The following is an excerpt of  Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again by Eric Topol

Better prediction of an important diagnosis in real time is another direction of AI efforts, as we’ve seen, and this issue is of huge importance in hospitals, as one of the major challenges that hospitals face is treating infections that patients catch while hospitalized. Sepsis, a deadly infection common in hospitals, is responsible for 10 percent of intensive care unit admissions in the United States. Treating it costs more than $10 billion per year, and treatment often fails: sepsis accounts for 20 to 30 percent of all deaths among hospitalized patients in the United States.

Timely diagnosis is essential since patients can deteriorate very quickly, often before appropriate antibiotics can be selected, let alone be administered and take effect. One retrospective study by Suchi Saria at Johns Hopkins Medicine used data from 53,000 hospitalized patients with documented sepsis, along with their vital signs, electronic medical records, labs, and demographics, to see whether the condition could be detected sooner than it had been. Unfortunately, the accuracy of the algorithm (ROC ~.70) was not particularly encouraging. A second deadly hospital-acquired infection, Clostridium difficile or C. diff, is also a target of AI. The data to date look a bit more positive. C. diff kills about 30,000 people each year in the United States, out of more than 450,000 patients diagnosed. Erica Shenoy and Jenna Wiens developed an algorithm to predict the risk from 374,000 hospitalized patients at two large hospitals using more than 4,000 structured EHR variables for each. Their ROCs were 0.82 and 0.75 for the two hospitals, with many features that were specific to each institution. With automated alerts to clinicians of high C. diff risk, it is hoped that the incidence of this life-threatening infection can be reduced in the future.

Preventing nosocomial infections, which one in every twenty-five patients will acquire from a caregiver or the environment, is also an important challenge for hospitals. For example, we know that lack of or suboptimal handwashing is a significant determinant of hospital- acquired infections. In a paper titled “Towards Vision-Based Smart Hospitals,” Albert Haque and colleagues at Stanford University used deep learning and machine vision to unobtrusively track the hand hygiene of clinicians and surgeons at Stanford University hospital with video footage and depth sensors. The technology was able to quantify how clean their hands were with accuracy levels exceeding 95 percent. Such sensors, which use infrared light to develop silhouette images based on the distance between the sensors and their targets, could be installed in hospital hallways, operating rooms, and at patient bedsides in the future to exploit computer vision’s vigilance.

Indeed, machine vision has particular promise for deep learning patterns in the dynamic, visual world of hospitals. The intensive care unit is another prime target for machine vision support. Reinforcement learning has been used as a data-driven means to automate weaning of patients from mechanical ventilation, which previously has been a laborious and erratic clinically managed process.

We get even bolder with the planned “extinction” of the hospital, at least as we know it today. Although we clearly need ICUs, operating rooms, and emergency rooms, the regular hospital room, which makes up the bulk of hospitals today, is highly vulnerable to replacement. Mercy Hospital’s Virtual Care Center in St. Louis gives a glimpse of the future. There are nurses and doctors; they’re talking to patients, looking at monitors with graphs of all the data from each patient and responding to alarms. But there are no beds. This is the first virtual hospital in the United States, opened in 2015 at a cost of $300 million to build. The patients may be in intensive care units or in their own bedroom, under simple, careful observation or intense scrutiny, but they’re all monitored remotely. Even if a patient isn’t having any symptoms, the AI surveillance algorithms can pick up a warning and alert the clinician. Their use of high-tech algorithms to remotely detect possible sepsis or heart decompensation, in real time, before such conditions are diagnosed, is alluring. Although being observed from a distance may sound cold, in practice it hasn’t been; a concept of engendering “touchless warmth” has taken hold. Nurses at the Virtual Care Center have regular, individualized interactions with many patients over extended periods, and patients say about the nurses that they feel like they “have fifty grandparents now.”