Artificial Intelligence (AI) is becoming increasingly prevalent across all industries over the past decade, and healthcare is not an exception. Given the complexity and rise of data in healthcare, AI has been more and more important in the industry.
Healthcare stakeholders and medical professionals can use many types of AI applications, such as machine learning (ML), natural language processing (NLP), and deep learning (DL), to discover healthcare problems and solutions faster and more accurately, leveraging data patterns to make informed clinical or business decisions.
AI applications are already being applied in several medical fields, such as diagnostics, treatment recommendations, drug development, and patient engagement. AI can evaluate enormous amounts of data maintained by healthcare organizations in the form of images, clinical research trials, and medical claims. It can spot patterns and insights that humans typically miss. Also, it can benefit healthcare professionals when it comes to administrative tasks, such as staff planning, or reporting, increasing operational efficiencies, and simplifying difficult procedures.
As a consequence, there is a wide range of stakeholders in healthcare benefiting from the usage of AI:
Here are 9 applications of AI in healthcare that have the potential to transform many aspects of patient care, as well as administrative processes within provider, payer and pharmaceutical organisations.
AI facilitates clinicians' review of photos and scans in case triage. This allows radiologists and cardiologists to find crucial information for prioritising critical patients, avoiding potential errors in reading electronic health records (EHRs), and establishing more exact diagnoses.
In light of an increase in the number of medical imaging ML systems receiving regulatory approval, healthcare professionals will have access to additional tools to automate tasks and support their decision making, saving time and reducing errors. (1)
New drugs are currently being developed by using AI in several phases, from discovery to screening and optimization. In comparison with traditional methods, AI can speed up the process of synthesis and testing of new drugs by 40 to 50 per cent, reducing costs by as much as $26 billion annually. Using ML and DL, the simulation of drug properties provides the information necessary to select the most effective compounds. (2)
Due to massive volumes of health data and medical records, clinicians frequently struggle to stay current with the latest medical developments while providing quality patient-centred treatment.
Machine learning algorithms can quickly analyze EHRs and biomedical data curated by medical units and professionals to provide doctors with fast, accurate replies.
Patients' health records and medical information are frequently stored as unstructured data, making them difficult to read and access. Instead of searching, identifying, collecting, and transcribing the solutions they need from piles of paper-formatted EHRs, AI can seek, collect, store, and standardise medical data regardless of format, assisting repetitive tasks and supporting clinicians with fast, accurate, tailored treatment plans and medicine for their patients.
Various medication applications can be identified using AI algorithms, which can then be traced back to their hazardous potential and mechanisms of action. With this technology, the business was able to build a drug discovery platform that allows them to repurpose existing medications and bioactive chemicals.
The ML methods are designed to extract information from biological datasets that are too complicated for human interpretation, reducing the danger of human bias. Big Pharma corporations are interested in finding new uses for existing pharmaceuticals since repurposing and repositioning existing drugs is less expensive than developing them from the start.
Acute kidney injury (AKI) is a condition that can be difficult for clinicians to recognize, but it can cause patients to deteriorate rapidly and become life-threatening. With an estimated 11% of hospital mortality due to a failure to identify and treat patients, early detection and treatment of these instances can significantly reduce life-long care and kidney dialysis costs.
The time between dialling the emergency number and receiving an ambulance is critical for recovery during a sudden heart attack. To take prompt action and maximize the odds of survival, emergency dispatchers must be able to recognize the symptoms of a cardiac arrest. To establish a diagnosis from a distance, AI can examine both verbal and nonverbal evidence.
ML has the potential to be extremely useful in assisting emergency medical personnel. Medical units could use the technology in the future to respond to emergency calls with drones equipped with automatic defibrillators or CPR-trained volunteers, increasing the chances of survival in cases of cardiac arrest that occur in the community.
Clinicians may be more productive with their workflows, medical choices, and treatment plans by transforming EHRs into AI-driven prediction tools. NLP and ML can read a patient's whole medical history in real-time and connect it to symptoms, chronic affections, or a disease that affects other family members. They can use the data to create a predictive analytics tool to detect and treat conditions before they become fatal. As a result, the process of diagnosing and giving treatment plans can be shortened, allowing the clinician to save valuable production hours. Time is money in any industry, so AI has the potential to save a lot of money.
AI is also being successfully employed to aid in the speedy discovery and development of drugs. Changed molecular phenotypes, such as protein binding, promote genetic disorders. Predicting these changes entails predicting the emergence of hereditary illnesses. This can be accomplished by gathering information on all identified chemicals and biomarkers relevant to specific clinical studies. Patients and physicians gain from the discovery and development of genetic medicine since it lowers the cost of treating uncommon disorders.
In a less shiny domain than patient care, AI can provide substantial efficiencies in administrative activities. It is estimated that, on average, healthcare workers spend 70% of their work time on regulatory and administrative activities.
AI aids in streamlining procedures, the automation of activities, the quick sharing of data, and the organisation of operations, all of which relieve medical personnel of the burden of juggling too many jobs.
The industry is facing unprecedented challenges, which have been aggravated by the pandemic, with a huge waitlist of patients, a record number of sick leaves, and a constantly stressed and burnt-out workforce.
That's when AI can kick in and help healthcare organisations turn around and become more efficient in managing their workforce.
Healthcare planners from Byåsen in Trondheim have reduced significantly 50% of their administrative tasks and saved 6.7% of their annual costs by using SynPlan.
AI has come a long way in the medical field, yet human supervision is still required. Health practitioners may see important behavioural insights that can assist in identifying or avoiding medical issues. AI has been present for several decades and is still evolving. More interaction between healthcare professionals and technology experts will occur as this field develops. Machine learning might not be able to replace doctors but doctors who learn and apply AI will be able to replace traditional doctors who have not kept up with the digital revolution.
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.
Get a demo or read more about the product at: synplan.ai
Source:
(1): https://link.springer.com/article/10.1007/s00134-020-06316-8
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