AI/ML-Empowered Data Analytics for Digital Health
The healthcare industry’s transformation is based on digitization and telehealth. These services are predominantly driven forward by big health data.
Mordor Intelligence research states that the need to reduce expenditures, optimize workflows, improve the quality of care delivery, and remove inefficiencies inside the healthcare domain is constantly growing. What’s more, these challenges have only accelerated during the pandemic.
This is where ML/AI-enhanced data analytics becomes an essential solution for industry improvement and optimization.
Since healthcare transformation has attracted new deep-tech players, the market competition is also increasing. Thus, this requires a significant change in business models to win over customers.
As a result, business models like value-based for health providers and risk-based contracting for payers are beginning to take the leading positions in terms of utilization.
This article presents the fundamental health analytics challenges and lists the ML tools to solve them. Once read, you will have information about four leading health analytics, namely:
- Health descriptive analytics
- Health diagnostics
- Predictive health analytics
- Prescriptive & preventive care
Later, we detail the practices applied by Neurons Lab in working with ML/AI in healthcare and ways to overcome industry challenges.
So, let’s get straight to the point!
1. Descriptive Analytics in Working with Massive Health Data
For starters, let’s pay attention to descriptive analytics. This type is the simplest but no less important than the others.
As the name implies, this type of analytics describes medical data and answers the question, ‘what happened?’
Just imagine the fact that the amount of data collected in clinics and medical institutes has grown exponentially over the past decade.
All this data must be structured, stored, processed at high speed, analyzed for additional value, and visualized. These and similar challenges can be covered by descriptive analytics once utilized correctly.
Here is a list of the top tools for descriptive analytics and the challenges they resolve:
- Data mining – this method includes working with sources like medical records (text, audio), pictures (X-rays, TMS), and biosignals (ECG, EEG) for data structuring and evaluation. In those cases, the main LM-tools are NLP, CV, and DSP. As a result, the benefits gained include the ability to improve electronic medical records.
- Data visualization and reporting – these techniques use classical statistics libraries and visualization libraries to improve health reporting for doctors and providers.
- Data cloud integration, discovery, and processing – these means derive insights and build additional value. These days, cloud services like Amazon provide infrastructure for working with health data. This approach allows digital health services to be delivered.
To sum things up, descriptive analytics optimize healthcare data, drive additional value, increase data quality, and reduce the cost of data.
2. Diagnostic Health Analytics for Better Medical Treatments
The second type of health analytics is diagnostics. This is one of the most popular types and one of the most important since it is used to diagnose various pathologies and diseases.
Diagnostic analytics answer the question, ‘why did it happen?’
Diagnostic analytics use predictive data, such as symptoms or test results, to make a diagnosis. Diagnoses made by physicians are concluded by following the rules and the process can be generalized. In its turn, some AI/ML models can learn these rules.
AI/ML models typically provide a list of possible diagnoses and their probabilities. This data is then used in clinics to support decision-making by practitioners.
The main challenges and tools for diagnostic analytics are as follows:
- Symptom-based diagnosing – this method uses classification models (single-, multi-label, or even multi-output regression) based on Logistics Regression, NN, or decision trees, to serve as the best choice. The obvious application is for clinical decision support systems.
- Biomedical image analysis – this technique uses Computer Vision (CV) and is usually based on deep (deep learning) convolutional neural networks (CNN). This approach allows for direct pathology detection and computer-assisted diagnosis.
- Big data and bioinformatics – this approach uses almost all ranges of ML/AI algorithms for early detection and population health management.
Thus, diagnostic analytics are designed to reduce the workload of medical professionals, minimize medical errors, and improve early diagnosis.
3. Predictive Health Analytics to Foresee Risks
The following vital type of data analytics in healthcare is predictive analytics.
Predictive analytics answer the question, ‘what will happen?’
The essence of this type of analytics is to predict specific processes or results. For example, this includes the development or duration of the disease, recovery rate, or even changes in the demand for certain medical services. These predictions can cover individual medical cases, groups of clients, or even whole clinics.
The methods utilized in predictive analytics and the burdens they help overcome are as follows:
- Trend and pattern recognition – this approach offers essential tools for predictive analytics. In particular, decision trees, regressions, and neural networks are successfully applied for outbreak and chronic disease management.
- Multivariate statistics – these methods are actually used in classic ML tools and are able to find complex dependencies that are not often obvious. These hidden dependencies can be helpful for outcomes, readmission, and risk foreseeing.
- Data modeling – this technique, in line with mathematical and statistical models, provides powerful instruments for medical and clinical research.
Therefore, the main results of using predictive analytics lie in the prompt delivery of medical care and optimal service planning, reducing the cost and improving the quality of care.
4. Prescriptive Analytics for Health Improvement
Last but not least, we will explain prescriptive, also called predictive, analytics.
Prescriptive analytics answer the question, ‘how can we make it happen?’
This type is used mainly to improve treatment efficiency, optimize chronic disease management, and provide preventive care to improve population health.
The cornerstone issues prescriptive analytics resolves and the tools it uses are as follows:
- Anomaly detection (or outlier detection) – these algorithms use Multivariate statistical analysis or NN in an unsupervised manner to search for suspicious events. These approaches are highly prospective at eliminating medication errors.
- Multi-objective optimization – this technique can be realized with mixed-integer nonlinear programming (MINLP) or Genetic Algorithms (GA) for care plan personalization.
- Big data and bioinformatics – these methods are used for risk-based preventive care.
AI/ML in prescriptive health analytics improves the quality of treatments and maintains population health at the highest level. As a result, this will ultimately reduce payers’ costs like insurance companies.
The image below summarizes the information about health analytics, their types, and the ML/AI technologies and tools for their delivery.
Neurons Lab Solutions for Creating Health Analytics Products
Neurons Lab has been operating in the AI/ML domain for 10+ years and has completed a few dominant projects that improved the healthcare sector.
Our team provides unique opportunities for health analytics products and start-ups. Our specialization and proven methodologies allow you to receive:
- Expertise in the intersection of AI, advanced science, and business that provides a unique team composed of a PhD-level applied scientists, recognized DS/ML/AI Engineers, and MLOps.
- Managed capacity engagement model with fast team allocation as well as efficient and transparent agile delivery process.
- Solution accelerators with a fast iterative R&D deployment approach for POC/MVP delivery and cloud/edge deployment.
As one of the examples, let us present the utilization of AI and the use of ML/AI data analytics for a scientific start-up in Sweden. The organization produces Remote Patient Monitoring (RPM) items for healthcare providers.
You are more than welcome to contact us for a more detailed consultation. We will explain how to utilize the latest practices and analytics to create novel products for healthcare – far quicker than your competitors.