In the somewhat landmark paper, the authors speak to the internet of things and quantified-self in that, “Global adoption of mobile and wearable technology has added yet another dimension to machine learning, allowing the uploading of large amounts of personal data into learning algorithms. Now, within closed-loop feedback systems, mobile technology (e.g., a smartphone) is not just a biometric device (e.g., measuring blood glucose levels) but ultimately could become a platform from which to deliver tailored interventions based on algorithms that continually optimize for personal information in real time. Available for many years, implantable cardioverter-defibrillators have saved lives by using algorithms to detect ventricular fibrillation and immediately deliver a defibrillating shock to the heart. Now, wearable devices promise to improve diabetes care—a small glucose meter adherent to the upper arm can regularly sample glucose levels, which are then wirelessly fed to the patient’s smartphone to inform the patient and treating physician”

A more recent article appearing in the New England Journal of Medicine noted three key ways that machine learning will be transformative in medicine. The authors’ point is that impact will be less from big data, per se, but better algorithms.

CALL OUT

Basically, “Dr. Watson” is better than Dr. Kildare. This is due to the fact that machine learning enables the processing of millions of variables and weights their valence of influence in combination with other comorbidities and demographics.

Thanks to ever-increasing computational horsepower, “… computers can look for anomalies at the pixel level of radiographs,” for example, something tough for a human, even if an expert. The three key, disruptive areas noted are:

1. “Establishing a prognosis: Data drawn from electronic health records or claims databases can help refine these models. They say prognostic algorithms will be used within five years, though several more years of data will be needed for validation.

2. Taking over much of the work of radiologists and anatomical pathologists. They also see algorithms used on streaming data taking over aspects of anesthesiology and critical care within years, not decades.

3. Improving diagnostic accuracy, suggesting high-value tests, and reducing overuse of testing. This will happen more slowly, they say, because some conditions don’t present clear or binary standards like radiology--malignant or benign--which make it harder to train algorithms (because of the prevalence of unstructured data in EHRs and because each diagnosis would require its own model).”

Not everyone has a rosy acceptance or enthusiasm for medical applications of machine learning. Authors of the enjoyably titled paper, Voodoo Machine Learning for Clinical Predictions, examined two popular cross-validation methods and found that one approach massively overestimated the prediction accuracy of machine learning algorithms used to support clinical decision making. Furthermore, they also found that studies that used accelerometers, wearable sensors, or smartphones to predict clinical outcomes tended to use the more error-prone approach to cross-validation.

The staff at DeepMind Health (a branch of Alphabet) is working in the UK, with the National Health Service, perhaps the largest healthcare system in the world, treating about a million patients every 36 hours! About 10 percent of those folks that go to a hospital will experience a medical error or some iatrogenic harm. DeepMind Health aims “…to support clinicians by providing the technical expertise needed to build and scale technologies that help them provide the best possible care to their patients.” They are applying machine learning to medical research in the analysis of medical data with the goal to “improve how illnesses are diagnosed and treated…” and to “…help clinicians to give faster, better treatment to their patients…”

Such potentials I believe could scale to third-party payers such as private insurance companies as well as Medicare, who in turn could then scale back on case management needs (and costs and hassles), and provide more appropriate levels of reimbursement based on more accurate value-based quality of care, while providing them with cost-savings as well.