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5 Ways AI-based Decision-Making Is Changing Healthcare Right Now, by GiniMachine
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5 Ways AI-based Decision-Making Is Changing Healthcare Right Now

Artificial intelligence (AI) in healthcare is growing year-on-year. From its 2020 value of over $6.7 billion, it’s set to increase at a CAGR of 41.8% between now and 2028. However, it’s not just the AI in healthcare figures that are growing, its use cases and potential are expanding too. 

The power of AI decision-making is already being implemented in several key healthcare areas, such as healthcare planning, but in the near future, we will see more and more AI-based solutions, from digital healthcare solutions to the way we do insurance. Below, we’ve brought together the top industry trends and predict what we’ll be likely to see in the next five years.  

5 Ways AI Decision-Making Is Transforming the Healthcare Industry 

5 Ways AI Decision-Making Is Transforming the Healthcare Industry, by GiniMachine

Data is everything, and it’s only through being able to analyze it and do so smartly that businesses, including those that deal with healthcare can expect to survive and thrive in the modern market. 

1. Disease prediction based on statistical data

As the world’s population ages, the trend in healthcare could be switching from disease curing to disease prevention. With approximately a 56% growth rate expected in the numbers of those over 60 by 2030, that will make up around 1.5 billion people who are considered older adults. While the increase in lifespan speaks to the successes of medicine, it also presents a challenge to healthcare systems worldwide as older populations tend to have greater and more complex healthcare needs.

This is where AI prediction models come in. Big data from chronic diseases can be analyzed more carefully, and conditions can be diagnosed and treated at earlier stages. In addition, it would also be possible to combine data from a patient’s medical history and predict their individual recovery plan, meaning less costs on the healthcare system overall. 

2. Managing pandemics

If 2020 has shown us anything, it’s how vulnerable we still are to emerging illnesses, and studies suggest (as much as we mightn’t want to believe it) that the coronavirus pandemic might not be the only one we see. However, just as viruses emerge so too does the technology that helps us fight them. AI decision-making tools in pandemic management can be used in a number of ways.

For example, using AI to analyze Big Data can help predict the behavior of a virus or pathogen—plotting its appearance, symptoms, typical incubation period, etc. All of which would help medical professionals make the right care decisions at the correct time. 

In addition, on a more global scale, AI could analyze and predict how a pandemic moves, and suggest strategies to lower its spread—specific area lockdowns—or eradicate it completely, while causing minimum issues for the wider population.  

3. Digital healthcare solutions

With remote healthcare gaining popularity, and not only due to the pandemic, AI-based decision-making solutions are changing how medicine is done. In recent years, we’ve observed an increase in healthcare apps, which engage AI tools, such as data monitoring to alert to potential health conditions. This not only helps the patient get the right treatment when they need it, but also collects vital data for the physician over a period of time. 

In addition, techniques, such as natural language programming (NLA) can be used to translate written or spoken notes into usable patient data. This can then be utilized to support the patient care process and deliver the right treatment.  

4. Financial management & insurance

Every year in the US, approximately $91 billion, yes billion, is wasted in the healthcare sector. Some of the reasons listed include failure of care delivery, care coordination, overtreatment, pricing, fraud, and administrative issues, all of which combine to make one hefty healthcare bill. So, how can AI help reduce waste and improve efficiency?

There are several options (and the list of potential solutions is growing). For example, let’s take the financial management of a care facility. By knowing more about patients, their needs, and staffing levels, care facilities could more accurately predict needs and avoid overstaffing, medication waste, and more, while still delivering services that meet the needs of the population.

Or what about insurance? Affordable healthcare insurance is a problem for millions, if not billions of people worldwide. AI-based decision-making tools put the power in the hands of insurance providers to offer more tailored policies in line with individual risk, and provide individual coverage, and at the same time reduce the risk of fraud through smarter security procedures. 

In addition, for those seeking to lend for healthcare, whether in terms of individuals or to organizations, such as research facilities, AI-based decision-making tools like those offered by GiniMachine, give lenders the capabilities to use a wider range of available data and assess lending risk based on individual circumstances. 

5. Risk and success assessments

Clinic Decision Support Systems are being adopted in healthcare facilities as a way of reducing risk and increasing success when it comes to patient care. These AI-based, decision-making systems utilize the power of neural networks and machine learning (ML) techniques to assist doctors and other medical staff in their work. 

They do so by collating and analyzing available data based on the patient and their condition to suggest diagnoses, risk factors, and outcomes, in terms of specific patients. This can then be used by a trained physician to help a patient understand their risks and come up with a workable treatment plan. 

Many still debate the ethics of AI-based solutions, that draw into question the role the ‘computer’ has over the lives of humans. However, many argue that it could prove a valuable tool in reducing the rate of error and increasing the changes of treatment success. 

Next steps for healthcare providers

With numerous solutions out there, healthcare providers will need to intensely assess their needs, that of their patients, and budgets before diving into an AI-based decision-making care solution. While no technology is cheap, it’s important to get the most value from any solution as it will naturally draw away from first-line resources. That said, the long-term potential is immense. 

For those providers seeking a lending-based solution, we recommend looking at GiniMachine’s offering, which delivers a smarter way to lend and get results. 

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