• Analytics
  • 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.

  • Top ten algorithms that can help healthcare

    As algorithms become more prevalent in eHealth, it’s important to have a clear development path for their use. Two core principles are:

    No single algorithm works best for every problemA learning a target function (f) maps input variables (X) to an output variable (Y), so: Y = f(X), used for predictive modelling.

    An article by James Lee in Towards Data Science sets out ten top algorithms. They’re: 

    Linear regression, a long-standing techniques from some 200 years ago, but a good starting pointLogistic regression, suitable for binary classification problems and their two class valuesLinear discriminant analysis, where prediction rely on calculating a discriminate value for each class and making a prediction for the class with the largest valueClassification and regression trees represented by a binary treeNaive Bayes, a simple, powerful algorithm for predictive modelling using two types of probabilities, one of each class, the other the conditional probability for each class given each x valueK-Nearest Neighbours (KNN), a simple and effective algorithm, where predictions are derived from  new data points by searching  entire data sets for the K most similar instances, the neighbours, and summarizing output variables for those K instancesLearning Vector Quantisation (LVQ), a KNN relative, and an artificial neural network algorithm enabling choices of the number of instances to hang onto, learning precisely what the instances should look likeSupport Vector Machines (SPV) are possibly one of the most popular, using a hyperplane to separate points in input variables spaces by their class, either class 0 or class 1Bagging and Random Forest (BBR), another popular algorithm, called Bootstrap Aggregation or bagging, and can estimate quantities from data samplesBoosting and AdaBoost, an ensemble technique aiming to create strong classifiers from several weak classifiers by building a model from training data then creating a second model that attempts to correct the errors from the first model.

    Selecting algorithms in eHealth uses, four questions need answering, what’s:

    The size, quality, and nature of the dataThe available computational timeThe urgency of the taskThe data to be used for.

    The answers aren’t easy to find. Lee points out that experienced data scientist can’t tell which algorithm’s best before trying different ones. It seems that Africa’s eHealth needs time to ponder these before settling on a preferred short list.

  • Analytics offers expanding opportunities for better health

    EHRs alone are no longer enough. Their rich source of data alongside other readily available data such as social media sources, can improve EHRs cost and benefit curves. A whitepaper from Insight, available from Health IT Analytics sets out a way to do it. As Africa’s health systems move their programmes for EHRs forward, they need to run analytics in parallel to maximise benefits for all types of stakeholders. 

    Achieving Success in the Big Data Analytics Era With Microsoft SQL Server says healthcare

    faces new realities about clinical care and business processes, with patient satisfaction scores, performance metrics and risk-based arrangements becoming routine. It’s switching decision on short-term expediency to using data make the choices for raising quality, improving populations’ health and lowering costs.

    Many healthcare organisations are unprepared, even though almost 75% of hospitals’ chief financial officers say EHRs are insufficiently sophisticated enough for complex risk modelling needed to improve performance One solution’s Microsoft SQL Server.

    It’s a database engine plus a full suite of components, resources, connections and community that supports organisations’ entire data platforms. It can help to analyse historical data and reveal current performance and trends that need addressing. Examples are avoiding patient harm, closing healthcare gaps and preventing duplicated or unnecessary services.

    Its set of services includes: 

    Reporting,  to create interactive reportsAnalysis,  to mine and manipulate  data for actionable insightsIntegration, streamlining Extraction, Transformation and Load (ETL) processesR, to develop and deploy applications to enhance data assets’ usefulness and  reduce the time to insight.

    As analytics becomes more routine, Africa’s health systems will need both skills and tools to benefit from them. Insight offers a tool. Health systems will have to invest in the skills too.

     

  • 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.

  • Predictive analytics needed for better infectious disease tracking

    When outbreaks of new diseases emerge, public health’s impact inevitably follows events. Eventually, it catches up. The US Government Accountability Office (GAO) report in May 2017, Emerging Infectious Diseases Actions Needed To Address the Challenges of Responding to Zika Virus Disease Outbreaks, found that the Zika virus case counts from the national disease surveillance system underestimate the total number infections because: 

    Infected people may not seek medical care because they have only mild or no symptoms, or other reasons,May not be diagnosed because of limitations in Zika virus diagnostic testingIncomplete surveillance reporting.                                                                                                                                   

    Better, prompt and accurate information’s still needed. Three Congress representatives have written to the GAO boss, the Comptroller General, suggesting a subsequent study into predictive models and systematically integrating modelling into outbreak responses. They think the US can respond more effectively bt learning if:

    Federal agencies use predictive modelling to inform planning for emerging infectious diseases?How federal agencies use models to inform regulatory decisions about infectious disease outbreaks?Do medical product sponsors use predictive modelling?What funding’s available for infectious disease predictive modelling?What challenges do predictive modellers face?If and how, federal agencies validate models’ predictions? 

    With predictive modelling and analytics expanding their potential, these seem like a good set of questions that all countries should ask. Answers can inform eHealth strategies and strengthen the role and impact of public health professionals.

  • 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.

  • Is machine learning the new buzzword for healthcare?

    By now, it’s old news that big data will transform healthcare. Electronic health records and health information systems have arrived, data flows, and there’s a lot of it. However, all this data, is only useful when it has been analyzed, interpreted and acted on. So will it be algorithms that perform this analysis that will really transform healthcare?

    Access to lab results via a mobile app, has helped clinicians diagnose and treat patients faster. Imagine how much more useful these results would be if they also showed the patient’s risk for cardiovascular disease or renal failure, based on the last several years of the patient’s lab reports. This is where machine learning might help physicians to make better decisions at point of patient care.

    Machine learning is an area of artificial intelligence (AI) that is starting to attract interest in healthcare. It is a set of algorithms that help a system to automatically learn and predict outcomes, after continuous exposure to variable datasets. The value of machine learning in healthcare is its ability to process huge amounts of clinical information, beyond that of human capability, and then reliably convert analysis of that data into clinical insights. This will help physicians plan better and ultimately lead to better outcomes, lower costs of care and increased patient satisfaction.

    Machine learning is already making headlines in healthcare. Google has developed a machine learning algorithm to help identify cancerous tumors on mammograms. Stanford is using a machine learning algorithm to identify skin cancer. A JAMA article, last year, reported the results of a deep machine-learning algorithm that was able to diagnose diabetic retinopathy in retinal images. Others, like Philips, are transforming TB screening, using machine learning algorithms that can offer an objective opinion to improve efficiency, reliability, and accuracy.

    Machine learning puts a new arrow in the quiver of clinical decision-making.

  • EU’s BigData@Heart aims to improve heart disease treatments

    A report on Cardiovascular Disease (CVD) published by Springer says CVD prevalence in sub-Saharan Africa’s increasing. Limited access to prevention and continuing care are seen as constraints to improvements. The EU’s BigData@Heart project may contribute to the development of treatments for heart disease patients. It has lessons for Africa’s health systems.

    It’s a large-scale, five-year, €19 million project. Its aim’s to use data and advanced analytics to develop a translational research platform of phenotypical resolution to improve patient outcomes and reduce societal burdens of atrial fibrillation (AF), heart failure (HF) and acute coronary syndrome (ACS). Data sources include real-world evidence, best-practices in drug development and personalised medicines.

    Four outputs are:

    ·         New universal, computable, definitions of diseases and outcomes relevant for patients, clinicians, industry and regulators

    ·         Informatics platforms linking, visualising and harmonising data sources, completeness and structures

    ·         Data science techniques to develop new definitions of disease, identify new phenotypes, and construct personalised predictive models

    ·         Guidelines that allow for cross-border use of big data sources acknowledging ethical and legal constraints and data security.

    It can have considerable value for Africa. The continents health systems and cardiologists could move their CVD services ahead in the wake of BigData@Heart’s progress. 

  • EU’s BigData@Heart aims to improve heart disease treatments

    A report on Cardiovascular Disease (CVD) published by Springer says CVD prevalence in sub-Saharan Africa’s increasing. Limited access to prevention and continuing care are seen as constraints to improvements. The EU’s BigData@Heart project may contribute to the development of treatments for heart disease patients. It has lessons for Africa’s health systems. 

    It’s a large-scale, five-year, €19 million project. Its aim’s to use data and advanced analytics to develop a translational research platform of phenotypical resolution to improve patient outcomes and reduce societal burdens of atrial fibrillation (AF), heart failure (HF) and acute coronary syndrome (ACS). Data sources include real-world evidence, best-practices in drug development and personalised medicines. 

    Four outputs are:

    New universal, computable, definitions of diseases and outcomes relevant for patients, clinicians, industry and regulatorsInformatics platforms linking, visualising and harmonising data sources, completeness and structuresData science techniques to develop new definitions of disease, identify new phenotypes, and construct personalised predictive modelsGuidelines that allow for cross-border use of big data sources acknowledging ethical and legal constraints and data security.

    It can have considerable value for Africa. The continents health systems and cardiologists could move their CVD services ahead in the wake of BigData@Heart’s progress.

  • Rothman Index predicts patients’ increasing risks

    It’s often reassuring to hear that hospital patients are in a stable condition.  When they’re deteriorating, it’s not so good. It’s even worse when the deterioration’s a set of marginal steps that are challenging to find, but lead to catastrophic states. This was the motivation for the Rothman Index (RI), a predictive health analytics tool.

    Florence Rothman was diagnosed with aortic stenosis, narrowing of the exit of her heart’s left ventricle. She had a low-risk surgical procedure and seemed to be recovering, then became weaker and started a slow, steady, subtle decline that detected when her condition became critical. Relevant data was recorded in her EHR, but the trends weren’t easy to see, so wasn’t used by the skilled and caring healthcare professionals. She was discharged, and four days later, collapsed and died in the ER.

    Michael and Steven, Florence’s sons, one an engineer, the other a scientist, both skilled in data analysis, were inspired reveal EHRs’ crucial insights to improve healthcare. They created the Rothman Index (RI), a statistically validated patient acuity score across all diseases and conditions. It presents patients’ real time conditions and can be trended and visualised, alerting doctors and nurses of deterioration before it’s critical.

    Pera Health, formerly Rothman Healthcare Corporation until 2012, provides RI. It includes graphical user interfaces so healthcare professionals can visualise trends in health status from patients’ data in their EHRs. Regularly updated health scores are derived from vital signs, nursing assessments and lab results. The model transforms each input into a common representation of univariate risk, enabling heterogeneous data to be summed, solving the data fusion problem. Outputs are continuous measures of patients’ conditions integrated into their EHRs. Trends enable deteriorating and vulnerable patients to be identified, often with less than24 hours warning, and with minimal false alerts.

    The company says RI correlates well with:

    24 hour mortalityUnplanned transfers to ICUsICU readmissionsCode Blue events to for cardiac or respiratory arrests.Readmissions within 30 days of dischargeLengths of stay.

    RI can be a part of Africa’s EHR programmes to build predictive health analytics into hospitals; routines. It’ll help to maximise their EHRs’ benefits.