Affidea Staff Classifier-Croatian Text Classifier

Example

From a survey saying, 'Dr. Marko was very attentive and helpful', the classifier would extract 'Dr. Marko' as a staff member and 'doctor' as his role.

Scenario

This function is particularly useful in processing large volumes of patient feedback to identify which staff members are mentioned and in what capacity, helping healthcare providers monitor and evaluate individual and team performance.

Example

In a comment like, 'I was unhappy with the dismissive attitude of therapist Ana', the classifier identifies the negative sentiment associated with 'therapist Ana'.

Scenario

Understanding the sentiment of feedback allows healthcare facilities to address specific issues directly, enhancing patient satisfaction and care quality.

Example

If a comment ambiguously mentions 'the nurse in the second shift was great', the classifier might assign a lower confidence level due to the lack of a specific name.

Scenario

This aids in filtering and prioritizing data, focusing on high-confidence reports for follow-up or in-depth analysis, thus optimizing resource allocation in patient care management.