The impact of work-related stress on medication errors in Eastern Region Saudi Arabia

The impact of work-related stress on medication errors in Eastern Region Saudi Arabia Abstract Objective To examine the relationship between overall level and source-specific work-related stressors on medication errors rate. Design A cross-sectional study examined the relationship between overall levels of stress, 25 source-specific work-related stressors and medication error rate based on documented incident reports in Saudi Arabia (SA) hospital, using secondary databases. Setting King Abdulaziz Hospital in Al-Ahsa, Eastern Region, SA. Participants Two hundred and sixty-nine healthcare professionals (HCPs). Main Outcome Measures The odds ratio (OR) and corresponding 95% confidence interval (CI) for HCPs documented incident report medication errors and self-reported sources of Job Stress Survey. Results Multiple logistic regression analysis identified source-specific work-related stress as significantly associated with HCPs who made at least one medication error per month (P < 0.05), including disruption to home life, pressure to meet deadlines, difficulties with colleagues, excessive workload, income over 10 000 riyals and compulsory night/weekend call duties either some or all of the time. Although not statistically significant, HCPs who reported overall stress were two times more likely to make at least one medication error per month than non-stressed HCPs (OR: 1.95, P = 0.081). Conclusion This is the first study to use documented incident reports for medication errors rather than self-report to evaluate the level of stress-related medication errors in SA HCPs. Job demands, such as social stressors (home life disruption, difficulties with colleagues), time pressures, structural determinants (compulsory night/weekend call duties) and higher income, were significantly associated with medication errors whereas overall stress revealed a 2-fold higher trend. stress, work, medication errors, healthcare professionals, injury Introduction Medication errors are a global concern that create serious medical consequences for patients [1]. Despite increased awareness about patient safety and quality of care, medication errors and adverse patient outcomes occur frequently in clinical practice [2, 3]. In 2010, an estimate of 3.5 million US hospital patients suffered adverse outcomes due to medical errors [2, 4]. Medical errors caused 200 000 American deaths and cost $17 billion in additional inpatient and outpatient services [2]. As of 2010, medication errors accounted for more than 50% of all medical errors [5]. The median medication error rate (interquartile range) per hospital dose was 19.6% (8.6–28.3%), depending on the healthcare setting [6, 7]. Medication errors include administration of wrong drug, unauthorized or unordered administration of a drug, missed or wrong dose administered and wrong route of administration [8]. Researchers report an association between healthcare professionals’ (HCPs’) wellness and medication error rates [6, 9]. Stress rooted in an office environment and organizational climate increases the probability of medication errors among HCPs in the USA [10]. Organizational climate can promote adverse working conditions through job demands and structural determinants (staffing, work overload). Interpersonal psychosocial conditions such as social stressors (disruption of home life, conflicts with hospital staff) and time pressures are associated with work-related stress [11]. High levels of emotional exhaustion and depersonalization along with low levels of personal accomplishment are components of the Burnout Syndrome [12]. Several studies report a linear association between burnout and frequency of self-reported medical errors among interns and residents [13], and physicians at all levels of experience [12]. Declining mental well-being is associated with burnout and decreased adherence to safety practices, such as due to substance abuse [14], and depression [15]. Despite evidence on the association between medication errors and stress in Western countries, little research regarding the impact, source and level of work-related stress was conducted in a Saudi Arabian (SA) healthcare setting [16, 17]. There is evidence that HCPs in SA work under high stress conditions [16, 17]. The difference in culture, pharmacy infrastructure, training and levels of professional experience may create different experiences between Saudi and the Western healthcare systems. In SA, 40 000 medical error complaints are filed annually with 80% never resolved [18]; results from previous studies suggest that SA nurses underreport medication errors because of fear of reprisal from the administration [19, 20]. There are no published studies on the relationship between medication errors and work-related stressors in the Middle East or SA. Thus, the purpose of the study was to explore the relationship between overall level of work-related stress and 25 sources of work-related stressors on medication errors among HCPs working in SA. In addition, this study is unique in that the relationship between overall stress, source-specific work-related stress and documented incident reports-based medication errors for SA HCPs rather than self-reported errors as used in prior published studies. Methods This was a cross-sectional retrospective study that assessed the relationship between documented safety incident report-based medication errors and sources of work-related stress experienced by HCPs at King Abdulaziz Hospital (KAH). The 269 participants were selected from a cross-sectional survey of 626 HCPs (physicians/residents, nurses and pharmacists) working at KAH and Imam Abdulrahman bin Faisal Hospital (IAFH) in Dammam as part of the National Guard Health Affairs in Eastern SA between November 2012 and April 2013 [17]. We excluded 211 from IAFH due to unavailable mediation error data and 146 from KAH due to missing badge identification numbers of HCPs. The sample size was large enough to detect an effect size of 0.3 with 90% power and 5% level of significance [21]. The quality management department of KAH provided safety incident reports filed during November 2012–April 2013 which included details on medication errors, type of error, employee badge number and the reported date, time and location of the error. A research questionnaire used in the study consisted of five demographic questions, 14 job characteristic questions and the validated 25-item Job Stress Survey (JSS) [17, 22]. The JSS is a Likert scale, including 0 (not at all), 1 (a little), 2 (quite a bit) and 3 (a lot). The response categories 0 (not at all) and1 (a little) were coded as not stressful whereas categories of 2 (quite a bit) and 3 (a lot) represent stressful situations [22–24]. The overall stress score was derived by combining the 25 specific stress scores into a single compiled score for each participant. HCPs with total stress scores ≤25 were considered ‘not stressed’ while those with total scores >25 were considered ‘stressed’ [24]. Using a written information sheet, participants were informed about the aim of the study, and that their participation was voluntary. A cover letter was attached to the questionnaire, with details on informed consent as well as instructions for completing and returning the survey. The HCPs badge number was linked to their respective survey data (demographic, job characteristics and work-related stress) and the number of medication errors. The linked safety incident reports and survey data were de-identified by the quality management department prior to forwarding to researchers. Statistical analysis All statistical analyses were performed using Statistical Package for Social Sciences (SPSS) Version 22. Descriptive results are presented as mean ± standard deviation for continuous variables (e.g. Age) and number (percentage) for all categorical variables (e.g. gender). The dependent variable (medication errors) was not normally distributed and did not have a linear relationship with the independent variable (overall level of work-related stress). Consequently, two binary variables were computed from the total stress score and number of medication errors. Total stress scores ≤25 were recoded as 0 (not stressed) while those with total scores >25 were recoded as 1 (stressed) indicating that HCP reported at least one stressor contributed a little or a lot of stress [24]. The safety incident reports where medication errors are reported, covered a 5-month period but individual number of medication errors were calculated through the date that the HCPs completed the survey. Medication error was recoded = 1 if the safety incident report indicated that HCP made at least one medication error per month and if HCP did not commit any medication errors per month, then medication error was recoded as 0. A binary logistic regression statistic was calculated to determine the impact of overall level and sources of work-related stress on medication error following adjustment for the demographic and job characteristics of HCPs. The Wald test was computed on each predictor to determine which were significant. An odds ratio (OR) and corresponding 95% confidence interval (CI) was reported. A two-sided P-value <0.05 was considered statistically significant. Results Most of the HCPs were female (58%), with an average age of 38.7 years, and the average degree holder had a bachelor’s degree. The majority of the study sample consisted of foreign nationals, which is consistent with the demographic proportions at KAH in particular and the Saudi healthcare industry in general [25]. The distribution of health professionals was composed of nurses (54.6%), physicians/residents (41.3%) and pharmacists (4.1%). A majority of HCPs reported sometimes or all the time working at night/weekend on-call duties (76.7%) and more than half (57.3%) reported exposure to any stressful events outside of work within the year prior to the study (Table 1). Table 1 Socio-demographic, job characteristics and medication errors for HCPs sample Socio-demographic and job characteristics  Overall  Medication error  Odds ratio and 95% confidence interval  P-value  n = 269  At least one (n = 48)  None (n = 221)  Gender   Male  113 (42.0%)  42 (87.5%)  71 (32.1%)  14.79 (6.01–36.40)  <0.001   Female  156 (58.0%)  6 (12.5%)  150 (67.9%)  1    Age in years  38.7 ± 9.8  34.2 ± 8.9  39.8 ± 9.8  0.94 (0.89–0.97)  0.001  Educational level   Diploma or Bachelor degree  199 (74.5%)  26 (55.3%)  173 (78.6%)  1  0.001   Master or PhD or Board qualified  68 (25.5%)  21 (44.7%)  47 (21.4%)  2.97 (1.54–5.75)  Nationality   Saudi  59 (22.2%)  23 (50.0%)  36 (16.4%)  5.11 (2.59–10.08)  <0.001   Non-Saudi  207 (77.8%)  23 (50.0%)  184 (83.6%)  1    Income level   >10 000 SR  155 (59.4%)  42 (91.3%)  113 (52.6%)  9.48 (3.28–27.36)  <0.001   ≤10 000 SR  106 (40.6%)  4 (8.7%)  102 (47.4%)  1    Professional group   Nurses  147 (54.6%)  2 (4.2%)  145 (65.6%)  1     Physicians/residents  111 (41.3%)  41 (85.4%)  70 (31.7%)  42.5 (9.9–180.6)  <0.001   Pharmacists  11 (4.1%)  5 (10.4%)  6 (2.7%)  60.4 (9.7–377.3)  <0.001  Years of work experience  11.9 ± 9.3  7.8 ± 7.6  12.7 ± 9.4  0.93 (0.89– 0.97)  0.002  Workload   >50 h/week  83 (31.9%)  34 (72.3%)  49 (23.0%)  8.75 (4.28–17.88)  <0.001   ≤50 h/week  177 (68.1%)  13 (27.7%)  164 (77.0%)  1    Are you working night shifts?   All the time  34 (12.8%)  11 (23.4%)  23 (10.6%)  5.02 (1.67–15.14)  0.004   Sometimes  162 (61.1%)  30 (63.8%)  132 (60.6%)  2.38 (0.95–6.03)  0.07   Not at all  69 (26.0%)  6 (12.8%)  63 (28.9%)  1    Are you working on weekends?   All the time  36 (13.4%)  10 (20.8%)  26 (11.8%)  13.1 (1.57–108.74)  0.02   Sometimes  197 (73.5%)  37 (77.1%)  160 (72.7%)  7.86 (1.04–59.29)  0.05   Not at all  35 (13.1%)  1 (2.1%)  34 (15.5%)  1    Are you working at night/weekend call duties in addition to your daily work?   All the time  38 (14.2%)  14 (29.2%)  24 (11.0%)  8.46 (2.53–28.33)  0.001   Sometimes  167 (62.5%)  30 (62.5%)  137 (62.6%)  3.175 (1.07–9.42)  0.04   Not at all  62 (23.2%)  4 (8.3%)  58 (26.5%)  1    Were you exposed to any stressful event within a year outside of your work?   No  114 (42.7%)  18 (38.3%)  96 (43.6%)  1     Yes  153 (57.3%)  29 (61.7%)  124 (56.4%)  1.25 (0.65–2.38)  0.50  Socio-demographic and job characteristics  Overall  Medication error  Odds ratio and 95% confidence interval  P-value  n = 269  At least one (n = 48)  None (n = 221)  Gender   Male  113 (42.0%)  42 (87.5%)  71 (32.1%)  14.79 (6.01–36.40)  <0.001   Female  156 (58.0%)  6 (12.5%)  150 (67.9%)  1    Age in years  38.7 ± 9.8  34.2 ± 8.9  39.8 ± 9.8  0.94 (0.89–0.97)  0.001  Educational level   Diploma or Bachelor degree  199 (74.5%)  26 (55.3%)  173 (78.6%)  1  0.001   Master or PhD or Board qualified  68 (25.5%)  21 (44.7%)  47 (21.4%)  2.97 (1.54–5.75)  Nationality   Saudi  59 (22.2%)  23 (50.0%)  36 (16.4%)  5.11 (2.59–10.08)  <0.001   Non-Saudi  207 (77.8%)  23 (50.0%)  184 (83.6%)  1    Income level   >10 000 SR  155 (59.4%)  42 (91.3%)  113 (52.6%)  9.48 (3.28–27.36)  <0.001   ≤10 000 SR  106 (40.6%)  4 (8.7%)  102 (47.4%)  1    Professional group   Nurses  147 (54.6%)  2 (4.2%)  145 (65.6%)  1     Physicians/residents  111 (41.3%)  41 (85.4%)  70 (31.7%)  42.5 (9.9–180.6)  <0.001   Pharmacists  11 (4.1%)  5 (10.4%)  6 (2.7%)  60.4 (9.7–377.3)  <0.001  Years of work experience  11.9 ± 9.3  7.8 ± 7.6  12.7 ± 9.4  0.93 (0.89– 0.97)  0.002  Workload   >50 h/week  83 (31.9%)  34 (72.3%)  49 (23.0%)  8.75 (4.28–17.88)  <0.001   ≤50 h/week  177 (68.1%)  13 (27.7%)  164 (77.0%)  1    Are you working night shifts?   All the time  34 (12.8%)  11 (23.4%)  23 (10.6%)  5.02 (1.67–15.14)  0.004   Sometimes  162 (61.1%)  30 (63.8%)  132 (60.6%)  2.38 (0.95–6.03)  0.07   Not at all  69 (26.0%)  6 (12.8%)  63 (28.9%)  1    Are you working on weekends?   All the time  36 (13.4%)  10 (20.8%)  26 (11.8%)  13.1 (1.57–108.74)  0.02   Sometimes  197 (73.5%)  37 (77.1%)  160 (72.7%)  7.86 (1.04–59.29)  0.05   Not at all  35 (13.1%)  1 (2.1%)  34 (15.5%)  1    Are you working at night/weekend call duties in addition to your daily work?   All the time  38 (14.2%)  14 (29.2%)  24 (11.0%)  8.46 (2.53–28.33)  0.001   Sometimes  167 (62.5%)  30 (62.5%)  137 (62.6%)  3.175 (1.07–9.42)  0.04   Not at all  62 (23.2%)  4 (8.3%)  58 (26.5%)  1    Were you exposed to any stressful event within a year outside of your work?   No  114 (42.7%)  18 (38.3%)  96 (43.6%)  1     Yes  153 (57.3%)  29 (61.7%)  124 (56.4%)  1.25 (0.65–2.38)  0.50  Results are expressed as mean ± standard deviation, number (%), Odds ratio and 95% confidence interval for odds ratio. Reference category is indicated by OR = 1. P-value has been calculated using Wald test statistics. Table 1 Socio-demographic, job characteristics and medication errors for HCPs sample Socio-demographic and job characteristics  Overall  Medication error  Odds ratio and 95% confidence interval  P-value  n = 269  At least one (n = 48)  None (n = 221)  Gender   Male  113 (42.0%)  42 (87.5%)  71 (32.1%)  14.79 (6.01–36.40)  <0.001   Female  156 (58.0%)  6 (12.5%)  150 (67.9%)  1    Age in years  38.7 ± 9.8  34.2 ± 8.9  39.8 ± 9.8  0.94 (0.89–0.97)  0.001  Educational level   Diploma or Bachelor degree  199 (74.5%)  26 (55.3%)  173 (78.6%)  1  0.001   Master or PhD or Board qualified  68 (25.5%)  21 (44.7%)  47 (21.4%)  2.97 (1.54–5.75)  Nationality   Saudi  59 (22.2%)  23 (50.0%)  36 (16.4%)  5.11 (2.59–10.08)  <0.001   Non-Saudi  207 (77.8%)  23 (50.0%)  184 (83.6%)  1    Income level   >10 000 SR  155 (59.4%)  42 (91.3%)  113 (52.6%)  9.48 (3.28–27.36)  <0.001   ≤10 000 SR  106 (40.6%)  4 (8.7%)  102 (47.4%)  1    Professional group   Nurses  147 (54.6%)  2 (4.2%)  145 (65.6%)  1     Physicians/residents  111 (41.3%)  41 (85.4%)  70 (31.7%)  42.5 (9.9–180.6)  <0.001   Pharmacists  11 (4.1%)  5 (10.4%)  6 (2.7%)  60.4 (9.7–377.3)  <0.001  Years of work experience  11.9 ± 9.3  7.8 ± 7.6  12.7 ± 9.4  0.93 (0.89– 0.97)  0.002  Workload   >50 h/week  83 (31.9%)  34 (72.3%)  49 (23.0%)  8.75 (4.28–17.88)  <0.001   ≤50 h/week  177 (68.1%)  13 (27.7%)  164 (77.0%)  1    Are you working night shifts?   All the time  34 (12.8%)  11 (23.4%)  23 (10.6%)  5.02 (1.67–15.14)  0.004   Sometimes  162 (61.1%)  30 (63.8%)  132 (60.6%)  2.38 (0.95–6.03)  0.07   Not at all  69 (26.0%)  6 (12.8%)  63 (28.9%)  1    Are you working on weekends?   All the time  36 (13.4%)  10 (20.8%)  26 (11.8%)  13.1 (1.57–108.74)  0.02   Sometimes  197 (73.5%)  37 (77.1%)  160 (72.7%)  7.86 (1.04–59.29)  0.05   Not at all  35 (13.1%)  1 (2.1%)  34 (15.5%)  1    Are you working at night/weekend call duties in addition to your daily work?   All the time  38 (14.2%)  14 (29.2%)  24 (11.0%)  8.46 (2.53–28.33)  0.001   Sometimes  167 (62.5%)  30 (62.5%)  137 (62.6%)  3.175 (1.07–9.42)  0.04   Not at all  62 (23.2%)  4 (8.3%)  58 (26.5%)  1    Were you exposed to any stressful event within a year outside of your work?   No  114 (42.7%)  18 (38.3%)  96 (43.6%)  1     Yes  153 (57.3%)  29 (61.7%)  124 (56.4%)  1.25 (0.65–2.38)  0.50  Socio-demographic and job characteristics  Overall  Medication error  Odds ratio and 95% confidence interval  P-value  n = 269  At least one (n = 48)  None (n = 221)  Gender   Male  113 (42.0%)  42 (87.5%)  71 (32.1%)  14.79 (6.01–36.40)  <0.001   Female  156 (58.0%)  6 (12.5%)  150 (67.9%)  1    Age in years  38.7 ± 9.8  34.2 ± 8.9  39.8 ± 9.8  0.94 (0.89–0.97)  0.001  Educational level   Diploma or Bachelor degree  199 (74.5%)  26 (55.3%)  173 (78.6%)  1  0.001   Master or PhD or Board qualified  68 (25.5%)  21 (44.7%)  47 (21.4%)  2.97 (1.54–5.75)  Nationality   Saudi  59 (22.2%)  23 (50.0%)  36 (16.4%)  5.11 (2.59–10.08)  <0.001   Non-Saudi  207 (77.8%)  23 (50.0%)  184 (83.6%)  1    Income level   >10 000 SR  155 (59.4%)  42 (91.3%)  113 (52.6%)  9.48 (3.28–27.36)  <0.001   ≤10 000 SR  106 (40.6%)  4 (8.7%)  102 (47.4%)  1    Professional group   Nurses  147 (54.6%)  2 (4.2%)  145 (65.6%)  1     Physicians/residents  111 (41.3%)  41 (85.4%)  70 (31.7%)  42.5 (9.9–180.6)  <0.001   Pharmacists  11 (4.1%)  5 (10.4%)  6 (2.7%)  60.4 (9.7–377.3)  <0.001  Years of work experience  11.9 ± 9.3  7.8 ± 7.6  12.7 ± 9.4  0.93 (0.89– 0.97)  0.002  Workload   >50 h/week  83 (31.9%)  34 (72.3%)  49 (23.0%)  8.75 (4.28–17.88)  <0.001   ≤50 h/week  177 (68.1%)  13 (27.7%)  164 (77.0%)  1    Are you working night shifts?   All the time  34 (12.8%)  11 (23.4%)  23 (10.6%)  5.02 (1.67–15.14)  0.004   Sometimes  162 (61.1%)  30 (63.8%)  132 (60.6%)  2.38 (0.95–6.03)  0.07   Not at all  69 (26.0%)  6 (12.8%)  63 (28.9%)  1    Are you working on weekends?   All the time  36 (13.4%)  10 (20.8%)  26 (11.8%)  13.1 (1.57–108.74)  0.02   Sometimes  197 (73.5%)  37 (77.1%)  160 (72.7%)  7.86 (1.04–59.29)  0.05   Not at all  35 (13.1%)  1 (2.1%)  34 (15.5%)  1    Are you working at night/weekend call duties in addition to your daily work?   All the time  38 (14.2%)  14 (29.2%)  24 (11.0%)  8.46 (2.53–28.33)  0.001   Sometimes  167 (62.5%)  30 (62.5%)  137 (62.6%)  3.175 (1.07–9.42)  0.04   Not at all  62 (23.2%)  4 (8.3%)  58 (26.5%)  1    Were you exposed to any stressful event within a year outside of your work?   No  114 (42.7%)  18 (38.3%)  96 (43.6%)  1     Yes  153 (57.3%)  29 (61.7%)  124 (56.4%)  1.25 (0.65–2.38)  0.50  Results are expressed as mean ± standard deviation, number (%), Odds ratio and 95% confidence interval for odds ratio. Reference category is indicated by OR = 1. P-value has been calculated using Wald test statistics. Table 2 describes the number of medication errors made per month within a 5-month period. Based on the safety incident reports, more than four out of five of the HCPs (82.2%) did not make any medication errors per month whereas almost one out of five respondents (17.8%) made at least one medication error per month. Table 2 Incidence of medication errors Factor  Frequency (%)  Medication error     No medication error made at all per month  221 (82.2)   Made at least one medication error per month  48 (17.8)  Total number of medication errors   0  221 (82.2)   1  25 (9.3)   2  8 (2.9)   3  4 (1.5)   4  4 (1.5)   5  3 (1.0)   6  1 (0.4)   7  1 (0.4)   10  1 (0.4)   21  1 (0.4)  Factor  Frequency (%)  Medication error     No medication error made at all per month  221 (82.2)   Made at least one medication error per month  48 (17.8)  Total number of medication errors   0  221 (82.2)   1  25 (9.3)   2  8 (2.9)   3  4 (1.5)   4  4 (1.5)   5  3 (1.0)   6  1 (0.4)   7  1 (0.4)   10  1 (0.4)   21  1 (0.4)  Table 2 Incidence of medication errors Factor  Frequency (%)  Medication error     No medication error made at all per month  221 (82.2)   Made at least one medication error per month  48 (17.8)  Total number of medication errors   0  221 (82.2)   1  25 (9.3)   2  8 (2.9)   3  4 (1.5)   4  4 (1.5)   5  3 (1.0)   6  1 (0.4)   7  1 (0.4)   10  1 (0.4)   21  1 (0.4)  Factor  Frequency (%)  Medication error     No medication error made at all per month  221 (82.2)   Made at least one medication error per month  48 (17.8)  Total number of medication errors   0  221 (82.2)   1  25 (9.3)   2  8 (2.9)   3  4 (1.5)   4  4 (1.5)   5  3 (1.0)   6  1 (0.4)   7  1 (0.4)   10  1 (0.4)   21  1 (0.4)  Demographics and job characteristics were found to have a statistically significant association with the number of medication errors (Table 1). The male gender (P < 0.001), higher education level (P < 0.001), Saudi nationality (P < 0.001), and higher income level (P < 0.001) were positively associated with medication errors whereas age had a negative effect. Physicians/residents (85.4%) and pharmacists (10.4%) were highly associated with making at least one medication error compared with nurses (4.2%, P < 0.001). Workloads above 50 h were significantly associated with at least one medication error versus workloads under 50 h (72.3% vs. 27.7%, P < 0.001). HCPs who were working on the weekend ‘all the time’ and ‘sometimes’ were significantly associated with medication errors (20.8%, P = 0.02, and 77.1%, P = 0.05, respectively) compared with those who were not working at all on weekend (2.1%). The likelihood of making at least one medication error is significantly higher amongst HCPs who work the night shift ‘all the time’ (OR: 5.02, P = 0.004) and ‘sometimes’ (OR: 2.38, P = 0.07) compared with those who do not work the night shift. Weekend and night shift work in combination with regular daily work increased the odds of making at least one medication error per month. The odds of making at least one medication error per month among those who reported working ‘all the time’ (OR: 8.46, P = 0.001), and ‘sometimes’ (OR: 3.18, P = 0.04) compared with those who were not working at all (Table 1). The overall stress score was derived by combining the 25 specific stress scores into a single compiled score for each participant. Overall stress ranged from 1 to 75 with mean total stress score of 30.8 (SD = 11.7). Overall stressed HCPs revealed a trend that was two times (OR: 1.95, 95% CI: 0.9–4.1) more likely to make at least one medication error per month than those not stressed but it was not statistically significant (P = 0.081; Fig. 1). Figure 1 View largeDownload slide Relationship between overall level of work-related stress and medication error. Figure 1 View largeDownload slide Relationship between overall level of work-related stress and medication error. Table 3 details a multiple binary logistic regression model used to identify significant independent factors associated with the number of medication errors made, following adjustment for covariates. Significant predictors were: disrupted home life due to long work hours, income over 10 000 riyals, workload above 50 h per week, employees were asked to work nights and weekends in addition to their daily duties ‘all the time’ and ‘sometimes’, those reporting pressure to meet deadlines, and those encountering difficulties in relationship with their colleagues (Table 3). Table 3 Multiple binary logistic regression model to identify independent predictors of number of medication errors after adjusting for covariates Factor  Adjusted odds ratio  95% CI for AOR  P-value  Disruption of your home life through spending long hours at work?   No  1       Yes  2.66  1.04–6.79  0.041  Feeling under pressure to meet deadlines?   No  1       Yes  0.39  0.16–0.93  0.034  Encountering difficulties in relationship with colleagues?   No  1       Yes  0.06  0.01–0.57  0.014  Income level   ≤10 000 SR  1       >10 000 SR  4.01  1.25–12.82  0.019  Workload   ≤50 h/week  1       >50 h/week  4.27  1.72–10.60  0.002  Are you working at night/weekend call duties in addition to your daily work?   All the time  6.99  1.5–32.53  0.013   Sometimes  2.25  0.6–9.25  0.260   Not at all  1      Factor  Adjusted odds ratio  95% CI for AOR  P-value  Disruption of your home life through spending long hours at work?   No  1       Yes  2.66  1.04–6.79  0.041  Feeling under pressure to meet deadlines?   No  1       Yes  0.39  0.16–0.93  0.034  Encountering difficulties in relationship with colleagues?   No  1       Yes  0.06  0.01–0.57  0.014  Income level   ≤10 000 SR  1       >10 000 SR  4.01  1.25–12.82  0.019  Workload   ≤50 h/week  1       >50 h/week  4.27  1.72–10.60  0.002  Are you working at night/weekend call duties in addition to your daily work?   All the time  6.99  1.5–32.53  0.013   Sometimes  2.25  0.6–9.25  0.260   Not at all  1      Note. AOR, adjusted odds ratio; CI, confidence Interval. Results are expressed as AOR and 95% confidence interval for AOR. Reference category is indicated by OR = 1. P-value has been calculated using Wald test statistics. Table 3 Multiple binary logistic regression model to identify independent predictors of number of medication errors after adjusting for covariates Factor  Adjusted odds ratio  95% CI for AOR  P-value  Disruption of your home life through spending long hours at work?   No  1       Yes  2.66  1.04–6.79  0.041  Feeling under pressure to meet deadlines?   No  1       Yes  0.39  0.16–0.93  0.034  Encountering difficulties in relationship with colleagues?   No  1       Yes  0.06  0.01–0.57  0.014  Income level   ≤10 000 SR  1       >10 000 SR  4.01  1.25–12.82  0.019  Workload   ≤50 h/week  1       >50 h/week  4.27  1.72–10.60  0.002  Are you working at night/weekend call duties in addition to your daily work?   All the time  6.99  1.5–32.53  0.013   Sometimes  2.25  0.6–9.25  0.260   Not at all  1      Factor  Adjusted odds ratio  95% CI for AOR  P-value  Disruption of your home life through spending long hours at work?   No  1       Yes  2.66  1.04–6.79  0.041  Feeling under pressure to meet deadlines?   No  1       Yes  0.39  0.16–0.93  0.034  Encountering difficulties in relationship with colleagues?   No  1       Yes  0.06  0.01–0.57  0.014  Income level   ≤10 000 SR  1       >10 000 SR  4.01  1.25–12.82  0.019  Workload   ≤50 h/week  1       >50 h/week  4.27  1.72–10.60  0.002  Are you working at night/weekend call duties in addition to your daily work?   All the time  6.99  1.5–32.53  0.013   Sometimes  2.25  0.6–9.25  0.260   Not at all  1      Note. AOR, adjusted odds ratio; CI, confidence Interval. Results are expressed as AOR and 95% confidence interval for AOR. Reference category is indicated by OR = 1. P-value has been calculated using Wald test statistics. Discussion This cross-sectional study examined the relationship between work-related stress and medication errors in a Saudi Arabian healthcare setting. HCPs were asked to complete a survey on socio-demographic factors, job characteristics, and a scale on work-related stress. The department of quality management provided de-identified data of documented medication errors linked to the self-administered survey; the data were linked using employee identification badge number and reflected medication errors made by the employee. The history of medication errors was linked as to coincide with the date of the survey to avoid possible bias from the attention of the survey topic on increased awareness of medication errors and behavior change. The incident report prevalence of medication error was 17.8% and similar to numerous global studies 19.6% (8.6–28.3%), depending on the healthcare setting [6, 7]. Although the majority of HCPs reported feeling stressed (68.4%), our study revealed that source-specific work-related stress, rather than overall stress is strongly associated with medication errors. Multiple studies indicate a significant relationship between stress and medication errors among HCPs although most of these studies were based on self-reported medication errors [9, 10]. Although sources of work-related stress were significantly correlated with medication errors, others were associated with a reduction in medication errors. A disruption to home life predisposed an HCP to make medication errors at a rate 2.66 times higher than those whose home life was not disrupted due to work, while HCPs experiencing pressure to meet deadlines were 61% less likely to make an error. These findings may indicate that HCPs were more careful when under pressure. Findings from a prospective study corroborate that high social stressors and time pressure contribute to perceived poor quality of patient care [11]. Difficulties with colleagues led to a 94% lower chance of error over those who did not have difficulties, which may indicate HCPs worked more carefully to avoid being embarrassed in front of their colleagues or because existing conflict led to finger-pointing. The relationship between specific sources of work-related stress and medication errors remained significant after adjusting for demographic and job characteristics. Although multiple sources of work-related stress could contribute to the overall perception of stress, there were specific sources that had the strongest relationships such as disrupted home life due to long work hours and excessive workload (above 50 h per week) and working night/weekend call duty ‘all the time’ ‘some time’ on top of existing daily duties. The relationship between medication errors and disrupted home life due to long work hours, workload and night/weekend call duty confirmed previous findings [4, 7, 26]. Our findings contribute to the literature by providing data on the association between specific sources of work-related stress and medication errors. This occurred despite the assumption that stress could contribute to human error. Two general approaches have been followed for stress relief in the work environment. The first one is the individual focused interventions through employee assistance programs. This has been successful in stress relief at work. A study from the USA showed that staff involved in at least one of seven training programs covering one or more aspects of stress management experienced significant reductions in psychological distress, depression and anxiety immediately after the intervention [27]. Follow-up of these subjects for 9–16 months revealed further reduction in psychological distress and emotional exhaustion [27]. The second approach is organization-based interventions [28]. Introducing better consistent working hours, reducing overlap of extra shifts with daily duties, and maintaining a healthy home life could all reduce medication error rate. These adjustments could create a positive long-term impact on the overall and specific sources of stress levels of individuals working in these medical environments and lower negative outcomes associated with higher overall levels of stress. The former approach has been associated with more positive outcomes at the personal level, while the second intervention has been associated with more positive outcomes at the organizational level [28]. Our study is the first to assess the relationship between overall and specific sources of work-related stress using documented safety incident reports rather than self-reported medication errors among HCPs. The finding on overall stress is important because it reveals that while stress may play a role in making medication errors, generalized stress occurring from work was not significantly associated with these errors. Our findings on the relationship between source-specific work-related stress and medication errors suggest that an analysis of a workplace environment should focus on specific source of work-related stress. Limitations of the study The quantitative retrospective approach carries some inherent limitations. Although the medication error data was documented prospectively and prior to survey, the use of a cross-sectional study design prevents us from proposing a temporal relationship between work-related stress and medication errors. In addition, a quantitative study does not capture the context of process improvement through a workflow process [29, 30]. Direct observation and mapping of clinical workflow provides the sequence of clinical events that lead to the medication errors, such as frequent interruptions and preparation of medications for multiple patients [29, 30]. Cognitive awareness on the implications from reporting medication errors by the administration could bias the validity of self-reported findings. While the reported incidence of medication errors in our nurse HCPs appears low compared with levels reported in developed countries, previous studies suggest that SA nurses underreport medication errors because of fear of reprisal from the administration [19]. Further work is needed to investigate whether there is a potential cognitive bias in self-reported medication errors by SA HCPs or if the current reporting system has limitations for promoting HCPs to report their errors [19, 20]. Another limitation of the study is bias from self-report responses about work-related stress, as subjective data cannot be independently verified. We cannot exclude unmeasured confounders (poor sleep quality, exhaustion) and potential selection bias such that stressed-out HCPs refused to participate [11]. Lastly, the generalizability of the results is impacted by the single center data and the fact that the HCPs work under a different culture (pharmacy infrastructure, training and levels of professional experience) which may create different experiences between Saudi and the Western healthcare systems. The study’s reliance on retrospective data could be supported with further prospective and direct observation studies. Conclusion Although multiple studies have assessed the relationship between stress and medication errors, the relationship between source-specific stress to medication errors had not been addressed. Most studies rely on self-reported medication errors or other subjective measures, such as perceived quality of life and burnout syndrome factors. The evidence from our study that source-specific workplace-based stressors (disruption of home life) are associated with an increase in medication errors may help inform comprehensive policies addressing these issues. Socio-demographic and employment characteristics point to a culture of empowerment and perhaps relaxed behaviors in avoiding medication errors. Those who were more likely to make medication errors, included those with higher income (4.01 times), organizational systemic factors (higher workload per week, and working night and weekend shifts in addition to regular duty), factors that affect HCPs with increased fatigue, burnout and medication errors. Follow-up studies on changes to policy addressing organizational climate such as workplace hours can be carried out to confirm and expand on our findings. Researchers suggest that shift work and disruption of home life can be improved through staffing schedules and staff recruitment to ensure less need for double shifts, and a culture of work-balance. Surprisingly, source-specific work-related stressors (pressure to meet deadlines, difficulties with colleagues) predicted less medication errors. Deadlines imposed by management may overcome distractions that normally would lead to medication errors and add more focused work practices. Further research on how the dynamic between difficulties with colleagues can guided in a healthy manner to reduce medication errors needs to exploration. Future assessment of stress at work should consider these specific stressors rather than a general stress assessment. Finally, the association between stress and incidence of medication errors does not address the sequence of clinical events that led to errors; the importance of medication administration workflow using direct clinical observation cannot be overemphasized. Acknowledgements The team is grateful for the assistance offered by Rommel Acunin, King Abdullah International Medical Research Center in data management. We would also like to extend our gratitude to Analyn Crisostomo of King Abdulaziz Hospital for assisting in data entry, and The Quality Management Department of King Abdulaziz Hospital for providing the team with the documented incident medication error data. Rabia Ali Khan at the Academic Health Services (Hamad Medical Corporation) reviewed the final manuscript. Funding This work was supported by a research grant (number: RA13/01l/A) from King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Saudi Arabia. Guidelines followed The manuscript was prepared as per the STROBE guidelines. References 1 World Health Organization (WHO). Reporting and learning systems for medication errors: the role of pharmacovigilance centres. Geneva, Switzerland; 2014. p. 110. Available from: http://apps.who.int/iris/bitstream/10665/137036/1/9789241507943_eng.pdf 2 Andel C, Davidow SL, Hollander M et al.  . The economics of health care quality and medical errors. J Health Care Finan  2012; 39: 39– 50. 3 Waeschle RM, Bauer M, Schmidt CE. Errors in medicine. Causes, impact and improvement measures to improve patient safety. Anaesthesist  2015; 64: 689– 704. Google Scholar CrossRef Search ADS PubMed  4 Christy G, Linda B, Beverly AL et al.  . Frequency of and risk factors for medication errors by pharmacists during order verification in a tertiary care medical center. Am J Health Syst Pharm  2015; 72: 1471– 4. Google Scholar CrossRef Search ADS PubMed  5 Zineldin M, Zineldin J, Vasicheva V. Approaches for reducing medical errors and increasing patient safety: TRM, quality and 5 Qs method. Tot Qual Manag  2014; 26: 63– 74. 6 Keers RN, Williams SD, Cooke J et al.  . Prevalence and nature of medication administration errors in health care settings: a systematic review of direct observational evidence. Ann Pharmacother  2013; 47: 237– 56. Google Scholar CrossRef Search ADS PubMed  7 Albara A, Val W, Patricia MD. Joanne L. Families, nurses and organisations contributing factors to medication administration error in paediatrics: a literature review. Int Pract Dev J  2015; 5: 1– 14. 8 Bergqvist M, Karlsson EA, Björkstén KS et al.  . Medication errors by nurses in Sweden: classification and contributing factors. Open Access Sci Rep  2012; 1: 1– 4. doi:10.4172/scientificreports.527. 9 Samsuri SE, Pei Lin L, Fahrni ML. Safety culture perceptions of pharmacists in Malaysian hospitals and health clinics: a multicentre assessment using the Safety Attitudes Questionnaire. Br Med J Open Access  2015; 5: e008889. 10 Shanafelt TD, Balch CM, Bechamps G et al.  . Burnout and medical errors among American surgeons. Ann Surg  2010; 251: 995– 1000. Google Scholar CrossRef Search ADS PubMed  11 Krämer T, Schneider A, Spieß E et al.  . Associations between job demands, work-related strain and perceived quality of care: a longitudinal study among hospital physicians. Int J Qual Health Care  2016; 28: 82409. 12 Sulaiman CF, Henn P, Smith S et al.  . Burnout syndrome among non-consultant hospital doctors in Ireland: relationship with self-reported patient care. Int J Qual Health Care  2017; 29: 679– 84. Google Scholar CrossRef Search ADS PubMed  13 Kang EK, Lihm HS, Kong EH. Association of intern and resident burnout with self-reported medical errors. Korean J Fam Med  2013; 34: 36– 42. Google Scholar CrossRef Search ADS PubMed  14 Oreskovich MR, Shanafelt T, Dyrbye LN et al.  . The prevalence of substance use disorders in American physicians. Am J Addict  2015; 24: 30– 8. Google Scholar CrossRef Search ADS PubMed  15 de Oliveira GS, Chang R, Fitzgerald PC et al.  . The prevalence of burnout and depression and their association with adherence to safety and practice standards: a survey of United States anesthesiology trainees. Anesthesia & Analgesia.  2013; 117: 182– 93. Google Scholar CrossRef Search ADS   16 Alosaimi FD, Kazim SN, Almufleh AS et al.  . Prevalence of stress and its determinants among residents in Saudi Arabia. Saudi Med J  2015; 36: 605– 12. Google Scholar CrossRef Search ADS PubMed  17 Salam A, Abu-Helalah M, Jorissen SL et al.  . Job stress and job satisfaction among health care professionals. Eur Scie J  2014; 10: 156– 73. 18 Al-Saleh KS, Ramadan MZ. Studying medical errors among hospital-staff at Saudi Health Providers: teaching hospital in Saudi Arabia. J Mater Sci Eng  2012; 2: 41– 52. 19 Almutary HH, Lewis PA. Nurses’ willingness to report medication administration errors in Saudi Arabia. Qual Manag Health Care.  2012; 21: 119– 26. Google Scholar CrossRef Search ADS PubMed  20 Sadat-Ali M, Al-Shafei BA, Al-Turki RA et al.  . Medication administration errors in Eastern Saudi Arabia. Saudi Med J  2010; 31: 1257– 9. Google Scholar PubMed  21 West CP, Tan AD, Habermann TM et al.  . Association of resident fatigue and distress with perceived medical errors. J Am Med Assoc  2009; 302: 1294– 1300. Google Scholar CrossRef Search ADS   22 Ramirez AJ, Graham J, Richards MA et al.  . Mental health of hospital consultants: the effects of stress and satisfaction at work. Lancet  1996; 347: 724– 8. Google Scholar CrossRef Search ADS PubMed  23 Grunfeld E, Zitzelsberger L, Coristine M et al.  . Job stress and job satisfaction of cancer care workers. Psychooncology  2005; 14: 61– 9. Google Scholar CrossRef Search ADS PubMed  24 Teasdale E, Drew S, Taylor C et al.   Hospital consultants’ job stress and satisfaction questionnaire. Cancer Research UK London, Psychosocial Group, 2008. Available from https://www.scribd.com/document/92245468/Soal-Selidik-Stress-Manual 25 Saudi Arabian Ministry of Health. Statistics book. Retrieved from http://www.moh.gov.sa/en/ministry/statistics/book/pages/default.aspx. 2015. 26 Kim KS, Kwon SH, Kim JA et al.  . Nurses’ perceptions of medication errors and their contributing factors in South Korea. J Nurs Manag  2011; 19: 346– 53. Google Scholar CrossRef Search ADS PubMed  27 Li‐An H. Meditation, learning, organizational innovation and performance. Indus Manag Data Syst  2011; 111: 113– 31. Google Scholar CrossRef Search ADS   28 Lamontagne AD, Keegel T, Louie AM et al.  . A systematic review of the job-stress intervention evaluation literature: 1990–2005. Int J Occup Environ Health  2007; 13: 268– 80. Google Scholar CrossRef Search ADS PubMed  29 Huang YH, Gramopadhye AK. Systematic engineering tools for describing and improving medication administration processes at rural healthcare facilities. Appl Ergon  2014; 45: 1712– 24. Google Scholar CrossRef Search ADS PubMed  30 Huang YH, Gramopadhye AK. Recommendations for health information technology implementation in rural hospitals. Int J Health Care Qual Assur  2016; 29: 454– 74. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2018. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal for Quality in Health Care Oxford University Press

Loading next page...
 
/lp/ou_press/the-impact-of-work-related-stress-on-medication-errors-in-eastern-HJS6HxIU5S
Publisher
Oxford University Press
Copyright
© The Author(s) 2018. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com
ISSN
1353-4505
eISSN
1464-3677
D.O.I.
10.1093/intqhc/mzy097
Publisher site
See Article on Publisher Site

Abstract

Abstract Objective To examine the relationship between overall level and source-specific work-related stressors on medication errors rate. Design A cross-sectional study examined the relationship between overall levels of stress, 25 source-specific work-related stressors and medication error rate based on documented incident reports in Saudi Arabia (SA) hospital, using secondary databases. Setting King Abdulaziz Hospital in Al-Ahsa, Eastern Region, SA. Participants Two hundred and sixty-nine healthcare professionals (HCPs). Main Outcome Measures The odds ratio (OR) and corresponding 95% confidence interval (CI) for HCPs documented incident report medication errors and self-reported sources of Job Stress Survey. Results Multiple logistic regression analysis identified source-specific work-related stress as significantly associated with HCPs who made at least one medication error per month (P < 0.05), including disruption to home life, pressure to meet deadlines, difficulties with colleagues, excessive workload, income over 10 000 riyals and compulsory night/weekend call duties either some or all of the time. Although not statistically significant, HCPs who reported overall stress were two times more likely to make at least one medication error per month than non-stressed HCPs (OR: 1.95, P = 0.081). Conclusion This is the first study to use documented incident reports for medication errors rather than self-report to evaluate the level of stress-related medication errors in SA HCPs. Job demands, such as social stressors (home life disruption, difficulties with colleagues), time pressures, structural determinants (compulsory night/weekend call duties) and higher income, were significantly associated with medication errors whereas overall stress revealed a 2-fold higher trend. stress, work, medication errors, healthcare professionals, injury Introduction Medication errors are a global concern that create serious medical consequences for patients [1]. Despite increased awareness about patient safety and quality of care, medication errors and adverse patient outcomes occur frequently in clinical practice [2, 3]. In 2010, an estimate of 3.5 million US hospital patients suffered adverse outcomes due to medical errors [2, 4]. Medical errors caused 200 000 American deaths and cost $17 billion in additional inpatient and outpatient services [2]. As of 2010, medication errors accounted for more than 50% of all medical errors [5]. The median medication error rate (interquartile range) per hospital dose was 19.6% (8.6–28.3%), depending on the healthcare setting [6, 7]. Medication errors include administration of wrong drug, unauthorized or unordered administration of a drug, missed or wrong dose administered and wrong route of administration [8]. Researchers report an association between healthcare professionals’ (HCPs’) wellness and medication error rates [6, 9]. Stress rooted in an office environment and organizational climate increases the probability of medication errors among HCPs in the USA [10]. Organizational climate can promote adverse working conditions through job demands and structural determinants (staffing, work overload). Interpersonal psychosocial conditions such as social stressors (disruption of home life, conflicts with hospital staff) and time pressures are associated with work-related stress [11]. High levels of emotional exhaustion and depersonalization along with low levels of personal accomplishment are components of the Burnout Syndrome [12]. Several studies report a linear association between burnout and frequency of self-reported medical errors among interns and residents [13], and physicians at all levels of experience [12]. Declining mental well-being is associated with burnout and decreased adherence to safety practices, such as due to substance abuse [14], and depression [15]. Despite evidence on the association between medication errors and stress in Western countries, little research regarding the impact, source and level of work-related stress was conducted in a Saudi Arabian (SA) healthcare setting [16, 17]. There is evidence that HCPs in SA work under high stress conditions [16, 17]. The difference in culture, pharmacy infrastructure, training and levels of professional experience may create different experiences between Saudi and the Western healthcare systems. In SA, 40 000 medical error complaints are filed annually with 80% never resolved [18]; results from previous studies suggest that SA nurses underreport medication errors because of fear of reprisal from the administration [19, 20]. There are no published studies on the relationship between medication errors and work-related stressors in the Middle East or SA. Thus, the purpose of the study was to explore the relationship between overall level of work-related stress and 25 sources of work-related stressors on medication errors among HCPs working in SA. In addition, this study is unique in that the relationship between overall stress, source-specific work-related stress and documented incident reports-based medication errors for SA HCPs rather than self-reported errors as used in prior published studies. Methods This was a cross-sectional retrospective study that assessed the relationship between documented safety incident report-based medication errors and sources of work-related stress experienced by HCPs at King Abdulaziz Hospital (KAH). The 269 participants were selected from a cross-sectional survey of 626 HCPs (physicians/residents, nurses and pharmacists) working at KAH and Imam Abdulrahman bin Faisal Hospital (IAFH) in Dammam as part of the National Guard Health Affairs in Eastern SA between November 2012 and April 2013 [17]. We excluded 211 from IAFH due to unavailable mediation error data and 146 from KAH due to missing badge identification numbers of HCPs. The sample size was large enough to detect an effect size of 0.3 with 90% power and 5% level of significance [21]. The quality management department of KAH provided safety incident reports filed during November 2012–April 2013 which included details on medication errors, type of error, employee badge number and the reported date, time and location of the error. A research questionnaire used in the study consisted of five demographic questions, 14 job characteristic questions and the validated 25-item Job Stress Survey (JSS) [17, 22]. The JSS is a Likert scale, including 0 (not at all), 1 (a little), 2 (quite a bit) and 3 (a lot). The response categories 0 (not at all) and1 (a little) were coded as not stressful whereas categories of 2 (quite a bit) and 3 (a lot) represent stressful situations [22–24]. The overall stress score was derived by combining the 25 specific stress scores into a single compiled score for each participant. HCPs with total stress scores ≤25 were considered ‘not stressed’ while those with total scores >25 were considered ‘stressed’ [24]. Using a written information sheet, participants were informed about the aim of the study, and that their participation was voluntary. A cover letter was attached to the questionnaire, with details on informed consent as well as instructions for completing and returning the survey. The HCPs badge number was linked to their respective survey data (demographic, job characteristics and work-related stress) and the number of medication errors. The linked safety incident reports and survey data were de-identified by the quality management department prior to forwarding to researchers. Statistical analysis All statistical analyses were performed using Statistical Package for Social Sciences (SPSS) Version 22. Descriptive results are presented as mean ± standard deviation for continuous variables (e.g. Age) and number (percentage) for all categorical variables (e.g. gender). The dependent variable (medication errors) was not normally distributed and did not have a linear relationship with the independent variable (overall level of work-related stress). Consequently, two binary variables were computed from the total stress score and number of medication errors. Total stress scores ≤25 were recoded as 0 (not stressed) while those with total scores >25 were recoded as 1 (stressed) indicating that HCP reported at least one stressor contributed a little or a lot of stress [24]. The safety incident reports where medication errors are reported, covered a 5-month period but individual number of medication errors were calculated through the date that the HCPs completed the survey. Medication error was recoded = 1 if the safety incident report indicated that HCP made at least one medication error per month and if HCP did not commit any medication errors per month, then medication error was recoded as 0. A binary logistic regression statistic was calculated to determine the impact of overall level and sources of work-related stress on medication error following adjustment for the demographic and job characteristics of HCPs. The Wald test was computed on each predictor to determine which were significant. An odds ratio (OR) and corresponding 95% confidence interval (CI) was reported. A two-sided P-value <0.05 was considered statistically significant. Results Most of the HCPs were female (58%), with an average age of 38.7 years, and the average degree holder had a bachelor’s degree. The majority of the study sample consisted of foreign nationals, which is consistent with the demographic proportions at KAH in particular and the Saudi healthcare industry in general [25]. The distribution of health professionals was composed of nurses (54.6%), physicians/residents (41.3%) and pharmacists (4.1%). A majority of HCPs reported sometimes or all the time working at night/weekend on-call duties (76.7%) and more than half (57.3%) reported exposure to any stressful events outside of work within the year prior to the study (Table 1). Table 1 Socio-demographic, job characteristics and medication errors for HCPs sample Socio-demographic and job characteristics  Overall  Medication error  Odds ratio and 95% confidence interval  P-value  n = 269  At least one (n = 48)  None (n = 221)  Gender   Male  113 (42.0%)  42 (87.5%)  71 (32.1%)  14.79 (6.01–36.40)  <0.001   Female  156 (58.0%)  6 (12.5%)  150 (67.9%)  1    Age in years  38.7 ± 9.8  34.2 ± 8.9  39.8 ± 9.8  0.94 (0.89–0.97)  0.001  Educational level   Diploma or Bachelor degree  199 (74.5%)  26 (55.3%)  173 (78.6%)  1  0.001   Master or PhD or Board qualified  68 (25.5%)  21 (44.7%)  47 (21.4%)  2.97 (1.54–5.75)  Nationality   Saudi  59 (22.2%)  23 (50.0%)  36 (16.4%)  5.11 (2.59–10.08)  <0.001   Non-Saudi  207 (77.8%)  23 (50.0%)  184 (83.6%)  1    Income level   >10 000 SR  155 (59.4%)  42 (91.3%)  113 (52.6%)  9.48 (3.28–27.36)  <0.001   ≤10 000 SR  106 (40.6%)  4 (8.7%)  102 (47.4%)  1    Professional group   Nurses  147 (54.6%)  2 (4.2%)  145 (65.6%)  1     Physicians/residents  111 (41.3%)  41 (85.4%)  70 (31.7%)  42.5 (9.9–180.6)  <0.001   Pharmacists  11 (4.1%)  5 (10.4%)  6 (2.7%)  60.4 (9.7–377.3)  <0.001  Years of work experience  11.9 ± 9.3  7.8 ± 7.6  12.7 ± 9.4  0.93 (0.89– 0.97)  0.002  Workload   >50 h/week  83 (31.9%)  34 (72.3%)  49 (23.0%)  8.75 (4.28–17.88)  <0.001   ≤50 h/week  177 (68.1%)  13 (27.7%)  164 (77.0%)  1    Are you working night shifts?   All the time  34 (12.8%)  11 (23.4%)  23 (10.6%)  5.02 (1.67–15.14)  0.004   Sometimes  162 (61.1%)  30 (63.8%)  132 (60.6%)  2.38 (0.95–6.03)  0.07   Not at all  69 (26.0%)  6 (12.8%)  63 (28.9%)  1    Are you working on weekends?   All the time  36 (13.4%)  10 (20.8%)  26 (11.8%)  13.1 (1.57–108.74)  0.02   Sometimes  197 (73.5%)  37 (77.1%)  160 (72.7%)  7.86 (1.04–59.29)  0.05   Not at all  35 (13.1%)  1 (2.1%)  34 (15.5%)  1    Are you working at night/weekend call duties in addition to your daily work?   All the time  38 (14.2%)  14 (29.2%)  24 (11.0%)  8.46 (2.53–28.33)  0.001   Sometimes  167 (62.5%)  30 (62.5%)  137 (62.6%)  3.175 (1.07–9.42)  0.04   Not at all  62 (23.2%)  4 (8.3%)  58 (26.5%)  1    Were you exposed to any stressful event within a year outside of your work?   No  114 (42.7%)  18 (38.3%)  96 (43.6%)  1     Yes  153 (57.3%)  29 (61.7%)  124 (56.4%)  1.25 (0.65–2.38)  0.50  Socio-demographic and job characteristics  Overall  Medication error  Odds ratio and 95% confidence interval  P-value  n = 269  At least one (n = 48)  None (n = 221)  Gender   Male  113 (42.0%)  42 (87.5%)  71 (32.1%)  14.79 (6.01–36.40)  <0.001   Female  156 (58.0%)  6 (12.5%)  150 (67.9%)  1    Age in years  38.7 ± 9.8  34.2 ± 8.9  39.8 ± 9.8  0.94 (0.89–0.97)  0.001  Educational level   Diploma or Bachelor degree  199 (74.5%)  26 (55.3%)  173 (78.6%)  1  0.001   Master or PhD or Board qualified  68 (25.5%)  21 (44.7%)  47 (21.4%)  2.97 (1.54–5.75)  Nationality   Saudi  59 (22.2%)  23 (50.0%)  36 (16.4%)  5.11 (2.59–10.08)  <0.001   Non-Saudi  207 (77.8%)  23 (50.0%)  184 (83.6%)  1    Income level   >10 000 SR  155 (59.4%)  42 (91.3%)  113 (52.6%)  9.48 (3.28–27.36)  <0.001   ≤10 000 SR  106 (40.6%)  4 (8.7%)  102 (47.4%)  1    Professional group   Nurses  147 (54.6%)  2 (4.2%)  145 (65.6%)  1     Physicians/residents  111 (41.3%)  41 (85.4%)  70 (31.7%)  42.5 (9.9–180.6)  <0.001   Pharmacists  11 (4.1%)  5 (10.4%)  6 (2.7%)  60.4 (9.7–377.3)  <0.001  Years of work experience  11.9 ± 9.3  7.8 ± 7.6  12.7 ± 9.4  0.93 (0.89– 0.97)  0.002  Workload   >50 h/week  83 (31.9%)  34 (72.3%)  49 (23.0%)  8.75 (4.28–17.88)  <0.001   ≤50 h/week  177 (68.1%)  13 (27.7%)  164 (77.0%)  1    Are you working night shifts?   All the time  34 (12.8%)  11 (23.4%)  23 (10.6%)  5.02 (1.67–15.14)  0.004   Sometimes  162 (61.1%)  30 (63.8%)  132 (60.6%)  2.38 (0.95–6.03)  0.07   Not at all  69 (26.0%)  6 (12.8%)  63 (28.9%)  1    Are you working on weekends?   All the time  36 (13.4%)  10 (20.8%)  26 (11.8%)  13.1 (1.57–108.74)  0.02   Sometimes  197 (73.5%)  37 (77.1%)  160 (72.7%)  7.86 (1.04–59.29)  0.05   Not at all  35 (13.1%)  1 (2.1%)  34 (15.5%)  1    Are you working at night/weekend call duties in addition to your daily work?   All the time  38 (14.2%)  14 (29.2%)  24 (11.0%)  8.46 (2.53–28.33)  0.001   Sometimes  167 (62.5%)  30 (62.5%)  137 (62.6%)  3.175 (1.07–9.42)  0.04   Not at all  62 (23.2%)  4 (8.3%)  58 (26.5%)  1    Were you exposed to any stressful event within a year outside of your work?   No  114 (42.7%)  18 (38.3%)  96 (43.6%)  1     Yes  153 (57.3%)  29 (61.7%)  124 (56.4%)  1.25 (0.65–2.38)  0.50  Results are expressed as mean ± standard deviation, number (%), Odds ratio and 95% confidence interval for odds ratio. Reference category is indicated by OR = 1. P-value has been calculated using Wald test statistics. Table 1 Socio-demographic, job characteristics and medication errors for HCPs sample Socio-demographic and job characteristics  Overall  Medication error  Odds ratio and 95% confidence interval  P-value  n = 269  At least one (n = 48)  None (n = 221)  Gender   Male  113 (42.0%)  42 (87.5%)  71 (32.1%)  14.79 (6.01–36.40)  <0.001   Female  156 (58.0%)  6 (12.5%)  150 (67.9%)  1    Age in years  38.7 ± 9.8  34.2 ± 8.9  39.8 ± 9.8  0.94 (0.89–0.97)  0.001  Educational level   Diploma or Bachelor degree  199 (74.5%)  26 (55.3%)  173 (78.6%)  1  0.001   Master or PhD or Board qualified  68 (25.5%)  21 (44.7%)  47 (21.4%)  2.97 (1.54–5.75)  Nationality   Saudi  59 (22.2%)  23 (50.0%)  36 (16.4%)  5.11 (2.59–10.08)  <0.001   Non-Saudi  207 (77.8%)  23 (50.0%)  184 (83.6%)  1    Income level   >10 000 SR  155 (59.4%)  42 (91.3%)  113 (52.6%)  9.48 (3.28–27.36)  <0.001   ≤10 000 SR  106 (40.6%)  4 (8.7%)  102 (47.4%)  1    Professional group   Nurses  147 (54.6%)  2 (4.2%)  145 (65.6%)  1     Physicians/residents  111 (41.3%)  41 (85.4%)  70 (31.7%)  42.5 (9.9–180.6)  <0.001   Pharmacists  11 (4.1%)  5 (10.4%)  6 (2.7%)  60.4 (9.7–377.3)  <0.001  Years of work experience  11.9 ± 9.3  7.8 ± 7.6  12.7 ± 9.4  0.93 (0.89– 0.97)  0.002  Workload   >50 h/week  83 (31.9%)  34 (72.3%)  49 (23.0%)  8.75 (4.28–17.88)  <0.001   ≤50 h/week  177 (68.1%)  13 (27.7%)  164 (77.0%)  1    Are you working night shifts?   All the time  34 (12.8%)  11 (23.4%)  23 (10.6%)  5.02 (1.67–15.14)  0.004   Sometimes  162 (61.1%)  30 (63.8%)  132 (60.6%)  2.38 (0.95–6.03)  0.07   Not at all  69 (26.0%)  6 (12.8%)  63 (28.9%)  1    Are you working on weekends?   All the time  36 (13.4%)  10 (20.8%)  26 (11.8%)  13.1 (1.57–108.74)  0.02   Sometimes  197 (73.5%)  37 (77.1%)  160 (72.7%)  7.86 (1.04–59.29)  0.05   Not at all  35 (13.1%)  1 (2.1%)  34 (15.5%)  1    Are you working at night/weekend call duties in addition to your daily work?   All the time  38 (14.2%)  14 (29.2%)  24 (11.0%)  8.46 (2.53–28.33)  0.001   Sometimes  167 (62.5%)  30 (62.5%)  137 (62.6%)  3.175 (1.07–9.42)  0.04   Not at all  62 (23.2%)  4 (8.3%)  58 (26.5%)  1    Were you exposed to any stressful event within a year outside of your work?   No  114 (42.7%)  18 (38.3%)  96 (43.6%)  1     Yes  153 (57.3%)  29 (61.7%)  124 (56.4%)  1.25 (0.65–2.38)  0.50  Socio-demographic and job characteristics  Overall  Medication error  Odds ratio and 95% confidence interval  P-value  n = 269  At least one (n = 48)  None (n = 221)  Gender   Male  113 (42.0%)  42 (87.5%)  71 (32.1%)  14.79 (6.01–36.40)  <0.001   Female  156 (58.0%)  6 (12.5%)  150 (67.9%)  1    Age in years  38.7 ± 9.8  34.2 ± 8.9  39.8 ± 9.8  0.94 (0.89–0.97)  0.001  Educational level   Diploma or Bachelor degree  199 (74.5%)  26 (55.3%)  173 (78.6%)  1  0.001   Master or PhD or Board qualified  68 (25.5%)  21 (44.7%)  47 (21.4%)  2.97 (1.54–5.75)  Nationality   Saudi  59 (22.2%)  23 (50.0%)  36 (16.4%)  5.11 (2.59–10.08)  <0.001   Non-Saudi  207 (77.8%)  23 (50.0%)  184 (83.6%)  1    Income level   >10 000 SR  155 (59.4%)  42 (91.3%)  113 (52.6%)  9.48 (3.28–27.36)  <0.001   ≤10 000 SR  106 (40.6%)  4 (8.7%)  102 (47.4%)  1    Professional group   Nurses  147 (54.6%)  2 (4.2%)  145 (65.6%)  1     Physicians/residents  111 (41.3%)  41 (85.4%)  70 (31.7%)  42.5 (9.9–180.6)  <0.001   Pharmacists  11 (4.1%)  5 (10.4%)  6 (2.7%)  60.4 (9.7–377.3)  <0.001  Years of work experience  11.9 ± 9.3  7.8 ± 7.6  12.7 ± 9.4  0.93 (0.89– 0.97)  0.002  Workload   >50 h/week  83 (31.9%)  34 (72.3%)  49 (23.0%)  8.75 (4.28–17.88)  <0.001   ≤50 h/week  177 (68.1%)  13 (27.7%)  164 (77.0%)  1    Are you working night shifts?   All the time  34 (12.8%)  11 (23.4%)  23 (10.6%)  5.02 (1.67–15.14)  0.004   Sometimes  162 (61.1%)  30 (63.8%)  132 (60.6%)  2.38 (0.95–6.03)  0.07   Not at all  69 (26.0%)  6 (12.8%)  63 (28.9%)  1    Are you working on weekends?   All the time  36 (13.4%)  10 (20.8%)  26 (11.8%)  13.1 (1.57–108.74)  0.02   Sometimes  197 (73.5%)  37 (77.1%)  160 (72.7%)  7.86 (1.04–59.29)  0.05   Not at all  35 (13.1%)  1 (2.1%)  34 (15.5%)  1    Are you working at night/weekend call duties in addition to your daily work?   All the time  38 (14.2%)  14 (29.2%)  24 (11.0%)  8.46 (2.53–28.33)  0.001   Sometimes  167 (62.5%)  30 (62.5%)  137 (62.6%)  3.175 (1.07–9.42)  0.04   Not at all  62 (23.2%)  4 (8.3%)  58 (26.5%)  1    Were you exposed to any stressful event within a year outside of your work?   No  114 (42.7%)  18 (38.3%)  96 (43.6%)  1     Yes  153 (57.3%)  29 (61.7%)  124 (56.4%)  1.25 (0.65–2.38)  0.50  Results are expressed as mean ± standard deviation, number (%), Odds ratio and 95% confidence interval for odds ratio. Reference category is indicated by OR = 1. P-value has been calculated using Wald test statistics. Table 2 describes the number of medication errors made per month within a 5-month period. Based on the safety incident reports, more than four out of five of the HCPs (82.2%) did not make any medication errors per month whereas almost one out of five respondents (17.8%) made at least one medication error per month. Table 2 Incidence of medication errors Factor  Frequency (%)  Medication error     No medication error made at all per month  221 (82.2)   Made at least one medication error per month  48 (17.8)  Total number of medication errors   0  221 (82.2)   1  25 (9.3)   2  8 (2.9)   3  4 (1.5)   4  4 (1.5)   5  3 (1.0)   6  1 (0.4)   7  1 (0.4)   10  1 (0.4)   21  1 (0.4)  Factor  Frequency (%)  Medication error     No medication error made at all per month  221 (82.2)   Made at least one medication error per month  48 (17.8)  Total number of medication errors   0  221 (82.2)   1  25 (9.3)   2  8 (2.9)   3  4 (1.5)   4  4 (1.5)   5  3 (1.0)   6  1 (0.4)   7  1 (0.4)   10  1 (0.4)   21  1 (0.4)  Table 2 Incidence of medication errors Factor  Frequency (%)  Medication error     No medication error made at all per month  221 (82.2)   Made at least one medication error per month  48 (17.8)  Total number of medication errors   0  221 (82.2)   1  25 (9.3)   2  8 (2.9)   3  4 (1.5)   4  4 (1.5)   5  3 (1.0)   6  1 (0.4)   7  1 (0.4)   10  1 (0.4)   21  1 (0.4)  Factor  Frequency (%)  Medication error     No medication error made at all per month  221 (82.2)   Made at least one medication error per month  48 (17.8)  Total number of medication errors   0  221 (82.2)   1  25 (9.3)   2  8 (2.9)   3  4 (1.5)   4  4 (1.5)   5  3 (1.0)   6  1 (0.4)   7  1 (0.4)   10  1 (0.4)   21  1 (0.4)  Demographics and job characteristics were found to have a statistically significant association with the number of medication errors (Table 1). The male gender (P < 0.001), higher education level (P < 0.001), Saudi nationality (P < 0.001), and higher income level (P < 0.001) were positively associated with medication errors whereas age had a negative effect. Physicians/residents (85.4%) and pharmacists (10.4%) were highly associated with making at least one medication error compared with nurses (4.2%, P < 0.001). Workloads above 50 h were significantly associated with at least one medication error versus workloads under 50 h (72.3% vs. 27.7%, P < 0.001). HCPs who were working on the weekend ‘all the time’ and ‘sometimes’ were significantly associated with medication errors (20.8%, P = 0.02, and 77.1%, P = 0.05, respectively) compared with those who were not working at all on weekend (2.1%). The likelihood of making at least one medication error is significantly higher amongst HCPs who work the night shift ‘all the time’ (OR: 5.02, P = 0.004) and ‘sometimes’ (OR: 2.38, P = 0.07) compared with those who do not work the night shift. Weekend and night shift work in combination with regular daily work increased the odds of making at least one medication error per month. The odds of making at least one medication error per month among those who reported working ‘all the time’ (OR: 8.46, P = 0.001), and ‘sometimes’ (OR: 3.18, P = 0.04) compared with those who were not working at all (Table 1). The overall stress score was derived by combining the 25 specific stress scores into a single compiled score for each participant. Overall stress ranged from 1 to 75 with mean total stress score of 30.8 (SD = 11.7). Overall stressed HCPs revealed a trend that was two times (OR: 1.95, 95% CI: 0.9–4.1) more likely to make at least one medication error per month than those not stressed but it was not statistically significant (P = 0.081; Fig. 1). Figure 1 View largeDownload slide Relationship between overall level of work-related stress and medication error. Figure 1 View largeDownload slide Relationship between overall level of work-related stress and medication error. Table 3 details a multiple binary logistic regression model used to identify significant independent factors associated with the number of medication errors made, following adjustment for covariates. Significant predictors were: disrupted home life due to long work hours, income over 10 000 riyals, workload above 50 h per week, employees were asked to work nights and weekends in addition to their daily duties ‘all the time’ and ‘sometimes’, those reporting pressure to meet deadlines, and those encountering difficulties in relationship with their colleagues (Table 3). Table 3 Multiple binary logistic regression model to identify independent predictors of number of medication errors after adjusting for covariates Factor  Adjusted odds ratio  95% CI for AOR  P-value  Disruption of your home life through spending long hours at work?   No  1       Yes  2.66  1.04–6.79  0.041  Feeling under pressure to meet deadlines?   No  1       Yes  0.39  0.16–0.93  0.034  Encountering difficulties in relationship with colleagues?   No  1       Yes  0.06  0.01–0.57  0.014  Income level   ≤10 000 SR  1       >10 000 SR  4.01  1.25–12.82  0.019  Workload   ≤50 h/week  1       >50 h/week  4.27  1.72–10.60  0.002  Are you working at night/weekend call duties in addition to your daily work?   All the time  6.99  1.5–32.53  0.013   Sometimes  2.25  0.6–9.25  0.260   Not at all  1      Factor  Adjusted odds ratio  95% CI for AOR  P-value  Disruption of your home life through spending long hours at work?   No  1       Yes  2.66  1.04–6.79  0.041  Feeling under pressure to meet deadlines?   No  1       Yes  0.39  0.16–0.93  0.034  Encountering difficulties in relationship with colleagues?   No  1       Yes  0.06  0.01–0.57  0.014  Income level   ≤10 000 SR  1       >10 000 SR  4.01  1.25–12.82  0.019  Workload   ≤50 h/week  1       >50 h/week  4.27  1.72–10.60  0.002  Are you working at night/weekend call duties in addition to your daily work?   All the time  6.99  1.5–32.53  0.013   Sometimes  2.25  0.6–9.25  0.260   Not at all  1      Note. AOR, adjusted odds ratio; CI, confidence Interval. Results are expressed as AOR and 95% confidence interval for AOR. Reference category is indicated by OR = 1. P-value has been calculated using Wald test statistics. Table 3 Multiple binary logistic regression model to identify independent predictors of number of medication errors after adjusting for covariates Factor  Adjusted odds ratio  95% CI for AOR  P-value  Disruption of your home life through spending long hours at work?   No  1       Yes  2.66  1.04–6.79  0.041  Feeling under pressure to meet deadlines?   No  1       Yes  0.39  0.16–0.93  0.034  Encountering difficulties in relationship with colleagues?   No  1       Yes  0.06  0.01–0.57  0.014  Income level   ≤10 000 SR  1       >10 000 SR  4.01  1.25–12.82  0.019  Workload   ≤50 h/week  1       >50 h/week  4.27  1.72–10.60  0.002  Are you working at night/weekend call duties in addition to your daily work?   All the time  6.99  1.5–32.53  0.013   Sometimes  2.25  0.6–9.25  0.260   Not at all  1      Factor  Adjusted odds ratio  95% CI for AOR  P-value  Disruption of your home life through spending long hours at work?   No  1       Yes  2.66  1.04–6.79  0.041  Feeling under pressure to meet deadlines?   No  1       Yes  0.39  0.16–0.93  0.034  Encountering difficulties in relationship with colleagues?   No  1       Yes  0.06  0.01–0.57  0.014  Income level   ≤10 000 SR  1       >10 000 SR  4.01  1.25–12.82  0.019  Workload   ≤50 h/week  1       >50 h/week  4.27  1.72–10.60  0.002  Are you working at night/weekend call duties in addition to your daily work?   All the time  6.99  1.5–32.53  0.013   Sometimes  2.25  0.6–9.25  0.260   Not at all  1      Note. AOR, adjusted odds ratio; CI, confidence Interval. Results are expressed as AOR and 95% confidence interval for AOR. Reference category is indicated by OR = 1. P-value has been calculated using Wald test statistics. Discussion This cross-sectional study examined the relationship between work-related stress and medication errors in a Saudi Arabian healthcare setting. HCPs were asked to complete a survey on socio-demographic factors, job characteristics, and a scale on work-related stress. The department of quality management provided de-identified data of documented medication errors linked to the self-administered survey; the data were linked using employee identification badge number and reflected medication errors made by the employee. The history of medication errors was linked as to coincide with the date of the survey to avoid possible bias from the attention of the survey topic on increased awareness of medication errors and behavior change. The incident report prevalence of medication error was 17.8% and similar to numerous global studies 19.6% (8.6–28.3%), depending on the healthcare setting [6, 7]. Although the majority of HCPs reported feeling stressed (68.4%), our study revealed that source-specific work-related stress, rather than overall stress is strongly associated with medication errors. Multiple studies indicate a significant relationship between stress and medication errors among HCPs although most of these studies were based on self-reported medication errors [9, 10]. Although sources of work-related stress were significantly correlated with medication errors, others were associated with a reduction in medication errors. A disruption to home life predisposed an HCP to make medication errors at a rate 2.66 times higher than those whose home life was not disrupted due to work, while HCPs experiencing pressure to meet deadlines were 61% less likely to make an error. These findings may indicate that HCPs were more careful when under pressure. Findings from a prospective study corroborate that high social stressors and time pressure contribute to perceived poor quality of patient care [11]. Difficulties with colleagues led to a 94% lower chance of error over those who did not have difficulties, which may indicate HCPs worked more carefully to avoid being embarrassed in front of their colleagues or because existing conflict led to finger-pointing. The relationship between specific sources of work-related stress and medication errors remained significant after adjusting for demographic and job characteristics. Although multiple sources of work-related stress could contribute to the overall perception of stress, there were specific sources that had the strongest relationships such as disrupted home life due to long work hours and excessive workload (above 50 h per week) and working night/weekend call duty ‘all the time’ ‘some time’ on top of existing daily duties. The relationship between medication errors and disrupted home life due to long work hours, workload and night/weekend call duty confirmed previous findings [4, 7, 26]. Our findings contribute to the literature by providing data on the association between specific sources of work-related stress and medication errors. This occurred despite the assumption that stress could contribute to human error. Two general approaches have been followed for stress relief in the work environment. The first one is the individual focused interventions through employee assistance programs. This has been successful in stress relief at work. A study from the USA showed that staff involved in at least one of seven training programs covering one or more aspects of stress management experienced significant reductions in psychological distress, depression and anxiety immediately after the intervention [27]. Follow-up of these subjects for 9–16 months revealed further reduction in psychological distress and emotional exhaustion [27]. The second approach is organization-based interventions [28]. Introducing better consistent working hours, reducing overlap of extra shifts with daily duties, and maintaining a healthy home life could all reduce medication error rate. These adjustments could create a positive long-term impact on the overall and specific sources of stress levels of individuals working in these medical environments and lower negative outcomes associated with higher overall levels of stress. The former approach has been associated with more positive outcomes at the personal level, while the second intervention has been associated with more positive outcomes at the organizational level [28]. Our study is the first to assess the relationship between overall and specific sources of work-related stress using documented safety incident reports rather than self-reported medication errors among HCPs. The finding on overall stress is important because it reveals that while stress may play a role in making medication errors, generalized stress occurring from work was not significantly associated with these errors. Our findings on the relationship between source-specific work-related stress and medication errors suggest that an analysis of a workplace environment should focus on specific source of work-related stress. Limitations of the study The quantitative retrospective approach carries some inherent limitations. Although the medication error data was documented prospectively and prior to survey, the use of a cross-sectional study design prevents us from proposing a temporal relationship between work-related stress and medication errors. In addition, a quantitative study does not capture the context of process improvement through a workflow process [29, 30]. Direct observation and mapping of clinical workflow provides the sequence of clinical events that lead to the medication errors, such as frequent interruptions and preparation of medications for multiple patients [29, 30]. Cognitive awareness on the implications from reporting medication errors by the administration could bias the validity of self-reported findings. While the reported incidence of medication errors in our nurse HCPs appears low compared with levels reported in developed countries, previous studies suggest that SA nurses underreport medication errors because of fear of reprisal from the administration [19]. Further work is needed to investigate whether there is a potential cognitive bias in self-reported medication errors by SA HCPs or if the current reporting system has limitations for promoting HCPs to report their errors [19, 20]. Another limitation of the study is bias from self-report responses about work-related stress, as subjective data cannot be independently verified. We cannot exclude unmeasured confounders (poor sleep quality, exhaustion) and potential selection bias such that stressed-out HCPs refused to participate [11]. Lastly, the generalizability of the results is impacted by the single center data and the fact that the HCPs work under a different culture (pharmacy infrastructure, training and levels of professional experience) which may create different experiences between Saudi and the Western healthcare systems. The study’s reliance on retrospective data could be supported with further prospective and direct observation studies. Conclusion Although multiple studies have assessed the relationship between stress and medication errors, the relationship between source-specific stress to medication errors had not been addressed. Most studies rely on self-reported medication errors or other subjective measures, such as perceived quality of life and burnout syndrome factors. The evidence from our study that source-specific workplace-based stressors (disruption of home life) are associated with an increase in medication errors may help inform comprehensive policies addressing these issues. Socio-demographic and employment characteristics point to a culture of empowerment and perhaps relaxed behaviors in avoiding medication errors. Those who were more likely to make medication errors, included those with higher income (4.01 times), organizational systemic factors (higher workload per week, and working night and weekend shifts in addition to regular duty), factors that affect HCPs with increased fatigue, burnout and medication errors. Follow-up studies on changes to policy addressing organizational climate such as workplace hours can be carried out to confirm and expand on our findings. Researchers suggest that shift work and disruption of home life can be improved through staffing schedules and staff recruitment to ensure less need for double shifts, and a culture of work-balance. Surprisingly, source-specific work-related stressors (pressure to meet deadlines, difficulties with colleagues) predicted less medication errors. Deadlines imposed by management may overcome distractions that normally would lead to medication errors and add more focused work practices. Further research on how the dynamic between difficulties with colleagues can guided in a healthy manner to reduce medication errors needs to exploration. Future assessment of stress at work should consider these specific stressors rather than a general stress assessment. Finally, the association between stress and incidence of medication errors does not address the sequence of clinical events that led to errors; the importance of medication administration workflow using direct clinical observation cannot be overemphasized. Acknowledgements The team is grateful for the assistance offered by Rommel Acunin, King Abdullah International Medical Research Center in data management. We would also like to extend our gratitude to Analyn Crisostomo of King Abdulaziz Hospital for assisting in data entry, and The Quality Management Department of King Abdulaziz Hospital for providing the team with the documented incident medication error data. Rabia Ali Khan at the Academic Health Services (Hamad Medical Corporation) reviewed the final manuscript. Funding This work was supported by a research grant (number: RA13/01l/A) from King Abdullah International Medical Research Center, Ministry of National Guard Health Affairs, Saudi Arabia. Guidelines followed The manuscript was prepared as per the STROBE guidelines. References 1 World Health Organization (WHO). Reporting and learning systems for medication errors: the role of pharmacovigilance centres. Geneva, Switzerland; 2014. p. 110. Available from: http://apps.who.int/iris/bitstream/10665/137036/1/9789241507943_eng.pdf 2 Andel C, Davidow SL, Hollander M et al.  . The economics of health care quality and medical errors. J Health Care Finan  2012; 39: 39– 50. 3 Waeschle RM, Bauer M, Schmidt CE. Errors in medicine. Causes, impact and improvement measures to improve patient safety. Anaesthesist  2015; 64: 689– 704. Google Scholar CrossRef Search ADS PubMed  4 Christy G, Linda B, Beverly AL et al.  . Frequency of and risk factors for medication errors by pharmacists during order verification in a tertiary care medical center. Am J Health Syst Pharm  2015; 72: 1471– 4. Google Scholar CrossRef Search ADS PubMed  5 Zineldin M, Zineldin J, Vasicheva V. Approaches for reducing medical errors and increasing patient safety: TRM, quality and 5 Qs method. Tot Qual Manag  2014; 26: 63– 74. 6 Keers RN, Williams SD, Cooke J et al.  . Prevalence and nature of medication administration errors in health care settings: a systematic review of direct observational evidence. Ann Pharmacother  2013; 47: 237– 56. Google Scholar CrossRef Search ADS PubMed  7 Albara A, Val W, Patricia MD. Joanne L. Families, nurses and organisations contributing factors to medication administration error in paediatrics: a literature review. Int Pract Dev J  2015; 5: 1– 14. 8 Bergqvist M, Karlsson EA, Björkstén KS et al.  . Medication errors by nurses in Sweden: classification and contributing factors. Open Access Sci Rep  2012; 1: 1– 4. doi:10.4172/scientificreports.527. 9 Samsuri SE, Pei Lin L, Fahrni ML. Safety culture perceptions of pharmacists in Malaysian hospitals and health clinics: a multicentre assessment using the Safety Attitudes Questionnaire. Br Med J Open Access  2015; 5: e008889. 10 Shanafelt TD, Balch CM, Bechamps G et al.  . Burnout and medical errors among American surgeons. Ann Surg  2010; 251: 995– 1000. Google Scholar CrossRef Search ADS PubMed  11 Krämer T, Schneider A, Spieß E et al.  . Associations between job demands, work-related strain and perceived quality of care: a longitudinal study among hospital physicians. Int J Qual Health Care  2016; 28: 82409. 12 Sulaiman CF, Henn P, Smith S et al.  . Burnout syndrome among non-consultant hospital doctors in Ireland: relationship with self-reported patient care. Int J Qual Health Care  2017; 29: 679– 84. Google Scholar CrossRef Search ADS PubMed  13 Kang EK, Lihm HS, Kong EH. Association of intern and resident burnout with self-reported medical errors. Korean J Fam Med  2013; 34: 36– 42. Google Scholar CrossRef Search ADS PubMed  14 Oreskovich MR, Shanafelt T, Dyrbye LN et al.  . The prevalence of substance use disorders in American physicians. Am J Addict  2015; 24: 30– 8. Google Scholar CrossRef Search ADS PubMed  15 de Oliveira GS, Chang R, Fitzgerald PC et al.  . The prevalence of burnout and depression and their association with adherence to safety and practice standards: a survey of United States anesthesiology trainees. Anesthesia & Analgesia.  2013; 117: 182– 93. Google Scholar CrossRef Search ADS   16 Alosaimi FD, Kazim SN, Almufleh AS et al.  . Prevalence of stress and its determinants among residents in Saudi Arabia. Saudi Med J  2015; 36: 605– 12. Google Scholar CrossRef Search ADS PubMed  17 Salam A, Abu-Helalah M, Jorissen SL et al.  . Job stress and job satisfaction among health care professionals. Eur Scie J  2014; 10: 156– 73. 18 Al-Saleh KS, Ramadan MZ. Studying medical errors among hospital-staff at Saudi Health Providers: teaching hospital in Saudi Arabia. J Mater Sci Eng  2012; 2: 41– 52. 19 Almutary HH, Lewis PA. Nurses’ willingness to report medication administration errors in Saudi Arabia. Qual Manag Health Care.  2012; 21: 119– 26. Google Scholar CrossRef Search ADS PubMed  20 Sadat-Ali M, Al-Shafei BA, Al-Turki RA et al.  . Medication administration errors in Eastern Saudi Arabia. Saudi Med J  2010; 31: 1257– 9. Google Scholar PubMed  21 West CP, Tan AD, Habermann TM et al.  . Association of resident fatigue and distress with perceived medical errors. J Am Med Assoc  2009; 302: 1294– 1300. Google Scholar CrossRef Search ADS   22 Ramirez AJ, Graham J, Richards MA et al.  . Mental health of hospital consultants: the effects of stress and satisfaction at work. Lancet  1996; 347: 724– 8. Google Scholar CrossRef Search ADS PubMed  23 Grunfeld E, Zitzelsberger L, Coristine M et al.  . Job stress and job satisfaction of cancer care workers. Psychooncology  2005; 14: 61– 9. Google Scholar CrossRef Search ADS PubMed  24 Teasdale E, Drew S, Taylor C et al.   Hospital consultants’ job stress and satisfaction questionnaire. Cancer Research UK London, Psychosocial Group, 2008. Available from https://www.scribd.com/document/92245468/Soal-Selidik-Stress-Manual 25 Saudi Arabian Ministry of Health. Statistics book. Retrieved from http://www.moh.gov.sa/en/ministry/statistics/book/pages/default.aspx. 2015. 26 Kim KS, Kwon SH, Kim JA et al.  . Nurses’ perceptions of medication errors and their contributing factors in South Korea. J Nurs Manag  2011; 19: 346– 53. Google Scholar CrossRef Search ADS PubMed  27 Li‐An H. Meditation, learning, organizational innovation and performance. Indus Manag Data Syst  2011; 111: 113– 31. Google Scholar CrossRef Search ADS   28 Lamontagne AD, Keegel T, Louie AM et al.  . A systematic review of the job-stress intervention evaluation literature: 1990–2005. Int J Occup Environ Health  2007; 13: 268– 80. Google Scholar CrossRef Search ADS PubMed  29 Huang YH, Gramopadhye AK. Systematic engineering tools for describing and improving medication administration processes at rural healthcare facilities. Appl Ergon  2014; 45: 1712– 24. Google Scholar CrossRef Search ADS PubMed  30 Huang YH, Gramopadhye AK. Recommendations for health information technology implementation in rural hospitals. Int J Health Care Qual Assur  2016; 29: 454– 74. Google Scholar CrossRef Search ADS PubMed  © The Author(s) 2018. Published by Oxford University Press in association with the International Society for Quality in Health Care. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices)

Journal

International Journal for Quality in Health CareOxford University Press

Published: May 7, 2018

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off