Bridging the Communication Gap in Healthcare with a Text Analysis Tool
The problem
Often, medical professionals communicate in a technical and complex manner that can be difficult for non-medical personnel to understand. This lack of clarity can lead to delays in treatment, inefficiencies in healthcare plans, and potential harm to patients' health.
The lack of engagement between medical professionals and patients negatively impacts the user experience, health outcomes, and revenue for healthcare entities. Non-medical individuals often struggle to comprehend medical jargon, requiring a shift in approach to communicate important information effectively.
People need a clear understanding of healthcare information to prevent illnesses effectively. However, healthcare experts' use of technical language poses a challenge for the general public. Communicating medical information clearly for patients, medical professionals, and the wider public across various platforms is essential.
The solution
We created a text analysis tool that allows users to rate their texts based on their readability and the amount of jargon (slang, complex words, etc). This tool enables users to determine whether there is still room for improvement, aiming to make their content accessible to a broad audience, including individuals who may not have expertise in healthcare.
Light-it's team developed a platform where healthcare technicians can write or paste their content and automatically get a specific score on how difficult the text is to comprehend as a non-medical user.
Tech Stack
Solution tech's specifics
This custom-built app counts with two different scores to determine the level of complexity of the text:
A score based on a mathematical formula is transformed into an algorithm that considers the number of syllables and words within the text.
A unique Wellframe score that contemplates a list of wordings that are considered complex or very specific to the healthcare niche outsiders. The platform identifies the number of times those specific words are repeated within the text, delivering a final score.
These scores create six categories to help users grasp the complexity of their text and motivate them to take steps to make it more accessible.
Once they validate the necessity of making another draft, physician users fill in their data. They are directed to a demo with Wellframe, where they can evaluate options to generate an optimum version with their team.
Technical Challenges
Creating and developing the platform separately from their existing web and making a seamless integration between this tool and their current website.
Deep diving into the different mathematical formulas that exist to analyze text, besides the grammatical rules, to minimize the margin of error in the final scores.
Iterating and changing the formula and updating the whole algorithm in the mid-process to seek the optimum results.
