Table 1.
Summary of individual studies.
| Reference | Study date and design | Study setting and participants | Intervention | Comparison | Main outcomes |
|---|---|---|---|---|---|
| Hospital care | |||||
| Terrell et al.32 | 2009 RCT | Emergency department of US hospital 63 emergency physicians; 5162 ED visits |
CDSS provided through CPOE; age-specific alerts and recommendations for alternative agents whenever one of 9 PIMs were ordered | Usual care | PIMs prescribed in 2.6% of ED visits in intervention group
versus 3.9% control group (OR = 0.55; 95% CI = 0.34–0.89; p = 0.02) |
| Awdishu et al.33 | 2016 RCT | Clinics and inpatient settings of US university
hospital; 1278 patients with chronic kidney disease |
CDSS tool targeting 20 medications that utilized dynamic assessment of kidney function of individual patients to generate recommendations for drug discontinuation or dose reduction to intervention physicians, either as prospective alerts when drug first prescribed or look-back alerts following initial prescription | Usual care whereby alerts generated but not revealed to control physicians | Prescribing orders appropriately adjusted in 17% of instances
among intervention group versus 5.7% of
instances in control group (OR = 1.89; 95% CI 1.45–2.47); results adjusted for GFR, sex, age, hospitalization status, length of stay, alert type, time from study start, clustering within prescribing physician |
| Peterson et al.34 | 2005 Interrupted time series | Inpatients of US hospital; 3718 older patients |
CPOE tool which generated age-specific dosing suggestions and recommendations for alternative drugs whenever one of 12 PIMs (psychotropic medications) were ordered for the first time; sequential off–on–off–on activation of CPOE over 24 weeks | Usual care | Rate of PIM orders decreased from 10.8% (on-periods) to 7.6%
(off-periods; p < 0.001); associated with lower in-hospital fall rate: 0.28 versus 0.64 falls per 100 patient-days (p = 0.001) |
| Galanter et al.35 | 2005 Prospective before–after study | Inpatients of US hospitals; 331 patients |
CPOE system generated alerts recommending cancellation of prescribing orders for patients whose estimated creatinine clearance was less than minimum threshold for that medication | Usual care | Ordering of PIMs decreased from 89% of 98 patients to 47% of 233
patients (p < 0.001); alert compliance higher in males (57% versus 38%, p = 0.02) and those with more severe renal insufficiency (p = 0.007) |
| Agostini et al.36 | 2007 Prospective before–after study | Inpatients of US hospital; 24,509 older patients |
CPOE targeting four sedative–hypnotic medications which generated reminders to prescribers to check indication, and provided information on potential adverse effects and recommendations for nonpharmacological sleep protocols or alternative sedative–hypnotic combinations | Usual care | Inappropriate prescribing decreased from 18 to 14% (OR 0.82; 95% CI 0.76–0.87) |
| Mattison et al.37 | 2010 Prospective before–after study | Inpatients of US hospital | Medication-specific warning system within CPOE alerted providers of alternative medications or dose reductions when ordering one of 16 PIMs to older patients | Usual care | Reduction in PIM orders from mean (SE) of 11.6 (0.4) per day to 9.9 (0.1) per day [difference 1.6 (0.3), p < 0.001] |
| Ghibelli et al.38 | 2013 Prospective before–after study | Inpatients of Italian hospital; 134 older patients |
Proprietary CDSS applied to EMR-listed records at hospital admission which offered recommendations for improving quality of prescribing | Usual care with no recommendations | Among intervention group (n = 74) rate of PIMs decreased from 41.7% on admission to 11.6% at discharge (p < 0.001) but remained unchanged in control group (39.1% versus 37.8%) |
| Peterson et al.39 | 2014 Prospective cohort study | Inpatients of US hospital; 797 older patients |
Computerized PIM dashboard linked to CPOE system flagging individuals with one or more PIMs from a list of 240 PIMs or medications with high calculated anticholinergic score, with clinical pharmacists then estimating ADE risk and delivering point-of-care recommendations to prescribing physicians | No comparison group | Dashboard flagged 179 (22%) individuals and 485 patient–medication pairs for pharmacist review; recommendation provided for 22 patients receiving 40 PIMs, which were enacted in 31 instances (78%) by prescribers |
| Ambulatory care | |||||
| Tamblyn et al.40 | 2003 Cluster RCT | Primary care practices in Canada; 107 physicians; 12,560 older patients |
CDSS with access to all current or past prescriptions on individual patients generated specific alerts and provided information on alternative options | Usual care | Number of newly prescribed PIMs per 1000 visits decreased from
52.2 in control group to 43.8 in intervention group (18%
relative reduction; (RR = 0.82; 95% CI 0.69–0.98) No difference overall between groups in rates of PIM discontinuation |
| Simon et al.41 | 2006 Cluster RCT | Primary care practices in US HMO; 15 practices; 239 clinicians; 50,294 patients |
CDSS generated age-specific prescribing alerts whenever one of
five PIM classes were ordered and academic detailing regarding PIM use |
CDSS generated age-specific prescribing alerts whenever one of five PIM classes were ordered | Both groups saw between 8.5% and 13.5% reduction in quarterly usage rates of PIMs per 10,000 members between start and end of study period; academic detailing exerted no additional effect |
| Weber et al.42 | 2006 RCT | Primary care practices in US; Geisenger Health Plan; 620 older patients |
Standardized medication review by clinical pharmacist of patients at risk for falls based on age and medication use, with targeting of psychoactive medications; alerts and recommendations sent to prescribers via EMR | Usual care | No differences between groups in total number of
medications; significant inverse relationship between intervention and number of medications started during study period (p < 0.001) and number of psychoactive medications prescribed (p < 0.05) |
| Tamblyn et al.43 | 2012 Cluster RCT | Primary care practices in Canada; 81 family physicians; 5628 patients |
CDSS with patient-specific risk estimates of drug-related injury generated alerts whenever psychotropic medications were ordered through a CPOE | CDSS generating generic commercial drug alerts | Intervention physicians reviewed therapy in 83.3% of visits and
reduced dose or discontinued psychotropic medications in 24.6%
of visits; intervention patients suffered 1.7 fewer injuries/1000 patients (95% CI 0.2 to 3.2/1000 patients; p = 0.02) versus control patients |
| Price et al.44 | 2017 Cluster RCT | Primary care practices in Canada 8 primary care practices; 28 physicians |
40 fully automated STOPP rules embedded within EMR which generated alerts according to clinical circumstances | EMR provided non-STOPP alerts | No significant differences between groups in PIM rates |
| Fried et al.46 | 2016 RCT | Primary care clinic in US VA medical centre; 128 older patients receiving 7 or more medications |
Web-based tool [Tool to Reduce Inappropriate Medications (TRIM)] which inputted data from EMR and used automated algorithm to identify PIMs and generate recommendations to prescribers | Usual care | No difference between groups in rates of PIM or number of medications |
| Smith et al.47 | 2006 Interrupted time series | Primary care clinics in US HMO; all older patients |
EMR-linked automated alerts targeting older patients ordered one of several nonpreferred medications (long-acting benzodiazepines and tricyclic antidepressants) | Usual care | 22% relative decrease (reduction of 5.1 prescriptions per 10, 000 patients per month; p = 0.004) in use of nonpreferred medications over 2 years postalert period |
| Vanderman et al.48 | 2017 Retrospective before–after study | Primary care clinics in US VA medical centre; 3029 older patients |
Age-specific alerts relating to 15 PIMs provided at point of care within CPOE system | Usual care | No differences between groups in overall rate of new PIM orders |
| Meulendijk et al.49 | 2015 Case-control study | Primary clinic in Netherlands; 42 physicians |
Web-based tool [Systematic Tool to Reduce Inappropriate Prescribing (STRIP)] linked with EMR generated patient-specific advice based on clinical guidelines in assisting medication reviews of patients with polypharmacy | Usual care involving matched patients | Rate of appropriate prescribing orders increased from 58% among controls to 76% among cases; rates of inappropriate orders decreased from 42% to 24% (p < 0.001 for both comparisons) |
| Residential care | |||||
| Donovan et al.50 | 2010 Cluster RCT | US long-term care facility; 813 residents |
CDSS linked to CPOE for 22 psychotropic medications which generated alerts to either avoid or decrease dose | Usual care | No differences between groups in rates of PIM |
| Field et al.51 | 2009 RCT | Canadian long-term care facility; 833 residents |
CDSS alerts for prescribing in patients with renal insufficiency displayed to prescribers with recommendations directly related to level of renal insufficiency for each of 62 drugs | Usual care | Fewer orders for PIMs among intervention group (3.5 versus 5.2 per 1000 resident days; RR = 0.68; 95% CI 0.45–0.0) |
| Colón-Emeric et al.52 | 2009 Before–after quasi-experimental study | Two US VA nursing homes; 42 clinicians; 265 residents |
CPOE algorithms for detecting geriatric problems based on clinical guidelines presented to clinicians with various diagnostic and treatment options and means for communicating these among interdisciplinary teams | Usual care | No differences between groups in rates of PIM orders |
ADE, adverse drug event; CDSS, computerized decision support systems; CI, confidence interval; CPOE, computerized physician order entry; ED, Emergency Department; EMR, electronic medical record; GFR, glomerular filtration rate; HMO, health maintenance organization; OR, odds ratio; PIM, potentially inappropriate medication; RACF, residential aged care facility; RCT, randomized controlled trial; RR, relative risk; SE, standard error of the mean; STOPP, screening tool of older people’s prescriptions; US, United States; VA, Veterans Affairs.