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Our book Decisively Digital is the number 1 new release in Organizational Change on Amazon!
The paperback of Decisively Digital became available yesterday in the US, with other territories soon to follow.
It took a village to finish this book. Many thanks to all of you:
My new book Decisively Digital — From Creating a Culture to Designing Strategy is now available for pre-order at most bookstores. The ebook is already available:
Book’s website | Amazon | Barnes & Noble | Books A Million | IndieBound | Thalia | published by Wiley
Check out 24 gripping interviews with Elissa Fink, Mohamed Abdel Hadi, Dr. Henna Karna, Derek Roos, Edna Conway, Kerem Tomak, André Rabold, Bora Beran, Florian Ramseger, Tatyana Yakushev, Patrick Kirchgäßner, Jordan Morrow, Yilian Villanueva Martinez, Lee Feinberg, Mark Kromer, Sarah Burnett, Andreas Kopp, Cameron Turner, Christy Marble, Prof. Dr. Patrick Glauner, Vladimir Alexeev, Sofie Blakstad, Sven Sommerfeld, and Ian Choo. Thanks also to Bernard Marr for the foreword, Patrick Walsh for editing, and everyone for supporting me in the preparation and reviews.
More about the book — from the back cover:
INSIGHTS AND APPLICATIONS FROM 24 LEADERS OF THE DIGITAL REVOLUTION FROM CREATING A CULTURE TO DESIGNING STRATEGY
Today’s business world is Decisively Digital. Across the business landscape, the leaders rising to the top are the ones who can think big-picture about data, AI, analytics, and beyond. How do we build new capabilities around digital, so we can push into the future with full steam? That’s the question at the heart of the twenty-four incisive interviews inside this unique collection of up-to-the-minute expertise from the people who are moving business forward.
With this book, your mentors are the very digital masterminds behind some of today’s top global organizations. Discover how tech giants are reinventing the world of work, how the financial sector is streamlining with data analytics, and what the latest AI research means for the businesses of today and tomorrow. You’ll also gain access to a toolkit of updates, further reading, and digital strategy ideas on the included companion website.
Discover the inspiration you need to evolve your business for the digital age and learn to:
“Alexander […] has brought together some of the brightest voices and smartest thought leaders from leading organisations across many industries, to bring you unmissable insights and real-world examples to showcase how technology can improve your businesses and drive business results today and tomorrow.”
—Bernard Marr, Futurist, influencer and best-selling author of Tech Trends in Practice and The Intelligence Revolution
“Decisively Digital is decisively executive. Great experts, great interviews, and great insights combine to a crisp, cohesive and powerful story. It would be quite a mistake for any (aspiring) leader not to dive into this book, so I will definitely put it on the list of mandatory readings for my future students!”
—Markus Maedler, Director Executive MBA Programmes at Frankfurt School of Finance & Management
Thank you all who helped me to complete this book — and please feel free to share the news:
Good health is a fundamental need for all of us. Hence, it’s no surprise that the total market size of healthcare is huge. Developed countries typically spend between 9% and 14% of their total GDP on healthcare.
The digital transformation in the healthcare sector is still in its early stages. A prominent example is the Electronic Health Record (EHR) in particular, and, in general poor quality of data. Other obstacles include data privacy concerns, risk of bias, lack of transparency, as well as legal and regulatory risks. Although all these matters have to be addressed in a Digital Strategy, the implementation of Artificial Intelligence (AI) should not hesitate!
AI has to potential to save millions of lives by applying complex algorithms to emulate human cognition in the analysis of complicated medical data. AI furthermore simplifies the lives of patients, doctors, and hospital administrators by performing or supporting tasks that are typically done by humans, but more efficiently, more quickly and at a fraction of the cost. The applications for AI in healthcare are wide-ranging. Whether it’s being used to discover links between genetic codes, to power surgical robots or even to maximize hospital efficiency, AI is reinventing modern healthcare through machines that can predict, comprehend, learn and act.
Let’s have a look at ten of the most straightforward use cases for AI in healthcare that should be considered for any Digital Strategy:
1. Predictive Care Guidance:
AI can mine demographic, geographic, laboratory and doctor visits, and historic claims data to predict an individual patient’s likelihood of developing a condition. Using this data predictive models can suggest the best possible treatment regimens and success rate of certain procedures.
2. Medical Image Intelligence:
AI brings in advanced insights into the medical imagery specifically the radiological images. Using AI providers can gain insights and conduct automatic, quantitative analysis such as identification of tumors, fast radiotherapy planning, precise surgery planning, and navigation, etc.
3. Behavior Analytics:
AI helps to solve patient registry mapping issues for and help the Human Genome Project map complicated genomic sequences to identify the link to diseases like Alzheimer’s.
4. Virtual Nursing Assistants:
Conversational-AI-powered nurse assistants can provide support patients and deliver answers with a 24/7 availability. Mobile apps keep the patients and healthcare providers connected between visits. Such AI-powered apps are also able to detect certain patterns and alert a doctor or medical staff.
5. Research and Innovation:
AI helps to identify patterns in treatments such as what treatments are better suited and efficient for certain patient demography, and this can be used to develop innovative care techniques. Deep Learning can be used to classify large amounts of research data that is available in the community at large and develop meaningful reports that can be easily consumed.
6. Population Health:
AI helps to learn why and when something happened, and then predict when it will happen again. Machine Learning (ML) applied to large data sets will help healthcare organizations find trends in their patients and populations to see adverse events such as heart attacks coming.
7. Readmissions Management:
By analyzing the historical data and the treatment data, AI models can predict, flag the causes of readmissions, patterns, etc. This can be used to reduce the hospital readmission rates and for better regulatory compliance by developing mitigating strategies for the identified causes.
8. Staffing Management:
Predictive models can be developed by analyzing various factors such as historical demand, seasonality, weather conditions, disease outbreak, etc. to forecast the demand for health care services at any given point of time. This would enable better staff management and resource planning.
9. Claims Management:
AI detects any aberrations such as – duplicate claims, policy exceptions, fictitious claims or fraud. Machine learning algorithms recognize patterns in data looking at trends, non-conformance to Benford’s law, etc. to flag suspicious claims.
10. Cost Management:
AI automates the cost management through RPA, cognitive services, which will help in faster cost adjudication. It will also enable analysis, optimization, and detection by identifying patterns in cost and flagging any anomalies.
Conclusion:
As these examples show, the wide range of possible AI use cases can improve healthcare quality and healthcare access while addressing the massive cost pressure in the healthcare sector. Strategic sequencing of use cases is mandatory to avoid implementation bottlenecks due to the scarcity of specialized talent.
Which use cases for AI in healthcare would you add to this list?
Share your favorite AI use case in the blog post comments or reply to this tweet:
🧪 Which use cases for #AI in #healthcare would you add to this list? Find my top 10 here: https://t.co/PxnGEyW8Ik#DigitalStrategy #ML #DeepLearning
— Alexander Loth (@xlth) March 12, 2020
This post is also published on LinkedIn.
Nachdem ich bereits Erfahrung als Buchautor (hier und hier) gesammelt habe, hatte ich kürzlich die Gelegenheit als Technical Reviewer ein sehr spannendes Buchprojekt zu unterstützen. Das Buch Machine Learning kompakt: Alles, was Sie wissen müssen, geschrieben von Andriy Burkov, fand ich dabei dermaßen interessant, dass ich es gerne im Folgenden kurz vorstellen werde:
Machine Learning kompakt von Andriy Burkov ist ein hervorragend geschriebenes Buch und ein Muss für jeden, der sich für Machine Learning interessiert.
Andriy Burkov gelang ein ausgewogenes Verhältnis zwischen der Mathematik, intuitiven Darstellungen und verständlichen Erklärungen zu finden. Dieses Buch wird Neulingen auf dem Gebiet als gründliche Einführung zu Machine Learning zugutekommen. Darüber hinaus dient das Buch Entwicklern als perfekte Ergänzung zu Code-intensiver Literatur, da hier die zugrunde liegenden Konzepte beleuchtet werden.
Machine Learning kompakt eignet sich außerdem als Lehrbuch für einen allgemeinen Kurs zu Machine Learning. Ich wünschte, ein solches Buch gäbe es, als ich studiert habe!
Protip: viele der im Buch vorgestellten Machine-Learning-Algorithmen können Sie einfach und bequem in Microsoft Azure Machine Learning Studio selbst ausprobieren: https://aka.ms/mlst
During the Q&A session of a recent talk on Data Strategy, I was challenged with a rather technical question: I was asked how to identify the variables that are heavily influencing a certain measure – with an interactive solution that matches a modern data strategy as suggested in my presentation.
Of course, this could be done by executing a script. The result however would be static and it would be not convenient for a Business Analyst to run it over and over again. Instead of applying a script every time the data changes, it would be much more innovative to get the answer immediately with every data update or interactivity such as a changed filter.
So why not solve this with Tableau? The magic underneath this easy-to-use Tableau dashboard is a nifty R script, embedded in a calculated field. This script calls a statistical method known as Random Forest, a sophisticated machine learning technique used to rank the importance of variables as described in Leo Breiman’s original paper.
The Tableau Packaged Workbook (twbx) is available here. Do you have more ideas or use cases? Feel free to leave a comment or send me an email: aloth@tableau.com
Tableau: How to find the most important variables for determining Sales https://t.co/4OekGZydiG pic.twitter.com/wCdL0rUXIS
— Alexander Loth (@xlth) October 18, 2016
