Artificial intelligence copies the human brain’s ability to learn and apply new knowledge in practice. Predictive modeling in healthcare implies research of relevant data to further pattern identification and a forecast. Machine learning algorithms cope with such tasks, as they can process medical data from electronic health records (EHR) and scientific literature, scan images, and interpret test results. Therefore, the possibilities of AI can complement traditional medical approaches and aid doctors in predictive analytics.
What is AI predictive modeling in healthcare?
Predicting modeling in medicine is the way to forecast the development of a disease, like stages of diabetes and sepsis. It also predicts side effects of medications, reactions to taking multiple pills simultaneously, and misuse of drugs. Predictive modeling contains the following types of medical data:
- EHRs: the records include all the information about a patient visiting any healthcare provider.
- Clinical studies: this data includes clinical research. When a patient undergoes a particular treatment according to medical protocols, this information appears in clinical trial databases, such as ClinicalTrials.gov. However, the information from this source is limited, as not every patient is acceptable for a clinical study.
- Medical literature: the data from the National Library of Medicine. The content is reliable, but it might be obsolete, as it takes time to print the results of any medical research.
- Sensor data: this relatively new type of data is collected from smart devices that people use to track their health conditions. Smart watches, smart scales, helmets, etc., can tell a person much about their blood pressure, heart rate, and sleep quality.
How does AI aid predictive analytics?
EMR and EHR
The electronic medical records system (EMR) is a collection of patients’ medical histories. Implementing AI features with EMR data allows experts to examine the information quickly, summarize the key cases, draw conclusions, and assess risks. The algorithms analyze different groups of patients and make generalizations about their typical health issues. Race, ethnicity, age, gender, education, and social status are taken into account.
Electronic health record systems (EHR) allow sharing of data among different medical providers. It is convenient for patients, as their history moves with them, and doctors may see a broader picture of their health.
According to the Belitsoft expert Dmitry Baraishuk, the integration of modern cloud-based EHRs with BI tools allows for a large scope of tasks. For example, such solutions can analyze historical data, extract information to suggest diagnoses, visualize these insights on BI dashboards, and send reports to decision-makers. Besides, they make it possible to scale resources following seasonal outbreaks of diseases when the workload is the highest.
Risk scoring
Risk scoring divides the population into groups according to their medical condition. Each group needs a particular type of regular screening. For instance, the Finnish FINDRISK score for diabetes contains questions about age, waist circumference, genetics, blood glucose, and diet. High scores in this test indicate high risks of developing diabetes.
AI algorithms examine the questionnaire data and analyze them. Any algorithm uses a set of rules and special conditions. If those conditions are met, the system understands it as a sign of a diagnosis, and AI comes up with preventive measures and recommendations. Doctors further examine those suggestions and adapt them to a real patient.
Risk scoring and storing the results are also helpful for chronic disease treatment. It includes measures to improve a patient’s quality of life and conduct regular tests to check the health condition. Predicting health risks with the help of AI technologies and assessing medication efficiency help to see the course of the disease in the long run, whether it requires changes in the treatment plan or more radical steps.
Diagnoses
Scientists from the University of Michigan have developed an AI predictive model to evaluate the effect of throat cancer treatment. HPV-positive patients will know if the treatment will work for them in advance. Standard screening allows the assessment of the treatment results only after a few months. Conversely, predictive models will demonstrate if it is reasonable to start.
Predictive analytics will reduce the number of pseudoprogression cases that often happen with different types of cancer. When the medication starts working, tumors sometimes grow before the final shrinkage. Doctors might interpret scans falsely in this situation. However, with predictive tools, the blood test will indicate a positive effect of the medicine.
Benefits of AI predictive analytics in healthcare
- Personalized approach to every patient. As mentioned by Deloitte, personalized treatment plans represent the main benefit of BI and data analytics in healthcare for both doctors and patients. Business intelligence solutions with AI integration allow doctors to examine and consider related data and tailor it to a particular case.
- Detection of at-risk groups. Predictive analytics compares the data and might indicate connections between a chronic disease and the possibility of developing another health condition.
- Chronic disease treatment. Chronic diseases, such as heart disease and cancer, cause nearly three-quarters of deaths worldwide. Those conditions, together with diabetes, obesity, and kidney disease, cost 75% of the national US expenditures. People suffering from chronic diseases have to constantly maintain and control their health. Predictive modeling of pill and drug efficiency reduces the risk of developing side effects and shows possible future scenarios.
What are the challenges?
Data standards
Machine learning algorithms require crystal-clear data. It means all the medical input has to be standardized. However, clinical data might contain ambiguous facts, unclear terminology, and acronyms. Medical staff are often short on time to provide such high-quality input data.
How to address it? Any healthcare software application or system must comply with data regulations and policies. The staff should be trained to provide correct input into EHRs or EMRs.
Lack of objectivity
Doctors analyze many conditions and health parameters to diagnose patients. Relying on AI may lead to a wrong diagnosis and consequently inadequate treatment. For instance, AI may take a single test result as an indicator of a disease or attribute too much weight to some tests, leading to false-positive or false-negative results. Some social groups of people tend to visit doctors irregularly, so the data about their typical health conditions is insufficient for the AI.
How to address it? Critical thinking and competence of medical experts help doctors relevantly interpret AI predictions. Human interference and checks are required, as AI technologies assist people, not work instead of them.
Ethical implications
Communicating with AI implies sharing personal information with a machine. It includes health data as well as financial and insurance information. Phishing was reported to be the most frequent cyber crime in 2023. People are reluctant to transfer their sensitive data to third parties.
How to address it? Make sure your software complies with HIPAA and GDPR. The HIPAA security rule demands the administrative and technical credibility of software. It protects medical information (health status, prescriptions, medication, test results, surgical manipulations, therapist notes) and financial data (invoices, billings, refunds, and insurance claims).
Follow-up
Machine learning tools improve predictive modeling in healthcare. Experts access relevant medical data and may complement their decisions with AI suggestions. Compliance with data regulations makes the processes safe for the patients and medical providers.