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Review ArticleClinical Review

Ambulatory Medication Safety in Primary Care: A Systematic Review

Richard A. Young, Kimberly G. Fulda, Anna Espinoza, Ayse P. Gurses, Zachary N. Hendrix, Timothy Kenny and Yan Xiao
The Journal of the American Board of Family Medicine May 2022, 35 (3) 610-628; DOI: https://doi.org/10.3122/jabfm.2022.03.210334
Richard A. Young
JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Family Medicine and Osteopathic Manipulative Medicine, North Texas Primary Care Practice-Based Research Network, University of North Texas Health Science Center, Fort Worth, TX (KGF, AE); Armstrong Institute Center for Health Care Human Factors, School of Medicine, Bloomberg School of Public Health, Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University (APG); University of Texas at Arlington, Arlington, TX (ZNH); Maine Medical Center, Portland, ME (TK); College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, TX (YX).
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Kimberly G. Fulda
JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Family Medicine and Osteopathic Manipulative Medicine, North Texas Primary Care Practice-Based Research Network, University of North Texas Health Science Center, Fort Worth, TX (KGF, AE); Armstrong Institute Center for Health Care Human Factors, School of Medicine, Bloomberg School of Public Health, Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University (APG); University of Texas at Arlington, Arlington, TX (ZNH); Maine Medical Center, Portland, ME (TK); College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, TX (YX).
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Anna Espinoza
JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Family Medicine and Osteopathic Manipulative Medicine, North Texas Primary Care Practice-Based Research Network, University of North Texas Health Science Center, Fort Worth, TX (KGF, AE); Armstrong Institute Center for Health Care Human Factors, School of Medicine, Bloomberg School of Public Health, Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University (APG); University of Texas at Arlington, Arlington, TX (ZNH); Maine Medical Center, Portland, ME (TK); College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, TX (YX).
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Ayse P. Gurses
JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Family Medicine and Osteopathic Manipulative Medicine, North Texas Primary Care Practice-Based Research Network, University of North Texas Health Science Center, Fort Worth, TX (KGF, AE); Armstrong Institute Center for Health Care Human Factors, School of Medicine, Bloomberg School of Public Health, Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University (APG); University of Texas at Arlington, Arlington, TX (ZNH); Maine Medical Center, Portland, ME (TK); College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, TX (YX).
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Zachary N. Hendrix
JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Family Medicine and Osteopathic Manipulative Medicine, North Texas Primary Care Practice-Based Research Network, University of North Texas Health Science Center, Fort Worth, TX (KGF, AE); Armstrong Institute Center for Health Care Human Factors, School of Medicine, Bloomberg School of Public Health, Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University (APG); University of Texas at Arlington, Arlington, TX (ZNH); Maine Medical Center, Portland, ME (TK); College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, TX (YX).
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Timothy Kenny
JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Family Medicine and Osteopathic Manipulative Medicine, North Texas Primary Care Practice-Based Research Network, University of North Texas Health Science Center, Fort Worth, TX (KGF, AE); Armstrong Institute Center for Health Care Human Factors, School of Medicine, Bloomberg School of Public Health, Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University (APG); University of Texas at Arlington, Arlington, TX (ZNH); Maine Medical Center, Portland, ME (TK); College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, TX (YX).
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Yan Xiao
JPS Hospital Family Medicine Residency Program, Fort Worth, TX (RAY); Department of Family Medicine and Osteopathic Manipulative Medicine, North Texas Primary Care Practice-Based Research Network, University of North Texas Health Science Center, Fort Worth, TX (KGF, AE); Armstrong Institute Center for Health Care Human Factors, School of Medicine, Bloomberg School of Public Health, Malone Center for Engineering in Healthcare, Whiting School of Engineering, Johns Hopkins University (APG); University of Texas at Arlington, Arlington, TX (ZNH); Maine Medical Center, Portland, ME (TK); College of Nursing and Health Innovation, University of Texas at Arlington, Arlington, TX (YX).
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    Table 1.

    Noninterventional Studies

    Lead Author (Year)SettingNumber of Patients or PrescriptionsHigh-Risk Subpopulation?Definition of Medical ErrorError RateOther Outcomes
    Abramson15 (2011)PC in NY2432 paper prescriptions at baseline and 2079 electronic at 1 yearNoPIP—IOM definition of prescribing errors16.0%
    Abramson16 (2012)PC in NY1629 prescriptions at 3 months postimplementation, 1738 at 1 yearNoPIP—IOM definition of prescribing errors4.5%
    Al-Busadi17(2020)Oman PC377 patientsAges 65+PIP—Beers, STOPP12.7%-17.2%
    Almeida18 (2019)Brazilian PC227 patients≥ 60 years of agePIP—Beers53.7%-63.4%
    Amos19 (2015)Italy PC865,354 patientsAges 65+PIP—own definition (Maio)28% had at least one PIP8%, 10%, and 14% of individuals were prescribed at least one medication that “should always be avoided,” is “rarely appropriate,” and has “some indications but [is] often misused,” respectively.
    Aspinall20 (2002)Pennsylvania Veterans Affairs PC198 patient/provider pairsNo, but limited to a VA outpatient populationADE—provider or patient report26%83 ADEs reported in active surveillance versus 1 in passive reporting
    Aubert21 (2016)Swiss university PC1002 patientsAges 50-80PIP—STOPP
    PPO—START
    PIP 6.7%, PPO 27.5%> 65 years, 5.6% PIP, 32.2% PPO
    Avery22 (2013)England PC6048 prescriptions for 1777 patientsNoPIP—own definition4.9%
    Awad23 (2019)Kuwait PC478 patients, 2645 prescriptionsAges 65+PIP—Beers, STOPP, FORTA, MAI44.3%-53.1%
    Barry24 (2016)Northern Ireland PC6826 patientsMedicine for dementia dispensedPIP—STOPP64.4%
    Ble25 (2015)UK PC13,900 patientsAges 65+PIP—Beers38.4% any, 17.4% long-term
    Bregnhoj26 (2007)Danish GP patients212 patients, 1621 prescriptionsAge of 65+, taking 5 or medicationsPIP—MAI94.3%
    Brekke27 (2008)Norwegian GP patients85,836 patientsAges 70+PIP—own definition18.4%
    Bruin-Huisman28 (2017)Dutch GP patients4537 patients per yearAges 65+PIP—STOPP
    PPO—START
    34.7% PIP, 84.8% PPO
    Cahir29 (2014)Irish PC931 patientsAges 70+PIP—STOPP42% PIPPatients with ≥ 2 PIP indicators were twice as likely to have an ADE (adjusted OR 2.21), have a significantly lower mean HRQoL utility (adjusted coefficient −0.09), and nearly a 2‐fold increased risk in the expected rate of A&E visits (adjusted IRR 1.85).
    Castillo-Paramo30 (2014)Spanish PC272 patientsAges 65+PIP—STOPP
    PPO—START
    37.5%-50.7%
    Chen31 (2005)England PC37,940 patientsNoPIP—own definition0.19% drug-drug, 0.49% drug-diseaseTwo thirds of PIP medications on PC medication list were started by hospital doctors
    Clark32 (2007)Scotland PC2513 ADR reports in year 2000 and 1455 ADR reports in 2001NoADE—own definitionThe “top 10” medications accounted for 1715 of 2817 (60.9%, 95% CI 59.1, 62.7) ADE reports but only 2.2 million out of a total of 128 million primary care prescriptions (1.7%).
    Corona-Rojo33 (2009)Mexico public health centers1400 patientsAges 70+PIP—own definition53%
    Dhabali34 (2011)Malaysia University PC17,288 patientsNoPIP—own definition5.3%
    Dhabali35 (2012)Malaysia University PC23,733 patientsNoPIP—own definition0.87%
    Diaz Hernandez36 (2018)US federally funded PC2218 patientsAges 65 + with at least one chronic condition who received pharmacy services with 2 or more medications and experienced a medication error or an ADEPotential ADE and ADE—own definition, several sourcesMedication errors 12.5/100, potential ADE 9.4/100, ADE 5.0/100
    Doubova Dubova37 (2007)Mexico PC624 patientsAges 50+ with nonmalignant pain syndrome who received prescriptions of nonopioid analgesicsPIP—own definition80%
    Fiss38 (2011)German PC744 patientsAges 50+ who regularly took one or more drugs, rural areas of Germany, GP home visitsPIP—Beers18%
    Gnadinger39 (2017)Switzerland PC197 cases of medication incidents 180 physicians (GP and pediatricians) at 144 practicesNo“Medication incidents” self-described2.07 per GP per year = 46.5 per 100,000 contacts.
    Goren40 (2017)Turkish PC1206 patientsAges 65+PIP—own definition33%They detected 29 (0.9%) A, 380 (11.8%) B, 2494 (77.7%) C, 289 (9%) D, and 18 (0.6%) X risk rating category PIPs
    Guthrie41 (2011)UK PC139,404 patients“Particularly vulnerable” defined by age, pre-existing disease, or pre-existing coprescription.PIP—STOPPPPO—START13.9%
    Jayaweera42 (2020)US PC111,461 PCPs who specialized in family medicine, internal medicine, general practice, and geriatric medicineMedicare Part D patientsPIP—Beers4.9%PIP varied widely across PCPs with the bottom quartile at 1.2% and the top quartile at 10.1%
    Kheir43 (2014)Qatar PC52 patients, 175 DRPs were identified with an average of 3.4 DRPs per patientNoDRP—own definition3.4 DRPs per patientThe most commonly reported DRPs were nonadherence to drug therapy (31%), need for education and counseling (23%),and ADRs (21%)
    Khoja44 (2011)Saudi Arabia PC463 prescriptions from public clinics and 2836 from private clinicsNo“Prescription errors”—own definition18.7%Type B errors were detected in 8.0% versus 6.0% of drugs prescribed by public and private clinics, respectively, and type C errors were found in 2.2% versus 1.1% drugs prescribed by public and private clinics, respectively
    Komagamine45 (2018)Japan hospital PC671 patients65+PIP—Beers54.8% in patients exempt from payment, 36.0% for others
    Kovacevic46 (2017)Serbian PC388 prescriptions“Elderly” with polypharmacyDRP—own definition98.2% with at least one DRP
    Kunac47 (2014)New Zealand PC376 voluntary reportsNoMedication errors—own definition14.7% of reports listed a patient harm
    Miller49 (2006)Australian PC8215 patients Each GP was asked to record whether or not each of 30 patients had experienced an ADE in the preceding 6 monthsNoADE—own definition; frequency of hospitalization852 patients (10.4%) had experienced ADEA GP severity rating for the most recent ADE was provided for 551 patients. Over half (53.9%) were rated as having a “mild” event(s), with a third rated as “moderate.” A “severe” rating was given for 55 patients (10.0% of those with an ADE or 6.7 per 1000 patients sampled). Responses to the question on hospitalization were received for 223 patients in survey 2. Of these, 7.6% (95% CI, 3.6 to 11.6) had been hospitalized as a result of the most recent ADE (9.7 per 1000 patients in the total sample). Preventability was judged for 327 patients in survey 3. GPs classified the ADE as preventable for 23.2% (95% CI, 17.4 to 29.1), made up of 19.9% of “mild” events, 25% of “moderate” and 32% of “severe” events
    Oliveira50 (2015)Brazilian family health units142 patientsAges 60+PIP—Beers, STOPP33.8%-51.8%
    Perez51 (2018)Ireland PC38,229 patientsAges 65+PIP—STOPP45.3%-51.0%
    Ryan52 (2009)Ireland PC500 patientsAges 65+ and at least 1 medicationPIP—Beers and IPET13%
    Ryan53 (2009)Ireland PC1329 patientsAges 65+ and at least 1 medicationPIP—Beers, STOPP
    PPO—START
    18.3%-21.4%
    22.7%
    177 (61.8%) of the potential PIPs identified were of “high severity”
    Stocks54 (2015)UK PC949,552 patientsNoPIP—own definition5.26%
    Trinkley55 (2017)Ohio University PC1160 patients A pharmacist performed a comprehensive EHR review and conducted a telephone interview with each of the respective participants at 7-21 days (first screen) and 30- 60 days (second screen) following a medication changeNoADE—own definitionOf the 701 participants and 1368 unique medication changes, 226 (32%) suspected ADEs were identified; 30% of the suspected ADEs were deemed to be “definite” or “probable” following causality assessment, 21% of the 68 ADEs were preventable, and 40% were ameliorableAll ADEs were considered significant; however, only 2 were serious or life-threatening
    Wallace56 (2017)Ireland PC605 patients for ADE interview; 662 patients for EQ-5 Days-3L questionnaire; 806 patients for chart reviewAges 70+PIP—Beers, STOPP
    ADE—own definition
    HRQoL—Euro Quol-5 Dimensions (EQ-5 Days)-3L
    40% STOPP
    26% Beers
    74% ≥ 1 ADE
    In multivariable analysis ≥2 Beers 2012 PIP was not associated with ADEs (adjusted incidence rate ratio 1.00 [95% CI 0.78, 1.29]), poorer HRQoL (adjusted coefficient −0.05 [95% CI −0.11, 0.003]), A&E visits (adjusted OR 1.54 [95% CI 0.88, 2.71]), or emergency admission (adjusted OR 0.72 [95% CI 0.41, 1.28]). At baseline, the prevalence of ≥ 1 PIP was 40% (n = 243), with 362 (60%) participants prescribed no PIP, 142 (24%) 1 PIP, and 101 (16%) ≥ 2 PIPs
    Wauters57 (2016)Belgium PC503 patients in the Belfrail-Med cohortAges 80+PIP—STOPP
    PPO—START
    PIP 56%
    PPO 67%
    Increase risk of hospitalization (HR 1.26) and mortality (HR 1.39) for underuse but not overuse
    Wucherer58 (2017)Germany PC446 patientsAges 70+ with positive screening for dementiaDRP—PIE-Doc®-System92.8%Problems related to administration and compliance were the most common group of DRPs (59.9% of registered DRPs; n = 645), followed by problems with drug interactions (16.7%; n = 180), problems with inappropriate drug choice (14.7%; n = 158), problems with the dosage (6.2%; n = 67), and problems with ADEs (2.5%; n = 27)
    • Abbreviations: A&E, accident & emergency; ADE, adverse drug event; ADR, adverse drug reaction; Beers, Beer's criteria; DRP, drug-related problem; EHR, electronic health record; FORTA, fit for the aged; GP, general practitioner; HRQoL, health-related quality of life; IOM, Institute of Medicine; MAI, medication appropriateness index; PC, primary care; PCP, primary care physician; PIP, potentially inappropriate prescribing; PPO, potential prescribing omission; START, screening tool to alert to right treatment; STOPP, screening tool of old people's prescriptions.

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    Table 2.

    Interventional Studies

    Lead Author (Year)SettingNumber of Patients or PrescriptionsHigh-Risk SubpopulationDefinition of Medical ErrorInterventionError RateOther Outcomes
    Benson59 (2018)Australian GP patients493 patientsPolypharmacy (5+ medications), diabetes, adherence concerns, asthma/chronic obstructive pulmonary disease, inadequate response to therapy, suspected adverse reaction, patient request, pain management, recent hospital discharge, and medication with a narrow therapeutic indexDRP—own definitionFeedback by pharmacist to GP1124 DRPs in 493 consultations, 685/984 (70%) recs accepted. 94% of patients had at least 1 DRPPharmacists made a total of 984 recommendations in relation to the 1140 DRPs identified, of which 685 (70%) were recorded as actioned by the GP
    Harms not measured
    Clyne60 (2015)Ireland PC196 patientsAges 70+PIP—own definitionIntervention GP participants received a complex, multifaceted intervention
    Control practices received simple, patient-level PIP feedback
    Baseline PIP: 1.31 drugs/patient intervention group, 1.39 in control group
    Completion PIP: 100% to 52% in the intervention group,100% to 77% in the control group (P = .02)
    0.7 PIP per patient intervention, 1.18 control (P = .02)
    Harms not measured
    Clyne61 (2016)Ireland PC196 patients—follow-up of primary studyAges 70+PIP—own definitionPharmacist feedback as above.51% patients with PIP in the intervention group, 76% in the control group (P = .01). The mean number of PIP drugs in the intervention group was 0.61, 1.03 in the control group (P = .01)Harms not measured
    Gibert62 (2018)France PC172 patientsAges 75+ who were taking at least 5 drugsPIP—STOPPGPs taught to use STOPP criteria on their own patientsGP's intervention decreased the number of PIMs according to STOPP criteria to 106 and was beneficial for 44.9% of the patients (n = 44). The mean MAI score of all medications and PIMs decreased by 14.3% (P < .001) and 39.1% (P < .001) respectivelyHarms not measured
    Howard63 (2014)UK PC72 general practices 2038 patient records reviewedTaking one of 8 classes of potentially hazardous medicationsPotentially hazardous prescribing—own definitionIntervention practices received simple feedback plus a pharmacist-led information technology complex intervention (PINCER) lasting 12 weeksPharmacists recommended 2105 interventions in 74% (95% CI 73, 76; 1516/2038) of cases and 1685 actions were taken in 61% (95% CI 59, 63; 1246/2038) of cases;control group not reportedHarms not measured
    Leendertse64 (2013)Netherlands PC364 intervention and 310 control patientsPatients with a high risk on medication-related hospitalizations based on old age, use of 5 or more medicines, nonadherence and type of medication usedMedication-related hospital admissions, ADE, survival, quality of life (EQ5D/Visual Analog scale).The intervention consisted of a patient interview and evaluation of a pharmaceutical care plan. The patient's own pharmacist and GP carried out the intervention.
    The control group received usual care and was cared for by a GP other than the intervention GP
    6 (1.6%) admissions in intervention group, 10 in control group (3.2%), p = NSThe secondary outcomes were not statistically significantly different either
    Lenander65 (2014)Sweden PC209 patientsAges 65+ and 5+ medicationsDRP—own definitionThe pharmacist reviewed all medications (prescription, nonprescription, and herbal) regarding recommendations and renal impairment, giving advice to patients and GPs. Each patient met the pharmacist before seeing their GP.Control patients received their usual careNo significant difference was seen when comparing change in DRPs between the groupsGroups not balanced at beginning of trial.
    Harms not measured
    Lopez-Picazo66 (2011)Spain PC81,805 patients of 265 family physiciansNoPotentially serious drug interactions—own definitionSpecially designed software analyzed EHR data and generated reports. Physicians and their patients randomized into 4 groups: control, report, sessions, and face-to-face personal interviewsOverall, a baseline mean of 6.7 interactions per 100 patients, which was reduced to 5.3 interactions after follow-up
    No difference between the control and report groups
    Harms not measured
    Peek67 (2020)UK PC47,413 patients in 43 general practicesHave 1 or more risk factors for any of the 12 medication safety indicators at the start of the intervention12 medication safety indicators (10 relating to potentially hazardous prescribing and 2 to inadequate blood-test monitoring) developed for PINCERSMASH comprised (1) training of clinical pharmacists to deliver the intervention; (2) a web-based dashboard providing actionable, patient-level feedb ack; and (3) pharmacists reviewing individual at-risk patients and initiating remedial actions or advising general practitionerson doing soAt baseline, 95% of practices had rates of potentially hazardousprescribing (composite of 10 indicators) between 0.88% and 6.19%. The prevalence of potentially hazardous prescribing reduced by 27.9% (95% CI 20.3% to 36.8%, P < .001) at 24 weeks and by 40.7% (95% CI 29.1% to 54.2%, P < .001) at 12 monthsHarms not measured
    Singh68 (2012)New York PC1125 patients preintervention; 1050 patients postinterventionAges 65+ADE—own definitionThis was a cluster randomized trial in which 12 practices were each randomized to one of 3 states (4 practices each): (1) team resource management intervention;
    (2) team resource management intervention with PEA; (3) no intervention (comparison group).
    In the “Intervention with PEA” group there was a statistically significant decrease in the overall rate of preventable ADEs after the intervention compared to before (7.4 per 100 patient-years vs 12.6, P = .018) and in the rate of moderate or severe (combined) preventable ADEs (1.6 vs 6.4, P = .035).Examples of preventable errors include missed allergy, wrong dosage, errors of dispensing, administration errors, and failure to order or complete laboratory monitoring.Harms not measured.Groups were not balanced at baseline
    Vanderman69 (2017)Veterans Affairs PC in North Carolina1539 patients preinterv ention; 1490 patients postinterventionAges 65+PIP—BeersComputerized physician order entry in Epic EHRPIP rate 12.6% preintervention, 12.0% post (p = NS)Top 10 PIPs 9.0% to 8.3%, (P = .016)
    Harms not measured
    Wessell70 (2008)South Carolina PC124,802 patientsAges 65+PIP—BeersQuarterly performance reports, on-site visits, and annual meetings for 4 yearsAlways inappropriate 0.41% to 0.33%, rarely appropriate medication decreased from 1.48% to 1.30%Harms not measured
    Wessell71 (2013)20 PC sites in 14 US states49,047 patientsHigh-risk medication use based on 44 indicatorsPIP—own definitionLocal performance review, quarterly reports, and academic detailingImproved 3/5 measures by 2.9% to 4.0%; 2/5 measures unchanged over 2 yearsHarms not measured
    • Abbreviations: ADE, adverse drug event; Beers, Beer's criteria; DRP, drug-related problem; EHR, electronic health record; GP, general practitioner; MAI, medication appropriateness index; PC, primary care; PEA, practice enhancement associate; PIM, potentially inappropriate medication; PIP, potentially inappropriate prescribing; STOPP, screening tool of old people's prescriptions.

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The Journal of the American Board of Family Medicine: 35 (3)
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Ambulatory Medication Safety in Primary Care: A Systematic Review
Richard A. Young, Kimberly G. Fulda, Anna Espinoza, Ayse P. Gurses, Zachary N. Hendrix, Timothy Kenny, Yan Xiao
The Journal of the American Board of Family Medicine May 2022, 35 (3) 610-628; DOI: 10.3122/jabfm.2022.03.210334

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Ambulatory Medication Safety in Primary Care: A Systematic Review
Richard A. Young, Kimberly G. Fulda, Anna Espinoza, Ayse P. Gurses, Zachary N. Hendrix, Timothy Kenny, Yan Xiao
The Journal of the American Board of Family Medicine May 2022, 35 (3) 610-628; DOI: 10.3122/jabfm.2022.03.210334
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