Is AI Shaping the Future of the Healthcare Industry?



The COVID-19 pandemic created a broad spectrum of new challenges for healthcare organizations and medical professionals. Starting from staff shortage to unavailability of beds – healthcare providers were struggling to cope with the novel coronavirus.

As with most sectors, the healthcare industry also regained its footing with the use of modern technology. Amongst all the forms of tech innovations used in healthcare, artificial intelligence (AI) is emerging as a game-changer.

While the concept of using AI in healthcare isn’t new, modern AI-based technologies are transforming the way physicians diagnose and treat diseases. Today, AI-based healthcare technology is no longer limited to performing robot-assisted surgeries and automating administrative tasks.

Judicious use of artificial intelligence will go a long way to improve the quality of patient care. Moreover, it will help cut treatment costs and skyrocket the efficiency of healthcare organizations. It can be instrumental in eliminating human errors and delays in the diagnosis and treatment of rare diseases.

In this blog, we will take a closer look at a few important applications of AI in the healthcare industry. Let us get started.


AI-Analyzed Crowdsourcing

The concept of crowdsourcing isn’t new in healthcare. Physicians often seek the opinions of their peers and other experts to diagnose rare diseases and identify correct treatment options.

But the problem with traditional crowdsourcing is that it overwhelms healthcare providers with a plethora of data. This is especially true when trying to assess and compare the effectiveness of various treatments outside the pristine environment of clinical trials, in the real world – Deriving actionable, reliable insights from crowdsourced data is a herculean task.

That is where artificial intelligence steps into the picture.

AI-based crowdsourcing platforms like StuffThatWorks, collect, process and analyze patient data from member reports. This, in turn, provides patients and medical professionals valuable insights about symptoms and effective treatments for different diseases based on people’s real-world experiences.

AI helps eliminate inaccuracies and guesswork from the process of crowdsourcing

As long as patients have access to a stable internet connection, they can learn more about any health condition using a platform like StuffThatWorks. The website features crowdsourced data and insights on more than 560 medical conditions.

Also, it is worth noting here that AI-analyzed crowdsourcing will help identify alternative therapies and natural remedies for various disorders. While the technology can’t replace a medical professional’s expertise, it can go a long way to empower patients to take control of their wellbeing.


Remote Patient Monitoring

Remote patient monitoring (RPM) solutions are designed to help physicians provide the required care without an in-person consultation. According to a recent study, RPM helps reduce readmissions and emergency room visits by 38% and 25%, respectively. Also, it helps improve patient satisfaction by nearly 25%.

AI-driven RPM solutions were particularly useful during the pandemic. They helped healthcare providers virtually monitor and treat patients with chronic conditions. This, in turn, eliminated the need for patients to visit clinics or hospitals, thus avoiding exposure to the novel coronavirus.

For instance, Cardiomo, a wearable device, uses biosensors and an AI algorithm to monitor and analyze a patient’s vitals. It constantly monitors the patient’s cardiac health and issues an alert up to 2 hours before a heart event.

These alerts can be relayed to a patient’s primary care provider and cardiologist to ensure that they receive proper and timely treatment.

RPM can also help minimize the cost of patient care, thus benefiting both patients and healthcare organizations. Moreover, AI-powered wearable gadgets make patients more conscious of their physical and mental health.


Syndromic Surveillance

In addition to monitoring a patient’s health, AI can also be used to predict their risk of developing specific disorders. Machine learning (ML) algorithms are equipped to analyze patient data and identify their predisposition to medical conditions, including cancer, cardiovascular ailments and more.

Syndromic surveillance technology can also be integrated with a hospital’s database to predict disease patterns and risks. For instance, an ML algorithm can be used to analyze EHR (electronic health record) data of multiple patients and scan for symptoms related to specific conditions, such as COVID-19.

Similarly, Airdoc, a Beijing-based startup, has developed an AI/ML system for retinal health risk assessment. The device captures, scans and analyzes images of a patient’s retina to identify early signs of chronic conditions. The conditions detected can be hypertension, diabetes, myopia and macular degeneration.

Syndromic surveillance technology can also help physicians identify patients who have a higher risk of developing complications and requiring hospitalization from an ailment.

As with RPM, syndromic surveillance has been extremely useful during the COVID-19 pandemic. The technology has helped medical professionals identify high-risk groups during hospitalization and vaccine administration.


Improved Data Analysis

Healthcare organizations have access to a plethora of patient data, including EHRs, pathological reports, scans, and physician’s notes. The only problem is that most of the data is stored in an unstructured format. That renders it unusable for disease prediction and mitigation.

These limitations can be overcome with the use of a natural language processing (NLP) algorithm. NLP is an AI-based technology that analyzes and interprets human language to derive actionable insights.

In the healthcare sector, NLP technology can be used to make sense of unstructured EHR data. It is particularly useful for identifying a patient’s eligibility for clinical trials and medical research.


AI in Healthcare: The Way Forward

In 2021, the question isn’t about whether AI is ushering in a new era in healthcare. Instead, healthcare organizations and medical professionals should familiarize themselves with how AI is changing the industry.

From remote patient monitoring and wearable devices to AI-analyzed crowdsourced data – modern technology will improve the quality of patient care while cutting costs and escalating efficiency.

Off-shoots of artificial intelligence, such as ML and NLP, will come in handy for analyzing patient records and converting them into actionable insights. These technologies will also help improve the quality of clinical trials and scientific studies. Also, they’ll make reliable healthcare services accessible to patients in hard-to-reach locations.

In the future, AI-based solutions will be used to identify infectious disease patterns and predict epidemics and outbreaks. Similarly, AI will be used for intelligent drug discovery to identify effective treatments for various diseases. Also, it will be instrumental in empowering patients with more information about their ailments.