The application of artificial intelligence (AI) in healthcare is vast and growing. From chatbots providing symptom relief to predictive analytics identifying at-risk patients, AI is quickly becoming an integral part of health economics and care.
Health Economics and Outcomes Research (HEOR) is critical to understanding the value of new medicines, but the field has been slow to adopt artificial intelligence (AI). That’s changing, as AI is increasingly used in HEOR to help unlock value for patients, payers, and society.
What is AI in HEOR?
The significance of AI in health economics and outcomes research (HEOR) is its potential to improve patient outcomes, reduce adverse medication events, save money, and increase safety.
AI can be used to:
- Predict the duration of hospitalization for patients
- Identify risk for diseases in populations
- Speed up the approval process for drugs
- Assess the effectiveness of drugs already in use
- Understand social determinants of health and tailor interventions to specific populations’ needs.
AI can also remind patients to take their medications on time by sending text or app notifications and identifying and extracting text from various sources regarding a patient’s adherence to medication. Ultimately, AI has the potential to revolutionize healthcare systems by predicting diseases more accurately and providing better access for patients.
10 Applications of AI in HEOR
Here are some applications of Artificial Intelligence in Health Economics and Outcomes Research.
1. Automating Healthcare Data Collection and Analysis
AI can automate healthcare data collection and analysis by automatically ingesting data from multiple sources, such as electronic health records (EHRs), social media platforms, patient surveys, and more.
AI can then use this data to generate predictions on patient outcomes or predict the effectiveness of healthcare treatments. This allows healthcare providers to better tailor treatments for specific patients, target key populations and improve their overall care pathways.
2. Predictive Analytics for Patient Health Outcomes
Artificial intelligence can predict patient health outcomes by analyzing large amounts of data points, such as care pathways, medical and social needs, and health-related behaviors. This helps healthcare providers better target and rank healthcare providers who treat a disproportionate share of key populations for further intervention opportunities and identify potential at-risk patients for fall-related injuries.
AI can also be used to create predictive models that identify patients at risk of hospital readmissions to improve overall care.
3. Clinical Decision Support Systems
Clinical decision support systems (CDSS) are computer-based systems that help healthcare professionals make more accurate decisions by analyzing large amounts of data. CDSS use AI to identify and predict variability within routine clinical practice and control for common inefficiencies in healthcare, leading to better patient outcomes.
AI techniques such as sequencing and unsupervised clustering are used in order to create models that accurately capture patient experiences over longer time horizons.
4. Developing Personalized Medicine and Treatments
AI can be used to develop personalized medicine and treatments by leveraging supervised learning methods, Markov influence diagrams, and the enhanced patient profiles provided by AI-powered platforms. Supervised learning allows for better classification of individuals compared to traditional regression techniques, while Markov influence diagrams reduce the need for micro-simulations and state explosion.
AI-enriched patient profiles can also help identify hard-to-identify patients as well as medical and social needs that require further attention. By pinpointing health economic impacts and intervention opportunities, AI can support personalized medicine treatments with improved efficacy.
5. Automating Routine Tasks in Hospitals and Clinics
AI can be used to automate tasks in hospitals and clinics by predicting patient outcomes, identifying hard-to-identify patients, helping to identify under-penetrated segments of the population, and pinpointing health economic impacts.
Automation can also improve efficiency by ranking healthcare providers (HCPs) based on their ability to treat key populations. Furthermore, AI can help generate predictions about medical risks to enhance care through automated data ingestion.
6. Improving Drug Discovery and Development
AI can be used to improve drug discovery and development by helping to speed up the process. It can assist researchers in automatically ingesting data from many sources and generating predictions, providing insights into a drug’s potential side effects or interactions with other medications, foods, and beverages.
AI also has the potential to identify new targets for drugs or develop better ways of testing them in vitro or in vivo.
7. Optimizing Patient Care Pathways
AI can optimize patient care pathways by leveraging data from observation, imputation, and long-term outcomes. AI can also identify inefficiencies and improve the structure of models by enriching patient profiles with information such as 2,000 healthcare features and predicted outcomes. By targeting key populations, AI can help create care pathways and identify under-penetrated segments.
Additionally, AI has the potential to reveal health economic impacts that may not have been previously understood or recognized.
8. Enhancing Patient Engagement and Experience
AI can be used to enhance patient engagement and experience by identifying hard-to-identify patients and under-penetrated segments. AI technology can analyze data from different sources such as medical records, claims databases, and surveys to determine which patients would benefit most from a given product or service, allowing healthcare providers to better target their offerings.
Additionally, AI can help rank providers based on how many at-risk patients they treat to allocate resources for intervention.
9. Improving the Accuracy of Diagnosis
AI can improve the accuracy of diagnosis in healthcare by utilizing big data and predictive analytics to identify trends over longer time horizons. Machine Learning (ML) can further help models reflect outcomes based on prespecified time horizons, making it possible for diagnoses to be more accurate.
10. Improving the Efficiency of Medical Records and Billing
AI can improve the efficiency of medical records and bills by predicting patient outcomes, identifying under-served populations, targeting healthcare providers who treat a disproportionate number of patients in targeted groups, and reducing the cost of hospital readmission. By doing so, AI can help reduce costs while providing better patient care.
Conclusion
AI has the potential to revolutionize healthcare, but it is essential to understand the risks and benefits before implementing any new technology. This guide has provided an overview of AI in HEOR and how it can improve patient care. By understanding the potential of AI, you can make informed decisions about how to best use it in your organization.
