How to Implement Artificial Intelligence Models in Healthcare to Optimize Disease Treatment

‍Artificial intelligence in healthcare has come to revolutionize disease treatment and improve efficiency in healthcare systems. Addressing chronic diseases requires significant resources and high operational costs, which, if not managed properly, can create an unsustainable burden for both the system and the patient. In this context, artificial intelligence (AI) is a crucial tool that not only optimizes resource management but also improves diagnostic accuracy, personalizes treatments, and reduces response times in patient care.The approach taken by companies like Arkangel Ai, a leader in implementing AI in healthcare across 18 countries and impacting 68 million people, demonstrates the transformation that AI can achieve. Through predictive and non-predictive models, AI addresses significant challenges: treatment adherence, cost reduction, and improved diagnostic times.However, for these models to work effectively, a clear and careful methodology is essential. This article outlines the essential steps in implementing AI models in healthcare, providing a guide to maximize the potential of this disruptive technology.

You may be interested in this use case: How to Improve Hospital Stay Management with AI to Reduce Costs and Enhance Care

‍Why Implement Artificial Intelligence in the Healthcare Sector?

‍Chronic diseases account for more than 40% of healthcare costs, and with an aging global population, this percentage continues to increase. These conditions require constant monitoring and effective management to avoid complications and prolonged hospitalizations. Detecting and treating these conditions early not only improves patients' lives but also enables hospitals, insurers, and government agencies to manage their resources more effectively. This is where artificial intelligence plays a decisive role in optimizing the diagnosis, prognosis, and treatment of these diseases.However, implementing AI in the healthcare sector is no simple task. It requires detailed planning, selecting the right model, validating results, and adhering to ethical and privacy standards. Below, we explain each key step in this implementation process.

Clearly Define the Problem

‍Before selecting any artificial intelligence model, the first step is to clearly define the problem to be solved. In the healthcare industry, objectives can vary widely: from improving treatment adherence, reducing operational costs, to increasing diagnostic accuracy. Identifying this objective is crucial, as it will guide the development and selection of the AI model.According to Arkangel Ai’s experience, a common mistake in this phase is that many organizations have a general understanding of the problem but lack insight into the specific causes that originate it. For instance, if a hospital faces challenges in increasing access to treatments, it's essential to ask why this limitation occurs and what data is needed to understand the context. This thorough analysis can take hours to weeks and is the foundation for successful AI implementation aligned with business objectives.

Collect and Organize the Necessary Data

‍Once the problem is identified, the next step is to collect and organize the data to train the model. Information in healthcare is often fragmented across multiple systems and formats, making it difficult to analyze. However, AI technology can process and structure large volumes of data, providing a solid foundation for decision-making.Arkangel Ai notes that data disorganization is not a barrier. AI can help organize this information and start generating results quickly, even within weeks, with the right tools and approach. Data collection is one of the most critical phases of the project, as it determines the accuracy and effectiveness of the AI model.

Selecting the Appropriate Artificial Intelligence Model

‍With data ready, the next step is to select the artificial intelligence model best suited to the identified problem. There are two main types of models in healthcare: predictive and non-predictive, each with specific characteristics and applications.

• Non-Predictive Models: These models operate by applying predefined rules. For example, an AI system might indicate that if a patient with chronic kidney disease has a specific creatinine level and exceeds a certain age, the system will automatically determine the risk of complications. These models are useful in situations where rules and guidelines are clear and established, though they can be limited in complex cases where conditions vary or data is incomplete.
• Predictive Models: Offering more advanced and personalized analysis, predictive models are trained with historical data and can anticipate behaviors such as treatment adherence, predict hospital bed demand, or make early diagnoses. Thanks to their capacity to learn from data, predictive models allow for customized treatment, prioritization of urgent cases, and optimization of resources.Arkangel Ai has implemented these models to improve patients' quality of life in the 18 countries it operates, enabling faster and more effective diagnoses that ultimately reduce hospitalization time and costs.

Model Validation and Testing in Controlled Environments

‍With the model selected, the next step is to test and validate the results in a controlled environment. This stage is critical to ensuring the model functions correctly and meets the required safety and privacy standards in the healthcare sector. Arkangel Ai adheres to ISO 27001 standards to ensure that all data and models are protected in line with current regulations.Testing typically lasts one to two months and allows developers to identify improvement areas and adjust model parameters to maximize accuracy. Additionally, this process offers the opportunity to test the real impact of AI in clinical practice before full deployment.

Model Implementation and Ongoing Monitoring

‍Once validated, the model is deployed within the organization’s system, enabling AI to start generating value immediately. This implementation process takes approximately 15 days, allowing the model to integrate effectively with existing systems.However, artificial intelligence is not static. During the first six months after implementation, Arkangel Ai continuously monitors model performance and retrains the system with real data. This constant adjustment allows the model to improve over time and maintain effectiveness in a constantly changing environment.This monitoring also helps assess key performance indicators (KPIs), such as cost reduction, optimized care times, and patient health impact. Thanks to this methodology, the AI models implemented by Arkangel Ai have helped healthcare institutions make faster, more accurate, and cost-effective decisions.

Want to learn more? Request a free consultation

Conclusion: The Impact of Artificial Intelligence on Healthcare

‍Artificial intelligence in healthcare has become an invaluable ally in improving disease treatment, especially for chronic conditions. By implementing AI models, healthcare organizations can optimize resources, reduce costs, and provide patients with more personalized and precise care. The methodology of companies like Arkangel Ai, which already impacts millions of people in 18 countries, demonstrates that this technology is not only effective but also scalable and capable of meeting the strictest security and privacy standards.AI is revolutionizing the healthcare sector, and those who don’t integrate it into their processes risk falling behind. Careful and strategic implementation of this technology maximizes its benefits, improves patients' quality of life, and builds a more efficient and sustainable healthcare system.If your organization seeks to incorporate artificial intelligence into its operations, Arkangel Ai’s experience shows how to leverage this technology at every stage, from planning to continuous monitoring.

‍