HealthMedia & Technology

Artificial Intelligence & Data Gathering in Healthcare

Early Benefits

Part1: Early Sceptics

Since as long ago as the 1850’s scientists have realized the benefits to public health of collecting mass data to establish where people’s mannerisms and habits at work and play affected their environment concerning diseases.

In the renowned case of the Cholera epidemic in London’s Soho district back in 1854, a famous Doctor John Snow walked the streets and knocked on doors to map out where the disease was concentrated to prove his theory against the medical establishment of the times, that the disease was borne by foul water. That was one of the first examples of Data gathering to assist healthcare.

Many of us are suspicious of the authorities’ gathering data on the population, and rightly so when used to track our views and politics, but where healthcare is concerned, it has proved essential. The comparison of personal data across the discovery of treatments and the progress of new viruses are now used worldwide to find cures for the benefit of humankind.

From the early 1900s, Ernest Codman, a medical reformer who ruffled a few establishment feathers in the USA, stated that the use of data was “one of the greatest moments in medicine.” He was a driving force in the use of data-driven reforms. One successful campaign involved a “Registry of Bone Sarcoma” in 1920, aiming to collect information across the US on bone cancers. From that data, he could standardize and diagnose various treatments’ effectiveness.

He got doctors to send everything involved in their treatment and diagnosis, including x-rays, case reports, and tissue samples, into their central registry, where Codman and his pathologists compiled the evidence. His research was so successful that by 1954, the American College of Surgeons found a home for his registry, which had 2400 records of completed caseloads.

Data Against the Establishment

Codman was a fiery campaigner for data gathering in healthcare, much against the medical establishment’s views of the time. Eventually, the success of his registry and his methods of forcing the professionals to send in their data changed how medical knowledge was shared widely and not just between a few supposedly talented seniors. But, as is often the case, he had to fight a class-ridden establishment to get his methods accepted as the norm.

After Codman’s registry’s success in the US, Britain began the “Mass Observation Project” in the 1930s, a data-gathering exercise to discover the representative averages. It collected many diverse and different characteristics and information into one efficient unit. That use of representative averages could then be applied to many and various subjects, from diseases to people’s features, even to their voting intentions. Learning from reading that data, once collected and collated, was soon picked up by the military to apply to their increasing early interest in computer science.

Codman’s successes encouraged others to follow. As a result, the art of statistical analysis grew so that by the 1960s and 1970s, renowned scientists had discovered the link between smoking and lung cancer, yet still had to convince a skeptical medical establishment of the value of statisticians.

Despite the increasing proof of the successes using data and statistics, it wasn’t until the 1990s that computers began to be recognized as a help in compiling the data to manage healthcare. Likewise, in America, it wasn’t until 2004 that official Presidential reports were released criticizing the medical institution’s failures in embracing artificial or information technology. As a result, a national coordinator for health information technology was established to create an electronic medical record for all US citizens by 2014.



Part 2: Changes in Future Treatments:

Despite an ongoing battle between the medical establishment and those that saw the benefits of data gathering, the results dictated the future. By 2019 a US study highlighted the way forward using statistical algorithms in diagnosis but there were still battles to be overcome. The worry that Artificial Intelligence would replace actual physicians drove much of the reluctance, but many medical professionals began to see the positives of the assistance it could give them in their decision-making when in 2020 a clinical oncologist at Addenbrooke’s Hospital in Cambridge stated that it was important for patients to know that AI was a big help professionally and that they still had the ability to override it if they judged differently.

The study of genetics using the data of the US Human Genome project highlighted the differences in racial profiling in the collection of the data when they discovered that 96% of the information gathered was from Europeans. The collection of the data had to become more personalized. This discovery has then highlighted the fact that pharmaceutical companies would only devise drugs to suit mass markets because of their profit-driven requirements.

Subsequent independent surveys into how the public viewed the advance of the use of AI in healthcare showed that most patients believed it would improve treatment though wary of how much of a role it would have in their diagnosis or treatment.

Patients may benefit from education on how AI is being incorporated into care and the extent to which clinicians rely on AI to assist with decision-making. Future work should examine how views evolve as patients become more familiar with AI.

With doctors in short supply the use of AI worldwide, where it can link experts across the globe, can help speed up treatments, and introduce up-to-the-minute benefits and changes in methods, while patient numbers increase.

We are in the throes of a digital revolution in medicine where computers can generate models of proposed procedures before a new treatment is trialed. Doctors can carry out comparisons between virtual computer-generated trials with actual clinical procedures. The data generated is shared with medics across the world. (Watch the video in the economist link in the sources below)

AI in healthcare has massive potential from mobile coaching solutions to drug discovery coming under the umbrella of what can be achieved with machine learning and data gathering.

At the World Economic Forum’s annual meeting, they predicted that by 2030 the breakthroughs in powerful technology, data science, and artificial intelligence, would connect experts and help health systems deliver proactive, predictive healthcare.

They stated that by 2030, healthcare systems will be able to predict when people are at risk of developing a chronic disease and diagnose preventative measures before they get worse. Chronic diseases involved most deaths worldwide in 2020. These improvements in AI have meant that rates of diabetes, congestive heart failure and COPD (chronic obstructive heart disease), which are all strongly influenced by social determinants are finally on the decline.

In 2030, a hospital will not be one big building covering a broad range of diseases; instead, will be made up of smaller hubs such as clinics, same-day surgery centers, specialist treatment clinics, and even people’s homes.

These various hubs will be connected via the digital infrastructure. Centers analyze clinical data to track supply and demand throughout the network. As well as using AI to identify patients at risk, this network will remove bottlenecks in the system and ensure that patients and doctors are directed to where they can get the best treatment when required.

In 2030, AI-powered predictive healthcare networks will reduce wait times, and assist staff workloads along with the paperwork side. With the increase in the use of AI in clinical practice, the medical staff will grow to believe in its uses to assist in surgery and diagnosis.

The information gleaned from the increased data gathering from every patient AI will dictate healthcare results, reduce staff shortages, and assist funding. By connecting treatments using those computer-generated hubs, and improving hardware, and software the web of networks will improve health and well-being globally.

Between now and 2030 governments, health systems, and private companies worldwide must continue working together to ensure AI systems are connected and prevent inequality. As healthcare continues to globalize, so will the need for international standards that protect how AI uses personal data as an urgent priority.

Countries must utilize AI’s power in improving human capabilities, not replace them. At the heart of connected care isn’t new technology, it’s people: the people are the market who drive needs as are the clinical staff who work so hard to deliver it to all of us.





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