The Artificial Intelligence and machine learning for health and their role has changed the thinking in medical sector or it is right to say in healthcare industry.
The Introduction to machine learning in digital healthcare epidemiology are rocking and the world get the benefits of machine learning in health care sector.
Machine Learning for healthcare
- 1 Machine Learning for healthcare
- 2 Role of Prior authorizations in Healthcare & AI
- 3 What are the limitless opportunities for machine learning in healthcare?
- 4 Reference:
Machine learning is already starting to make an impact on the healthcare industry. Using predictive analysis, machine learning is helping healthcare providers make more accurate and quicker diagnoses. Predictive analysis is also allowing healthcare providers to notice trends in medical records to be on the lookout for future diagnoses.
The limitless opportunities of machine learning in healthcare are that it cannot only help providers predict future health patterns but can also be used for treatment or prevention. Machine learning could be used to opt the best methods to treat a patient or what medications are best to use to cure an illness.
When you don’t feel good, the last thing you want to do is get out of bed and go to the doctor’s office, wait in the waiting room enjoying whatever left over germs are there, then wait in a examining room enjoying those germs of the previous sick patients.
With the evolution of self-driving cars people are not yet ready to trust the driving experience.
But what if you can call up a self-driving car to come to your home or office as a healthcare pod. Enter the pod’s code texted to you. Once in the seat the screen in front will prompt for user’s information, insurance.
An operator comes on the screen with questions regarding needs. The seat weighs you, determines your height. Disposable pads to check your temperature.
Possibly check your blood pressure. The pod is equipped with a camera that can look focusing in your throat or in your ear collecting those images for future reference for your file and sharing with specialists or your general practitioner.
Then the doctor comes on the screen to ask the relevant questions due to your aches and pains. If the doctor deems it necessary for further diagnostics, they can transfer you to a specialist to have them view you in real time or share the findings for assessment and call back.
If a cardiac event starts, the pod can take to the hospital. Once a user has left the pod the pod self-fumigates the interior of all germs giving confidence to the next user that all contaminants have been eliminated.
Prior authorizations (PA) have largely evolved as an entirely manual process requiring time and human resources to manage from initiation through follow up to conclusion.
Artificial Intelligence (AI) and cloud computing technology introduces game-changing automation that allows hospital systems and healthcare providers to proactively manage each patient’s care and significantly reduce administrative burdens.
Not only does this have a direct impact on an organization’s bottom line, but it provides patients with timely care and reduces discontinuation or even abandonment of treatment.
As insurance carriers change and update their policies, the machine learning algorithms learn and adapt to these changes and then apply them immediately to PAs in progress.
The platform also identifies whether the prior authorization request requires a clinical review accurately predicting the turnaround time for a specific case so providers can give their patients optimal scheduling options.
While technology is advancing to the point that many see completely automated solutions as the logical choice, a machine simply can’t do it all.
AI exists to help humans make better decisions. Decision making, as it applies to the healthcare industry, still requires human intelligence and human empathy. The best possible outcomes in prior authorization are the result of good intelligence and great execution.
The most significant value proposition for AI is taking on the burden of repetitive administrative tasks and allowing healthcare providers and staff members to focus on the patient encounter. The most valuable solutions help people make better decisions and actions by continuously learning from the data gathered from previous experiences.
The AI component of prior authorization is designed to continuously learn and improve from all data and interactions as well as provide prescriptive insights for decision making with increasing transparency into the process.
With expanding modules that include automated solutions to verifying insurance eligibility and estimating patients’ copays and coinsurance amounts, AI enables intelligent decisions that directly affect bottom line results and the patient experience, thus improving outcomes.
Someday AI may evolve to the point of completely automating the prior authorization process, but until that day, we will need to incorporate specialized human intelligence in order to proactively resolve requests that are unique or unusual.
With massive amounts of incoming data, the potential for machine learning to transform healthcare is more drastic than any other sector.
Machine learning is changing medical practice since its applications are expanding into areas that were previously thought to be only solved by human experts.
Machine learning will affect the way hospitals operate, as it will play a vital role in clinical decision support, enabling timely identification of disease and tailored treatment plans to ensure optimal outcomes for both the patients and hospitals.
The impact of machine learning on healthcare is improving the efficiency while reducing the cost of care.
What are the limitless opportunities for machine learning in healthcare?
The list below is by no means complete but provides some useful applications of Machine Learning in the healthcare industry.
Identifying Diseases and Diagnosis
Identification and diagnosis of diseases which are otherwise considered difficult to diagnose is one of the main ML applications in healthcare. Diagnosis can include anything from cancer during the initial stages, to other genetic diseases.
Drug Discovery and Manufacturing
Early-stage drug discovery process is one of the primary clinical applications in machine learning. This also includes precision medicine which can help in finding alternative paths for therapy of multi factorial diseases.
Some machine learning techniques can also identify patterns in data. These techniques can be applied to derive insights about specific patients heading towards a diagnosis of a chronic disease.
Medical Imaging Diagnosis
Computer Vision is a field which works on image diagnostic tools for image analysis. Data sources from medical imagery can be used to identify any disease through the test results.
Medical imaging is often used for preventive screenings for cancers, such as breast cancer. Using ML to identify fractures, dislocations could allow to be more confident in their treatment choices.
Health systems can reduce mortality rates by predicting the likelihood of death within some specific time period of patient discharge and then by dealing with those matching patients by providing appropriate support.
Clinical Trial and Research
Machine learning has various potential applications in the field of clinical trials and research. Clinical trials are time and money expensive. Using ML-based analytics to identify potential clinical trial candidates can help researchers to select a group of data for research.
You can also use Machine learning for real-time monitoring of the trial participants and use this data for your research.
By applying machine learning to multiple data sources such as genetic data, sensor data, environmental and lifestyle data, personalized treatments for diseases are possible.
This type of treatment is known as precision medicine which is based on analysis of patient characteristics such as medical history, genetic makeup, and data recorded by wearable devices.
A patient’s medical history data can be leveraged to generate various treatment plans. By Blair Heckel