5 Use Cases for Machine Learning in Healthcare

Healthcare has always relied on humans delivering patient care based on observations, experience and their medical knowledge. But with the evolution of machine learning in healthcare, medical professionals can harness the power of data to develop more precise, targeted and effective treatments. 

Machine learning algorithms analyze vast amounts of information, including genomic data, medical imaging and electronic health records (EHRs). They identify patterns and make predictions with increasing accuracy. Machine learning improves the efficiency and cost-effectiveness of healthcare delivery — and saves lives by detecting medical conditions earlier and suggesting tailored interventions

Learn more about common machine learning use cases in the healthcare industry, the ethical implications of this technology and how to get started.

How Machine Learning Is Used in Healthcare

Machine learning is a subset of artificial intelligence that relies on algorithms to automatically identify patterns in data and learn from them. The goal of machine learning is to develop models that can make predictions or decisions without explicit programming. To achieve this,  algorithms are trained on large amounts of healthcare data.

Common machine learning algorithms include deep learning, decision trees, support vector machines and neural networks. These algorithms can be applied to different types of data, including images, text and time-series data. 

One longstanding challenge with healthcare data is that it’s often unstructured, which complicates processing it with traditional statistical methods. Machine learning can more easily extract meaning from this data, which often comes from EHRs, genomic data or data lakes. 

5 Examples of Machine Learning in Healthcare

Machine learning can improve patient outcomes by contributing to tailored treatment plans, earlier detection of disease and better clinical decision-making. Here are five examples of machine learning applications in healthcare.

Developing Predictive Modeling

Predictive modeling is an application of machine learning in which models are trained on historical data. Algorithms then identify patterns in the data and make predictions accordingly. Predictive modeling can improve patient outcomes when used to identify at-risk patients and suggest treatments. 

For example, machine learning algorithms can be used to identify patients who are at risk for developing sepsis, a life-threatening condition that can occur in hospitalized patients. By analyzing patient data, including vital signs, laboratory values and medical history, machine learning models can be developed that can predict which patients are at highest risk for developing sepsis. This insight allows healthcare professionals to intervene and‌ save lives.

Improving Diagnostic Accuracy 

Clinical decision support tools are another way that machine learning is being used to improve patient outcomes. These tools use data from a patient’s EHR to help clinicians choose the most effective course of action. Machine learning can enhance the effectiveness of this approach.

For example, a clinical decision support tool could inform a clinician’s choice of the most effective antibiotic for a patient with a bacterial infection. The tool would consider the patient’s age, weight, allergies and other factors to recommend the best course of treatment.

Personalizing Patient Care

Machine learning mines large healthcare datasets to identify patterns and trends that could improve patient care and develop personalized treatment plans. This approach is faster than any human-led analysis and allows healthcare providers to gain broad insights into treatment approaches without compromising patient privacy.

For example, by analyzing data from EHRs, it may be possible to identify early warning signs of disease or adverse drug reactions. This information can then be used to develop new treatments or safety protocols.

Automating Clinical Workflows

By automating parts of the healthcare process, machine learning streamlines workflows and provides a simpler experience. For example, it can automate administrative tasks such as billing, scheduling and record-keeping, which helps patients, providers and insurance companies.

Interacting With Patients

Machine learning can be used to develop chatbots and virtual assistants that help patients with managing chronic conditions such as diabetes or asthma. These chatbots provide patients with personalized advice on managing their medical conditions based on data such as blood glucose levels or lung function. 

With the critical shortage in medical staff, one area of focus has been providing a better interface with the patient to make it easier for the doctor to see all the answers the patient has provided up to that point. A chatbot can be used at intake to gather information and depending on the EHR to pull relevant information that the doctor can review prior and during a consultation. This will allow for a more personalized interaction and give the doctor a deeper understanding of the care the patient already received. 

Ethical Implications of Using Machine Learning in Healthcare

Machine learning in healthcare has tremendous potential, but like most technological advances in this field, there are ethical considerations to its use because patient health and privacy are at stake. Any use of machine learning should prioritize patient outcomes. Here are key ethical implications to think about.

Algorithms Can Be Biased

Bias is already an ongoing concern in patient treatment by humans, but bias also exists in machine learning, particularly when a model is trained on data that isn't representative of the larger population. For example, if a model is trained on data from a single hospital, the findings might not represent the norm across a wider sample. If the data is overweighted toward a particular demographic group, it might make inaccurate assessments or treatment recommendations for other groups of people. 

Bias in machine learning can contribute to inaccurate, unfair and even life-threatening decisions about patient care — and medical professionals might not be able to recognize an algorithm’s bias until it’s too late.

Increased Transparency Is Needed

Patients put their trust in your medical team, and they need transparency about how medical decisions are being made — especially when machine learning or other cutting-edge technologies are involved. Patients should be made aware of when and why machine learning is being used, as well as what it is being used for. 

For example, if machine learning helps choose which treatment options are best for a patient, your medical professionals should make that clear. Patients have a right to know how their personal data is being used so they can make informed decisions about their care.

Privacy Could Be Compromised

Patient privacy is paramount in healthcare, but using technology to share, interact with and analyze data creates risks in terms of privacy and cybersecurity. 

While your healthcare institution is well-aware of regulations related to patient privacy, you must consider how machine learning will affect your ability to stay in compliance. Start by ensuring your privacy safeguards apply to machine learning models, including strict controls around who can access data sets containing sensitive information.

How to Get Started With Machine Learning and AWS

Machine learning can offer incredible benefits, including better patient care and more efficient operations. But the delicate nature of healthcare means it’s critical to prioritize data privacy, security and accuracy throughout. That starts with a thorough understanding of the fundamentals of machine learning, the data you’ll work with and your machine learning options, including Amazon Web Services (AWS). 

One important aspect of preparing your models is gathering the data you need and getting it into a format that is useful. AWS has created Amazon HealthLake which lets you ingest data and put it into FIHR format for easy downstream processing. AWS has also improved its tools to use DICOM files which is the medical standard. Additionally, AWS has enhanced many of their standard AI services to work with medical data such as comprehend medical. 

Here are the basic steps to get started with machine learning and AWS.

Determine the Machine Learning Task

Determine what machine learning task or tasks your organization needs to accomplish. Examples include analyzing medical images or text, predicting health outcomes or processing natural language. By appropriately defining your task, you have a better chance of selecting the right machine learning service and algorithms.

Choose an AWS Machine Learning Service

Once you have determined the task, the next step is to choose an AWS machine learning service that best fits the project requirements. There are many services available, each with their own unique capabilities. Amazon SageMaker is ideal for building custom machine learning models; Amazon Comprehend is designed for natural language processing; and Amazon Rekognition is built for image and video analysis.

Gather the Necessary Data

To train a machine learning model, you'll need data — and lots of it. This data can come from patient records, medical images or other healthcare datasets. 

Clean and Prepare the Data

Before data can train machine learning models, it needs cleaning and preparation. This process includes removing duplicates, dealing with missing values and formatting data for consistency. Data preparation is an essential step in making sure your model produces accurate results.

Choose an Algorithm

The next step is to choose the appropriate algorithm. Algorithm types include linear regression, decision trees and neural networks. Your choice depends on what problem you’re trying to solve, the data you’re using and what you hope to achieve. 

For example, the algorithm you choose might differ depending on whether you’re using structured, unstructured or text data. Consider the algorithm’s accuracy and speed, along with how easy it is to interpret the results.

Train the Model

Training involves feeding your cleaned and prepared data into the algorithm and tuning parameters to achieve the best possible performance. Training the model is an iterative process that can involve multiple rounds of testing and refinement.

Evaluate Model Performance

Even if you’ve done everything right so far, you need to understand whether you’re getting the right results from your machine learning model. Evaluating model performance involves metrics such as accuracy, precision and recall. By evaluating performance, you can identify areas for improvement and fine-tune the model.

Follow Compliance Best Practices

Protecting patients’ protected health information (PHI) is paramount. The Health Insurance Portability and Accountability Act (HIPAA) and HITECH Act are federal laws that govern how you safeguard PHI — and there are penalties for noncompliance.

Understanding the shared responsibility model between you and your machine learning infrastructure partner is crucial. AWS is an ideal choice for health care and life science applications because it offers HIPAA-eligible managed services that allow you to build and deploy compliant applications rapidly.

AWS enforces key security measures, including encryption of PHI in transit and at rest. This keeps your data secure and protected at each stage. AWS also enables detailed logging and auditing, so you can quickly identify any breaches and keep centralized logs for audits. With AWS, you can also provision real-time backup and disaster recovery.

What’s Next for Machine Learning in Healthcare? 

Machine learning in healthcare has already made significant advancements, and there's little doubt that it will continue improving patient outcomes and healthcare delivery. Expect algorithms to become more sophisticated, accurate and helpful as they benefit from the explosion of data generated by digital medical records, wearables and other sources.

Besides helping with patient treatment decisions, look for machine learning also to improve screening methods. Many medical conditions are difficult or impossible to detect in the early stages, and machine learning could drive tremendous advances there. Whatever your focus, you can get started today with machine learning in healthcare — especially with the right infrastructure partner to guide your efforts.

If you’re ready to take the next step toward leveraging its power in your organization, learn how to advance your machine-learning strategy and leverage the cloud to meet your unique needs.

FAQ

How does machine learning in healthcare address data privacy and security concerns, especially with sensitive patient information?

When it comes to addressing data privacy and security concerns in the healthcare sector, particularly with the utilization of machine learning (ML) with sensitive patient information, the industry adheres to stringent standards and regulations, such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States. These regulations ensure that any ML application involving patient data is developed and deployed with robust encryption and anonymization techniques. Additionally, ML models are often trained so that they can operate on encrypted data, ensuring that personal health information remains secure even in the analysis process. Implementing access controls, audit logs, and regular security assessments further safeguards patient data against unauthorized access and breaches.

What are the challenges and limitations of implementing machine learning solutions in low-resource settings or developing countries?

Implementing machine learning solutions in healthcare settings with limited resources or in developing countries presents unique challenges. These can range from a lack of digital infrastructure to support ML technologies to insufficient training data that reflects the local population's health conditions. Overcoming these obstacles often requires innovative approaches, such as using transfer learning techniques where a model developed in one setting is adapted for use in another or leveraging mobile technology, which is widely available in many developing countries. Partnerships with local governments and international health organizations are also crucial in developing tailored ML solutions that can operate within these constraints while still significantly improving patient care and disease management.

How can machine learning integrate with healthcare IT systems, such as electronic health records (EHRs) and clinical decision support systems (CDSS)?

Integrating machine learning with existing healthcare IT systems, like electronic health records (EHRs) and clinical decision support systems (CDSS), is a complex but achievable goal. This process typically involves developing application programming interfaces (APIs) or using interoperability standards such as Health Level 7 (HL7) and Fast Healthcare Interoperability Resources (FHIR). These technologies enable ML algorithms to access and analyze data from EHRs and other healthcare databases securely and efficiently. Additionally, to ensure that healthcare professionals can effectively use ML tools, user interfaces are designed for seamless integration into the existing workflows, allowing for the insights generated by ML models to be easily accessed and acted upon within the clinical decision-making process. This harmonious integration empowers healthcare providers to leverage the predictive power of ML within their existing infrastructure, enhancing patient outcomes without disrupting established operational procedures.