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Featured Articles
Machine intelligence in the OR: optimizing the effectiveness of surgeons with IT tools
Medicine is undergoing a historic transition, moving away from a trial-and-error model of care, towards individualized treatment strategies based on patient-specific knowledge management of disease and treatment. Not only the biomedical systems sciences and engineering, mathematics, medical imaging and medical informatics but also the discipline of machine intelligence and in a wider sense computer assisted radiology and surgery (CARS) are enablers of this new paradigm. With an appropriate ICT platform, for example, medical workstations for domain-specific applications, they provide the methods and tools for knowledge management and specifically for a patient-specific medicine.
Important aspects of these dramatically evolving and ICT based methodologies and tools are possibilities for:
- Modelling of human organ systems, pathologies and clinical processes from scientifically based evidence, medical guidelines and data mining.
- Analysis (e.g. inferencing) and adaptation (e.g. domain-specific learning) of models from data and information gathered from specific patients through imaging and biosensors in order to generate knowledge models of patient-specific situations to improve accuracy of diagnosis and appropriateness of treatment processes.
- Higher quality of therapeutic interventions by means of real time integration of information in patient-specific models and therapeutic processes through computer assisted workflow, knowledge and decision management
With an appropriate employment of these methods and tools, they become enablers of intelligent infrastructures and processes in medical diagnosis and therapy, hopefully making complex situations and processes in healthcare more comprehensible, visible, reproducible, transparent and understandable for the human, i.e. for the physician and patient……
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Using joined-up data to transform healthcare services
Data sharing between healthcare organizations can improve clinical efficiency and enable better decision-making. This is essential at a time when health services in Europe are facing perhaps their greatest challenge to date. Providers must accommodate increasing demand for services due to ageing populations, the rising prevalence of chronic diseases and budgets being constrained. The ability to compare costs to benchmarks will be crucial for healthcare providers needing to maintain or improve standards of care and achieve cost reductions and efficiency targets without cutting frontline staff. However, it will be necessary to look at key performance indicators (KPIs) more from a clinical achievement perspective than an operational cost or activity-based perspective. Data collection and dissemination will also be central to achieving these aims, as hospitals and providers adopt a more collaborative and integrated approach to healthcare.
by Peter Osborne
Growth in healthcare spending per capita has slowed or fallen sharply in real terms in almost all European countries since 2010 [see Figure 1]. Previous to the economic crisis, it had been rising faster than the rest of the economy, according to the European Commission (EC) and Organization for Economic Co-operation and Development (OECD).
As a result, there is a pressing need to change the way healthcare services are commissioned and delivered. Although national health systems in Europe are diverse, with models varying from predominantly single payer health services, to systems of competing insurers and providers, there is a growing recognition that advanced analytics employed at every point of care will play a key role in advising on how to move forward.
According to analysts at Frost & Sullivan, hospitals and other healthcare providers will need to invest in advanced data analytics solutions to monitor end-to-end care delivery across a variety of settings, as well as provide comprehensive reporting on performance and quality measures to a variety of stakeholders.
Up until recently however, the healthcare sector has suffered from disjointed approaches to collecting data, with highly-fragmented systems used by various payers, providers, and government agencies. Furthermore, many have yet to adopt any form of analytical approach to the clinical, financial and administrative data they collate.
A recent Accenture survey of 3,700 physicians across eight countries globally found that the main barriers to health information exchange were: IT systems that are unable to
Transforming healthcare services with connected health innovation
Healthcare providers must find new ways to increase efficiency and quality of care. As an evolution of telehealth and telemedicine services, connected health is seeing new levels of capability being realized in areas such as user interfaces, storage, smartphones, low power connectivity, and data processing and analytics. When combined with medical sector advances around novel sensing and imaging technologies, as well as microfluidics, haptic feedback, and robotics, it delivers practical solutions to some of the most pressing healthcare issues.
by David Pettigrew
Ageing populations and the growing prevalence of chronic diseases are placing healthcare infrastructure under greater pressure than ever before. At a time of significant budgetary constraint, technology is proving a key force in combating these growing problems, particularly in the form of connected health.
Broadly defined as the use of technology to provide healthcare at a distance, connected health is an evolution from existing delivery models such as telehealth and telemedicine services. The latter are focused on the transmission of raw data between two locations
Checking those vital signs
Medical devices and equipment used to check for and monitor people