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Is it time to adopt a predictive model? -Med-Tech Innovation




Lucy Mackillop, Sensyne Health CMO, analyzes the role of predictive models and algorithms in healthcare and why new technologies are key to the future.

Since the beginning of the pandemic, the healthcare industry has changed and technology has become central. At the time of this writing, more than 47 million people in the United Kingdom have been vaccinated at least once with the COVID-19 vaccine, a vaccine developed and tested within a few months.

Research into SARS-CoV-2 efforts is taking place at an alarming rate, accelerating the adoption of technology to continue medical services while minimizing the risk of infection.

By adopting the latest technology and incorporating new processes, you also have the opportunity to consider further innovation in the form of machine learning (ML). ML technology can be used to quickly analyze the vast amount of data generated by the adoption of technology in health care and provide insights to clinicians to assist in decision making.

The government has pledged an additional $ 250 million to underscore the growing confidence and importance of these technologies to enhance the role of artificial intelligence (AI). Its potential role in enabling healthcare professionals to work more efficiently, better understand individual patients and provide more personalized care is exciting. There are many examples of technology already having a positive impact on the development of advanced algorithms that have significantly changed the way clinicians provide patient care during a pandemic.

Advantages of predictive models

Predictive models and algorithms are generated through the analysis of large sets of real-world data such as electronic medical records. One of the benefits of this is that you can predict the likelihood of a particular health condition occurring in a particular patient group. This facilitates early intervention and may help identify the treatments that are most likely to be effective for a particular patient.

There are many reasons why the healthcare sector can take a long time to adopt these types of technologies. For example, the fact that human interaction, not technology, is at the heart of the clinician-patient relationship. However, the use of technology has become widely accepted by clinicians as their capabilities develop and evidence of their clinical effects becomes apparent.

An actual example of this is SYNE-COV, a clinical AI algorithm launched by Sensyne Health. It provides NHS clinicians with real-time decision support. Developed in collaboration with the Chelsea & Westminster NHS Foundation Trust, SYNE-COV provides risk scores for three potential outcomes when patients with COVID-19 are hospitalized. Admission to the intensive care unit; invasive ventilation and in-hospital mortality.

A pandemic that emphasizes to clinicians that modern technology can enhance the decision-making process, provide clinicians with preventive care, and reduce pressure on medical services.

Technology to support the health of pregnant women

Pregnant health is a good example of how technology has already been accepted by clinicians and patients to help ensure the safety of pregnant women and their babies.

Regular examinations and monitoring are an important part of pregnant women’s care. Virtual processes were introduced after the government advised to reduce face-to-face bookings during a pandemic. In other words, pregnant women were supported through remote examinations.

Like much of our lives during the blockage, video calling has become the norm for healthcare. According to a UK survey, more than half (57%) of people find the ability to meet medical professionals remotely during a pandemic to be important and useful. And for the health of pregnant women, these continuous virtual connections were important in their continuous support for pregnant women.

In addition to remote monitoring, predictive modeling and algorithms have made it easier and safer for clinicians to identify when a pregnant woman needs a particular treatment or care route. They should either women are more likely to need medication to control diabetes during pregnancy, or which women should adopt specific lifestyle measures and take early interventions to help them. You can better predict what it is.

What’s next for predictive medicine?

In addition to maternal health, this predictive medical model may apply to other areas in the future. Chronic diseases such as diabetes, obstructive lung disease, and heart failure begin to benefit from predictive modeling and remote patient monitoring on a daily basis, not only accelerating disease identification, but also improving patient care.

It is difficult to be able to manage long-term care remotely, but certain short-term intensive support can be adequately addressed for patients suffering from chronic illness, especially when new treatments need to be initiated. For example, patients with type 2 diabetes who are transitioning to insulin therapy or patients with heart failure after discharge can provide intensive support and care with short-term monitoring.

With COVID-19, technology has become an more irreplaceable asset for the healthcare sector than ever before. As the understanding and acceptance of these technologies grows, so do the opportunities for them to make a difference and revolutionize the way health care is delivered and received. Healthcare may have taken longer to adopt newer technologies than other sectors, but AI and ML are now helping drive the digital healthcare revolution.




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