Over the last 100 years, the rapid advancements in sanitation, pharmacology, and medical science have resulted in the exponential growth of life expectancy worldwide.
However, this trend coupled with rising patient expectations, and the ever-increasing popularity of healthy lifestyles mean that healthcare organizations will have to provide care for more patients than ever before. According to WHO, 15 million more health workers will be needed by 2030.
While the first solution that comes to mind is to attract more healthcare practitioners, it’s technology that will play a pivotal role in making healthcare sustainable. Artificial intelligence and machine learning technologies are posed to improve the efficiency of care delivery and free healthcare professionals’ time for more important tasks.
In this article, we discuss how machine learning helps healthcare institutions revolutionize their daily workflows and view the top use cases and real-life examples. We also outline the main implementation challenges and see how machine learning consultancy can solve them, as well as provide a future outlook on this technology.
Machine learning in healthcare is used to draw insights from large medical data sets to enhance clinicians’ decision-making, improve patient outcomes, automate healthcare professionals’ daily workflows, accelerate medical research, and enhance operational efficiency.
Here are some of the advantages healthcare organizations gain by implementing machine learning:
| 1 | Automated routine tasks | From streamlining EHR processes to virtual nursing, machine learning can help healthcare professionals to automate many routine and repeatable tasks. |
| 2 | Improved decision-making | By detecting patterns in enormous healthcare data sets, machine learning helps clinicians to streamline diagnostics and improve decision-making. |
| 3 | Enhanced patient experience | With the help of chatbots and virtual assistants, healthcare organizations can improve patient experience by streamlining access to basic healthcare services. |
| 4 | Accelerated innovation | By optimizing drug discovery and development, machine learning helps pharmaceutical companies to reduce time-to-market and dramatically decrease research costs. |
| 5 | Reduced operational costs | Machine learning-enabled automation of back-office tasks allows healthcare organizations to cut costs and improve resource management. |
| 6 | Improved patient outcomes | With well-thought-out decision-making systems powered by machine learning, healthcare professionals can make more informed decisions and improve patient outcomes. |
| 7 | Extended access to healthcare | With efficiencies unlocked by machine learning, organizations can help more patients without compromising care quality. |
| 8 | Reduced risks | Predictive analytics allows for early detection of serious illnesses, reduced risks during robot-assisted surgeries, and quick identification of high-risk patients. |
| 9 | Increased staff satisfaction | By allocating routine tasks to machine learning algorithms and enabling quick extraction of the most important data from patients’ cases, healthcare organizations can substantially increase the satisfaction of their medical staff. |
| 10 | Refined data governance | Machine learning algorithms can help healthcare organizations to consolidate disparate data sources and make sense of their large datasets. |
Here are the most popular machine learning algorithms in healthcare:
Artificial neural network (ANN) is often referred to as the most ‘humanized’ machine learning algorithm. ANNs sequentially filter incoming information based on set parameters and usually require minimum human involvement during training. In the healthcare context, they are often used for medical imaging in radiology as well as text and speech recognition.
Logistic regression is typically used to predict which outcome out of two is likely to happen. Its binary nature makes it comparatively easy to implement, which is why it’s one of the most popular machine learning algorithms in healthcare. Besides predicting an outcome probability, logistic regression allows users to see how important each variable is for the final outcome. Healthcare professionals use logistic regression for medical diagnosis, treating at-risk patients, and adjusting behavior plans.
Unlike linear regression algorithms, support vector machines (SVMs) are generally used for classification problems. In simple terms, the further the data points are from the y axis on the graph below, the higher the probability is that they belong to the respective classes. SVMs are frequently used to classify data from incomplete datasets with missing values and can be applied to a range of healthcare tasks including drug development, medication adherence prediction, and image and text segmentation.
Many prevalent diseases are manageable or even avoidable. For example, type 2 diabetes, obesity, and heart diseases in some cases can be avoided by practicing a healthier lifestyle. However, adjusting the lifestyle requires a change in behavior, which is not a one-time effort but one requiring constant reminders and follow-ups. For this purpose, machine learning algorithms can aggregate data incoming from patients’ connected health devices and sensors to generate insights into these patients’ behavior and guide them during this transformational journey.
Altering smoking habits with SmokeBeat
SmokeBeat is an innovative application that passively gathers data on the user’s smoking behavior. The application uses an accelerometer on a smartwatch or a smart band to detect hand-to-mouth gestures. SmokeBeat processes this data and offers real-time Cognitive Behavior Therapy incentives. User responses to those incentives are constantly measured and recorded to improve effectiveness. Additionally, SmokeBeat compares users’ smoking data with their peers of choice, creating a sort of supportive social network.
In today’s busy hospital environment, nurses can become overworked and struggle to offer enough personalized support to each patient. Healthcare facilities count on virtual nurses to solve this issue. Virtual nurses are computer-generated avatars that can interact with patients like humans. They are designed to be social, empathic, and informative.
Virtual nurses can interact with patients more regularly than human nurses and answer questions in-between doctor visits. They offer quick answers (faster than waiting for a nurse) and they are available 24/7.
Streamlining remote care with Molly
One example of a virtual nurse is Molly. This is a female avatar able to remotely monitor medical conditions, which would be challenging to monitor on the spot. Molly receives data such as blood pressure and weight from monitoring devices connected via Bluetooth. These devices are positioned in patients’ homes, which makes it convenient to take measurements as often as needed. Molly can recognize speech and verbally answer patients’ queries. Additionally, Molly understands human speech and can respond in kind. It also offers a chatbot for private discussions.
Even with all the advancements in technology, medical image analysis is a tedious task prone to human error since it requires great attention to detail. With the help of machine learning, it’s possible to detect even the subtlest changes in medical scans. Furthermore, traditional scan analyses (such as CAT scans and MRI) are time-consuming.
Improving image quality with SubtleMR
For example, SubtleMR developed by Subtle Medical is a machine learning-based software solution that improves the quality of MRI protocols. With the help of denoising and resolution enhancement, SubtleMR can improve image quality and sharpness with any MRI scanner and field strength. For example, RadNet, a US leader in outpatient imaging with 335 centers across the country, accelerated its protocols by 33-45% after adopting SubtleMR technology.
By combining machine learning-powered pattern recognition and automation, clinicians can considerably reduce the time it takes to identify high-risk patients.
Detecting high-risk patients with UiPath and Amitech
For example, UiPath, an AI- and RPA-enabled automation platform, was used by UiPath’s partner Amitech to help a large healthcare provider automate patient analysis.
A mixture of optical character recognition and natural language processing is used to structure and organize patients’ medical documents. Then an RPA bot feeds this data to a machine learning system that scores patients for risks, provides them with a tailored healthcare plan, and alerts the appropriate clinicians and care managers about high-risk patients so the former can take immediate action. Besides significantly improving patient outcomes, instead of four weeks, the process now takes only minutes.
Using robots in healthcare is not a new trend. Robotic assistance in surgery increases precision, allows access to different areas of the human body with minimal penetration and alleviates pressure from human surgeons as robots can take over some parts of the work.
Improving surgical outcomes with Senhance Surgical system
Senhance Surgical system is a console-based, multiarmed surgical system that allows surgeons to remotely control it. The system heavily relies on machine learning and deep learning models to bring the most challenging healthcare ideas to reality. For example, during the preoperative stage, a machine learning-driven database allows surgeons to go through simulation training. During surgeries, based on data from the eye-tracking camera, the system’s Intelligent Surgical Unit can automatically adjust the camera view and predict when a surgeon needs to zoom in or enhance images in real-time.
Drug discovery is an expensive and long process. Thousands of elements need to be tested together, and only one of them might end up as a viable drug.
Machine learning algorithms are used in the drug discovery process for the following purposes:
Accelerating oncology research with IBM Watson
Pfizer, a pharmaceutical company, is using IBM Watson for its immune-oncology research. While a human researcher can read around 300 articles a year, Watson was able to process one million journal articles and data on four million patents. Given the ability of machine learning systems to ingest a huge amount of information, Pfizer employees can identify non-obvious connections and help create treatment plans out of drug combinations.
Finding cures to diseases with Google’s Deep Mind
Another example is a machine learning-powered system called AlphaFold that can automatically predict protein structure built by Google’s DeepMind and revealed in 2020. Reliably predicting how different proteins interact with each other is an immense biological breakthrough as it can significantly accelerate drug screening and development. Last year, Google launched Isomorphic Labs, a company that will use AlphaFold’s technology to find cures for prevalent diseases.
In general, far too many operations in healthcare institutions are undermined by ineffective management practices. With the ever-growing demand for healthcare services, hospitals’ enterprise management systems are becoming increasingly more chaotic. Carefully tuned machine learning-based systems can make sense of administrative data and handle the majority of hospitals’ administrative functions.
Optimizing staffing with Globus.ai
For example, Globus.ai is a Norway-based company that created a system to help healthcare institutions to streamline staffing. With the help of natural language processing and machine learning, the system can match healthcare employees to specific tasks based on their skill sets, making task scheduling far more efficient. Importantly, Globus.ai’s system considers legal requirements when making scheduling decisions. For example, in some cases, the law limits the number of working hours or requires a professional with particular expertise to be present during a certain procedure.
Nowadays, a huge amount of data can be collected from satellites. This includes real-time data from social media and other historical web data. Machine learning algorithms help aggregate this data and make predictions about potential disease outbreaks. One example is predicting malaria outbreaks by analyzing data including monthly rainfall, temperature, and similar parameters.
This can be particularly relevant in third-world countries that lack medical infrastructure and the necessary education to combat those diseases. Knowing about such critical outbreaks upfront will allow precautions to be taken in order to minimize their negative impact and save lives.
ProMED reporting disease outbreaks online
ProMED (the Program for Monitoring Emerging Diseases) offers an online real-time reporting system showing outbreaks of infectious diseases worldwide and any exposure to toxins affecting human or animal health. ProMED aggregates data from sources such as official reports, media reports, local observers, and reports contributed by its subscribers. An expert team reviews these reports before they are accepted into the system.
The data provided by ProMED is aggregated by HealthMap to visualize disease outbreaks in every country.
In healthcare, inaccurate or incomplete diagnosis of diseases can be detrimental to patient outcomes, and, in the worst-case scenarios, lead to death. To address one of the most apparent healthcare challenges, many companies are tapping into machine learning to make medical diagnostics more accurate.
Predicting syndromes with Face2Gene
A great example is the Face2Gene app, a machine learning-enabled facial recognition software that helps clinicians to more accurately diagnose rare diseases. With the help of machine learning, Face2Gene can detect phenotypes, reveal relevant facial features, and evaluate the probability of a patient having a particular syndrome.
Health insurance is an essential component of the healthcare industry and plays a critical role in enhancing access to healthcare. However, there is plenty of room for improvement in the mostly manual-based processes of health insurance.
For example, machine learning-enabled pattern recognition algorithms can assist in early fraud detection. Rule-based fraud detection systems that the majority of health insurers currently use can flag too many claims as potentially fraudulent. Machine learning systems, on the other hand, learn and gradually decrease the probability of false positives.
Machine learning can also be useful for the automation of different health insurance processes, including credit underwriting, risk assessment, claims to process, and customer support.
Optimizing health insurance with Maya Intelligence
For example, Temple University Health System (TUHS), a nationally-recognized academic health system in Philadelphia partnered with Accolade, a company that provides the Maya Intelligence platform to help patients choose the most appropriate healthcare coverage option. The system utilizes machine learning to analyze medical claims, lab results, and other relevant patient information to offer tailored healthcare plans to patients. As a result of the implementation, TUHS has managed to save more than $2 million in healthcare claim costs, and achieve a 50% increase in employee engagement.
While machine learning has immense potential to transform healthcare, it’s critical to consider the challenges and risks associated with its implementation.
1. Lack of dataThe lack of clean, structured data is an overarching problem for organizations across every industry, but training and deploying value-adding machine learning models at scale requires companies to reimagine their approaches to data governance. Given that datasets from one organization rarely suffice for model training, engineers typically resort to obtaining patient data from other healthcare organizations. The problem is that the majority of these datasets are incompatible with each other. Enforcement of industry-wide data governance frameworks is paramount at this point. Standardization of medical data requires equal effort from both governmental bodies and industry players. Thankfully, such initiatives are already taking place. In 2022, the White House-led National AI Research Resource (NAIRR) team issued a comprehensive interim report that outlines recommendations around data collection and aims to extend access to medical data for more AI companies and researchers. |
2. BiasWhile all machine learning applications can suffer from bias, its implications in healthcare are justifiably the most concerning. To put it simply, given that it’s humans who train machine learning algorithms, our existing biases inevitably creep in. What’s even more daunting is that ML models do not only sustain these prejudices but often amplify them. Fortunately, the importance of unbiased and equitable data is getting recognized at the governmental level. In 2021, the Biden administration formed the Equitable Data Working Group to ensure that historically underserved communities get equal access to healthcare services. Importantly, enforcing robust standards to make different datasets interoperable is also on the group’s agenda. Both NAIRR’s and Equitable Working Group’s initiatives are instrumental in maximizing the effectiveness of machine learning and reducing the probability of bias. |
3. Lack of strategyIn the absolute majority of cases, machine learning brings tangible long-term benefits when all parts of the organization support its adoption. Given that machine learning has a much more drastic impact on conventional healthcare workflows than the majority of other technologies, companies should make an effort to redefine team roles, invest in change management, and launch workforce reskilling programs. Understandably, many organizations are reluctant to undergo such significant changes for the sake of technology adoption. However, according to a recent Deloitte survey, the majority of industry leaders believe that these initiatives will inevitably prove worthwhile in the long term. |
4. Lack of in-house expertiseIntegrating such a complex technology as machine learning into intricate healthcare workflows requires both excellent technical skills and a deep understanding of medical science. On the one hand, many ambitious AI startups fail to incorporate clinical expertise during the early phases of development, while, on the other hand, many credible and experienced clinicians have insufficient understanding of machine learning to provide tangible input. This is why for artificial intelligence startups tapping into healthcare, assembling a multifaceted team is a prerequisite for success. It’s not only important to hire exceptional talent in multiple fields but to also ensure that data scientists, machine learning engineers, medical professionals, legal advisers, and other experts can collaborate with each other. |
1. Viz.ai
The Viz.ai tool helps care teams to optimize care coordination and improve communication between healthcare professionals with machine learning algorithms. Viz.ai streamlines care by quickly connecting frontline healthcare providers to specialists, resulting in faster case resolution and improved patient outcomes.
2. Deep Genomics
The Deep Genomics’ artificial intelligence-powered platform accelerates research by helping healthcare professionals quickly find candidates for the development of drugs for specific disorders.
3. Oncora Medical
Oncora Medical is a Philadelphia-based startup that streamlines cancer research and treatment. By collecting large amounts of medical data, Oncora’s platform can assess care quality and suggest better treatment methods.
4. Intuitive Surgical
Intuitive Surgical are the developers of the most widely used machine learning-powered surgical system called Da Vinci. Da Vinci Surgical System allows surgeons to perform robotic-assisted, minimally invasive surgeries that significantly improve surgery outcomes.
5. PathAI
PathAI uses machine learning to help pathologists to make more informed diagnostic decisions. PathAI works with renowned drug developers and healthcare organizations to extend the reach of artificial intelligence and machine learning in healthcare.
While the benefits of machine learning in healthcare are apparent and indisputable, a very careful and slow approach to its implementation is unsurprising. It goes without saying that human lives are the most important thing we can trust technology to handle. However, as global access to healthcare becomes a growing concern, banking on technology continues to emerge as a clear solution.
For machine learning and artificial intelligence to solve healthcare’s legacy challenges, it’s paramount to transition from tests and pilot projects to having machine learning as a fully-functional capability.
September 21, 2023