Impact of AI on the healthcare workforce
Healthcare systems are dealing with greater demand from patients. The relentless drivers driving demand include population ageing, shifting patient expectations, and a shift in lifestyle choices. Among these are the effects of an ageing population. By 2050, one in four individuals in Europe and North America will be over 65, putting more patients with complicated medical needs on the healthcare system's plate. Managing such patients is costly, and it necessitates a paradigm shift away from episodic treatment toward one that is considerably more proactive and focused on long-term care management.
The cost of healthcare is just not keeping up with inflation. Healthcare systems will only be able to remain viable if big structural and transformational changes are made. Health systems also require more extensive staff, but the World Health Organization estimates that almost 10 million physician, nurse, and midwife shortages will exist globally by 2030, even though the global economy might provide 40 million new health-sector jobs by 2030. Not only do we need to recruit, educate, and retain more healthcare professionals, but we also need to make sure that their time is spent where it is most valuable—caring for patients.
Artificial intelligence (AI) has the potential to revolutionise healthcare by improving care outcomes and the productivity and efficiency of care delivery by letting healthcare practitioners spend more time looking after patients and raising staff morale and improving retention.
AI has enormous potential to improve productivity and efficiency in health systems and make them more sustainable – but more importantly, it has the potential to deliver better health outcomes for patients.
AI usage is increasing
AI in healthcare is a broad definition that covers a wide spectrum from natural language processing (NLP), image analysis, to predictive analytics based on machine learning, which can be applied to both clinical and non-clinical settings.
A study by EIT Health and McKinsey & Company looked at 23 current uses and included case studies for 14 of them. While there are many questions regarding the use of artificial intelligence in healthcare today, these examples demonstrate how AI can affect a wide range of fields, from patient-controlling apps to online symptom checks and e-triage AI tools, virtual agents that can do hospital jobs, and bionic pancreas that helps diabetic patients.
Some aid in the improvement of healthcare operations by optimising scheduling or bed management; others aid in the improvement of population health by predicting the risk of hospital admission or assisting in the early detection of specific cancers, allowing for intervention that can lead to improved survival rates; and still others aid in the optimisation of healthcare R&D and pharmacovigilance. Many solutions are still limited in scale, but their rising adoption at the health-system level implies that change is speeding up. Most of the time, the question isn't whether AI can influence but rather how to maximise impact while improving the user experience and driving adoption.
Clinicians may discover that the benefits of AI extend beyond task automation and include more data in their decision-making. Predictive tasks are already being incorporated into healthcare processes, with machine-learning models with clinical decision-support capabilities enhancing diagnostics and illness classification.
AI will eventually make sense of the massive amounts of data generated by genetics, biosensors, smartphone apps, electronic health records, unstructured notes, and data on social determinants of health, allowing physicians to provide high-quality, patient-centred treatment.
However, for machine learning to be valuable to physicians, the data must be accurate—a need that may force healthcare personnel to take on additional responsibilities related to data integrity. They may need to gain new digital skills like digital and AI knowledge, data awareness, and agility.
Staff will need to learn new skill sets and competencies to take advantage of AI's capabilities. The educational pipeline will need to prepare people entering the healthcare field with unique talents.
AI can reduce the potential risks of using Electronic Health Records (EHRs)
EHRs have aided the healthcare industry's transition to digitalisation; however, the change has resulted in many issues, such as cognitive overload, endless documentation, and user burnout. EHR developers are now using artificial intelligence to design more intuitive interfaces and automate some of the repetitive tasks that take up so much of a user's time.
The three tasks users spend the most time on are clinical documentation, order entry, and sorting through the in-basket. Although voice recognition and dictation assist in improving clinical documentation, natural language processing (NLP) techniques may need to go further.
DNA Genotyping and Sequencing via Unsplash
How AI will change the healthcare workforce
Automation and AI will affect the number of jobs in healthcare, as the demand for occupations is set to increase, even allowing for the fact that approximately 10 per cent of nursing activities could be freed up by automation.
AI can help healthcare practitioners refocus on and improve patient care by removing or minimising time spent on routine, administrative tasks, and by improving the speed and accuracy in the use of diagnostics, giving practitioners faster and easier access to more knowledge, and enabling remote monitoring and patient empowerment through self-care. Similarly, predicting the number of patients and staff sick leave allows organisations to have a more proactive plan and allocate resources more efficiently, putting less pressure on nurses to work overtime.
Saving time isn't the only advantage. Using an AI-powered prediction solution, Trondheim municipality reported that it reduced 50% of its reliance on agency staff, which is always a costly option when there are not enough nurses. Also, AI will increase the speed and accuracy of healthcare services, resulting in better patient outcomes, increased productivity, and improved care delivery efficiency.
New professionals will emerge at the intersection of medical and data-science expertise, including medical leaders, designers, data architects, specialists in genomic medicine, and genomic counsellors. Institutions will have to develop teams with expertise in partnering with, procuring, and implementing AI products.
Keep moving forward
To create a sustainable healthcare system, we need to leverage the advantages of AI technology to cope with the increasingly rising demand for patients. While it might need time to maximise the impact of AI in clinical settings, leveraging the technology in operational settings could bring many benefits to healthcare practitioners by freeing them up from administrative tasks. As a result, they will be more efficient and be able to refocus on taking care of their patients.
If you wanna try to start applying AI in your daily operation activities, you can start with SynPlan, which offers predictions of sick leaves and patients, as well as budgets for healthcare organisations.
Interested in what SynPlan can predict? Have a chat or request a demo with us!