Smart Tools for Physicians: Automating Patient Care with AI

AI is changing healthcare. It gives doctors powerful tools for patient care. These solutions improve diagnostics and simplify many processes. They reduce healthcare professionals' workloads ( ai solutions in healthcare ). These innovative solutions not only improve diagnostic accuracy but also simplify many processes, effectively reducing the workload on healthcare professionals.

In this article, we explore how we developed an “Assistant” for our client’s clinic, designed to support doctors in their daily tasks. The tool was developed for the "Healthcare Platform Project". It integrates an AI-powered physician assistant for medical facilities.

Our Client

A private clinic with a large, expert team approached us. They have provided medical services and consultations for many years. Given the high patient volume, the clinic’s physicians experience a significant workload. The client expressed a need to enhance efficiency and accuracy in daily operations while improving patient experience. To help ease this burden, our client reached out for assistance.

Client’s Challenges and Objectives

Our client tasked us with reducing the paperwork burden on their doctors. This involves automating daily, routine data collection from patients.

The goal was also to eliminate the human factor in patient interactions. The goal was to streamline the collection of patient histories and symptoms. This would create a better view of each patient's health before consultations began. Doctors are only human. They may miss key details in consultations. This can hurt accurate diagnoses and treatment recommendations. Our objective was to create a system that would always capture these details and even offer preliminary diagnoses.

AI Collects and Analyzes Patient Documents

One of the key features of our system is the automation of collecting and analyzing patient medical data, such as test results and scans. This solution not only saves physicians time but also ensures that no critical information is overlooked. Now, doctors no longer need to gather this info from new patients.

The "Assistant" handles it automatically. The AI analyzes the patient's test results. It finds anomalies and sends the findings to the physician.

How it Works

After a patient books an appointment, the "Assistant" connects to their medical record via the doctor's profile and platform. AI queries the API using a function-calling method. Then, large language models process the data, looking for anomalies. Logical Volume Manager (LVM) technology helps organize the data, while LLaMA 3.2 analyzes it. The model uses vast data sources. It compares patient metrics, like test results, against standard values from official medical references. These include ICD-10 codes for disease classification. Retrieval Augmented Generation (RAG) supports the retrieval of this information.

The processed data is then sent to the doctor through RESTful API, allowing seamless integration with medical systems like CRM. This process provides an efficient, systematic review of patient history without the risk of human error or oversight.

Problem Solved

The AI system provides pre-structured reports with preliminary diagnoses. This frees doctors from routine tasks and helps them prepare for consultations. This automation saves time. It also reduces diagnostic delays. This benefits both physicians and patients.

AI physician assistant 

On the appointment day, we collected the patient's data and shared it with the doctor. He can now rely on the "Assistant" to interact with the patient without needing to take notes. During the consultation, the AI supports physicians by listening, recording, and summarizing important patient information. The AI secretary won't miss a thing. It will record the whole conversation, find key points, and create complete medical documents. The full-text transcript is also available for the doctor’s review.

How it Works

The ASR tech, using Deepgram's Speech-to-Text API, records chats between the doctor and the patient. The data is then processed by LLaMA 3.2, which highlights critical points from the conversation. The system can differentiate between questions and responses, allowing for easier organization of relevant information.

Neural network-based sentiment analysis tech helps assess patients' emotions. For document creation, AI utilizes Robotic Process Automation (RPA), which automates the generation of standard medical documents, customized to reflect each patient’s data.

Problem Solved

Doctors no longer need to document each patient complaint manually. The AI now generates medical reports that can be quickly reviewed and edited. The automated system cuts documentation time. This lets doctors focus on clinical decisions, not admin work.

Solution Benefits

A personalized approach for each patient: The AI assistant adapts to each patient. It tailors questions and prompts to their unique recovery journey. This customized interaction helps build a sense of personal care and attention, allowing patients to feel valued and heard.

Regular check-ins ensure up-to-date information on recovery progress and allow medical staff to respond quickly to any deviations from expected outcomes. By monitoring each patient's health, the assistant can catch subtle changes early. This helps prevent minor issues from becoming serious complications.

All patient data collected by the AI assistant is stored securely. Physicians can easily access it. This ensures transparent, reliable treatment records. This data management speeds up reviews for medical staff. It also helps track long-term patient outcomes. This enables better care.

Conclusion

AI-powered assistants open new avenues for improving healthcare quality. These tools optimize workflows. They keep the human touch in patient interactions. By automating routine tasks, doctors gain valuable time to focus directly on patient care, leading to better health outcomes and greater patient satisfaction. Personalized patient monitoring helps to quickly respond to complications. It allows doctors to spend more time on complex cases.

The "Assistant" boosts patient engagement. It lets patients share their symptoms and progress, providing support outside the hospital. As AI continues to evolve, we anticipate even more opportunities to enhance patient-centered care, making healthcare more accessible, efficient, and effective for everyone.