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Pharmacotherapy within a learning healthcare system: rationale for the Dutch Santeon Farmadatabase
  1. Ewoudt M W van de Garde1,2,
  2. Bram C Plouvier2,
  3. Hanneke W H A Fleuren3,
  4. Eric A F Haak4,
  5. Kris L L Movig5,
  6. Maarten J Deenen6,
  7. Marinus van Hulst2,7
  1. 1 Department of Clinical Pharmacy, St Antonius Hospital, Nieuwegein, The Netherlands
  2. 2 Santeon, Utrecht, The Netherlands
  3. 3 Department of Clinical Pharmacy, Canisius Wilhelmina Hospital, Nijmegen, The Netherlands
  4. 4 Department of Clinical Pharmacy, OLVG, Amsterdam, The Netherlands
  5. 5 Department of Clinical Pharmacy, Medisch Spectrum Twente, Enschede, The Netherlands
  6. 6 Department of Clinical Pharmacy, Catharina Hospital, Eindhoven, The Netherlands
  7. 7 Department of Clinical Pharmacy, Martini Hospital, Groningen, Netherlands
  1. Correspondence to Dr Ewoudt M W van de Garde, Department of Clinical Pharmacy, St. Antonius Hospital, 3435 CM Nieuwegein, The Netherlands; e.van.de.garde{at}antoniusziekenhuis.nl

Abstract

Objectives The increasing number of available, often expensive, medicines asks for continuous assessment of rational prescribing. We aimed to develop a simple and robust data infrastructure in order to monitor hospital medicine utilisation in real time.

Methods Within a collaboration (Santeon) of large teaching hospitals in the Netherlands, we set up a process for extraction, transformation, anonymisation and load of individual medicine prescription data and major clinical outcomes from different hospital information systems into a central database. Quarterly reports were constructed to monitor and validate the quality of the uploaded data.

Results A central database has been developed that includes data from all patients from 2010 onwards and is refreshed on a weekly basis by an automated process. Beginning in 2017, the database holds data from almost 800 000 patients with prescriptions. All hospitals provide at least 18 mandatory data items per patient. Provided data include, among others, individual prescriptions, diagnosis data, and hospitalisation and survival data. The database is currently used to benchmark the level of biosimilar prescribing and to assess the impact of novel systemic treatments on survival rates in metastatic cancers.

Conclusion We showed that it is feasible for a group of hospitals to construct their own database that can serve as a tool to benchmark the positioning of medicines and to start with monitoring their impact on clinical outcomes.

  • database
  • benchmarking
  • pharmacotherapy
  • hospital
  • learning healthcare system

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

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