Discussing The Impact of AI in Healthcare

The volume and complexity of data being generated throughout our healthcare settings, is offering increasing opportunities for the use of artificial intelligence (AI) applications.

Whilst already prevalent in both business and society, AI and its related technologies are being adopted a little more slowly across the healthcare sector. This may seem understandable given the ethical and legal considerations surrounding its use.

However, several types of AI have demonstrated enormous potential over the past several years – particularly within the areas of medical diagnostics, administration, patient engagement and healthcare management. Research studies have even shown that AI performs as well as, or even better, than humans at some crucial healthcare tasks, the AI algorithms outperforming experienced radiologists at spotting malignant tumours, for example.

Despite the fact that it is likely to be a number of years before AI replaces humans for many medical processes, there will be a growing acceptance and reliance on AI capabilities. Facility managers can expect to see the technology being incorporated more and more into healthcare systems, so will need to ensure there is a constant source of clean energy to power it.

As with all technology, the use of AI applications will demand an uninterruptible electrical supply. This will need to be combined with a robust backup power protection strategy to support daily operations in the event of mains failure.

In this article we will look at how AI is currently being used to automate and transform aspects of patient care, together with the actions needed to ensure these critical applications keep running smoothly 24/7.

                                                                                                                                     

How AI is Being Adopted in Healthcare  

Pharmaceutical organisations, healthcare and health plan providers are already utilising a range of different AI technologies, which include:

• Machine learning
• Natural language processing
• Physical robots
• Robotic process automation
• Diagnosis and treatment applications
• Patient engagement and adherence applications
• Administrative applications

 

Machine Learning

One of the most recognised forms of AI, machine learning is most commonly used within precision medicine for predicting what treatment protocols are the most likely to succeed based on the varying patient attributes and context of treatment.

A more complex version of this is known as the ‘neural network’, a technology form that has been well established in healthcare research since the 1960’s. Used for categorisation applications, it views problems in terms of inputs, outputs and the variables associated with this type of information, so is suitable for determining whether a patient is likely to acquire a particular disease in the future. 

The most complex form of machine learning however is ‘deep learning’ which incorporates many levels of features or variables to predict an outcome. It is increasingly being applied to radiomics, the detection of clinically relevant features in imaging data that fail to be appreciated by the naked eye. The AI algorithms can effectively analyse extensive collections of medical images to identify and classify cancerous tumours, offering oncologists insightful information about a tumour’s stage, rate of growth and potential for metastasis.  

 

Natural Language Processing

Natural language processing (NLP) includes applications such as speech recognition, text analysis, translation and a variety of other language objectives.

The dominant applications used within healthcare involve the creation, understanding and classification of clinical documentation and published research. Systems have the ability to analyse unstructured clinical notes that have been made on a patient, prepare reports (for example on radiology examinations), transcribe patient interactions and conduct conversational AI.

 

Physical Robots

Whilst robots are sometimes used in hospital settings to deliver a range of supplies, it is the surgical robots that provide today’s surgeons with a whole range of extended super powers. They improve a surgeon’s ability to see, create accurate minimally invasive incisions and stitch wounds - thus making them particularly suitable for procedures within gynaecological, prostate, head and neck surgery.

 

Robotic Process Automation

In comparison to other forms of AI, this technology is easier to program, less expensive and more transparent therefore ideal for performing structured administrative tasks within information systems.

Whilst described as ‘robotics’, the term uses computer programs on servers, integrating with these to complete repetitive tasks such as updating patient records and billing.

 

Diagnosis and Treatment Applications

The use of AI algorithms to analyse patient data, health records, lab results, clinical data and medical images from X-Rays, MRIs and CT scans, can yield an abundance of insightful information to help with medical diagnosis in a fraction of the time.

The algorithms analyse vast amounts of data to find hidden associations, biomarkers and disease-associated risks that practitioners might not otherwise be able to see. This allows for more precise diagnosis of complicated conditions and assists specialists in selecting the most appropriate individualised treatment strategy. 

The application of AI to medical imaging helps radiologists view areas of the body from differing viewpoints, enhancing diagnostic accuracy and decreasing the possibility of misinterpreting scan results.

 

Patient Engagement and Adherence Applications

AI has significant potential to advance both preventative and customised healthcare.

It is recognised that patient engagement and adherence has long been an issue within healthcare – many clinical leaders reporting that patients were either non-compliant or not fully engaged with making the behavioural adjustments required to support recovery.

Using AI powered systems would allow providers to generate personalised disease prevention and treatment solutions, taking into consideration a patient’s genetic makeup, medical records and lifestyle habits. Messaging alerts and relevant targeted content could then be generated along the care continuum to help drive patient behaviour in a more positive direction.

 

Administrative Applications

There are numerous AI applications currently being used in healthcare that provide efficiency solutions for a range of daily activities. These include clinical documentation, revenue and medical records management, patient chatbots, appointment systems and ordering of repeat prescriptions.   

The technology can also be used by medical insurers, matching data across different databases to assess the probability of legitimate claims.

 

What does that mean for Critical Power.

As we’ve seen in the above, the use of AI in Healthcare will increase the demand for Critical Power UPS backup systems and the electrical infrastructure and distribution.

 This would either be for AI data and automation technologies held locally within a hospital, or AI processing within Data Centre hosting cloud services.

Understanding is then required as to the UPS Power demand is to fall under the HTM guidelines or other regulations for local AI technologies within the hospital.  Either way, reliable UPS topologies suitably sized for the process and mechanical demand is critical.

 Remote cloud services hosted by Data Centre will have their own Critical Power UPS systems. Consideration will be needed to make sure these are suitable for Healthcare demands and regulations.

 

Conclusion

As demonstrated above, AI is revolutionising many different areas of healthcare. Whilst there will be numerous ethical and legal considerations to take into account during any implementation process, we are highly likely to see the adoption of further AI technologies over the coming months and years. 

AI has the capability to simplify and enhance conventional healthcare practices. It enables in-depth analyses and forecasts, and when combined with other pioneering technologies will be capable of producing highly customisable patient monitoring in the future.

As mentioned earlier, like most technology AI applications demand a consistent electrical supply. Without this, they will simply not function as they should and all benefits of this pioneering and innovative technology will be lost.

As we look to amalgamate wider forms of AI into our daily life, the need for 24/7 uninterruptible power must remain high on the agenda of each healthcare facility. Those responsible for facilities management must look towards their backup power protection strategies to ensure they have the appropriate solution in place to keep systems operational in the event of unwanted power anomalies. 

Power Control Ltd has over 25 years’ experience in the provision of UPS solutions for medical and healthcare settings. With an extensive product portfolio to hand, the company has the ability to deliver the most appropriate strategy that is capable of meeting both current and future AI application demands.

For information on how Power Control can help, or to discuss the company’s range of advanced UPS equipment, please contact 01246 431 431 or email info@powercontrol.co.uk.