AI in Healthcare: Optimising Data Search with Tovie Data Agent

June 05, 2024

7 min read

Alexandra Khomenok

Data Agent


Healthcare and medical organisations are dealing with massive amounts of data, striving to deliver better patient services, meeting regulatory requirements, managing complex claims processes, and handling huge volumes of information. Tovie Data Agent can be integrated into various scenarios to enhance efficiency, speed, connectivity, and innovation in healthcare.


Data Agent is an intelligent search application that allows you to interact with your organisation’s data through a chat interface. It connects with multiple data sources to provide accurate responses across your organisation.


Data Agent improves operational effectiveness by increasing worker productivity and saving time in handling paperwork and searching for information. Additionally, it supports medical research by extracting relevant studies and patient records.

If you want to learn more about the use cases of generative AI in healthcare — from diagnostics to new drug discovery — check out our blog article.

Data Agent use cases in healthcare

1. Automating routine tasks for healthcare providers

Physicians and primary care providers often work long hours, with up to two-thirds of their time spent on administrative tasks, leading to burnout.


Data Agent helps alleviate this burden by streamlining the retrieval of critical information, acting much like a personal assistant. It understands natural language and delivers instant responses, pulling up medical records for diagnosis, handling insurance policies, and following organisational protocols.


Data Agent’s capabilities include summarising complex clinical documents for quick review and drafting 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 worker satisfaction and a better work-life balance.


Additionally, AI tools like Data Agent promote more empathetic interactions between healthcare providers and patients. By taking on routine tasks, smart IT solutions for healthcare industry 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, enhancing the doctor-patient relationship.


2. Improved patient services

You can train Tovie Data Agent on patient guidelines, medical policies, and other helpful information in your databases. The Agent then provides personalised and quick responses to patients’ queries.


For example, suppose you are a medical clinic specialising in certain types of surgeries. Your staff often spends considerable time online providing post-surgery information and treatments, referring to medical standards and best practices. With Data Agent, patient queries can be automated via a chatbot. It will give personalised responses to all their questions from 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 your 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 you 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, allowing them to respond swiftly. That is to say that AI automation in healthcare offers significant benefits for both healthcare workers and patients.


3. Enhanced productivity for healthcare workers 

Data Agent can streamline 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 and prepare document drafts. 


Data Agent can also help create official letters, medical reports, and other documents based on internal databases. 


Employees in healthcare organisations that have comprehensive repositories of medical research papers and treatment protocols may need help accessing needed information promptly. Integration of Data Agent into vast databases will facilitate data retrieval and collaboration among healthcare professionals and improve 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 aid organisations in fully complying with stated policies, pulling rules out of the thousands of pages of regulation documents for one specific purpose without human effort and time consumption. 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 the help of Data Agent in extracting the client’s histories, records, and medical policies and guidelines, drafting a response to the denial becomes more quickly. 


In countries like the U.S., these procedures can be particularly time- and resource-consuming. Data Agent helps streamline 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 categorisations 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, thus reducing manual work and improving the speed of claims resolution and reimbursement. This would mean faster claims settlements and refunds.


However, it is essential 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 utilising 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 stringent laws and regulations.


Tovie Data Agent is designed with robust security measures to ensure the safety of your information. The application is controlled in terms of access to only specified staff members, allowing 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 streamline workflows, reduce administrative tasks, and enhance efficiency. Healthcare data solutions like Data Agent help manage patient information effectively, minimise paperwork, and improve patient care.


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