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TELEPHARMACY ANALYTICS AND DATA VISUALIZATION THROUGH BUSINESS INTELLIGENCE

European Statement

Clinical Pharmacy Services

Author(s)

Cristina González Pérez, Laura Llorente Sanz, Ángel Liras Medina, Ana Andrea García Sacristán, María Molinero Muñoz, Lidia Ybañez García, José Alberto Peña Pedrosa, Henar González Luengo, María Luaces Méndez, José Manuel Martínez Sesmero

Why was it done?

Telepharmacy implementation in the context of SARS-CoV-2 pandemic conducted us through the management of a high volume of complex, real-time both clinical and economic data. A multidisciplinary working group (biomedical engineers from the Innovation Unit, clinicians, managers and hospital pharmacists) developed a software tool in April-May 2021.

What was done?

The design of an agile, customizable and dynamic dashboard for the visualization and analysis of Telepharmacy key performance indicators (KPI) through Business Intelligence (BI).

How was it done?

Phases:
1. Situation analysis. KPI definition. Ethics committee approval submission.
2. Extraction and processing of raw databases (Telepharmacy database, outpatient dispensing program, hospital admission database, drug catalog) through data mining.
3. Co-creation of the comprehensive dashboard in PowerBI®, by integrating database sources. Different panels have been designed where filters such as age, time frame, medical service, pathology, etc. can be applied.
• Description of general variables: patients, demography, shipments, time frame, medical department.
• Geolocation of the destinations of the patients’ home delivery.
• Pharmacological profile: top 10 drugs, distribution by active ingredient and drug classification group.
• Relative analysis of the beneficiary patients of Telepharmacy vs global outpatients
4. Pilot project by different types of users (administrative staff, clinicians and managers)
5. Structure design for automatic updating of the panels from the successive updates of the source databases
The quality of the raw databases can be a limitation. It has been necessary to define how to handle missing and duplicate data. Pre-processing, normalization and transformation data processes have been applied too.
Working within the hospital network ensures that there are no security gaps in terms of patient data protection.
For the external use of the dashboard, the granularity of the data is modulated to ensure enough clustering to avoid the identification of individual patients.

What has been achieved?

Processing the huge dataset (more than 2.4 million records) was possible by BI tools that synthesizes data, provides dynamic and engaging visualization (charts and graphs), allows the interactive reports customization for more effective communication of results and apply analysis based on Artificial Intelligence.

What next?

Applying new technologies will help us improve strategic decisions: interactions, behaviors and trends perceiving, weak points identifying, uncertainty reducing and over time monitoring.

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