For me, one of the major future changes in diagnostic medicine will be the use of predictive analytics based on deep learning and big data (see: Integrated Clinical Research Databases: A New Way to "Monetize" Clinical Data; What Are the Consequences of Big Tech Entering the Healthcare Market?). This new science will enable the prediction of future "outcomes" for patients. This trend was emphasized by former Google CEO Eric Schmidt in a lecture at HIMSS 2018 (see: HIMSS 2018: ‘Run to the Cloud,’ says Former Google CEO Eric Schmidt). He referred to the trend as leveraging the power of predictive analytics. Below is an excerpt from the article:
Chief among innovations — what Schmidt called “the really powerful stuff at the edge” of his work — are predictive algorithms, he said. While it’s one thing to be able to classify, it’s another thing entirely to be able to predict next steps. “We have physicians within our company who believe that if these algorithms for prediction work, we can predict outcomes in the ER, for example, 18 to 24 hours earlier than any other observation system,” Schmidt said. “We can’t predict our own fates, but machines can. That’s what I want as I age: I want the computer and all this work I’ve done over my whole career to make sure that I have a healthy life.”
It's interesting that Eric Schmidt chose to emphasize the value of predicting outcomes in the ER during his recent lecture at HIMSS. ER visits are a major source of revenue for hospitals in that they account for a large percentage of inpatient admissions (see: The Hospital Emergency Department Is Now the Admissions Department). ER visits can also result in malpractice lawsuits for a hospital (see: AMA Study: EPs Rank 5th in Liability Claims Frequency). One common challenge are ER patents presenting with a history of chronic headaches. ER physicians may order expensive brain scans with computed tomography (CT) or magnetic resonance imaging (MRI) for such patients, a practice often viewed as defensive medicine (see: Signs and symptoms of patients with brain tumors presenting to the emergency department). Emergency physicians are thereby caught between a rock and a hard place, obliged to rule out serious disease but also subjecting themselves to criticism for over-utilization of services.
The ER is an example of a hospital unit where predictive analytics could potentially provide guidance for the workup of ER patients presenting with headaches. The analysis of many thousands of records of patients presenting with a history of headaches and perhaps no other obvious symptoms could yield a useful set of recommendations that would avoid over-utilization of services. The best way to diagnose early brain tumors may depend on some subtle differences in routine blood tests or physical changes that are not recognized at the present time. I like the quote used by Schmidt in the excerpt above relating to predictive analytics: We can’t predict our own fates, but machines can.