From billing to patient discharge, healthcare has far too many manual processes even today. While there are many areas where technology can play an important role, one of the more critical ones would be in helping improve patient care. With time, advancements in electronic medical records have been incredible. However, the information they offer isn’t much better than the paper charts that they have substituted. One of the best ways to have technology enhance patient care is by improving the quality and speed of patient information being offered to the doctors via machine learning and analytics.
Machine learning is getting increasingly to be a need and gaining popularity in the healthcare industry. It is being used to help clinicians and patients in multiple ways. One of the most common uses of machine learning is in clinical decision support, medical billing, and the development of clinical care guidelines. It is seeing gradual acceptance in the healthcare industry. This gives us a glimpse of a future where data, analysis, and innovation will work synchronously to assist patients. It will increase the efficiency of new treatment options that had been unavailable before.
Let’s take a look at a few applications of machine learning in healthcare.
Analyzing Diseases and
One of the primary uses of ML in the field of healthcare is identifying and diagnosing an ailment or disease that is otherwise considered difficult to be diagnosed. It can be anything from genetic diseases to cancers that is difficult to detect in the initial stages. Companies are leveraging AI for developing therapeutic treatments in fields like oncology. Some medical organizations are looking for ways to create a commercially feasible way to identify a disease and offer treatment in a routine clinical condition.
Discovering and Manufacturing
Another great clinical application of machine learning is its application in the early stages of the drug discovery procedure. It includes R&D technologies like precision medicine and next-generation sequencing that can help find alternative paths for therapies for multifactorial diseases. At present, machine learning techniques comprise unsupervised learning that can recognize the pattern in data without offering any predictions.
By combining individual healthcare with predictive analytics and better disease assessment, personalized treatments can be made more effective. At present, physicians have to choose from a certain set of diagnoses or reckon the risk to the patient based on the symptoms, history, and genetic information available. However, machine learning in medicine is making great improvements. Some medical organizations are at the forefront of the movement as they leverage patient medical history for generating multiple treatment choices.
In the future, biosensors and more devices with good health measurement abilities will be introduced in the market. This means more data will be readily available for machine-learning-based healthcare technical choices.
Smarter Health Records
Maintaining updated health records can be exhaustive in healthcare, especially in senior management. Sure, technology has played a great role in easing out the data entry process. However, the truth is, most of the processes take a lot of time to complete. The primary role of machine learning is to ease out the process. This will save time, money, and effort.
Machine learning and AI are being used to monitor and predict epidemics across the world. These days, scientists have access to a large collection of data from social-media updates, satellites, website information, etc. With artificial neural networks, collate information and predict outbreaks. This can be highly useful in senior management.
One of the most popular applications of this technology in healthcare is Radiology. Medical image examination has several discrete variables that can arise at a certain moment. There are many types of cancer, lesions, etc., that cannot be detected using complex equations. Since machine learning-based algorithms learn from various samples on-hand, it becomes convenient to diagnose and seek the variable.
Machine learning in medicine is used for image analysis to classify objects like lesions into categories like abnormal or normal. It is being used to detect the difference between cancerous and healthy tissue.
Crowdsourcing Data Collection
Crowdsourcing is in rage in the field of medicine. It allows practitioners and researchers to access a large amount of data uploaded by people depending on their consent. The live health data has significant ramifications on the way medicine will be perceived in the future. Advances are being made to apply machine learning in facial recognition for treating Parkinson’s disease.
With improvements in IoT, the healthcare industry is discovering ways in which to use the information and handle tough-to-diagnose cases and help with an overall betterment of medication or diagnosis.
Medical Image Diagnosing
Machine learning lends itself to process better than others. Algorithms can offer immediate benefit to disciplines with procedures that are standardized or reproducible. Moreover, those with big image datasets like pathology, cardiology, and radiology are important candidates. With machine learning, you can look at images, recognize abnormalities, and point to the aspects that require attention, thereby increasing the accuracy of the whole process. Machine learning, in the long-term, will benefit all aspects of healthcare.
The Ethics to Use Algorithms in Healthcare
Machine learning in the healthcare field serves as the brain of doctors. So, the question is, will physicians take machine learning as an unwanted second opinion?
There was a time when auto workers were scared that robotics is going to erase their jobs. In the same manner, there might be several physicians who are worried that machine learning is the start of the process that can make them obsolete.
However, it is the art of medicine, which can never be replaced. Patients in healthcare are always going to require human touch and care. Future technologies like machine learning will not eliminate the need for doctors or physicians. They only become tools that can augment their practice and use to improve patient care.
Healthcare has to move from taking machine learning as a futuristic concept to taking it as a real-time tool that can be used today. In situations where machine learning plays a role in healthcare, an incremental approach needs to be adopted. Professionals should find specific use cases in which the capabilities of machine learning offer value from a certain technological application.
Not everything can be done by machine. Healthcare information for machine learning needs to be prepared in such a manner that computers can look for patterns and inferences. It is generally done by human tagging elements of the data that is called an annotation over the input. Nevertheless, for machine learning applications in the field of healthcare to learning effectively and efficiently, the annotation done on data has to be relevant and accurate to the task of extracting key concepts.
Why should business leaders adopt this technology?
It goes without saying that such robust techniques like machine learning can be applied to large-scale health systems combined with individual patient care. With a clear line of sight of goals and desired business outcomes, the senior management and leadership teams in the healthcare industry can develop many potentially valuable Machine Learning use cases.
With a mission to help the healthcare industry actualize data, Resolve strives to deliver maximum value from data to improve healthcare outcomes. From helping in understanding patient readmission risk to data security to accelerating drug discovery, Resolve offers a wide range of machine learning and AI services for the healthcare industry.
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