Each year, millions of Americans walk out of a doctor’s office with a misdiagnosis or missed-diagnosis. Physicians try to be systematic when identifying illness and disease, but they lack the appropriate medical devices.
Now a group of researchers in Canada and Serbia have introduced artificial intelligence (AI) to remedy many of the all to familiar human shortcomings that lead to mis-diagnosis and missed-diagnosis in heartcare in Primary Care.
In Primary Care, heartcare is one of the biggest challenges faced by GP’s as they don’t have the specialized knowledge of the heart, making them mere gatekeepers to Cardiology. Yet, they are the first-responders for diagnosing heart disease onset – that patient both expect and rely on.
The challenge is twofold, GP’s need to detect disease onset, and then to find a solution that would assist GP’s identify which patients need to see a cardiologist and which can simply undergo preventative or pharmacological treatments.
The role of AI to assist the GP in these challenges.
The current State-of-the-Art non-invasive diagnostic device now in use in Primary Care is the ECG (Electrocardiograph). Although GP’s learn some ECG in med-school, it takes years to gain effective practical experience. Although algorithmic augmentation has creeped into the newest devices, for example, the “Glasgow Algorithm”[1], and is assisting GP’s, the ECG itself has limitations that no amount of algorithmic augmentation can overcome – basically, ECG can only detect and diagnose heart diseases that have or leave an electrical signature. Physiological diseases are not included.
In this case, AI can only be as effective as the underlying technology it depends on.
Relative to all heart disease commonly prevalent in clinical practice, ECG’s effectiveness is limited to a miserly 44%. This ineffectiveness is not enough for GP’s in Primary Care for the widespread screening for heart disease of their patient population – it will lead to many mis-diagnosis or false positives and to missed-diagnosis or False positives.
We’re not saying ECG is bad, no, because what diseases ECG can detect, it detects very accurately and very specifically. A patient with an ECG detectable disease will likely be accurately detected. However, ECG was for years engineered for low sensitivity – to reduce FP’s, it was calibrated for use in Cardiology. This is not optimal for Primary Care as it sacrifices early onset detection for certainty of diagnosis. This is problematic for Primary Care as the first-responder for heart disease, creating a higher rate of FN, resulting in more patients returning by ambulance.
So what is a Primary Care GP to do?
For AI to truly be a Physician Assistant in Primary Care it has to help with the detection of a wider range of heart diseases, dysfunctions and anomalies, and do it earlier. As a stop gap measure intended to compensate for ECG’s ineffectiveness in Primary Care, the Standard of Care (clinical workflows) requires that an Over-reading Cardiologist (ORC) must review all patient ECG’s. This adds costs and delays to routine examinations discouraging its use on asymptomatic patients.
Although AI can assist by complementing the ORC, ECG limitations, however, cannot be addressed through AI alone. The problem is in the electrical nature of ECG signal – it’s is only good diseases that have or leave and electrical signature, limiting ECG’s effectiveness to less than ½ of all common heart diseases. That means that over ½ of all common heart diseases cannot be detected in Primary Care.
Patients, simply don’t understand this limitation of ECG, putting themselves at greater risk. In this context, AI would not have been of assistance. So only if you’re lucky enough to have a disease that is detectable by ECG, AI can assist the GP, if not, then… the situation is deceiving.
For AI Assistance to be effective, the right bio-signal technology must exist that can provide the data on all of the most common disease conditions, not just less than ½.
The most glaring omission from ECG signals is morphological and physiological heart information. Other than when that electrical signal is disturbed from its pre-set pathway, the ECG signal cannot identify with physiologically or morphologically natured diseases – those diseases that make up the other ½ of all common heart diseases. In this case, AI must be combined with technological innovations in the form of novel bio-signals to be of assistance.
However, an innovation designed for use in Primary Care is rare, because it’s not “sexy” and there is little money in Primary Care diagnostics. The big money is on the bigger ticket wonder-toys used in cardiology. The problem then becomes one of access, i.e. patients have to get into Cardiology to get access to the wonder-toys.
The access to Cardiology, however, is through Primary Care, or ambulance ride!
There is hope however as there is a startup that has for the last many years been developing a novel bio-signal whose nature is essentially physiological. When combined with ECG and powered by AI, it promises to detect well over 90% of all common heart diseases making it the ideal diagnostic device for use in Primary Care.
Compared to ECG’s 44%, this startup is going to reconfigure the Primary Care landscape.
Currently completing a retrospective clinical study in Serbia, this Canadian startup is aiming to have this device available by the fall of 2019. It’s a game-changing technology for Primary Care heartcare and extend a level of heartcare assistance to GP’s not otherwise available in Primary Care.
Called Cardio-HART™ or CHART, it will provide the GP with substantial assistance that would in essence, do the heavy lifting related to disease diagnosis. By applying AI to this innovation in bio-signal, the diagnostic learning curve is mitigated and the GP has the tool to help them do what they should – effectively diagnose the patient. The retrospective Clinical Study confirms that this the case.
AI assistance provides the GP with effective assistance at the point-of-care, where patients expect it.
In a secondary benefit, AI assistance can be just as beneficial for the ORC. At the halfway mark in the study, when comparing ECG only with CHART, ORC’s revised their diagnosis in 17% of the cases. They also expressed higher diagnostic confidence in 67% of their cases, because there was better medical justification. The only exception was in purely ECG detectable diseases, where there was no difference, as would be expected.
In this study, GPs were paired such that one reviewed one of two tests, the ECG test results and the CHART test results for the same patient. At the ½ point in the study, the results show that the when relying only on ECG, the GP’s tended to refer a patient to the cardiologist 33% more often than when compared to the CHART reading physician for the same patient. They claimed that there were two reasons for these results. First, they were generally more unsure when viewing the ECG only results, and second, but related, they were more sure after seeing the patient's CHART results, because CHART provided more complete medical information leading to higher confidence in their diagnosis. These results were consistent for both FP and FN results, even when compared to those of the ORC results.
After Primary Care, each participant received an Echo examination to validate the initial diagnosis. This was then followed-up, by a consensus phase, when 3 independent cardiologists reviewed the ECHO to determine the ground truth. This ensured that the actual FP and FN could be truly identified.
AI assistance to the GP when combined with the right bio-signal technology, is clearly leading to better patient outcomes in Primary Care.
As this prospective clinical study continues, it is clear that AI on its own is not a sufficiently effective assistant – AI needs to be combined with the right type of diagnostic technologies.
In this study, AI did not replace the GPs or the ORC but instead helped “up” their game. The assistance provided by the AI, especially through CHART, greatly improved the diagnostic outcomes for all patients but particularly in the small cities and towns where the most junior physicians were located. This front-line use of AI to provide GPs in Primary Care diagnostic assistance is clearly leading to better patient outcomes.
[1] The Glasgow Algorithm is an ECG algorithm developed by the University of Glasgow and is one of the best and most prevalent algorithms licensed for use in many ECG devices, since the mid-1980s.
