TY - JOUR
T1 - Pharmacotherapy within a learning healthcare system
T2 - Rationale for the Dutch Santeon Farmadatabase
AU - Van De Garde, Ewoudt M.W.
AU - Plouvier, Bram C.
AU - Fleuren, Hanneke W.H.A.
AU - Haak, Eric A.F.
AU - Movig, Kris L.L.
AU - Deenen, Maarten J.
AU - Van Hulst, Marinus
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - benchmarking
KW - database
KW - hospital
KW - learning healthcare system
KW - pharmacotherapy
UR - http://www.scopus.com/inward/record.url?scp=85049118671&partnerID=8YFLogxK
U2 - 10.1136/ejhpharm-2017-001329
DO - 10.1136/ejhpharm-2017-001329
M3 - Article
AN - SCOPUS:85049118671
SN - 2047-9956
VL - 26
SP - 46
EP - 50
JO - European Journal of Hospital Pharmacy
JF - European Journal of Hospital Pharmacy
ER -