With digitalization disrupting every industry, including healthcare, the ability to capture, share and deliver data is becoming a high priority. Machine learning, big data and artificial intelligence (AI) can help address the challenges that vast amounts of data pose.
Machine learning can also help healthcare organizations meet growing medical demands, improve operations and lower costs.
At the bedside, machine learning innovation can help healthcare practitioners detect and treat disease more efficiently and with more precision and personalized care.
An examination of machine learning in healthcare reveals how technology innovation can lead to more effective, holistic care strategies that could improve patient outcomes.
What Is Machine Learning? An Overview
Machine learning is one of the most common forms of AI. It processes and finds patterns in large data sets to enable decision-making.
Machine learning applications consist of algorithms: a collection of instructions for performing a specific set of tasks. The algorithms are designed to learn from the data independently, without human intervention.
Over time, machine learning algorithms improve their prediction accuracy without requiring programming.
A deep dive into what machine learning is reveals three critical components of algorithms: representation, evaluation and optimization.
Representation means that data must be classified in a form and language that a computer can handle. This component sets the stage for the next component, evaluation, to determine whether the data classifications are useful.
Then, as part of the optimization process, the algorithm finds the best model for the most effective and accurate outputs.
Types of Learning
Machine learning allows machines to go through a learning process. It does this by developing foundational models to solve problems. The machine learning algorithm alters the model every time it combs through the data and finds new patterns. This approach enables learning and provides increasingly accurate outputs.
An algorithm goes through this learning process without requiring programming. Machine learning can be supervised, unsupervised, semisupervised or reinforced.
- Supervised learning. According to Gartner, supervised learning, a type of machine learning in which historical and classified input and output data is fed into algorithms, will continue to be the most used through 2022.
- Unsupervised learning. In unsupervised learning, algorithms can independently identify patterns in data without previous classification. It has various applications. For example, in industrial sectors, it can identify faults in factory systems before they happen through predictive maintenance.
- Semisupervised learning. Semisupervised learning falls in between supervised and unsupervised. Semisupervised learning algorithms can use a mix of classified and unclassified data to build problem-solving models. A recent study shows the promise of a semisupervised learning model in accelerating drug discovery.
- Reinforcement learning. Reinforcement learning teaches algorithms through a rewards system. Algorithms produce various outputs and learn to select the correct ones; they get rewarded for desired actions and punished for undesired actions. According to a recent study, various applications, including autonomous robotic nanofabrication, can use reinforcement learning.
Machine Learning Applications in Healthcare
Machine learning applications can potentially improve the accuracy of treatment protocols and health outcomes through algorithmic processes.
For example, deep learning, a type of complex machine learning that mimics how the human brain functions, is increasingly being used in radiology and medical imaging. Using neural networks that can learn from data without any supervision, deep learning applications can detect, recognize and analyze cancerous lesions from images.
Faster processing speeds and cloud infrastructures allow machine learning applications to detect anomalies in images beyond what the human eye can see, aiding in diagnosing and treating disease.
Future advancements in machine learning in healthcare will continue to transform the industry. Machine learning applications under development include a diagnostic tool for diabetic retinopathy and predictive analytics to determine breast cancer recurrence based on medical records and images.
How Machine Learning in Health Informatics Impacts Healthcare
The following sections discuss three areas in which machine learning in health informatics impacts healthcare.
Machine learning in health informatics can streamline recordkeeping, including electronic health records (EHRs). Using AI to improve EHR management can improve patient care, reduce healthcare and administrative costs, and optimize operations.
One example includes natural language processing, which enables physicians to capture and record clinical notes, eliminating manual processes.
Machine learning algorithms can also make EHR management systems easier to use for physicians by providing clinical decision support, automating image analysis and integrating telehealth technologies.
Gaps in healthcare information can result in machine learning algorithms making inaccurate predictions, which can negatively impact decision-making in clinical settings.
Since healthcare data is originally intended for EHRs, the data must be prepared before machine learning algorithms can effectively use it.
Health informatics professionals are responsible for maintaining data integrity. Activities that health informatics professionals perform include gathering, analyzing, classifying and cleansing the data.
The combination of machine learning, health informatics and predictive analytics offers opportunities to improve healthcare processes, transform clinical decision support tools and help improve patient outcomes. The promise of machine learning’s changing healthcare lies in its ability to leverage health informatics to predict health outcomes through predictive analytics, leading to more accurate diagnosis and treatment and improving physician insights for personalized and cohort treatments.
The following resources can provide a greater understanding of the relationship between machine learning and health informatics:
- National Center for Biotechnology Information, “Machine Learning and Electronic Health Records: A Paradigm Shift”: This study discusses the application of machine learning methods to EHRs and its potential in improving diagnosis and treatment.
- Forbes, “The 9 Biggest Technology Trends That Will Transform Medicine and Healthcare In 2020”: This article highlights some of the biggest and most transformative technology trends in healthcare, including machine learning.
- HIMSS, “Artificial Intelligence in Health: Ethical Considerations for Research and Practice”: Though healthcare technology innovation continues to transform medicine, one must consider machine learning’s ethical implications.
- gov, Health IT Curriculum Resources for Educators: More than 10,000 educators around the world have used this resource to access a health IT curriculum.
- Wired, “From Diagnosis to Holistic Patient Care, Machine Learning Is Transforming Healthcare”: This article provides examples of how machine learning is transforming healthcare.
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Other Applications of Machine Learning in Healthcare
Machine learning can positively impact patient care delivery strategies. For example, it can help clinicians identify, diagnose and treat disease. Applications of machine learning in healthcare can also streamline healthcare tasks and optimize surgery planning, preparation and execution.
Here are some examples of machine learning applications in healthcare.
Disease Identification and Diagnosis
Machine learning algorithms can detect patterns associated with diseases and health conditions by studying thousands of healthcare records and other patient data.
Recent developments in machine learning can help increase healthcare access in developing countries and innovate cancer diagnosis and treatment. Entrepreneur reports that a deep learning-based prediction model developed at the Massachusetts Institute of Technology can predict breast cancer development years in advance.
Additionally, according to an AMA Journal of Ethics article, AI applications in healthcare “can now diagnose skin cancer more accurately than a board-certified dermatologist.” The article points to machine learning’s additional benefits, including diagnostics speed and efficiency and a shorter time frame for training an algorithm versus a human.
Medical Imaging Diagnosis
CAT scans, MRIs and other imaging technologies offer such high-resolution detail that going through the megapixels and data can challenge even experienced radiologists and pathologists.
Machine learning has demonstrated its value in helping clinical professionals improve their productivity and precision. Common use cases for machine learning in medical imaging include identifying cardiovascular abnormalities, detecting musculoskeletal injuries and screening for cancers.
Machine learning can use real-time data, information from previous successful surgeries and past medical records to improve the accuracy of surgical robotic tools. The benefits include reduced human error, aid during more complex procedures and less invasive surgeries.
For example, robots can precisely conduct operations to unclog blood vessels and even aid in spine surgery. Surgical robotics can also offer more than mechanized assistance to surgeons by planning workflows and executions for surgical procedures.
Robotic Patient Support Tasks
Robots can help augment patient abilities directly. Examples include helping paralyzed patients regain walking ability and performing tasks such as taking blood pressure and providing medication reminders to patients. Robots can even provide companionship to sick and older patients.
Health data from various sources, including EHRs and genetic data, can help advance personalized care. A health practitioner doesn’t have enough time in a day to analyze all the data to provide precision medicine to patients.
However, machine learning, with its ability to leverage big data and predictive analytics, creates opportunities for researchers to develop personalized treatments for various diseases, including cancer and depression.
Examining the Ethics of AI in Healthcare
The improvements to healthcare efficiency and patient care delivery that machine learning provides come with ethical concerns.
Health informatics professionals can play a pivotal role in addressing challenges with AI as well as the ethics of AI in healthcare, including those in the following sections.
Sharing Patient Information
Concerns with patient confidentiality, the federal law restricting release of medical information, and informed consent all have to do with sharing patient information.
The basis of effective machine learning is data. Still, privacy and confidentiality laws are meant to protect patient information from vulnerabilities such as a data breach. Statutes prohibit clinicians from sharing patient information, unless for medical reasons, for example, when a doctor shares medical information about the patient with an oncologist or a cancer specialist to improve health outcomes.
Patient autonomy issues also exist. Many older and psychiatric patients are incapable of making healthcare decisions independently. Machine learning can harness data from EHRs and other medical sources to help with critical decisions in these circumstances.
On the other side of the argument, an automated process shouldn’t fully replace patient autonomy. However, machine learning could become a valuable tool that aids in medical decision-making.
Patient Safety and Outcomes
The quality of data input in machine learning algorithms determines the reliability of the output. Erroneous or flawed data can undermine system reliability, which then calls into question whether decisions based on the data are right or wrong.
Another concern with flawed data is that it can lead to a lack of cultural competency. For example, since data typically underrepresents minority populations, it can put people at risk of overdiagnosis or underdiagnosis.
The bottom line is that concerns about system reliability and lack of cultural competency from faulty data that machine learning algorithms may use can generate erroneous outputs, lead to misinformed medical decision-making, and ultimately impact patient safety and outcomes.
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What Is the Future of Healthcare Technology?
Health informatics professionals stand at the entryway of opportunity, playing a key role in enabling machine learning’s integration into healthcare and medical processes. Their in-depth knowledge of technology and how it can be applied to improve patient care and outcomes offers enormous value to an evolving healthcare industry increasingly reliant on data.
The deep-learning algorithms of machine learning can trim the time it takes to review patient and medical data, leading to faster diagnosis and speedier patient recovery.
Machine learning has already proven useful in the current global pandemic. As HealthITAnalytics reports, a deep-learning tool can predict COVID-19 surges in U.S. counties with nearly 65% accuracy.
As healthcare organizations seek to integrate machine learning into healthcare and medical processes, a primary responsibility of health informatics professionals—to ensure that healthcare data is reliable—becomes a high priority.
Various technology-driven healthcare concepts show promise in improving care delivery in the coming years. These innovations will also transform the health informatics professional’s role.
What’s the future of healthcare technology? Here are examples of technologies that will impact healthcare in the years to come.
Virtual reality (VR) is changing healthcare by transforming patients’ lives and making it easier to train doctors.
For example, surgeons wearing special VR headsets can stream operations and provide medical students with a unique view of a surgical procedure. In another example, VR is being used to help speed up recovery in physical therapy.
Patients going through physical therapy often endure strenuous physical activities that can feel burdensome. Through VR training exercises with machine learning, recovery programs can be personalized and make physical therapy activities more enjoyable and engaging.
Augmented reality (AR) is among the top three technologies transforming healthcare, according to The Medical Futurist. Similar to VR, AR applications in healthcare can help better prepare medical students. AR technologies can provide students with opportunities to learn directly from surgeons performing real-life surgeries.
For example, AR enables medical students to get detailed, accurate depictions of human anatomy without studying real human bodies.
From counting steps to monitoring heart rhythms, various types of consumer wearable technologies provide information that can help people become more fit.
Other wearable technologies can provide doctors with vital information about patient health, including heart rhythm, blood pressure, temperature and heart rate.
According to Pew Research Center, about 21% of Americans use wearable technologies, such as fitness trackers and smartwatches. As more people embrace wearable technologies, health informatics professionals can help improve the communication and accuracy of data shared between these devices and health information systems that doctors use.
Genomic data can help doctors create personalized treatment plans for their patients. Machine learning in health informatics enables genetic mutations to be analyzed much faster and helps in diagnosing conditions that can lead to disease. Genome sequencing, made possible through machine learning applications, can impact cancer diagnosis and treatment and mitigate the impact of infectious disease.
The first-ever human genome sequencing project cost more than $3 billion. It took more than 13 years to complete, according to the World Economic Forum.
Today, individuals can pay less than $600 to have their genome sequenced and get results within a week. As genome sequencing becomes more affordable and machine learning becomes smarter, health informatics professionals can help advance genomic medicine to treat the world’s deadliest diseases.
According to the National Nanotechnology Initiative, nanotechnology is defined as “the understanding and control of matter at the nanoscale, at dimensions between approximately 1 and 100 nanometers.”
Nanotechnology application in healthcare is referred to as nanomedicine. Nanotechnology can help execute tasks such as drug delivery in which molecules, cellular structures and DNA are at work.
For example, future nanotechnology medicine includes drug delivery methods that “enable site-specific targeting to avoid the accumulation of drug compounds in healthy cells or tissues,” according to Engineering.com.
In other words, drugs can be delivered to targeted regions bypassing areas in the human system that aren’t affected by diseases.
According to a study published in the Journal of Polymers and the Environment, 3D printing in biomedicine offers opportunities in the health sector. 3D printing processes allow for the efficient manufacture of drug formulations, implants, prostheses, biosensor devices, and even human tissues and organs. It creates opportunities for personalizing medical treatments, improves healthcare quality, reduces costs and minimizes production risks.
Transforming the Healthcare Industry
According to Imaging Technology News, the market for AI in healthcare will expand to more than $31.3 billion by 2025—a growth of more than 40% since 2018. Individuals seeking to extend their healthcare informatics careers to include machine learning can begin by exploring educational opportunities. This can include enrolling in graduate degree programs in health informatics. With the advanced skills and knowledge they gain in graduate programs, they can help transform the healthcare industry.