Machine Learning – A Blessing for the Healthcare Sector

Machine Learning has become quite the catchphrase across several sectors, let alone healthcare and it’s justified to given the opportunity it presents for businesses. In this piece, we shall try and understand what exactly is Machine Learning and how is it poised to bring about revolutionary changes in the Healthcare Sector.

 

Machine Learning?

Machine Learning or ML is a type of Artificial Intelligence that helps software applications to continuously study incoming data and predict the most likely outcomes without being programmed specifically to do so. The key element in this process is that the computer programs access data, interpret it and give a solution, all on their own – without any human intervention.

Today evidences of ML are all around us whether we have realized it or not. The highly relevant ‘Friends suggestions’ that you receive on Facebook, that’s ML. Google Maps notifying you to leave early for work since there is ‘higher than usual traffic’ on your route, that’s ML. Uber being able to accurately ‘pin your location’ while you book a cab is ML. You landing on this page could actually be because of Machine Learning.

 

As there are continuous advancements in the field of Artificial Intelligence, ML algorithms are gaining greater eminence and their importance to businesses will only see a surge in the times to come.

 

The Healthcare Sector

The Healthcare Sector essentially exists to keep all of us healthy and includes all individuals and entities that work towards the maintenance or improvement of our health. This space is buzzing at the moment thanks to our living conditions and lifestyle choices. The workload and challenges of entities in this sector are only going to increase and become more complex.

The sheer number of individuals this sector has to cater to currently and will have to cater to in the future, is mind boggling. Service providers in this sector have no other option but to look at technology enabled solutions to function smoothly.

Technological advancements don’t disappoint them either. Today solutions are available for something as trivial as billing to something as significant as predicting the occurrence of say cancer, long before it’s onset in a patient.

 

Machine Learning and Healthcare

ML has made its presence felt in the field of healthcare since its early days. The Healthcare Sector benefits from ML in several avenues like handling patient records, development of new medicines, predicting health risks, accurately finding results of pathological tests and the like. ML not only helps in analysing huge complex databases for further medical study, but also helps in providing more advanced medical solutions. Let’s take a look at some areas where ML is making its mark.

 

Managing administrative processes in hospitals

ML is being used to continuously analyse the huge database of hospitals to standardise recording systems.

Benefits:

It ensures efficient, accurate and quick retrieval of information whenever needed.

ML can also be used to organise data in such a way so as to use memory space effectively to reduce costs of maintaining the ever-increasing patient record data.

It can lead to better inter-departmental co-ordination which can reduce duplication of work.

Effective automated systems for communication with patients can be established.

 

Mapping and treating infectious diseases

ML can be used to track pathological tests of patients and study patterns to identify infectious diseases before they result in an outbreak.

 

Benefits:

It helps doctors and caregivers to devise treatments beforehand to ensure the situation is in control.

This can also be useful to spread awareness among the vulnerable set of people.

It will lead to a reduction in the load on hospitals and caregivers thus improving their efficiency.

A smooth supply of required medicines can be ensured in case of an epidemic.

It will result in an overall reduction in cost and time of treatment.

 

Personalizing medical treatments

ML can help by studying individual historic data of patients like family history and genetics, in connection with several external environmental factors that may influence the well-being of that patient and help to suggest personalized care for that patient.

 

Benefits:

This would not only help in avoiding several illnesses, but also be able to highlight gaps in treatment needed and provided.

The effectiveness of such studies would be far greater as the treatment is tailored to suit the patient.

Several hereditary and chronic diseases can be nipped in the bud.

Such historical data of patients presents a huge opportunity for clinical trials.

ML can effectively match patients with the most suitable doctors.

 

Drug discovery and development

ML can be of use to pharma companies in developing new drugs by efficiently studying medicinal compounds and their impact on diseases.

 

Benefits:

ML can identify newer patterns and combinations which can aid new drug development.

More effective medicines can be developed and produced in a shorter time span.

Reduction in failure rates translate huge savings of time and money for pharma companies.

Diseases can be cured quickly and effectively at lower costs.

ML can play a great role in those streams of medicine where effective cures are yet to be found.

 

Data insights

ML can be of great use for pathologists and physicians if there is a large database and variety of patient data. An exciting phase in healthcare research has been ushered with the advent of wearable technology. The continuous automated data points it provides for ML spell out promising opportunities in the future.

 

Benefits:

Standardization in clinical test procedures reduces the time taken to determine pathological findings.

Patients can be given quick and accurate diagnoses of the pathological tests they undergo.

Accurate treatments can be suggested without taking much of the physicians’ time.

Physicians can prescribe different combinations of treatment more confidently in case the patient doesn’t respond to a particular type of treatment.

Diseases which might develop at a later stage can be predicted and thus avoided.

ML can also play a significant role in the field of radiology by analysing images based on several parameters and spotting things that may go undetected by a human eye.

 

Assisting surgical procedures

Efforts are being made to use ML while conducting surgeries and such other critical procedures to avoid undesirable outcomes while treating patients. There are several ongoing studies to increase automation in

surgical procedures. Robotic surgeries are a case in point here.

 

Benefits:

ML can be used to monitor body vitals and specific body parameters while carrying out surgical procedures.

Since ML can provide real-time monitoring, timely corrective action can be taken to reduce life threats and other complications that may arise.

Surgeons can receive real-time feedback on the effectiveness of the exercise.

This will reduce the average time a patient needs to be kept in the hospital thereby allowing the hospital to tend to a greater number of patients.

Automation in procedures will mean that skilled surgeons and technicians will have more time on their hands to treat more patients.

It will also help ease surgeon fatigue in case of lengthy surgeries.

 

Clinical Trial and Research

ML can be of immense help in the area of clinical trials and research.

 

Benefits:

Organizations can benefit from the huge savings ML can provide in terms of time and money spent on conducting trials.

ML can help researchers to identify potential clinical trial candidates based on their bifurcation in several attributes.

These candidates can be monitored on a real-time basis to speed to draw conclusions faster.

Data errors can be reduced significantly.

 

The ways in which ML can be of use in the Healthcare Sector is only limited to one’s imagination. Each passing day brings forward innumerable avenues for ML to leave its mark. A scenario in which caregivers are completely overtaken by machines to provide healthcare, doesn’t seem hypothetical anymore.

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