5 Productivity Hacks to improve your Meeting Culture

Everyone has experienced days that are almost completely filled with meetings. Since business trips have become redundant due to the Covid-19 pandemic and you no longer need to plan in any travel time, it is very tempting to fill in the remaining gaps in your schedule with new tasks – and in the worst case, there is no time for lunch.

Is this the type of modern work we want to experience? Below we have put together some ideas and suggestions that can help to make your working day more pleasant.

1. 5-minute breaks after meetings

A 5-minute break after a meeting can be incredibly revitalizing – especially when meetings are often back-to-back. Outlook gives you the option to automatically schedule meetings 5 minutes shorter:

Once you have shortened your meetings by 5 minutes, you need to make sure that everyone sticks to it.

2. Blocker for lunch breaks, daycare, etc.

To make sure nobody schedules a meeting during your lunch break, a lunch blocker can help you here. Just create an appointment series:

If all the colleagues in your team create a lunch blocker for the same time, it’s (almost) like having lunch together.

If you also have children who need to be taken to daycare, kindergarten, or school, an appointment series can serve the same purpose here. As it is usually possible to make calls while in the car, you can also leave a note with your phone number in the appointment series so that your colleagues know how to reach you when you’re on the road.

3. Chat und Call Etiquette

When pinging colleagues on Teams, don’t simply write “Hello”, as each message distracts them from their current task. While you are typing the remaining message, your colleague is very likely to wait until you have sent it. Even though it might seem impolite or even rushed at first, it is easier for your colleagues if you get right to the point. It is therefore a good idea to type the whole message and send it in one go.

The same goes for calls. Instead of pinging a colleague before calling them and typing “Hello” or “Hello, are you free for a quick call?” it’s better to give them some information beforehand, such as the topic and the estimated duration of the call. For example, you could write “Hello, do you have 3 minutes to discuss topic XYZ with me?” That allows your counterpart to estimate whether they can take the time for this particular call.

For more information on chat and call etiquette, check out this link: aka.ms/NoHello

4. Reduce meetings

To reduce the number of meetings you need to attend, it is helpful to ask yourself the following questions before sending out meeting invites:

  • Can the question be clarified by chat or email?
  • Is this matter urgent or can it wait until the next regular team meeting?
  • Do we really need to involve everyone or are fewer participants enough?

Each meeting should be critically questioned and the most important meetings prioritized. Before attending a meeting, it helps to ask yourself the question: Do I have an active contribution to make to the meeting, or do I only need to read the meeting minutes?

5. Using AI-based technologies

Do not hesitate to actively leverage AI-based technologies. MyAnalytics gives you the option to automatically block focus times. With just one click, not only dedicated times can be blocked for you, but these blockers also automatically change your status on Teams to “Don’t Disturb”. Thus you can simulate, for example, your travel times. More information about the features of MyAnalytics can be found by following this link.

Outlook also gives you various options that can help you save time and focus on the essentials. You can use email rules to automatically move mail to different Outlook folders. For example, you can determine that all cc messages are placed in a separate folder. That allows you to dedicate time to reading these messages as required. The goal is that at the end of the day your inbox is empty (zero-inbox policy) so that you can start afresh the next day. You can also deactivate Outlook push notifications so that you are not distracted by pop up notifications during important activities.

What are your ideas for a more productive workday? We’d like to read your suggestions in the comments below.

Written by Sophia Cullen and Alexander Loth. This post is also published on LinkedIn.

Recap of the 17th Data & AI Meetup: Data & AI in Healthcare

Analyzing Medical Images: Detecting Pneumonia with Custom Vision
Analyzing Medical Images: Detecting Pneumonia with Custom Vision AI

Recently we had the 17th edition of our Data & AI Meetup. This meetup focused on Data & AI in Healthcare. Let’s have a quick recap!

Agenda:

16:00 – Willkommen & Intro
16:05 – BI as a Service fĂŒr eine bessere Healthcare Supply Chain
Christopher Glogger, Sana Einkauf & Logistik
16:35 – AI Trends und Use cases
Andreas Kopp, Microsoft
17:20 – Visuelle Datenanalyse rund um CoViD-19
Markus Raatz, Ceteris AG
17:55 – Wrap up

Session recording:

Further information:

The next Data & AI Meetup?

The next Data & AI Meetup will be announced on the Data & AI LinkedIn group and on the Data & AI Meetup page. Feel free to join!

If you’ve dreamed of sharing your Data & AI story with many like-minded Data & AI enthusiasts, please submit your session proposal.

Recap of the 16th Data & AI Meetup: Azure Bootcamp

Azure Synapse Analytics Screenshot
Azure Synapse Analytics demo shown during the meetup

Yesterday we had the 16th edition of our Data & AI Meetup. This meetup was a hands-on Azure Bootcamp. Let’s have a quick recap!

Agenda:

  1. Welcome & Intro
  2. Azure SQL DB
  3. Azure Data Factory
  4. Azure Synapse Analytics
  5. Visual Analytics with Power BI on Azure

Session recording:

Further information:

The next Data & AI Meetup?

The next Data & AI Meetup will be announced on the Data & AI LinkedIn group and on the Data & AI Meetup page. Feel free to join!

If you’ve dreamed of sharing your Data & AI story with many like-minded Data & AI enthusiasts, please submit your session proposal.

10 Use Cases for AI in Healthcare as part of your Digital Strategy

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:

This post is also published on LinkedIn.

Recap of the 15th Data & AI Meetup: Reinforcement Learning; TensorFlow on Azure; Visual Analytics

200 attendees at the 15th Data & AI Meetup at DB Systel in Frankfurt, Germany
200 attendees at the 15th Data & AI Meetup at DB Systel in Frankfurt, Germany

Yesterday we had an amazing Data & AI Meetup in Frankfurt! Let’s have a quick recap!

The venue: DB Systel’s Silberturm

DB Systel kindly hosted the 15th iteration of our Data & AI Meetup on the 30th floor of the Silberturm in Frankfurt, Germany.

Welcome & Intro

Darren Cooper and I had the pleasure to welcome 200 Data & AI enthusiasts! Furthermore, we were happy to announce that our Data & AI Meetup group has 1,070 members and our brand new Data & AI LinkedIn group already has 580 members.

Reinforcement Learning of Train Dispatching at Deutsche Bahn

Dr. Tobias Keller, Data Scientist at DB Systel, showed in his session how Deutsche Bahn aims at increasing the speed of the suburban railway system in Stuttgart (S-Bahn) using Artificial Intelligence. In particular, a simulation-based reinforcement learning approach provides promising first results.

TensorFlow & Co as a Service

Sascha Dittmann, Cloud Solution Architect for Advanced Analytics & AI at Microsoft, showed in his presentation, how TensorFlow and other ML frameworks can be used better in a team through appropriate Microsoft Cloud services. He presented different ways of how data science experiments can be documented and shared in a team. He also covered topics such as versioning of the ML models, as well as the operationalization of the models in production.

Visual Analytics: from messy data to insightful visualization

Daniel Weikert, Expert Consultant at SIEGER Consulting, showed in his session the ease of use of Microsoft Power BI Desktop. He briefly highlighted the AI Capabilities which Power BI provides and showed a way on how to get started with messy data, doing data cleaning and visualize results in an appealing way to your audience.

Speaking at an upcoming Data & AI meetup?

If you’ve dreamed of sharing your Data & AI story with many like-minded Data & AI enthusiasts, please submit your session proposal or reply to the recap tweet: