Today, healthcare companies face many challenges, including a low rate of patient engagement with their own healthcare and compliance with treatment plans, such as filling prescriptions, making behavioral changes or attending follow-up appointments.
In a survey of more than 300 clinical leaders and healthcare executives, more than 70% of respondents reported having fewer than 50% of their patients highly engaged.
Some 42% of respondents said fewer than 25% of their patients were highly engaged.
This has implications not only for patient wellbeing but also for healthcare organizations, which may lose business opportunities and revenue. Technologies such as Machine Learning applied to healthcare can help resolve this issue.
In this article, we will show how ML technology benefits healthcare organizations and how they can put ML technology into practice.
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Using Machine Learning in healthcare can actually cut tech costs.
For instance, Quotient Health, a Denver-based company, “reduces the cost of supporting EMR (electronic medical records) systems” using ML-based software.
The company is improving care at lower cost by optimizing and standardizing the way those systems are designed.
Machine Learning in healthcare helps doctors improve their efficiency and speed delivery of treatment to patients.
PathAI’s patented technology, for example, helps physicians make accurate diagnoses and identify the most useful therapies for a particular patient.
Machine Learning allows healthcare organizations to reduce the human factor in data processing and related risks.
For example, BioSymetrics’ ML-based system “enables customers to perform automated ML and data pre-processing.”
Organizations in a wide range of fields such as biopharmaceuticals, tech, and healthcare can automate routine tasks and increase their accuracy.
Let’s look at some ways healthcare organizations can implement Machine Learning and put it into practice.
From detecting cancer in the early stages to dealing with common infections, Machine Learning technology can help with detecting the disease and making a diagnosis.
For example, in oncology, the biopharma giant Berg leverages AI to create and develop therapeutic treatments.
IBM Watson Genomics integrates genome-based tumor sequencing and cognitive computing to speed up diagnosis.
One great application of Machine Learning in healthcare may be in the early stages of discovering new drugs.
Precision medicine and next-generation sequencing may improve the delivery of current treatments and help find alternative options.
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Microsoft developed Project Hanover, which uses ML-based techniques for a variety of initiatives, such as personalizing drug combinations for AML (Acute Myeloid Leukemia) and is developing AI-based technology for cancer treatment.
A technology called Computer Vision combines Machine Learning and Deep Learning to help the InnerEye initiative work on image diagnostic solutions.
As Machine Learning in healthcare becomes more widespread, healthcare organizations can improve their diagnostic processes by receiving data from new sources and making this data more visible and accessible.
Personalized treatment is made more effective using predictive analytics. It can also boost future medical developments and research.
At the moment, doctors rely on the patient’s medical history and symptoms to choose from a limited number of known treatments. ML technology can be valuable in this context.
IBM Watson Oncology helps study cancer patients to quickly offer multiple treatment options.
In the future, a variety of biosensors, systems, and devices will help collect patient biometric data to personalize treatment and make it more effective.
Processing and storing large volumes of medical data is a time-consuming, exhaustive process that can be greatly simplified with the help of technology.
In this area, Machine Learning can save costs and worker time.
MATLAB’s machine learning-based handwriting recognition technology and Google’s Cloud Vision API are working in this direction, developing vector machines and various document classification methods.
Machine Learning technology has several potential applications in the sphere of research and clinical trials.
Any professional involved in clinical research knows this is a long and expensive process that typically takes years.
Applying Machine Learning–based predictive analytics can help identify potential clinical trial candidates.
That helps researchers build a pool of suitable patients based on their medical history, visits to doctors, complaints, medical records, and so on.
Second, Machine Learning in healthcare can be used in the research itself, making it more productive and reducing risk by removing the human factor in data analysis.
The Prognos Registry, for example, contains 19 billion records for 185 million patients.
Using Machine Learning, Prognos’s AI system highlights opportunities for clinical trials, pinpoints therapy requirements, facilitates early disease detection, and notes gaps in care and other factors for a number of conditions.
Today, healthcare companies face many challenges that can be addressed with Machine Learning technology. It can help such organizations reduce their risks and costs, as well as increase staff efficiency.
In practice, any healthcare organization can implement ML technology and make it part of its work processes. Healthcare companies can implement Machine Learning to:
If you want to learn more about the application of Machine Learning technology or want to start developing a project, feel free to contact us.