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Machine Learning in Healthcare:
Real-World Use Cases and What You Need to Get Started

Table of Contents
Application of Machine Learning and Artificial Intelligence in Medicine

Machine learning in healthcare has the potential to revolutionize the industry, from automating medical claims to facilitating cancer research. Machine learning algorithms and techniques are being harnessed to analyze vast amounts of medical data, enabling healthcare professionals to make more accurate diagnoses, develop personalized treatment plans, and enhance patient outcomes.

Let’s look at how machine learning is already impacting healthcare, touching on existing applications, benefits, risks, and the future outlook of artificial intelligence for healthcare and medical research.

First, we’ll delve into why machine learning in healthcare is hard to implement, before outlining how healthcare organizations can prepare their existing data architecture to ensure the success and scalability of AI in their businesses.

Pockets of Innovation and the Trouble Implementing Machine Learning in Healthcare

Realizing the benefits of AI, and successfully deploying it at scale, is a daunting task for many in the healthcare industry due to data-related challenges:

  • Fragmented Medical Records: Patient data often resides in multiple organizations, leading to fragmented medical records. This lack of data centralization impedes holistic patient care and hinders AI-driven insights.
  • Data Quality Issues: Healthcare data is notorious for its variability, including unstructured free text, inconsistent use of medical terminologies, and incomplete coding. Ensuring data quality is critical for reliable AI outcomes.
  • Privacy Concerns and Regulatory Compliance: Privacy considerations, ethical data use, and adherence to strict regulatory frameworks (e.g., HIPAA, GDPR) are paramount. Healthcare organizations must navigate patient consent, data access, and sharing with utmost care.

In addition to these data-centric challenges, healthcare organizations also grapple with broader technology-related challenges:

  • Data trapped in application and analytics silos across the organization
  • Dependence on legacy technologies not built for modern streaming data and real-time, app-driven analytics use cases
  • Traditional data architectures that are too rigid to respond quickly to changing business needs and variables
  • Convoluted cloud data architectures that add more complexity, making the challenge of delivering on business objectives even harder

That’s not all — healthcare organizations often face a shortage of skilled personnel with expertise in both healthcare and machine learning.

While data-related challenges require considerations such as healthcare interoperability, this article will focus primarily on technical-related challenges in healthcare AI implementation to provide a deeper understanding of the technological hurdles healthcare organizations face.

The above issues have resulted in data scientists typically working on AI and healthcare machine learning projects in isolation throughout their organizations, experimenting with deep learning models and machine learning applications targeted to answer very specific questions or perform explicit tasks.

For instance, AI might be used to analyze radiological images, helping radiologists identify anomalies or abnormalities more efficiently. In another context, AI can be utilized to enhance hospital operations, optimizing bed allocation based on historical patient data and current bed availability.

But for each of these discrete implementations, data scientists and data engineering teams will build data ingestion pipelines, pulling in data from a variety of sources, so that it can be prepared for use by machine learning models.

As a result, we often end up with individual teams focused on building their own AI models, creating specialized feature repositories, and developing data pipelines to address specific, narrow use cases.

All these data stores or feature stores are only accessible to the teams and models for which they were created, creating a cascade of problems:

  • Complicated and costly data pipelines built by siloed data engineering and data science teams.
  • Models that solve only discrete problems and use cases.
  • The application teams and the data science and engineering teams still operate in isolation of one another, creating data assets that are not common or shared between them.
  • Individual teams may duplicate efforts by collecting and storing similar data separately. This redundancy not only wastes resources but also introduces inconsistencies in data quality and definitions.
  • Maintaining data governance and ensuring compliance with regulations become more complex when data is dispersed across multiple teams and systems.
  • The AI response can not be easily leveraged for real-time customer interactions.
  • Narrowly focused AI models and data repositories may miss opportunities for cross-functional insights and broader applications. This limits the organization's ability to harness the full potential of AI.
The Issue with Scaling Machine Learning Systems

This process of isolated feature store and data pipeline creation requires a significant amount of extra work.

The sheer effort involved with bringing data in from sources, defining features (that contain relevant data transformation rules), training a model, and then integrating that model into an actual business application, often inhibits the organization's ability to bring wide-scale AI adoption to the fore.

The efforts expended to coalesce and transform data into something broadly applicable and meaningful are great, but the outcomes are not. Taken to its logical conclusion, we would need hundreds of separate feature stores and data pipelines to satisfy all of the individual business use cases.

As a consequence, it’s not uncommon for many of the models that are built within a healthcare organization to simply ‘die on the vine’, and never see the daylight of production systems.

This is simply not practical, and a better approach is needed.

Creating a Single Source of Truth for Machine Learning in Healthcare Industry

When it comes to customer-facing applications, software delivery teams have long benefited from adopting the principles of DDD, or ’domain-driven design and development.’ There are many concepts and principles in DDD, but in essence, we can reduce complexity by having software and delivery teams architect systems, and develop business logic using a central domain model. The software, the people, the data and data sources, and the business logic, are all bound together in the context of a business capability or function.

For instance, patient diagnosis or treatment planning would be good examples of domains within healthcare.

Given the complexities associated with legacy data models and source systems, domain-driven teams will often create operational data stores, or operational data layers (ODLs) - datastores where the breadth of information from legacy systems can be collected, and distilled into a single version of the truth, or ‘single-views’ as they often are referred to.

Single-view operational data stores supporting domain-driven software delivery teams define a common domain data model for which new software can be written, and they can help encapsulate the complexities of the underlying source systems and data.

A typical healthcare provider, for example, may want to deliver digital capabilities to their patients through a single mobile application, but their interactions with the health system may be administered in dozens of different systems. Rather than spending years, and tens of millions of dollars replacing legacy systems, the problem can instead be solved using the ODL approach.

Defining and building a billing ODL, where all of the customer billing details are held and changes flow in from source systems in real-time, can dramatically simplify the effort of building new software. and driven products on top of that data. The developers writing the new code can do so using a new, common domain data model, versus what might otherwise be multiple, complex legacy models, and they can access all of the information through common API’s against a single data store.

This approach is a practical and pragmatic way to move an organization away from the constraints of legacy concepts and the spider web of systems and towards a digital strategy based on a modern, single-domain data model.

It’s a way to mitigate the risks associated with legacy systems, and at the same time, deliver new digital capabilities, backed by AI, to customers.

The ODL - an LLM Enabler illustration
Tasks that Machine Learning in Healthcare Can Handle

Machine learning algorithms can already handle a wide array of repetitive tasks in the healthcare industry, ranging from medical imaging analysis to predicting treatment outcomes. Let's delve into some of the key areas where ML is making a significant impact:

Machine Learning in Medical Imaging

ML algorithms can analyze medical images such as X-rays, CT scans, and MRIs, assisting radiologists in the early stages of detection of diseases, including cancer, tumors, and cardiovascular conditions. By using convolutional neural networks (CNNs) and deep learning techniques, ML can automate image interpretation, providing faster and more accurate diagnoses.

Predictive Approach to Treatment

Machine learning models can predict the outcomes of specific treatments based on patient data, allowing healthcare providers to make informed decisions regarding the most effective treatment plans. These models can consider various factors in patient records, and analyze data such as medical history, demographics, and genetic information, to generate personalized treatment recommendations.

Clinical Decision Support Systems

Machine learning algorithms can be integrated into clinical decision support systems to aid healthcare professionals in their medical diagnosis and making critical decisions. These systems leverage ML techniques to analyze patient data, medical literature, clinical trials, and clinical guidelines, providing real-time recommendations to medical professionals on diagnostics, treatment options, and medication prescriptions.

Personalized Medicine

ML algorithms can analyze large-scale genomic and proteomic datasets, identifying biomarkers and genetic patterns associated with certain diseases. This enables the development of personalized medicine approaches, where treatment plans can be tailored to individual patients based on their unique genetic makeup, increasing treatment efficacy and reducing adverse effects.

Accelerated Drug Discovery and Development

Machine learning algorithms can analyze vast amounts of biomedical data, including drug interactions, molecular structures, clinical trials, and clinical trial results. By identifying patterns and relationships within this data, ML can facilitate the whole drug discovery process, and development of new drugs, significantly reducing the time and cost required for the entire drug discovery and development process.

Identifying high-risk patients

ML algorithms can analyze patient data to identify individuals who are at a higher risk of developing specific diseases or experiencing adverse health events. By both collecting data and proactively identifying high-risk patients, healthcare providers can implement preventive measures and interventions, improving patient outcomes and reducing healthcare costs.

Benefits of Machine Learning in Healthcare

The incorporation of machine learning technologies in healthcare offers many benefits that have the potential to transform healthcare delivery. Key advantages include:

Patient Safety

Machine learning algorithms can enhance patient care and safety by reducing medical errors and improving the accuracy of diagnoses. ML models can analyze patient data, identify potential risks, and provide real-time alerts to healthcare providers, preventing adverse events and improving patient care.

Infectious Disease Outbreak Prediction

ML algorithms can analyze vast amounts of data, including demographic information, environmental factors, and disease prevalence, to predict and track infectious disease outbreaks. This early detection allows public and health professionals and agencies to take preventive measures, allocate resources efficiently, and minimize the spread of diseases.

Advanced Diagnostics and Predictive Analytics

Machine learning algorithms can analyze large volumes of medical data, including electronic health records (EHRs), other medical records, images, and genomic data, to identify patterns in clinical data and make accurate diagnoses. ML can also enable predictive analytics to anticipate disease progression and identify high-risk patients for early intervention.

Precision Medicine and Personalized Treatment

Machine learning algorithms can leverage patient-specific data, such as genetic profiles, medical history, and lifestyle factors, to develop personalized treatment plans. ML enables the identification of optimal therapies, reducing adverse effects and improving treatment efficacy.

Healthcare Operations Optimization

ML algorithms can optimize healthcare operations by analyzing clinical data, to improve resource allocation, enhance scheduling, and streamline clinical workflows. This can lead to better utilization of healthcare resources, reduced wait times, and improved patient satisfaction.

Integration of Artificial Intelligence (AI)

Machine learning will continue to integrate with other AI technologies, such as natural language processing and computer vision, to enhance clinical expertise in healthcare applications. This integration can facilitate improved clinical decision support, medical image analysis, and automated patient monitoring.

Explainable and Interpretable ML Models

Efforts will be made to develop ML models that provide explanations for their decisions, ensuring transparency and trust. Explainable AI techniques will help healthcare professionals understand how ML models arrive at their conclusions, enabling better decision-making and acceptance of AI technology in healthcare.

Collaborative Data Sharing and Federated Learning

Healthcare organizations and medical companies are recognizing the need for collaborative data sharing while ensuring privacy and security. Federated machine learning, where ML models are trained on decentralized data, allows organizations to share insights without sharing raw data.

Risks of Machine Learning in Healthcare

While the benefits of machine learning in the healthcare system are significant, there are inherent risks that need to be addressed to ensure improved patient outcomes, safety, and ethical considerations. Let's examine some of the key risks:

Ethics of Employing Machine Learning in Healthcare

The ethical implications of using machine learning algorithms in healthcare are critical. ML models may inherit biases from the training data, leading to discriminatory outcomes. It is essential to develop algorithms that are fair, transparent, and free from bias to ensure equitable healthcare delivery.

Privacy and Data Security

Machine learning relies heavily on patient data, including personal health information (PHI), which raises concerns about privacy and data security. Healthcare organizations must implement robust data protection measures, such as encryption and access controls on medical devices, to safeguard patient privacy during data collection and prevent data-based errors, unauthorized access, or data breaches.

The complexity of machine learning algorithms can make it challenging to understand how decisions are made. Lack of transparency can undermine trust in healthcare systems. It is crucial to ensure transparency in ML models by providing explanations for the decisions made. Additionally, obtaining informed consent from patients for data usage and ML applications is essential to maintain trust in healthcare institutions and respect patient autonomy.

In conclusion, the integration of machine learning in healthcare holds immense promise, ushering in a new era of precision medicine, streamlined operations, and improved patient outcomes. While navigating the complex landscape of data challenges, technological hurdles, and ethical considerations, healthcare organizations stand at the forefront of transformative change. As we address the risks and work towards collaborative solutions, the potential for machine learning to revolutionize diagnostics, treatment, and healthcare delivery remains a beacon of hope. Embracing a unified approach, leveraging domain-driven design, and fostering transparency will be key to unlocking the full potential of artificial intelligence in the pursuit of a healthier future for all.

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