What is AI in healthcare?
Artificial intelligence (AI) is a broad term used to describe algorithms and models that perform tasks that would normally require human intelligence, such as problem-solving, reasoning, and making predictions. Some AI applications can help navigate staff shortages with robot-assisted surgery, quality of care through virtual nursing assistance tools, administrative workflow assistance, dosage error reduction and automated image diagnosis.
According to the Canadian Association of Professionals in Regulatory Affairs, AI is anticipated to impact multiple areas of healthcare by optimizing processes, conducting preclinical research, developing and discovering new drugs, preventing burnout, and developing clinical pathways. AI can help health professionals improve patient outcomes by optimizing emergency medicine, diagnostics, treatments, preventative medicine, and care delivery.
Types of AI in healthcare
AI is an umbrella term for various algorithms and systems, which can be broken down into specific functions. In healthcare, AI systems can include several methodologies to improve patient care while alleviating :
Machine learning (ML)
Machine learning (ML) is one of the most common forms of AI and involves training an algorithm to perform tasks by learning from patterns in data. The healthcare industry can use ML to detect diseases and plan personalized treatment. It can also predict what treatment protocols will likely succeed in a patient based on various patient attributes and the treatment context. To train an ML program, a human divides data into two types:
- Training sets: A human indicates whether an outcome of interest is present or absent.
- Validation sets: The system uses what it learns to indicate the potential outcome.
Artificial neural network
Artificial neural networks are more complex deep learning systems used in applications such as determining whether a patient will acquire a particular disease. They calculate based on inputs, outputs and weights of variables that associate inputs with outputs. The network adapts to the information provided (such as images) and learns, through a series of layered calculations, what features can be used to determine specified outputs, such as the presence or absence of a condition. It creates an adaptive system that computers use to learn from their mistakes and is designed to improve continuously.
A typical application of deep learning in healthcare is the recognition of potentially cancerous lesions in radiology images.
Natural language processing (NLP)
Natural language processing (NLP) includes applications such as speech recognition, text analysis, translation, and other language-related goals. In healthcare, the dominant applications of NLP involve creating, understanding, and analyzing clinical documentation (such as electronic medical records) and published research. NLP systems can analyze clinical notes on patients, prepare reports (such as on radiology examinations), and transcribe patient interactions.
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Using AI in Canadian healthcare
Data collected from electronic health records (EHR), clinical and pathological images, and wearable connected sensors have become increasingly available in the healthcare sector. This data is used to train algorithms and provide more opportunities for these systems to practice and learn. Canada’s current healthcare landscape has been noted to be conducive to adopting AI.
AI technologies are anticipated to improve healthcare using tools to increase diagnostic accuracy, improve treatment planning, and forecast care outcomes. According to a Government of Canada Report, AI has developed applications to improve:
- medical decision-making in direct patient care regarding diagnostics, prognosis, selecting treatment methods
- optimization of hospital workflows and improved inventory management
- assessment and prediction of patient needs for homecare use of wearable devices and sensor
- clinical application in image-intensive fields, including radiology, pathology, ophthalmology, dermatology, image-guided surgery and disease surveillance
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How AI could change Canadian healthcare
AI-based technologies can enhance human capabilities and improve accuracy, efficiency, and administrative processes. For employers experiencing an ongoing shortage of nursing staff and other healthcare workers, the assistance of AI could help alleviate this pressure. Investments in AI are increasing and will probably be focused on four key areas:
1. Biomedical research
Researchers are working on applying AI to solve challenges in biomedical research as more data becomes available. This type of AI promises to reduce the costs and timelines for developing and providing drugs to patients. These kinds of AI solutions may enable more innovative drug development processes and more precise medical solutions.
2. Quality of care
Research is ongoing to demonstrate AI’s capability to improve the quality of care by incorporating it into research, care delivery and recovery monitoring. AI is currently being used in advanced wound care, where clinicians can provide digital wound-management solutions. AI is also used in chronic cardiac conditions to automatically recognize relevant neural signals from patients and adjust the timing and degree of stimulation required in real time to improve treatment efficacy.
AI remote monitoring systems are another solution that will assist in determining what resources are required in care facilities and reduce the need for more workers during a shortage in healthcare staff. They can track and analyze patient vital signs in real time and quickly detect anomalies, which will be particularly beneficial for patients with chronic conditions or for those living in remote areas where access to healthcare facilities and distance are significant barriers. This approach could improve the overall quality of care and reduce the need for travelling nurses while optimizing system management.
3. Administrative tasks
AI can automate administrative tasks such as clinical documentation, support real-time staffing schedules, improve billing accuracy, reduce clinicians’ administrative burden, and allow them more time to focus on patient care. By optimizing clinic scheduling, scheduling software can improve care coordination and patient experiences by analyzing critical metrics such as frequency of visits, intensity of symptoms, and appointment preferences. Where staffing shortages exist, AI can help alleviate this burden with voice-to-text transcription, report writing, and prescription management. AI can process large amounts of data on a daily basis, reducing the time staff spends on administrative tasks.
4. System management
AI can also strengthen healthcare system management through improved bed utilization. The Canadian population is ageing, and predicting capacity and adjusting resources will become more important for managing healthcare resources effectively. This management could include optimizing supply and demand to minimize workforce shortfalls, a major priority across the Canadian healthcare system. It could also ensure accurate forecasting and the assignment of physicians, nurses, and other health professionals where they are most needed.
5. Robot-assisted surgeries
Using AI systems to assist with surgeries shows promise for increasing precision and relieving nursing aftercare. These robotic systems can integrate pre-operative medical records and present metrics in real time to surgeons. They have been most successful so far in delicate and complex surgeries. These procedures are minimally invasive, so they result in faster recoveries and shorter hospital stays, allowing hospitals to increase the number of surgeries while reducing the time spent in hospital for patients.
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Cost savings potential
Canada could experience a cost saving each year in the healthcare system by using AI to improve the efficiency of administrative tasks, the quality of care, patient and staff experiences, and optimize system management overall. AI also has the potential to lower healthcare spending in Canada while improving outcomes and experience.
Integrating AI into healthcare can transform patient care, streamline administrative tasks, and enhance system management, all while reducing costs. By improving diagnostics, treatment planning, and resource allocation, AI has the potential to address key challenges in the system, ultimately leading to better outcomes for patients and healthcare professionals alike.
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