Healthcare
Reducing healthcare staffing shortages with AI solutions
The unprecedented pressure on the healthcare industry during the fight against the coronavirus has exposed many lacunae within the industry. The pandemic caught the world napping. In spite of the technological advances, we were found wanting in many areas. One of the major challenges the industry faced was the unprecedented pressure on health care professionals (HCP). The 2-year long battle against the virus overworked the industry’s resources leading to fatigue, burnout, mental stress and dissatisfaction among the HCPs. The nature of work in nursing is such that they had to work in close quarters with the patients. The stringent biosecurity measures, increased workload, and witnessing innumerable patient deaths led to severe stress on physical, mental and emotional levels. This resulted in a high and expensive turnover in the sector.
The American Nurses Association (AMA) has predicted that Registered Nurse (RN) jobs would have more demand and supply than any other profession in 2022. The US Bureau of Labour Statistics has predicted that RN will continue to be the top growth occupation with a 9% increase through 2030. The average cost of turnover for one staff RN has also been increasing by over 8% per 2021 data. The shortage of nurses, especially bedside nurses, is real and is affecting patient care. The surging cost of HCP, new therapies and infrastructure have added to the overall increased expenditure of hospitals and reduced their revenue. Countries like the US have an increasing ageing population with a longer life expectancy (77 years per 2020 data). The dependency on continued support of health services means that the industry has to find sustainable solutions to the staffing shortages. As the world now settles into the endemic phase of the infection, we must urgently tackle the shortage of healthcare resources – both human and infrastructure. The industry has to look at multi-pronged approaches to deal with these challenges.
Artificial Intelligence (AI)
had its origins in the 1950s and has since undergone sea changes with respect to its build and applications. AI has long made in-roads in diagnostic accuracy, precision medicine, prognosis evaluation, computer-aided detection in oncology, radiology, gastroenterology and neurology to name a few. The four main fields of AI which have accomplished these are Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP) and Computer Vision. AI is now being leveraged in new ways to navigate the shortages in the workforce while improving efficiencies in processes, managing unpredictable capacity demands, backlog management, and replacing repetitive tasks.
A report by Accenture has predicted that there could be $150 billion in annual savings due to AI applications in healthcare by 2026. Some of the top AI applications that can help navigate through staff shortages are robot-assisted surgery, virtual nursing assistants, administrative workflow assistance, dosage error reduction and automated image diagnosis.
Diagnosis
AI can help in the diagnosis of malignant tumours with far more efficiency than the human eye. Computer Aided Detection (CAD) and Image analysis have made radiology tests more efficient and precise.
Streamlining Processes
Operating Rooms (OR) can bank on predictive technology tools to optimally manage the schedules and resources available and also keep tabs on changing environments – both micro and macro. AI can help in better communication within the hospital and other stakeholders. Intelligent scheduling with AI can lead to optimal utilisation of resources.
AI tools
The burden on clinicians can be reduced by providing tools like AI-enabled symptom checkers enabled with urgent care settings to ensure the use of emergency services only when needed. This would address about 20% of unmet clinical demands.
Robot-assisted surgery
Robotic systems that assist surgeons by increasing precision, flexibility and control during surgeries are a boon for patient safety. These systems have the ability to integrate all preoperative medical records across platforms and present the metrics real-time. These are most useful for delicate and complex procedures. The minimally invasive surgeries result in shorter hospital stays and quicker recovery, so hospitals can cover more ground with an increased number of surgeries.
Virtual nursing assistants
With the use of these assistants, patient symptoms are monitored remotely and alerts are issued to clinicians only when needed. Reports suggest that almost 20% of nursing time can be saved by employing these tools so that unwarranted hospital visits can be avoided.
Workflow assistant capabilities
Activities such as voice-to-text transcription, report writing, prescription recording and printing, and processing different types of data generated on a daily basis, can be handled by AI. These systems can consume voluminous data and provide insights on workflow improvements as well.
Though there is a lot of promise in integrating AI in different aspects of healthcare, AI adoption has to address many aspects of legal, ethical, medical and societal questions in parallel.
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