Democratizing Healthcare by Machine Learning

<p><em>Machine learning is revolutionizing businesses, through valuable insights, predicted outcomes, and facilitated decision-making. In healthcare, the models efficiently analyze patient data, identify anomalies and offload staff. But besides a reliable algorithm are specific considerations, e.g., process adaptation, ethical aspects and continuous improvement. Despite these hurdles, machine learning is outlooked as a critical tool to democratize healthcare.</em></p>
<p><strong>Machine learning unlocks untapped business potential</strong><br />Machine learning has become a buzzword in the business world, but for good reason. The use of algorithms to analyze and learn from data enable businesses to gain insights, predict outcomes, and facilitate decision making. As a result, unlocking endless opportunities, for instance customer behavior predictions, improved supply chain efficiency and reduced costs, to gain and sustain a competitive edge in fast-paced business landscapes.</p>
<p style="text-align: center;"><em><strong>”Algorithms have been deployed to analyze ECGs and X-Rays, significantly leveraging efficiency, reducing costs and mitigating shortages”</strong></em></p>
<p>An industry with untapped potential for machine learning is healthcare. Machine learning efficiently analyze vast amounts of patient data to identify anomalies and make predictions. For example, algorithms have been deployed to analyze ECGs and X-Rays, significantly leveraging efficiency, reducing costs and mitigating shortages. But the potential upside comes with specific challenges, that need to be carefully considered.</p>
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<p><strong>Healthcare specific machine learning considerations</strong><br />Besides the challenge of developing a predictive and reliable  algorithm trained on a vast amount of high-quality data, are  other challenges that must be specifically considered to enable machine learning models in a real-world clinical setting. Healthcare predominant aspects include:</p>
<p><strong>•Process adaptation:</strong> To make the machine learning work in practice, healthcare workflow and responsibilities must be re-defined. Considerations include how to use the model, for what clinical context, and its influence over decision-making</p>
<p><br /><strong>•Ethical aspects:</strong> Besides complying with regulations regarding privacy and security of patient data, a structure and governance for fault management must be developed to handle potential life-threatening misdiagnoses by the model</p>
<p><br /><strong>•Continuous improvement:</strong> To continuously refine the model post implementation, a defined ownership in conjunction with a systematic structure to feed the model with patient data are requisites, to improve accuracy and mitigate misclassification<br />Managing listed considerations require efforts, but meanwhile establish favorable conditions for increased efficiency long-term.</p>
<p><strong>Machine learning outlook in the Healthcare industry</strong><br />It is evident that machine learning in healthcare require caution, but the massive scalability enabled by machine learning models is foreseen to be a critical enabler to democratize healthcare, increase efficiency and reduce stress on heavy loaded workforce.</p>
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