TABLE 1.

Study characteristics and CDSS description of the included studies

Authors	Study setting	Patients	CDSS description	CDSS input	CDSS output
Arvisais et al. (2015) 26 Canada	Hospital, all (except four) wards	n = 182 (200 patient‐days) Mean age = 84 Female = 36%	Computerized alert system (CAS), used by pharmacists	n = 5 geriatric explicit criteria by expert panel based on Beers criteria and prevalence	Alerts (on HTML page), daily generated by database
Azaz‐Livshits et al. (1998) 46 Israel	Hospital, medical ward	n = 150 patients Mean age = NR Female = 50%	ADR detection tool	n = 14 ADR signals from laboratory data	List of alerts
Buckley et al. (2018) 47 USA	Hospital, ICU and general ward	n = 634 patients Mean age = 60 non‐ICU, 59 ICU Female = 7% non‐ICU, 42% ICU	DRHC triggers	n = 20 unique trigger alerts	Paper and electronic reports (webpage within hospital)
Cossette et al. (2019) 29 Canada	Primary care	n = 369 patients Mean age = 77 Female = 71%	Computerized alert system (CAS)	n = 11 types of geriatric explicit criteria based on 2015 Beers and STOPP v2 criteria	List of patients with PIMs and alert reasons
Dalton et al. (2020) 27 Ireland	Hospital	n = 204 patients Mean age = 77 Female = 49%	SENATOR software, PIM CDSS for physicians	n = 111 recommendations based on STOPP/START v2	Report per patient on paper and per email
DiPoto et al. (2015) 43 USA	Three hospitals, ICU and general ward	n = 623 patients Mean age = 60 non‐ICU, 58 ICU Female = 49% non‐ICU, 39% ICU	Automated surveillance trigger alerts for DRHCs	n = 93 logic‐based trigger rules	Alerts in centralized database
Dormann et al. (2000) 48 Germany	Hospital, medical ward	n = 379 patients Age = 51 Female = 35%	Computer‐based ADR monitoring system	n = 20 automated laboratory signals	Daily list of alerts with patient name, date of event
Eppenga et al. (2012) 44 Netherlands	Hospital	n = 619 patients Mean age = 53 Female = 54%	Two medication surveillance CDSSs: (1) Centrasys (iSOFT) (2) Pharmaps advanced CDSS	n = NR CDSS 1 = G‐standard (Dutch drug database), CDSS 2 = G‐standard + additional expert‐based rules	CDSS 1: Alerts at one point in time CDSS 2: Generation of alerts once a day
Ferrández et al. (2017) 45 Spain	Hospital	n = 17 878 patients Mean age = 55 Female = 49%	DRP alert in pharmacy warning system, used by pharmacists	n = 414 medications (7879 alerts), based on literature, selected by two pharmacists	Extra line in pharmacy module. Alerts include patient, dose and recommendations
Fritz et al. (2012) 34 Switzerland	Hospital, two general internal wards	n = 100 patients Median age = 59 Female = 42%	Three medication surveillance CDSSs: (1) Pharmavista (2) DrugReax (3) TheraOpt	NR	Alerts
Garcia‐Caballero et al. (2018) 28 Spain	Nursing home	n = 115 patients Mean age = 79 Female = 62%	PIP screening (polimedication)	n = NR, STOPP criteria	Automated alerts
Hammar et al. (2015) 35 Sweden	Two geriatric clinics, three primary care units	n = 254 patients Mean age = 84 Female = 64%	Electronic expert support system (EES) for DRPs, used by physicians	n = NR, DDIs based on Swedish databank	Paper‐based reports with potential DRPs
Hedna et al. (2019) 24 Sweden	Data from 110 outpatient clinics, 51 primary care units, 29 departments in three hospitals	n = 745 Mean age = 75 Female = 57%	PHARAO, risk scores for ADE	n = 9 (p)ADE, algorithm, database of potential interactions	Low, intermediate and high risk with advice
Hwang et al. (2008) 49 Korea	Hospital, two ICUs, five general wards	n = 598 patients Age = NR Female = NR	ADE monitor, integrated in HIS	n = 46, based on previously studied CDSS in literature, modified by panel	List of alerts with report including medication, ADE, bed location
Ibáñez‐Garcia et al. (2019) 36 Spain	Hospital	n = 25 449 admissions Age = NR Female = NR	ADE CDSS (HIGEA)	n = 211 clinical rules based on bibliographic search, clinical practice, team	Real‐time list of alerts
Jha et al. (1998) 50 USA	Hospital, 9 units	n = 21 964 patient‐days Age = NR Female = NR	ADE monitor	n = 52 ADE detection rules based on literature, study team	List of alerts with report including name, bed, event, condition
Jha et al. (2008) 37 USA	Hospital, 5 units	n = 2407 patients Av. age = 74 Female = 53%	Dynamic pharmacovigilance	n = NR, rule base, using combination of laboratory, medication, patient demographic data	List of alerts
Levy et al. (1999) 51 Israel	Hospital, medical ward	n = 192 patients Age = ~75% > 60 Female = 47%	ADR detection tool	n = NR, ADR signals from laboratory data (same tool as Azaz‐Livshits et al.)	List of alerts
Miguel et al. (2013) 52 Portugal	Hospital	n = 118 patients Mean age = 60 Female = 41%	ADR detection tool, stand‐alone (patient data needs to be filled in by hand)	n = 10 ADRs, selection drugs and ADRs based on hospital formulary, INFARMED, book	Alerts with suggested ADRs and frequent ADR for prescribed drugs
Peterson et al. (2014) 25 USA	Hospital, general medicine, orthopaedics, urology	n = 179 patients Mean age = 72 Female = NR	PIM review dashboard	n = NR, triggers based on Beers and STOPP criteria, anticholinergic risk scale, formulary	List of patients with PIM(s) sorted by highest risk
Quintens et al. (2019) 30 Belgium	Hospital	n = NR Age = 47–74 Female = NR	Check of medication appropriateness (CMA)	n = 78 clinical rules for PIMs, DRPs and ADEs based on literature and guidelines, selected by team	Once a day generation of list of alerts on a worklist
Raschke et al. (1998) 38 USA	Hospital, non‐obstetrical patients	n = 9306 admissions Age = NR Female = NR	ADE system	n = 37 ADE rules defined by authors	Alerts printed in pharmacy
Rommers et al. (2011) 31 Netherlands	Hospital, general internal ward	n = NR Age = NR Female = NR	ADE alerting system (ADEAS), pharmacists	n = 121 clinical expert‐based rules	Every morning, list of alerts
Rommers et al. (2013) 39 Netherlands	Hospital, six wards	n = 931 patients Age = NR Female = NR	ADE alerting system (ADEAS), pharmacists	n = 121 clinical expert‐based rules	List of alerts for patients with possible ADE
Roten et al. (2010) 33 Switzerland	Hospital, internal medicine and geriatric wards	n = 501 patients Age = NR Female = NR	DRP screening tool	n = 6 DRP queries based on literature, experience pharmacists, another hospital	List of patients with possible DRPs
Schiff et al. (2017) 23 USA	Outpatients	n = NR Age = NR Female = NR	Medication errors, outlier detection screening (MedAware)	n = 1706 alerts, machine learning algorithms identifying outliers	Alerts with short explanation
Silverman et al. (2004) 32 USA	Hospital	n = NR Age = NR Female = NR	ADE detection system	n = NR, ADE detection rules (modified version of Jha et al.)	List of alerts
Segal et al. (2019) 22 Israel	Hospital, internal medicine department	n = 3160 patients Age = NR Female = NR	Medication errors/ADE CDSS (MedAware)	n = NR, machine learning algorithms identifying outliers	Alerts after change in clinical state patient
de Wit et al. (2015) 40 Netherlands	Hospital, nursing home	n = 900 patients Age = NR Female = NR	Medication surveillance CDSS, stand‐alone, pharmacists	n = 39 clinical rules based on product info, known ADEs, prescribing mistakes	Alerts
de Wit et al. (2016) 41 Netherlands	Hospital, geriatric ward	n = 33 Mean age = 83 Female = 45%	Medication review CDSS, stand‐alone, used by pharmacists	n = 469 clinical rules based on literature, guidelines, protocols, multidisciplinary team	DRP alerts with advice to prevent ADE

ADE, adverse drug event; ADR, adverse drug reaction; CDSS, clinical decision support system; DRHC, drug‐related hazardous conditions; DRP, Drug‐related problems; ICU, intensive care unit; NR, not reported; PIM, potentially inappropriate medication; PIP, potentially inappropriate prescribing.