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, itÔÇÖs 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 itÔÇÖs 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 patientÔÇÖs 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 AlzheimerÔÇÖs.

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.

Machine Learning kompakt: Alles, was Sie wissen m├╝ssen

Machine Learning Kompakt Cover und Deep-Learning-Kapitel
Machine Learning kompakt und Blick in das Kapitel “Neuronale Netze und Deep Learning”

Nachdem ich bereits Erfahrung als Buchautor (hier und hier) gesammelt habe, hatte ich k├╝rzlich die Gelegenheit als Technical Reviewer ein sehr spannendes Buchprojekt zu unterst├╝tzen. Das Buch Machine Learning kompakt: Alles, was Sie wissen m├╝ssen, geschrieben von Andriy Burkov, fand ich dabei derma├čen interessant, dass ich es gerne im Folgenden kurz vorstellen werde:

Machine Learning kompakt von Andriy Burkov ist ein hervorragend geschriebenes Buch und ein Muss f├╝r jeden, der sich f├╝r Machine Learning interessiert.

Andriy Burkov gelang ein ausgewogenes Verh├Ąltnis zwischen der Mathematik, intuitiven Darstellungen und verst├Ąndlichen Erkl├Ąrungen zu finden. Dieses Buch wird Neulingen auf dem Gebiet als gr├╝ndliche Einf├╝hrung zu Machine Learning zugutekommen. Dar├╝ber hinaus dient das Buch Entwicklern als perfekte Erg├Ąnzung zu Code-intensiver Literatur, da hier die zugrunde liegenden Konzepte beleuchtet werden.

Microsoft Azure Machine Learning Studio
Microsoft Azure Machine Learning Studio

Machine Learning kompakt eignet sich au├čerdem als Lehrbuch f├╝r einen allgemeinen Kurs zu Machine Learning. Ich w├╝nschte, ein solches Buch g├Ąbe es, als ich studiert habe!

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Machine Learning kompakt: Alles, was Sie wissen m├╝ssen (mitp Professional)
  • Andriy Burkov
  • Publisher: mitp
  • Edition no. 2019 (30.06.2019)
  • Broschiert: 200 pages

Tableau: How to find the most important variables for determining Sales

Random Forest Animation
Interactive dashboard displaying the most important variables for determining the Sales measure in Tableau 10.0 (click screenshot to enlarge)

During the Q&A session of┬áa recent talk on Data Strategy, I was challenged with a rather technical question: I was asked how to identify the┬ávariables that are heavily influencing a certain measure – with an interactive solution that matches a modern data strategy as suggested in my┬ápresentation.

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