Big data in medical science and healthcare management

Peter Langkafel (Ed.) Big Data in Medical Science and Healthcare Management Also of interest Advanced Data Management Lena Wiese, 2015 ISBN 978-3-11-044140-6, e-ISBN 978-3-11-044141-3, e-ISBN (EPUB) 978-3-11-043307-4 Healthcare Mario Glowik, Slawomir Smyczek (Eds.), 2015 ISBN 978-3-11-041468-4, e-ISBN 978-3-11-041484-4, e-ISBN (EPUB) 978-3-11-041491-2, Set-ISBN 978-3-11-041485-1 Data Mining Jürgen Cleve, Uwe Lämmel, 2014 ISBN 978-3-486-71391-6, e-ISBN 978-3-486-72034-1, e-ISBN (EPUB) 978-3-486-99071-3 Sebastian Müller BIG DATA ANALYSEN PROGRAMMIEREN IN DER CLOUD SOFTWARETECHNIK Big Data Analysen Sebastian Müller, 2017 ISBN 978-3-11-045552-6, e-ISBN 978-3-11-045780-3, e-ISBN (EPUB) 978-3-11-045561-8, Set-ISBN 978-3-11-045781-0 Big Data in Medical Science and Healthcare Management  Diagnosis, Therapy, Side Effects Published by Dr med Peter Langkafel, MBA ISBN 978-3-11-044528-2 e-ISBN (PDF) 978-3-11-044574-9 Set-ISBN 978-3-11-044575-6 Library of Congress Cataloging-in-Publication Data A CIP catalogue record for this book has been applied for at the Library of Congress Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at http://dnb.dnb.de With kind permission of medhochzwei Verlag The german edition of Langkafel, Peter (Ed.), “Big Data in Medizin und Gesundheitswirtschaft – Diagnosen, Therapien, Nebenwirkungen” has been published by medhochzwei Verlag, Alte Eppelheimer Straße 42/1, 69115 Heidelberg, Germany © 2014 medhochzwei Verlag GmbH, Heidelberg www.medhochzwei-verlag.de Translation: Thorsten D Lonishen, CL-Communication GmbH, Germany: www.cl-communication.com © 2016 Walter de Gruyter GmbH, Berlin/Boston Cover image: Shironosov/iStock/Thinkstock Typesetting: Lumina Datamatics Printing and binding: CPI books GmbH, Leck ♾ Printed on acid free paper Printed in Germany www.degruyter.com For Gudrun, Arved, Napurga and Anio You are my best Big Datas! Autopilot and “Doctor Algorithm”? The first flight with the help of an autopilot was shown at the world exhibition in Paris in the year 1914: Both the vertical as well as the horizontal directions of flight were controlled by two gyroscopes These were driven by a wind-powered generator located behind the propeller What was a fascinating novelty at the time is today taken for granted Nowadays, airline pilots on an average flight actually pilot the plane by themselves for only about minutes.¹ The rest is controlled by modern automation using a variety of sensors and onboard computers Most readers will probably not pilot modern aircrafts very often – but autopilots in modern cars have already become a part of daily life, and these days most drivers will no longer want to without them Initially, digital maps may have led to some bizarre accidents – such as driving into a lake on a clear day But in modern passenger cars Big Data is taking on an ever increasing part of driving behavior: speed, distance, braking behavior, directional stability, etc The range of auxiliary functions is constantly growing, which is also true for their acceptance A modern car today incorporates more information technology than the Apollo rocket, which enabled a manned mission to the moon in 1969 Medicine has evolved over the last 100 years – but medical autopilots are not available, or should we say not yet available? For some, this may be an inspiring vision of the future – for others the damnable end of treatment with a “human touch.” Particularly in the field of medicine, a huge, unmanageable amount of information is generated every day – but computers as coaches and not as data servants: Is that vision a long way off or have we almost reached it already? The issue concerns much more than mere technical feasibility If we want to understand Big Data in medicine, we have to take a broader view of the matter In this book, more than 20 different experts, from the broader field of and around medicine, have written articles concerning completely different aspects of the issue – thus including the conditions and possibilities of “automation of everyday (medical) life.” Roughly 100 years ago the philosopher and mathematician Alfred North Whitehead described civilization as something “that develops by increasing the number of important tasks that we can perform without thinking about them.”² However, the impact in the 21st century is quite different: “Automation does not simply replace human activities but actually changes them, and often does so in a manner that was neither intended nor envisaged by its developers.” Nicholas Carr: Die Herrschaft der Maschinen: Blätter für deutsche und internationale Politik, 2/2014 Raja Parasuraman, quoted from: Carr: Die Herrschaft der Maschinen: Blätter für deutsche und internationale Politik, 2/2014 VIII  Autopilot and “Doctor Algorithm”? Big Data – in medicine, too – may be changing the world more than we can yet understand or want or wish to acknowledge Special thanks goes out to all authors for their contributions to this kaleidoscope: The texts are hopefully also reflected in each other We often see kaleidoscopes as a “mere” children’s toy With a little technical skill, however, they can also be used as a “microscope” or “telescope” – but even here, software has already been developed that can simulate these effects… I hope you enjoy the reading experience and may your insights be small or Big! Peter Langkafel Contents Peter Langkafel Intro Big Data for Healthcare?  Schepers Josef and Martin Peuker Information Management for Systems Medicine – on the Next Digital Threshold  33 Albrecht von Müller Some Philosophical Thoughts on Big Data  45 Thomas Brunner Big Data from a Health Insurance Company’s Point of View  53 Harald Kamps Big Data and the Family Doctor  63 Alexander Pimperl, Birger Dittmann, Alexander Fischer, Timo Schulte, Pascal Wendel, Martin Wetzel and Helmut Hildebrandt How Value is Created from Data: Experiences from the Integrated Health Care System, “Gesundes Kinzigtal” (Healthy Kinzigtal)  69 Rainer Röhrig and Markus A Weigand Ethics  89 Karola Pötter-Kirchner, Renate Höchstetter and Thilo Grüning The New Data-Supported Quality Assurance of the Federal Joint Committee: Opportunities and Challenges  101 Werner Eberhardt Big Data in Healthcare: Fields of Application and Benefits of SAP Technologies  115 Axel Wehmeier and Timo Baumann 10 Big Data – More Risks than Benefits for Healthcare?  123 Marcus Zimmermann-Rittereiser and Hartmut Schaper 11 Big Data – An Efficiency Boost in the Healthcare Sector  131 234  Peter Langkafel Statista Online: http://de.statista.com/; search term: Gesundheit, [accessed on: August 22, 2014] van der Meet/Assendelft, et al.: Cost-Effectiveness of Internet-Based Self-Management Compared with Usual Care in Asthma Online: http://www.plosone.org/article/info%3Adoi%2F10.1371 %2 Fjournal.pone.0027108, [accessed on: August 27, 2014] Wikipedia: Flugzeugabsturz Online: http://de.wikipedia.org/wiki/Flugzeugabsturz, [accessed on: August 26, 2014] Publisher and Index of Authors Published by Dr med Peter Langkafel, MBA Born in 1968, studied human medicine and has worked in, among other things, obstetrics and general medicine In addition to “digital projects” in research, clinical practice and teaching, he also studied medical informatics which he completed with a Master of Business Administration (MBA) He was the founder and CEO of a healthcare IT startup At SAP AG, he is General Manager of Public Sector and Healthcare for the Middle and Eastern Europe (MEE) region, and in that role advises national and international clients from the healthcare industry in their strategic (IT) orientation Previously, he was responsible for the strategic development of Charité Berlin Peter Langkafel is chairman of the Professional Association of Medical Informatics Scientists (Berufsverband Medizinischer Informatiker e V [BVMI e V.]) for the Berlin/ Brandenburg region and lecturer at the School of Economics and Law in Berlin With contributions by Dipl.-Inform Med Timo Tobias Baumann Born in 1974, MBA, Vice President of the Portfolio Management Clinic at Deutsche Telekom Healthcare & Security Solutions GmbH (DTHS) Thomas Brunner Born in 1967, diploma in economics, has worked for AOK since the end of the 90’s, on various projects such as the implementation of SAP BW in the framework of SAM/oscare® Currently working as Product Manager for Business Intelligence at AOK Systems GmbH Birger Dittmann Born in 1985, IT business engineer (B.Sc.), Business Intelligence & Data Warehouse Developer at OptiMedis AG, Hamburg 236  Publisher and Index of Authors Dr Werner Eberhardt MBA Born in 1963, global responsibility for business development and strategic clients and partners in healthcare as well as in the pharmaceutical and biotechnology industries at SAP AG PhD in proteomics at the Max Planck Institute for Biochemistry in Martinsried Dipl Inform Michael Engelhorn Born in 1949, medical computer scientist, CIO and CTO of ExperMed GmbH Berlin, www.expermed.de Co-founder of CARS, TELEMED Berlin and KIS-RIS-PACS conventions in Mainz Member of BVMI Alexander Fischer Born in 1984, health economist (M Sc with a focus on political science and networked supply structures in healthcare) Health Data Analyst at OptiMedis AG, Hamburg Dr med Thilo Grüning Anesthetist and intensive care specialist, Master of Science in Health Services Management Since 2010 Head of Quality Assurance and Intersectoral Care Concepts for the Federal Joint Committee, Berlin Helmut Hildebrandt Born in 1954, pharmacist and health scientist Member of the management board of OptiMedis AG, Managing Director of Gesundes Kinzigtal GmbH, Co-Chairman of the Health Commission of the Heinrich Böll Foundation, board member of the German Managed Care Association (BMC) Renate Höchstetter Physician in Dermatology and Venereology, Master of Public Health, Master of Business Administration Since 2010 Deputy Head of Quality Assurance and Intersectoral Care Concepts of the Federal Joint Committee, Berlin Publisher and Index of Authors  237 Anika Kaczynski, M.Sc Master of Science in Public Health and Administration in 2013 Since 2013 research associate at the Institute for Health Economics and Medicine Management (IGM) at the University of Applied Sciences, Neubrandenburg Harald Kamps Born in 1951, studied medicine in Bonn Worked in Norway between 1982 and 2002 as a general practitioner, project manager and university lecturer Working in Berlin since 2002 as a general practitioner and head of a primary care center since 2005 (www.praxis-kamps.de) Dr med Sebastian Krolop, M.Sc Born in 1971, Director of Accenture Strategy Health for Germany, Austria and Switzerland Author of the European Hospital Rating Report, various hospital rating reports and the rehabilitation and nursing home rating report, which are published every year in cooperation with the RWI Qualified doctor (PhD) with master’s degree in healthcare management in the health sector Member of the WorldWide Board of Directors of the HIMSS and the International Health Economics Association Lecturer at the Hochschule Fresenius Peer Laslo Born in 1963, Business Economist and Management Consultant at SAP Germany AG & Co KG, Business Development Manager in the process industry with a focus on the production and product tracking of pharmaceutical products Dr rer oec Axel Mühlbacher Professor for Health Economics & Medicine Management at the University of Applied Sciences Neubrandenburg, Institute of Health Economics and Medicine Management Since 2012 Senior Research Fellow at the Center for Health Policy & Inequalities Research of the Duke Global Health Institute, Duke University, USA In 2010‒2011 Harkness Fellow in Healthcare Policy and Practice at the Duke Clinical Research Institute and the Fuqua School of Business at the Duke University, USA Between 2009 and 2013 head of the “Conjoint Analysis” pilot study for the Institute for Economy and Quality in Healthcare (IQWiG) 238  Publisher and Index of Authors Prof Dr Albrecht von Müller Head of the Parmenides Center for the Study of Thinking; teaches philosophy at the Ludwig-Maximilians-University of Munich (LMU) His main fields of interest are the concept of time and the phenomenon of thinking After his doctoral thesis on “Time and Logic” at LMU in 1982, he worked for several years at the Max Planck Society and taught philosophy at the University of Munich In the 1980 s he worked on international security and arms control Von Müller developed the EIDOS methodology for visual thinking for a better promotion of complex thought and complex decision-making processes Von Müller is an external member of two multidisciplinary research institutions of the University of Munich: the Human Science Center and the Munich Center for Neuroscience He is also a member of the Board of Trustees of the Max Planck Institute of Neurobiology and Biochemistry Martin Peuker Born in 1973, Deputy CIO of the Charité University Medicine Berlin, consultant in various institutions of the European healthcare industry The Industrial Engineering graduate began his career at Mummert and Partner, Siemens AG and is an expert in the areas of reporting for hospitals and inMemory databases such as SAP HANA Dr Alexander Pimperl Born in 1983, Health Economist, Head of Controlling & Health Data Analytics at OptiMedis AG, Hamburg Karola Pötter-Kirchner Master of Public Health with a degree in social pedagogy Since 2010 lecturer in the Department of Quality Assurance and Intersectoral Care Concepts of the Federal Joint Committee, Berlin Dr Rainer Röhrig Born in 1970, physician and medical informatics scientist Head of the Department of Medical Computer Science in Anesthesia and Intensive Care, a member of the Ethics Committee of the Faculty of Medicine, Justus Liebig University Chairman of the technology and methods platform for networked medical research (TMF e V.) Publisher and Index of Authors  239 Hartmut Schaper Born in 1962, mathematician and computer scientist (MSc) Over 25 years of experience in the areas of software and IT in various positions (Principal Consultant, Head of SW Development, CTO (Chief Technology Officer) at companies such as Boston Consulting Group, IXOS AG, SAP AG) Today Senior Vice President and Head of Health Services International, Siemens AG, Healthcare Sector, Erlangen Dr Josef Schepers Born in 1959, physician and health economist Has worked on a variety of projects in healthcare informatics and health system management Cooperative member of the Institute for Healthcare Systems Management (HCMB) Berlin eG Employed at TMF e V (Technology and methods platform for networked medical research) Timo Schulte Born in 1983, degree in business management and MBA in health management Health Data Analyst at OptiMedis AG, Hamburg Florian Schumacher Born in 1980, Florian Schumacher is a consultant and founder of Quantified Self Germany and trendscout of Wearable Technologies AG An engineer and trained design thinker, Florian Schumacher deals with digital sports, health and wellness products, and their economic and social innovation potential Schumacher advises companies in the design, development and implementation of Quantified Self software and hardware As the host of Quantified Self events, keynote speaker and author, Schumacher, a selftracking pioneer, promotes discussion on the collection and use of personal data He deals with the latest trends and developments in the area of wearables and quantified self in his blog igrowdigital.com Henri Souchon, M.Sc Born in 1984, a consultant at Accenture GmbH in the areas of healthcare and public administration Focus on hospital benchmarking and coauthor of the Accenture European Hospital Rating Report Master of Science (M.Sc.) in management from the University of Edinburgh and Bachelor of Science (B.Sc.) in economics from the University of Münster 240  Publisher and Index of Authors Dr Axel Wehmeier Born in 1966, CEO of Deutsche Telekom Healthcare & Security Solutions GmbH (DTHS) Dr Thilo Weichert Born in 1955, lawyer and political scientist/State Commissioner for Privacy Protection Schleswig-Holstein and thus Head of the Independent Center for Privacy Protection, Kiel, www.datenschutzzentrum.de Prof Dr Markus Alexander Weigand Born in 1967, Director of the Clinic for Anesthesiology, Operational Intensive Medicine and Pain Therapy at the Justus Liebig University/University Clinic Giessen und Marburg GmbH, located in Gießen Pascal Wendel Born in 1972, qualified IT specialist for application development Business Intelligence & Data Warehouse Developer, Data Analyst, System and Database Administrator at OptiMedis AG, Hamburg Martin Wetzel Born in 1958, registered specialist for general medicine Chairman of Medizinisches Qualitätsnetz Ärzteinitiative Kinzigtal e V Marcus Zimmermann-Rittereiser Born in 1958, graduate engineer in technical healthcare with Master of Business and Marketing (MBM) in the Department of Economics of the Free University of Berlin Over 26 years of experience in the areas of International Healthcare and Healthcare IT at Siemens AG Today Head of Strategy, Health Services International, Siemens AG, Healthcare Sector, Erlangen Glossary 23 Things You’ve Always Wanted to Know About Statistics ¹ Natural frequency The simplest method of evaluating the occurrence of events or characteristics is to simply count how often they occur In contrast to probabilities and relative frequencies, natural frequencies are quasi raw data, that is, they are not normalized with respect to the basic share of the event or characteristic For example: A doctor examines 100 people, 10 of which have a particular disease, but 90 of which not Of the 10 with the disease, show particular symptoms, whereas of the 90 that not have the disease also show the symptoms These 100 cases can now be divided into four different groups: disease and symptoms: 8; disease and no symptoms: 2; no disease and symptoms: 4; no disease and no symptoms: 86 These four numbers are the natural frequencies Average A measure of the central tendency of a number of measurements and observations The average usually refers to the arithmetic mean, but sometimes also to the median For example: The annual income of five agents is EUR 80,000, EUR 90,000, EUR 100,000, EUR 130,000 and EUR 600,000 The arithmetic mean of these amounts – i e., their sum divided by their number – is EUR 200,000 If one arranges the individual values in increasing order (as here), the median is the value that has an equal number of values on either side of it, in this case EUR 100,000 If the distribution is asymmetrical, which is often the case with income, then arithmetic mean and median will differ from each other It is therefore possible that most people have an income that is below average because a few have very high incomes Here’s an amusing fact: Did you know that almost all people have more legs than the average? Risk If the uncertainty associated with an event or characteristic can be evaluated on the basis of empirical observations or causal knowledge (design), this is called risk Frequencies and probabilities can express risks Unlike its use in everyday language, the term risk does not necessarily have to be associated with adverse effects or consequences, but can equally refer to a positive, neutral or negative event or attribute This is partially an excerpt from the definitions by the Harding Center for Risk Literacy Online: https://www.harding-center.mpg.de/de/gesundheitsinformationen/wichtige-begriffe, [accessed on: August 26, 2014] as well as Wikipedia entries 242  Glossary Conditional probability The probability that an event A occurs when event B has occurred is usually written as p(A|B) An example of this is the probability that a screening mammogram will be positive if breast cancer is present; it is around 0.9 % In contrast, p(A) is not a conditional probability Conditional probabilities are often misunderstood in two different ways The first is that the probability of A under the condition of B is confused with the probability of A and B The other is that the probability of A under the condition of B is confused with the probability of B under the condition of A These errors can be avoided by replacing the conditional probabilities with the natural frequencies Absolute risk reduction A measure of the effectiveness of a treatment (or behavior) It refers to the proportion of people who have been healed or saved by the respective treatment If, for example, a therapy can reduce the number of deaths caused by the disease in question from to out of 1,000 patients, then the absolute risk reduction is two out of 1,000 or 0.2 % Relative frequency One of the three major interpretations of probabilities (in addition to the degree of conviction and design) The probability of an event is defined as the relative frequency in a reference quantity Historically, frequencies found their way into probability theory through the mortality statistics, which in turn formed the basis of life insurance calculations Relative frequencies are limited to repeated events that are observable in large numbers Relative risk reduction A measure of the effectiveness of a treatment It refers to the proportion of patients that were saved by the respective treatment For example: A therapy lowers the proportion of those dying from a disease from to out of 1,000 This means the relative risk reduction amounts to out of or 33 % The relative risk reduction is often given because its numerical value is greater than that of the absolute risk reduction (in the same example this would be two out of 1,000 or 0.2 %) When specifying the relative values it remains unclear how big the risk actually is, which often leads to misinterpretations or misunderstandings If, for example, a therapy reduces the number of deaths from to in 10,000 (instead of 1000), then the relative risk reduction, at 33.3 %, is the same, although the absolute risk reduction is now only 0.02 % Reliability This is a quality criterion that indicates the certainty with which a repetition of the test would deliver the same results under other conditions – for example in the case of repeated measurements High reliability is necessary but does not guarantee high validity Glossary  243 Sensitivity The sensitivity of a test for a disease is the proportion of individuals that tested positive out of all the tested persons who have the disease in question The sensitivity is thus equal to the conditional probability p(positive I sick) of a positive test result, if the disease is present Sensitivity and false-negative rate add up to 100 % The sensitivity is also called the “hit rate.” 10 Specificity (Literally: “peculiarity”, “special feature”) The specificity of a test for a disease is the proportion of individuals who tested negative of all the tested persons who not have the disease in question The specificity is thus equal to the conditional probability p(negative I not sick) of a negative test result, if the disease is not present Specificity and false-positive rate add up to 100 % 11 Independence Two events or attributes are independent of one another if knowledge of one of the events or attributes says nothing about whether the other will occur or exist Two events A and B are formally independent of each other, if the probability p(A and B) that both will occur is equal to the product of p(A) and p(B), i e., equal to the product of the probability of the two events Independence plays a role, for example, if the match between the DNA profile of a suspect and a trail of blood at a crime scene has to be assessed Assuming that only one in 1,000,000 people will show such a match, the probability will be one in 1,000,000 that the DNA profile of a randomly selected person will match that of the trail of blood But if the suspect has an identical twin, then – not taking into account possible evaluation errors – the probability that the twin will show a match will be one instead of 1:1,000,000 And if the suspect has brothers, the probability of a match for them will also be significantly higher than for the general population This means that the probability of a DNA match is not independent of whether people are related to each other or not 12 Validity A criterion that indicates how well a test measures what it purports to measure A high reliability is necessary, but not sufficient for high validity 13 Number needed to treat (NNT) This is the number of patients that have to be treated or screened in order to save one human life Therefore, the NNT is a measure of the effectiveness of a therapy For example: If a two-year mammography screening saves the life of one in 1000 participating women, then the NNT is equal to 1000 Put another way: The remaining 999 women will not benefit in terms of mortality reduction However, you can also specify NNT if you want to measure the risk of treatment If, for example, thromboembolism occurs in one in 7000 women who take birth control pills, the NNT for 244  Glossary birth control pills and thromboembolism is equal to 7000 In other words: 6999 women will not display this side effect 14 Number needed to harm (NNH) The NNT is put into relation using the NNH, i e., the number of treatments necessary to reach the desired therapy goal in a patient is compared to the number of treatment procedures necessary to cause the patient damage 15 False-negative rate The proportion of negative tests in people with the disease (or feature) in question is called the false-negative rate It is usually expressed as a conditional probability and normally stated in percent For mammography screening, for example, it is between and 20 %, depending on the age of the tested women That means that in to 20 % of the examined women with breast cancer, the test result was negative, i e., the cancer was overlooked False-negative rate and sensitivity of a test add up to 100 % 16 False-positive rate The proportion of positive test results in people without the disease (or feature) in question is called the false-positive rate It is usually expressed as a conditional probability and normally stated in percent For mammography screening, for example, it is between and 10 %, depending on the age of the tested women This means that to 10 % of the tested women without breast cancer had a positive result, i e., a suspected carcinoma was detected although none actually exists False-positive rate and specificity of a test add up to 100 % The false-positive rate and false-negative rate of a test depend on each other: If you decrease one, you generally increase the other 17 Placebo effect A placebo is a “fake drug” that contains no medicinal product and can thus have no pharmacological effect caused by any such substance In a broader sense, other fake interventions are also called placebo, for example, fake operations Placebo effects are positive changes in the subjective condition and objectively measurable physical functions that are attributed to the symbolic importance of treatment They can occur with any kind of treatment, not only in fake treatments Placebos are used in placebo-controlled clinical trials in order to detect the therapeutic efficacy of different procedures, called verums, as accurately as possible 18 Nocebo effect The nocebo effect (from the Latin nocere: to harm, I shall harm) is – similarly to the placebo effect (from the Latin placebo: I will please) – an apparent negative effect of a drug It refers to an effect on the well-being or health of a patient caused by a substance or measure, or rumored substance or measure that changes the environment of Glossary  245 a patient In contrast to the positive effect of the placebo effect, the nocebo effect produces a negative reaction The nocebo effect was discovered when the administration of drugs without any active agents – so-called placebos – caused negative pathogenic effects in patients 19 Nominal scale Various properties, no predetermined order (e.g., gender, location) 20 Ordinal scale The values can be sorted, but you cannot specify distances between them (e.g., rankings, school grades) 21 Interval scale The distance between two values can be measured; the zero point is set arbitrarily (e.g., dates, temperature in °C) 22 Ratio scale There is a natural zero point, so you can specify both the difference and the ratio of two values (e.g., age, income) Such data provides the most information 23 Innumeracy The inability to deal with numbers correctly In the context of statistics, this is the inability to correctly represent and assess uncertainties It manifests itself in an uncertainty about risks, in confusing conveying of risks and nebulous thinking Like dyslexia, innumeracy is also curable, as it is by no means a mere “internal” mental weakness, but is at least partially externally produced or provoked by an inadequate presentation of the respective values Therefore an external remedy is possible Testimonials Due to the increasing digitization, storage and use of healthcare data, Big Data will have a dramatic effect on the healthcare industry PD Dr Günter Steyer, Honorary Chairman of the German Society for Health Telematics (Deutschen Gesellschaft für Gesundheitstelematik [DGG]) and Honorary Member of the Professional Association of Medical Informatics (Berufsverband Medizinischer Informatiker [BVMI]) Being able to provide all the necessary information at any time during the treatment process and to interlink it accordingly will be critical for future medical care PD Dr med Lutz Fritsche MBA, Chief Medical Officer at Paul Gerhard Diakonie The growth of data within the information-driven healthcare industry is enormous The diffusion of modern IT technology is progressing continuously: Hospitals are digitizing more and more paper-based processes, and patients and citizens are increasingly using healthcare apps The result is that the data volumes continue to rise dramatically, but are only available in separate forms Big Data provides the opportunity of interlinking this data to generate efficiency potential for both the primary and secondary healthcare market and to create innovations that are sustainably affordable Dr Michael Reiher, Professor for HealthCare Management Optimal intra- and inter-organizationally oriented collaborations must be effectively controlled in order to function Effective controllability requires comprehensive and standardized information in “real time” – “Big Data” is one of the most important, if not the most important, levers! Dr Pierre-Michael Meier, CEO of März AG and Deputy Spokesperson of IuiG-Initiativ-Rat ENTSCHEIDERFABRIK Providing well-structured, large amounts of data in high quality for a large variety of application scenarios in medicine is a key issue of the future Helmut Greger, CIO of the Charité, Humboldt University of Berlin Big Data will open up new opportunities for start-ups in medicine Dr Joachim Rautter, Managing Director of Peppermint VenturePartners GmbH The networking and sharing of information provides great potential for the entire healthcare industry, and particularly for patients Professor Arno Elmer, CEO of gematik (Gesellschaft für Telematikanwendungen der Gesundheitskarte mbH) 248  Testimonials We are merely at the beginning of the upheavals that Big Data will cause in the healthcare industry We are currently still occupied with the question of how to best manage the provision of healthcare services But soon the possibilities regarding the diagnosis and treatment of our patients will play a much more important role This will show us how we, as a society, are able (or willing) to handle such possibilities For example, Andrew McAfee, Director of the Center for Digital Business at MIT, predicts that even if computers are not yet the best at making diagnoses, they soon will be Dr med Florian Schlehofer, MBAClustermanager Health Economy/Life Sciences, ZAB ZukunftsAgentur Brandenburg GmbH ... Medical Big Data and Data Protection  139 Sebastian Krolop and Henri Souchon 13 Big Data in Healthcare from a Business Consulting (Accenture) Point of View  151 Peer Laslo 14 Influence of Big. .. Mr Big Data in Medicine? If you turn a telescope around, you get a microscope So in the following years, researchers tirelessly examined all kinds of living and non-living things under Intro Big. .. Big Data?  1.3 Mr Big Data in Medicine?  1.4 Mr Interpret Big Data  1.5 The Only Statistics You Can Trust, Are …  1.6 Mr Understand Data  1.7 Datability  1.8 Big Data in the Bundestag 