• Hospitals
  • China’s tertiary hospitals’ social media offer a good strategic template

    Social media’s increasingly prevalent and important for health care. This doesn’t mean that it’s easy to succeed with it. Charles Mingus, the jazz bassist, composer, bandleader and legend, said “Making the simple complicated is commonplace; making the complicated simple, awesomely simple, that’s creativity.”  A survey of social media use in the Journal of Medical Internet Research (JMIR) in China’s tertiary hospitals provides insights and experiences that other hospitals can use as a foundation for their creative social media initiatives.

    The objective of Social Media Landscape of the Tertiary Referral Hospitals in China: Observational Descriptive Study was to map out the social media use in patient engagement by China’s best tertiary hospitals, 705 of them. They’re usually in city centres, serving as medical hubs providing specialised medical care for several regions. Their social media initiatives using Sina Weibo and WeChat are often seen as pioneering and innovative in connecting and communicating with their patients.

    Data collected and analysed had three main characteristics:

    Hospital characteristics of time since established, number of beds, hospital type, and regions or localitiesStatus of social media use of China’s two most popular local social media platforms defined as post-initiation time, number of followers and number of tweets or postsA logistic regression model to test the association between hospital characteristics and social media adoption.

    About 76%, 537, hospitals have created official accounts on either Sina Weibo or WeChat, with the latter being more frequent. The larger and newer the hospitals, the greater resources were for social media. Hospitals type and affiliation with universities were not significant predictors of social media adoption. 

    The highest penetration rate was about 97%, the lowest 20%. The investment profiles is perhaps more important than the penetration rate. Since 2009, investment in Sino Weibo climbed steadily then flattened. Later, from 2014, WeChat investment plodded along until 2016 when it surged and became the most used in 2017. In parallel, Sino Weibo use we sustained. Now, both services are used.

    WeChat’s bigger profile is only part of the comparative profiles. While it has about 75% more hospitals as users compared to Sino Weibo, over 25% of the hospitals had inactive Sino Weibo accounts over six months. WeChat’s equivalent rate was less than 7%. Their time scales were different too. Sino Weibo’s nine years compares to WeChat’s four.

     

  • AI passes a stiff test at London’s Moorfields Eye Hospital

    England’s Grand National run at Aintree is gruelling. It has 30 fences, two with open ditches, in a distance of 2.25 miles that’s completed twice. AI has just moved up the field in the eHealth equivalent. 

    An AI project at London’s Moorfields Eye Hospital with Google’s DeepMind has accurately diagnosed eye conditions from scans. As ophthalmologists’ workloads and their complexities increase, diagnostic imaging is expanding faster than specialists can interpret the results. AI already has a constructive reputation in classifying two-dimensional photographs of some common diseases it’s reached the performance of expert clinicians in a real-world clinical pathway with three-dimensional diagnostic scans. 

    At Moorfields, a novel, deep learning architecture is now applied to a clinically heterogeneous set of three-dimensional optical coherence tomography scans from patients. The research found that after training on 14,884 scans, AI’s referral recommendations of sight-threatening retinal diseases reached, and sometimes exceeded that of experts. 

    Other benefits include:

    Tissue segmentations produced by the architecture are device-independent representationsReferral accuracy’s maintained when using tissue segmentations from a different devicePrevious barriers to wider clinical use without prohibitive training data requirements across several pathologies have been removed.

    After training, the algorithm assigned diagnoses to 1,000 patients’ scans whose clinical outcomes were already known. The same scans were shown to eight clinicians. Four were leading ophthalmologists, four were optometrists. They classified the diagnoses into four referral types,  urgent, semi-urgent, routine and observation. AI performed as well as two of the world's leading retina specialists. The error rate was 5.5%. More strikingly within this performance, the algorithm didn’t miss any urgent cases.

    The impact of the project’s global. For Africa’s health systems, the challenge’s entering the AI Grand National and making sure they don’t fall at any of the daunting fences. It offers an eHealth strategic scenario that extends what is now relatively conventional EHRs and mHealth. AI can extract more value from them than originally imagined.

  • mHealth can help to reduce hospital readmissions

    Using mHealth to improve hospital services’s a common theme in Africa’s eHealth strategies and plans. Reducing readmissions’s an important part of these initiatives. A report from MobileSmith says how three mHealth solutions can help. 

    How to Reduce Preventable Readmissions with Healthcare IT describes:

    Efficient mHealth strategies for reducing hospital readmissionsStrategic use cases for prompt implementationSix best practices for cost-effective apps for engaging patient and doctors. 

    Efficient mHealth should provide:

    Relevant discharge communicationFamily and carer engagementImproved medication adherenceChronic disease control.

    The six best practices are: 

    Research and know target patient groupsThink big, start small, act fast, so avoid mHealth that does everything for everybody, so unlikely to be user-friendlyPolish user interfaces and experiencesKeep mHealth freshEstablish secure data exchangesAdopt analytics. 

    Underpinning each of these’s the core goal to empower patients. mHealth’s the bridge that healthcare can leveraging now to empower patients. It can only work with easy-to-use mHealth so patients are encouraged to become more proactive towards their health. These themes need expanding in Africa’s next wave of mHealth strategies and plans. They also need setting alongside high priority patient groups and clinical conditions.

  • Robotic surgery is revolutionising prostate care

    Robotic surgery is a remote-control operation. Movements of a surgeon are translated through the tiny robotic arms of a machine.  The surgeon is often not in the same room and can even be on a different continent.

    Surgeons and patients are thrilled with the results.  Specifically, in prostate surgery, the Da Vinci robotic surgical machine has been used successfully in the UK and Africa countries to perform over 10,000 surgeries in men with prostate cancer, with marked improvements. Procedures are quicker, safer, and with fewer side effects than conventional open surgery or laparoscopic radical prostatectomy.  A review of 104 studies covering 230,000 patients confirmed it.

    Robotic surgery demonstrated superiority in:

    Operative timeLength of hospital staysBlood lossTransfusions requiredRate of post-operative erectile dysfunction and incontinenceLong term cost, due to the quick recovery timePositive surgical margin (PSM), which indicate whether the entire extent of the cancer was extracted during the operation. 

    The review is in line with other research on robotic surgery, which shows improved erectile function and reduced urinary continence compared to open surgery.

    South Africa has seen an increasing uptake of the robotic procedure since it was first implemented at the Urology Hospital in Pretoria in 2013. It is now more widely available.

    Doctors and patients benefit from these types of innovations. Long term net cost-benefits are likely too. The challenge for our health systems is how to find space for these, alongside other healthcare challenges, in ways that are affordable and sustainable.

    Watch a You Tube video about it here.

  • Saudi’s eHealth programme aims for efficiency and effectiveness gains

    Saudi Arabia’s Vision 2030 aims to improve the efficiency of the health care sector through information technology and digital transformation.

    The ministry has launched the beta version of the e-health system at three hospitals

    The e-health system will be implemented across hospitals in the Kingdom in phases

    RIYADH: The Ministry of Health is implementing a cutting-edge e-health system at hospitals to improve health care efficiency in the Kingdom and provide patients with standardized e-health records by 2020. 

    Three Saudi Arabian hospitals, Yanbu General Hospital, Al-Bukayriyah General Hospital and Al-Kharj Maternity and Children’s Hospital, are set to use eHealth to improve their efficiency. An article in Arab News says it’s the start of National Transformation Program 2020 (NTP 2020), a Kingdom-wide programme to use eHealth in all medical departments including reception, emergency departments, clinics and wards. It’s part of the health ministry’s Vision 2030 to use eHealth to improve healthcare efficiency and effectiveness. 

    Integrated eHealth system will simplify data saving and access, reduce medical errors and ensure that appropriate health services are provided to patients. Benefits stem from the right resources being in the right place at the right time and reduced waste. 

    While Africa’s health systems may find the affordability of such a strategy challenging, the strategy seems transferrable. They’d have much longer timescales, which can create other challenges, such as obsolescence creeping in. These need identifying and addressing with risk mitigation plans as long-term requirements.

     

  • 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.

     

  • Robot Tug to help nurses in hospitals

    The field of robotics is making great leaps in healthcare today.  Take for example, Tug, the robot nurse. The aim of this robot is to improve patient care in hospitals by doing the mundane tasks like hauling food, linens, specimens and medications around the facility. This enables the healthcare workers to focus on other relevant duties and patient care.

    Appearance wise, Tug does not look like a typical humanoid robot. Instead, it looks like an oven that has wheels.  Staff begin the day by uploading activities that they would like Tug to do and then it wheels itself around the hospital performing those duties. It is programmed in such a way that employees can change the order of the tasks based on urgency.

    Tug navigates a facility using dozens of lasers therefore it is able to make quick decisions such as stopping when a person is in the way. It can carry up to 1,000 pounds on its back which can also be swapped with different models to meet other needs besides medical deliveries and food.

    This is a great use case for the overburdened, understaffed hospitals in Africa – a robot to aid nurses and health workers with their daily activities.  Will we being seeing Tug in African hospitals soon?

  • e-Hospital portals can improve hospitals

    The world we live in has seen a revolution of digitisation in mobile phones, banks and the internet are examples.  People want to avoid or reduce the time they spend doing things manually, and would rather opt for using the latest technology.

    An e-Hospital portal is an example. It’s a project introduced by the government of India. An aim's to use technology to empower people and help them connect to areas of health.

    e-Hospital enables the public to book outpatient appointments, view diagnostic reports, check the availability of blood, lab reports and pay their fees. Using this type of eHealth offers quicker access, remote diagnosis and faster medical prescriptions. Time taken to receive treatment or see a medical expert is expected to be reduced considerably.

    Services provided by an e-Hospital portal maximises the contribution of existing healthcare professionals. It enables networks of nurses and doctors to achieve more efficient treatment and monitoring.

    The large scale of the project offers an insight for Africa’s eHealth strategies. Planning directly for national coverage can offer bigger scale benefits, even where, as in India, roll out's phased.

  • Voice recognition reduces Tanzania's patient waiting times

    Patients at the Muhimbili National Hospital in Dar es Salaam no longer have to endure long waiting times for their radiology results.  This is thanks to a new technology installation in the department.  Voice recognition or speech recognition technology is now being used to encode doctors notes on patients so that they can easily be transferred to the radiology department. 

    With this new technology, Tanzanian medical professionals are able to dictate into their computers, in the normal course of speaking and have the speech engine recognise what the clinician wants, and then apply the commands or structured words, respectively, to obtain a radiology report for a patient.  There has been some concern around the effect of speech accents on the technology, but this has posed no problems since implementing it at the hospital.  

    The speech engine is also capable of showing the cardiology report template populated with the name of the patient and other demographic data. By dictating the cardiology report narrative, the computer recognises the narrative context and intent and condenses a complete, correct, and structured document.

    This translates to shorter waiting times for patients, greater operational efficiency within the hospital and reduced workload on medical staff who are required to take notes of patient examinations and consultations.  The technology, which uses natural language processing, is constantly learning speech behaviour through repetitive exposure to terms and complex algorithms that organise speech patterns into recognisable behaviour. 

    This bold technology implementation in Tanzania could be a useful pilot for overburdened health care systems in Africa hoping to achieve the same benefits.