GLOBAL HOME HEALTHCARE MARKET 2015-2021 (USD BILLION)
2.3.3. Restructuring healthcare delivery systems, from supply-driven to patient-orientedsupply-driven to patient-oriented
2.3.3.1.
VisionToday healthcare costs are mainly based on demanding fixed prices for fixed, predefined, treatments at moments when the health problem unavoidably disturbs the life of the patient. Large amounts of images will be combined with other sensor data and biomedical models to get precise, quantified information about the person’s health condition.
Low–latency, massive image processing is the main information source for AI based automation, visualisation and decision support within the whole care cycle. Precise quantified imaging is needed at many levels: from molecular imaging up to whole body imaging. Additionally, imaging will be improved to become less harmful and less expensive.
For chronic patients, the costs are determined by long-term pharmaceutical prescriptions and irregular treatments, when the disease gets severe. However, patient centric healthcare demands prevention, early diagnosis before suffering, and continuous care in the case of (multiple) chronic diseases. Outcome based healthcare will predict the outcome of a disorder and its treatment for a patient much faster. At its core is maximising value for patients: that is, achieving the best outcomes at the lowest cost 51. Not the number of treatments but the optimal value for the patients will become the driver of care. In addition, care centres will not be organised according to general hospitals that serve any disease. Instead, they will specialise to Ò
51 https://hbr.org/2013/10/the-strategy-that-will-fix-health-care
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specified disease types, to improve the total outcome. These centres will act not only for visiting patients but, increasingly, through remote access, for patients anywhere in the world.
2.3.3.2.
Scope and ambitionThe goal of outcome based healthcare is to improve value for patients, without increasing the costs, and preferably lowering the costs. This demands that reimbursement schemes have to be changed, allowing more cost-effective care involving prevention, early detection and treatment, and continuous monitoring of chronically ill patients. Outcome based healthcare will learn and adopt optimisation practices common in industry. The ambition is that outcome based healthcare will improve the predictability of diagnosis and treatment.
2.3.3.3.
High priority R&D&I areas Holistic healthcare involving all imbalanced health situations of the patient
Use of the (growing) whole body of medical knowledge during diagnosis, (image-guided) treatment and monitoring
EHR involving patient health models supporting precise communication between different care givers
EHR involving health models that exactly describe the outcome heath values for the patients, both short and long term
Transform large healthcare systems to optimise hospital workflow, automatically optimise diagnostic imaging and tracking of therapy results, enable preventive maintenance and generation of requirements and test cases for new generations of systems
Predictable and repeatable outcome of diagnostic imaging. Current diagnostic imaging is often of a qualitative nature, meaning that comparison over time or with other patient cases is impossible
Apply generic standards (e.g. industry 4.0) to diagnostic and therapy systems and use of big data principles to reduce cost of ownership
Create and apply biomedical models for AI based automation, visualisation and decision support, to get precise, quantified information of the person’s health condition. This needs large amounts of images and other sensor data at many levels: from molecular imaging up to whole body imaging
Less harmful and less expensive imaging modalities at several levels: from molecular imaging up to whole body imaging, in the prevention, diagnosis, therapy and monitoring phases
Humanoid robots applying interpreted human body language and emotion in care delivery
Robotics to improve treatments either in the operating room, minimal invasively inside the body, at general practitioners or at home
3D Printing and CNC (Computerised Numerical Control machining): printing implants and prosthetics for individuals, create patient-specific anatomical models, e.g. create powered exoskeleton to help paraplegics to walk again
2.3.3.4.
Competitive situationTwo of the three major diagnostic imaging and image-guided intervention companies (Philips and Siemens) are based in Europe. Competition from China (e.g. United Imaging) and Korea (Samsung) is emerging, driving the need for faster innovation at lower cost in Europe.
2.3.3.5.
Expected achievements Healthcare delivery for patients with co-morbidities
Preventive and early warnings for (combine) diseases
Image analysis and decision support for diagnosis, treatment and monitoring, using large medical knowledge bases
Quantitative, less harmful and less expensive imaging for diagnosis and therapy
Patient health models on complex heath conditions
Outcome based treatment, diagnosis and monitoring health models
Healthcare systems, IoT with big data learning for optimising workflow, usage, capabilities and maintenance
Repeatable and quantifiable outcome of diagnosis (including the use of biomarkers) and treatment
2.3.4.
Engaging individuals more actively in their own health and wellbeing2.3.4.1.
VisionIn 2030, digitalisation will be common in our society, and will bring healthcare from clinical centres into the everyday life of the citizen, in health and vitality promotion as much as for healthcare and disease prevention.
Highly motivated individuals will improve their vitality by using wearables and connected software, thus enabling adjustment of personalised models that make them increasingly aware about the impact of body movement, food and nutrition on their health. The development of digital health ecosystems (comprising digital health platforms, health monitoring wearables and devices, mobile applications and online services) will empower individuals to monitor against a norm, manage, track and improve their own health. This will open new markets of solutions and services directly targeted at both healthy and patient individuals, and positively impact the effect of preventive practices as well as on the application of treatments earlier.
2.3.4.2.
Scope and ambitionA motivated and educated population will take preventive measures. Collection of long-term data will contribute to early capture of a disease or disorder, which will increase probability of fast and successful treatment.
The ecosystem of healthcare solutions targeted at individuals is growing fast. The competition is pushing the improvement of features provided and quality of service as well as the specialisation. Smart algorithms for different purposes will be developed and specialised marketplaces will emerge; the demand and supply of data analytics will push the creation and sharing of data sources; precision medicine will follow the two previous building blocks.
2.3.4.3.
High priority R&D&I areas Wearables or minimally invasive implants, Internet of Things, simple analysers for home use;
reliable data collection and analysis – focus on input data quality assessment (we need to know whether we evaluate useful data or noise and artefacts); standardisation of calibration, process interoperability
Devices or systems for utilising/extracting/sharing new knowledge in the most informative and efficient manner (e.g. vitality data, molecular profiling, biotechnology, diagnostics, ICT tools) in the most appropriate personalised setting (e.g. healthcare system, at home)
Devices or systems for protecting and enforcing individual health-related information: ownership and secure storage of health data, data sharing with healthcare providers, and rendering real-time anonymity for wider data analytics
Devices or systems improving security for executing transactions in healthcare and wellbeing, like blockchains to improve health or personal records exchanges and interact with stakeholders
Devices or systems for integration of health and prevention ICT solutions in national health systems.
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2.3.4.4.
Competitive situationA major development is the rapid development in the wearable technology and devices market. According to a research report conducted by Transparency Market Research, the wearable devices market, or the remote patient monitoring devices market, is anticipated to reach USD 0.98 billion by the end of 2020. This represents 14.2% CAGR. Top players are Biotricity Inc., Abbott Laboratories, Apple, Alphabet, Business Machines Corp.
Three groups are fighting a war for control of the “healthcare value chain”:
One group comprises “traditional innovators” – pharmaceutical firms, hospitals and medical-technology companies such as GE Healthcare, Siemens, Medtronic and Philips.
A second category is made up of “incumbent players”, which include health insurers, pharmacy-benefit managers (which buy drugs in bulk), and single-payer healthcare systems such as UK NHS.
The third group are the technology “insurgents”, including Google, Apple, Amazon and a host of hungry entrepreneurs that are creating apps, predictive-diagnostics systems and new devices.
These firms may well profit most handsomely from the shift to digital.
2.3.4.5.
Expected achievements Repeatable and quantifiable outcome of vitality and prevention.
Early diagnostics based on assessment of longitudinal patient data.
New models of person-centred health delivery, also integrating health and social care and considering the environment and community setting of the individual. Transition to a decentralised model, from traditional healthcare venues like hospitals to integrated care models (e.g. transfer of records to patients);
Empowerment of the individual to manage his data: individuals taking greater ownership of his/
her state of health, especially for those with chronic conditions.