR) and machine learning are included. They’re:

    Top 10 Challenges of Big Data Analytics in HealthcareTop 4 Machine Learning Use Cases for Healthcare ProvidersWhat is the Role of Natural Language Processing in Healthcare?Judy Faulkner: Epic is Changing the Big Data, Interoperability GameHow Healthcare can Prep for Artificial Intelligence, Machine LearningExploring the Use of Blockchain for EHRs, Healthcare Big DataHow Big Data Analytics Companies Support Value-Based HealthcareBasics to Know About the Role of FHIR in InteroperabilityData Mining, Big Data Analytics in Healthcare: what’s the Difference?Turning Healthcare Big Data into Actionable Clinical Intelligence. 

    It’s a valuable checklist for Africa’s health informatics and ICT professionals for there personal development plans. eHealth leaders can use it too to ensure their eHealth strategies either include initiatives for the top ten, or lay down the investigative and business case processes for future plans.