Business Analytics in Health Care Industry

Business Analytics in Health Care Industry 1
Business Analytics in Health Care Industry
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Business Analytics in Health Care Industry 2
Introduction
Hospital information systems represent a gold mine: healthcare facilities store and
process very large volumes of data which constitute an extremely rich reservoir, in particular of
clinical data: patient results, clinical trial data, genetic data, bioclinical data, pathological data,
pharmacy prescriptions, laboratory results, medical data of all kinds. The follow-up of patients at
home, for example, will enrich this reservoir of new data collected by the devices connected to
home patients. Modern technologies, smartphones, genomic chips, GPS sensors to measure the
movements or activity of patients, are the source of a huge amount of information usable in
epidemiology, information that would have been impossible to collect by traditional approaches
(Hawrylak, Schimke, Hale and Papa 2012). The data available cover a growing field: diet,
pollution in all its forms, lifestyle, lifestyle, travel, infections, drug treatments, stress, etc. The
total volume of e-health data in the world doubles every 73 days. This large volume of data
opens the field to expert systems. The paper will explore the emergence and adoption of business
analytics in health care industry
Business Analytics in health care industry
Today, modern medicine has become almost inconceivable without the use of digitized
personal data. The emergence of e-health, telemedicine, m-health, NBIC (nanotechnology,
biotechnology, informatics, and cognitive science), and data analytics are changing healthcare
delivery, doctor patient relationship, and scientific understanding of the human body and
diseases (Hawrylak, Schimke, Hale and Papa 2012). The time has now come to promote access
to this massive data in Health and the interoperability of information systems in order to set up
"clinical data centers" (referred to below as "bio-heterogeneous warehouses"), and to allow

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cross-referencing of health and research data that will allow multiparametric analyzes correlating
epidemiological, medical-technical, clinical, sensor-derived data (Marb 2015).
Taking all these data into account in epidemiological studies raises expectations and
hopes in terms of understanding the causes and mechanisms of diseases and the customization of
medical monitoring. Thanks to the predictivity offered by new tools implemented on Big Data, a
proactive practice of medicine is being developed, integrating the complex analysis on the
multiple available data: biological, pathological, their evolution, the environmental data
(Hawrylak, Schimke, Hale and Papa 2012). The cross-fertilization of all this information and the
elaborate calculations of indicators, will make it possible to orient the medicine towards
innovative therapeutic axes. These axes foreshadow the medicine of tomorrow: Predictive and
Preventive - acting early before the onset of symptoms - and which is also Personalizes and
Participative - adapting treatments and interventions to the characteristics and individual
reactions (Hawrylak, Schimke, Hale and Papa 2012).
Drivers
In the field of health, business analytics and big data corresponds to the set of socio-
demographic and health data, available from different sources that collect them for various
reasons. The exploitation of these data has many interests: identification of disease risk factors,
assistance with diagnosis, choice and monitoring of the effectiveness of treatments,
pharmacovigilance, epidemiology.
It must be admitted that the vastness of the knowledge necessary for the practice of
medicine in accordance with current scientific data can no longer be mastered by a single man
who no longer has the time necessary to update his knowledge during his professional life

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already busy (Marb 2015). One of the ways to deal with this complexity has been to divide
medicine into many specialties by placing the general practitioner as the pivot of care. Today,
alongside the traditional means of postgraduate education, the computer serves more and more
mnemonic prosthesis to the doctor and expert systems of decision support have developed since
the seventies, eighties. Today, knowledge bases and medical ontologies provide essential
assistance to physicians to access the latest advances in art and medical science. Since the 1980s,
they have been an essential tool for scientific progress in medicine. This development has been
considerably accelerated with the rise of the Internet since the early 1990s. Between 1967 and
2000, at least ten thousand medical decision support systems were proposed (Terwiesch 2009).
Perceived benefits
One of the perceived benefits of business analytics is that it minimizes risks and errors.
When recruiting patients for clinical trials, one of the major concerns is the risk of overlap, that
is to say, with two clinical trials for the same therapeutic indication but slightly different
subpopulations (Alexandrov et al., 2014). Big Data and business analytics techniques can
provide insight into the extent of this overlap and help determine if the tests will be in direct
competition. Where appropriate, the site and / or sample may be changed for both tests (Corley,
Cook, Mikler and Singh 2010). Risk plays a key role in safety analysis and the more information
about it is available, the more the safety of the test can be guaranteed. Currently, Big Data and
business analytics makes it possible to: Create Profit / Risk profiles that feed into risk
management plans; to control the risks associated with populations treated with certain
compounds or certain diseases in order to evaluate the potential effects; and to support ethical
decision-making - based on the known elements on the molecule tested, to decide whether the
treatment of patients with certain medical history is unethical (Colloc and Lery 1997).