AI Powered Heartcare. Root-cause cost cutting in an intelligent but not artificial way.

Recently, research findings suggested that a meaningful reduction in variations of care is more effective than traditional cost-cutting. As an American president once said “its all about the economy stupid”. Any meaningful reduction in variations of care is really about focusing on the essential services the hospital needs to deliver in the community it serves, including the primary care community.

Heartcare represents a significant portion of any hospital budget. With heart disease responsible for more death, trauma, and morbidity than all the cancers combined, any meaningful reduction in variations of care must focus on the variations of heart care.

Yet a root-cause analysis of when and how heartcare costs are triggered reveals that they are triggered well before the patient arrives at the hospital, in primary care.

That means that an effective strategy for a meaningful reduction in hospital-related heartcare costs should really start in primary care. Until then patients will continue to be wheeled into the hospital through the ED doors, the most expensive doors to open in healthcare - not a red carpet but a carpet of red. 

But little has changed in primary care. It is still the same basic, archaic, system that “feeds” the hospital with the same basic centuries-old technologies.

A strategic new vision to cost-cutting is needed to address the issues at source. Two innovations can easily, and cost-effectively, enable that vision.

The first innovation should enable more direct collaboration between the hospital and primary care. Hospitals have the expertise that if applied at an earlier stage of disease onset, when it is first discovered in primary care, would lead to more effective and meaningful care, thereby reducing downstream, a.k.a. hospital, costs.

The earlier the onset of heart disease is detected, the more cost-effective the resulting treatment options and the more meaningful the cost savings. And patients remain in Primary Care. 

The bottleneck to this interaction is the technology between the hospital and the primary care clinics – the focus has to be on simpler diagnostic level collaboration.

Too often EHR interoperability is invoked as the panacea for collaboration when in fact it is really a red herring, a minefield of complexity, hidden costs and user problems – a shot-gun wedding is more fun.

This interaction should focus on a simple and effective solution that both fosters and enables “collaborative-triage”, where the hospital’s expertise is made available as part of the Standard of Care to help with patient triage at the primary point of care. The goal, more solid medical-justification as the basis of feeding patients to the hospital.  

This leads us to our second point. To enable effective collaborative-triage requires better Primary Care telemedicine-based diagnostic devices that can provide that provide more robust medical-justification. The right telemedicine diagnostic device is the key, it’s also the hard part.

Currently, ECG is the main “go-to” medical device for diagnosing heart disease in Primary Care (and this only with cardiologists over-read). Yet, despite its iconic status, ECG remains woefully inadequate and ineffective as a widespread screening device for use in Primary Care as the range of diseases it can diagnose is limited to 44% of common heart diseases[1]. This is a serious limitation “gap”, that patients are rarely aware of.

Currently, that gap can only be bridged when and if the patient has access to hospital-level heartcare services, which are not cheap and not readily available - you need a referral based on medical justification. A catch-22?

You need access to higher levels of heart care, to get the medical justification needed to get access to higher levels of heartcare! Yup, Catch-22!

To address this limitations gap requires advanced technological innovations that will both augment and complement ECG with new bio-signal technology powered by Artificial Intelligence (AI). 

Where are the innovators?

Conclusion

For hospitals, effective root-cause cost cutting starts with greater primary care collaboration. Novel innovations in AI-powered bio-signals will help bridge the diagnostic capabilities gap in current technologies enabling more meaningful and effective primary care level screening.

Improvements in Collaborative-triage innovations help bring together primary care and hospital level care that will help hospitals address root cause cost cutting whilst ironically providing more effective and meaningful care.

Cost reductions don’t have to mean care reductions.

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[1] Common heart diseases are defined as all heart diseases that can be identified either by Echocardiography, ECG and are not rare. Rare diseases are defined as having a prevalence of less than 1:10,000+ and do not require specialized tests or specialized equipment to detect it.  

Not for Primary Care - The Promise of AI.

By Marc Torok,

Healthcare AI specialist.

Feb 13^th, 2019.

The RSNA 2018 Annual Meeting, demonstrated the rapid rise of AI and ML in Healthcare. Around the world, the application of artificial intelligence (AI) and machine learning (ML) in medical imaging is a hot topic. While research work will undoubtedly continue and expand, there are also growing examples of AI and ML being used in real-world, clinical settings. It also targets where it is not being applied – Primary Care!

Thus far, two main areas of focus have emerged as the most promising for AI and ML applicability, the first is healthcare information and second is medical imaging. Healthcare produces a wealth of disconnected patient-related information where AI and ML promise to find trends and patterns that could lead to better patient management and better clinical decisions. Whereas in medical imaging, AI and ML are leading to better detection of disease conditions in diagnostic images that promise to assist the specialists.

The progress has been rapid, as the goliaths of tech including Googles, Facebook, Amazon, Microsoft, IBM, … and a host of others, rush-in to capture the pole position in the new race into an emerging branch of value healthcare. As AI heads into clinical use, these early use cases are helping both technology vendors and healthcare providers understand how to integrate these systems into its clinical workflows. So too, healthcare institutions are learning how to value and monetize their data, the life-blood of AI in imaging and decision support systems.

And thus far, the promise of AI is all but absent from Primary Care.

The problem - the lack of first-use medical devices designed for use in Primary Care that can be AI augmented, or AI Powered. And the data needed to feed the AI beast. Only two current Primary Care devices are ripe for AI augmentation, the ECG and the stethoscope. Other heart disease-related devices, are not for use in Primary Care, such as the Echocardiograph, CT, MRI, which requires specialized knowledge and training.

The promise of AI in Primary Care should be to increase the devices effectiveness. For ECG this means AI would have to increase specificity, which, at roughly +97%, is already at the top of its game. AI can add little to this. However, ECG is essentially a bio-signal of an electrical nature and therefore limited to diagnosing diseases that only have or leave an electrical trace. ECG cannot identify with the morphological or acoustic aspects of the heart unless the ECG pathways are altered by a disease.

The other measure of ECG is based on sensitivity. Here AI has a modest possibility to increase sensitivity, which typically is between 60-70%, dependent on the device but yet again is limited to the diseases the ECG bio-signal can detect. However, ECG has already been algorithmically enhanced, which means that again AI would unlikely be able to deliver on its promise.

However, ECG’s greatest limitation is that it can only detect about 44% of all common heart diseases typically found in Primary Care. This is understandable as ECG bio-signal is limited by its electrical nature. This also disqualifies it for use as a first-use device for detecting heart disease in general, particularly in Primary Care. It’s simple, with ECG, too many diseases would remain hidden, undetectable. So although AI could make it more sensitive, it does not make more sensitive to a broader and wider range of common heart diseases typically found in Primary Care. AI would fail on its promise.

So current algorithmically augmented ECG devices, like the Glasgow Algorithm produced by the University of Glasgow, although not true AI, already do for ECG what AI promises.   but its effectiveness remains limited to the nature of the device. However, what it can detect, it detects very well – but this is not enough for use as a widespread screening device in Primary Care!

What is needed in Primary Care is an effective first-use device that can detect over 90% of all common heart diseases. 90% is specified because above 90%, the heart diseases become very rare, and very numerous, with very low prevalence in the population. At 90% effectiveness, such a device can be relied upon by Primary Care providers to enable a reliable systematic widespread screening of the at-risk patient population, both symptomatic and asymptomatic. However, even with AI or algorithmically augmented, ECG bio-signals are simply not enough as both the acoustic and morphological aspects of the heart cannot be diagnosed, leaving more than ½ of all common heart diseases undetectable in Primary Care.

New AI powered heart bio-signal enabled first-use devices are needed. Only then, will AI be able to assist Primary Care physicians with their diagnostic responsibilities as it is in Primary Care, where patients have the first contact with the healthcare system, and expect the detection of heart disease onset to be diagnosed first. The promise of AI, is to not betray that trust.