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AI for Patient Records Management
Architecture, Functionality, and Costs
In AI since 1989 and in healthcare IT since 2005, ScienceSoft designs secure and compliant AI solutions for medical providers and healthcare software product companies.
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AI-Driven Patient Records Management in a Nutshell
Artificial intelligence (AI) solutions for patient record management leverage natural language processing, predictive text, intelligent search, and other techniques to optimize administrative workflows. AI algorithms can streamline clinical documentation by automating patient data entry, categorization, and retrieval, allowing clinicians to focus on patient care.
Medical AI Market Overview
The global healthcare AI market was valued at $20.9 billion in 2024 and is estimated to reach $148.4 billion by 2029, registering a CAGR of 48.1% during the forecast period. The key market drivers include the generation of large and complex healthcare datasets, the pressing need to reduce healthcare costs, and the shortage of healthcare workers. 56% of physicians consider administrative workflow automation the best way to leverage AI technology.
How AI for Patient Records Works
Below, ScienceSoft’s healthcare AI engineers share a high-level architecture of a comprehensive patient records management solution powered by artificial intelligence. It can be expanded, reduced, or otherwise adjusted based on the needs of a specific provider or target user group.
How AI Can Support Patient Records Management
Collecting unstructured clinical data
Extracting data from disparate sources (e.g., lab tests, clinical notes, handwritten health records) to structure and categorize it.
Error and fraud detection
Cross-referencing information from various sources, including historical patient records, terminology services, and third-party databases, to reveal potential inaccuracies and fraud.
Voice control and auditory input
Transforming live conversations into clinical documentation and using voice commands for information search and retrieval.
Intelligent search
Providing smart entry suggestions and showing the most relevant records based on the context and semantics.
Text-to-speech
Reading patient data from the records out loud for a physician’s convenience.
Smart billing
Automating medical coding and insurance eligibility checks.
Clinical summaries
Summarizing patient history or lab test results for a physician and highlighting key facts (e.g., past diagnoses, detected abnormalities, or potential correlations between health conditions managed by different medical specialties).
AI Implementation for Records Management: Success Story
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Atrium Health, North Carolina’s 70,000-employee hospital network, adopted Microsoft’s DAX™ Copilot to streamline administrative processes across the organization. The solution enables the automated creation of draft clinical summaries from in-person exams or telehealth patient conversations that can be immediately reviewed and finalized in the EHR system. As a result, clinicians save up to 40 minutes per day, and 84% of them report an improved documentation experience. |
Technologies ScienceSoft Uses to Build AI for Patient Record Management
Preventing Common Pitfalls of AI for Records Management
With 35 years of experience in AI development, we know that leveraging AI for managing patient data presents certain challenges. Below, we explain how to tackle the most common ones:
PHI security risks
Some healthcare providers are hesitant to implement AI solutions for records management due to concerns about patient data privacy and compliance. As with any healthcare software, standard security procedures like encryption for data at rest and in transit, strong access controls, and multi-factor authentication should be implemented.
Another piece of advice concerns the environment, in which the AI model is trained and deployed. Many healthcare providers opt for the cloud due to its scalability, cost-effectiveness, and ease of access. In such cases, it’s best to rely on HIPAA-compliant cloud providers. For instance, you can deploy Azure AI Speech or a third-party AI model in Microsoft Cloud for Healthcare, which would serve as an isolated HIPAA-compliant ecosystem.
Low algorithm accuracy
AI-produced errors in patient records can lead to incorrect diagnoses and treatment plans, harming patients and bringing financial and reputational losses to healthcare providers. There are several important aspects that should be covered to ensure the high accuracy of your AI model:
- High training data quality
Providing high-quality data is essential for comprehensive model training. Training datasets should be clean, relevant, and representative of the future input that AI will process. The preliminary data cleaning process should involve removing duplicates, inconsistencies, and outliers. If some data is missing, data scientists can apply traditional imputation methods like mean, median, and mode imputation (replacing missing entries with statistical summaries of observed data) or interpolation techniques like linear or quadratic interpolation (estimating missing values by finding patterns or relationships in the known data points). - Tuning and monitoring for anomalies
With time, the data you have to analyze will grow and change, which may result in decreased model accuracy. We recommend setting up a monitoring system (e.g., Model Monitor for Amazon SageMaker) to detect performance anomalies or accuracy declines. This way, if an “AI drift” is revealed, you can tune the hyperparameters and retrain the model on a new data set reflecting the shifts in data patterns.
Humans in the loop are essential for medical AI effectiveness
During the development stage, medical coders should review AI-generated documentation and edit the output when necessary. For example, if an AI-powered transcriber misinterprets a drug name, the clinician corrects it. The model can then learn from the corrections to improve its accuracy. The human-in-the-loop (HITL) approach not only helps ensure AI’s reliability but also builds care providers’ trust in the new solution as they learn to work with it.
Costs of AI-Driven Records Management Solutions
Key cost factors to consider:
- The scope of AI functionality.
- Algorithm complexity.
- The number of data sources and the volume of data for processing.
- The number and complexity of integrations with other solutions (e.g., patient portals).
- Performance, usability, security, and compliance requirements.
The costs of custom AI-enabled software for patient records management may vary from $30,000 to $800,000. Below are sample cost ranges for commonly requested solutions.
$30,000–$70,000
For a standalone AI module that enables a single data management process (e.g., summarizing patient history or scanning handrwritten text).
$150,000–$300,000
For an AI virtual assistant that can transcribe patient-doctor conversations and suggest fixes for potential errors in the physician’s input.
$400,000–800,000+
For a custom AI-powered EHR system with features like dictation, virtual assistance, and smart billing (enables AI-driven insurance eligibility checks and in-line suggestions of billing codes).
What makes ScienceSoft different
We achieve project success no matter what
ScienceSoft does not pass off mere project administration for project management, which, unfortunately, often happens on the market. We practice real project management, achieving project success for our clients no matter what.