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Digital health and digital biomarkers – enabling value chains on health data

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Current Directions in Biomedical Engineering
From the journal Current Directions in Biomedical Engineering Volume 2 Issue 1

Abstract

Smart Devices, IoT and Co. are changing healthcare. The possibilities for pervasive sensing and analysis are fast increasing and new therapy concepts as well as business models are arising, related to the term “Digital Health”. The paper will give a brief overview on the history and ask, whether Digital Health is more than Telemedicine 4.0. Furthermore, we will ask for the prospective currency to participate in new preventive offer. Data is the new oil – Digital Biomarkers enable new value chains on health data and allow for the personalization of healthcare. We will present ongoing work of Fraunhofer ISST on a Digital Biomarker called beHealthy Health Score.

Keywords: digital biomarker; digital health; mobile health; pervasive sensing

1 Introduction

There is a recognizable change in healthcare – IoT, Smart Devices, Big Data and Co. are changing the way of living a healthy life. Digitalization is the forthcoming term for the integration of technology in existing processes to foster their efficacy, efficiency and effectivity. Focused on health one is talking about Digital Health [1].

Technologies enabling pervasive sensing and analysis within the healthcare sector are fast increasing. Smart Watches and other Smart Devices increase the amount of heterogeneous data by being able to collect sensor data (e.g. motion detection, location-based services, Bluetooth-connected devices) at any time and any place. The pervasiveness of such technologies and the overall availability of data require the thinking of new data driven therapy concepts and business models to ensure high quality and efficient patient centred treatment. Data could have an enormous not quantifiable value to enable more patient-centered preventive and therapeutic offers [2].

Besides the potentiality of digitalization to change our way of thinking Becker et al. [3] pointed out, that a number of challenges need to be adequately addressed: From a psychological perspective, high attrition rates, digital divide of society, and intellectual capabilities of the users are key issues when implementing such technologies. Sriram et al. [4] completed from the technical perspective the necessity to ensure high data quality. But, from a user’s point of view data quality is an assumed requirement.

Within this paper we’ll give an overview on Digital Health, its definition and challenges to ensure high quality treatment. Later on the paper will introduce the term and meaning of Digital Biomarkers as a way to enable value chains and business models on health data. At last the paper will show case study results of beHealthy a PPG-based app to deduce a health score.

2 Digital health

A bird’s eye view into the history of the term Digital Health is required to answer the question: Is Digital Health some kind of Telemedicine 4.0 or even more (see Figure 1)?

Figure 1 From telematics to Digital Health – A brief history.
Figure 1

From telematics to Digital Health – A brief history.

Starting in the early 70s telematics was the leading expression for the fusion of telecommunication and informatics to exchange data between at least two different information systems. Focused on health the World Health Organization (WHO) introduced the term telemedicine as the “delivery of health care services, where distance is a critical factor […] using information and communication technologies (ICT) […]” [5].

As time went by, especially the distance as well as the data exchanging and consuming parties got discussed within the community. The usage of ICT shouldn’t be limited to the therapeutic parts anymore and the patient should obtain an active part within his treatment. As a conclusion the European Commission decided to introduce the term E-Health to pay attention to the increasing amount of ICT services for the improvement of prevention, diagnosis, treatment, monitoring and management as well as the access to care and quality of care [6].

The ongoing establishment of mobile devices, e.g. smart phones, led to the definition of mHealth defined by the WHO as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices” [7]. Thus, “mobile Health” or “mHealth,” broadly can be defined as medical or public health practice supported by mobile devices [8], encompassing a variety of contexts: use of mobile phones to improve point of service data collection, care delivery, patient communication, use of alternative wireless devices for real-time medication monitoring, and adherence support [9].

Starting from 2015, the new term Digital Health tries to express the convergence of the digital as well as the genomic-proteomic revolution within healthcare, taking also everyday life and social aspects into account, like mentioned by Sonnier. The aim is to foster and increase the degree of self-responsibility of individuals for a healthy living. Furthermore, also aspects like efficiency, efficacy and effectivity as well as quality are taken into account to ensure a personalized and high-quality medical treatment [1], [10].

Regarding the question Telemedicine 4.0 versus Digital Health one can resume, that health and digitalization within the 21. Century are going far beyond the initial definition of telemedicine. Large and well known internet companies show how digitalization and data processing can turn health supply upside down. They are the key drivers for digital innovations using gadgets to realize self-reflexive and individualized preventive healthcare. Former patients are getting more and more customers, asking for digital health promotion solutions.

Thus, talking about Digital Health implies to talk about customers’ needs or business models. The actual German market survey of the D21 Initiative [11] shows an increased usage of digital products and digital infrastructures: 53.9% of the Germans over 14 years used mobile internet. Also the usage of mobile devices increased (tablets 2015: 35%, 2014: 28%; smartphones 2015: 60%, 2014: 53%). Many other analyses and forecasts have been realized by organizations like PwC, Frost & Sullivan as well as GSMA. With more than 6 billion mobile phone subscribers, it is estimated that 75% of the world population has access to mobile communication [7], [12], [13]. Probably more than a third or 500 million of the global smartphone users will have installed some type of mobile medical application in 2015 and it is expected that more than 50% of them have downloaded at least one mobile health application in 2018 [14]. In 2017 mHealth could potentially save a total of €99 billion (€69 billion on prevention, €32 billion on treatment/monitoring) in healthcare costs in the EU [12]. By 2016, there will be a projected 10 billion mobile devices in use worldwide [15]. The number of devices with broadband capabilities increased to more than 1 billion worldwide [13].

New preventive offers are more and more related to self-paying patients. But what would be the “currency”? Data is the new oil was postulated by Gerhardt [16]. In the following we’ll discuss the opportunity to use highly distributed, heterogeneous data for the deduction of Digital Biomarkers to personalize therapeutic offers and enable new business models.

3 Digital biomarkers

Today, the diagnosis and the follow-up within therapy are often accomplished by using well known time and money consuming biomarkers. They can be defined as objective, quantifiable characteristics of biological processes and have some relations to medical signs, symptoms, surrogate endpoints or clinical endpoints [17]. Looking at Digital Health and the usage of smart devices enabling a pervasive sensing of surrounding parameters like vital signs, there is an ongoing research on how heterogeneous data can be accessed to deduce equivalents to traditional biomarkers.

The increasing data volumes, the variety of data as well as the velocity of data input and output are typical challenges [18]. Bonnie [19] stated that healthcare data will explode from 500 petabytes in 2012 up to 25,000 petabytes in 2016, also forced by the extensive usage of IP-based devices. Nowadays, a forthcoming term to describe the increasing amount of heterogeneous data and the need for integrated data analysis technology is Big Data. There is no common definition of the term but Gartner proposed its 3V’s model to describe todays situation in information society [18]: Increasing data volumes demand for new storage and analysis technologies. The challenge is to transform the variety of data available into decision supporting information and cope with the velocity.

The diversity of data types and information has the potential to optimize and personalize treatment or therapy concepts by identifying critical points for intervention. A detection of critical situations in earlier stages can result in a more easy and effective treatment [20]. With respect to biomarkers, the forthcoming term to bring relevant situations in health data and patients general conditions together is called Digital Biomarker. Great work on this term has been done by RockHealth within their article “The Emerging Influence of Digital Biomarkers on Healthcare defining Digital Biomarkers as “consumer-generated physiological and behavioral measures collected through connected digital tools” [21].

Traditional biomarkers like blood aim to determine a biological marker of illness whilst digital markers are more focused on the continuous assessment of phenotypic markers of illness. Digital Biomarkers can benefit from a longitudinal data collection, retrospectively processing the data to forecast possible critical or relevant medical situations. Furthermore, a digital system has the potential to react on a given situation in terms of intervention [20], [21].

The digitalization of health and the possibility to deduce relevant medical situations by processing data enables new business models. In Germany, a not inconsiderable part of health insurances are evaluating the usage of digital products to implement new reimbursement models. Data are striking promotors for new business models. Contrary to the value chain defined by Porter [22] a revision is needed to pay more attention to the data preparation process [23], [24], [25]. Furthermore, the patient or citizen wants to benefit from the monetization of his data [26]. Besides “willingness to accept” or “willingness to pay”, the immaterial subject “Data” should be materialized [27].

4 beHealthy health score

The ongoing work of beHealthy of Fraunhofer ISST is based upon the idea of aggregating data up to a single health score to realize a Digital Biomarker for the overall medical conditions [28]. To realize a pervasive sensing easy to use technology is required to measure e.g. vital signs like heart rate variability, blood pressure and resting pulse rate. A bunch of technologies like body-worn accelerometers are widespread used caused by a low financial entrance but any additional sensor a) costs money and b) has to worn every time or carried by the user. Fraunhofer ISST decided for the usage of photoplethysmography (PPG). PPG is a method that allows a low-cost and large-area health screening and a health assessment. It detects blood volume changes in the microvascular bed of tissue [29]. According to Tamura et al. [30] are red and infrared (IR) light-emitting diodes (LEDs) commonly used as the light source. The PPG sensor monitors changes in the light intensity via reflection from or transmission through the tissue. The changes in light intensity are associated with small variations in blood perfusion of the tissue and provide information on the cardiovascular system, in particular, the pulse rate. This concept offers a cheap solution to observe the vital signs of a patient 24/7. The combination of PPG and the smartphone boost is the ideal foundation for delivering vital signs for pervasive sensing and this creates new assignments, such as the prevention of stress disorders, the screening of risk groups or fitness optimization of athletes by themselves.

The beHealthy app (see Figure 2) allows the measurement of vital signs using PPG, connecting external sensors (e.g. pulse oximeter) as well as entering data manually (especially the subjective well-being). Methods of Machine Learning and Data Mining are used to identify patterns characterizing the health situation of a user bringing physical and psychological aspects together. Several parameters, e.g. Heart Rate Variability, are well-known to support the objectification of psychological states of a human being. With the actual state of evaluation we can show that PPG can be sufficient to approximate vital signs. beHealthy allows the measurement of equivalents for pulse, heart rate, HRV and blood pressure (pulse transit time). Compared to certified medical sensors pulse and heart rate can be measured with a deviation of < 5%, HRV < 10% and blood pressure < 15% under controlled conditions (e.g. smartphone type restriction). Further work will focus on the optimization of the signal processing chain, especially to prevent noise during the measurement of blood pressure. Although the number of subjects to evaluate the correlation between objective and subjective well-being expressed as health score was limited to 10 persons (see Figure 3).

Figure 2 beHealthy app.
Figure 2

beHealthy app.

Figure 3 Evaluation of health score (n = 5).
Figure 3

Evaluation of health score (n = 5).

Author’s Statement

Research funding: The author state no funding involved. Conflict of interest: Authors state no conflict of interest. Material and Methods: Informed consent: Informed consent is not applicable. Ethical approval: The conducted research is not related to either human or animal use.

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Published Online: 2016-9-30
Published in Print: 2016-9-1

©2016 Sven Meister et al., licensee De Gruyter.

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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https://doi.org/10.1515/cdbme-2016-0128
Keywords for this article
digital biomarker; digital health; mobile health; pervasive sensing
Creative Commons
BY-NC-ND 4.0

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  1. Synthesis and characterization of PIL/pNIPAAm hybrid hydrogels
  2. Novel blood protein based scaffolds for cardiovascular tissue engineering
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  32. Prediction of immediately occurring microsleep events from brain electric signals
  33. Determining cardiac vagal threshold from short term heart rate complexity
  34. Classification of cardiac excitation patterns during atrial fibrillation
  35. An algorithm to automatically determine the cycle length coverage to identify rotational activity during atrial fibrillation – a simulation study
  36. Deriving respiration from high resolution 12-channel-ECG during cycling exercise
  37. Reducing of gradient induced artifacts on the ECG signal during MRI examinations using Wilcoxon filter
  38. Automatic detection and mapping of double potentials in intracardiac electrograms
  39. Modeling the pelvic region for non-invasive pelvic intraoperative neuromonitoring
  40. Postprocessing algorithm for automated analysis of pelvic intraoperative neuromonitoring signals
  41. Best practice: surgeon driven application in pelvic operations
  42. Vasomotor assessment by camera-based photoplethysmography
  43. Classification of morphologic changes in photoplethysmographic waveforms
  44. Novel computation of pulse transit time from multi-channel PPG signals by wavelet transform
  45. Efficient design of FIR filter based low-pass differentiators for biomedical signal processing
  46. Nonlinear causal influences assessed by mutual compression entropy
  47. Comparative study of methods for solving the correspondence problem in EMD applications
  48. fNIRS for future use in auditory diagnostics
  49. Semi-automated detection of fractional shortening in zebrafish embryo heart videos
  50. Blood pressure measurement on the cheek
  51. Derivation of the respiratory rate from directly and indirectly measured respiratory signals using autocorrelation
  52. Left cardiac atrioventricular delay and inter-ventricular delay in cardiac resynchronization therapy responder and non-responder
  53. An automatic systolic peak detector of blood pressure waveforms using 4th order cumulants
  54. Real-time QRS detection using integrated variance for ECG gated cardiac MRI
  55. Preprocessing of unipolar signals acquired by a novel intracardiac mapping system
  56. In-vitro experiments to characterize ventricular electromechanics
  57. Continuous non-invasive monitoring of blood pressure in the operating room: a cuffless optical technology at the fingertip
  58. Application of microwave sensor technology in cardiovascular disease for plaque detection
  59. Artificial blood circulatory and special Ultrasound Doppler probes for detecting and sizing gaseous embolism
  60. Detection of microsleep events in a car driving simulation study using electrocardiographic features
  61. A method to determine the kink resistance of stents and stent delivery systems according to international standards
  62. Comparison of stented bifurcation and straight vessel 3D-simulation with a prior simulated velocity profile inlet
  63. Transient Euler-Lagrange/DEM simulation of stent thrombosis
  64. Automated control of the laser welding process of heart valve scaffolds
  65. Automation of a test bench for accessing the bendability of electrospun vascular grafts
  66. Influence of storage conditions on the release of growth factors in platelet-rich blood derivatives
  67. Cryopreservation of cells using defined serum-free cryoprotective agents
  68. New bioreactor vessel for tissue engineering of human nasal septal chondrocytes
  69. Determination of the membrane hydraulic permeability of MSCs
  70. Climate retainment in carbon dioxide incubators
  71. Multiple factors influencing OR ventilation system effectiveness
  72. Evaluation of an app-based stress protocol
  73. Medication process in Styrian hospitals
  74. Control tower to surgical theater
  75. Development of a skull phantom for the assessment of implant X-ray visibility
  76. Surgical navigation with QR codes
  77. Investigation of the pressure gradient of embolic protection devices
  78. Computer assistance in femoral derotation osteotomy: a bottom-up approach
  79. Automatic depth scanning system for 3D infrared thermography
  80. A service for monitoring the quality of intraoperative cone beam CT images
  81. Resectoscope with an easy to use twist mechanism for improved handling
  82. In vitro simulation of distribution processes following intramuscular injection
  83. Adjusting inkjet printhead parameters to deposit drugs into micro-sized reservoirs
  84. A flexible standalone system with integrated sensor feedback for multi-pad electrode FES of the hand
  85. Smart control for functional electrical stimulation with optimal pulse intensity
  86. Tactile display on the remaining hand for unilateral hand amputees
  87. Effects of sustained electrical stimulation on spasticity assessed by the pendulum test
  88. An improved tracking framework for ultrasound probe localization in image-guided radiosurgery
  89. Improvement of a subviral particle tracker by the use of a LAP-Kalman-algorithm
  90. Learning discriminative classification models for grading anal intraepithelial neoplasia
  91. Regularization of EIT reconstruction based on multi-scales wavelet transforms
  92. Assessing MRI susceptibility artefact through an indicator of image distortion
  93. EyeGuidance – a computer controlled system to guide eye movements
  94. A framework for feedback-based segmentation of 3D image stacks
  95. Doppler optical coherence tomography as a promising tool for detecting fluid in the human middle ear
  96. 3D Local in vivo Environment (LivE) imaging for single cell protein analysis of bone tissue
  97. Inside-Out access strategy using new trans-vascular catheter approach
  98. US/MRI fusion with new optical tracking and marker approach for interventional procedures inside the MRI suite
  99. Impact of different registration methods in MEG source analysis
  100. 3D segmentation of thyroid ultrasound images using active contours
  101. Designing a compact MRI motion phantom
  102. Cerebral cortex classification by conditional random fields applied to intraoperative thermal imaging
  103. Classification of indirect immunofluorescence images using thresholded local binary count features
  104. Analysis of muscle fatigue conditions using time-frequency images and GLCM features
  105. Numerical evaluation of image parameters of ETR-1
  106. Fabrication of a compliant phantom of the human aortic arch for use in Particle Image Velocimetry (PIV) experimentation
  107. Effect of the number of electrodes on the reconstructed lung shape in electrical impedance tomography
  108. Hardware dependencies of GPU-accelerated beamformer performances for microwave breast cancer detection
  109. Computer assisted assessment of progressing osteoradionecrosis of the jaw for clinical diagnosis and treatment
  110. Evaluation of reconstruction parameters of electrical impedance tomography on aorta detection during saline bolus injection
  111. Evaluation of open-source software for the lung segmentation
  112. Automatic determination of lung features of CF patients in CT scans
  113. Image analysis of self-organized multicellular patterns
  114. Effect of key parameters on synthesis of superparamagnetic nanoparticles (SPIONs)
  115. Radiopacity assessment of neurovascular implants
  116. Development of a desiccant based dielectric for monitoring humidity conditions in miniaturized hermetic implantable packages
  117. Development of an artifact-free aneurysm clip
  118. Enhancing the regeneration of bone defects by alkalizing the peri-implant zone – an in vitro approach
  119. Rapid prototyping of replica knee implants for in vitro testing
  120. Protecting ultra- and hyperhydrophilic implant surfaces in dry state from loss of wettability
  121. Advanced wettability analysis of implant surfaces
  122. Patient-specific hip prostheses designed by surgeons
  123. Plasma treatment on novel carbon fiber reinforced PEEK cages to enhance bioactivity
  124. Wear of a total intervertebral disc prosthesis
  125. Digital health and digital biomarkers – enabling value chains on health data
  126. Usability in the lifecycle of medical software development
  127. Influence of different test gases in a non-destructive 100% quality control system for medical devices
  128. Device development guided by user satisfaction survey on auricular vagus nerve stimulation
  129. Empirical assessment of the time course of innovation in biomedical engineering: first results of a comparative approach
  130. Effect of left atrial hypertrophy on P-wave morphology in a computational model
  131. Simulation of intracardiac electrograms around acute ablation lesions
  132. Parametrization of activation based cardiac electrophysiology models using bidomain model simulations
  133. Assessment of nasal resistance using computational fluid dynamics
  134. Resistance in a non-linear autoregressive model of pulmonary mechanics
  135. Inspiratory and expiratory elastance in a non-linear autoregressive model of pulmonary mechanics
  136. Determination of regional lung function in cystic fibrosis using electrical impedance tomography
  137. Development of parietal bone surrogates for parietal graft lift training
  138. Numerical simulation of mechanically stimulated bone remodelling
  139. Conversion of engineering stresses to Cauchy stresses in tensile and compression tests of thermoplastic polymers
  140. Numerical examinations of simplified spondylodesis models concerning energy absorption in magnetic resonance imaging
  141. Principle study on the signal connection at transabdominal fetal pulse oximetry
  142. Influence of Siluron® insertion on model drug distribution in the simulated vitreous body
  143. Evaluating different approaches to identify a three parameter gas exchange model
  144. Effects of fibrosis on the extracellular potential based on 3D reconstructions from histological sections of heart tissue
  145. From imaging to hemodynamics – how reconstruction kernels influence the blood flow predictions in intracranial aneurysms
  146. Flow optimised design of a novel point-of-care diagnostic device for the detection of disease specific biomarkers
  147. Improved FPGA controlled artificial vascular system for plethysmographic measurements
  148. Minimally spaced electrode positions for multi-functional chest sensors: ECG and respiratory signal estimation
  149. Automated detection of alveolar arches for nasoalveolar molding in cleft lip and palate treatment
  150. Control scheme selection in human-machine- interfaces by analysis of activity signals
  151. Event-based sampling for reducing communication load in realtime human motion analysis by wireless inertial sensor networks
  152. Automatic pairing of inertial sensors to lower limb segments – a plug-and-play approach
  153. Contactless respiratory monitoring system for magnetic resonance imaging applications using a laser range sensor
  154. Interactive monitoring system for visual respiratory biofeedback
  155. Development of a low-cost senor based aid for visually impaired people
  156. Patient assistive system for the shoulder joint
  157. A passive beating heart setup for interventional cardiology training
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