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.

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.

How China is winning in the Age of Artificial Intelligence

Alibaba Campus
Alibaba Campus

Currently, I’m on a 4-week China trip, visiting many cities. In Hangzhou, I met CEIBS peers who work for Alibaba. While the Alibaba campus is quite impressive, I got even more impressed by Alibaba’s leadership culture, which is encouraging its employees to innovate as intrapreneurs.

If you start your own project (a new mobile app, a patent, a scientific paper, etc.), you’re doing it in your own pace, you’re not being micro-managed and you’ll receive a bonus based on success. Intrapreneurship at Alibaba is just one of many examples where we (Europeans) can learn a lot from China!

Yue and me, Hangzhou West Lake

While traveling in China I was reading AI Superpowers: China Silicon Valley, and the New World Order by Kai-Fu Lee, a book that is a must-read to get an idea where China’s AI ambitions are heading to. What matters most for AI innovation these days, the author argues, is access to vast quantities of data—where China’s advantage is overwhelming.

AI Superpowers: China, Silicon Valley, and the New World Order
  • Kai-Fu Lee
  • Publisher: Houghton Mifflin Harcourt
  • Gebundene Ausgabe: 272 pages

A quite entertaining book focusing on the new mindset of China’s young generation is this one: Young China: How the Restless Generation Will Change Their Country and the World by Zak Dychtwald.

YOUNG CHINA
  • ZAK DYCHTWALD
  • Publisher: MACMILLAN USA
  • Gebundene Ausgabe: 304 pages

[Update 2 May 2019]: Which other cities in China did I visit? Check out my Tableau Public viz:

Data Operations: Wie Sie die Performance Ihrer Datenanalyse und Dashboards steigern

#dataops: Folgen Sie der Diskussion auf Twitter
#dataops: Folgen Sie der Diskussion auf Twitter

Sind Sie mit der Geschwindigkeit Ihrer Datenanlyse unzufrieden? Oder haben Ihre Dashboards lange Ladezeiten? Dann können Sie bzw. Ihr Datenbank-Administrator folgenden Hinweisen nachgehen, die sich je nach Datenquelle unterscheiden können.

Allgemeine Empfehlungen zur Performance-Optimierung

Möchten Sie die Geschwindigkeit der Analyse verbessern? Dann beachten Sie folgende Punkte:

  • Benutzen Sie mehrere »kleinere« Datenquellen fĂŒr individuelle Fragestellungen anstatt einer einzigen Datenquelle, die alle Fragestellungen abdecken soll.
  • Verzichten Sie auf nicht notwendige VerknĂŒpfungen.
  • Aktivieren Sie in Tableau die Option »Referentielle IntegritĂ€t voraussetzen« im »Daten«-MenĂŒ (siehe Abbildung 2.20). Wenn Sie diese Option verwenden, schließt Tableau die verknĂŒpften Tabellen nur dann in die Datenabfrage ein, wenn sie explizit in der Ansicht verwendet wird*. Wenn Ihre Daten nicht ĂŒber referentielle IntegritĂ€t verfĂŒgen, sind die Abfrageergebnisse möglicherweise ungenau.
Aktivierte Option „Referentielle IntegritĂ€t voraussetzen“ im „Daten“-MenĂŒ
Abbildung 2.20: Aktivierte Option »Referentielle IntegritĂ€t voraussetzen« im »Daten«-MenĂŒ

* So wird beispielsweise der Umsatz anstatt mit der SQL-Abfrage SELECT SUM([Sales Amount]) FROM [Sales] S INNER JOIN [Product Catalog] P ON S.ProductID = P.ProductID lediglich mit der SQL-Abfrage SELECT SUM([Sales Amount]) FROM [Sales] ermittelt.

Empfehlungen fĂŒr Performance-Optimierung bei Dateien und Cloud-Diensten

Achten Sie insbesondere beim Arbeiten mit Dateiformaten, wie Excel-, PDF- oder Textdateien, oder Daten aus Cloud-Diensten wie Google Tabellen zusÀtzlich auf folgende Punkte:

  • Verzichten Sie auf Vereinigungen ĂŒber viele Dateien hinweg, da deren Verarbeitung sehr zeitintensiv ist.
  • Nutzen Sie einen Datenextrakt anstatt einer Live-Verbindung, falls Sie nicht mit einem schnellen Datenbanksystem arbeiten (siehe Wann sollten Sie Datenextrakte und wann Live-Verbindungen verwenden).
  • Stellen Sie sicher, dass Sie beim Erstellen des Extrakts die Option »Einzelne Tabelle« wĂ€hlen, anstatt der Option »Mehrere Tabellen« (siehe Abbildung 2.21). Dadurch wird das erzeugte Extrakt zwar grĂ¶ĂŸer und das Erstellen des Extrakts dauert lĂ€nger, das Abfragen hingegen wird um ein Vielfaches beschleunigt.
AusgewĂ€hlte Option „Einzelne Tabelle“ im „Daten extrahieren“-Dialog
Abbildung 2.21: AusgewÀhlte Option »Einzelne Tabelle« im »Daten extrahieren«-Dialog

Empfehlungen fĂŒr Performance-Optimierung bei Datenbank-Servern

Arbeiten Sie mit Daten auf einem Datenbank-Server, wie Oracle, PostgreSQL oder Microsoft SQL Server, und möchten die Zugriffszeiten verbessern? Dann achten Sie bzw. der dafĂŒr zustĂ€ndige Datenbankadministrator zusĂ€tzlich auf folgende Punkte:

  • Definieren Sie fĂŒr Ihre Datenbank-Tabellen sinnvolle Index-Spalten.
  • Legen Sie fĂŒr Ihre Datenbank-Tabellen Partitionen an.

Dieser Beitrag ist der dritte Teil der Data-Operations-Serie:

Teil 1: Daten fĂŒr die Analyse optimal vorbereiten
Teil 2: Wann sollten Sie Datenextrakte und wann Live-Verbindungen verwenden
Teil 3: Wie Sie die Performance Ihrer Datenanalyse und Dashboards steigern

Außerdem basiert dieser Blog-Post auf einem Unterkapitel des Buches “Datenvisualisierung mit Tableau“:

Datenvisualisierung mit Tableau
  • Alexander Loth
  • Publisher: mitp
  • Edition no. 2018 (31.07.2018)
  • Broschiert: 224 pages

How to research LinkedIn profiles in Tableau with Python and Azure Cognitive Services

A few weeks after the fantastic Tableau Conference in New Orleans, I received an email from a data scientist who attended my TC18 social media session, and who is using Azure+Tableau. She had a quite interesting question:How can a Tableau dashboard that displays contacts (name & company) automatically lookup LinkedIn profile URLs?

Of course, researching LinkedIn profiles for a huge list of people is a very repetitive task. So let’s find a solution to improve this workflow…

1. Python and TabPy

We use Python to build API requests, communicate with Azure Cognitive Services and to verify the returned search results. In order to use Python within Tableau, we need to setup TabPy. If you haven’t done this yet: checkout my TabPy tutorial.

2. Microsoft Azure Cognitive Services

One of many APIs provided by Azure Cognitive Services is the Web Search API. We use this API to search for name + company + “linkedin”. The first three results are then validated by our Python script. One of the results should contain the corresponding LinkedIn profile.

3. Calculated Field in Tableau

Let’s wrap our Python script together and create a Calculated Field in Tableau:

SCRIPT_STR("
import http.client, urllib, base64, json
YOUR_API_KEY = 'xxx'
name = _arg1[0]
company = _arg2[0]
try:
headers = {'Ocp-Apim-Subscription-Key': YOUR_API_KEY }
params = urllib.urlencode({'q': name + ' ' + company + ' linkedin','count': '3'})
connection = http.client.HTTPSConnection('api.cognitive.microsoft.com')
connection.request('GET', '/bing/v7.0/search?%s' % params, '{body}', headers)
json_response = json.loads(connection.getresponse().read().decode('utf-8'))
connection.close()
for result in json_response['webPages']['value']:
if name.lower() in result['name'].lower():
if 'linkedin.com/in/' in result['displayUrl']:
return result['displayUrl']
break
except Exception as e:
return ''
return ''
", ATTR([Name]), ATTR([Company]))

4. Tableau dashboard with URL action

Adding a URL action with our new Calculated Field will do the trick. Now you can click on the LinkedIn icon and a new browser tab (or the LinkedIn app if installed) opens.

LinkedIn demo on Tableau Public

Is this useful for you? Feel free to download the Tableau workbook (don’t forget to add your API key), leave a comment and share this tweet: