Data Resource Profile: The Scottish Combined Medicines Dataset (SCoMeD)

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Authors: Tanja Mueller, Lynne Jarvis, Victoria Stark, Morven Millar, Amy Hynd, Elaine Pauline, Amanj Amanj Kurdi, Laura Stobo, Stuart McTaggart, Marion Bennie
Cited by: 2

Abstract

Introduction: Prescribing data has been collected electronically in Scotland for many years; however, data are collated in individual, non-overlapping datasets based on the origin of the prescription (e.g., primary or secondary care). The vision was to create a unified view of all prescribing data to provide a longitudinal dataset of medicines use for patients treated by the National Health Services (NHS) Scotland, irrespective of where or how that care was provided. Methods: The Scottish Combined Medicines Dataset (SCoMeD) is, in essence, a data virtualisation tool collating information from three previously available prescribing datasets: the Prescribing Information System (PIS); the Hospital Electronic Prescribing and Medicines Administration (HEPMA) national dataset; and the Homecare Medicines (HCM) dataset. This allows the creation of study cohorts (patient groups of interest) that meet specified criteria across all prescribing settings and facilitates the retrieval of the prescribing history for individuals pre-identified from other datasets. Records contain a unique patient identifier (Community Health Index number) which is used to identify patients for inclusion in the dataset and also enables linkage to other routinely collected data, including hospital admission episodes and death records. Results: SCoMeD contains details on the patient (age, sex, geographical information) and on the medication prescribed. Medication-related information includes what was received and when; strength and dose information are also available. The earliest date of data availability depends on the source (PIS, 01/2010; HEPMA, 07/2022; HCM, 01/2019). Data is held by Public Health Scotland. Conclusion: SCoMeD facilitates a range of different studies, including cross-sectional/point-prevalence studies and drug utilisation studies as well as longitudinal studies, e.g., cohort and case-control studies. With the possibility to link to other relevant datasets, additional areas of interest may include health policy evaluations and health economics studies. Access to data is subject to approval; researchers need to contact the electronic Data Research and Innovation Service in the first instance.

Fields of Study & Subjects

Electronic Health Records Systems Medical Coding and Health Information Machine Learning in Healthcare

Publication details

Journal / source
International Journal for Population Data Science
Publisher
Swansea University
Volume
8
Issue
6
Pages
3006–3006
Publication date
15 January 2026
Language
EN