Top 6 Applications for Machine Learning (ML) in Healthcare
The healthcare industry is constantly evolving and looking for new ways to improve patient care.
Machine learning is a valuable tool for the healthcare industry, as it helps make sense of the vast amounts of data generated by electronic health records and the ever-growing IoT (internet of things) devices in hospitals and healthcare facilities. With machine learning algorithms, healthcare providers can uncover patterns and insights that would be impossible to find manually. The widespread adoption of machine learning in healthcare allows for a more predictive approach, leading to improved care delivery and patient-based processes. Everyday use cases for machine learning in healthcare include automating medical billing, providing clinical decision support, and developing clinical practice guidelines within health systems.
Here are a few AI/ML use cases for the healthcare industry:
1. Medical Imaging Diagnosis
The healthcare industry faces challenges with medical imaging diagnosis, such as a high volume of images and a shortage of radiologists. The breakthrough technology, called Amazon Rekognition, has deep learning-based visual search and image classification capabilities. This technology can analyze and interpret medical images, such as X-rays, CT scans, and MRIs, which makes it easier for healthcare professionals to make an accurate and timely diagnosis.
It can also detect and identify specific abnormalities and patterns in medical images, helping radiologists and other medical specialists to quickly and efficiently make informed decisions about patient care.
Amazon Rekognition’s integration with AWS makes it a fast, scalable, reliable and secure option for healthcare organizations looking to gain new insights and revenue opportunities from their medical image library.
2. Analyzing unstructured data
Clinicians often face challenges in keeping up with the latest medical advances while providing quality patient-centered care due to the vast amount of health data and medical records. This task becomes much easier with the support of Machine Learning (ML) technologies such as Amazon Comprehend Medical: a HIPAA-eligible natural language processing (NLP) service.
Using machine learning models that have been pre-trained to understand and extract health data from medical text, such as prescriptions, procedures, or diagnoses, can quickly analyze unstructured population health data, improving health outcomes and identifying gaps in care. It also helps in enhancing hospital efficiency by assisting with repetitive tasks and supporting clinicians with fast, accurate, tailored treatment plans and medicine for their patients.
3. Personalized Medicine
The healthcare industry faces several challenges regarding personalized treatment, such as a lack of data standardization, limited data sharing, lack of interoperability, and limited understanding of genetic and genomic data. We can improve personalized healthcare treatments with predictive analytics, and machine learning is making great strides in this field.
Custom ML models can be developed and deployed for healthcare applications, such as predicting patient outcomes or identifying patterns in patient data. One tool that healthcare practitioners can use to take advantage of these advancements is Amazon SageMaker. In the coming years, with the increasing amount of data available from sophisticated health measurement devices and biosensors, Amazon SageMaker will become even more powerful in providing healthcare practitioners with the ability to make more accurate predictions and assessments. This technology will help in delivering better patient outcomes and treatment.
4. Forecasting Patient Demand, Resource allocation & Inventory Management
Accurately forecasting patient demand, resource allocation, and inventory management is crucial for efficient healthcare delivery systems to match resources to service demand over time. AWS Forecast, a Machine Learning (ML) model, can help in this regard. It allows decision-makers to anticipate the need for services and make informed decisions about how to manage resources and purchase supplies over time by forecasting count data in healthcare systems under stochastic uncertainty, such as counts of patients arriving at emergency departments and counts of prescription medications distributed by pharmacies. This process helps provide better patient outcomes and manage resources effectively.
5. Conversational Voice Transcribing
Conversational voice transcribing is critical to clinical documentation and overall caregiver satisfaction. Amazon Transcribe Medical, an ML model, can help in this regard. It allows healthcare and life science customers to build transcription applications that capture physician-patient conversations in real time without disrupting the interaction. It converts transcribed speech into written text, such as for creating medical reports and notes. Other text analytics tools can then process this output to identify critical entities like medication, dosage, strength, and frequency before final entry into electronic health record (EHR) systems. The extreme accuracy in clinical documentation thus achieved will improve the workflows and the overall satisfaction of the caregivers.
6. Communication with patients speaking a different language
Communication is vital in healthcare, and it becomes even more critical when patients speak a different language. With AWS Translate, healthcare providers can break down language barriers and provide equal and accessible treatment to all patients. This ML model can translate medical documents and reports, making it easier for healthcare providers to communicate with patients and other healthcare providers who speak different languages leading to a higher quality of care, fewer complications and improved satisfaction. Patients can easily explain symptoms and pain levels they experience, and providers can use this information to make accurate diagnoses and prescribe the necessary treatment. With this tool, healthcare providers can ensure that they provide high-quality care to all patients regardless of their language.
About Thortech Solutions
ThorTech Solutions provides businesses with software architecture, DevOps and IT automation services for over 20 years. Our mission is to drive business value utilizing technology to enable businesses and individuals to excel today in ways they could not in the past.
We have been part of the Amazon Web Services Partner Network since 2012 and are proud to help businesses navigate one of the most disruptive shifts in technology with the advent of the public cloud.