An Experimental Healthcare System: Essence and Challenges

An Experimental Healthcare System: Essence and Challenges

Miroslav M. Bojović, Veljko Milutinović, Dragan Bojić, Nenad Korolija
Copyright: © 2021 |Pages: 8
DOI: 10.4018/978-1-7998-7156-9.ch019
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Abstract

Contemporary healthcare systems face growing demand for their services, rising costs, and a workforce. Artificial intelligence has the potential to transform how care is delivered and to help meet the challenges. Recent healthcare systems have been focused on using knowledge management and AI. The proposed solution is to reach explainable and causal AI by combining the benefits of the accuracy of deep-learning algorithms with visibility on the factors that are important to the algorithm's conclusion in a way that is accessible and understandable to physicians. Therefore, the authors propose AI approach in which the encoded clinical guidelines and protocols provide a starting point augmented by models that learn from data. The new structure of electronic health records that connects data from wearables and genomics data and innovative extensible big data architecture appropriate for this AI concept is proposed. Consequently, the proposed technology may drastically decrease the need for expensive software and hopefully eliminates the need to do diagnostics in expensive institutions.
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2. Existing Technologies For Healthcare Systems

Computers are utilized in healthcare for decades (Korolija, 2013), (Korolija, 2019). Contemporary healthcare systems tend to utilize the technology advancements for reducing costs and producing suggestions with higher accuracies. Many of them are based on AI. There are many AI methods. Most of them are oriented towards producing better results, but using decision trees, a health care professional could follow the decisions that the computer made, enabling him to change the decision by adjusting the path that the computer has taken. However, in general case, decision trees do not produce the same quality output as those methods that do not reveal the decision process. Recent advances in big data processing enable high quality decisions, while at the same time healthcare professionals are not eliminated from the process of making decisions.

2.1. Contemporary Healthcare Systems

In most contemporary healthcare systems, healthcare professionals are expected to make rational decisions, weighing up available knowledge and making choices about patients’ needs. In some cases, computers could aid in estimating the probability of diseases based on measured parameters of a patient (Wilson, 2000), (Arenson, 2000), (McLane, 2005). However, based on the knowledge about healthcare needs that both computers and human professions have, they might come to the opposite conclusions. Typically, a healthcare professional is responsible for making a decision. However, the knowledge used by computers, as well as the decision policies, are usually closed to computers, so that the computers act as black boxes. On the other hand, it would be beneficial if healthcare professions could navigate through the decision process, as authoritative medical knowledge could intersects with knowledge from other sources.

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