Machine Learning in Healthcare

Machine learning is often associated with self-driving cars and fraud detection for credit card companies, but there’s a vast amount of potential for machine learning in the healthcare industry.

In fact, machine learning models already play a significant role in the medical field. Healthcare professionals leverage various machine learning algorithms for disease diagnosis, risk calculations, and treatment recommendations.

As machine learning in healthcare evolves, it will become more prevalent in the healthcare industry, making this an exciting time to be part of this field. This article explores how machine learning can be instrumental in achieving improved patient outcomes, streamlining operations and clinical workflows, and fostering innovation across the board.

Understanding Machine Learning in Healthcare

Medical professionals, executives, and administrative staff deal with mountains of data daily. From reviewing terabytes of electronic health records to scouring patient history files, there’s no shortage of medical data. The challenge becomes making sense of all this raw data collected to come to meaningful conclusions that can help with everything from early disease detection to finding the right treatment protocols to improve patient outcomes.

The Basics of Machine Learning

Machine learning (ML) is a subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed. In healthcare, key ML subfields include deep learning, which uses neural networks to model complex patterns for tasks like image recognition and natural language processing (NLP) which helps computers understand and interpret language for analyzing clinical notes and patients’ records.

Machine learning in healthcare involves the use of various algorithms and statistical models, including:

• Deep learning: Deep learning allows a machine to take in large amounts of unstructured data and process it in a way that mimics the human brain. This is powerful because it results in machine learning algorithms coming to similar conclusions that medical professionals do – just much more quickly!
• Natural language processing: Referred to as NLP for short natural language processing, is machine learning that’s associated with understanding, analyzing, and even generating human language. This machine learning technology is useful for healthcare organizations because it allows them to pull healthcare data from an electronic health record, for example, and translate it into everyday language.

The above examples are just the tip of the iceberg regarding machine learning in healthcare. Robotics is another exciting field that can assist with a variety of applications, including performing medical procedures and improving patient safety.

Why Healthcare?

The healthcare sector is known for having complex data, which can be challenging for healthcare providers to accurately diagnose disease in the early stages and treat conditions economically and effectively with precision medicine applications.

The complexity of data healthcare professionals encounter is astounding, including electronic health records, medical images, medical research, and clinical data. Data availability and data collection aren’t necessarily the challenges – it’s making sense of medical records that can be difficult.

After all, no matter how fantastic a doctor is, they are not necessarily data scientists. Being able to analyze all of that clinical data without the help of machine learning algorithms can be incredibly challenging. However, when medical professionals utilize these powerful deep-learning models, they can get actionable insights to improve patient care and clinical decision-making.

The Spectrum of Machine Learning Applications in Healthcare

The sources of health data that assist in machine learning in healthcare stem from the medical practice itself and the knowledge collected by healthcare providers. When this medical data is aggregated, especially across various areas of the healthcare industry, it can become a powerful data source for machine learning algorithms.

Diagnostic Assistance and Predictive Analytics

In healthcare, machine learning systems collate information about what treatments are given in clinical practice and the patient results from those treatments. This information is then combined with results from clinical trials, research, studies, and textbooks to begin training various machine learning models. Once there are enough data points, machine learning can take over with the help of engineers to build out rules for interpreting healthcare data.

Ultimately, this leads to healthcare professionals becoming more adept at diagnosing diseases and predicting health outcomes. One of the most famous examples of this artificial intelligence model being used to improve patient care is IBM’s “Watson for Oncology” system, which leveraged machine learning data to provide treatment recommendations for oncologists.

Personalized Medicine

Being able to forecast the treatments that a patient potentially needs and tailoring those treatment plans to the individual patient is at the heart of providing quality patient care.

Without having access to machine learning in healthcare, diagnosing disease and recommending treatments is often based on experience or third-party research. The problem with that model is that every patient is unique. Trying to initiate a one-size-fits-all treatment plan based on “similar” patients is a recipe for disaster.

Machine learning can vastly improve this current model and lead to better patient outcomes by analyzing larger volumes of data, including millions of patient records that include notes, lab work, vitals, and demographic data.

Collecting data in this way allows the machine learning algorithm to do a better job at creating an individual patient profile and recommending the best course of treatment, even for high-risk patients. This also, understandably, reduces the need for healthcare professionals to engage in repetitive tasks, like sorting through stacks of medical imaging and patient data samples because the deep learning algorithm has already done the work and is ready to provide a diagnosis or recommendation.

Operational Efficiency

While healthcare professionals prioritize treating a patient in the best way possible, there’s a lot that goes on behind the scenes in healthcare systems that machine learning can optimize.

Some of the ways that machine learning and medicine work together from an operational efficiency perspective include:

• Patient scheduling: Machine learning in healthcare can be a powerful ally because it allows healthcare organizations to predict appointment demand, proactively identify scheduling preferences, and reduce appointment wait times.
• Resource allocation: The use of artificial intelligence models can optimize resource allocation by predicting patient flow, bed occupancy, and staffing levels. This allows healthcare organizations to manage resources more effectively and allocate them to other areas that can better serve the patient or practice.
• Patient communication: Healthcare machine learning can be used to improve communication touchpoints with patients. In addition to speech recognition, chatbots, and autoresponders, machine learning technology can also be used to analyze patient data, like demographics, personal preferences, and medical history to personalize each message.
  For example, this structured data can tailor messages, reminders, and education materials to suit a patient’s needs.
• Healthcare delivery: Being able to effectively streamline the management of resources and various processes and workflows not only optimizes patient care but can also reduce costs and improve health outcomes.

Success Stories and Real-World Impact

As machine learning in healthcare becomes more prevalent, the healthcare industry is collecting piles of success stories and promising case studies.

Case Studies

Some of the most promising real-world applications of machine learning in healthcare are in the areas of drug discovery and earlier detection of disease.

Drug discovery: Pharmaceutical companies are always looking for the next big thing, and a deep learning model can certainly assist in this area. There are several recent healthcare examples, including one from a San Francisco company, Insitro.
The company currently uses machine learning to boost the efficiency and cost-effectiveness of drug development. Part of the process includes using machine learning to identify trends that allow researchers to make nearly instant adjustments to drug formulas in clinical trials.

Early detection of disease: A disease that is particularly troubling to society is Alzheimer’s Disease. While there is not yet a cure for this cognitive disease, earlier detection can treat symptoms and help patients develop various coping mechanisms. Machine learning in healthcare is being applied to detect Alzheimer’s (along with other degenerative conditions) earlier.

Patient Outcomes and Quality of Care

Implementing machine learning in healthcare can directly improve health outcomes and the quality of the care prescribed because it provides quality data that can enhance clinical decision-making, optimize treatment strategies, and provide personalized patient care.

Early disease detection is one of the most promising healthcare examples of machine learning because it can lead to prompt interventions that slow down or reverse disease progression, improving the patient prognosis and survival rates.

Further, by embracing machine learning in healthcare, medical practices can be better equipped to engage in personalized treatment planning because patient data like genetic profiles, biomarker patterns, and treatment responses will be available at a clinician’s fingertips.

Challenges and Ethical Considerations

Despite the numerous advantages of machine learning in healthcare, machine learning does have some challenges and ethical considerations that shouldn’t be taken lightly.

For example, if the data quality is not top-notch, it could lead to an inaccurate diagnosis or dangerous recommendation. With 63% of physicians reporting being burnt out, relying on a collection of patient records with inaccurate notes could be problematic. This is why aggregating healthcare data from multiple sources, not just “clinical expertise,” is imperative for the continued success of machine learning in healthcare settings.

Data Privacy and Security

Keeping patient information private and secure has always been a top priority in the healthcare industry. Machine learning can complicate this because it necessitates providing sensitive patient information across various networks.

There are several safeguards that medical professionals and administrators can implement to keep data private and secure, including:

• Encrypting personal data
• Providing secure storage solutions
• Limited access to data to key personnel
• Requiring two-factor authentication
• Implementing data anonymization and de-identification
• Ensuring compliance with all regulatory standards
• Conducting regular audits and vulnerability assessments to ensure security protocols are being properly maintained
• Learning from industry best practices about how to effectively protect data

Ethical Use of AI and ML

With any new technology, including machine learning in healthcare, there are bound to be opportunities for unscrupulous individuals to game the system and cause harm. Even in the absence of malice, mistakes can happen.

For example, it’s important to understand that much of the data provided to machine learning algorithms is human-generated. This means that there could be inherent biases in terms of gender, ethnicity, and other demographic factors. It’s also important that a patient gives informed consent before their patient data or any information is used for any purpose, including machine learning.

The Future of Machine Learning in Healthcare

The future looks bright for machine learning in healthcare, as data continues to be collected daily and the machine learning algorithms continue to get smarter and improve. Be on the lookout for the following emerging trends in machine learning, which are bound to disrupt the industry (in a good way)!

Emerging Trends

Much of the machine learning in healthcare has been focused on processing data and using that to develop advanced insights and treatment protocols. However, that’s just the beginning. New trends are emerging that provide additional applications in the field.

For example, wearable devices with sensor and machine learning algorithms can constantly monitor patients and collect a high volume of meaningful health data. This leads to remote patient monitoring, where a clinician does not have to be with a patient to gather helpful data.

The Road Ahead

Future machine learning developments are in the works that focus on the interoperability of data collection and integration methodologies. This is imperative to enable seamless sharing of information across platforms, including practice management tools like Weave, which can encourage powerful collaborations.

As more data is collected, the more information we’ll have at our fingerprints for informed decision-making. 

Conclusion

Machine learning has already shown great promise in elevating the healthcare industry. As it becomes more advanced, it is important not to lose sight of the best interests of the patients. If anything, clinicians should strive to use machine learning in healthcare to help patients at every touchpoint.

Weave is an early adopter of machine learning principles for practice management. To see Weave in action, schedule a demo.

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