The Information Commissioner’s Officer (ICO) ruled, on 3 July 2017, that the Royal Free NHS Foundation Trust (the Trust) had failed to comply with the Data Protection Act 1998 (DPA) when it provided 1.6 million patient details to Google DeepMind as part of a trial diagnosis and detection system for acute kidney injury, and required the Trust to sign an undertaking. The investigation brings together some of the most potent and controversial issues in data privacy today; sensitive health information and its use by the public sector to develop solutions combined with innovative technology driven by a sophisticated global digital company. This analysis provides insight on the investigation into Google DeepMind with focus on how the General Data Protection Regulation (Regulation (EU) 2016/679) (GDPR) may impact the use of patient data going forward. All About Testing The area of focus in the ICO’s investigation was the clinical testing phase for Streams, the app that Google DeepMind was developing with the Trust to improve diagnosis of acute kidney injury. The development phase had already taken place but that phase had used synthetic data or dummy data so did not raise any data privacy issues. From the perspective of the Trust, and their obligations under the Health and Social Care Act 2012, any new technology must properly undergo clinical safety testing before being deployed. In order to properly test the clinical safety of a new technology, the Trust argued that personal data of patients had to be provided. Significantly, neither the current DPA nor the GDPR specifically refer to the way personal data can be used in a ‘testing phase’ before the benefit of a technology (whether for controller or individual or both) is provided. However, importantly the ICO did not rule in its covering letter to the Trust that testing activity cannot use personal data but indicated that the issue required further exploration and should be considered more fully in a Data Protection Impact Assessment (DPIA). Lawful Grounds Assuming that the use of patient personal data is not totally out of hand for testing new technology, what lawful grounds would be available to the Trust under the GDPR? Under Schedule 2 of the DPA, the legitimate interests ground can be relied upon by public authorities such as the Trust so long as they can demonstrate that their legitimate interests are not overridden by the prejudice to the privacy rights of the patients. However, under Article 6 of the GDPR, the Trust has bigger problems since the GDPR specifically states that the legitimate interests ground is not available to public authorities in the performance of their tasks, as it is for the legislator to provide by law for the legal basis for public authorities to process personal data. Although there is nothing in the GDPR that outlaws a public authority obtaining consent from individuals to data processing, the requirement that consent must be freely given means that it cannot be relied upon when there is a clear imbalance between the individual and controller which is in particular when the controller is a public authority. Consequently the Trust would have to be able to prove that patient consent is freely given to overcome the implication in the GDPR that consents given to a public authority are unlikely to be freely given. So where does this leave the Trust? Effectively, the processing must be specifically laid down in EU or Member State law to be lawful under Article 6 of the GDPR. This seems to be a tough conclusion, but is backed up by the grounds for processing special categories of data (under Article 9(2) (h), (i) and (j) of the GDPR) which, although providing a broader description of acceptable purposes (preventative or occupational medicine, provision of health treatment, reasons of public interest in the area of public health, and scientific research purposes), still indicates that the processing must be necessary on the basis of EU or Member State law. The one exception to this requirement appears to be that processing for the purposes of preventive or occupational medicine, for the assessment of the working capacity of the employee, medical diagnosis, provision of health or social care or treatment or the management of health or social care systems and services can be made pursuant to a contract with a health professional (and not specifically under EU or Member State law) so long as the health professional is subject to obligations of professional secrecy under EU or Member State law or processing is by another person also subject to an obligation of secrecy under EU or Member State law. This means that the Trust could meet Article 9 of the GDPR so long as it can demonstrate that the use of patient personal data in the testing phase of Streams was necessary for the purpose of preventative medicine (i.e. to prevent morbidity) or medical diagnosis (i.e. diagnose kidney problems) and was only processed on the basis of contracts with health professionals or other persons subject to obligations of secrecy under EU or Member State law. While it is not unreasonable to argue that those in the Trust who can access the data from the testing for Streams are likely to be either healthcare professionals or employees subject to equivalent obligations of secrecy, would this ground also cover Google DeepMind personnel? It seems unlikely to, if Google DeepMind is in reality a controller (as some commentators have argued it is in the live phase though probably not in the testing phase) then it seems quite a stretch to argue that this ground applies. In addition, if Google DeepMind is a controller it will be difficult to resolve this conundrum of the lawful ground. Similarly, if Google DeepMind is a processor (as the contracts between the Trust and Google DeepMind firmly state and the ICO accepted) then surely the requirement under Article 9 would not apply to Google DeepMind’s access anyway. This is because, the act of disclosing personal data to a processor in itself does not give rise to a separate obligation on a controller to rely on a lawful ground. In any event if the Trust cannot fully rely on there being appropriate contracts in place with healthcare professionals or proper secrecy obligations, the Trust is faced with either having to rely on explicit consent (and demonstrating that the consents given are wholly free) or relying on EU or Member State law that explicitly gives public health authorities the ability to use personal data for testing new technology (which may have significant public benefits) and to additionally engage third party technology companies to assist with the testing. It would be perverse if, amidst the enthusiasm for innovation in public healthcare and backing from those in both the EU and European Governments, the fundamentals of data protection law did not provide sufficient clarity and certainty for public authorities to be able to explore new technologies that could deliver significant public benefits, ease the circumstances of those with health conditions and save lives. Certainly, it is to be hoped that the UK Government’s recent call for views on the necessary GDPR derogations will produce a clear way forward for public authorities on the use of sensitive personal data in these types of circumstances. Wider Data Protection Compliance And yet all such forays must be balanced against the need for other fundamentals of data protection compliance – points that the ICO raised in its letter to the Trust and subsequently agreed to by the Trust in the signed undertaking. To comply with the GDPR, these types of projects must devote sufficient time and resources to: Transparency – ensuring that all affected individuals understand the purpose of the data use and how it will and could impact them Fairness – ensuring that all such uses of the personal data are within the expectations of individuals. Proportionality – considering the amount and type of personal data which needs to be processed at a testing phase (as opposed to a live phase) and being able to justify the decision taken. Individuals’ control of their personal information – giving individuals the right not to be part of the project from the outset and additionally giving participants further information about their rights if they choose to become part of the project. Data security – putting appropriate contracts in place that comply with Article 28 of the GDPR with any processor(s) as well as suitable internal security measures. DPIA – carrying out a comprehensive and balanced DPIA before the roll out of the project so that the Trust can demonstrate to the regulator, should it become necessary, that they have a written assessment taking account of all the issues, risks and mitigating steps before actual processing of personal data takes place. While not specifically mentioned by the ICO, any parties embarking on this kind of public health big data project should always seriously consider consulting the relevant data protection authority (DPA). Although the GDPR abolishes the general requirement to file with DPA, there is still a requirement to consult with DPAs where a DPIA indicates that the processing would result in a high risk in the absence of measures taken by the controller to mitigate the risk. Indeed, even if a public health authority considers it is not technically required to consult the DPA, it could still be a prudent step especially if private sector partners are involved in the processing, in order to demonstrate transparency and good faith should the matter be publicly criticised at a later stage. The Trust would furthermore need to be able to demonstrate accountability (through policies, procedures, training and audits) concerning how the patient personal data will be used in the context of testing (and later live use) as well as ensuring privacy by design are embedded in the technology from the beginning. The Role of Governments and Regulators From what is publicly available, it appears that the Royal Free and Google DeepMind data use would need to meet additional compliance steps to satisfy GDPR requirements. This is not to say that all similar types of data use would be prohibited under the GDPR. But there is an obligation on Governments and regulators to provide public health authorities with appropriate guidance and clarity in order to understand how they can use patient personal data lawfully under the GDPR in testing and live environments. This is essential for public health authorities to be able to use technology efficiently for the public good and in a way that preserves patient privacy. In this regard, in its covering letter to the Trust, the ICO recognised the benefits that can be achieved by using patient data for wider public good and, where appropriate, the ICO supported the development of innovative technological solutions that help improve clinical care however, such schemes must comply with data protection law. This article was originally published in Data Protection Leader in August 2017.

October 5th 2017: Zesty has appointed Andy Williams as a Board Advisor. Andy was CEO of NHS Digital, formerly HSCIC from January 2014 until March 2017. Health Secretary Jeremy Hunt commented on Williams 3 year tenure of NHS Digital saying “Andy had a tireless commitment to improving the quality of health and social care for patients through the power of data and technology during his tenure. He has made a decisive contribution to setting the NHS on course to be far better digitally integrated and advanced, and I want to wish him all the best for the future.” Andy joins Zesty at an exciting time in the company’s growth phase. In addition to building digital patient facing services for Milton Keynes University Hospital NHS Trust and Guy’s & St Thomas NHS Trust, winning the 5 year Pan London Sexual Health Digital Transformation Project as part of a consortium with Preventx, Chelsea & Westminster NHS Trust and Lloyds Online Pharmacy, Zesty has launched 20+ new online booking services for various NHS Trusts across the UK. Zesty will shortly announce new digital outpatient projects with one of the Global Digital Exemplar trusts in England, one of the largest Scottish NHS Trusts and one of Europe’s largest private healthcare providers in Germany. Andy has extensive experience in overseeing large transformational technology projects in the UK and around the world having led teams in companies such as IBM, Alcatel-Lucent and CSC during the last three decades. He has worked across a variety of industry sectors, usually being involved in programme governance, introducing complex new technology and managing change. He has an international reputation and the companies he has worked with include many well-known names both in the UK and abroad. Speaking about the appointment Zesty CEO, James Balmain said: “Andy’s extensive experience gained within the public and private European healthcare sectors made him a natural choice for us and this role. His vast knowledge of the UK healthcare market, impressive network of NHS contacts and operational experience will certainly add value to our board. We are very excited and honoured Andy has agreed to help us with our mission to improve access to healthcare with digital patient facing services.” For media enquiries please email lloyd@zesty.co.uk.

When I was at medical school, I was taught that the four most important words you can say to a patient are ‘You don’t have cancer’. I’m sure you would agree that this statement would be of utmost importance to get right. In order to make this statement, the observer must be as certain as is feasibly possible that the statement is indeed correct at that moment in time (that cancer has been proven to be absent). In statistical terminology, this statement qualifies what is known as a True Negative. Unfortunately, medicine is not black and white, and doctors can get it wrong. This doesn’t happen too often (well, more often than we would like, but not often enough for everyone to just give up), but when it does - the results can be catastrophic for patients. When it comes to AI systems making diagnoses or reporting findings, it is important to ensure that novel technologies are also accurate, or at least as accurate as humans. This is done by a process of clinical validation, the aim of which is to assess the ‘accuracy’ of a system. We certainly don’t want AI to start incorrectly telling people that they do or don’t have cancer more than humans! In this article, I’ll go over the most relevant metrics for reporting accuracy for AI developers in a clinical setting. What is Accuracy anyway? ‘Accuracy’ is not a defined scientific statistical term, but it essentially encompasses the notion of how well an Index Test (the system being tested) performs, often compared to another pre-existing system, which in the context of AI is usually human doctors. ‘Accuracy’ can be assessed using surrogate measurements such as reliability, sensitivity, precision and error-rate, to name but a few. ‘Accuracy’ of an Index Test is ideally measured by comparison to a Gold Standard error-free test. However, in the field of medical diagnosis (or other areas such as radiology or pathology findings), there is often no error-free Gold Standard. Therefore, the best available method for establishing a ‘ground truth’, known as a Clinical Reference Standard (CRS), should be used. The CRS could be defined as ‘the consensus from a group of qualified doctors’, or more simply ‘the expert opinion of one qualified doctor’. The whole point of the CRS is to be as close as possible to a perfect system. The definition of the CRS is important when approaching clinical validation, as it will affect the methodology, analysis and results of any statistical tests. Too often I see published literature on the accuracy of AI systems compared to only one or two doctors, and this may not be enough. However, it is probably unreasonable to take up hundreds of doctors’ time to, so do your best! Several statistical methodologies exist to measure ‘accuracy’. However, it must be pointed out that most statistical tests are designed to assess ‘accuracy’ of a test for one target condition in a binary classification (a given disease, condition or physiological measurement), not a large number of variables. Alternatively, inter-test comparisons (kappa agreement, concordance, reliability) may be used to assess for ‘accuracy’, especially if there is a lack of a specifically defined target condition. However, these tests are only a measure of agreement between AI and humans, and do not reflect ‘accuracy’. e.g. Poor agreement does not tell you which of the two tests is better, only that they disagree. Therefore, I’ll be ignoring these tests in this article. Finally, any methodological assessment of ‘diagnostic accuracy’ should, as far as possible, remove bias, be applicable to the context, be transparent, and appropriately powered. For this reason, the international STARD Criteria for reporting ‘diagnostic accuracy’ were produced, a peer-reviewed framework that enables researchers to adequately document and rationalise their studies and findings. Measuring ‘Accuracy’ of Binary Classifications In environments where results are dichotomous (binary, either present or absent), without indeterminate results, statistical binary classifiers can be used. By charting the Index Test as compared to a Gold Standard, a simple 2x2 tabulation known as a Confusion Matrix or Contingency Table can be created. Standard confusion matrix Calculations based on true and false positive/negatives can provide surrogate measures of ‘accuracy’. In general, the more ‘accurate’ a system, the greater number of True Positives and True Negatives occur, and the fewer False Positives and False Negatives. Two Worlds Collide There is considerable overlap between medical & data science metrics when reporting diagnostic ‘accuracy’. In the clinical world, the main ratios used are the true column ratios — True Positive Rate and True Negative Rate (Sensitivity and Specificity). In data science, the main ratios are the true positive ratios — Positive Predictive Value (PPV) and True Positive Rate (TPR) known respectively as Precision and Recall. Traditionally, AI systems in non-medical sectors are rated on Precision and Recall only. This is because the True Negatives may not matter when applying a model to a non-clinical problem (e.g. a system that is designed for document or information retrieval). Precision Precision (green cells) is a measurement of how relevant a positive result is, and is also known as the Positive Predictive Value (PPV). It is calculated as follows: Precision = TP / (TP + FP) Precision is a useful measurement of ‘accuracy’ when the Gold Standard is entirely error-free. However, caution must be taken when there is possible error in the Gold Standard. This is because the Index Test may actually be more ‘accurate’ (e.g. the AI is better than a human doctor), but because it is only being compared to the CRS, any True Positives produced by the Index Test may erroneously be classed as False Positive, artificially decreasing the reported Precision. (e.g the consensus opinion of doctors is that there is a cancer, when there isn’t. The AI system correctly says there isn’t a cancer. In this case, the AI system is correct, but because we are only comparing the result to the incorrect consensus opinion of doctors, the Precision of the AI system is affected negatively). Recall / Sensitivity Recall (red cells) is a measurement of the proportion of correct positive results, and is also known as the True Positive Rate, or Sensitivity. It is calculated as follows: Recall = TP / (TP + FN) Recall / Sensitivity can only be truly useful when the Gold Standard is error-free, as it assumes that all positive results from the Gold Standard are indeed positive. If a human is incorrect and a diagnosis is actually a True Negative, then reported recall will be artificially decreased. It is worth noting that neither Precision nor Recall / Sensitivity take into account the True Negatives, and therefore do not provide a complete picture of ‘accuracy’. For this reason, the traditional data science metrics of Precision and Recall are not sufficient in a clinical setting for assessing accuracy. Remember — ‘You don’t have cancer’ is a True Negative, and neither Precision nor Recall tell us anything about whether an AI system can make that statement. Specificity Specificity (yellow cells) is a measurement of the proportion of correct negative results, and is also known as the True Negative Rate. It is calculated as follows: Specificity = TN / (TN + FP) True Negative Rate is crucial in diagnostic tests, as it is a measure of how often an Index Test correctly negates a diagnosis. This is important clinically, as ruling out a diagnosis has a large impact on the level of triage / further investigations / treatment required, not to mention the emotional impact a False Negative can have on a patient. However, the True Negative rate can only be measured if the Gold Standard produces true negatives, which doctors tend not to do when making clinical diagnostic decisions. Imagine a doctor’s differential diagnosis list had to include all possible negative diagnoses — it would be almost impossible to do so! In practice, it can be assumed that if a doctor does not include a diagnosis in a differential that all other possible diagnoses are automatically excluded. Negative Predictive Value Negative Predictive Value (NPV, blue cells) is a measurement of how relevanta negative result is. It is calculated as follows: NPV = N / (FN + TN) As for the True Negative Rate, it can only be calculated if the Gold Standard produces True Negatives. An AI system with a high NPV and Specificity and Sensitivity would be very likely to approved for clinical use. Combining Binary Classifications ROC Curve Plotting 1-Specificity and Sensitivity pairs for each diagnostic cut-off gives a Receiver Operating Characteristic Curve (ROC Curve). The higher the curve towards the upper left corner, the more ‘accurate’ the diagnostic test is. The Area Under the Curve (AUC) gives a single numeric indicator of the discriminative power of a diagnostic test. AUC is generally more useful when comparing two different diagnostic tests, rather than comparing an Index Test against a Gold Standard, as it does not give information on how good a test is at ruling-in or excluding a diagnosis or finding. It also requires a statistical significance calculation (p value) to be performed in order to place the result into context. (I’m not even going to touch on the arguments for and against p values!). If two ROC curves do not intersect, then one method dominates the other, as it outperforms the other in every sense. If two curves intersect, then that indicates that there is a balance point at which one system is better than the other for a certain task, and above which the other system is better. For instance, AI systems may be far more specific than humans, but humans may be more sensitive. This might manifest as two curves intersecting at the point at which both metrics are performed equally well by both systems. It is interesting to note than an AI system, when compared to a perfect Gold Standard, can never have a better AUC. This is because it is impossible to improve on the perfect Gold Standard. The best you could hope for is equivalency. Luckily for AI developers, but not for patients, humans are error prone! F1 Score The weighted average of Precision and Recall (purple cells) is known as the F1 score, and is a commonly used measure of classification systems in machine learning. It is calculated as follows: F1 score = 2x ((Precision x Recall)/(Precision + Recall)) Importantly however, this score does not take into account True Negatives (TN). The negative rate of AI systems would be important to ascertain (the diagnostic ‘miss’ rate), and hence specificity and/or NPV should also be attempted to be reported. Likelihood and Diagnostic Odds Ratios In order to encompass both positive and negative rates, a combined single indicator incorporating the probability of detection and the probability of false alarm is required. False Positives are ‘false alarms’, and False Negatives are ‘false re-assurances’. The Positive Likelihood Ratio (LR+ = TPR / FPR) is the probability of detection, and a high LR+ value is a good indicator for including a diagnosis. The Negative Likelihood Ratio (LR- = FNR / TNR) is the probability of false alarm, and a low LR- value is a good indicator for ruling out a diagnosis. These two ratios (incorporating all 4 pink cells) can be combined to form the Diagnostic Odds ratio (DOR) which is a measurement of the diagnostic accuracy of a test. It is calculated as follows: DOR = LR+ / LR- = (TPR/FPR) / (FNR/TNR) The advantages of the DOR are that it does not depend on underlying prevalence, is a single indicator that doesn’t require statistical significance, has a simple calculation for 95% Confidence Intervals, and is medically recognised. Effectiveness The effectiveness of a test is probably the closest to the lay-definition of ‘accuracy’, in that it is a measure of the proportion of correct outputs by the Index Test from all cases. It is calculated as follows: Effectiveness = (TP + TN) / (TP = TN + FP + FN) However, effectiveness is considerably affected by underlying prevalence, making it an inferior statistical test. For example, given a set sensitivity and specificity, a disease with high prevalence in a population is more likely to be diagnosed by the Index Test than one with a low prevalence. For that reason, effectiveness is never reported as a single indicator, and is always reported alongside other measures such as PPV and NPV. In the case of testing an AI system, extremely high prevalence may be an issue when calculating effectiveness, as every single case tested has a diagnosis. Summary Studies not meeting strict methodological standards usually over- or under-estimate the indicators of test performance, as they limit the applicability of the results of the study. There are several statistical methodologies available to AI developers, each with their own advantages and disadvantages. When validating AI systems in a clinical setting it is important to consider whether True Negatives will be reported, as this will affect which statistical tests can be applied. Currently, doctor’s differential diagnoses, reports or findings do not include all possible negatives (this would be too exhaustive), so an assumption will have to be made that an excluded diagnosis/finding is a negative. However, this could give some erroneous results by incorrectly counting diagnoses/findings were excluded due to error/fatigue/knowledge bias as negatives. Conversely, without True Negatives, only Precision, Recall and F1 score can be deduced, which do not provide the complete picture in terms of medically relevant negative diagnoses/findings and omissions. The final message is that there is no 100% correct way of reporting how accurate an AI system is in clinical settings, as each one has its own quirks! My advice would be to report ROC curves, AUC and DOR — that way you are covering all aspects of positive and negative rates using statistical language that medics understand. Thankfully ROC curves are gaining popularity in machine learning circles, so for those of you applying your skills to medical problems, you’ll be able to make your results understood to the doctors you encounter. References: http://www.stard-statement.org/ http://bmjopen.bmj.com/content/6/11/e012799 http://www.ifcc.org/ifccfiles/docs/190404200805.pdf http://gim.unmc.edu/dxtests/roc3.htm

The UK Digital Healthcare Council met for the first time today at the Royal Society of Medicine, Chandos House, 2 Queen Anne St, Marylebone, London W1G 9LQ. 40 people from across the UK digital health industry debated a number of key points and discussed the aims & objectives of having an industry body represented at both a local and national stages. The Council’s stated purpose is to: 1) Advocate the benefits of technology in healthcare for patients 2) Provide a vehicle to promote ‘data does good’ to the wider system 3) Collaborate to influence policy decisions from organisations such as CQC, Department of Health, NHS England and NHS Digital 4) Explore opportunities for partnership 5) Share innovation and best The Council aims are: A) to ensure there are open lines of communication with the key stakeholders, as named above B) The Council aims to advocate for the views of members and ensure these are considered by key stakeholders when making policy decisions C) The Council aims to work with key stakeholders to ensure barriers to innovation in the system are removed The full list of attendee's were: Simon Abrams, CEO, Datapharm Zenon Andeou, Clinical Director, DrEd.com Paul Bate, Director of NHS Services, Babylon Matteo Berlucci, CEO, Your.MD Jonathan Boor, Director of Clincal Information, System C Healthcare Stephen Bourke, Founder and CEO, Echo Miles Boyden, Clinical Director, iDoc Andy Burton, Lloyds Pharmacy Online Doctor Jonathon Carr-Brown, Managing Director, Lost4Words Simon Chaplin-Rogers, Chief Medical Officer, iDoc Shellane Crisostomo, Co-founder, Vala Health Neil Daly, CEO, Skin Analytics James Davies, CEO, DrEd.com Mark Davies, Chairman, Your.MD Murray Ellender, CEO, eConsult webGP Farzad Entikab, Co-founder & Chief Medical Officer, Doctor Care Anywhere Costas Fantis, Business Development Manager, Doctor Morton’s Ian Gallifant, CEO, Medelinked Hamish Grierson, CEO and Founder, Thriva Alex Heaton, Founder, Live Smart Mark Jenkins, UK Managing and Medical Director, Oviva Alex Kafetz, Managing Director, ZPB Associates Jason Keane, CEO, Patient Suzanne Lawrence, Medical Director, Care UK Tanya Lawson, Director of Quality & Governance, Dr Morton’s Michelle Lea, Chief Operating Officer, OBS Medical Dermott Mullins, Chairman, iDoc Tim Ng, Chief Technology Officer, DrNow Claire Novorol, Co-founder and Chief Medical Officer, Ada Mary O'Brien, CEO, VideoDoc Ltd Adam Odessky, CEO, Sensely Eren Ozagir, CEO, Push Doctor Mike Pallett, CEO, Cupris Joaquim Pereira, CEO, PharmaDoctor Lloyd Price, Co-Founder & Chief Operating Office, Zesty Owain Rhys Hughes, CEO & Founder, Cinapsis Paul Roberts, CEO, GPDQ Sam Rodgers, Medical Director, Medichecks Joel Schoppig, Director of Strategy, Healthy.io Katherine Ward, Chief Commercial Officer, Managing Director UK and Europe, Healthy.io Tom Whicher, Founder, DrDoctor

The Global market for health technology will expand by a third every year into the near future. While this sector expands, so too will the proportion of the UK population that is elderly, with a quarter of the population due to be over 65 by 2050. It is often reported that the elderly are left behind by technology, with the over 65’s frequently cited as ‘the generation that technology forgot’. The same arguments have been made in regards to health technology. But how true is this? The answer is to some degree. There is an increasing number of health technologies aimed at the elderly but these are mainly passive technologies. For example Fayet have developed a walking stick that monitors its users and can alert caretakers should the patient fall over. It comes with a variety of other functions, such as the recording of walking habits which are monitored for change, again a message will then be sent to relatives. Sensor Care has developed new technologies which can monitor an elderly persons breathing, heart rate, sleep patterns and stress. The sensors can be placed under chairs and mattresses to monitor patients and alert for any worrying health indicators. With many outlets claiming that the health technology and technology sector as a whole is finally waking up to the needs of the elderly, this is true but this increase is largely in passive technologies related to the elderly. In these examples the primarily technology participant will be relatives and healthcare professionals who engage with the technology. An increasing number of tools, such as Care Sourcer, are offering families the chance to find care for their elderly loved ones using online platforms that rate carers and give families far more informed choices about care. Again though, the role of the elderly themselves is largely passive in regards to use. Where health tech needs to catch up is in technologies which involve active elderly participation. To do this some argue that some very simple rules must be followed which will lead to an older person embracing health technologies. These are larger buttons, extra loud speakers, hearing aid compatibility and longer battery life. Stripped back version of technologies such as mobile phones would serve as the model for this. AgeUK offers the OwnFone, a mobile with big buttons that merely sends and receives calls. For so long these kind of attitudes towards elderly embrace of technology have been the conventional wisdom. There is only one problem with this approach, they can be viewed as slightly patronising. Tech Radar saw this trend in 2010, declaring ‘stop patronising the elderly with crappy technology’. The main scorn of the writers are the exact features that I have described, big buttons and numbers. Tech Radar called it ‘expensive crap a toddler would find too simple’. These kind of idiot proof solutions are not solving the problem of the elderly struggling to use technology but skirting around it, essentially removing the technology and leaving the elderly to use its most simplistic components. The solution is simple, actually teach the elderly how to use real technology. It begs the question ‘has this approach ever really been tried? Or have we presumed that the elderly just won’t be able to do it? With the likely trend in the future being the online booking of GP appointments only, this teaching needs to occur fairly rapidly. The solution may be complex and a significant amount of money may need to be spent. The families of the elderly also need to become more involved. But we need to move this situation forward and actually devise an approach to solving the problem, not avoiding it by stripping technology away and leaving the elderly to use what is left. DataArt Julie Pelta is Director of Business Development for DataArt Healthcare and Life Sciences division. DataArt is a global technology consultancy that designs, develops and supports unique software solutions, helping clients take their businesses forward. Recognised for their deep domain expertise and superior technical talent, DataArt teams create new products and modernize complex legacy systems that affect technology transformation in select industries. DataArt has earned the trust of some of the world’s leading brands and most discerning clients, including Nasdaq, S&P, Oneworld Alliance, Ocado, Artnet, Betfair, and Skyscanner. Organized as a global network of technology services firms, DataArt brings together expertise of over 2,200 professionals in 20 locations in the US, Europe, and Latin America. http://www.dataart.com/

Julie Pelta is Director of Business Development for DataArt Healthcare and Life Sciences division. DataArt is a global technology consultancy that designs, develops and supports unique software solutions, helping clients take their businesses forward. Recognised for their deep domain expertise and superior technical talent, DataArt teams create new products and modernize complex legacy systems that affect technology transformation in select industries. DataArt has earned the trust of some of the world’s leading brands and most discerning clients, including Nasdaq, S&P, Oneworld Alliance, Ocado, Artnet, Betfair, and Skyscanner. Organized as a global network of technology services firms, DataArt brings together expertise of over 2,200 professionals in 20 locations in the US, Europe, and Latin America. http://www.dataart.com/ Technology is transforming our lives. Digital health, AI, TeleHealth, machine learning, all terms that excite individuals from pharmaceutical executives to venture capitalists. But what if the advent of technology is actually hurting us? I’m referring specifically to technology in surgery. Surgery for hundreds of years was a trial and error process of learning and developing through hands on skills and many challenging hours in the operating room. The growth of Laparoscopic (keyhole) surgery in the 90s saw a huge change in how surgery was performed. Laparoscopic surgery reduces infections (due to the keyhole incisions), improves outcomes and patients recover quicker than they would have with open surgery. The developments powered by the advances in medical devices, to speed up procedures and increase capacity really did transform surgery. But there was a step change again when the robots arrived! The DaVinci robot was first to market, and is now being followed by new players. A surgical robot is a huge investment for a hospital and with clear surgical benefits in visualisation, movement and access. Being able to turn a robot’s “hand” 360 degrees in the surgical field laparoscopically is clearly an improvement to the movement of a human hand! I thought for some time that the robot was a natural progression in surgery. But then a coffee room conversation with an older surgeon, Mr X made me think rather differently about how technology is impacting us. Mr X described his early career and surgical training, the very long hours he had to endure (prior to the European working time directive!), but that the investment in time during his training meant his understanding of the anatomy, the procedure and potential complications and how to manage them was outstanding. Mr X had put in long hours and had a huge amount of experience. Then laparoscopic surgery arrived and Mr X needed to re-train, he realised the “nintendo generation” of younger surgeons had the dexterity and ability to visualise on screen more quickly than he could but also recognised the benefits and the reduced infection rates, quicker recovery times etc. Robots in the Surgery Room And then came the robots, yes he acknowledged their benefits and ability to get into anatomical spaces and visualise areas that in open and basic laparoscopic surgery were hard to access. But what happens when the robot stops working? Devices and machines break, and at that point one has to revert to basic training and open the patient to complete the surgery. This was the impactful part: Mr X admitted that some of the juniors that were joining his department didn’t know how to do an open appendectomy or remove a gallbladder, two very basic procedures for a general surgeon. In fact the advent of progression in the form of the surgical robot had in fact de-skilled the junior Dr's and this concerned him greatly. It makes me think, do we just get a bit carried away with “progress” and ignore the associated risks. An airline pilot still undertakes extensive training and can take full control of a plane if the autopilot fails. We think this is the same for surgeons, sadly it’s not. The medical device industry is rapidly becoming a commodity market place with open and laparoscopic instruments no longer competing in innovation but price. Traditional medical device companies need to innovate as their core business is getting swallowed up by the “me-to” products being produced at scale and significantly cheaper. Innovation is being led by the robotic companies and a few niche start ups with a handful of products. More recently a new partnership Verb Surgical, a start up founded by Verily (Google/Alphabet) and Ethicon (Johnson and Johnson) is developing an intelligent digital surgery platform that will incorporate robotics, visualisation, advanced instrumentation, machine learning and connectivity. It’s an interesting partnership and I’m looking forward to seeing what they bring to market, the partnership appears to be a good example of an extension of the product life cycle in the medical device industry. The innovative products used to command a higher price tag, but with the increasing pressure on budgets it will be interesting to see how this plays out, particularly at scale. Can the system tolerate the innovation that is moving into the market? Having worked in this industry for over 20 years I will always keep a keen eye on how things are moving in medical devices. But I wonder if innovation in medical devices is really what’s needed to fix a broken health system?

As the pressures on the UK’s National Health Service continue to mount and appointment waiting times worsen, it’s no surprise that in recent years we’ve seen the likes of Jeremy Hunt et al begin to champion the ever-increasingly important role that digital health services have to play in the future of the NHS. As the recent Health and Care Innovation Expo in Manchester showed, digital healthcare is a hot topic at the moment, and there were some great examples of new innovation on display. Events such as the Expo show the amazing progress being made in the digital health industry at the moment; it’s very encouraging to see key figures in the NHS finally begin to take note and understand just what telehealth and mHealth have to offer and the incredible benefits that technology could bring to the UK’s health service. However, just talking about innovation isn’t enough; we need to see real change being implemented directly into the NHS, as a matter of urgency. As modern society becomes increasingly demanding and more and more “on-the-go”, the concept of waiting over a week (or in some cases two) to see your GP is simply not acceptable. Similarly, the process of receiving medicines – going to the doctors, getting a prescription and then going to a pharmacy to collect it – seems somewhat archaic. Since Now Healthcare Group was founded back in 2014, we’ve harboured ambitions of bringing more convenient and accessible primary healthcare to as many people as possible. We’ve made incredible strides as a business through several high profile partnerships with private and corporate clients, with our user base expected to hit the 20 million mark by the end of 2017 thanks to new upcoming contracts. However, our long-term ambitions are obviously for our services to be utilised on a much larger scale, and the possibility of integrating our technology into the NHS is an incredibly exciting proposition for us. Secure Smartphone Apps For those of you not too familiar with terms such as “telehealth” and “mHealth”, our platforms are smartphone apps that utilise secure and scalable technology to connect patients with GPs, nurses and pharmacists via live remote video consultation or live chat triage service. We also offer patients a 24/7 telephone triage service, too, allowing people to access more convenient care and helping to alleviate the pressures on A&E units, local clinics and pharmacies. Our well-known Dr Now and Now GP products continue to make great progress from a private healthcare point of view. However, our brand new platform – Now Patient – is set to revolutionise the way that over 15 million NHS patients with chronic care conditions access healthcare and medicines – all for free. Now Patient is the first telehealth service to offer users a complete end-to-end primary care solution; users can book a remote video appointment with an MRCGP-certified, NHS-trained GP, manage their medicine usage with our medical adherence platform and get free nationwide delivery of their repeat prescriptions. We also offer an in-app chat facility and triage service to connect worried patients to GPs and nurses. This new NHS “all-in-one” service is completely free to the patient, and we believe it will empower people to manage their own healthcare whilst significantly reducing the strain on our country’s increasingly overstretched CCGs, surgeries and GP practices. Telehealth and the NHS Jeremy Hunt has recently once again reiterated his desire to integrate telehealth services into the NHS “in the near future”, but the truth is that the solution that the NHS is looking for is here now. We’re already integrating our Now Patient services into NHS CCGs, surgeries and practices to help make healthcare more convenient for millions of people across the country. Manor Park Medical Practice in Leicester, run by Dr D. Jawahar, is one of the first NHS practices to introduce the Now Patient offering into its system. “As a GP and Federation Chair we fully support innovation,” said Dr Jawahar. “It’s great to see what can be achieved when the NHS and the independent sector combine forces to improve patients’ lives – we’re proud to be a part of it.” In the coming months we hope to roll out our services to a huge audience, transforming the way that primary healthcare is accessed and managed across the country. Through Now Patient and its ground-breaking innovative technology, we’re truly offering people tomorrow’s healthcare, now.

In the space of a few short years, Twitter has grown from several strands of inconsequential drivel to an information powerhouse. Originally conceived as a way to keep up to date with a small network of friends and family, the micro-blogging model, which sees the rapid exchange of quick-fire information, was soon recognised as an invaluable resource for professional organisations. An area that has benefited in particular from this format is the healthcare industry, where the output of short bursts of relevant news and developments can mean the difference between life and death. Social media sites such as Twitter now represent the largest source of healthcare discussion in the world. Just as celebrities have found Twitter a useful platform for communicating directly with fans, Tweets cut out the media coverage middleman when it comes to providing accurate news in real time from healthcare professionals. Read on to find out how medical authorities and industry experts are using twitter accounts to change the way healthcare is delivered. Reaching a wider audience From Health Secretary Jeremy Hunt, to NHS organisations, hospitals and individual GPs, twitter offers an effective and efficient means of communication. With only 140 characters to work with, even the heavyweights in healthcare still have time to Tweet. Unlike Facebook, where privacy settings dictate the visibility of the status updates, Twitter works on a more open and accessible system. As the length of Tweets are limited, the information is also more likely to be regularly updated. So when a hospital needs to alert staff and patients about problems with a service, there’s a good chance the Tweet will have saved a lot of time and hassle all round. Direct communication with followers The Internet has changed the landscape of public opinion and freedom of speech forever. Nowhere is this more apparent than in the stream of Twitter feeds where anyone with an interesting or controversial opinion can suddenly be propelled to Twitter stardom. The healthcare industry can only thrive if the relationship between patient/consumer and provider is nurtured. By receiving direct communication from the people who use their services everyday in the form of criticism, comments and suggestions, the healthcare industry can better understand what areas require improvement. The power of the hashtag Twitter takes some aspects of traditional forms of media and combines them with the best practices in social media that encourage cross-network conversation between users. Hashtags help to collect information about a topic and present them for anyone wishing to find opinions about that topic or contribute to the discussion. For users wishing to get involved in conversations about social media in healthcare, a useful hashtag is #HCSM where you can find all the latest news from healthcare innovators such as ‘medical futurist’ Bertalan Mesko. Emergency Situations Twitter has been in invaluable resource in emergency situations around the world, such as during the aftermath of typhoon Haiyan in the Philippines. A study by the Red Cross show that people are increasingly using social media as a vital resource for organising relief, helping families and friends find each other and discovering the scale of damage caused. On the 25th September Twitter announced the launch of a new alert system to help organisations ‘enhance the visibility of critical Tweets.’ Services using the alert system, including the London Fire Brigade, Foreign Office and Environment Agency, can mark an important Tweet with an orange bell. Followers will then receive a text alert in the event of an emergency situation. It is hoped that these Tweets will act as warnings of imminent danger, provide evacuation instructions and information about resources. Twitter analytics The scope for reaching meaningful conclusions through analysis of Twitter feeds is immense. Researchers have already learned a lot about the way healthcare information travels through social media and how to successfully track epidemics. Symplur is a company that aims to ‘connect the dots in healthcare social media’. So far Symplur’s big data includes 170 million healthcare Tweets, 3.4 million healthcare Twitter profiles and 1,500 health communities. These analytics may benefit ‘public health organisations, governmental agencies, communication agencies, pharmaceutical companies, healthcare researches and academia.’ One of Symplur’s major undertakings is The Healthcare Hashtag Project, which aims to make finding relevant hashtags easier for the healthcare community and providers. The most astonishing benefit for healthcare providers is in Twitter’s proven capacity to accurately track and predict outbreaks of disease and epidemics. In the same way that Google has been tracking the spread of influenza through related search terms since 2008, Twitter analysis has shown the same principle can be applied to Tweets. One good example of this is the cholera outbreak that effected Haiti in 2010. Researchers found that analysis of real-time Tweets could have led to early detection of the outbreak, as this information was available up to two weeks earlier than reports from official sources. In a study conducted at John Hopkins University it was discovered that many people put very revealing health information on their Twitter feeds, such as their symptoms, self-diagnosis, the medication they are using to treat the problem and the effects it is having. The researchers supposed that much of this information is not recorded in any official health record and could provide insights into common health misconceptions that need to be addressed, such as when to use antibiotics.

Augmented reality (AR) has shown an amazing development curve since Boeing researcher, Thomas Caudell coined the term “augmented reality” in 1990. The technology changed how an NFL football game is perceived through television. Emmy award winning Sportvision introduced the yellow first down line painted on the field in 1998, and the game has never been the same. Yet, AR does not only have a transformative impact on sports broadcasting but also on navigation, architecture, tourism, military or healthcare education and all the sub-fields of education in general. According to the latest forecasts, the AR device market is expected to reach $659.98 million by 2018. Its counterpart, virtual reality (VR) has a somewhat similar innovation pattern and it is also expected to boom in the next couple of years. Around $407 million growth is estimated on the VR device market by 2018. That’s why the two technologies are often mixed up, although there are significant differences between the two. While AR lets users see the real world and projects digital information onto the existing environment, VR shuts out everything else completely and provides an entire simulation. It is a logical consequence that VR is more immersive, and certain, so far unfounded fears also surfaced about the possibility of its addictive nature. Both AR and VR have an aptitude for changing healthcare for the better in the future, they just have different functions in it. In the case of AR, the use of technology in medicine and healthcare is basically a natural consequence of the technological development and data boom. Plenty of information, but also very specific kind of data has been flooding physicians for years. The paper format was changed for electronic health records (EHR), but the information flow remained very static so far. It must become more and more seamless and help the healing process. Patients’ lives cannot depend on whether the doctor can access the latest and most relevant data – and AR can help us with it. As data access and information processing technologies are already on an advanced level, the next step is to bring significant, even life-saving information into the doctors’ field of vision. For example, if there is a complicated operation, there might be very little time for checking whether the patient has a certain type of allergy. So, instead of searching among papers or in the EMR, the surgeon could see the relevant data on his AR screen in seconds. Yet, not only data but also other types of medical information, such as the location of the veins or organs might be projected onto the environment helping physicians doing their job. Google Glass One of the most popular platforms for working out medical AR solutions is the Google Glass, with which Shafi Ahmed carried out the first operation streamed live in 2014. The wearable computer with an optical head-mounted display was made available to testers and developers in 2013. However, it failed to catch on with the broader mainstream market, in the last years, the old technology is taking on a new function in healthcare. Since December 2013, doctors used Google Glass at Boston’s Beth Israel Deaconness Medical Center to see whether it can facilitate either doctor-patient interactions or the input of data. Huge QR codes hang on the walls and doors of patient rooms. These can be scanned when the doctor steps into the room, and Google Glass transmits the relevant patient records and information. The device makes it possible for doctors to keep eye contact with the patient while receiving pertinent information right away. Yet, there are plenty of other innovative ventures as well aiming to bring AR to healthcare. I decided to put together a list with the most relevant companies developing groundbreaking augmented reality solutions. HoloLens by Microsoft Case Western Reserve University and the Cleveland Clinic have partnered with Microsoft to release a HoloLens app called HoloAnatomy to visualize the human body in an easy and spectacular way. The app offers such an amazing insight into the biology of the human organism, that it landed first place for the immersive virtual reality and augmented reality category during the 2016 Jackson Hole Wildlife Film Festival Science Media Awards competition. With Microsoft’s HoloLens VR Headset, app users are able to see everything from muscles to the tiniest veins before their eyes on a dynamic holographic model. I believe it will revolutionize medical education, as students will be able to see the human body in 3D instead of the usual working method: black-and-white pictures and written descriptions in books.

https://www.hottopics.ht/stories/health/top-100-health-tech-influencers/ I am proud and honoured to have been included in the Hot Topics most influential people in Health Tech list 2016. To be listed alongside my fellow practitioners, founders, investors, digital leaders, government representatives, consultants and pharmaceutical heads leaves me humbled and motivated to work even harder :) Healthcare is currently undergoing a digital revolution, and its rapidly developing relationship with technology is beginning to shape potentially one of the largest industry sectors in the world. The digital sector and its stakeholders began to acknowledge the role of technology within the healthcare system barely five years ago, but it has since allowed a reservoir of investor capital, startup creation, and consumer adoption to explode. By 2020, it is projected that $102 billion will be spent on health and wellness technology across nine different markets, and the growing value of the global Health Tech market pushes well over $100 trillion. It’s development has been hotly anticipated: the healthcare industry was initially slow to adopt innovative solutions within its services, but recent traction points towards a rewarding future. Driving this growth is not only technology innovation, but behavioural changes in the general public’s attitude towards health and wellbeing, regulatory changes – in the US in particular – are beginning to open up the space to entrepreneurship, and changing demographics are forcing governments to ameliorate the effects of an ageing population. These macro-trends go some way to explain the perfect storm scenario that Health Tech is predicted to experience this year. Crucial to the safe, responsible, efficient and productive delivery of each Health Tech innovation are the many people, healthcare influencers, across the world, that seek to improve the consumption and experience of care. Of those, there are 100 Health Tech influencers who operate as practitioners, founders, investors, digital leaders, government representatives, consultants and pharmaceutical heads that have a particular knowledge of their field, product or service, that truly makes them influential in these important, early, days of Health Tech. The list was defined in association with Hotwire PR, and the challenges we face in shaping the future of healthcare are vast, but these 100 Health Tech influencers represent a digital sub-sector that could be fundamental to the future of our species. https://www.hottopics.ht/stories/health/top-100-health-tech-influencers/