AI in Healthcare: How intelligent data is transforming hospitals and clinics.
- Marcelo Firpo
- 4 days ago
- 7 min read
AI-generated summary:
The application of GenAI and Machine Learning in the healthcare sector is revolutionizing everything from patient experience to the operational efficiency of institutions. Process automation, improved diagnostics, and clinical decision support are just some of the innovations made possible by AI. However, for these solutions to be effective, a solid foundation in engineering and data analysis is essential.
Imagine a patient who, upon leaving a medical appointment, immediately receives on their mobile device a clear and concise summary of the diagnosis and recommendations discussed, as well as prescriptions for medications to be purchased or scheduling of requested tests. At the same time, the doctor, relieved of administrative tasks, can dedicate more attention and empathy to the next patient. Scenarios like this are becoming a reality thanks to the application of Generative Artificial Intelligence (GenAI) and Machine Learning (ML) in the healthcare sector.
The advancement of new technologies is redefining the healthcare sector, driving innovations ranging from the automation of administrative processes and patient interfaces to advanced data analysis for more accurate diagnoses and personalized treatments. Technologies such as Generative AI (GenAI) and Machine Learning (ML) are transforming how hospitals, clinics, doctors, nursing teams, laboratories, insurers, and managers make decisions, improving efficiency, reducing costs, and enhancing the patient experience.
On the other hand, issues such as data governance, regulatory aspects, and compliance with the values of genuinely ethical practice raise concerns. In this dynamic scenario, understanding how these tools are shaping the healthcare field is essential for organizations and professionals seeking to offer faster, more accurate, and more accessible care.
The importance of data engineering and data analytics.
The effectiveness of GenAI and ML solutions depends directly on a solid foundation of data engineering and analysis. Accurate data collection, organization, and interpretation ensure that algorithms are trained with relevant, high-quality information, resulting in more efficient and reliable models. According to Gabriel Casara, CGO of BlueMetrics, it's crucial to understand that a good AI solution stems from a well-structured data framework. “Data is foundational. Models will work with it, and it's where we need to start. To use an analogy with the healthcare world, before we begin treatment, we need to have our pharmacy well-organized.”
The revolution in patient experience and operational efficiency.
The adoption of GenAI and ML in the healthcare sector is not just about technology, but a fundamental transformation in the patient experience and operational efficiency of healthcare institutions. These solutions enable the optimization of bureaucratic processes, the automation of repetitive tasks, and the delivery of personalized insights, benefiting both patients and healthcare professionals.
Through the analysis of large volumes of clinical data, AI can reduce waiting times, improve diagnoses, and personalize treatments. Healthcare professionals also benefit, as they spend less time on administrative tasks and more time focused on direct patient interaction.
AI in healthcare: discover some use cases
Patient and healthcare professional engagement
The interaction between patients and healthcare professionals is fundamental to the effectiveness of treatments. GenAI-based tools are transforming this dynamic by automating bureaucratic tasks and improving communication. For example, the Jiménez Díaz Foundation in Madrid implemented the "Mobility Scribe" AI system, developed by the Quirónsalud group. This system listens to conversations between doctors and patients, generates understandable medical reports, and proposes treatments that doctors validate. As a result, doctors can focus more on the patient during the consultation, increasing the quality of care.
Diagnostic support
Diagnostic Support Programs (DSPs) and Patient Support Programs (PSPs) play an essential role throughout the entire patient journey. These initiatives operate from the diagnostic phase to access and adherence to treatment, ensuring humanized and personalized care. The use of GenAI and ML in these programs allows for the automation of workflows, improved interaction quality, and a more fluid experience for patients and their caregivers. Through digitized care guidelines, it is possible to offer individualized support, ensuring greater treatment adherence and improving clinical outcomes.
For example, Roche developed a digitized support program for patients with chronic diseases, using AI to personalize interactions and remind patients about treatment adherence. The program not only improved the patient experience by offering a continuous support channel, but also helped reduce treatment dropout rates, ensuring better clinical outcomes.
Patient segmentation and sentiment analysis
Understanding patients' emotions and perceptions is vital to providing personalized care. Machine learning algorithms can analyze patient feedback, segmenting it based on expressed feelings and specific needs. This allows healthcare institutions to tailor their approaches, improving satisfaction and clinical outcomes.
The recent study “Probabilistic emotion and sentiment modelling of patient-reported experiences,” from the universities of Adelaide and James Cook in Australia, introduced an innovative methodology for modeling patient emotions from online narratives about their experiences. Using advanced topic modeling techniques, the researchers analyzed patient reports on the Care Opinion portal, identifying key emotional themes related to interactions between patients and healthcare professionals, as well as clinical outcomes. This probabilistic approach allowed for the prediction of emotions and feelings with high accuracy, providing valuable insights for personalizing care and improving healthcare services.
Next Best Action (NBA)
In the healthcare context, the Next Best Action approach uses AI to analyze real-time patient data, determining the most appropriate interventions at any given time. This allows healthcare professionals to offer personalized treatments, increase therapeutic effectiveness, and improve the patient experience.
Although the following example belongs to the financial sector, its principle can be adapted to healthcare: EY implemented an Accounts Receivable Collection Assistant with AI technology that uses machine learning to prioritize accounts, identify at-risk clients, and recommend the next best course of action. In healthcare, similar systems could analyze patient data to suggest timely and personalized clinical interventions.
Synthesis of medical literature
The sheer volume of medical publications is growing exponentially, making it challenging for healthcare professionals to stay up-to-date. GenAI and ML tools can automate the analysis and synthesis of large volumes of medical literature, providing concise and relevant summaries that aid in informed clinical decision-making.
MedSearch is an AI-based digital assistant developed by Arkangel AI, designed to streamline the search and analysis of medical literature in PubMed. It provides healthcare professionals with up-to-date and relevant information in an efficient and contextualized manner, improving the accuracy and speed of access to scientific evidence.
Analysis of clinical trials
Clinical trials generate a substantial amount of data that requires meticulous analysis to identify patterns and significant outcomes. Applying AI to this data can accelerate interpretation, improve the accuracy of results, and aid in the development of new treatments.
The study "Accelerating Clinical Evidence Synthesis with Large Language Models" presented TrialMind, a GenAI-based platform that automates the systematic review of clinical studies. Using advanced language models, TrialMind improves the efficiency in identifying and extracting relevant data, facilitating the synthesis of clinical evidence for researchers and healthcare professionals.
Support for clinical decision-making and care coordination.
GenAI and ML tools assist healthcare professionals in making clinical decisions by analyzing large volumes of data to provide evidence-based recommendations. Furthermore, these technologies improve coordination among medical teams, ensuring that patients receive integrated and efficient care.
The Cardiomentor project, for example, developed by Tecnalia and the Barcelona Supercomputing Center in collaboration with the Spanish Society of Cardiology, resulted in the first Spanish public application based on AI. Initially focused on providing general practitioners with easy access to up-to-date information on heart failure, the tool plans, in its second phase, to use anonymized patient data to improve its predictive and diagnostic capabilities. This aims to offer recommendations based on similar cases, optimizing patient treatment.
Improved diagnostics and medical training
GenAI and ML are revolutionizing medical education by providing interactive and personalized platforms for training healthcare professionals. These technologies enable realistic simulations and access to vast repositories of knowledge, facilitating continuous learning and updating in medical practices.
The Son Llàtzer University Hospital in Mallorca has implemented an AI algorithm capable of predicting a sepsis diagnosis within 24 hours, with 96% accuracy. While the primary focus is improving patient care, the algorithm also serves as an educational tool for doctors and nurses, enhancing clinical training and understanding of the early signs of the condition. Meanwhile, the Doctor Balmis General Hospital in Alicante has implemented an AI-based image reading system for chest and bone X-rays, allowing it to detect pathologies with 90% accuracy.
Automation of authorizations and improvements in customer experience.
Administrative processes, such as prior authorization for medical procedures, can be time-consuming and prone to errors. Implementing GenAI and ML automates these tasks, reducing waiting times and increasing accuracy in treatment approvals, which benefits both patients and healthcare providers.
Furthermore, the use of AI in patient care platforms, with agents capable of scheduling new appointments and exams, as well as chatbots able to access patient history and offer contextualized suggestions, are examples of new horizons opening up in the continuous improvement of the customer experience in healthcare services.
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BlueMetrics' experience in projects for the healthcare epsistema.
BlueMetrics has extensive experience in developing Data, GenAI, and Machine Learning solutions for companies in the healthcare ecosystem, helping to optimize operations, improve decision-making, and drive financial results.
An example of this is the work done for a large American company in the sector, which mediates emergency services, such as ambulances, and patients' health plans. With the growth of its operation, the company needed more transparency and accuracy in its reports to meet regulatory and operational demands.
To address this challenge, we developed a robust data infrastructure, implementing a data lake and a data warehouse, making reports more agile, detailed, and adaptable to new business rules. As a result, the company attracted four new payment processing systems, connecting hundreds of businesses and expanding its customer base.
This is a case study in Data & Analytics, a foundational area for any Artificial Intelligence project. With a structured database, new functionalities using GenAI and Machine Learning are already planned in the roadmap, bringing even more efficiency and innovation to the sector.
Conclusion:
The advancement of Artificial Intelligence in healthcare is not just a distant promise, but a constantly evolving reality. From automating administrative processes to personalizing medical care, including the analysis of clinical trials and optimization of the patient journey, GenAI and Machine Learning are reshaping the sector. At the heart of this revolution are data engineering and data analytics – fundamental pillars for the development of reliable and scalable solutions.
With proven expertise in GenAI, Machine Learning, and Data & Analytics projects, BlueMetrics accelerates the AI journey in healthcare ecosystem companies, ensuring a more efficient, innovative, and patient-centered future.
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