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Business intelligence for the visualization and data analysis of Telepharmacy activity indicators in a hospital
Cristina González-Pérez1, Laura Llorente-Sanz2, Macarena Torrego-Ellacuría3
1Servicio de Farmacia Hospitalaria. Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos, Madrid Unidad de Innovación. Hospital Clínico San Carlos, Instituto de Investigación Sanitaria Hospital Clínico San Carlos, Madrid. gonzalezperezc@gmail.com.
Objective:
The consolidation of Telepharmacy during the COVID-19 pandemic has raised the need for managing large volumes of real-time activity data through data analysis. The aim of this project was to design a dynamic, user- friendly, customizable scorecard in a hospital pharmacy service for the visualization and analysis of Telepharmacy activity indicators through the use of advanced business intelligence technology.
Method:
The software tool was developed by a multidisciplinary team between April and May 2021, driven from the hospital pharmacy service. Once the Telepharmacy indicators of interest were established, datasets were extracted from raw databases (administrative databases, Telepharmacy database, outpatient dispensing software, drug catalogues) through data analysis. The different data sources were integrated in a scorecard using PowerBI®. The criteria for processing missing and duplicated data were defined, and data pre-processing, normalization and transformation were performed. Once the pilot scorecard was validated by different profiles of users, the structure was designed for the panels to automatically update as databases were updated.
Results:
Design and implementation of a scorecard of Telepharmacy activity: general descriptive panel (demographic profile of patients, count and delivery conditions, program and medical service); geolocation of destination; pharmacological profile; relative analysis of patients involved in the Telepharmacy program with respect to the total of outpatients. In the last updating as of January 2022, data from 16,000 dispensations to more than 4,000 patients had been collected. This means that 21.93% of outpatients had benefited at some time point from the Telepharmacy service. Filters enable the visualization of timeline progress and patient characterization, and measure Telepharmacy activity by program.
Conclusions:
The processing of large Telemedicine datasets from various sources through Business Intelligence in a hospital pharmacy service makes it possible to synthesize information, generate customized reports, and visualize information in a dynamic and attractive format. The application of this new technology will help us improve strategic clinical and management decision making.
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