• AI
  • 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. 

  • AI’s on the move in healthcare

    Perhaps the biggest display so far of AI potential and enthusiasm was at the Neural Information Processing Systems (NIPS) conference. It’s role in healthcare was a core theme of applied research, as reported in The Economist. 

    Initiatives presented at NIPS 

    Australia’s Maxwell MRI combines MRI and deep learning to improve prostate cancer diagnosesJohannes Kepler University has an AI system to track cell proteins to identify underlying biologyNorth Carolina’s Duke University uses machine learning to use a pocket colposcope to find cervical cancer. 

    Mining EHRs and doctors’ notes to estimate unplanned readmissions is increasing too. Another application’s categorising and understanding children’s allergic reactions. AI algorithms identify the use and distribution of Naloxone, a drug to reverse effects of narcotic drug, treat pain and block the effects of opioids. 

    With AI marching on, it adds to Africa’s eHealth priority challenge. How can it invest simultaneously in mainstream eHealth and AI? There’s no easy answers.

  • Will AI improve cyber-security?

    AI is seen as a big step up in eHealth and healthcare. Will it help to improve cyber-security too? Forrester, a strategy firm, says it will. Its report Artificial Intelligence Will Revolutionize Cybersecurity But Security Leaders Must View All Vendor Claims With Skepticism also offers caution.

    While AI can help, pure AI, the sci-fi version won’t. It’s the building block technologies of pragmatic AI that can provide applications that can support cyber-security in dealing with about current and future threats. Like all solutions, AI’s not a silver bullet, but it’s part of the cyber-security armoury that can help analysts to keep up with new and emerging threats and the daily deluge of alerts and events they have to deal with every day. This emphasises an important AI theme. Human knowledge is paramount and can be enhanced by AI.

    AI for cyber-security’s a second joint priority. About 34% of organisations say it’s their objective, the same percentage as improving analytics and insights. Better ICT efficiency’s the top priority at about 40%.

    Some AI vendors are incorporating one of more components into their services. The range includes: 

    Biometrics to authenticate users unique physical characteristicsNatural language processing (NLP) technology to reads and understand people’s textMachine learning, composed of tools, techniques, and algorithms to analyse dataDeep learning, a branch of machine learning focusing on algorithms that construct artificial neural networkSecurity automation and orchestration (SAO) to help with cyber-threat investigations and responses.Cyber-security analytics.

    Forrester sets out six ways to scale cyber-security with machine learning. It identifies and advantage and disadvantage of each one. The core role is automatically identifying suspicious, anomalous patterns and user behaviour that appear faster. The techniques are:

    1. Thresholds set on continuous metrics to detect anomalies. Advantage: thresholds are very simple to configure. Disadvantage: they may detect situations after the fact, not before

    2. Built-in rules using vendors’ years of expertise can automatically raise alerts based on this internal. Advantage: built-in rules require little setup and codify vendors’ expertise with other customers. Disadvantage: rules may not exist for all threat surfaces and may be based on outdated information

    3. Customisable rules to let cyber-security professionals apply their experience using their organisations’ own unique complex combinations of software and systems. Advantage: security professionals can codify their expertise in the solutions. Disadvantage: they may create rules based on theories instead of concrete data

    4. Built-in models, can go beyond rules created by people to address complex relationships from historical data faster and find complex, nuanced relationships than people can. Advantage: models are created by machine learning algorithms that analyse historical cyber-security data, yielding better predictions that improve over time. Disadvantage: models need more data science knowledge to tune and maintain.

    5. Built-in models can learn the peculiarities of organisations’ cyber-threat surface. Advantage: predictive models are based on actual data collected from infrastructure and analysed by machine learning algorithms. Disadvantage: false positives and false negatives are often problems with predictive models generated by machine learning

    6. External, importable models let organisations’ communities share knowledge. Advantage: organisations can share and reuse AI models used for cyber-security. Disadvantage: community models may vary widely in efficacy and applicability to specific organisations.

    The report provides Africa’s health systems sophisticated, balanced insights into AI’s wider user. It is essential to include its perspectives into their eHealth strategies with AI having more than one role in frontline healthcare. It adds a new, constructive dimension to eHealth’s essential cyber-security strategies and plans.

  • mHealth keeps expanding, but Africa and South America are trailing

    The mHealth market’s been growing steadily, and will keep it up. In its report mHealth App Economics 2017 Current Status and Future Trends in Mobile HealthResearch2Guidance (R2G), a strategy advisory and market research company, assesses how digital intruders are taking over the healthcare market. 

    This year, there are 325,000 health and fitness apps available from all major app stores. It’s 78,000 more than last year.

    Most eHealth practitioners come from Europe, 47%, and 36% from the US, a combined 83%. Asia-Pacific accounts for 11%. South America and Africa trail at 4% and 2% respectively, confirming the need for increased human capacity development.

    Other findings include:

    Android’s overtaking Apple in health app numbers84,000 health app publishers release appsWidening demand and supply gap, with high number of developers and low downloads growth ratesUS$5.4bn investment in eHealth start-ups fuelling the marketUsers will download an estimated 3.6bn apps in 201718% are not developing health apps because of uncertain regulations53% of eHealth practitioners expect health insurances to be  the future distribution channel with best market potentialAn estimated 28% pure eHealth market players in the eHealth industry.

    Two app types may have a big healthcare impact. Artificial Intelligence (AI) is seen as the most disruptive technology.  It’s seen as combining with remote monitoring to be the technologies that will disrupt healthcare most. The profile’s:

    AI 61%Remote monitoring and assistance 43%Wearables 34%IoT 30%Virtual reality and intelligence 27%3D printing 22%Blockchain 18%5G 8%Other 5%. 

    It seem that there’s an opportunity for Africa’ health systems to support and expand their local health app supply side. An integrated demand and supply strategy could do it.