Digitalisierung und Nachhaltigkeit im Fokus: Gespräch mit Philipp Güth – Neue Folge von “Die Digitalisierung und Wir”
In unserer neuesten Folge von Die Digitalisierung und Wir tauchen wir in zwei Schlüsselthemen unserer Zeit ein: Digitalisierung und Nachhaltigkeit. Unser Gast Philipp Güth, der Gründer des Startups Wilson & Oskar, gibt uns spannende Einblicke in die Welt der digitalen Startups. Begleiten Sie uns, Florian Ramseger und Alexander Loth, auf dieser aufschlussreichen Reise.
Digitalisierung und Nachhaltigkeit: Imperative für die Gesellschaft
In unserer sich rasch verändernden Welt sind Digitalisierung und Nachhaltigkeit keine leeren Begriffe, sondern dringende Imperative für die Gesellschaft. Die Digitalisierung hat das Potenzial, unseren Lebensstil effizienter und bequemer zu gestalten, während Nachhaltigkeit darauf abzielt, diese Veränderungen auf eine Weise zu bewerkstelligen, die unseren Planeten schont.
Durch intelligente Technologien, darunter Künstliche Intelligenz, können wir den Energieverbrauch optimieren, Abfall reduzieren und Ressourcen effizienter nutzen. Auf der anderen Seite bietet die Digitalisierung auch Werkzeuge, um die Nachhaltigkeit zu überwachen und zu messen. Apps für den CO2-Fußabdruck, smarte Landwirtschaft und sogar Blockchain für transparente Lieferketten sind nur einige Beispiele dafür, wie die beiden Konzepte ineinandergreifen können.
Aber es ist nicht nur Sache von Startups und Unternehmen, diese Veränderungen voranzutreiben. Jeder Einzelne hat die Möglichkeit und die Verantwortung, beides in seinen Alltag zu integrieren. Mit dem richtigen Ansatz und den richtigen Tools kann jeder von uns aktiv an der Digitalisierung teilnehmen, ohne die Bedürfnisse unseres Planeten zu ignorieren.
Deshalb ist es so wichtig, dass wir weiterhin Diskussionen über Digitalisierung und Nachhaltigkeit führen und wie sie gemeinsam eine besser vernetzte und nachhaltigere Gesellschaft schaffen können.
AI has to potential to save millions of lives by applying complex algorithms | Photo Credit: via Brother UK
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:
My TC18 sessions in New Orleans: „Rock your Social Media Data with Tableau“
Anyone can analyze basic social media data in a few steps. But once you’ve started diving into social analytics, how do you bring it to the next level? This session will cover strategies for scaling a social data program. You’ll learn skills such as how to directly connect to your social media data with a Web Data Connector, considerations for building scalable data sources, and tips for using metadata and calculations for more sophisticated analysis.
Here are some key takeaways and links (i.e. additional resources) featured during my TC18 sessions to help you formulate your social media data program in order to build a stronger presence and retrieve powerful insights:
Step 1: Understand How to Succeed with Social Media
Apple has officially joined Instagram on 7th August 2017. This isn’t your average corporate account as the company doesn’t want to showcase its own products. Instead, Apple is going to share photos shot with an iPhone:
And there are plenty takeaways for every business:
Wrap your data around your customers, in order to create business value
Interact with your customer in a natural way
Understand your customer and customer behaviour better by analyzing social media data
Step 2: Define Your Social Objectives and KPIs
A previous record-holding tweet: In 2014, actor and talk show host Ellen DeGeneres took a selfie with a gaggle of celebrities while hosting the Oscars. That photo has 3.44 million retweets at the time of writing:
Social listening means that you look beyond your own content. E.g. Talkwalker offers AI for image recognition and ggregation for online/offline media: http://bit.ly/tc17_talkwalker
Are you ready for Tableau Conference 2018? Don’t miss my Social Media Analytics sessions!
Why do we need Social Media Analytics?
Social Media Analytics transforms raw data from social media platforms into insight, which in turn leads to new business value.
What will your learn in this sessions?
Once you dive into Social Media Analytics, how do you bring it to the next level? Social data can offer powerful insights right away. In this session, you will learn how to build a mature social data program from that foundation and strategies for scaling a social data programme, as well as how to connect directly to your social media data with a web data connector; considerations for building scalable data sources; and tips for using metadata and calculations for more sophisticated analysis.
Where and when are the sessions?
Do you want to learn more about Social Media Analytics with Tableau? Meet me at the 2018 Tableau Conferences in London or New Orleans and join my sessions:
Immersive und interaktive Analyse von Finanzdaten mit Argumented Reality (Blockchain-Dashboard)
Jedes Jahr (2015, 2016, 2017 und 2018) stelle ich Digitalisierungstrends vor, die der Finanzbranche ein großes Potenzial bieten. Dabei geht es vor allem um einen Überblick darüber, welche Trends und Technologien zukünftig eine größere Rolle spielen werden oder könnten.
Im Folgenden habe ich die fünf Digitalisierungstrends identifiziert, die für Banken und Versicherungen in Zukunft besonders spannend sein dürften:
1. Maschine Learning
Maschine Learning und Deep Learning werden im Investment Banking angewandt, um Unternehmensbewertungen schneller und zuverlässiger durchzuführen. Mehr Daten denn je können hinzugezogen werden. Eine Gewichtung der Daten erfolgt komplett autonom. Da manuelle Analyse weitgehend entfällt, werden Entscheidungsprozesse drastisch beschleunigt. Investoren, die mit konventionellen Werkzeugen arbeiten, haben das Nachsehen.
2. Künstliche Intelligenz
Durch Künstliche Intelligenz gesteuerte Chatbots vermitteln den Kunden eine menschlichen-ähnliche Betreuung. Chatbots werden darüber hinaus in existierende Cloud-basierende Assistenten, wie Alexa oder Siri, eingebunden und sind in der Lage mittels Natural Language Processing, auch komplexere Anfragen zu verstehen. Recommender-Systeme liefern maßgeschneiderte Lösungen, die speziell auf die Bedürfnisse der Kunden abgestimmt sind.
3. Internet of Things
Wearables und in Kleidung eingearbeitete Sensoren (Internet of Things, IoT) liefern ausreichend Daten, um den Lebensstil der Kunden vollständig zu vermessen. Dadurch können individuelle Raten für Versicherungen und Finanzprodukte berechnet werden. Außerdem bieten die IoT-Daten eine weitere Datenquelle für die Recommender-Systeme.
4. Blockchain
Verträge werden kostengünstig, fälschungssicher und irreversibel in der Blockchain gespeichert. Die Blockchain dienst sogenannten Smart Contracts als dezentrale Datenbank. Darüber hinaus liefern Blockchain-Implementierungen, wie Ethereum, das Ausführen von Logik, die beispielsweise monatliche Zahlungen prüfen und ggf. auch die Erfüllung von Vertragsbestandteilen (z.B. im Schadenfall) steuern.
5. Argumented Reality
Arbeitsplätze werden mit Technik ausgestattet, die Argumented Reality ermöglicht. Lösungen wie Microsoft’s Hololense ermöglichen Analysten und Händlern eine immersive und interaktive Analyse von Finanzdaten in Echtzeit. Insbesondere fällt dadurch auch die Zusammenarbeit mit Kollegen leichter, da Plattformen zur visuellen Kollaboration traditionelle Meetings weitgehend ablösen.
Welcher ist der 6. Trend?
Helfen Sie den 6. Digitalisierungstrend zu benennen? Nehmen Sie hierzu an der Twitter-Umfrage teil. Selbstverständlich freue ich mich auch über Kommentare und eine spannende Diskussion.
Digitale Banken: Welche Digitalisierungstrends bewegen die Finanzbranche 2018: https://t.co/3wvEsofy8M Welchen Trend sehen Sie noch?
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