Case Study | Hospital del Mar and Ferrer

Project aimed at understanding thousands of medical discharges available and extracting clinical intelligence from them.

Folksonomy Text Analytics allows the extraction of information from unstructured data, whether in the form of text, images, video, audio, etc.



The following questions are set as immediate objectives:

  • Objective 1: to find out what hypoglycaemic treatment we are treating patients with renal disease with and what treatment has been changed.
  • Objective 2: to find out which patients with a history of renin angiotensin system inhibitors take inhibitors at discharge.
  • Objective 3: to find out which patients who do not have a history of depression are taking an antidepressant drug (group N06A).


Managing through team empowerment has taken the form of three types of big data:

  • Descriptive big data: to assess health outcomes, identify previously unknown relationships, connect all sources of data generated.
  • Predictive big data: has allowed them to predict clinical events.
  • Prescriptive big data: to make real-time decisions based on best practices.


  • Automatically extract all the variables of the patients attended by filtering by the desired search criteria.
  • Extraction of knowledge from unstructured discharge data.
  • Support for decision making in real time.
  • Determine population epidemiology, generate clinical research hypotheses, perform observational studies, predict clinical casuistries before they occur.
  • Response to objective 1: 39.91% of patients admitted to nephrology are diabetic, a total of 651, of which 89 are treated with metformin.

Analysis of more than 1,600 medical discharges


Results in a short period of time

Real-time decision making

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