P4 medicine: how systems medicine will transform the healthcare sector and society

New medical big data for P4 medicine on allergic conjunctivitis

Allergic conjunctivitis affects approximately 15-20% of the global population and can permanently deteriorate one's quality of life (QoL) and work productivity, leading to societal work force costs. Although not fully understood, allergic conjunctivitis is a multifactorial disease with a complex network of environmental, lifestyle, and host contributory risk factors. To effectively enhance the quality of treatment for patients with allergic conjunctivitis, as well as other allergic diseases, the field must first comprehend the pathology underlying various individualized subjective symptoms and stratify the disease according to risk factors and presentations. Such competent stratification and societal reconstruction that targets the alleviation of the damage due to allergic diseases would greatly help ramify personalized treatments and prevent the projected increase in societal costs imposed by allergic diseases. Owing to the rapid advancements in the information and technology sector, medical big data are greatly accessible and useful to decipher the pathophysiology of many diseases. Such data collected through multi-omics and mobile health have been effective for research on chronic diseases including allergic and immune-mediated diseases. Novel big data containing vast and continuous information on individuals with allergic conjunctivitis and other allergic symptoms are being used to search for causative genes of diseases, gain insights into new biomarkers, prevent disease progression, and, ultimately, improve QoL. The individualized and holistic data accrued from new angles using technological innovations are helping the field realize the principles of P4 medicine: predictive, preventive, personalized, and participatory medicine.

Untargeted longitudinal analysis of a wellness cohort identifies markers of metastatic cancer years prior to diagnosis

We analyzed 1196 proteins in longitudinal plasma samples from participants in a commercial wellness program, including samples collected pre-diagnosis from ten cancer patients and 69 controls. For three individuals ultimately diagnosed with metastatic breast, lung, or pancreatic cancer, CEACAM5 was a persistent longitudinal outlier as early as 26.5 months pre-diagnosis. CALCA, a biomarker for medullary thyroid cancer, was hypersecreted in metastatic pancreatic cancer at least 16.5 months pre-diagnosis. ERBB2 levels spiked in metastatic breast cancer between 10.0 and 4.0 months pre-diagnosis. Our results support the value of deep phenotyping seemingly healthy individuals in prospectively inferring disease transitions.

Shared decision making in peri-operative medicine: Miles to go in Indian scenario

Shared Decision Making (SDM) in peri-operative medicine is increasingly encouraged as an ideal model of treatment decision making in the medical encounter. Moreover, it has the potential to improve the quality of the decision-making process for patients and ultimately, patient outcomes. This review focuses on several published literature on SDM in peri-operative medicine, its Implementation, barriers faced by Patient and the Provider, Myths regarding SDM and current scenario of SDM in India. Within the anesthetic community, patient consent is vigorously guided. However, this community suffers from lack of advancements in implementing the patient-focused rather than doctor-focused characteristics of SDM. Out of the several barriers, the most common barrier towards the implementation of SDM is the lack of time from the provider community. Within the anesthesia domain, the consultations discussed directly preceding the surgery do not pursue the customary and highly organized stages of typical outpatient consultations. Under these backgrounds and to be successfully implemented, it becomes imperative to begin the process of SDM pre-operative assessment clinic targeting both the high- and low-risk patients. It is critical to summarise that SDM does not end at the time of anesthesia for the peri-operative healthcare professional, but it gets to carry forward until patient discharge. Therefore, it is carried as the Pinnacle of Patient-Centred Care.

Bibliometric Study of Technology and Occupational Health in Healthcare Sector: A Worldwide Trend to the Future

Since the eighties, technological tools have modified how people interact in their environment. At the same time, occupational safety and health measures have been widely applied. The European Agency for Safety and Health at Work considers that information and communication technologies are the main methods to achieve the goals proposed to improve working life and the dissemination of good practices. The principal objective was to determine the trends of publications focused on these technologies and occupational safety in the healthcare sector during the last 30 years. A bibliometric study was carried out. The 1021 documents showed an increased trend per country, especially for the United States (p < 0.001) and year (p < 0.001). The citations per year showed significant differences between citations of articles published before 2007 (p < 0.001). The year was also linked to the increase or decrease of articles (72.2%) and reviews (14.9%) (p < 0.001). The analysis of journal co-citations also showed that the main journals (such as Infection Control and Hospital Epidemiology) were linked to other important journals and had a major part in the clusters formed. All these findings were discussed in the manuscript and conclusions were drawn.

A P4 Medicine Perspective of Gut Microbiota and Prediabetes: Systems Analysis and Personalized Intervention

Type 2 diabetes (T2D) accounts for approximately 90% of diabetes worldwide and has become a global public health problem. Generally, individuals go to hospitals and get healthcare only when they have obvious T2D symptoms. While the underlying cause and mechanism of the disease are usually not well understood, treatment is for the symptoms, but not for the disease cause, and patients often continue to progress with more symptoms. Prediabetes is the early stage of diabetes and provides a good time window for intervention and prevention. However, with few symptoms, prediabetes is usually ignored without any treatment. Obviously, it is far from ideal to rely on the traditional medical system for diabetes healthcare. As a result, the medical system must be transformed from a reactive approach to a proactive approach. Root cause analysis and personalized intervention should be conducted for patients with prediabetes. Based on systems medicine, also known as P4 medicine, with a predictive, preventive, personalized, and participatory approach, new medical system is expected to significantly promote the prevention and treatment of chronic diseases such as prediabetes and diabetes. Many studies have shown that the occurrence and development of diabetes is closely related to gut microbiota. However, the relationship between diabetes and gut microbiota has not been fully elucidated. This review describes the research on the relationship between gut microbiota and diabetes and some exploratory trials on the interventions of prediabetes based on P4 medicine model. Furthermore, we also discussed how these findings might influence the diagnosis, prevention and treatment of diabetes in the future, thereby to improve the wellness of human beings.

Guiding Pay-As-You-Live Health Insurance Models Toward Responsible Innovation in Health

While the transition toward digitalized health care and service delivery challenges many publicly and privately funded health systems, patients are already producing a phenomenal amount of data on their health and lifestyle through their personal use of mobile technologies. To extract value from such user-generated data, a new insurance model is emerging called Pay-As-You-Live (PAYL). This model differs from other insurance models by offering to support clients in the management of their health in a more interactive yet directive manner. Despite significant promises for clients, there are critical issues that remain unaddressed, especially as PAYL models can significantly disrupt current collective insurance models and question the social contract in so-called universal and public health systems. In this paper, we discuss the following issues of concern: the quantification of health-related behavior, the burden of proof of compliance, client data privacy, and the potential threat to health insurance models based on risk mutualization. We explore how more responsible health insurance models in the digital health era could be developed, particularly by drawing from the Responsible Innovation in Health framework.

Building Consensus on the Priority-Setting for National Policies in Health Information Technology: A Delphi Survey

Objectives

With growing attention on the healthcare industry as a potential market for big data and artificial intelligence in the Fourth Industrial Revolution, countries around the world are introducing and developing various policies and projects related to health information technology (HIT). To assist prioritizing HIT topics in policy making, this study adopts the Delphi technique to garner expert opinions from various fields of health informatics.

Methods

Data were collected from November 2019 to February 2020 using the Delphi technique through two rounds of surveys administered via email. The Delphi panel consisted of 16 experts with a high level of experience in health informatics. They were from the Health Information Policy Advisory Committee of the Ministry of Health and Welfare of Korea, and the board of directors of the Korean Society of Medical Informatics. The experts were asked to assess the importance, urgency, and difficulty of HIT topics in three domains: technology, application, and infrastructure.

Results

Of the 40 topic items, a 100% agreement was reached for the importance of 6 items, including 2 items in technology, 1 item in application, and 3 items in infrastructure domains. Especially, Quadrant I of a 2×2 matrix showing high importance and high urgency included 7 items in the technology domain, 2 items in the application domain, and 13 items in the infrastructure domain.

Conclusions

Most items with high importance and urgency belonged to the infrastructure domain. The findings indicated that fostering an infrastructural environment should be polices with top priorities of HIT.

Dynamic predictions of kidney graft survival in the presence of longitudinal outliers

In kidney transplantation, dynamic predictions of graft survival may be obtained from joint modelling of longitudinal and survival data for which a common assumption is that random-effects and error terms in the longitudinal sub-model are Gaussian. However, this assumption may be too restrictive, e.g. in the presence of outliers, and more flexible distributions would be required. In this study, we relax the Gaussian assumption by defining a robust joint modelling framework with t-distributed random-effects and error terms to obtain dynamic predictions of graft survival for kidney transplant patients. We take a Bayesian paradigm for inference and dynamic predictions and sample from the joint posterior densities. While previous research reported improved performances of robust joint models compared to the Gaussian version in terms of parameter estimation, dynamic prediction accuracy obtained from such approach has not been yet evaluated. Our results based on a training sample from the French DIVAT kidney transplantation cohort illustrate that estimates for the slope parameters in the longitudinal and survival sub-models are sensitive to the distributional assumptions. From both an internal validation sample from the DIVAT cohort and an external validation sample from the Lille (France) and Leuven (Belgium) transplantation centers, calibration and discrimination performances appeared to be better under the robust joint models compared to the Gaussian version, illustrating the need to accommodate outliers in the dynamic prediction context. Simulation results support the findings of the validation studies.

Hardware Prototype for Wrist-Worn Simultaneous Monitoring of Environmental, Behavioral, and Physiological Parameters

We designed a low-cost wrist-worn prototype for simultaneously measuring environmental, behavioral, and physiological domains of influencing factors in healthcare. Our prototype continuously monitors ambient elements (sound level, toxic gases, ultraviolet radiation, air pressure, temperature, and humidity), personal activity (motion tracking and body positioning using gyroscope, magnetometer, and accelerometer), and vital signs (skin temperature and heart rate). An innovative three-dimensional hardware, based on the multi-physical-layer approach is introduced. Using board-to-board connectors, several physical hardware layers are stacked on top of each other. All of these layers consist of integrated and/or add-on sensors to measure certain domain (environmental, behavioral, or physiological). The prototype includes centralized data processing, transmission, and visualization. Bi-directional communication is based on Bluetooth Low Energy (BLE) and can connect to smartphones as well as smart cars and smart homes for data analytic and adverse-event alerts. This study aims to develop a prototype for simultaneous monitoring of the all three areas for monitoring of workplaces and chronic obstructive pulmonary disease (COPD) patients with a concentration on technical development and validation rather than clinical investigation. We have implemented 6 prototypes which have been tested by 5 volunteers. We have asked the subjects to test the prototype in a daily routine in both indoor (workplaces and laboratories) and outdoor. We have not imposed any specific conditions for the tests. All presented data in this work are from the same prototype. Eleven sensors measure fifteen parameters from three domains. The prototype delivers the resolutions of 0.1 part per million (PPM) for air quality parameters, 1 dB, 1 index, and 1 °C for sound pressure level, UV, and skin temperature, respectively. The battery operates for 12.5 h under the maximum sampling rates of sensors without recharging. The final expense does not exceed 133€. We validated all layers and tested the entire device with a 75 min recording. The results show the appropriate functionalities of the prototype for further development and investigations.

Network and Systems Medicine: Position Paper of the European Collaboration on Science and Technology Action on Open Multiscale Systems Medicine

Introduction: Network and systems medicine has rapidly evolved over the past decade, thanks to computational and integrative tools, which stem in part from systems biology. However, major challenges and hurdles are still present regarding validation and translation into clinical application and decision making for precision medicine. Methods: In this context, the Collaboration on Science and Technology Action on Open Multiscale Systems Medicine (OpenMultiMed) reviewed the available advanced technologies for multidimensional data generation and integration in an open-science approach as well as key clinical applications of network and systems medicine and the main issues and opportunities for the future. Results: The development of multi-omic approaches as well as new digital tools provides a unique opportunity to explore complex biological systems and networks at different scales. Moreover, the application of findable, applicable, interoperable, and reusable principles and the adoption of standards increases data availability and sharing for multiscale integration and interpretation. These innovations have led to the first clinical applications of network and systems medicine, particularly in the field of personalized therapy and drug dosing. Enlarging network and systems medicine application would now imply to increase patient engagement and health care providers as well as to educate the novel generations of medical doctors and biomedical researchers to shift the current organ- and symptom-based medical concepts toward network- and systems-based ones for more precise diagnoses, interventions, and ideally prevention. Conclusion: In this dynamic setting, the health care system will also have to evolve, if not revolutionize, in terms of organization and management.

Is the Quality of Toothache-Related Information Published in Brazilian Websites Adequate to Assist People in Seeking Dental Treatment?

PURPOSE

The aim of this study was to evaluate the readability and the quality of toothache-related information found in Brazilian websites.

MATERIALS AND METHODS

Fifty-five websites retrieved from Google Search, Baidu, Yahoo! and Bing were evaluated by two independent examiners using the DISCERN questionnaire, the Journal of American Medical Association (JAMA) benchmark criteria and the Flesch Reading Ease adapted to Brazilian Portuguese (FRE-BP). Additionally, the websites were categorised according to their information, adopting four criteria related to: (i) endodontic pain, (ii) toothache relief or treatment, (iii) the self-resolution of pain, and (iv) the promotion of home remedies usage. The statistical analysis was performed using Spearman's rank correlation coefficient, Mann-Whitney U test, hierarchical clustering analysis by Ward's minimum variance method, Kruskal-Wallis test, post-hoc Dunn's test and Chisquare test. P < 0.05 was considered statistically significant.

RESULTS

The overall means (± SD) of DISCERN and FRE-BP were, respectively, 31.02 (± 5.56) and 61.20 (± 11.79), without quality-based differences between the websites with health- and non-health-related authors, and distinct clusters.

CONCLUSION

Therefore, the quality of toothache-related information found in this sample of Brazilian websites was classified as simple, accessible and of poor quality, which can hamper the personal decision-making process of seeking dental treatment, leading to damages caused by the non-effective self-management of toothache.

An Individualized, Data-Driven Digital Approach for Precision Behavior Change

Chronic disease now affects approximately half of the US population, causes 7 in 10 deaths, and accounts for roughly 80% of US health care expenditure. Because the root causes of chronic diseases are largely behavioral, effective therapies require frequent, individualized interventions that extend beyond the hospital and clinic to reach patients in their day-to-day lives. However, a mismatch currently exists between what the health care system is equipped to provide and the interventions necessary to effectively address the chronic disease burden. To remedy this health crisis, we present an individualized, data-driven digital approach for chronic disease management and prevention through precision behavior change. The rapid growth of information, biological, and communication technologies makes this an opportune time to develop digital tools that deliver precision interventions for health behavior change to address the chronic disease crisis. Building on this rapid growth, we propose a framework that includes the precise targeting of risk-producing behaviors using real-time sensing technology, machine learning data analysis to identify the most effective intervention, and delivery of that intervention with health-reinforcing feedback to provide real-time, individualized support to empower sustainable health behavior change.

Expectations to data: Perspectives of service providers and users of future health and wellness services

The healthcare and wellness sector currently attempts to provide more proactive service models with data-driven solutions. This study examines the expectations and values related to personal data i.e. data valences from the perspective of service providers and individual users. The study is based on the analysis of extensive empirical material collected through interviews and a collaborative workshop. The data was collected in one cultural context, Finland. The results suggest that the potential service providers and users have similar expectations regarding self-evidence of data while the main differences concern the expectations of transparency. The results of the study propose some basic requirements for the development of personalised data-driven services in future. The study suggests that basic requirements for the development of future data driven services concern expectations to usable data visualisations, data as a motivator, data accuracy and data transparency. Even though there are varying expectations to personal health data and even some concerns, it can be seen that here different ecosystem actors primarily perceived the wider use of personal health and wellness data as a positive trend. It can be concluded that collaborative personal data-driven service ecosystems are an integral part of development towards proactive service models in healthcare.

Big data in digital healthcare: lessons learnt and recommendations for general practice

Big Data will be an integral part of the next generation of technological developments-allowing us to gain new insights from the vast quantities of data being produced by modern life. There is significant potential for the application of Big Data to healthcare, but there are still some impediments to overcome, such as fragmentation, high costs, and questions around data ownership. Envisioning a future role for Big Data within the digital healthcare context means balancing the benefits of improving patient outcomes with the potential pitfalls of increasing physician burnout due to poor implementation leading to added complexity. Oncology, the field where Big Data collection and utilization got a heard start with programs like TCGA and the Cancer Moon Shot, provides an instructive example as we see different perspectives provided by the United States (US), the United Kingdom (UK) and other nations in the implementation of Big Data in patient care with regards to their centralization and regulatory approach to data. By drawing upon global approaches, we propose recommendations for guidelines and regulations of data use in healthcare centering on the creation of a unique global patient ID that can integrate data from a variety of healthcare providers. In addition, we expand upon the topic by discussing potential pitfalls to Big Data such as the lack of diversity in Big Data research, and the security and transparency risks posed by machine learning algorithms.

Situated Precision Healthcare in the Smart Medical Home: Bringing NASA’s Research Strategy down to Earth

This special issue is ambitious in that it calls for strategic transformation in research on Alzheimer’s Disease (AD) and related dementias, including innovation in both research design and value delivery, through lifestyle interventions that implicitly relate to a much broader range of comorbidities and diseases of aging. One response to this challenge is to venture beyond the boundaries of research that supports the healthcare industry. Toward this end, we introduce opportunities for research translation and knowledge transfer from NASA to the healthcare industry. Our intent is to show how NASA’s approach to research can guide innovation for a smart medical home, most notably for AD and other diseases of aging. The article is organized in four major sections: (a) aggregating fragmented research communities; (b) lifestyle interventions in the medical home; (c) multiscale computational modeling and analysis; and (d) lifespan approach to precision brain health. We provide novel motivations and transformative paths to a diversity of specific lines of research, across communities, that would be difficult to discover in common methods of networking within research communities and even through sophisticated bibliographic methods. We thus reveal how knowledge transfer between the public and private sector can stimulate development of broader scientific communities and achieve a more coherent strategic approach to integration and development of a diversity of capabilities including but not limited to technology.

Ultra-Low Power Wearable Infant Sleep Position Sensor

Numerous wearable sensors have been developed for a variety of needs in medical/healthcare/wellness/sports applications, but there are still doubts about their usefulness due to uncomfortable fit or frequent battery charging. Because the size or capacity of battery is the major factor affecting the convenience of wearable sensors, power consumption must be reduced. We developed a method that can significantly reduce the power consumption by introducing a signal repeater and a special switch that provides power only when needed. Antenna radiation characteristics are an important factor in wireless wearable sensors, but soft material encapsulation for comfortable fit results in poor wireless performance. We improved the antenna radiation characteristics by a local encapsulation patterning. In particular, ultra-low power operation enables the use of paper battery to achieve a very thin and flexible form factor. Also, we verified the human body safety through specific absorption rate simulations. With these methods, we demonstrated a wearable infant sleep position sensor. Infants are unable to call for help in unsafe situations, and it is not easy for caregivers to observe them all the time. Our wearable sensor detects infants’ sleep positions in real time and automatically alerts the caregivers when needed.

Translational Research Symposium-collaborative efforts as driving forces of healthcare innovation

The 5th Translational Research Symposium was organised at the annual meeting of the European Society for Biomaterials 2018, Maastricht, the Netherlands, with emphasis on the future of emerging and smart technologies for healthcare in Europe. Invited speakers from academia and industry highlighted the vision and expectations of healthcare in Europe beyond 2020 and the perspectives of innovation stakeholders, such as small and medium enterprises, large companies and Universities. The aim of the present article is to summarise and explain the main statements made during the symposium, with particular attention on the need to identify unmet clinical needs and their efficient translation into healthcare solutions through active collaborations between all the participants involved in the value chain.

Medical knowledge integration and "systems medicine": Needs, ambitions, limitations and options

Medicine today is an extremely heterogeneous field of knowledge, based on clinical observations and action knowledge and on data from the biological, behavioral and social sciences. We hypothesize at first that medicine suffers from a disciplinary hyper-diversity compared to the level of conceptual interdisciplinary integration. With the claim to "understand" and cure diseases, currently with the label "Systems Medicine" new forms of molecular medicine promise a general new bottom-up directed precise, personalized, predictive, preventive, translational, participatory, etc. medicine. Our second hypothesis rejects this claim because of conceptual, methodological and theoretical weaknesses. In contrary, this is our third hypothesis; we suggest that top-down organismic systems medicine, related to general system theory, opens better options for an integrative scientific understanding of processes of health and disease.

Plasma protein patterns as comprehensive indicators of health

Proteins are effector molecules that mediate the functions of genes1,2 and modulate comorbidities3-10, behaviors and drug treatments11. They represent an enormous potential resource for personalized, systemic and data-driven diagnosis, prevention, monitoring and treatment. However, the concept of using plasma proteins for individualized health assessment across many health conditions simultaneously has not been tested. Here, we show that plasma protein expression patterns strongly encode for multiple different health states, future disease risks and lifestyle behaviors. We developed and validated protein-phenotype models for 11 different health indicators: liver fat, kidney filtration, percentage body fat, visceral fat mass, lean body mass, cardiopulmonary fitness, physical activity, alcohol consumption, cigarette smoking, diabetes risk and primary cardiovascular event risk. The analyses were prospectively planned, documented and executed at scale on archived samples and clinical data, with a total of ~85 million protein measurements in 16,894 participants. Our proof-of-concept study demonstrates that protein expression patterns reliably encode for many different health issues, and that large-scale protein scanning12-16 coupled with machine learning is viable for the development and future simultaneous delivery of multiple measures of health. We anticipate that, with further validation and the addition of more protein-phenotype models, this approach could enable a single-source, individualized so-called liquid health check.

Real-time health monitoring through urine metabolomics

Current healthcare practices are reactive and based on limited physiological information collected months or years apart. By enabling patients and healthy consumers access to continuous measurements of health, wearable devices and digital medicine stand to realize highly personalized and preventative care. However, most current digital technologies provide information on a limited set of physiological traits, such as heart rate and step count, which alone offer little insight into the etiology of most diseases. Here we propose to integrate data from biohealth smartphone applications with continuous metabolic phenotypes derived from urine metabolites. This combination of molecular phenotypes with quantitative measurements of lifestyle reflect the biological consequences of human behavior in real time. We present data from an observational study involving two healthy subjects and discuss the challenges, opportunities, and implications of integrating this new layer of physiological information into digital medicine. Though our dataset is limited to two subjects, our analysis (also available through an interactive web-based visualization tool) provides an initial framework to monitor lifestyle factors, such as nutrition, drug metabolism, exercise, and sleep using urine metabolites.

Cooperating on Data: The Missing Element in Bringing Real Innovation to Europe's Healthcare Systems

Europe's growing awareness of gaps in its healthcare provision is not being matched by an increase in remedial action - despite the rich transformative potential of new approaches to data. The new availability of data offers policymakers tools that would allow Europe's huge investments in health to be far better spent, by being properly targeted. The result would be far better health for far more Europeans. But that requires a step that most European policymakers have not been ready to take. They need to cooperate so that the data can be shared and its full value realised. This paper explores the potential and the challenges that stand in the way of mobilising health data for wider health benefits. This paper goes on to summarise the results of a survey on how different components of the healthcare sector perceive the opportunities from mobilising data effectively, and the barriers to doing so. The responses demonstrated a widespread genuine will to promote research and innovation, and its take-up, for the betterment of healthcare. There was strong appreciation of the merits of data sharing and readiness - under the right circumstances - to share personal health data for research purposes and to undergo genetic sequencing. This paper also suggests the strategic direction that should influence policy formation. The solution can be found without changing the EU treaties, which already provide an adequate base for cooperation. Properly handled, the problems facing European healthcare can be turned into major assets for Europe and make it easier for citizens to have equal access to high-quality care through the meaningful use of digital innovations.

A Combinatorial Electrochemical Biosensor for Sweat Biomarker Benchmarking

Misclassification of an acute disease condition as chronic and vice versa by electrochemical sweat biomarker sensors can cause significant psychological, emotional, and financial stress among patients. To achieve higher accuracy in distinguishing between a chronic condition and an acute condition, there is a need to establish a reference biomarker to index the actual chronic disease biomarker of interest by combinatorial sensing. This work provides the first technological proof of leveraging the chloride ion content in sweat for a combinatorial sweat biomarker benchmarking scheme. In this scheme, the sweat chloride ion has been demonstrated as the reference/indexing biomarker, while sweat cortisol has been studied as the disease biomarker of interest. Label-free affinity biosensing is achieved by using a two-electrode electrochemical system on a flexible substrate suitable for wearable applications. The electrochemical stability of the fabricated electrodes for biosensing applications was studied by open-circuit potential measurements. Attenuated total reflectance-Fourier transform infrared spectroscopy spectra validate the crosslinker-antibody binding chemistry. Concentration-dependent analyte-capture probe binding induces a modulation in the electrical properties (charge transfer resistance and double-layer capacitance) at the electrode-sweat buffer interface, which are transduced by nonfaradaic electrochemical impedance spectroscopy (EIS). Calibration dose responses for the sensor for cortisol (5-200 ng/mL) and chloride (10-100 mM) detection were evaluated in synthetic (pH 6) and pooled human sweat (R² > 0.95). The variation in the cortisol sensor response due to fluctuations in sweat chloride levels and the significance of reporting normalized biomarker levels were demonstrated to further emphasize the need for biomarker benchmarking in electrochemical sensors.

Wearables Meet IoT: Synergistic Personal Area Networks (SPANs)

Wearable monitoring and mobile health (mHealth) revolutionized healthcare diagnostics and delivery, while the exponential increase of deployed "things" in the Internet of things (IoT) transforms our homes and industries. "Things" with embedded activity and vital sign sensors that we refer to as "smart stuff" can interact with wearable and ambient sensors. A dynamic, ad-hoc personal area network can span multiple domains and facilitate processing in synergistic personal area networks-SPANs. The synergy of information from multiple sensors can provide: (a) New information that cannot be generated from existing data alone, (b) user identification, (c) more robust assessment of physiological signals, and (d) automatic annotation of events/records. In this paper, we present possible new applications of SPANs and results of feasibility studies. Preliminary tests indicate that users interact with smart stuff-in our case, a smart water bottle-dozens of times a day and sufficiently long to collect vital signs of the users. Synergistic processing of sensors from the smartwatch and objects of everyday use may provide user identification and assessment of new parameters that individual sensors could not generate, such as pulse wave velocity (PWV) and blood pressure. As a result, SPANs facilitate seamless monitoring and annotation of vital signs dozens of times per day, every day, every time the smart object is used, without additional setup of sensors and initiation of measurements. SPANs creates a dynamic "opportunistic bubble" for ad-hoc integration with other sensors of interest around the user, wherever they go. Continuous long-term monitoring of user's activity and vital signs can provide better diagnostic procedures and personalized feedback to motivate a proactive approach to health and wellbeing.

A framework for analysing learning health systems: Are we removing the most impactful barriers?

INTRODUCTION

Learning health systems (LHS) are one of the major computing advances in health care. However, no prior research has systematically analysed barriers and facilitators for LHS. This paper presents an investigation into the barriers, benefits, and facilitating factors for LHS in order to create a basis for their successful implementation and adoption.

METHODS

First, the ITPOSMO-BBF framework was developed based on the established ITPOSMO (information, technology, processes, objectives, staffing, management, and other factors) framework, extending it for analysing barriers, benefits, and facilitators. Second, the new framework was applied to LHS.

RESULTS

We found that LHS shares similar barriers and facilitators with electronic health records (EHR); in particular, most facilitator effort in implementing EHR and LHS goes towards barriers categorised as human factors, even though they were seen to carry fewer benefits. Barriers whose resolution would bring significant benefits in safety, quality, and health outcomes remain.LHS envisage constant generation of new clinical knowledge and practice based on the central role of collections of EHR. Once LHS are constructed and operational, they trigger new data streams into the EHR. So LHS and EHR have a symbiotic relationship. The implementation and adoption of EHRs have proved and continues to prove challenging, and there are many lessons for LHS arising from these challenges.

CONCLUSIONS

Successful adoption of LHS should take account of the framework proposed in this paper, especially with respect to its focus on removing barriers that have the most impact.

Personalized treatment options for thyroid cancer: current perspectives

Thyroid cancer is one of the most common endocrine malignancies, with increasing incidence all over the world. In spite of good prognosis for differentiated thyroid carcinoma, for an unknown reason, about 5–10% of the patients, the cancer will show aggressive behavior, develop metastasis, and be refractory to treatment strategies like radioactive iodine. Regarding the genetic information, each thyroid cancer patient can be considered as an individual unique one, with unique genetic information. Contrary to standard chemotherapy drugs, target therapy components aim at one or more definite molecular pathway on cancer cells, so their selection is underlying patient’s genetic information. Nowadays, several mutations and rearrangements including BRAF, VEGF receptors, RET, and RET/PTC, KDR, KIT, PDGFRA, CD274, and JAK2 are taken into account for the therapeutic components like larotrectinib (TRK inhibitor), vemurafenib, sunitinib, sorafenib, selumetinib, and axitinib. With the new concept of personalized treatment of thyroid cancer diagnoses, planning treatment, finding out how well treatment will work, and estimating a prognosis has changed for the better over the last decade.

Susan G. Komen Big Data for Breast Cancer Initiative: How Patient Advocacy Organizations Can Facilitate Using Big Data to Improve Patient Outcomes

Integrating different types of data, including electronic health records, imaging data, administrative and claims databases, large data repositories, the Internet of Things, genomics, and other omics data, is both a challenge and an opportunity that must be tackled head on. We explore some of the challenges and opportunities in optimizing data integration to accelerate breast cancer discovery and improve patient outcomes. Susan G. Komen convened three meetings (2015, 2017, and 2018) with various stakeholders to discuss challenges, opportunities, and next steps to enhance the use of big data in the field of breast cancer. Meeting participants agreed that big data approaches can enhance the identification of better therapies, improve outcomes, reduce disparities, and optimize precision medicine. One challenge is that databases must be shared, linked with each other, standardized, and interoperable. Patients want to be active participants in research and their own care, and to control how their data are used. Many patients have privacy concerns and do not understand how sharing their data can help to effectively drive discovery. Public education is essential, and breast cancer researchers who are skilled in using and analyzing big data are needed. Patient advocacy groups can play multiple roles to help maximize and leverage big data to better serve patients. Komen is committed to educating patients on big data issues, encouraging data sharing by all stakeholders, assisting in training the next generation of data science breast cancer researchers, and funding research projects that will use real-life data in real time to revolutionize the way breast cancer is understood and treated.

Personalized laboratory medicine: a patient-centered future approach

In contrast to population-based medical decision making, which emphasizes the use of evidence-based treatment strategies for groups of patients, personalized medicine is based on optimizing treatment at the level of the individual patient. The creation of molecular profiles of individual patients was made possible by the advent of "omics" technologies, based on high throughput instrumental techniques in combination with biostatistics tools and artificial intelligence. The goal of personalized laboratory medicine is to use advanced technologies in the process of preventive, curative or palliative patient management. Personalized medicine does not rely on changes in concentration of a single molecular marker to make a therapeutic decision, but rather on changes of a profile of markers characterizing an individual patient's status, taking into account not only the expected response to treatment of the disease but also the expected response of the patient. Such medical approach promises a more effective diagnostics with more effective and safer treatment, as well as faster recovery and restoration of health and improved cost effectiveness. The laboratory medicine profession is aware of its key role in personalized medicine, but to empower the laboratories, at least an enhancement in cooperation between disciplines within laboratory medicine will be necessary.

Artificial intelligence as an emerging technology in the current care of neurological disorders

BACKGROUND

Artificial intelligence (AI) has influenced all aspects of human life and neurology is no exception to this growing trend. The aim of this paper is to guide medical practitioners on the relevant aspects of artificial intelligence, i.e., machine learning, and deep learning, to review the development of technological advancement equipped with AI, and to elucidate how machine learning can revolutionize the management of neurological diseases. This review focuses on unsupervised aspects of machine learning, and how these aspects could be applied to precision neurology to improve patient outcomes. We have mentioned various forms of available AI, prior research, outcomes, benefits and limitations of AI, effective accessibility and future of AI, keeping the current burden of neurological disorders in mind.

DISCUSSION

The smart device system to monitor tremors and to recognize its phenotypes for better outcomes of deep brain stimulation, applications evaluating fine motor functions, AI integrated electroencephalogram learning to diagnose epilepsy and psychological non-epileptic seizure, predict outcome of seizure surgeries, recognize patterns of autonomic instability to prevent sudden unexpected death in epilepsy (SUDEP), identify the pattern of complex algorithm in neuroimaging classifying cognitive impairment, differentiating and classifying concussion phenotypes, smartwatches monitoring atrial fibrillation to prevent strokes, and prediction of prognosis in dementia are unique examples of experimental utilizations of AI in the field of neurology. Though there are obvious limitations of AI, the general consensus among several nationwide studies is that this new technology has the ability to improve the prognosis of neurological disorders and as a result should become a staple in the medical community.

CONCLUSION

AI not only helps to analyze medical data in disease prevention, diagnosis, patient monitoring, and development of new protocols, but can also assist clinicians in dealing with voluminous data in a more accurate and efficient manner.

Effect of Telephone-Delivered Collaborative Goal Setting and Behavioral Activation vs Enhanced Usual Care for Depression Among Adults With Uncontrolled Diabetes: A Randomized Clinical Trial

IMPORTANCE

Depression symptoms are present in one-third of patients with diabetes, contributing to significant adverse consequences. Population screening of high-risk patients coupled with telephone delivery of evidence-based therapies for comorbid diabetes may address barriers to care.

OBJECTIVE

To evaluate the effectiveness of proactive population screening plus telephone delivery of a collaborative goal-setting intervention among high-risk patients with uncontrolled diabetes and depression.

DESIGN, SETTING, AND PARTICIPANTS

In this randomized clinical trial, 225 participants (intervention [n = 136] and control [n = 89]) were enrolled from a regional Veterans Healthcare System serving Southeast Texas from November 1, 2012, through June 24, 2016. Data were gathered at baseline and 6 and 12 months after intervention. Patients selected had uncontrolled diabetes (hemoglobin A1c [HbA1c] >7.5%]) and clinically significant depression (Patient Health Questionnaire-9 scores [PHQ-9] ≥10) and were living more than 20 miles from the Veterans Affairs medical center. Data collection was completed on December 6, 2016, and final analyses were completed by January 25, 2018. All analyses were intent to treat.

INTERVENTIONS

Healthy Outcomes Through Patient Empowerment (HOPE) included 9 telephone sessions with 24 trained health care professionals using collaborative goal-setting and behavioral activation methods. The control group received enhanced usual care (EUC) and notification of high-risk status.

MAIN OUTCOMES AND MEASURES

Change in depression symptoms using PHQ-9 and glycemic control using HbA1c from baseline to 6 months and to 12 months. Secondary analyses evaluated clinically significant responses for these measures.

RESULTS

Among 225 participants, 202 (89.8%) were men, the mean (SD) age was 61.9 (8.3) years, 145 (64.4%) were married, and 156 (69.3%) had some education beyond high school. For the overall study, 38 participants (16.9%) were lost to follow-up or withdrew at 6 months and another 21 (9.3%) were lost to follow-up or withdrew at 12 months. Repeated-measures analysis with multiple imputation for missing data assessing the interaction of treatment group (HOPE vs EUC) and time (baseline, 6 months, and 12 months) found no significant improvement in PHQ-9 (β, 1.56; 95% CI, -0.68 to 3.81; P = .17) or HbA1c (β, -0.005; 95% CI, -0.73 to 0.72; P = .82). Analyses using t test for change from baseline to 12 months showed a HOPE vs EUC between-group mean difference for PHQ-9 of 2.14 (95% CI, 0.18 to 4.10; P = .03) and for HbA1c of -0.06% (95% CI, -0.61% to 0.50%; P = .83). A secondary analysis of patients experiencing a clinical response found that 52.1% of HOPE participants had clinically significant responses in PHQ-9 at 12 months vs 32.9% in EUC (difference, 0.19; 95% CI, 0.04-0.33; P = .01).

CONCLUSIONS AND RELEVANCE

Telephone-delivered, collaborative goal setting produced clinically significant reductions in depression symptoms but not glycemic control among patients who remained engaged at 12 months compared with EUC among a population screened sample of high-risk patients with diabetes and depression. Although the intervention created some lasting effect for depression, additional strategies are needed to maintain engagement of this high-risk population within an interprofessional team approach to primary care.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT01572389.

Visual Analytics for the Representation, Exploration, and Analysis of High-Dimensional, Multi-faceted Medical Data

Medicine is among those research fields with a significant impact on humans and their health. Already for decades, medicine has established a tight coupling with the visualization domain, proving the importance of developing visualization techniques, designed exclusively for this research discipline. However, medical data is steadily increasing in complexity with the appearance of heterogeneous, multi-modal, multi-parametric, cohort or population, as well as uncertain data. To deal with this kind of complex data, the field of Visual Analytics has emerged. In this chapter, we discuss the many dimensions and facets of medical data. Based on this classification, we provide a general overview of state-of-the-art visualization systems and solutions dealing with high-dimensional, multi-faceted data. Our particular focus will be on multi-modal, multi-parametric data, on data from cohort or population studies and on uncertain data, especially with respect to Visual Analytics applications for the representation, exploration, and analysis of high-dimensional, multi-faceted medical data.

Contested futures: envisioning "Personalized," "Stratified," and "Precision" medicine

In recent years, discourses around "personalized," "stratified," and "precision" medicine have proliferated. These concepts broadly refer to the translational potential carried by new data-intensive biomedical research modes. Each describes expectations about the future of medicine and healthcare that data-intensive innovation promises to bring forth. The definitions and uses of the concepts are, however, plural, contested and characterized by diverse ideas about the kinds of futures that are desired and desirable. In this paper, we unpack key disputes around the "personalized," "stratified," and "precision" terms, and map the epistemic, political and economic contexts that structure them as well as the different roles attributed to patients and citizens in competing future imaginaries. We show the ethical and value baggage embedded within the promises that are manufactured through terminological choices and argue that the context and future-oriented nature of these choices helps to understanding how data-intensive biomedical innovations are made socially meaningful.

Personalized medicine for patients with COPD: where are we?

Chronic airflow limitation is the common denominator of patients with chronic obstructive pulmonary disease (COPD). However, it is not possible to predict morbidity and mortality of individual patients based on the degree of lung function impairment, nor does the degree of airflow limitation allow guidance regarding therapies. Over the last decades, understanding of the factors contributing to the heterogeneity of disease trajectories, clinical presentation, and response to existing therapies has greatly advanced. Indeed, diagnostic assessment and treatment algorithms for COPD have become more personalized. In addition to the pulmonary abnormalities and inhaler therapies, extra-pulmonary features and comorbidities have been studied and are considered essential components of comprehensive disease management, including lifestyle interventions. Despite these advances, predicting and/or modifying the course of the disease remains currently impossible, and selection of patients with a beneficial response to specific interventions is unsatisfactory. Consequently, non-response to pharmacologic and non-pharmacologic treatments is common, and many patients have refractory symptoms. Thus, there is an ongoing urgency for a more targeted and holistic management of the disease, incorporating the basic principles of P4 medicine (predictive, preventive, personalized, and participatory). This review describes the current status and unmet needs regarding personalized medicine for patients with COPD. Also, it proposes a systems medicine approach, integrating genetic, environmental, (micro)biological, and clinical factors in experimental and computational models in order to decipher the multilevel complexity of COPD. Ultimately, the acquired insights will enable the development of clinical decision support systems and advance personalized medicine for patients with COPD.

Health as the Moral Principle of Post-Genomic Society: Data-Driven Arguments Against Privacy and Autonomy

In Finland, as well as all over the globe, great weight is put on the possibilities of large data collections and ‘big data’ for generating economic growth, enhancing medical research, and boosting health and wellbeing in totally new ways. This massive data gathering and usage is justified by the moral principle of improving health. The imperative of health thus legitimizes data collection, new infrastructures and innovation policy. It is also supported by the rhetoric of health promotion. New arrangements in health research and innovations in the health sector are justified, as they produce health, while the moral principle of health also obligates individual persons to pursue healthy lifestyles and become healthy citizens. I examine how, in this context of Finnish data-driven medicine, arguments related to privacy and autonomy become silenced when contrasted with the moral principle of health.

Optimising mechanical ventilation through model-based methods and automation

Mechanical ventilation (MV) is a core life-support therapy for patients suffering from respiratory failure or acute respiratory distress syndrome (ARDS). Respiratory failure is a secondary outcome of a range of injuries and diseases, and results in almost half of all intensive care unit (ICU) patients receiving some form of MV. Funding the increasing demand for ICU is a major issue and MV, in particular, can double the cost per day due to significant patient variability, over-sedation, and the large amount of clinician time required for patient management. Reducing cost in this area requires both a decrease in the average duration of MV by improving care, and a reduction in clinical workload. Both could be achieved by safely automating all or part of MV care via model-based dynamic systems modelling and control methods are ideally suited to address these problems. This paper presents common lung models, and provides a vision for a more automated future and explores predictive capacity of some current models. This vision includes the use of model-based methods to gain real-time insight to patient condition, improve safety through the forward prediction of outcomes to changes in MV, and develop virtual patients for in-silico design and testing of clinical protocols. Finally, the use of dynamic systems models and system identification to guide therapy for improved personalised control of oxygenation and MV therapy in the ICU will be considered. Such methods are a major part of the future of medicine, which includes greater personalisation and predictive capacity to both optimise care and reduce costs. This review thus presents the state of the art in how dynamic systems and control methods can be applied to transform this core area of ICU medicine.

Validation of Four Smartwatches in Energy Expenditure and Heart Rate Assessment During Exergaming

Objective: Validated the Apple Watch (AW), Fitbit Surge HR (FS), TomTom Multisport Cardio Watch (TT), and Microsoft Band (MB) in energy expenditure (EE), average heart rate (HR), and peak HR assessment during exergaming. Materials and Methods: Twenty-one college students participated in this study in Spring 2016. A 20-minute boxing session was completed on the Nintendo^® Wii™. The AW and TT were placed on the left wrist and the FS and MB on the right. Each smartwatches' EE and HR data were compared with identical data provided by ActiGraph GT3X+-Bluetooth accelerometers and an associated ActiGraph HR strap. Results: Initial agreement was observed between the ActiGraph and: FS and TT EE (r = 0.62-0.69); AW, FS, and TT average HR (r = 0.47-0.74); and all smartwatches' peak HR (r = 0.59-0.65). However, post hoc comparisons indicated differences between the ActiGraph and: FS and TT EE measurements (P < 0.01) and MB average/peak HR measurements (P < 0.01). Low measurement bias/adequate precision observed for most smartwatches versus ActiGraph. Conclusions: Observations indicated smartwatch average/peak HR measurements as moderately valid. Smartwatch EE measurements were less valid.

Personalized medicine-concepts, technologies, and applications in inflammatory skin diseases

The current state, tools, and applications of personalized medicine with special emphasis on inflammatory skin diseases like psoriasis and atopic dermatitis are discussed. Inflammatory pathways are outlined as well as potential targets for monoclonal antibodies and small-molecule inhibitors.

Contemporary approaches for identifying individual risk for periodontitis

Key breakthroughs in our understanding of the etiology and principles of predictable treatment of patients with chronic periodontitis first emerged in the late 1960s and carried on into the mid-1980s. Unfortunately, some generalizations of the evidence led many to believe that periodontitis was a predictable result of exposure to bacterial plaque accumulations over time. For a brief period, the initial plaque concept was translated by some to implicate specific bacterial infections, with both concepts (plaque exposure and specific infection) being false assumptions that led to clinical outcomes which were frustrating to both the clinician and the patient. The primary misconceptions were that every individual was equally susceptible to periodontitis, that disease severity was a simple function of magnitude of bacterial exposure over time, and that all patients would respond predictably if treated based on the key principles of bacterial reduction and regular maintenance care. We now know that although bacteria are an essential initiating factor, the clinical severity of periodontitis is a complex multifactorial host response to the microbial challenge. The complexity comes from the permutations of different factors that may interact to alter a single individual's host response to challenge, inflammation resolution and repair, and overall outcome to therapy. Fortunately, although there are many permutations that may influence host response and repair, the pathophysiology of chronic periodontitis is generally limited to mild periodontitis with isolated moderate disease in most individuals. However, approximately 20%-25% of individuals will develop generalized severe periodontitis and probably require more intensive bacterial reduction and different approaches to host modulation of the inflammatory outcomes. This latter group may also have serious systemic implications of their periodontitis. The time appears to be appropriate to use what we know and currently understand to change our approach to clinical care. Our goal would be to increase our likelihood of identifying those patients who have a more biologically disruptive response combined with a more impactful microbial dysbiosis. Current evidence, albeit limited, indicates that for those individuals we should prevent and treat more intensively. This paper discusses what we know and how we might use that information to start individualizing risk and treat some of our patients in a more targeted manner. In my opinion, we are further along than many realize, but we have a great lack of prospective clinical evidence that must be accumulated while we continue to unravel the contributions of specific mechanisms.

Role of Digital Games in Self-Management of Cardiovascular Diseases: A Scoping Review

OBJECTIVE

Examine research on the use of digital games to improve self-management (SM) behaviors in patients diagnosed with cardiovascular diagnoses of hypertension, coronary artery disease, heart failure, or myocardial infarction.

MATERIALS AND METHODS

For this scoping review, the CINAHL, PubMed, and Web of Science databases were searched for studies published from January 1, 2008 to December 20, 2017 using terms relevant to digital games and cardiovascular diseases (CVDs).

RESULTS

Eight articles met the inclusion/exclusion criteria, seven of which presented studies with participants 50 years or older. Five of the eight studies assessed physical activity. Only two studies included a control group. Digital games significantly improved exercise capacity and energy expenditure but did not affect quality of life, self-efficacy, anxiety, or depression. Digital games were found enjoyable by 79%-93% of participants, including those with lower education or age; however, barriers to game use included being tired or bored, lack of interest in digital games, poor perception of fitness through games, sensor limitations, conflicts with daily life routine, and preferences for group exercise. Average adherence ranged from 70% to 100% over 2 weeks to 6 months of study duration, with higher adherence rates in studies that included human contact through supervision or social support.

CONCLUSION

Paucity of studies about digital games for CVD SM behaviors precludes the need to undertake a full systematic review. Future studies examining digital games should include larger sample sizes, longer durations, game-design guided by behavioral change theoretical frameworks, and CVD SM behaviors in addition to physical activity behaviors.