See how ML and AI can be applied to healthcare data analytics

AI has emerged as a defining invention in the healthcare sector, with the combination of machine learning and AI revolutionizing the medical sector. These technologies are becoming widely adopted in the healthcare sector and are said to revolutionize the way HL7 data analytics works by significantly improving the understanding of patient data, enhancing patient care, and improving logistics within the system.

However, this article is dedicated to applying real-life examples of ML and AI in healthcare data analytics and show how the field is being transformed.

Healthcare data analytics refers to the process of identifying health data to draw inferences and make decisions. This data includes, but is not limited to, data from electronic health records (EHRs), medical images, genetic data, wearable data, and other patient-owned sources.

With the help of ML and AI concepts, healthcare providers can model and predict what will be best in a particular case and how they can streamline the delivery of services.

More widespread use of machine learning and AI in healthcare data applications

Predictive analytics uses statistical models to forecast future trends after analyzing past actions and decisions. In healthcare, predictive analytics can use statistical data to forecast disease outbreaks, hospital readmission rates, and other issues.

For example: Using patient data in EHR, healthcare administrators can understand the many variables that lead to a patient’s risk of readmission and prevent readmissions accordingly, thus reducing not only the risks associated with readmissions but also the costs of readmissions.

It is true that advances in technologies such as AI have enhanced image recognition algorithms that can diagnose images and scans in the form of X-rays, MRIs and CT scans with a high degree of accuracy.

Example: AI algorithms that detect early symptoms of dangerous diseases such as cancer can reduce avoidable human errors and increase the chances of treatment.

The application of artificial intelligence in medicine helps in predicting faulty genes, analysing the patient's lifestyle and other details to find the best treatment for the patient. It also helps in enhancing the effectiveness of the treatment with little or no side effects to the patient.

Example: Cancer treatment maps will vary depending on the client's family presence and their response to previous treatments.

Natural Language Processing (NLP) tools help in performing text mining to extract insights from traditional structures such as texts, clinical notes, research papers, patient feedback, etc.

For example: Unlike asking direct questions, NLP has the ability to analyze conversations with doctors to find the right symptoms and recommended diagnostic tests and treatments.

Healthcare SCM can leverage AI and ML to automate and enhance administrative activities, resource scheduling, and operations within the healthcare sector.

For example: One application can predict patient parameters, allowing hospitals to forecast future admissions and better manage them in terms of staffing and resources.

So the first step in applying machine learning and artificial intelligence to healthcare data analytics is data acquisition and integration: All the information from EHRs, medical devices, lab results, and patient surveys is optimized for analysis.

Best Practice: Implement health information and knowledge integration and exchange through HL7 and FHIR compatible data standards.

A key characteristic of healthcare data is that it is rarely free of errors and may be incomplete and contain inconsistencies. Before performing data analysis, data cleaning is crucial to remove unwanted results or inappropriate results from the dataset.

Best practice: Always incorporate automated cleaning of your data to automatically remove missing values, outliers, and duplicates where appropriate. Assist with pre-processing by checking whether your data is normalized and that sufficient measures have been taken to depersonalize information.

Feature engineering defines the process of selecting and styling input variables to improve model performance, which might involve generating new information from the raw data (such as patient age groups or normalized patient test results).

Best practice: Consult subject matter experts to define features that integrate data-rich clinical representations to improve model efficiency and effectiveness.

The next level of abstraction is to choose the right type of ML model depending on the type of problem. Models employed in healthcare include logistic regression models, decision trees, support vector machines, artificial neural networks, etc. These models need to be trained using a labeled dataset. The labeled dataset used should be appropriate for the type of model.

Best practice: Implement strategies such as cross-validation to evaluate model performance and avoid cases where a model performs well when trained but degrades when tested on new data. Integrate new data into the model framework to ensure relevance with respect to time factors.

Testing different models: Accuracy, precision, recall measures, F1 score, etc. Cross-validation helps to check for overfitting of the model and obtain its capabilities to apply to more unknown data.

Best practice: To increase confidence in your model, we recommend that you perform extensive validation testing using diverse validation sets and perform external validation on data from various sources.

All developed models will be deployed in real-world healthcare settings for analysis and selection. They must be compatible with existing systems and interfaces, and their benefits must be embedded into care providers' existing practices and interfaces.

Best practice: For ease of implementation, include APIs in your model to integrate with EHR systems, patient monitoring devices, and CDSSs.

Monitoring should be part of a never-ending process to ensure your models are performing as planned and compatible with updated data. Use monitoring tools that help you track model performance and automate the process of identifying underperforming models.

Best practice: Implement checkpointing to notify you when your model drifts or performs poorly. It is important to update your model with new data so that it continues to be useful and provide accurate outputs.

When applying ML and AI to healthcare data analytics, there are several challenges that need to be addressed.

1. Data Privacy and Security: It is important to protect patient information and records from being leaked to inappropriate parties. This requires necessary measures such as strong layers of encryption, access controls, and compliance with existing applicable laws and legislation such as the Health Insurance Portability and Accountability Act (HIPAA), General Data Protection Regulation (GDPR), etc.

2. Bias and Fairness: Machine learning models are known to contain the impartiality and political bias of the individuals who input training data into the model. To address this risk, organizations should strive to incorporate fairness-conscious algorithms where applicable and perform regular bias assessments.

3. Interpretability: Referrals should be made by medical professionals who have a basic level of understanding of how the AI ​​system works and some trust in the AI ​​system's decisions. Make ML models fully interpretable in order to produce understandable explanations for the predictions required to accept the model's results.

4. Regulatory Compliance: Comply with laws and best practice guidelines regarding the adoption and use of artificial intelligence in the healthcare industry. Before deploying any model, it is important to ensure that it has been thoroughly checked and validated by the appropriate authorities.