Enhancing Service Operator Efficiency in HealthTech
NLP-powered video recommendation tool to help operators surface relevant health content faster.
Overview
A leading health tech company maintains an extensive library of educational videos spanning various health topics, designed to assist patients with their medical conditions, treatments, and overall well-being. By harnessing video metadata and NLP technologies, DataMax enabled operators to efficiently surface video content recommendations — solving the challenge of non-discoverability through text search.
Challenge
Patient service operators faced two compounding problems. First, information silos: the video library was not easily discoverable through text searches. Second, operator burden: staff had to manually match patients with relevant video content, often working from generic or non-specific information provided by patients. This made fast, accurate recommendations difficult.
Approach
We combined deep video metadata analysis with NLP to bridge the gap between patient intent and video content, and wrapped it in an operator-facing UI rather than a fully automated system — keeping the human in the loop for clinical decisions.
Solution
We developed advanced video metadata analysis tools to extract and categorise information from each video — titles, descriptions, tags, durations, medical specialties, and treatment types. Using BERT-based NLP models, we built a system that comprehends patient queries and medical records, extracts key terms and context, and surfaces the most relevant videos. Operators see a user-friendly interface displaying matched video metadata alongside the patient query, streamlining their decision-making without removing their judgement from the process.
Results
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