A study conducted in London found a significant correlation between waiting times in hospital emergency rooms (ED) and an increase in deaths during the following 30 days. This unique big data study shows that ED waiting times and delays in decision-making impact patient outcomes. Delays of 4-6 hours after arrival will lead to an 8% increase in the risk of mortality in the following 30 days, delays of 6-8 hours will lead to an increase of 14% and delays of 8 hours or more will lead to an increase of about 16%.
"Figure 1 shows the SMR for all-cause 30-day deaths plotted against duration in the ED from time of arrival. It demonstrates an increased SMR for patients admitted to the hospital after 5 hours. The SMR increases in an approximately linear fashion from this point until accurate data are unavailable after 12 hours. If a linear model is applied to the data between 240 and 720 minutes, then the SMR is found to increase by about 0.008 (95% CI 0.003 to 0.013) points per minute (p<0.001)."
In Israel, a third of patients will wait more than 5 hours for their first contact with a doctor. About a quarter of the patients will stay in the emergency room for more than 5 hours, even after the doctor decides whether to be hospitalized or released. More information can be found at the Israeli Ministry of Health (PDF download, Hebrew only). As can be seen in the graph, in Israel, the trend of staying over 5 hours in the emergency room worsens as the years go by.
At Maisha, we provide a predictive analytics SaaS platform for hospitals in Canada and in Israel. Research results and data from the Israeli Ministry of Health are consistent with the existing data we analyze at Maisha Labs - long stays in emergency rooms increase the risks of serious diseases and mortality. Our models allow us to support patient flow in the emergency room and the rest of the hospital. Improving the patient flow leads to lowering the load in the emergency room, reducing costs, and improving patient experience.
Our reliable forecasts are provided shortly after a patient's arrival to the ED, which allows the ED team to fast track appropriate cases, and allows faster discharge for those patients. All of which frees up ED capacity for those patients with higher acuity.
Our tools enable faster treatment while increasing the doctor-patient quality time, one of the most limited resources.
On a personal note, as a data scientist at Maisha Labs, I am happy to see how the models we develop are improving patient flow. It is a great feeling to know that my work helps reduce the load on our doctors and nurses. Working on KPIs like improving care quality, and reducing risks from prolonged hospital admissions is an endless source for our deep sense of purpose.
