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Benefits of Machine Learning in Healthcare & its Use Cases

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Know the real-world advantages and use cases of machine learning in healthcare to drive improvements for your clinical operations, care quality & patient outcomes.

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Could Machine Learning Reduce Healthcare Costs and Improve Care?

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Pulse is developing machine learning models to predict patient behavior and encourage improved outcomes.

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Even in this digital age, manual processes still constrain healthcare providers. Documenting clinical encounters, reviewing complex patient records (e.g., labs and imaging), tracking patient care gaps, and reviewing and refilling medications require significant time and effort. These cumbersome processes present an opportunity for technology to play a bigger role in reducing clinician burden and improving patient care.

To take the next step in using data-driven technology to improve care, health systems need to increase analytics and machine learning (ML) adoption, then leverage those capabilities to provide the most relevant information to clinical decision makers.

Machine Learning in Healthcare Delivers Valuable Insight at the Point of Decision Making

The digitization of healthcare via the EHR has provided clinicians with more information than ever. However, that information can quickly become more than a clinician can process, especially in the brief timeframe they have with a patient. For technology to improve care, solutions and tools need to make data meaningful to the clinician and impactful to patient care.

EHRs increase providers' access to a patient's basic health data, but they continue to fall short of making that data actionable. Organizations can bridge this gap by using advanced analytics and ML to deliver more valuable information at the point of care. Instead of the clinician reviewing a few blood pressure readings as they sit with the patient, an advanced ML algorithm could be reviewing the last five years of blood pressure readings, BMI, cholesterol, race, family history, and socioeconomic information.

With greater speed and precision, the algorithm could give the clinician critical information, such as the patient's risk for stroke, myocardial infarction, or kidney disease. The algorithm could also suggest possible treatment options based on the latest relevant literature.

Machine learning can process large amounts of patient data beyond the scope of human capability, then reliably convert that analysis into medical insights that help clinicians plan and deliver care. Clinicians can use these data-driven insights-based on the patient's data and data from other patients with similar conditions-to understand diagnoses and treatment options, achieve better outcomes, and lower costs of care.

Understanding How Machine Learning in Healthcare Is Critical to Widespread Adoption

Establishing trust in ML requires transparency in the process. Clinicians are used to reviewing medical science from the perspective of clinical trials, designed and implemented by experts. Once complete, the trial is peer reviewed and published with information about the processes, data, and outcomes. This transparent process lends itself to scrutiny, evaluation, and explanation.

Widespread adoption of ML in medicine will likely require similar processes. Subject matter experts will play an integral role in helping build and evaluate algorithms. Algorithms will also require transparency, explaining the reasoning behind recommendations, and how recommendations can improve clinical outcomes.

When providers understand how ML can support data-informed decision making, they will view the capability as a real-world tool to augment patient care. Analytics organizations can also use real-world scenarios to earn team member ML buy-in.

Radiology, for example, has been on the forefront of adopting ML in clinical practice. Instead of predictions that ML would replace radiologists, it is now seen as a tool that can augment workflow, efficiency, and diagnostic capabilities. Technology companies including the American College of Radiology and the U.S. Food Drug Administration are collaborating with large academic institutions with large data sets. This collaborative effort between medical subject matter experts, data scientists, and governing bodies is key to future ML adoption. As experts develop, test, and demonstrate algorithm success with clinical outcomes and publish results for evaluation, radiologists will incorporate these algorithms into their workflow to benefit patients.

Machine Learning in Healthcare Optimizes Standardized Processes

As team members learn to trust and adopt ML, they can apply the data science capability to standardized processes with large image data sets. Certain areas of medicine that involve pattern recognition, such as radiology, dermatology, and pathology, have seen increasing ML development. Data scientists can train ML models to look at images, identify abnormalities, and augment clinician interpretation, with the potential to improve the diagnostic accuracy and ultimately improve patient care.

For example, a high-risk skin cancer patient comes in for a routine mole check to screen for changes to the size, shape, and color of moles worrisome for melanoma. The dermatologist of the future has access to skin imaging, which takes a picture of the patient's skin and then kicks off an ML algorithm that can document each mole in minute detail, compare moles to past images, and directs the dermatologist to specific moles that may need additional evaluation.

Machine Learning Scours Millions of Datasets to Surface Critical Information

As health systems gain access to more data and invest in integration and interoperability infrastructure, ML will potentially reach all aspects of medicine. The most accurate ML models will typically come from organizations with big data sets and the supporting infrastructure, including a data platform and ML technology (e.g., Healthcare.AI by Health Catalyst). These tools are necessary to aggregate data from enterprise data warehouses, data platforms, and third-party data sources, then identify the relevant data inputs for ML. This process enables comparative effectiveness and research, resulting in unique, accurate ML algorithms.

The increase in healthcare data and accurate algorithms means ML-generated insights can reach more areas of medicine. For example, a primary care provider (PCP) treating a patient with hypertension could review ML-generated information during the clinic visit. The process could begin with an analysis of home-generated data from connected devices, including blood pressure readings, weight, fitness data, sleep data, compliance with medications, and nutritional data. Future wearable sensors may help collect additional data. An ML algorithm could combine that data with the health system's data, such as additional diagnosis, symptoms, lab tests, imaging, and genomic data. By integrating all available patient data in real time, ML will augment the PCP's ability to better understand the patient's current state and future health risks and enhance medical decision making to improve that patient's long-term outcomes.

This potential to augment care exists across all specialties of medicine as more data is available. For example, oncology will see advances in diagnosis with ML-augmented imaging and pathology, and ML's analysis of complex genetic data will improve clinical care and inform treatment. ML algorithms will be like an additional expert consultation, aggregating and informing oncologists with the latest clinical trial results across a broad spectrum of cancers, allowing easier access to newer treatment options and even helping refer patients to clinical trials with promising investigational drugs.

Machine learning-powered tools can sift through more data, including libraries of similar patients, diagnoses, and genetics, than one person can process. As such, ML opens data resources that include treatment options and predictions for each treatment's effectiveness, mortality rates, side effects, and cost. In this way, ML can put in infinitely more work behind the scenes, delivering real-time, accurate information to the point of care.

Moving Machine Learning in Healthcare from Theoretical to Clinical Reality

Many health systems already leverage ML in everyday clinical practice, advancing medicine into a new realm. While the benefits of ML to augment provider decision making seem endless, health systems first need to understand ML's role in healthcare and then invest in supporting tools and infrastructure. With this process, ML is becoming commonplace in healthcare.

Analyzing patients' real-time status along with that of similar patients at other health systems will enable providers to make the most informed decisions and more deeply understand the best course of action for each patient. The analytics engine and the ML algorithm can analyze millions of data sets and present relevant information to the clinician at the point of decision making, enabling care teams to spend less time with data and more time with patients.

Additional Reading

Would you like to learn more about this topic? Here are some articles we suggest:

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Real-World Benefits of Machine Learning in Healthcare

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Even in this digital age, manual processes still constrain healthcare providers. Documenting clinical encounters, reviewing complex patient records (e.g., labs and imaging), tracking patient care gaps, and reviewing and refilling medications require significant time and effort. These cumbersome processes present an opportunity for technology to play a bigger role in reducing clinician burden and improving patient care.

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The Healthcare industry is an essential industry that offers care to millions of citizens, while at the same time, contributing to the local economy. Artificial Intelligence is benefiting the healthcare industry in numerous ways. Information technology is revolutionizing the healthcare industry by providing a helping hand. As we know, AI is the development of computing systems that are capable of performing tasks that typically require human intelligence. These involve heavy tasks like decision making, solving complex problems, object detection, and much more. The benefits of technologies, such as, increased level of accuracy and high level of computation, which take days for humans to solve manually are getting implemented into the healthcare business to enhance the services and keep the data organized.

Machine learning is an application of AI which has impacted various domains including marketing, finance, the gaming industry, and even the musical arts. However, the largest impact of Artificial intelligence is on the field of the healthcare industry. According to a report of PwC, AI will contribute an additional $15.7 trillion to the world economy by 2030, while the greatest impact is to be estimated on the healthcare industry.

Causes for Sudden Growth of AI in Healthcare

Let us narrow down the reason behind the sudden growth and application of Machine Learning in the healthcare industry. There are two major points that have made AI so impactful in the field of healthcare.

High availability of medical data - With the availability of data, implementing artificial intelligence gets much easier. AI is based on technologies such as deep learning and machine learning which requires tons and tons of data. So it becomes easier for the healthcare industry to track the record of their firm by a properly organized data setup.

Development of complex algorithms - The introduction of complex algorithms led to the development of machine learning in the healthcare industry. Particularly the medical data is of very high dimensional in character, the data is very vast, it has thousands and thousands of attributes, machine learning played a major role in the healthcare industry through the complex problem-solving feature.

Applications of Machine Learning in the Healthcare Sector

The growing number of applications of machine learning in healthcare allows the health care industries to manage their data and enhance their services effectively. Let's look at a couple of applications of machine learning in the healthcare industry.

1. Managing Medical Data

Machine learning is actually advancing the health care industry by implementing cognitive technology in order to unwind a huge amount of medical records and also in order to perform any power diagnosis. Machine learning helps predict the intent of a user. Implementing machine learning in an organization's workflow can develop a personalized user experience that allows the company to make better decisions and better actions that enhance the customer's experience which benefits the organization. Therefore, machine learning helps to store, collect and reform data.

2. Helps in Medical Diagnosis

According to the Global Market Insights, Medical imaging and diagnosis powered by AI should witness more than 40% growth to surpass $2.5 billion by 2024. With the help of machine learning and deep learning models, AI is actually revolutionizing the image diagnosis field in medicine. The one major application of AI in medical diagnosis is MRI scans. AI has taken over the complex analysis of MRI scans and it has made it a much simpler process.

3. Detecting Diseases at an Earlier Stage

Machine learning played a very important role in the early predictions of medical conditions such as heart attacks and diabetes. There are many AI-based wearables that are being developed to monitor the health of a person and display any warnings when the devices observe something unusual or unlikely. For eg:- Fitbit and Apple watch. These devices monitor a person's heart rate, sleep cycle, breathing rate, activity level, blood pressure, so on. It keeps the record of these measures 24×7.

4. Machine Learning in Medical Assistance

As an engine for medical assistants has grown the development of artificial intelligence-based virtual nurses has increased according to a recent survey, virtual Nursing Assistants corresponds to a maximum of 20 billion u.s dollars by 2027. A virtual nurse helps to monitor patients' conditions and follow up with treatment between the doctor visits.

5. Machine Learning in Decision Making

AI has played a very important role in decision-making not only in the field of health care, but AI has also improved businesses by studying customer needs and evaluating any potential risk that a business might face. A powerful use case of artificial intelligence in decision-making is the use of surgical robots that can minimize errors and any variations and will eventually help in increasing the efficiency of your surgeons. They help implement complex surgeries with better flexibility and control than any other approach.

6. Personalized Medicine

The predictive analysis of Machine learning can help users to get personalized treatment. Generally, nurses are bound to choose from a specific set of diagnoses or predict the risk to the patient using a fixed formula based on the history and available genetic information. Whereas, machine learning in medicine predicts the data of the patient by analyzing the medical history to generate multiple treatment options. Due to these treatments being based on the user's data they're more likely to suit the patient and are more personalized.

7. Helps Analyze the Errors in Prescriptions

Machine learning can detect and analyze the errors in the prescriptions too. The intelligence can inspect the patient's health records with the given prescriptions to find and correct the possible errors in the medication.

Conclusion: In health care industries, the intelligence by various data science models, without any human intelligence, can provide accurate and efficient results with little to no time. The machine learning models help health care sectors in numerous ways, by establishing accurate and efficient suggestions or by reducing the manual tasks of healthcare professionals, hence allowing them to focus on the research area and enhance their performance in urgent cases.

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7 Applications of Machine Learning in Healthcare Industry - GeeksforGeeks

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The Healthcare industry is an essential industry that offers care to millions of citizens, while at the same time, contributing to the local economy. Artificial Intelligence is benefiting the healthcare industry in numerous ways. Information technology is revolutionizing the healthcare industry by providing a helping hand.

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Machine learning is a term that has been on the lips of many experts over the past few years. And for good reason - it has the potential to completely change entire industries. It is a field of artificial intelligence that deals with the design and development of algorithms that can learn from data and improve their performance over time.

It has already had a significant impact on many industries, including healthcare. In fact, it is predicted to radically alter healthcare as we know it. Let's look at the possibilities of machine learning in healthcare.

What Can Machine Learning do in Healthcare - A Brief Overview

Machine learning is a form of artificial intelligence that is particularly well suited for tasks like recognizing patterns and making predictions. In recent years, machine learning has been applied to a wide range of problems in healthcare, including the diagnosis of diseases. By analysing large data sets, machine learning algorithms can identify patterns that may be indicative of disease and difficult for humans to detect. This information can then be used to develop new diagnostic tests or to refine existing ones.

Moreover, machine learning algorithms can be trained to examine medical images and identify features that are associated with specific diseases. They can also be used to analyze patient data, looking for patterns that may indicate the presence of a particular condition.

Additionally, machine learning can be used to improve our understanding of how diseases develop and progress. This knowledge can be used to develop more effective treatments. In the future, machine learning will become an increasingly important tool in the fight against disease. By harnessing the power of machine learning, doctors and other healthcare professionals will be able to speed up the diagnosis of diseases, providing patients with prompt and effective treatment.

10 ways Machine Learning Will Change the Healthcare Industry

As it is important to diagnose the patients faster, diagnosing the patients right is also a crucial aspect. In fact, a major part of treating any disease is diagnosing it accurately. According to a study by John Hopkins, thousands of people become a victim of medical errors and lose their lives every year. And machine learning can help to bring down this number by assisting in the process.

The algorithms can analyze large volumes of data and identify the disease based on that provided data. This is especially useful in medical disciplines that rely more on standardized procedures such as pathology. If we combine it with pathology management systems such as ORNet Pathology that allow pathologists to examine the samples, mark points, add annotations, control different devices, and share the slide images with other specialists for consultation, the diagnosis process can be shortened. The AI and machine learning can act as pathologist's assistants, if not replace them entirely.

Another powerful and life-saving implication of machine learning and artificial intelligence in the field of healthcare is the monitoring for health epidemics by understanding the spread of infections and predicting possible patterns.

This specific application of machine learning also proved beneficial during the pandemic to understand patterns and spread. Moreover, it allowed countries to take essential steps to contain the virus. Along with these, there are countless usages of machine learning in healthcare industry that can benefit us all.

From improving diagnosis and treatment methods, to helping us live longer and healthier lives, here are ten ways machine learning will change healthcare for the better.

1. Speed up the diagnosis of diseases

2. Improve the accuracy of diagnoses

3. Personalize treatments for patients

4. Early detection of diseases

5. Reduce the cost of healthcare

6. Improve patient outcomes

7. Increase access to care

8. Empower patients and caregivers

9. Assist in Monitoring Health Epidemics

10. Facilitate the Medical Data Collection

The healthcare sector has started relying on machine learning and AI where the big names such as Microsoft, Pfizer, KenSci, and many others are bringing in huge advancements. So, let's see what the future holds.

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Impact of Machine Learning on Healthcare: 10 ways It will change things

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Machine learning is a term that has been on the lips of many experts over the past few years. And for good reason - it has the potential to completely change entire industries. It is a field of artificial intelligence that deals with the design and development of algorithms that can learn from data and improve their performance over time.

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Terms like "machine learning," "artificial intelligence" and "deep learning" have all become science buzzwords in recent years. But can these technologies be applied to saving lives?

The answer to that is a resounding yes. Future developments in health science may actually depend on integrating rapidly growing computing technologies and methods into medical practice.

Cosmos spoke with researchers from the University of Pittsburgh, in Pennsylvania, US, who have just published a paper in Radiology on the use of machine-learning techniques to analyse large data sets from brain trauma patients.

Co-lead author Shandong Wu, associate professor of radiology, is an authority on the use of machine learning in medicine. "Machine-learning techniques have been around for several decades already," he explains. "But it was in about 2012 that the so-called 'deep learning' technique became mature. It attracted a lot of attention from the research field not only in medicine or healthcare, but in other domains, such as self-driving cars and robotics."
More on machine learning: Machine learning for cancer screening
So, what is deep learning? "It's a kind of multi-layered, neural network-based model that is constantly mimicking how the human brain works to process a large set of data to learn or distill information," explains Wu.

The key to the increased "maturity" of machine-learning techniques in recent years is due to three interrelated developments, he says. These are the technical improvements in the algorithms of machine learning; the developments in the hardware being used, such as the improved graphical processing units; and the large volumes of digitised data readily available.

That data is key. Lots of it.

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Machine-learning techniques use data to "train" the model to function better, and the more data the better. "If you only have a small set of data, then you don't have a very good model," Wu explains. "You may have very good questioning or good methodology, but you're not able to get a better model, because the model learns from lots of data."

Even though the available medical data is not as large as, say, social media data, there is still plenty to work with in the clinical domain.

Machine-learning models and algorithms can inform clinical decision-making, rapidly analysing massive amounts of data to identify patterns, says the paper's other co-lead author, David Okonkwo.

"Human beings can only process so much information. Machine learning permits orders of magnitude more information available than what an individual human can process," Okonkwo adds.

Okonkwo, a professor of neurological surgery, focuses on caring for patients with brain and spinal cord injuries, particularly those with traumatic brain injuries.

"Our goal is to save lives," says Okonkwo. "Machine-learning technologies will complement human experience and wisdom to maximise the decision-making for patients with serious injuries.

"Even though today you don't see many examples, this will change the way that we practise medicine. We have very high hopes for machine learning and artificial intelligence to change the way that we treat many medical conditions - from cancer, to making pregnancy safer, to solving the problems of COVID."

But important safeguards must be put in place. Okonkwo explains that institutions such as the US Food and Drugs Administration (FDA) must ensure that these new technologies are safe and effective before being used in real life-or-death scenarios.

Wu points out that the FDA has already approved about 150 artificial intelligence or machine learning-based tools. "Tools need to be further developed or evaluated or used with physicians in the clinical settings to really examine their benefit for patient care," he says. "The tools are not there to replace your physician, but to provide the tools and information to better inform physicians."

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Are machine-learning tools the future of healthcare?

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Terms like "machine learning," "artificial intelligence" and "deep learning" have all become science buzzwords in recent years. But can these technologies be applied to saving lives? The answer to that is a resounding yes. Future developments in health science may actually depend on integrating rapidly growing computing technologies and methods into medical practice.