4 ways artificial intelligence is helping patients manage their chronic conditions

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Chronic disease management is one of the biggest challenges in the healthcare industry, costing patients and healthcare providers billions of dollars every year.

The current industrialized healthcare system of today was designed to increase life expectancy and has succeeded in bringing down mortality. However, it is not equipped to deal with chronic illness and conditions that require around the clock care.

Artificial intelligence is the future of medicine that can mimic human analysis techniques but can complete them much quicker and more accurately. AI has no limits, but in the case of chronic disease management, it is helping to change the entire landscape.

There have been many developments in this particular area – and that’s what this article is going to demonstrate.

Here are four ways artificial intelligence is helping patients manage their chronic conditions.

1. Measuring And Managing Chronic Pain

It’s no secret that many Americans, and people around the world for that matter, are living in chronic pain, but doctors and physicians can struggle to clinically diagnose it.

But thanks to PainChek AI, this may not have to be an issue any longer.

PainChek helps healthcare practitioners to detect chronic pain in patients who are unable to receive in-person care from their doctor or their carers. It has received clearance for use in Europe and Australia, making it the first of its kind.

The technology combines facial recognition with AI to monitor facial muscle movements in patients that can’t report the pain themselves. It then provides a score based off of the pain they’re experiencing.

All analysis and recordings are done via an app. The patient’s history is then sent and stored in the cloud, enabling those that require the information to easily access it.

To date, PainChek has been clinically researched and proven to be a reliable tool for assessing patients suffering from dementia. For example, in Australia, PainChek is currently used across several care homes, helping to reduce chronic pain in residents due to more accurate assessments and subsequent treatments.

2. Virtual Nurse Assistants

The University of California, San Francisco (UCSF) and the UK’s National Health Service (NHS) are using AI as virtual nurse assistants. Developed by Sensely, these assistants are referred to as avatars and are available in more than thirty different languages.

The avatars have a wide range of uses, from system assessment and accessing health records, to providing care to chronic patients.

In one example, a payer-provider service in the US is using Sensely to help patients manage chronic their own heart failure. Discharged patients are encouraged to download and install the Sensely app and are provided with scales and blood pressure devices that connect to their phones via Bluetooth.

Then, every morning, the patients wake up to a notification reminding them to complete their daily check-in. The AI avatar guides the patients to record their weight and blood pressure into the app.

Once the data has been received, the app does a risk assessment. It sends the information to the patients’ physicians so they can keep an eye on their health and supply any further treatment should it be required.

Upon trialling the technology, there was a 75% decrease in hospital readmissions in chronic heart failure patients and a further 66% reduction in costs.

3. Early Detection Of Worsening Conditions

Millions of Americans are living with diabetes, with a significant proportion of them showing signs of worsening conditions. Such an example is diabetic retinopathy which impacts on a patient’s vision.

Now, this is not a serious problem as long as it’s detected early, but it can only be diagnosed by expert ophthalmologists and high-quality equipment. Unfortunately, patients tend to put off having these checks until it’s too late, their vision deteriorates and it becomes a lot more difficult to treat.

In order to overcome this problem, a research team developed an AI called Dr Grader which makes it easier to detect diabetic retinopathy. As such, GPs can check if their patients have this condition.

Dr Grader uses a technique called deep learning, where the AI analyses lots of data to understand and provide a solution. In this case, Dr Grader compares the patient’s retinal scans with its database of disease eyes for a similar match.

This technology has had successful trials in recent years and it has been licensed for use across South East Asia.

4. Create A Positive Feedback Loop

As has already been discussed, thanks to artificial intelligence in healthcare, there is a great opportunity to enhance the work of physicians and clinicians.

This fourth method of helping to manage chronic conditions is more of a generalized benefit, rather than focusing on a specific application.

Through self-digital health management through the likes of smartphone apps and other self-care devices, healthcare organizations can collect a brand new dataset on a patient’s daily activity, outcomes and generate detailed insights.

From here, this data can be combined with, for example, their electronic health record (EHR). Machine learning techniques of the AI can then prioritize their members based on their real-time needs and deliver follow ups with providers.

AI will have created a positive feedback loop as a result, since patients are communicating with their clinician more often with timely and personalized support.

In turn, this generates more data, which then gives care teams greater insights into their health, leading to providing the best possible care at the right time, and the cycle continues.

The University of California, San Francisco (UCSF) and the UK’s National Health Service (NHS) are using AI as virtual nurse assistants. Developed by Sensely, these assistants are referred to as avatars and are available in more than thirty different languages.

The avatars created through the combination of AI and the NHS have a wide range of uses, from system assessment and accessing health records, to providing care to chronic patients.

Conclusion

These examples provide a small sample of artificial intelligence’s potential in helping patients manage their chronic conditions.

The healthcare industry is full of innovative ideas and technology, with AI being the most exciting, potentially of all time.

But it’s worth pointing out that the speed at which AI can be implemented into current systems depends on our tolerance to risk. Healthcare executives and decision-makers are aware of the clinical challenges and risks of practicing medicine, but it’s a whole other story when it comes to a machine making mistakes.

Nevertheless, it seems that it’s only a matter of when than if AI becomes standard within our healthcare system.