Ebook: Transforming Healthcare with the Internet of Things
The current trend in health and social care systems is a shift from care provision to citizen-driven health. Only a few years ago, the high-tech devices used in healthcare were limited to health cards and personal portable devices. These have since evolved dramatically to include wearables, sensors and devices for measuring health values. The application of such technologies has, for the most part been welcomed by both patients and professionals. It is the fact that these devices can be connected to and communicate with other connected devices and systems which has been the game changer in healthcare technology, not least because it has empowered and will empower patients to take more control of their own healthcare management.
This book presents the proceedings of the Special Topic Conference (STC) of the European Federation for Medical Informatics (EFMI), held in Paris, France, in April 2016. The special topic this year is 'Transforming Healthcare with the Internet of Things'. The papers are divided into four sections: transforming healthcare with the Internet of things; societal dimensions of the Internet of things; ontology and decision support; and clinical information systems and data reuse; with a further section for poster presentations.
The book will be of interest to all those involved in the provision and delivery of technology-based healthcare.
This volume contains the proceedings of the Special Topic Conference (STC) of the European Federation for Medical Informatics (EFMI). The organisation of the STC is part of a long tradition of EFMI working groups to organise scientific events focused on important trends in medical informatics and eHealth. In 2016, the special topic is “Transforming Healthcare with the Internet of Things” in relation to the EFMI working group Personal Portable Devices (PPD). STC 2016 takes place in Paris, France, organised by the Laboratoire d'Informatique Médicale et d'Ingénierie des Connaissances en e-Santé (LIMICS) under the auspices of EFMI and the French Association for Medical Informatics (AIM).
Only a few years ago, devices were limited to health cards and personal portable devices. Since then, devices have dramatically evolved to include wearables, sensors, and actuators for measuring health values. The application of such technologies in the field of health, social care and wellness has attracted the attention of both patients and members of the general public interested in supporting or improving their health and wellbeing. One of the characteristics of these ‘devices’ (sometimes too small to observe thanks to nanotechnology) is to be ‘connected’ and to communicate with other connected devices and systems. This has been the game changer, as it replaces the cumbersome and often error-prone intervention of the human being who was previously necessary to enter data.
The Internet of Things (IoT) is thus turning out to have a major impact on the information paradigm in healthcare. The patient can now become their own Chief Operational Officer, as described by Eric Topol in his recent book The Patient will see you now
Eric Topol, The Patient Will See You Now, Basic Books, New York, 2015.
Eric Topol, The Patient Will See You Now, Basic Books, New York, 2015.
STC 2016 deals with the convergence of a process originally fuelled by technical and scientific forces, and the current political forces driven by the sustainability agenda of health and social care. This emphasises the process to humanise the individual who is more and more connected and surrounded by the IoT. Our ambition is to concentrate on this debate and create a platform for these different dimensions of this unstoppable development.
As a conclusion, we quote the Blue Line Statement presented at the 31th PCSI Conference in The Hague on October 16th 2015 in order to emphasise the transition of health and social care systems and the shift from care to citizen-driven health:
In order to achieve meaningful improvements in the health of the population, it is essential to understand the combination of health and social care issues for people. This requires health and social care systems to be interoperable, both from a technological and semantic point of view. Care should be aligned around the person and strive for social interoperability between the professions serving them, and systems must be designed with empathy and respect as core underpinning values. The pillars of the Blue Line model represent key principles for the design and delivery of person-centred, integrated care systems. We encourage policymakers and health system leaders to adopt these principles and create the societal incentive framework to enable this vision to be realised. The Village Track participants of the PCSI Conference recommend that further support be sought to continuously develop and formalise the Blue Line principles as requirements for supporting holistic, person-centred, integrated care systems in the Netherlands, and beyond.
The call for papers has resulted in 70 submissions from 30 countries, which were peer-reviewed by over 160 highly appreciated experts of the EFMI biomedical informatics network. Over 400 authors and co-authors are involved in the accepted contributions that shape the programme. The conference starts with a key contribution from Bernard Benhamou entitled Internet of Things & Medicine: A European Perspective. Then, Peter Pharrow explains how ‘We are entering the era of the Internet of (every)-Thing’. Christian Lovis, as the third keynote speaker, ends the conference by addressing the key to ‘Moving from a care-driven system to a health-centred paradigm: active objects, data, and information’.
The reviewing process has demonstrated that, up to now, the scientific work related to the IoT in healthcare has been focused on a mix of technologically-driven issues, but the potential to reform the health and social care systems is still underexploited. This can be seen as a positive sign, as the scientific world first wants to understand and prove the potential of the IoT before widely implementing it within the healthcare system. Accepted contributions reflect the scientific work on the impact of the IoT and the societal dimensions of the IoT in the sessions related to the transformation of healthcare, while the sessions on ontologies, decision support, clinical information systems, and data reuse complete the programme. To enrich the scientific sessions, the conference also offers tutorials, workshops, posters, short communications, demonstrations and a plenary panel.
From Sunday 17th to Tuesday 19th April 2016, Paris will not only be the city of light, but also the centre for world-citizens involved in the changing of traditional health care. It is with respect for the history of French medicine that the LIMICS chose the “Ecole de médecine” (created in 1794), as the historic, almost sacred, venue for STC 2016 to discuss the future of healthcare.
On behalf of the Scientific Program Committee
Brigitte Séroussi, Christian Lovis, Floor Sieverink, Frédéric Ehrler and Adrien Ugon
Healthcare acquired infections are among the biggest unsolved problems in healthcare, implying an increasing number of deaths, extra-days of hospital stay and hospital costs. Performing hand hygiene is a simple and inexpensive prevention measure, but healthcare workers compliance with it is still far from optimal. Recognized hurdles are lack of time, forgetfulness, wrong technique and lack of motivation. This study aims at exploring gamification to promote nurses' HH compliance self-awareness and action. Real-time data collected from an indoor location system will provide feedback information to a group of nurses working in an ICU ward. In this paper both the research's motivation and methods is presented, along with the first round of results and its discussion.
A better monitoring of pregnant women, mainly during the third trimester of pregnancy and an easy communication between physician and patients are very important for the prevention and good health of baby and mother. The paper presents an integrated system as support for the Obstetrics – Gynaecology domain consisting in two modules: a mobile application, ObGynCare, dedicated to the pregnant women and a new component of the Obstetrics-Gynaecology Department Information System dedicated to the physicians for a better monitoring of the pregnant women. The mobile application informs the pregnant women about their status, permits them to introduce glycaemia and weight values and has as option pulse and blood pressure acquisition from a smart sensor and provides results in a graphic format. It also provides support for easy patient-doctor communication related to any health problems. ObGyn Care offers nutrition recommendations and gives the pregnant women the possibility to enter a social space of common interests using social networks (Facebook) to exchange useful and practical information. Data collected from patients and from sensor are stored on the cloud and the physician may access the information and analyse it. The extended module of the Obstetrics-Gynaecology Department Information System already developed supports the physicians to visualize weekly, monthly, or on a trimester, the patient data and to discuss with her through the chat module. The mobile application is in test by pregnant women and medical personnel.
Serious games could be used to improve cognitive functions in the elderly. We evaluated the adoption of a new tablet application dedicated to cognitive stimulation in the elderly. The Stim'Art application offers various serious games to work different cognitive functions (memory, attention, concentration, etc.). The usage of fifteen older adults was followed for six months. The type of the game, the number of launches for each game, the time spent on each game, the difficulty level, the success rate and perceived well-being of users have been studied and compared at the end of the first and the sixth months. The participants have played half an hour per day on average. The average time of playing per day in the sixth month was significantly higher than the average time of playing during the first month (p value < 7 * 10−4). The same result was found for the average number of game launches per day (p value < 7 * 10−4). However, older people seem not to launch more difficult levels in the last month. The success rate at sixth months was significantly higher than the success rate at the end of the first month (p value < 6.4 * 10−4). Generally, seniors have had an improvement in their wellbeing score judged by themselves. Our study showed that the mobile application receives a good admission from users. The results are promising and can pave the way for improving cognitive function in the elderly patients. The use of tablets and the constitution of serious games in close cooperation with health professionals and elderly patients (the end user), are likely to provide satisfactory results to improve healthcare provided for elderly patients suffering from cognitive disorders.
Sedentary behaviour is a major risk factor for chronic disease morbidity and mortality in aging. Measuring people activity through devices such as pedometers is a recognized intervention to motivate them for more physical activity. However, the feedback provided by these devices must be accurate in order to avoid overtraining and keep users' motivation alive. If the accuracy of pedometers has been validated for healthy people, their lack of accuracy for elderly people walking at slower pace has been reported in several studies. The emergence on the consumer's market of new devices that can be worn indifferently at the wrist or at the waist raises once more this concern. In order to evaluate whether pedometers' location influences their accuracy, we have tested three pedometers at different locations, and for several paces in a comparative study. Beyond confirming the decrease of pedometers' accuracy with speed reduction, our study reveals that pedometers should be worn at the waist rather than at the wrist. This leads us to recommend wearing pedometers at the waist when monitoring population with reduced mobility.
Polysomnography is the gold standard test for sleep disorders among which the Sleep Apnea Syndrome (SAS) is considered a public health issue because of the increase of the cardio-and cerebro-vascular risk it is associated with. However, the reliability of this test is questioned since sleep scoring is a time-consuming task performed by medical experts with a high inter- and intra-scorers variability, and because data are collected from 15 sensors distributed over a patient's body surface area, using a wired connection which may be a source of artefacts for the patient's sleep. We have used symbolic fusion to support the automated diagnosis of SAS on the basis of the international guidelines of the AASM for the scoring of sleep events. On a sample of 70 patients, and for the Apnea-Hypopnea Index, symbolic fusion performed at the level of sleep experts (97.1% of agreement). The next step is to confirm these preliminary results and move forward to a smart wireless polysomnograph.
The Internet of Things holds great promise for healthcare, but also embodies a number of risks. This analysis suggests that the risks are as yet poorly delineated (having features in common with the oracle Pythia, and with Pandora and her box), and that adopting the precautionary principle is appropriate.
A characteristic feature of the development of health-related social networks is the emergence of internet-based virtual communities, composed of patients. These communities go beyond the mere interchange of information concerning their conditions, intervening in the planning and execution of clinical research, including randomised controlled trials, in collaboration with health professionals. That was the case, in 2009, when patients suffering amyotrophic lateral sclerosis, a rare and severe disease, conducted a clinical trial in USA, organising themselves through an online platform. This initiative launched a new model for the planning and conduction of clinical research: “Participants-Led Research” (PLR). The distinctive particularities of this new research paradigm represent a challenge to the traditional standards used for judging the ethical soundness of clinical investigation. That is the case, for example, of informed consent. This article aims at identifying the ethical, legal, and social issues (ELSI) posed by PLR and the relevant concepts that may help in solving them. The following issues, in particular, are analysed, that may give place to a new social contract for the ethical assessment of clinical research: consent for participating in research and personal integrity; data protection and confidentiality; benefits sharing and intellectual property
Mobile health applications are expected to play a major role for the management of personal health in the future. For this purpose, the apps collect a lot of sensitive data from sensors or direct user input, combine it with automatic data such as GPS location data, store it locally and pass it on to web-platforms (often running in a public cloud), where the information can be managed and often shared with others in social networks. However, it is usually not transparent for the user how this sensitive information is handled and where it goes to. This paper shows the result of the analysis of mobile health applications regarding the handling of sensitive data especially with respect to transmission to third-parties.
Mobile technologies are constantly evolving and with the development of Internet of Things we can expect continuous increase of various applications. Mobile technologies have undeniable opportunities to play an important role in health services. Concerning purely technical aspects, almost every problem can be solved. However, there are still many unsolved and unclear issues related with ethics and governance mechanisms for mobile phone applications. These issues are even more critical in medical and health care applications of mobile technologies. This paper tries to analyse ethical, and privacy-related challenges that may occur when introducing Personal Portable Devices (PPD) to collect and record personal health data in health care and welfare environment.
Medication data is a crucial part of patient data. Medication data is stored in a centralized archive, and made accessible to health care professionals and citizens. Re-usability of medication data requires it to be not only interoperable, but also complete and reliable. We evaluated e-prescription data stored in a national archive. The data consists of 596 patients with 76411 e-prescriptions. The interim results show the data to be complete when stored, whereas data reliability would require more user training.
The number of patients that benefit from remote monitoring of cardiac implantable electronic devices, such as pacemakers and defibrillators, is growing rapidly. Consequently, the huge number of alerts that are generated and transmitted to the physicians represents a challenge to handle. We have developed a system based on a formal ontology that integrates the alert information and the patient data extracted from the electronic health record in order to better classify the importance of alerts. A pilot study was conducted on atrial fibrillation alerts. We show some examples of alert processing. The results suggest that this approach has the potential to significantly reduce the alert burden in telecardiology. The methods may be extended to other types of connected devices.
Ontologies are now widely used in the biomedical domain. However, it is difficult to manipulate ontologies in a computer program and, consequently, it is not easy to integrate ontologies with databases or websites. Two main approaches have been proposed for accessing ontologies in a computer program: traditional API (Application Programming Interface) and ontology-oriented programming, either static or dynamic. In this paper, we will review these approaches and discuss their appropriateness for biomedical ontologies. We will also present an experience feedback about the integration of an ontology in a computer software during the VIIIP research project. Finally, we will present OwlReady, the solution we developed.
This paper proposes a social network with an integrated children disease prediction system developed by the use of the specially designed Children General Disease Ontology (CGDO). This ontology consists of children diseases and their relationship with symptoms and Semantic Web Rule Language (SWRL rules) that are specially designed for predicting diseases. The prediction process starts by filling data about the appeared signs and symptoms by the user which are after that mapped with the CGDO ontology. Once the data are mapped, the prediction results are presented. The phase of prediction executes the rules which extract the predicted disease details based on the SWRL rule specified. The motivation behind the development of this system is to spread knowledge about the children diseases and their symptoms in a very simple way using the specialized social networking website www.emama.mk.
Amongst the positive outcomes expected from the Internet of Things for Health are longitudinal patient records that are more complete and less erroneous by complementing manual data entry with automatic data feeds from sensors. Unfortunately, devices are fallible too. Quality control procedures such as inspection, testing and maintenance can prevent devices from producing errors. The additional approach envisioned here is to establish constant data quality monitoring through analytics procedures on patient data that exploit not only the ontological principles ascribed to patients and their bodily features, but also to observation and measurement processes in which devices and patients participate, including the, perhaps erroneous, representations that are generated. Using existing realism-based ontologies, we propose a set of categories that analytics procedures should be able to reason with and highlight the importance of unique identification of not only patients, caregivers and devices, but of everything involved in those measurements. This approach supports the thesis that the majority of what tends to be viewed as ‘metadata’ are actually data about first-order entities.
The paper presents the results of the development and implementation of an expert system that automatically generates doctors' letters based on the results of laboratory tests. Medical knowledge is expressed using a first order predictate logic based language. The system was implemented and evaluated in the Helix laboratory service.
The aim was to study the opinions of tuberculosis patients and doctors and identify perceived opportunities and barriers to using a Web portal to optimize its use. The perceptions of 30 tuberculosis patients and 18 doctors (10 general practitioners and 8 tuberculosis specialists) from Tomsk, Russia were collected through semi-structured interviews. The responses were analysed for content using principles of the grounded theory and thematic analysis, in order to gain insight into the participants' beliefs and attitudes towards adopting tuberculosis web-portal to increase the efficiency of the treatment and rehabilitation process.
The objective of the work is to implement and evaluate the automated computation of 9 healthcare quality indicators, by data reuse of electronic health records, in the field of elderly surgical patients. Methods: Data are extracted from EHR, including administrative data, ICD10 diagnoses, laboratory results, procedures, administered drugs, and free-text letters. The indicators are implemented by a medical data reuse specialist. The conformity rate is automatically computed (3.5 minutes for 15,000 inpatient stays and 9 indicators). A medical expert reviews 45 stays per indicator. The precision is the proportion of non-conform inpatient stays among the cases detected as non-conform by the algorithms. Results: the paper describes the implemented algorithms, the conformity rates and the precisions. Two indicators have a precision of 0%, 3 indicators have a precision of 40 to 60%, and four indicators have a precision from 80 to 100% (for 2 of them, the conformity rate is lower than 2.5%!). This demonstrates that automated quality screening is possible and enables to detect threatening situations. The implementation of the indicators requires special skills in medicine, medical information sciences, and algorithmics. Failures of precision are mainly due to defaults of information quality (missing codes), and could benefit from text analysis.
In order to reuse data for clinical research it is then necessary to overcome two main challenges – to formalize data sources and to increase the portability. Once the challenge is resolved, it then will allow research applications to reuse clinical data. In this paper, three data models such as entity-attribute-value, ontological and data-driven are described. Their further implementation at University Hospitals of Geneva (HUG) in the data integration methodologies for operational healthcare data sources of the European projects such as DebugIT and EHR4CR and national project the Swiss Transplant Cohort Study are explained. In these methodologies the clinical data are either aligned according to standardised terminologies using different processing techniques or transformed and loaded directly to data models. Then these models are compared and discussed based on the quality criteria. The comparison shows that the described data models are strongly dependent on the objectives of the projects.
Data stored in operational databases are not reusable directly. Aggregation modules are necessary to facilitate secondary use. They decrease volume of data while increasing the number of available information. In this paper, we present four automated engines of aggregation, integrated into an anaesthesia data warehouse. Four instances of clinical questions illustrate the use of those engines for various improvements of quality of care: duration of procedure, drug administration, assessment of hypotension and its related treatment.
There is an increasing social pressure to train medical students with a level of competency sufficient to face clinical practice already at the end of their curriculum. The case-based learning (CBL) is an efficient teaching method to prepare students for clinical practice through the use of real or realistic clinical cases. In this regard, the Electronic Healthcare Record (EHR) could be a good source of real patient stories that can be transformed into educative cases. In this paper a formal approach to generate Health Case Studies from EHR is defined.