Better Diagnosis and Patient Treatment with Machine Learning in Healthcare

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Healthcare in the US alone is worth over $ 1668 trillion and yet there is a shortage of qualified personnel leading to overload and consequent reduction in quality of services. This situation can be remedied to a great extent by including machine learning development in healthcare services. Machine learning, along with artificial intelligence, goes a long way in reducing the time doctors spend on diagnosing patients and in providing more accurate diagnoses of health conditions. It cannot, of course, supplant doctors but it does help in accuracy and speed. There are different ways you can get ML development services to increase efficiency and reduce costs while providing better healthcare services.

Predicting treatment protocols

There are far too many variables involved in diagnostics and treatment protocols. Doctors, already working under pressure, cannot be expected to devote more time to examining minutiae. Engage experts in machine learning development to see a transformation in treatment quality as well as speed. ML along with neural networks works on datasets that factor in not only patient’s age, economic background and habits but also genetics and racial qualities to determine predilection for disease and arrive at the right treatment techniques that will succeed. For instance, advanced instruments like MRI, ultrasound and radiography deliver imagery that needs careful examination. This is done in a trice by ML that can even detect hidden image data that human eyes may overlook. A doctor simply looks at the report and analysis to put together the right preventive or therapeutic treatment, which could save patient lives.

Speeding up processes – documentation

Radiomics is just one area where ML delivers superior and positive outcomes. There is another area of healthcare where machine learning allied with deep learning and neural networks can provide beneficial and that is documentation. Doctors usually dictate their findings and such voice recording is transcribed, usually by some offshore transcription service with high possibilities of errors or time delay. Enter natural language processing that can analyze text, recognize speech and, importantly, a doctor’s particular pronunciation. ML can help healthcare in two ways. ML development services can develop solutions that lead to faster and more accurate transcription. They can just as easily create ML based systems that will classify clinical documentation and analyze data within to prepare reports. It can lead to future progress such as a conversational AI solution. If not in other areas, documentation is one area healthcare can take up on priority. Medical documentation is the first step to robotic automation of overall healthcare systems and documentation. This involved repetitive tasks such as authorization, patient record handling and billing.

The tracing app extended

Covid 19 has seen the emergence of a variety of tracing apps. These are somewhat basic, compared to what AI and ML can achieve in healthcare. Healthcare services, along with governments, can build on the basic theory and expand it to cover an entire population, providing preventative and diagnostic as well as therapeutic remedies. Preventive medicine, it is obvious, is far less expensive for all concerned, including patients, hospitals and governments and ML development services can help governments come up with a superior plan to monitor each individual’s health, detect conditions and make predictions about lifestyle disease or health risks due to environment at workplaces or living condition environment.

Drug response studies

Some diseases are straightforward in that infections respond to certain antibiotics. However, diseases like cancer may also be rooted in genetic profiles apart from environment, food and lifestyle. As such, for pharmaceutical companies, it is an uphill task to determine efficacy of drugs and treatment protocols. ML development helps drug development by factoring in genetic variations and internal as well as external factors to arrive at a more accurate response to drugs by various genetic types.

Also Read: 10 Reasons how technology is changing the future of higher education.

Patient follow up care

Treatment does not end with prescription or surgery. It is not humanly possible or practical for doctors or healthcare workers to follow up constantly and keep track of patient recovery. There is no way to know if patient is regular in taking medications or following prescribed procedures. Healthcare can go a long way towards ensuring precise follow up by getting ML development services to put in place post treatment procedures along with monitoring, especially necessary for patients with chronic conditions. ML solutions can assure continuity and better patient engagement too since they know they are under constant observation, even if it is by a machine. This is absolutely necessary considering that clinicians report that less than 50% of patients are committed to following doctor’s advice.

ML in healthcare is promising with a vast scope. It is not possible to cover every aspect into an all inclusive AI powered solution but working in sections, today’s healthcare can be ready for tomorrow’s needs for better quality, speed of response, accuracy and preventive health practices. Better health of the population certainly contributes to better economy of the nation.

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