Machine learning and its impact in various facets of healthcare

Updated: Aug 18, 2020 11:40 AM

Mandatory practices such as Electronic Health Records (EHR) have already improved the traditional healthcare processes by incorporating big data to perform state-of-the-art data analytics.

Machine learning, healthcare, Electronic Health Records, machine learning models, COVID -19 patients, art data analytics, covid 19 pandemic, latest news on coronavirus pandemicAI/ML tools are further destined to add value to this realm.

By Inderpreet Kambo

Data in the healthcare settings is used for generating day-to-day insights as well as continuous improvement of the overall healthcare system. Mandatory practices such as Electronic Health Records (EHR) have already improved the traditional healthcare processes by incorporating big data to perform state-of-the-art data analytics. AI/ML tools are further destined to add value to this realm. From analyzing radiographs, to identifying tissue abnormalities, to improving the accuracy of stroke prediction based on clinical signs, to benefiting family practitioner or internist at the bedside in supporting their clinical decisions, machine learning is providing the much needed objective opinion to improve efficiency, reliability, and accuracy in the healthcare flow.

Predictive algorithms and machine learning are as good as the training data behind these advanced models. As more data becomes available, there will be better information to build these machine-learning models. And hence we see that much of the initial machine learning successes have come from large organizations with big datasets – Google, Aurora Healthcare, NIH of England to name a few organizations which are able to harness the data. These firms with massive amount of data facilitate the development of center of excellences (CoEs) that produce unique and powerful machine learning algorithms. Having centralized and synchronous data repositories enable deployment of these algorithms across use cases in healthcare. Below are some of the major use cases, although not comprehensive, that outlines the deployment and use of machine learning in healthcare.

Personalized Medicine

Medicine has come a long way, starting from a generalized broad-spectrum antibiotic treatment approach to disease treatment and prevention approach that takes into account individual variability in genes, environment, and lifestyle for each person. InsightRX, for example, leverages quantitative pharmacology with machine learning to provide a customized and individualized patient’s response to various treatments. By combining clinical, pharmaceutical, and socioeconomic data with machine learning algorithms, researchers and providers are able to observe patterns in the effectiveness of particular treatments and identify the genetic variations that may be correlated with success or failure.

Healthcare Management

From automating routine front office and reporting to analyzing pharmaceutical marketing research, machine learning is making strides in multiple areas of operations and management. Founded in 2010, LeanTaaSis using machine learning to optimize hospital resources such as waiting period and operating rooms. With Goldman Sachs leading the investment backing, the company has raises over 100 million dollars worth of funding and is one of the pioneers in using machine learning for improvement in healthcare management.

Machine learning guided diagnosis

Data scientists working at Google have developed machine-learning algorithms to detect breast cancer by training the algorithm to differentiate cancer patterns from otherwise healthy surrounding tissue. The machine-learning algorithm entered vast amounts of data into its system and trained to differentiate abnormal tissue pattern from normal surrounding cells. Studies show that these machine learning, predictive analytics and pattern recognition technology has been adjudged to have over 89 percent accuracy as compared to below 75 percent trained pathologists and medical radiologists’ accuracy.

Research and Development

Pharmaceutical firms and healthcare organizations have been spending billions of dollars in R&D to identify factors affectingpatient’s response and improve healthcare outcomes. However, machine learning has revolutionized research by using these factors inter alia to identify which patients will have better outcomes than others. From enabling early cancer detection to identifying COVID -19 patients who require ventilator support, machine learning is enhancing outcome based research across the various facets of healthcare R&D.

Conclusion

AI and Machine Learning continue to grow in the healthcare industry with the ever-evolving technology advancements. There have been more healthcare focused startups that deploy machine learning than it has ever been. However machine-learning models have not been implemented to the same extent in healthcare as they have been in other verticals. Firstly, machine learning is a recent technology and is far away from the state of perfection. Whether its FDA, ICMR or EMA approval, it is a long, arduous and expensive process to test, validate and approve the technology in a healthcare setting. Secondly, data privacy and security are one of the biggest barriers of machine learning adoption in healthcare. In healthcare industry, the technologies and systems must be developed so as they comply with the respective data laws and rules of governing organizations.

Despite multitude of challenges in healthcare, a need for a breakthrough in healthcare delivery is much needed. From aging population of various countries, diminishing physician to patient ratio and higher scrutiny on accuracy of diagnosis, the need for new innovative solutions in healthcare is clear and explicit. The best opportunities for AI in healthcare is where clinicians are supported in diagnosis, treatment planning, and identifying risk factors, but where physicians retain ultimate responsibility for the patient’s care.

(The author is Associate Director, Axtria, a global provider of cloud software and data analytics to the Life Sciences industry. Views expressed are personal.)

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