The COVID-19 pandemic overwhelmed the existing healthcare infrastructure. It has been a rude reality check for clinical administrators worldwide. Now, as the contagion subsumes, the persisting rise in the global burden for non-communicable ailments like lifestyle disorders is likely to keep medical practitioners on their toes in the days ahead. With this, the demand for preventive measures is increasing. By using AI for healthcare, we may step ahead in this crisis.
Demand for AI In Healthcare
Today, the demand for preventive intervention is skyrocketing. But limited growth in clinical ranks indicates an ever-widening talent gap with cascading implications. WHO assessed a projected shortfall of 18 million healthcare workers by 2030. This gap is likely to be manifested primarily in low and lower-middle-income geographies. Medvocation, in one of its recent studies, found that nearly 44% of doctors worldwide are already breaking down under the immense workload and are unable to live happy and healthy lives.
The existing state of affairs can portend a crippling impediment as the global population ages. Reactive healthcare is becoming more costly, adding to the worries of patients and clinical professionals.
How is AI used in healthcare?
Fortunately, advancements in data-driven technologies like AI and machine learning have brought preventive medicine much closer to high-risk and healthy individuals. It has improved the possibilities of self-care and wellness like never before. For instance, today, AI applications can monitor every heartbeat and predict congestive heart failure (CHF) with remarkable accuracy. It permits the prospective patient a significant head start in seeking expert advice long before ending up on a gurney in an ICU.
Due to this the global market for intelligent self-care medical devices ($13 billion in 2020) is expected to reach a valuation of more than $30 billion by 2027, with a CAGR of over 8%.
Looking to implement AI in your healthcare solutions? Mindfire’s Healthcare Software Development Services help providers harness technologies like AI, RPA, and data analytics to build smart, scalable systems. From automated claims processing to intelligent diagnostics, we create custom solutions that improve efficiency, accuracy, and patient outcomes.
How IT Can Revolutionize Your Hospital with Automation?
Hospitals today face growing pressure to deliver faster, safer, and more cost-effective care. IT-driven automation is helping them meet this demand by streamlining routine tasks like patient registration, billing, appointment scheduling, and medical record management. By reducing manual work, hospitals can lower errors, cut down wait times, and free up staff to focus on patient care. From the front desk to the back office, automation creates smoother workflows that boost operational efficiency.
Advanced technologies like AI, machine learning, and RPA are making it easier to implement intelligent systems that adapt and improve over time. Whether it’s automating claims processing or enabling real-time patient monitoring, IT solutions are reshaping how hospitals operate day to day. With the right digital infrastructure, Healthcare Automation can turn complexity into simplicity, making hospitals more agile, accurate, and patient-centric.
AI in Medicine: A Comprehensive Approach
For some years, AI-powered applications have made significant inroads across various clinical procedures and treatments, from automating medical front desk services, drug repurposing, vaccine development, building Smart EHR systems, and improving pathology to successfully predicting drug reactions. Today, at least 90% of healthcare establishments have an AI strategy. Indeed, the benefits are substantial as cognitive computing allows practitioners to delegate repetitive tasks like clinical data extraction, assimilation, and report creation to the machines. Effectively, AI for healthcare professionals can help with better decisions and focus on what they do best: care for their patients.
However, the role of AI on the demand side of the story is equally spectacular! Proper self-care, yet elusive, is now feasible as wearable devices embedded with machine intelligence enable individuals to listen to their vital signs better. As algorithms operating on the edge get more intelligent and more empathetic, they will only expand the chances of proactively and precisely diagnosing physiological parameters and assessing the likelihood of acute events for individuals with chronic conditions. It, in turn, will preempt health risks, transform clinical deliveries, and ease the pressure on the existing medical infrastructure worldwide.
Factors Fueling The Influx Of AI For Healthcare
The trend is in no way isolated and wholly synced with the cognitive technology maturity curve. Several factors advocate making personal devices like Blood Glucose monitors, Insulin Pumps, Sleep Apnea Devices, Blood Pressure Monitors, and Smart Watches intelligent enough to improve self-care and wellness for their owners. None is more telling than the dichotomy that although there has been an explosive growth of health data collected institutionally in recent years, it may still fall short in enabling patient outcomes.
For instance, an asthma patient typically visits the physician every three months and spends over 2,100 hours in between when the symptoms are not actively monitored, undermining realistic assessment. Now, data continuously ingested through smart devices can bridge this gap. Other factors include:
– Advancement in cloud computing:
The computational power available for training AI models and algorithms has grown exponentially in recent years with the GPU revolution. Today, with easy access to bare metal servers from cloud infrastructure providers, it is easy to configure systems for running high-performance healthcare workloads.
– Development of Deep Neural Networks:
The development of Artificial Neural Networks today is supplementing ML capabilities, providing for much better and more precise modeling. ML procedures like Capsule Neural Networks and Transfer Learning can transform how ML models are built and deployed, leading to far more accurate predictions even when trained with limited datasets. It will indeed make self-care medical devices smarter and more cost-effective.
– Shift in the healthcare delivery philosophy:
As AI systems become more readily available, institutions worldwide are unmistakably considering how care is delivered and how precious healthcare resources and infrastructure are utilized. For instance, the National Institute of Health in the United Kingdom has launched an initiative to encourage the use of AI for healthcare of individuals to self-diagnose the onset of chronic conditions. The broader objective is to eliminate unnecessary outpatient visits and save operating costs, optimizing the resources available to the frontline care workers.
AI In Self-care: Use Cases
This research paper published by Nature.com manifests the overall apprehensions of patients around the role of AI in healthcare. However, advancements in cognitive technologies can bring in early and reliable insights and even formulate an effective response to some of the most prevalent chronic health conditions worldwide. These includes:
– Diabetes:
While the retroactive insights on blood sugar levels are currently derived from lab tests like A1C and self-service glucometer readings, AI-enabled devices can completely upend the diabetes treatment pathway. Intelligent insulin pumps can monitor blood glucose levels and other health metrics in real-time and auto-administer appropriate insulin doses based on the patient’s health condition and symptoms.
– Hypertension:
Collecting blood pressure readings periodically through cuff-based devices is only half the game, pending further diagnosis. However, with Smart wearable devices connected to the cloud, blood pressure data can be assimilated with multimodal data sets like genomics and behavioral to pinpoint anomalies and preempt acute instances.
– Asthma:
Asthma patients must regularly visit clinics for pulmonary function tests. They are monitored for environmental variables like air quality and moisture profile that can adversely impact their health. AI algorithms can extrapolate heart rate and blood oxygen level data from pulse oxymeters. Utilizing other pointers like pathophysiological analysis, natural history, seasonality, phenotypes, genetics, environmental monitoring, disease biomarkers, etc., AI predicts the possibilities of an asthma attack.
– Congestive Heart Failure (CHF):
Conventional detection for CHF is done through a clinical diagnosis like ECG and studying factors like hereditary prevalence and lifestyle choices. Nevertheless, AI algorithms can accurately predict heart health through raw electrocardiograms to predict the possibilities of CHF, almost with 100% accuracy.
– Depression:
Screening for mental health conditions depends on subjective evaluations to detect the root cause and respond to symptoms. However, AI algorithms can eliminate this subjectivity and clinical bias from the equation by evaluating symptoms through facial expressions, voice patterns, and online habits. Moreover, they can objectively assess treatment progress by interpreting brainwave profiles unique to patients with depression.
Implementing Artificial Intelligence in Health Insurance
AI is transforming the health insurance industry by streamlining claims processing, detecting fraud, and improving risk assessment. It leverages patient data, medical histories, and behavioral patterns to make faster, more informed decisions. Insurers are also using AI to offer personalized plans, predict future health events, and reduce administrative overhead. As adoption grows, it’s helping both providers and patients benefit from greater accuracy and efficiency. Learn more in our article on AI in health insurance.
Final Thoughts
Bringing AI to improve the state of self-care and wellness is an idea whose time has come. Cognitive technologies can play a pivotal role in preventing catastrophic health events and saving lives. However, considering the wide range of variables and the risks involved, expert implementation becomes as crucial as the technology to ensure the first-time-right outcomes. Therefore, alongside investment in technology, it becomes a strategic necessity to find an experienced technology partner who can aptly demonstrate the viability of self-care through AI.
Mindfire Solutions is one such leader in providing AI and ML development services, well-acclaimed in the global health tech market. Get in touch with one of their AI consultants to discover how the company simplifies self-care and wellness for millions worldwide.