TY - JOUR
T1 - Building Process-Oriented Data Science Solutions for Real-World Healthcare
AU - Fernandez-Llatas, Carlos
AU - Martin, Niels
AU - Johnson, Owen
AU - Sepulveda, Marcos
AU - Helm, Emmanuel
AU - Munoz-Gama, Jorge
N1 - Funding Information:
This activity has received funding from EIT Health (www.eithealth.eu) Value Project ID 20328, the innovation community on Health of the European Institute of Innovation and Technology (EIT, eit.europa.eu), a body of the EU, under Horizon 2020, the EU FP for Research and Innovation. https://www.eithealth.eu (accessed on 28 June 2022).
Publisher Copyright:
© 2022 by the authors.
PY - 2022/7/10
Y1 - 2022/7/10
N2 - The COVID-19 pandemic has highlighted some of the opportunities, problems and barriers facing the application of Artificial Intelligence to the medical domain. It is becoming increasingly important to determine how Artificial Intelligence will help healthcare providers understand and improve the daily practice of medicine. As a part of the Artificial Intelligence research field, the Process-Oriented Data Science community has been active in the analysis of this situation and in identifying current challenges and available solutions. We have identified a need to integrate the best efforts made by the community to ensure that promised improvements to care processes can be achieved in real healthcare. In this paper, we argue that it is necessary to provide appropriate tools to support medical experts and that frequent, interactive communication between medical experts and data miners is needed to co-create solutions. Process-Oriented Data Science, and specifically concrete techniques such as Process Mining, can offer an easy to manage set of tools for developing understandable and explainable Artificial Intelligence solutions. Process Mining offers tools, methods and a data driven approach that can involve medical experts in the process of co-discovering real-world evidence in an interactive way. It is time for Process-Oriented Data scientists to collaborate more closely with healthcare professionals to provide and build useful, understandable solutions that answer practical questions in daily practice. With a shared vision, we should be better prepared to meet the complex challenges that will shape the future of healthcare.
AB - The COVID-19 pandemic has highlighted some of the opportunities, problems and barriers facing the application of Artificial Intelligence to the medical domain. It is becoming increasingly important to determine how Artificial Intelligence will help healthcare providers understand and improve the daily practice of medicine. As a part of the Artificial Intelligence research field, the Process-Oriented Data Science community has been active in the analysis of this situation and in identifying current challenges and available solutions. We have identified a need to integrate the best efforts made by the community to ensure that promised improvements to care processes can be achieved in real healthcare. In this paper, we argue that it is necessary to provide appropriate tools to support medical experts and that frequent, interactive communication between medical experts and data miners is needed to co-create solutions. Process-Oriented Data Science, and specifically concrete techniques such as Process Mining, can offer an easy to manage set of tools for developing understandable and explainable Artificial Intelligence solutions. Process Mining offers tools, methods and a data driven approach that can involve medical experts in the process of co-discovering real-world evidence in an interactive way. It is time for Process-Oriented Data scientists to collaborate more closely with healthcare professionals to provide and build useful, understandable solutions that answer practical questions in daily practice. With a shared vision, we should be better prepared to meet the complex challenges that will shape the future of healthcare.
KW - artificial intelligence
KW - COVID-19
KW - healthcare
KW - process mining
KW - process-oriented data science
KW - Data Science
KW - COVID-19/epidemiology
KW - Delivery of Health Care
KW - Artificial Intelligence
KW - Humans
KW - Pandemics/prevention & control
UR - http://www.scopus.com/inward/record.url?scp=85135130345&partnerID=8YFLogxK
U2 - 10.3390/ijerph19148427
DO - 10.3390/ijerph19148427
M3 - Editorial
C2 - 35886279
AN - SCOPUS:85135130345
SN - 1661-7827
VL - 19
JO - International Journal of Environmental Research and Public Health
JF - International Journal of Environmental Research and Public Health
IS - 14
M1 - 8427
ER -