Machine Learning in Healthcare – Applications, Examples, Use Cases, Benefits and Importance

27 Oct Machine Learning in Healthcare – Applications, Examples, Use Cases, Benefits and Importance

Machine learning in healthcare assists medical practitioners in patient care and clinical data management. Driven by artificial intelligence, it involves programming computers to replicate human thinking and learning processes.

Machine learning in healthcare industry finds applications in diverse tasks such as patient data collection, trend identification, treatment recommendations, and more. Hospitals and healthcare organizations are increasingly acknowledging machine learning’s potential to enhance decision-making and reduce medical risks, ushering in a host of promising career prospects in the field.

Machine Learning Applications in Healthcare

While new machine learning applications continually surface, healthcare predominantly employs them to enhance care quality, optimize workflow, and uplift patient well-being. One can opt for specialization given the extensive range of healthcare machine learning applications.

Here are various applications of machine learning in healthcare listed below that can aid in selecting the module or specialization that aligns with your interests and career aspirations.

• Enhance Trauma Care Response: The time gap between arrival and life-saving treatment is reduced by developing sensors and devices capable of transmitting a patient’s vital data to the hospital prior to their arrival via ambulance or other emergency transport.

• Disease Prediction: Machine learning identifies trends and connections within vast datasets, aiding disease outbreak predictions and tracking habits leading to illnesses within communities.

• Accurate Health Records: Ensures patient records are current, precise, and easily transferable between clinics and medical professionals.

• AI-Assisted Surgery: Assists surgeons by performing complex tasks during surgeries, improving visibility, and modeling procedural steps.

• Biomedical Data Visualization: Machine learning generates three-dimensional visualizations of biomedical data, including RNA sequences, protein structures, and genomic profiles.

• Improved Disease Diagnosis: Identifies previously unnoticed symptom patterns and compares them with extensive datasets for earlier disease detection.

• Personalized Treatment Options: Analyzes multimodal data to make patient-specific treatment decisions based on all available options.

• Enhanced Medical Research and Clinical Trials: Improves participant selection, data collection, and results analysis in clinical trials through machine learning.

Importance of Machine Learning in Healthcare

The importance of machine learning in healthcare holds significant value, particularly for analyzing the vast amount of daily data generated in electronic health records. It uncovers patterns and insights that would be impractical to find manually.

With the widespread adoption of machine learning in healthcare, providers can embrace a predictive approach to precision medicine, resulting in a more unified system, enhanced care delivery, improved patient outcomes, and streamlined processes.

Common healthcare applications of machine learning involve automating medical billing, clinical decision support, and clinical practice guideline development within health systems.

For instance, at MD Anderson, data scientists employed deep learning to predict acute toxicities in head and neck cancer patients undergoing radiation therapy. In clinical workflows, deep learning rapidly identifies intricate patterns in healthcare data in clinical workflows and offers clinical decision support to primary care providers within electronic health records.

Moreover, nearly 80% of information stored in electronic health record systems comprises unstructured healthcare data, often in the form of patient information within text documents. Machine learning in healthcare, leverages natural language processing programs to convert this complex, non-uniform, and ambiguous data into analyzable information, making it a crucial component for deep learning applications.

How can Machine Learning be used in Healthcare?

Machine learning in healthcare is underpinned by its proficiency in managing intricate data. The healthcare sector is accumulating vast datasets, often raw, unstructured data within electronic health records (EHRs).

Employing Natural Language Processing (NLP), machine learning transforms this data into structured formats, enabling healthcare professionals to derive valuable insights swiftly.

Machine learning in healthcare industry, in tandem with AI, is also revolutionizing pharmaceutical research, with potential applications in central nervous system clinical trials. It promises to predict patient responses to diverse drugs and identify individuals most likely to benefit from specific medications.

Furthermore, telemedicine greatly benefits from these machine learning advancements. Certain machine learning firms are exploring methods to streamline the organization and delivery of patient information to physicians during telemedicine sessions and to capture data during virtual visits, thereby optimizing operational workflows.

Benefits of Machine Learning in Healthcare

Machine learning has a broad spectrum of potential applications in clinical healthcare, spanning from enhancing patient data management, diagnosis, and treatment to cost reduction and streamlining patient safety measures.

Below are several benefits of machine learning in healthcare:

1. Enhancing Diagnosis

Machine learning in healthcare empowers medical practitioners to create more precise diagnostic tools for analyzing medical images. For instance, a machine learning algorithm can be employed in medical imaging, like X-rays or MRI scans, employing pattern recognition to detect disease-indicative patterns. This type of machine learning has the potential to expedite and enhance diagnostic accuracy, ultimately leading to improved patient outcomes.

2. Innovating Treatments, Drug Discovery, and Clinical Trials

Healthcare organizations and pharmaceutical companies can utilize deep learning models to extract pertinent information from data, potentially driving drug discovery and the development of novel pharmaceuticals and treatments. For example, machine learning in healthcare can analyze clinical trial data and medical research to uncover previously unknown drug side effects. This healthcare approach within clinical trials can improve patient care, drug discovery, and the safety and efficacy of medical procedures.

3. Cost Optimization

Machine learning technologies enable healthcare organizations to enhance operational efficiency, potentially resulting in cost savings. For instance, machine learning in healthcare can lead to the creation of superior algorithms for managing patient records or appointment scheduling, reducing the wastage of time and resources on repetitive healthcare system tasks.

4. Faster Data Collection

Healthcare professionals employ wearable technology for swift real-time data acquisition, which machine learning can rapidly analyze and learn from. This represents a crucial application of machine learning in the healthcare sector.

5. Elevating Patient Care

Medical professionals can utilize machine learning in healthcare to elevate the quality of patient care. For example, the healthcare sector can harness deep learning algorithms to build systems that proactively monitor patients and alert medical devices or electronic health records to changes in their condition. This data-driven machine learning approach ensures patients receive timely, appropriate care.

Examples of Machine Learning in Healthcare

• Hospitalization Decision Support for Cancer Patients: One example of machine learning in healthcare is hospitalization decision support for cancer patients who frequently experience treatment-related adverse effects or secondary disorders. Identifying the cause of cancer-related symptoms can be challenging, making it crucial to determine the appropriate treatment and the right time for hospital visits.

Machine learning in healthcare, along with wearable data, simplifies this process by providing comprehensive information and indicators, facilitating improved management of acute diseases.

• Personalized Health Treatments: Machine learning in healthcare has revolutionized treatment options by enabling personalized therapy recommendations, enhancing doctors’ ability to prescribe treatments effectively.
• Reducing False Positives: Machine learning in healthcare also mitigates false positives and negatives in medical reports, enhancing result accuracy.
• Enhancing Medical Device Performance: Electronic medical devices have evolved with technological advancements, including machine learning for healthcare, contributing to improved result accuracy.
• Quality Control for Vaccine Cold Chain Storage and Distribution: Machine learning in healthcare significantly aids in predicting the appropriate freight temperature and optimizing logistics routes, ensuring the quality control of vaccine cold chains.

Machine Learning Use Cases in Healthcare

Machine Learning (ML) is making significant strides in healthcare, offering versatile uses from streamlining appointment management and improving medical image analysis to enhancing patient engagement and predicting diseases. Also, ML contributes to the evolving field of robotic surgery by automating processes and refining surgical capabilities.

Here are some machine learning use cases in healthcare:

1. Streamline Online Appointment Management:

Machine learning (ML) plays a multifaceted role in healthcare, extending its capabilities to diverse tasks. Beyond just billing, appointment rescheduling, and record-keeping, ML aids in providing patient guidance, configuring appointment reminders, efficiently managing emergency calls, and a wide array of additional functions.

However, it’s noteworthy that the most transformative contributions of ML in healthcare can be observed in the realms of medical imaging and precise disease diagnosis. These applications stand as a testament to the remarkable progress achieved through ML in the healthcare sector.

2. Elevating Patient Engagement:

Machine learning’s role in patient engagement goes beyond improving the therapeutic journey; it directly contributes to enhanced health outcomes. When integrated with the Internet of Medical Things (IoMT), machine learning (ML) enables the acquisition of more precise patient data and automates targeted message notifications. These notifications are strategically timed to encourage desired patient behaviors, ultimately leading to more favorable health results.

3. Medical Image Analysis:

Traditionally, radiologists spent substantial time reviewing CT scans to identify anomalies such as cancerous tumors or developing embolisms. Nevertheless, deep learning, a more advanced facet of machine learning, is poised to revolutionize this process.

4. Predicting Diseases:

One of the most remarkable uses of machine learning in healthcare is disease prediction. Healthcare providers are now utilizing digital solutions founded on machine learning models to anticipate events like strokes, heart attacks, sepsis, and other critical medical issues. These tools aggregate data from past patient records, perform daily assessments, and continuously monitor key indicators such as heart rate and blood pressure in real time. These technologies can alert healthcare professionals to impending patient risks and enable them to take proactive measures. 

5. Robotic Surgery:

While it may be premature to envision robots conducting all surgical procedures, they play a valuable role in assisting medical professionals with tasks like surgical instrument manipulation. Machine learning has found uses in various aspects, such as automating suturing, evaluating surgical skills, and refining robotic surgical materials and workflow modeling.

Conclusion

Machine learning in healthcare is a transformative force enhancing patient care, optimizing data management, and offering diverse career opportunities. It empowers healthcare professionals to make more accurate diagnoses, streamline processes, reduce costs, and engage patients proactively. From predicting diseases and personalizing treatments to improving medical imaging and surgical procedures, machine learning continues to shape the future of healthcare, promising better patient outcomes and more efficient healthcare delivery.

In this scenario, NuMantra’s Saas platform leverages the machine learning application to revolutionize healthcare by providing advanced solutions and services. Our expertise includes developing predictive models on the platform for disease prognosis and identifying patient-specific treatment plans. NuMantra’s consulting & support services provides the expertise to facilitate faster data analysis and collection of disparate data by integrating with the provider’s systems in a seamless manner.

Our solutions will help streamline online appointment management, optimize medical records, reduce false positives, and improve medical device performance, contributing to cost savings and elevating the overall quality of patient care.