The Sibyl technology comes from the developers of macro-eyes, which is a machine learning company centered on personalizing patient care. The new technology allows care practices to manage the points in the day when patients do not turn up for appointments. In the U.S., for example, it has been estimated that patients not turning up for medical appointments accounts for some 15 percent of all appointments made, although for'worst case' situations'no shows' can account for up to 40 percent of the day's bookings. Costing medical centers money According to Benjamin Fels, CEO of macro-eyes, in a message sent to Digital Journal: "No-Shows and lack of optimization in scheduling costs healthcare providers billions, hits morale, strains operations and has implications on care that can cost lives." This was the reason why his company developed Sibyl, aiming to solve the appointments gap problem with machine learning.
Care for some of the sickest Americans is decided in part by algorithm. New research shows that software guiding care for tens of millions of people systematically privileges white patients over black patients. Analysis of records from a major US hospital revealed that the algorithm used effectively let whites cut in line for special programs for patients with complex, chronic conditions such as diabetes or kidney problems. The hospital, which the researchers didn't identify but described as a "large academic hospital," was one of many US health providers that employ algorithms to identify primary care patients with the most complex health needs. Such software is often tapped to recommend people for programs that offer extra support--including dedicated appointments and nursing teams--to people with a tangle of chronic conditions.
Developed by UK tech company Babylon, the initiative follows widespread criticism of the much-maligned hotline, which has been beset by problems. The app, initially available to people in Camden, Islington, Enfield and Barnet, will address urgent but non-life-threatening conditions. Users will be able to type details of their ailments and the artificial intelligence will ask further questions to assess their condition, while matching the responses with medical databases. According to the Financial Times, the process requires 12 messages and an average of ninety seconds to make a provisional diagnosis. In comparison, the average call time for a 111 user spanned from 10 to 12 minutes.
A widely used algorithm that predicts which patients will benefit from extra medical care dramatically underestimates the health needs of the sickest black patients, amplifying long-standing racial disparities in medicine, researchers have found. The problem was caught in an algorithm sold by a leading health services company, called Optum, to guide health care decision-making for millions of people. But the same issue almost certainly exists in other tools used by other private companies, nonprofit health systems and government agencies to manage the health care of about 200 million people in the United States each year, the scientists reported in the journal Science . Correcting the bias would more than double the number of black patients flagged as at risk of complicated medical needs within the health system the researchers studied, and they are already working with Optum on a fix. When the company replicated the analysis on a national data set of 3.7 million patients, they found that black patients who were ranked by the algorithm as equally as in need of extra care as white patients were much sicker: They collectively suffered from 48,772 additional chronic diseases.
The founder of Gliimpse, Anil Sethi, said on LinkedIn that he created the app because "there's no single electronic health record that all physicians use ... As such, the app is primarily targeted at patients with complex health records, particularly those who suffer from chronic illnesses like diabetes, cancer and heart problems. Apple's HealthKit is used by half the hospitals in the US to monitor patients with serious health issues. Since Gliimpse collects confidential patient data with "rigorous technical security," the acquisition makes sense.