Ameera Hamid

eLearning Specialist and Disruptive Innovator in eHealth

  • Medical imaging’s the big gainer from AI and ML

    AI’s not new. It emerged in the 1960s. A blog from PLOS Speaking of Medicine says advertising hyperbole has led to scepticism and misunderstanding of what’s possible with machine learning (ML) and what’s not with. The blog sets about providing an accessible, scientifically and technologically accurate portrayal of ML’s current state in clinical translation.


    Medical imaging workflows are seen as benefiting most in the short-term. ML algorithms automatically processing two or three-dimensional scans to identify clinical signs of conditions, such as tumours and lesions, and determining likely diagnoses have been published. Some are progressing through regulatory steps toward the market. 


    Many use deep learning. It’s a form of ML based on layered representations of variables, ML’s neural networks. It’s benefited ophthalmology. A major UK eye hospital has used deep learning to deal with a clinically-heterogeneous set of three dimensional optical Coherence Tomography (CT) scans. Referral recommendations reached or exceeded experts’ decisions.


    Radiologic diagnoses are another ML beneficiary. An algorithm detected 14 clinically important pathologies from frontal-view chest radiographs. They included:


    • Pneumonia
    • Pleural effusion
    • Pulmonary masses and nodules.


    ML’s performance matched practicing radiologists. Another 


    There’s several other clinical activities where ML can benefit healthcare. They include: 


    • Triage and prevention
    • Clustering for discovery of disease sub-types
    • Anomaly detection to reduce medication errors
    • Augmented doctors.


    The blog’s an advance report. Its final version’ll be in PLOS Medicine at the end of December. It’s a valuable guide for Africa’s health systems’ eHealth strategies. An initial step’s to lay down data foundations.


  • Babylon’s AI is embedded in Rwanda’s primary care, and other countries

    Succeeding with UHC depends on extra healthcare resources. It depends on efficient and effective use of resources too. eHealth’s part of the solution. 


    Babylon Health uses AI to improve access to primary care. It’s planning to expand into chronic care. In England, it’s restricted geographically to London.  Regulations seem to prevent Babylon’s AI from providing diagnoses. Instead, it provides health information.  This could change as hard evidence becomes available.


    A review, reported in Digital Health, says Babylon’s AI claims lack convincing evidence. Babylon Health doesn’t agree. A report claims its AI beat human doctors’ average score of 72% in a range of 64% to 94%. Babylon scored 81% in a Royal College of General Practitioners (RCGP) exam using a representative sample of questions from the final assessment for GPs in training. The results have not been peer-reviewed.


    In Rwanda, it’s called Babyl. It uses AI to provide:


    • Consultations with doctors and nurses
    • Lab tests
    • Prescriptions
    • Referrals.


    It’s a core UHC component for the country. Where access isn’t feasible with conventional healthcare, Babyl seems crucial in meeting health and healthcare needs.


    Babylon’s AI uses machine learning created by a team of research scientists, engineers, doctors and epidemiologists. They have access to large data volumes from the medical community. Learning’s continuous through feedback from Babylon’s experts.


    It comprises: 


    • A knowledge graph
    • A user graph
    • An inference engine
    • Natural Language Processing (NLP). 


    It seems like a solution for all Africa. On its own, it may not be enough. Increasing referrals may need investment in extra healthcare capacity.



  • Nigeria uses mHealth to improve blood donations

    Blood shortages are common in many health systems. An initiative in Nigeria uses mHealth to create a community of voluntary blood donors, and connects hospitals with blood banks, and blood banks with donors. Life Bank, a Lagos start-up also provides a discovery platform on for hospitals to order blood


     LifeBank delivers requested blood in less than 45 minutes, in a WHO Blood Transfusion Safety compliant cold chain. An article in Disrupt Africa says it’ll add other medical products such as oxygen, vaccines and rare drugs to its services.


    Its founder, Giwa-Tubosun, began a non-profit service to encourage people to donate blood. She then moved on to address supply shortages and poor logistics. Two main goals are: 


    • Increasing access to blood
    • Reducing the number of Nigerian women who die from birth complications. 


    LifeBank’s resources include: 


    • AI
    • Blockchain
    • Cold chain
    • mHealth
    • Motorbikes.


    These combine to provide information about blood availability and avoid health workers’ wasted time and frustration seeking blood products. They also minimise ineffective blood transports that result in bacteria proliferation and consequences of health complications.


     Supporters include:



    Its impact is considerable. To date, LifeBank’s delivered some 11,000 products for over 400 hospitals. Over 6,300 people are registered as voluntary blood donors, with over 20% donating blood in the last two years. The result: over 2,100 lives saved.


    A challenge is convincing blood bank partners to use LifeBank. As this is  overcome, it’s it easy to envisage LifeBank eventually operating across Africa.


  • Heidelberg University launches an eHealth policy course.


    Three entities have combined to create a five-day residential course on eHealth policy at Heidelberg University. The other two are evaplan, a University Hospital Heidelberg consultancy, and the Institute for Global Health.


    Developing national digital health policy: Laying the Foundations is designed for health planners and policy advisers. It will help them to explain eHealth’s national requirements for success. A specific emphasis is on low and middle income countries. It aims to help participants to:


    • Understand how well-crafted eHealth strategies support smart investment
    • Use available toolkits to design and improve country’s eHealth policies
    • Strengthen participants’ eHealth adviser roles
    • Support decision making for interoperable eHealth and avoid further fragmentation
    • Understand organisational and behavioural changes needed to maximise eHealth benefits.


    The curriculum for the first four days includes: 


    • Health Strategies and eHealth strategies in developing countries
    • Developing eHealth strategies
    • Planning for interoperability
    • Management and behavioural change. 


    The dates are 4 to 8 February 2019 at the university’s Internationales Wissenschaftsforum Heidelberg (IWH), Germany. The final day includes a guided tour of Heidelberg and time for mentoring and networking. Presenters are Peter Drury and Michael Stahl, The course is in English. Applications close on 15 November 2018.


  • How far into the future should eHealth strategies look?

    By definition, eHealth strategies are about investing in the future. They’re also about taking existing eHealth investments forward, either by switching, enhancing and rolling out further. In 2006, Rosabeth Kanter identified several lesson for innovation strategies. They included an “innovation pyramid” where:

    • Not every innovation idea has to be a blockbuster
    • Sufficient numbers of small or incremental innovations can lead to big gains
    • Big bets at the top that get most of the investment
    • A portfolio of promising midrange ideas in test stage
    • A broad base of early stage ideas or incremental innovations.

    The last one’s relevant for a perspective set out in an eBook from Oracle. Technology Takes Healthcare to Next Level proposes strategies for disruptive technologies of:

    • AI
    • Blockchain
    • Chatbots
    • IoT. 


    Each one offers promise for healthcare. Combined, Oracle sees the sum of the parts as greater than the whole. Combining blockchain and IoT allows frictionless data exchange. AI and machine learning put data in motion with minimal human intervention. AI tools can study blockchain’s large volumes of data to find patterns that need responses


    For Africa’s health systems, investment in ICT foundations and patients’ clinical and demographic data’s needed to. The strategic challenge is to choose between sequential investment and progress in an innovation pyramid where these four technologies start their journey. While leaving the disruptive technologies into the future, it can defer the costs. It will also defer the benefits.

     


  • Robots could be good for your health

    In his book The Rise of the Robots, published by One World, Martin Ford proposes social and economic scenarios for robots that are good for output, but not so good for people. He sees significant upheaval and displacement from employment across a wide range of commercial and industrial activities and across middle and low income families. The drop in income, so spending power, will degrade economies.


    Simultaneously, robots aren’t paid and don’t spend money. He sees this as exacerbating the social and economic impact.


    Healthcare’s the activity that’s different. He sees the robots marching into healthcare that’s already over-stretched as needs and demands continuously outstrip supply. Four roles are crucial:


    • Artificial intelligence in medicine
    • Hospital and pharmacy robotics
    • Robots that care for the elderly
    • Unleashing the power of data. 


    For low and middle income countries and health systems, sustained investment in robots could be part of the solution. They can improve healthcare professionals’ productivity and help to meet demand.


    They should find a place in Africa’s eHealth strategies. Small scale investment will lay out a trajectory for the future.


  • Will the new Apple watch be a big hit?

    Apple is renowned for technological advancement and innovation.  During their annual product launch this year, Apple announced the new series 4 watch.  It’s not your average wearable.  This watch boasts mHealth features such as electrocardiogram (ECG) monitoring and fall detection, and allows you to share this data with your healthcare practitioner.

    These features are impressive.  Apple is snapping up opportunities to break into the rapidly expanding mHealth market, from fitness tracking to a health app and now wearable ECG.  But there’s a nagging concern too.  What impact will this wearable ECG have on the healthcare system?  Could Apple’s new innovation spur an increase in unnecessary healthcare utilisation?

    These are concerns that I seem to share with other healthcare practitioners who worry that consumers may incorrectly attempt to diagnose complex cardiac conditions themselves.  There are also concerns about the sensitivity and specificity of the device, which if not great, could spark a mass of panic-stricken consumers due to false positives.

    While this new innovation is an important movement towards better patient management, it is vital that the counsel of healthcare practitioners is not diminished.  Consumers must be advised to use it cautiously to augment their healthcare management rather than replace professional management. 

    Similarly, the medical community needs to work more closely with companies like Apple who drive much needed innovation.




  • Medical apps need better UX and UI

    With the ubiquitous use of smartphones today, mobile users have great expectations from their apps: fast loading time, intuitive workflows, ease of use and aesthetic appeal. Digital health and mHealth organisations hoping to compete successfully in this vivid ecosystem, can no longer ignore user experience(UX) and user interface (UI) design as an essential component of their product strategy.


    So, what is UX and UI? 


    UX is the process of researching, developing, and refining all aspects of a user’s interaction with a product to ensure that it is meeting the user’s needs. UI is more cosmetic and takes into consideration the visual interaction with a product, including the colour schemes, the size and colour of a button, the consistency of a theme and so on.  


    Simply put, UX makes apps useful, while UI makes apps beautiful. Together these aspects play an important role in highlighting the value of your product and creating a lasting connection with your users.  They also have a positive impact on the bottom line, by reducing development time, increasing sales and improving customer retention. 


    With over 318,000 health apps across the most popular app stores, the difference between a successful and unsuccessful mHealth app will lie in the quality of its UX and UI.  The importance of good UX and UI cannot be overemphasized.


  • AI helps to predict cancers’ trajectories

    Many years ago, people in the UK referred to cancers as “a growth.” While it might have lacked scientific precision, it encapsulated cancers’ changing characteristics. The country’s Institute of Cancer Research (ICR) at London’s Royal Marsden Hospital, and part of University College London (UCL), says tumours’ constantly changing nature’s one of the biggest challenges in treating cancer, especially when they evolve into drug-resistant forms.


    It reports that its ICR scientists, working with colleagues at Edinburgh University have used AI to identify patterns in DNA mutations in cancers. The information can forecast future genetic changes to predict how cancers will progress and evolve. The technique, Repeated Evolution of Cancer (REVOLVER), predicts cancers’ next moves so doctors can monitor tumour’s progress and design the most effective treatment for each patient. 


    Three organisations financed the research, published in Nature Methods. They were the Wellcome Trust, the European Research Council and Cancer Research UK. Their support for REVOLVER’s created what’s seen as a powerful AI tool. It’s revealed previously hidden mutation patterns located in complex data sets.


    Teams from ICR and the University of Edinburgh working with colleagues from the  Birmingham University, Stanford University and  Queen Mary Universities London found a link between some sequences of repeated tumour mutations and survival outcomes. It suggests that repeating patterns of DNA mutations could be prognoses indicators. This can help to specify future treatment.


    AI success stories provide material to consider in Africa’s new eHealth strategies, to support leading specialist hospitals to set up a wide range of AI initiatives. They could focus on Africa’s current and emerging health and healthcare priorities.


  • Will AI and Blockchain converge to enhance health analytics?

    While AI and Blockchain are seen by some to offer powerful tools, a view’s emerging that combining them offers significantly more potential for Big Data and health analytics. Or, is it just another dose of eHealth hype? An article in Health IT Analytics  says in the US, AI and Blockchain are now tools of choice for developers, providers and payers in improving their eHealth infrastructure.

    But, it acknowledges that both are near their hype curves peaks. Some providers and payers are reluctant to invest heavily at their maturity stages. Concerns over security, utility and Return on Investment (ROI) are justifications for some organisations to defer investment, leaving others to provide evidence that combining AI and Blockchain can succeed in secure the large data sets and exchanges that Big Data needs for innovative analytics.

    Access to data’s one obstacle. Most data resources are held securely and privately by several institutions. Opening them can create cybersecurity vulnerabilities. Despite this, ideas are fermenting of using Blockchain to produce metadata about the datasets available at several organisations. It can also provide secure, peer-to-peer data exchange. Blockchain can be a pointer to where full data sets are stored, allowing for discoverability without requiring data sets to move each time a transactions completed.

    This strategy enables organisations to keep sensitive data, such as Protected Health Information (PHI) and Personally Identifiable Information (PII) off Blockchain. It’ll reduce risks of breaches. Instead, minimal but sufficient data should be held in Blockchain.

    These comprise complex decisions and projects. It seems premature for Africa’s health systems to pursue combined AI and Blockchain strategies in the medium term. There are other eHealth priorities to address, such as using mHealth to support remote health workers with access to test results and improving their co-ordination with colleagues.

    If the AI and Blockchain are converging in healthcare, Africa’s health systems can watch trajectories and learn from them. If they deliver a significant proportion of their potential, a challenge for Africa’s health systems may be to avoid a sudden disruption to their eHealth strategies and plans. While this can be costly, missing new eHealth opportunities has a cost too, often of missed benefits.