• AI
  • 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.

     

  • Machine learning use cases for health points to the future

    Machine learning (ML) and artificial intelligence (AI) have quickly rocketed to the top of the industry’s buzzword list, driven partly by heightened interest in big data analytics amongst healthcare providers and vendors

    The allure of intelligent algorithms to mine masses of structured and unstructured data for innovative insights get’s health planners pretty excited. However, a fragmented health ICT landscape and sluggish analytics development have thus far kept that Holy Grail beyond reach.

    Regardless, ML is already making a difference.  Here are some examples;

    Imaging analytics and pathology

    ML can supplement the skills of human radiologists by identifying subtler changes in imaging scans more quickly and potentially leading to earlier and more accurate diagnoses.  At Stanford University, ML tools performed better than human pathologists when distinguishing between two types of lung cancer.  The computer also bested its human counterparts at predicting patient survival times.

    Natural language processing and free text data

    Using natural language processing (NLP), ML algorithms can turn images of text into editable documents, extract semantic meaning from those documents, or process search queries written in plain text to return accurate results.  Anne Arundel Medical Center is using a natural language interface, similar to any of the widely known internet search engines, to allow users to access data and receive trustworthy results.

    Clinical decision support and predictive analytics

    Identifying and addressing risks quickly can significantly improve outcomes for patients with any number of serious conditions, both clinical and behavioral. The University of California San Francisco’s Center for Digital Health Innovation (CDHI) and GE Healthcare are creating a library of predictive analytics algorithms for trauma patients in an attempt to speed up the delivery of critical care.

    Cybersecurity and ransomware

    At the end of 2016, IBM Watson launched its Cyber Security Program.  Watson’s ML and cognitive computing skills are used to flag cyber threats and check for suspicious activity against known malware or cyber crime campaigns.  This helps IT staff take better decisions based on known characteristics of malware.

    ML and AI are the keys to addressing health care inadequacies.  These technologies can help predict and control disease, expand and augment service delivery, and address several persistent social inequities. Ubiquitous health tech is by no means inevitable.  Successful rollout will entail an immense amount of concerted effort, capital, labor, and partnership.

  • How can Africa’s eHealth afford AI?

    As AI creates more potential and opportunities for better health and healthcare, it’ll need more investment in ICT. It’s a core theme of an eBook by Azeem Azhar, editor of the weekly blog Exponential View. For Africa’s health systems, it looks like an emerging and major component of their eHealth strategies. 

    Published by Medium, ArtificiaI Intelligence and the Future of Computing sets out his view of the future where machine learning creeps across enterprises and increases the demand for processing capacity significantly. Three main impacts on the ICT supply side, its hardware and software suppliers, practices and opportunities include:

    Considerable in the computing capacityFlourishing cloud servicesNew chip species.

    These are matched by users’ demand. They’ll need to invest too to pursue their AI goals and benefits. An indication of the scale of the ICT investment step-up’s Azhar’s comparison with Moore’s Law. It’s an average annual 60% improvement of transistor packing or performance every year, a significant expansion. He says the growth AI needs’ll be bigger than Moore’s Law.

    It’s not the only expansion. More algorithms and processing capacity are two. The third’s data, a component requiring Africa to increase its eHealth investment too. These work as a cycle. As processing power increases, more demanding algorithms are possible. These need more data, increasing the demand for more and better processing, leading to more complex algorithms.

    These AI forecasts have considerable implications for Africa’s eHealth. They raise questions like:

    How can Africa afford AI investment and general eHealth to provide baseline data?How much will it have to invest in human capacity and capability for AI and its algorithms?Within low healthcare budgets, how long will it take?What’s the relative strategic requirement for AI opportunities compared to other eHealth and initiatives such as IoT?

    Simple questions. No easy answers.

  • AI helps to identify schizophrenia

    Artificial Intelligence (AI) is on the move. It’s becoming an integral part of healthcare. Research by IBM and the University of Alberta published in a report in the Nature Partner Journal Schizophrenia, a Nature publication, shows that machine learning algorithms can predict schizophrenia with 74% accuracy, says an article in IT-Online.

    It also shows that the technology can predict the severity of patients’ specific schizophrenic symptoms based on correlations observed across different brain regions. This predication of symptom severity could help clinicians identify customised treatment plans for each patient.

    Schizophrenia’s a chronic neurological disorder affecting up to eight out of every 1,000 people. Patients experience hallucinations, delusions and cognitive and physical impairment.

    Dr Serdar Dursun, professor of Psychiatry and Neuroscienece at Alberata University’s optimistic that the new technology’s. “Innovative multidisciplinary approach opens new insights and advances our understanding of the neurobiology of schizophrenia, which may help to improve the treatment and management of the disease.”

    The study can also extend these techniques to other psychiatric disorders such as depression or post–traumatic stress disorder, both of which are often tricky to diagnose and harder to treat. 

    Using AI is one way that Africa’s stretched mental health services can expand their impacts. The research provides a justification for it as part of eHealth strategies.

  • Microsoft's joined the AI race

    The Artificial Intelligence (AI) supply side’s heating up. An article on the BBC says Microsoft is promoting its AI credibility and visibility. It brought its top scientists from across the world to London demonstrate their ideas, vision, research, initiatives and AI’s direction that over the next few years.

    One project’s Seeing AI. It does several clever things. It helps people with visual impairment access information using a smartphone’s camera. Pointing a phone at documents enables it to read them aloud. A set of bleeps guide people to barcoded on drinks cans to tell users what it is. 

    Creating Seeing AI took several years. Microsoft’s AI research programme’s been underway for 25 years. It has three main parts, speech, language and vision. Some of it’s now coming to fruition and seeing, recognising and understanding our world in a similar way we do. 

    Microsoft’s AI progress adds extra opportunities for Africa’s consumers and health systems. When will they become part of Africa’s eHealth strategies?