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Creating value from healthcare big data requires investing in essential human capital, not just machines and technology

October 08, 2021
Artificial Intelligence CT MRI Ultrasound X-Ray
Moira Schieke
By Dr. Moira Schieke

As a radiologist and physician founder, I understand how today’s data deluge is toxic to patient care. Radiologists provide a core value that exploded into use only weeks after the first x-ray was taken by Roentgen over 125 years ago. They have turned an increasing variety of medical image data (MRI, CT, Ultrasound, etc.) into value through visual insights, diagnostic decisions, and actions for patient care. Medical imaging volumes exploded by ~10-fold over the last 20 years. Meanwhile, radiologists have remained strapped to legacy viewers requiring review of each and every image, now at the dangerous pace of every ~3-4 seconds. Professional quality of work life dropped with record-level burnout rates. Resulting physician shortages were further fueled by the narrative that radiologists will be replaced by “AI.” Most concerning, patient care outcomes may be declining. We fueled a toxic supply chain, and we are now at a crisis point.

In response to the data deluge, we have seen a rise in new machine-centric visionary markets. “Artificial intelligence” for medical imaging (coined “AI Automation”) received billions in funding, yet now sits in the trough of disillusionment on the Gartner hype cycle. We see enormous AI failure rates in “open” clinical patient care environments (~93%) and almost all FDA approvals have recently been called into question. AI practitioners took direct aim at disrupting the value proposition of human experts, attempting to “simulate human-like intelligence” with machine autonomy in decision-making and actions. It failed.

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We can choose a smarter path. We can be more strategic and instead align with a patient-centric visionary market: decision intelligence, an identified top Gartner trend. At Cubismi, we define decision intelligence as a formalized approach to augment professional decision-making and actions using established decision pathways and modern algorithmic tools under expert control. Delineating how it is different from “artificial intelligence” rests in understanding AI’s limitations and the critical abilities of a trained human medical professional (figure 1).
According to Turing award winner Dr. Yann LeCun, AI is a categorizer that does not possess the human abilities that help us navigate the world. As physicians understand, cognitive “frames” for decision-making are formed by many years of education, residency training, and direct clinical experience. “Framing'' allows us to make good decisions from small datasets. They allow professional diagnosticians to turn data into value as we have done for centuries, yet now with a new technical twist. In my view, the cloud provides the best cyber secure core technology for decision intelligence to optimally augment professional performance (figure 2). It supports highly accurate categorizations of health data using modern algorithmic tools. Ingestion from innumerable patient care sites with varying levels of financial resources and geographic locations, including underserved areas, helps us best organize and leverage massive data. Yet, it also supports human-computer interaction (HCI) ergonomic management of data and leverages the essential value of trained professionals for optimized returns: high efficiency in making better decisions and taking better actions.

I argue that decision intelligence (DI) under expert control provides a superior patient-centric strategy and pathway to these optimized returns (figure 3). It creates a lever for decreasing costs, diminishes short-term and long-term risks, and increases potential benefits for patient care. We can predict low cost and cyber secure cloud ingestion will flatten the costs of machines and raw data as new combinations of existing data drive new insights. A DI strategy diminishes the high risks that “AI” won’t perform as AI practitioners predict and the potential for unexpected patient harm. It diminishes the risks of losing critical human capital trained on generations of validated clinical science who have been successfully turning imaging data (now 90% of the health data footprint) into value for over 125 years. Maintaining the well-established valuable patient-professional fiduciary relationships also implies higher patient care benefits. Some will assert that my predictions here are false; they will proclaim that “AI” just needs more and more data to deliver superhuman “closed system” outcomes. It is thus important to re-emphasize that DI HCI systems will leverage big data, but also small data through expert cognitive “frames,” a safer, proven, and scientifically based approach. Robust DI HCI systems will turn a wider variety of “open” clinical data into valuable clinical insights, high quality decisions, and better patient care actions.

Making predictions about the future is hard, yet we face enormous risks if we make the wrong choices based on speculation. We can’t afford to lose the essential human capital we need to turn exploding healthcare data into new value. It’s simply not a high-level long-term risk worth taking. We must instead empower professionals to help direct the best disruptive business models for healthcare’s technology supply chains that drive precision patient care at lower costs.

Success for healthcare’s digital transformation will hinge on investment in essential human capital, not just machines and technology.

Dr. Moira Schieke is the founder and CEO of Cubismi, a medtech company pioneering interactive digital diagnostics for personalized actionable visualizations and precision insights for each patient’s journey.

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