Transforming Healthcare with AI: How Tovie Data Agent Optimises Data Search
The healthcare industry is inundated with vast amounts of data as organisations strive to improve patient care, comply with regulations, handle complex claims processes, and manage enormous volumes of information. Tovie Data Agent can be integrated into various healthcare scenarios to boost efficiency, speed, connectivity, and innovation.
Data Agent is an intelligent search application that enables users to interact with an organisation’s data through a chat interface. By connecting with multiple data sources, it provides accurate responses across the organisation.
Data Agent enhances operational effectiveness by increasing worker productivity and saving time spent on paperwork and searching for information. It also supports medical research by extracting relevant studies and patient records.
Use Cases for Data Agent in Healthcare
1. Automating routine tasks for healthcare providers
Physicians and primary care providers often work long hours, with a significant portion of their time spent on administrative tasks, leading to burnout.
Data Agent helps ease this burden by simplifying the retrieval of critical information, acting as a personal assistant. It understands natural language and provides instant responses, accessing medical records for diagnosis, handling insurance policies, and following organisational protocols.
Data Agent can summarise complex clinical documents for quick review and draft official replies, emails, or messages. By handling clerical work, AI services in healthcare reduce the workload for doctors and nurses, allowing them to focus more on patient care. This leads to greater job satisfaction and a better work-life balance.
Moreover, AI tools like Data Agent promote more empathetic interactions between healthcare providers and patients. By taking on routine tasks, smart IT solutions allow medical staff to concentrate on the human aspects of care. AI-generated after-visit summaries, for instance, are often perceived as more personal than those written by doctors, improving the doctor-patient relationship.
2. Improved patient services
Tovie Data Agent can be trained on patient guidelines, medical policies, and other helpful information in an organisation’s databases. The Agent then provides personalised and quick responses to patients’ queries.
For example, consider a medical clinic specialising in certain types of surgeries. Staff often spend considerable time providing post-surgery information and treatments online, referring to medical standards and best practices. With Data Agent, patient queries can be automated via a chatbot, which will give personalised responses to all their questions using the same reliable data sources.
Data Agent is easily customisable and deployable across various channels, including website chatbots, providing real-time responses to natural language searches. It reduces call volumes for operators, increasing customer satisfaction and cutting costs. For more complex queries requiring access to personal patient information, patients can be referred to a doctor or the nearest healthcare professional.
Additionally, Data Agent provides analytics on the processed queries, keeping organisations updated on performance and customer satisfaction with the answers provided.
Medical workers can also use Data Agent to answer patient requests. This AI search tool allows quick access to medical data, treatments, and other relevant documents, enabling swift responses. In other words, AI automation in healthcare offers significant benefits for both healthcare workers and patients.
3. Enhanced productivity for healthcare workers
Data Agent can simplify many tasks, such as drafting medical reports and preparing summaries. It works across different data types, including MP3, so it can analyse audio from medical panels and other relevant sources to prepare document drafts.
Data Agent can also help create official letters, medical reports, and other documents based on internal databases.
Employees in healthcare organisations with comprehensive repositories of medical research papers and treatment protocols may struggle to access needed information promptly. Integrating Data Agent into vast databases will facilitate data retrieval and collaboration among healthcare professionals, ultimately improving patient care.
4. Helping regulatory compliance
Failing to stay updated with regulations in the medical and pharmaceutical sectors can be a costly mistake for companies. Data Agent makes it easy for organisations to meet their compliance requirements with industry regulations.
It can help organisations fully comply with stated policies by quickly extracting relevant rules from thousands of pages of regulation documents. As a result, the cost of full compliance is eventually reduced.
5. Handling appeals and claims
Preparing appeal letters for insurance denials is a highly time-consuming procedure. However, with Data Agent’s help in extracting client histories, records, and medical policies and guidelines, drafting a response to the denial becomes quicker.
In countries like the U.S., these procedures can be particularly time- and resource-intensive. Data Agent helps simplify this work, reducing the time staff in medical organisations spend on it. It rapidly sifts through unstructured medical notes, medications, lab results, and other health records. With the necessary information gathered, an AI language model can generate an appeal letter draft, which staff can then review.
6. Simplification of the claim submission process
The claims submission process in the medical industry involves the manual categorisation of a large volume of incoming claims, each with complex medical codes. Data Agent can enhance this process by improving both speed and accuracy.
Data Agents with access to claim category classifiers and proper algorithms could analyse incoming claims, reducing manual work and improving the speed of claims resolution and reimbursement. This would mean faster claims settlements and refunds.
However, it is important to address the potential biases of Large Language Models (LLMs) used in generative AI tools. To mitigate these biases, it is crucial to test different models. Data Agent employs a multimodel approach to achieve the most precise results, offering organisations a variety of deployment options. Careful data collection, adherence to correct guidelines, and continuous monitoring will also help minimise LLM biases.
Data security challenges
One of the primary concerns when using generative AI in healthcare is data protection. Health providers and healthcare organisations hold enormous amounts of personal data, mainly in the form of electronic health records. Protecting this health information is subject to strict laws and regulations.
Tovie Data Agent is designed with robust security measures to ensure the safety of your information. Access to the application is controlled, allowing only specified staff members to maintain control over who can search and access sensitive data.
Deploying Data Agent on-premises guarantees proper safeguarding of sensitive data and maintains control over the database’s access levels. By deploying LLMs on-premises in your private cloud, you can leverage their powerful capabilities while maintaining complete control over how your data is handled, stored, and managed.
We also employ data masking of sensitive information to prevent non-anonymised data from leaking and being inappropriately disclosed. This tool acts as a privacy firewall for LLMs, anonymising sensitive data when accessing cloud-based LLMs and ensuring that data does not leave your organisation.
However, when considering AI implementation in healthcare, it’s important to note that AI-generated results depend on the quality of the data used to train or fine-tune the LLMs. If the data is poorly prepared or contains biases, the models’ outcomes will reflect these issues, potentially damaging the business’s reputation. Careful data preparation and continuous monitoring are essential to ensure the reliability and trustworthiness of generative AI in medicine and healthcare.
AI solutions meeting global healthcare challenges
According to the World Health Organisation, the current number of health workers, including physicians, radiologists, and others, is inadequate to handle the rising caseload. The increased stress and burnout caused by such outcomes have also led many people to exit the labour market, further raising the shortage of practising workers.
Consequently, healthcare organisations worldwide are struggling with several global challenges, such as clinician burnout, shortages of healthcare workers, and long patient wait times. Expanding the use of AI in the healthcare industry has the potential to relieve some pressure on health systems, saving staff time and resources. Tovie Data Agent can increase efficiency and reduce the administrative burden on health sector professionals.
Digital transformation in the healthcare industry enables providers to simplify workflows, reduce administrative tasks, and enhance efficiency. Healthcare data solutions like Data Agent help manage patient information effectively, minimise paperwork, and improve patient care.
