Poor systems deliver poor results, and, in the case of US healthcare, the pile of problems has been growing for years. From lack of transparency to high costs and administrative inefficiency, the system has created an environment where patients and medical staff suffer. During the global pandemic of COVID-19, all of those pain points have only intensified and got worse.
This article will delve into only one of the many facets of the US healthcare system that need reform — inefficient patient flow. We will explore how overworked medical personnel in the United States are, cover the consequences of the coronavirus pandemic in terms of diagnostics efficiency, and demonstrate how automation and AI can help doctors serve more patients while staying safe and protected.
According to The Physicians Foundation 2018 Physician Survey, doctors in the US see 20 patients a day and work 51 hours a week, having to cover from 1,800 to 2,500 patients nationwide. In addition to that, almost 25% of their time is taken up with tedious non-clinical paperwork.
These figures perfectly correlate (and explain) to the findings of the 2018 study on burnout of healthcare professionals, according to which:
High workloads, stress, and burnout affect the well-being of healthcare workers and negatively impact patient care, causing higher mortality among patients and much faster dissemination of hospital-transmitted infections.
Burnout has reached significant levels among United States healthcare professionals, with over one-half of physicians and one-third of nurses experiencing symptoms. Burnout among physicians has increased since 2013.
In comparison, doctors in the European Union, on average, work no more than 48 hours a week, ranging from 37 hours for experienced physicians to 56 hours for junior doctors. Most countries in Europe employ more physicians per 1000 of the population. Nonetheless, doctor burnout is also a severe issue in the EU (primarily due to rapid population aging, reductions in financing, and a shortage of doctors).
High (and inefficient) patient flow is a universal challenge. Doctors suffer under workloads beyond their capacity in the US and countries as diverse as Germany, Japan, Argentina, and China. The coronavirus pandemic has only worsened the situation, increasing burnout rates while bringing down diagnostics efficiency and accuracy.
The COVID-19 pandemic has placed an immense physical and mental strain on healthcare workers across the globe. One of the earlier studies on the effects of coronavirus on doctors’ mental health showed that physicians treating COVID-19 patients in China suffered from depression, distress, anxiety, and insomnia at a disproportionately high rate.
The response by the governments and the healthcare systems has only mounted since the first days of the pandemic, which meant longer working days and a higher risk of acquiring COVID-19 for medical personnel. Those, combined with the lack of personal protective equipment (PPE) and testing kits, were the leading factors to cause stress and anxiety and, respectively, negatively impact the accuracy of diagnostic decision‐making.
To remedy the situation, “flatten the curve” measures, from quarantine to social distancing, were implemented. Eventually, the strategy enabled healthcare services to manage the same volume of patients better while allowing doctors and nurses to rest more at home.
Healthcare organizations were pushed to implement various diversion mechanisms to better manage capacity to the patient flow. By default, all patients were sent to alternative care sites to limit the spread of COVID-19 in hospitals.
Despite all the measures, the pandemic has made the US health care access problems even worse. For instance, while healthcare professionals on the front line of COVID-19 had to work double shifts, primary care doctors stayed out of work.
Fortunately for both doctors and patients, the solution to a significant portion of healthcare problems, encompassing care access, doctor workloads, diagnostics efficiency, and red tape — specifically in the dire times of coronavirus — lies in the domain of technology. Artificial intelligence (AI) and machine learning (ML) have all the “tools” to aid humanity in our combat against SARS-CoV-2.
High and efficient patient flow is critical to any healthcare system (specifically in times of crisis). Patients must be served with a minimal delay at every stage of care to increase their chances to rehabilitate without complications, on the one hand, and to drastically reduce the mortality rate for vulnerable populations, on the other.
In the case of the COVID-19 pandemic, it is of vital importance to optimize patient flow. Because the virus is highly contagious but often develops asymptomatically, care providers must be able to diagnose and triage faster than usual in a healthy and safe environment. Otherwise, healthy patients can be misdiagnosed (and catch the virus in the care facility), while medical personnel can also get infected.
COVID-19 has become a challenge of the century for the global healthcare systems. Faced with a surge of COVID-19 patients, they had to act reactively to increase capacity and improve patient flow. Doctors and nurses already suffering from burnout had to buckle up and fight against the pandemic, often without proper PPE and testing kits.
AI and machine learning might be the solution that overstrained healthcare systems need. Offering a wide range of automation and enhanced decision-making use cases can shift the burden from humans to machines to improve safety, increase diagnostics accuracy, and improve patient flow.
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