Pharmacist interventions in high-risk obstetric inpatient unit: a medication safety issue

Pharmacist interventions in high-risk obstetric inpatient unit: a medication safety issue Abstract Objectives The aim of this study was to report number, type and severity of prescribing errors and pharmacist interventions in high-risk pregnant and postpartum women. Design A prospective cross-sectional, observational study. Setting A high-risk obstetric inpatient unit of a Women’s Hospital in Brazil. Participants About 1826 electronic prescriptions for 549 women in the high-risk obstetrics inpatient unit were included. Interventions When the pharmacist detected potential prescribing errors, interventions were suggested. Main Outcome Measures Prescriptions were evaluated by clinical pharmacist to identify the type, frequency and severity of prescribing errors and rate of clinical pharmacist intervention acceptance in a high-risk obstetric inpatient. Results A total of 1826 prescriptions were reviewed with 128 errors (7.0%). The most frequent errors were drug interaction (43.8%), incorrect frequency (21.5%) and improper dose (13.1%). One-hundred and sixty-eight interventions were made by pharmacists, 98.8% of which were accepted by prescribers. Higher maternal age (OR 1.0 (95%CI 1.0–1.1)), higher number of prescribed medications (OR 1.2 (95%CI 1.1–1.3)), obstetric conditions (OR 2.2 (95%CI 1.4–3.3)) and non-breastfeeding postpartum women (OR 3.9 (95% CI 2.5–6.1)) were the independent factors associated with prescribing errors identified through multivariate analysis. Conclusions The most common prescription errors related to drug interactions, incorrect frequency and higher number of prescribed medications. The rate of pharmacist acceptance intervention was high. pharmacist intervention, prescribing error, medication safety, pregnancy, postpartum period Introduction Pharmacists are able to identify errors in the course of routine work [1, 2]. The clinical pharmacist in the inpatient medical team during daily rounds showed a reduction of preventable adverse drug events caused by prescribing errors [3]. Moreover, prescribing errors identified by clinical pharmacy services are associated with decreased costs of care [4]. A prescribing error happens when, as a result of a prescribing decision or prescription writing process, there is an unintentional reduction in the possibility of treatment being effective or an increase in the risk of harm when compared with generally accepted practice [5]. This can be associated with prescribing without taking into account the patient’s clinical status, failure to communicate essential information and/or transcription error [5]. A systematic review of the prevalence, incidence and nature of prescribing errors in hospital inpatients showed that these errors were common and affected 7% of medication orders posing a health public problem [6]. There are numerous gaps in knowledge on the consequences of drug prescription for both the mother and the fetus [7]. Furthermore, rational use of drugs in pregnancy is an essential component of prenatal care for women and their healthcare providers [8]. Studies conducted to describe pharmacist interventions and prescribing errors, especially in the obstetric inpatients unit, are still scarce. One study identified in this area reported prescribing errors intercepted by clinical pharmacists in obstetrics, as well as in pediatrics, in a tertiary hospital in Spain and demonstrated that pharmacist interventions can reduce prescribing errors and improve the quality and efficiency of care provided [9]. Therefore, the aim of this study was to report number, type and severity of prescribing errors and pharmacist interventions at inpatient high-risk pregnant and postpartum women. Patients and methods Study setting and design A prospective cross-sectional study was performed in a high-risk obstetric inpatient unit of a Women’s Hospital, in Brazil (142 beds) from September 2014 to March 2015. This study was approved by the Research Ethics Committee of the University of Campinas (approval number: 26709414.1.0000.5404) and physicians signed an informed consent form authorizing use of data. The Women’s Hospital at the University of Campinas is a teaching hospital where residents, under supervision of a medical professor, are responsible for prescriptions. A pharmaceutical service is provided from 7 am to 7 pm on weekdays. The high-risk obstetric inpatient unit has 20 beds. The obstetric team has 18 medical professors and assistants and 33 residents who work under the supervision of medical professor/assistant. The team changed according to their work schedule. There is, therefore, high staff turnover. Sample size was determined by statistical calculation based on prevalence of 8.4% of prescribing errors confidence level of 95% and acceptable absolute error of 0.015, resulting in a minimum of 1314 prescriptions [10, 11]. The statistical software used was Statistical Analysis System (SAS) 9.4 version for Windows. Eligibility criteria All medical prescriptions for patients in the high-risk obstetric inpatient, done on the weekday mornings. All physicians signined the informed consent. Analysis of medication orders and pharmacist interventions Since March 2003, this hospital has used the electronic prescription system developed in the institution: a barcode system is routinely included for both medication dispensing and administration. Daily, each prescription was accessed electronically and electronic patient records were checked to collect data such as age, characteristics of patients (pregnant women and breastfeeding or non-breastfeeding postpartum women), gestational age and trimester and reasons of hospitalization. Medication orders were evaluated by insertion of each drug name prescribed into the databases Micromedex® [12], Up to Date® [13] and E-lactancia [14], and also by consulting Briggs et al. [15]. In this assessment, prescription data were compared with the database regarding duration of treatment, dosage interval, the dosage, dosage form, route of administration, infusion rate, drug–drug interactions, drug–food interactions and therapeutic duplication. The pharmacist discussed the results of the prescription evaluations in the unit daily. If there are inconsistencies between prescription and literature/database cited (potential prescribing errors), the pharmacist suggested recommendations (pharmacist intervention). Demographics data, period of pregnancy-puerperal cycle, being or not breastfeeding and reasons of hospitalization also were evaluated and categorized for analysis. After medical assessment of published data and its association with women’s clinical condition, the physician accepted or rejected the proposed pharmacist intervention. The severity of the error and impact of the pharmacist intervention were classified. The data collection was performed by two clinical pharmacists, one of them a clinical pharmacist specialist employed by the Brazilian Society of Hospital Pharmacy and Health Services. The data analysis (classification of prescribing errors and pharmacist interventions) was performed by a third professional, a clinical pharmacy Master’s student under the supervision of a PhD professor. This third pharmacist was necessary to avoid bias. Classification of prescribing errors Prescribing errors found by the pharmacist were quantified and classified according to an adaptation of an index developed by the National Coordinating Council for Medication Error Reporting and Prevention (NCCMERP) [16] Also, two categories were included: drug not secure in pregnancy and drug not secure in breastfeeding, in relation to characteristics of the studied inpatient unit. The severity of the error was assessed using a scale proposed by Overhage et al. [17] generating a categorical variable with five possible categories: (1) potentially lethal; (2) serious; (3) significant; (4) minor and (5) no error. The Anatomical Therapeutic Chemical (ATC) classification was used to classify drugs involved in prescribing errors into therapeutic classes [18]. Moreover, these drugs were also classified into high-alert medication (HAMs) or not HAMs. HAMs are defined as medications that, if involved in erroneous medication order, carry a greater risk of significant harm or death [19]. In this study, HAMs were compiled from the Institute for Safer Medication Practices’ HAMs list [20]. Classification of pharmacist interventions The pharmacist interventions were subsequently quantified and classified according to an adaptation of the methods used by Leape et al. [3]. Therefore, pharmacist interventions were categorized. The impact of the pharmacist interventions on women care was assessed using a scale proposed by Overhage et al. [17] generating a categorical variable with six possible categories: (1) extremely significant; (2) very significant; (3) significant; (4) somewhat significant; (5) insignificant and (6) harmful. Pharmacist interventions were also evaluated by acceptance rate (accepted or not accepted). Recommendations were considered as accepted if the physician implemented the change suggested by the pharmacist within 24 h of the intervention. Demographics data, period of pregnancy-puerperal cycle, being or not breastfeeding and reasons of hospitalization also were evaluated and categorized for analysis Data analysis A database was created using Microsoft Excel® to record all data collected from records and prescriptions, including prescribing errors and their details, and pharmacist interventions and their details. This database did not include patients’ names or other identification information. Student’s t-test, chi-square test and Mann–Whitney test were used to compare demographics and reasons of hospitalization with or not prescribing errors. Fisher’s Exact and Kruskal–Wallis tests were performed to verify associations among some variables and prescribing errors/pharmacist interventions; multivariate logistic regression analysis of risk factors for prescribing errors was also performed; 2-sided P-values <0.05 were considered statistically significant (SAS, 9.4 version). All recommendations of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement were followed for this study [21]. RESULTS Physicians performed 1826 prescriptions for 549 women in the high-risk obstetric inpatient unit (3.3 ± 1.8 prescriptions/women). About 128 erroneous medication orders were found (7.0%), responsible for 130 interventions related to errors and 101 patients (18.4%) had at least one erroneous prescription. One-hundred and sixty-eight pharmacist interventions were made related to 128 prescriptions in 121 patients (38 not associated with errors). The acceptance rate of the pharmacist interventions by prescribers was 98.8%. Incidences of prescribing errors and interventions are shown in Table 1. Table 1 Prescribing errors and interventions incidence in high-risk obstetric inpatient unit Parameter  Value  Total number of patients  549  Total number of medical prescriptions  1826  Prescribing errors     Total number of medical prescriptions with      one prescribing error  126    two prescribing errors  2   Total number of patients with prescribing errors  101   Incidence of patients with error (%)  18.4   Incidence of prescription with errors (%)  7.0   Incidence of prescription with errors involving high-risk medications (%)  0.4  Pharmacist interventions     Total number of pharmacist interventions  168   Total number of erroneous medication orders intercepted by pharmacista  128   Total number of pharmacist intervention related with errors  130   Total number of pharmacist intervention not related with errors  38   Total number of patients with pharmacist interventions  121   Incidence of pharmacist interventions accepted (%)  98.8  Parameter  Value  Total number of patients  549  Total number of medical prescriptions  1826  Prescribing errors     Total number of medical prescriptions with      one prescribing error  126    two prescribing errors  2   Total number of patients with prescribing errors  101   Incidence of patients with error (%)  18.4   Incidence of prescription with errors (%)  7.0   Incidence of prescription with errors involving high-risk medications (%)  0.4  Pharmacist interventions     Total number of pharmacist interventions  168   Total number of erroneous medication orders intercepted by pharmacista  128   Total number of pharmacist intervention related with errors  130   Total number of pharmacist intervention not related with errors  38   Total number of patients with pharmacist interventions  121   Incidence of pharmacist interventions accepted (%)  98.8  aTwo prescriptions had two errors. Table 1 Prescribing errors and interventions incidence in high-risk obstetric inpatient unit Parameter  Value  Total number of patients  549  Total number of medical prescriptions  1826  Prescribing errors     Total number of medical prescriptions with      one prescribing error  126    two prescribing errors  2   Total number of patients with prescribing errors  101   Incidence of patients with error (%)  18.4   Incidence of prescription with errors (%)  7.0   Incidence of prescription with errors involving high-risk medications (%)  0.4  Pharmacist interventions     Total number of pharmacist interventions  168   Total number of erroneous medication orders intercepted by pharmacista  128   Total number of pharmacist intervention related with errors  130   Total number of pharmacist intervention not related with errors  38   Total number of patients with pharmacist interventions  121   Incidence of pharmacist interventions accepted (%)  98.8  Parameter  Value  Total number of patients  549  Total number of medical prescriptions  1826  Prescribing errors     Total number of medical prescriptions with      one prescribing error  126    two prescribing errors  2   Total number of patients with prescribing errors  101   Incidence of patients with error (%)  18.4   Incidence of prescription with errors (%)  7.0   Incidence of prescription with errors involving high-risk medications (%)  0.4  Pharmacist interventions     Total number of pharmacist interventions  168   Total number of erroneous medication orders intercepted by pharmacista  128   Total number of pharmacist intervention related with errors  130   Total number of pharmacist intervention not related with errors  38   Total number of patients with pharmacist interventions  121   Incidence of pharmacist interventions accepted (%)  98.8  aTwo prescriptions had two errors. There was an association between the number of prescribed drugs and the type of pharmacist intervention (P = 0.0003; Kruskal–Wallis test). Interventions for the management of drug interactions and therapeutic duplications were common for prescriptions with the highest number of prescribed drugs (mean 7.7 ± 2.5 and 8.0 ± 1.8 drugs, respectively). Prescriptions with interventions for management of improper doses and wrong drug due to adverse effects contained less prescribed drugs (mean 5.5 ± 2.3 and 4.5 ± 1.5 drugs, respectively). Demographics and reasons of women's hospitalization according to occurrence of prescribing errors described in Table 2. Table 2 Demographics and reasons for hospitalization of women according to the occurrence of prescribing errors Characteristics  With prescribing errors  Without prescribing errors  Total  P-value*  Number of patients  101  448  549    Mean age in years (±SD)  29.4 ± 7.5  27.4 ± 6.8  27.8 ± 7.0  0.0083**  Pregnancy-puerperal cycle (n, %)        <0.0001***   Pregnant women—third trimester  36 (35.6)  177 (39.4)  213 (38.8)     Pregnant women—second trimester  22 (21.8)  109 (24.4)  131 (23.9)     Pregnant women—first trimester  6 (5.9)  27 (6.0)  33 (6.0)     Breastfeeding postpartum women  12 (11.9)  110 (24.6)  122 (22.2)     Non-breastfeeding postpartum women  25 (24.8)  25 (5.6)  50 (9.1)    Mean gestational age in weeks (±SD)  27.0 ± 8.0  27.3 ± 8.6  27.2 ± 8.5  0.8**  Reasons of hospitalization (n, %)        0.3***   Fetal and neonatal causes  26 (26.3)  98 (22.0)  124 (22.7)     Pregestational co-morbidities  18 (18.2)  68 (15.3)  86 (15.8)     Obstetric conditions  15 (15.2)  51 (11.4)  66 (12.1)     Labor and postpartum issues  13 (13.1)  91 (20.4)  104 (19.1)     Others  27 (27.2)  139 (30.9)  166 (30.3)    Mean number of causes of hospitalization (±SD)  2.0 ± 1.1  1.9 ± 0.9  1.9 ± 1.0  0.1****  Mean number of prescribed medications (±SD)  7.2 ± 2.5  6.1 ± 2.3  6.4 ± 2.4  <0.0001****  Characteristics  With prescribing errors  Without prescribing errors  Total  P-value*  Number of patients  101  448  549    Mean age in years (±SD)  29.4 ± 7.5  27.4 ± 6.8  27.8 ± 7.0  0.0083**  Pregnancy-puerperal cycle (n, %)        <0.0001***   Pregnant women—third trimester  36 (35.6)  177 (39.4)  213 (38.8)     Pregnant women—second trimester  22 (21.8)  109 (24.4)  131 (23.9)     Pregnant women—first trimester  6 (5.9)  27 (6.0)  33 (6.0)     Breastfeeding postpartum women  12 (11.9)  110 (24.6)  122 (22.2)     Non-breastfeeding postpartum women  25 (24.8)  25 (5.6)  50 (9.1)    Mean gestational age in weeks (±SD)  27.0 ± 8.0  27.3 ± 8.6  27.2 ± 8.5  0.8**  Reasons of hospitalization (n, %)        0.3***   Fetal and neonatal causes  26 (26.3)  98 (22.0)  124 (22.7)     Pregestational co-morbidities  18 (18.2)  68 (15.3)  86 (15.8)     Obstetric conditions  15 (15.2)  51 (11.4)  66 (12.1)     Labor and postpartum issues  13 (13.1)  91 (20.4)  104 (19.1)     Others  27 (27.2)  139 (30.9)  166 (30.3)    Mean number of causes of hospitalization (±SD)  2.0 ± 1.1  1.9 ± 0.9  1.9 ± 1.0  0.1****  Mean number of prescribed medications (±SD)  7.2 ± 2.5  6.1 ± 2.3  6.4 ± 2.4  <0.0001****  SD = Standard deviation. *Comparison between patients with prescribing errors and patients without prescribing errors. **Student’s t-test; ***Chi-square test; ****Mann–Whitney test. Bold: Statistically significant P-values. Table 2 Demographics and reasons for hospitalization of women according to the occurrence of prescribing errors Characteristics  With prescribing errors  Without prescribing errors  Total  P-value*  Number of patients  101  448  549    Mean age in years (±SD)  29.4 ± 7.5  27.4 ± 6.8  27.8 ± 7.0  0.0083**  Pregnancy-puerperal cycle (n, %)        <0.0001***   Pregnant women—third trimester  36 (35.6)  177 (39.4)  213 (38.8)     Pregnant women—second trimester  22 (21.8)  109 (24.4)  131 (23.9)     Pregnant women—first trimester  6 (5.9)  27 (6.0)  33 (6.0)     Breastfeeding postpartum women  12 (11.9)  110 (24.6)  122 (22.2)     Non-breastfeeding postpartum women  25 (24.8)  25 (5.6)  50 (9.1)    Mean gestational age in weeks (±SD)  27.0 ± 8.0  27.3 ± 8.6  27.2 ± 8.5  0.8**  Reasons of hospitalization (n, %)        0.3***   Fetal and neonatal causes  26 (26.3)  98 (22.0)  124 (22.7)     Pregestational co-morbidities  18 (18.2)  68 (15.3)  86 (15.8)     Obstetric conditions  15 (15.2)  51 (11.4)  66 (12.1)     Labor and postpartum issues  13 (13.1)  91 (20.4)  104 (19.1)     Others  27 (27.2)  139 (30.9)  166 (30.3)    Mean number of causes of hospitalization (±SD)  2.0 ± 1.1  1.9 ± 0.9  1.9 ± 1.0  0.1****  Mean number of prescribed medications (±SD)  7.2 ± 2.5  6.1 ± 2.3  6.4 ± 2.4  <0.0001****  Characteristics  With prescribing errors  Without prescribing errors  Total  P-value*  Number of patients  101  448  549    Mean age in years (±SD)  29.4 ± 7.5  27.4 ± 6.8  27.8 ± 7.0  0.0083**  Pregnancy-puerperal cycle (n, %)        <0.0001***   Pregnant women—third trimester  36 (35.6)  177 (39.4)  213 (38.8)     Pregnant women—second trimester  22 (21.8)  109 (24.4)  131 (23.9)     Pregnant women—first trimester  6 (5.9)  27 (6.0)  33 (6.0)     Breastfeeding postpartum women  12 (11.9)  110 (24.6)  122 (22.2)     Non-breastfeeding postpartum women  25 (24.8)  25 (5.6)  50 (9.1)    Mean gestational age in weeks (±SD)  27.0 ± 8.0  27.3 ± 8.6  27.2 ± 8.5  0.8**  Reasons of hospitalization (n, %)        0.3***   Fetal and neonatal causes  26 (26.3)  98 (22.0)  124 (22.7)     Pregestational co-morbidities  18 (18.2)  68 (15.3)  86 (15.8)     Obstetric conditions  15 (15.2)  51 (11.4)  66 (12.1)     Labor and postpartum issues  13 (13.1)  91 (20.4)  104 (19.1)     Others  27 (27.2)  139 (30.9)  166 (30.3)    Mean number of causes of hospitalization (±SD)  2.0 ± 1.1  1.9 ± 0.9  1.9 ± 1.0  0.1****  Mean number of prescribed medications (±SD)  7.2 ± 2.5  6.1 ± 2.3  6.4 ± 2.4  <0.0001****  SD = Standard deviation. *Comparison between patients with prescribing errors and patients without prescribing errors. **Student’s t-test; ***Chi-square test; ****Mann–Whitney test. Bold: Statistically significant P-values. Drug–drug interaction (57 (43.8%)); frequency incorrect (28 (21.5%))—higher than correct (12 (9.2%)) or lower than correct (16 (12.3%)) and improper dose (17 (13.1%))—over-dosage (6 (4.6%)) or under-dosage (11 (8.5%)) were the most common errors, followed by duplication of therapy (14 (10.8%)); wrong drug (6 (4.6%)); wrong duration of treatment (4 (3.1%)); and unintentional omission of drug (4 (3.1%)), which were classified as significant (74 (56.9%)) and serious (56 (43.1%)) errors. A closer look at the ATC sub-therapeutic categories shows that alimentary tract and metabolism (94 (50.3%)) was the drug class most involved with prescribing errors, followed by genitourinary system and sex hormones (35 (18.7%)) and anti-infective for systemic use (19 (10.2%)). The most frequently recorded individual medications associated with an error were metoclopramide (58 (31.0%)), cabergoline (35 (18.7%)) and ranitidine (15 (8.0%)). It is observed that the total number of drugs involved in prescribing errors is higher than the total errors because one error may include one or more drugs involved. The HAMs involved in prescribing errors were enoxaparin, tramadol and promethazine. The associations between the most prescribing errors and pregnancy-puerperal cycle, ATC class and number of medications prescribed are shown in Table 3. Non-breastfeeding women had the highest drug–drug interaction due to improper cabergoline–metoclopramide association. Table 3 Association of prescribing errors and pregnancy-puerperal cycle, reasons for hospitalization, ATC class and number of prescribed medications Variable  Drug–drug interaction (n = 57)  Frequency less than correct (n = 16)  Duplication of therapy (n = 12)  P-value  Pregnancy-puerperal cycle, n = 128        <0.0001*   Pregnant women  10 (17.5)  12 (75.0)  4 (33.3)     Breastfeeding postpartum women  9 (15.8)  4 (25.0)  5 (41.7)     Non-breastfeeding postpartum women  38 (66.7)  0 (0.0)  3 (25.0)    Reasons for hospitalization n = 128        0.0003*   Fetal and neonatal causes  15 (26.3)  0 (0.0)  1 (8.3)     Infections  12 (21.1)  0 (0.0)  1 (8.3)     Obstetric conditions  9 (15.8)  11 (68.8)  7 (58.3)     Labor and postpartum issues  9 (15.8)  1 (6.3)  0 (0.0)     Others  3 (5.2)  0 (0.0)  0 (0.0)    ATC class, n = 187        <0.0001*   Alimentary tract and metabolism  58 (51.3)  9 (53.6)  13 (76.5)     Genitourinary system and sex hormones  34 (30.1)  0 (0.0)  0 (0.0)     Anti-infective for systemic use  1 (0.9)  7 (43.8)  0 (0.0)     Others  20 (17.7)  0 (0.0)  4 (7.5)    Number of prescribed medications, n = 128  7.6 (2.4)  7.1 (2.3)  8.0 (2.0)  0.0136**  Variable  Drug–drug interaction (n = 57)  Frequency less than correct (n = 16)  Duplication of therapy (n = 12)  P-value  Pregnancy-puerperal cycle, n = 128        <0.0001*   Pregnant women  10 (17.5)  12 (75.0)  4 (33.3)     Breastfeeding postpartum women  9 (15.8)  4 (25.0)  5 (41.7)     Non-breastfeeding postpartum women  38 (66.7)  0 (0.0)  3 (25.0)    Reasons for hospitalization n = 128        0.0003*   Fetal and neonatal causes  15 (26.3)  0 (0.0)  1 (8.3)     Infections  12 (21.1)  0 (0.0)  1 (8.3)     Obstetric conditions  9 (15.8)  11 (68.8)  7 (58.3)     Labor and postpartum issues  9 (15.8)  1 (6.3)  0 (0.0)     Others  3 (5.2)  0 (0.0)  0 (0.0)    ATC class, n = 187        <0.0001*   Alimentary tract and metabolism  58 (51.3)  9 (53.6)  13 (76.5)     Genitourinary system and sex hormones  34 (30.1)  0 (0.0)  0 (0.0)     Anti-infective for systemic use  1 (0.9)  7 (43.8)  0 (0.0)     Others  20 (17.7)  0 (0.0)  4 (7.5)    Number of prescribed medications, n = 128  7.6 (2.4)  7.1 (2.3)  8.0 (2.0)  0.0136**  ATC, Anatomical Therapeutic Chemical. *Fisher’s Exact test; **Kruskal–Wallis test (the result considers all types of prescribing error). Bold: Statistically significant P-values. Table 3 Association of prescribing errors and pregnancy-puerperal cycle, reasons for hospitalization, ATC class and number of prescribed medications Variable  Drug–drug interaction (n = 57)  Frequency less than correct (n = 16)  Duplication of therapy (n = 12)  P-value  Pregnancy-puerperal cycle, n = 128        <0.0001*   Pregnant women  10 (17.5)  12 (75.0)  4 (33.3)     Breastfeeding postpartum women  9 (15.8)  4 (25.0)  5 (41.7)     Non-breastfeeding postpartum women  38 (66.7)  0 (0.0)  3 (25.0)    Reasons for hospitalization n = 128        0.0003*   Fetal and neonatal causes  15 (26.3)  0 (0.0)  1 (8.3)     Infections  12 (21.1)  0 (0.0)  1 (8.3)     Obstetric conditions  9 (15.8)  11 (68.8)  7 (58.3)     Labor and postpartum issues  9 (15.8)  1 (6.3)  0 (0.0)     Others  3 (5.2)  0 (0.0)  0 (0.0)    ATC class, n = 187        <0.0001*   Alimentary tract and metabolism  58 (51.3)  9 (53.6)  13 (76.5)     Genitourinary system and sex hormones  34 (30.1)  0 (0.0)  0 (0.0)     Anti-infective for systemic use  1 (0.9)  7 (43.8)  0 (0.0)     Others  20 (17.7)  0 (0.0)  4 (7.5)    Number of prescribed medications, n = 128  7.6 (2.4)  7.1 (2.3)  8.0 (2.0)  0.0136**  Variable  Drug–drug interaction (n = 57)  Frequency less than correct (n = 16)  Duplication of therapy (n = 12)  P-value  Pregnancy-puerperal cycle, n = 128        <0.0001*   Pregnant women  10 (17.5)  12 (75.0)  4 (33.3)     Breastfeeding postpartum women  9 (15.8)  4 (25.0)  5 (41.7)     Non-breastfeeding postpartum women  38 (66.7)  0 (0.0)  3 (25.0)    Reasons for hospitalization n = 128        0.0003*   Fetal and neonatal causes  15 (26.3)  0 (0.0)  1 (8.3)     Infections  12 (21.1)  0 (0.0)  1 (8.3)     Obstetric conditions  9 (15.8)  11 (68.8)  7 (58.3)     Labor and postpartum issues  9 (15.8)  1 (6.3)  0 (0.0)     Others  3 (5.2)  0 (0.0)  0 (0.0)    ATC class, n = 187        <0.0001*   Alimentary tract and metabolism  58 (51.3)  9 (53.6)  13 (76.5)     Genitourinary system and sex hormones  34 (30.1)  0 (0.0)  0 (0.0)     Anti-infective for systemic use  1 (0.9)  7 (43.8)  0 (0.0)     Others  20 (17.7)  0 (0.0)  4 (7.5)    Number of prescribed medications, n = 128  7.6 (2.4)  7.1 (2.3)  8.0 (2.0)  0.0136**  ATC, Anatomical Therapeutic Chemical. *Fisher’s Exact test; **Kruskal–Wallis test (the result considers all types of prescribing error). Bold: Statistically significant P-values. Pharmacist intervention types are shown in Table 4. The majority of pharmacist interventions were very significant (74.4%) and significant (19.6%). There was no extremely significant intervention. Table 4 Pharmacist intervention types classification by the clinical significance of intervention   N (%a)  Intervention type  Very significant  Significant  Somewhat significant  No significance  Total  Drug interactions  51 (40.8)  32 (97.0)  0 (0.0)  0 (0.0)  83 (49.4)  Inappropriate dosage interval  25 (20.0)  0 (0.0)  2 (22.2)  0 (0.0)  27 (16.1)   Higher than correct  12 (9.6)  0 (0.0)  0 (0.0)  0 (0.0)  12 (7.2)   Less than correct  13 (10.4)  0 (0.0)  2 (22.2)  0 (0.0)  15 (8.9)  Drug dosages  17 (13.6)  0 (0.0)  0 (0.0)  0 (0.0)  17 (10.1)   Over-dosage  7 (5.6)  0 (0.0)  0 (0.0)  0 (0.0)  7 (4.2)   Under-dosage  10 (8.0)  0 (0.0)  0 (0.0)  0 (0.0)  10 (5.9)  Duplication of therapy  14 (11.2)  0 (0.0)  0 (0.0)  0 (0.0)  14 (8.3)  Provision of drug information  2 (1.6)  1 (3.0)  6 (66.5)  0 (0.0)  9 (5.3)  Identification of adverse drug event  4 (3.2)  0 (0.0)  1 (11.1)  1 (100)  6 (3.6)  Duration of treatment  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Medication omitted from prescription  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Indication  3 (2.4)  0 (0.0)  0 (0.0)  0 (0.0)  3 (1.8)  Route of administration  1 (0.8)  0 (0.0)  0 (0.0)  0 (0.0)  1 (0.6)  Total  125 (74.4)  33 (19.6)  9 (5.4)  1 (0.6)  168 (100)    N (%a)  Intervention type  Very significant  Significant  Somewhat significant  No significance  Total  Drug interactions  51 (40.8)  32 (97.0)  0 (0.0)  0 (0.0)  83 (49.4)  Inappropriate dosage interval  25 (20.0)  0 (0.0)  2 (22.2)  0 (0.0)  27 (16.1)   Higher than correct  12 (9.6)  0 (0.0)  0 (0.0)  0 (0.0)  12 (7.2)   Less than correct  13 (10.4)  0 (0.0)  2 (22.2)  0 (0.0)  15 (8.9)  Drug dosages  17 (13.6)  0 (0.0)  0 (0.0)  0 (0.0)  17 (10.1)   Over-dosage  7 (5.6)  0 (0.0)  0 (0.0)  0 (0.0)  7 (4.2)   Under-dosage  10 (8.0)  0 (0.0)  0 (0.0)  0 (0.0)  10 (5.9)  Duplication of therapy  14 (11.2)  0 (0.0)  0 (0.0)  0 (0.0)  14 (8.3)  Provision of drug information  2 (1.6)  1 (3.0)  6 (66.5)  0 (0.0)  9 (5.3)  Identification of adverse drug event  4 (3.2)  0 (0.0)  1 (11.1)  1 (100)  6 (3.6)  Duration of treatment  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Medication omitted from prescription  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Indication  3 (2.4)  0 (0.0)  0 (0.0)  0 (0.0)  3 (1.8)  Route of administration  1 (0.8)  0 (0.0)  0 (0.0)  0 (0.0)  1 (0.6)  Total  125 (74.4)  33 (19.6)  9 (5.4)  1 (0.6)  168 (100)  a%Age related to column. Table 4 Pharmacist intervention types classification by the clinical significance of intervention   N (%a)  Intervention type  Very significant  Significant  Somewhat significant  No significance  Total  Drug interactions  51 (40.8)  32 (97.0)  0 (0.0)  0 (0.0)  83 (49.4)  Inappropriate dosage interval  25 (20.0)  0 (0.0)  2 (22.2)  0 (0.0)  27 (16.1)   Higher than correct  12 (9.6)  0 (0.0)  0 (0.0)  0 (0.0)  12 (7.2)   Less than correct  13 (10.4)  0 (0.0)  2 (22.2)  0 (0.0)  15 (8.9)  Drug dosages  17 (13.6)  0 (0.0)  0 (0.0)  0 (0.0)  17 (10.1)   Over-dosage  7 (5.6)  0 (0.0)  0 (0.0)  0 (0.0)  7 (4.2)   Under-dosage  10 (8.0)  0 (0.0)  0 (0.0)  0 (0.0)  10 (5.9)  Duplication of therapy  14 (11.2)  0 (0.0)  0 (0.0)  0 (0.0)  14 (8.3)  Provision of drug information  2 (1.6)  1 (3.0)  6 (66.5)  0 (0.0)  9 (5.3)  Identification of adverse drug event  4 (3.2)  0 (0.0)  1 (11.1)  1 (100)  6 (3.6)  Duration of treatment  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Medication omitted from prescription  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Indication  3 (2.4)  0 (0.0)  0 (0.0)  0 (0.0)  3 (1.8)  Route of administration  1 (0.8)  0 (0.0)  0 (0.0)  0 (0.0)  1 (0.6)  Total  125 (74.4)  33 (19.6)  9 (5.4)  1 (0.6)  168 (100)    N (%a)  Intervention type  Very significant  Significant  Somewhat significant  No significance  Total  Drug interactions  51 (40.8)  32 (97.0)  0 (0.0)  0 (0.0)  83 (49.4)  Inappropriate dosage interval  25 (20.0)  0 (0.0)  2 (22.2)  0 (0.0)  27 (16.1)   Higher than correct  12 (9.6)  0 (0.0)  0 (0.0)  0 (0.0)  12 (7.2)   Less than correct  13 (10.4)  0 (0.0)  2 (22.2)  0 (0.0)  15 (8.9)  Drug dosages  17 (13.6)  0 (0.0)  0 (0.0)  0 (0.0)  17 (10.1)   Over-dosage  7 (5.6)  0 (0.0)  0 (0.0)  0 (0.0)  7 (4.2)   Under-dosage  10 (8.0)  0 (0.0)  0 (0.0)  0 (0.0)  10 (5.9)  Duplication of therapy  14 (11.2)  0 (0.0)  0 (0.0)  0 (0.0)  14 (8.3)  Provision of drug information  2 (1.6)  1 (3.0)  6 (66.5)  0 (0.0)  9 (5.3)  Identification of adverse drug event  4 (3.2)  0 (0.0)  1 (11.1)  1 (100)  6 (3.6)  Duration of treatment  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Medication omitted from prescription  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Indication  3 (2.4)  0 (0.0)  0 (0.0)  0 (0.0)  3 (1.8)  Route of administration  1 (0.8)  0 (0.0)  0 (0.0)  0 (0.0)  1 (0.6)  Total  125 (74.4)  33 (19.6)  9 (5.4)  1 (0.6)  168 (100)  a%Age related to column. Moreover, there was an association between type of pharmacist intervention and the pregnancy-puerperal cycle (P < 0.0001, Fisher’s exact test); 80.0% of prescriptions for non-breastfeeding postpartum women needed intervention for the management of drug interactions. Multivariate logistic regression analysis showed that age (increased age), number of prescribed medications (higher number of drugs), reasons of hospitalization (obstetric conditions) and non-breastfeeding postpartum women are risk factors for prescribing errors (Table 5). The effects of these variables were tested individually and controlled by other covariates (main effects model). Table 5 Multivariate logistic regression analysis of risk factors for prescribing errors (n = 1826 prescriptions) Variable  Odds ratio  95%CI  P-value  Age  1.0  1.0–1.1  <0.0001  Number of prescribed medications  1.2  1.1–1.3  <0.0001  Reasons for hospitalization/co-morbidities         Fetal and neonatal causes  1.3  0.7–2.4  0.4   Pregestational co-morbidities  0.8  0.5–1.3  0.3   Obstetric conditions  2.2  1.4–3.3  0.0003   Others  1.0  0.7–1.6  0.8  Pregnancy-puerperal cycle         Pregnant women—first trimester  1.6  0.9–2.5  0.067   Pregnant women—second trimester  1.1  0.8–1.5  0.5   Breastfeeding postpartum women  0.8  0.6–1.1  0.1   Non-breastfeeding postpartum women  3.9  2.5–6.1  <0.0001  Variable  Odds ratio  95%CI  P-value  Age  1.0  1.0–1.1  <0.0001  Number of prescribed medications  1.2  1.1–1.3  <0.0001  Reasons for hospitalization/co-morbidities         Fetal and neonatal causes  1.3  0.7–2.4  0.4   Pregestational co-morbidities  0.8  0.5–1.3  0.3   Obstetric conditions  2.2  1.4–3.3  0.0003   Others  1.0  0.7–1.6  0.8  Pregnancy-puerperal cycle         Pregnant women—first trimester  1.6  0.9–2.5  0.067   Pregnant women—second trimester  1.1  0.8–1.5  0.5   Breastfeeding postpartum women  0.8  0.6–1.1  0.1   Non-breastfeeding postpartum women  3.9  2.5–6.1  <0.0001  CI, confidence interval. Dependent variable = prescribing error (yes or no); Independent variables = age (quantitative continuous); number of prescribed medications (quantitative discrete); reasons for hospitalization/co-morbidities (categorical—reference: labor and postpartum) and pregnancy-puerperal cycle (categorical—reference: pregnant women—third trimester). Bold: Statistically significant P-values. Table 5 Multivariate logistic regression analysis of risk factors for prescribing errors (n = 1826 prescriptions) Variable  Odds ratio  95%CI  P-value  Age  1.0  1.0–1.1  <0.0001  Number of prescribed medications  1.2  1.1–1.3  <0.0001  Reasons for hospitalization/co-morbidities         Fetal and neonatal causes  1.3  0.7–2.4  0.4   Pregestational co-morbidities  0.8  0.5–1.3  0.3   Obstetric conditions  2.2  1.4–3.3  0.0003   Others  1.0  0.7–1.6  0.8  Pregnancy-puerperal cycle         Pregnant women—first trimester  1.6  0.9–2.5  0.067   Pregnant women—second trimester  1.1  0.8–1.5  0.5   Breastfeeding postpartum women  0.8  0.6–1.1  0.1   Non-breastfeeding postpartum women  3.9  2.5–6.1  <0.0001  Variable  Odds ratio  95%CI  P-value  Age  1.0  1.0–1.1  <0.0001  Number of prescribed medications  1.2  1.1–1.3  <0.0001  Reasons for hospitalization/co-morbidities         Fetal and neonatal causes  1.3  0.7–2.4  0.4   Pregestational co-morbidities  0.8  0.5–1.3  0.3   Obstetric conditions  2.2  1.4–3.3  0.0003   Others  1.0  0.7–1.6  0.8  Pregnancy-puerperal cycle         Pregnant women—first trimester  1.6  0.9–2.5  0.067   Pregnant women—second trimester  1.1  0.8–1.5  0.5   Breastfeeding postpartum women  0.8  0.6–1.1  0.1   Non-breastfeeding postpartum women  3.9  2.5–6.1  <0.0001  CI, confidence interval. Dependent variable = prescribing error (yes or no); Independent variables = age (quantitative continuous); number of prescribed medications (quantitative discrete); reasons for hospitalization/co-morbidities (categorical—reference: labor and postpartum) and pregnancy-puerperal cycle (categorical—reference: pregnant women—third trimester). Bold: Statistically significant P-values. Discussion This study at high-risk obstetric inpatient unit found 7.0% of prescribing errors. Recent studies showed a similar incidence in other medical areas, between 7.1 and 8.8% [2, 6, 11, 22]. The most common prescription errors were drug–drug interactions, incorrect frequency and improper dose. Higher maternal age, higher number of prescribed medications, reasons of and non-breastfeeding postpartum women were factors whom independent increasing the risk of prescribing errors. This study did not focus on the reasons for a prescription error occurring; however, some variables related to patients and prescriptions were studied. It was found that prescriptions containing errors were generally those with a greater number of drugs. Likewise, another study showed that for each additional medication item, the risk of a prescribing error increased by 14% [23]. Drug–drug interaction, incorrect frequency and improper dose were the most common prescribing errors in our study. Errors related to dose and frequency were also more prevalent in others the literature [6, 9, 24]. In a study conducted at the maternity and children’s hospital in Spanish, the prescriptions of obstetric and gynecology inpatients analyzed there was a prevalence of sequential therapy (54.5%) (maintenance of an intravenous medication when it is no longer is necessary), inappropriate dosage interval (16.6%) and dosage error (from 1.5- to 10-fold higher than normal) (6.3%) in the evaluated prescriptions [9]. Drug interactions were the highlight in our study, similarly to the prospective observational study conducted in three medical wards of the public teaching hospital in India which observed 68.2% of errors being caused by drug interactions [25]. In prospective study, including Brazilian pregnant and breastfeeding women admitted to the intensive unit care at the Women’s Hospital demonstrated 175 different combinations of potential drug interaction were identified in 305 prescriptions [26]. Moreover, an association between error type and pregnancy-puerperal cycle was found mainly because drug–drug interaction was strongly present in cases of non-breastfeeding postpartum women prescribing errors (specifically improper cabergoline–metoclopramide association). Alimentary tract and metabolism drugs were the most associated with errors. This result is different from other studies, which found that anti-microbials and cardiovascular system drugs were most frequently involved in errors, probably due to the specific characteristics of the study group [6, 9, 11, 25, 27]. HAMs can be involved in the most serious prescribing errors and we found a low incidence of HAMs involved with prescribing errors in the current study (0.4%). It is known that the nature of prescribing errors is multifactorial such as work environment (heavy workload, interruptions, pressure from the other staff), individual factors (tiredness and stress), task factors (lack of familiarity with medication) and patient factors (complex patient, poor communication with patient) [1, 22, 28]. The average age of 101 women which presented errors in theirs prescriptions was ~30 years, similar age was found in other studies in which women required admission to the obstetrics unit [27, 29]. In the study conducted in a women’s health unit of an Australian teaching hospital which a total of 454 potential medication-related problems were identified over the 5-week period among 241 patients, mean age in years with and without prescribing showed association statistically significant (P = 0.002), similar with our study (P = 0.0083) [27, 29]. In this study, pregnancy-puerperal cycle was associated with a significantly increased likelihood of experiencing with prescribing errors. This occurring given that pregnancy-puerperal cycle (mainly pregnant women in third trimester and breastfeeding postpartum or not breastfeeding) has been associated with number of prescribed medication, increased pregestational co-morbidities and obstetric complications [27, 30, 31]. Pharmacist interventions are essential to avoid drug-related problems, to contribute to the rationalization of drug therapy and to improve the quality of hospital prescribing [9, 11]. The factors may influence in frequency and acceptance interventions were the level of pharmacist’s individual skills, acknowledgment and professionalism with physicians. This study focused on 168 pharmaceutical interventions and 98.9% of the overall interventions were accepted. The results showed values similar in a study conducted by Fernandez-Llamazares et al. [9] in obstetric and gynecology with 89.9 % of the overall 702 interventions being accepted (P < 0.05). The majority of pharmacist interventions performed in this study were classified as ‘very significant’ and ‘significant’ in terms of their impact. These categories were recommendation on patient care for avoiding potential or existing dysfunction in a major organ or avoiding a serious adverse drug interaction or contraindication to use or to improve the quality of life of the patient, respectively. The study by Fernandez-Llamazares et al. [9] who also classified using the Overhage et al. [17] method observed 85.9% ‘significant’ interventions made by clinical pharmacists for obstetric and gynecology patients. The main types of intervention agreed with the main types of prescribing error, as expected. Data collection was done in a high-risk obstetric inpatient unit, where the most prevalent cases are hypertensive syndromes, diabetes associated with pregnancy and preterm labor. However, these most common complications are linked to assistance protocols of conduct that are strongly followed by residents. In the study inpatient unit, only women with newborn complication or fetal deaths are hospitalized and for this reason, these women cannot breastfeed. Breastfeeding women with their babies, who are the majority, do not stay in this sector. This leads to the conclusion that less frequent situations are also more associated with prescribing errors, situation that is very clear among postpartum women with lactation inhibition regardless of fetal death reason, HIV seropositive, use of immunosuppressant or other drugs that contraindicate the breastfeeding. The same to observe to HAM that involved the use of enoxaparin, tramadol and promethazine, medications that are not routinely used among pregnant women, which are also associated with more severe complications and use of a large number of medications. All of this reinforces the importance of pharmacist interventions, especially in the usual same cases in unit. This study is limited due to physicians were aware of the research once they had signed the informed consent; therefore it is hypothesized that they may have paid more attention to prescriptions and have made fewer prescribing errors. By having a high medical staff (professor/assistant), this may also have generated a bias. Thus, the issue of medication reconciliation was not included, and it should be done to identify/avoid mainly omission errors (the incidence of this error could be higher). Because this study was conducted at a single center, the conclusions that may be drawn are limited. As study strength, this research was performed in a teaching hospital with high patient turnover and 20 beds for high-risk pregnant and postpartum women’s care. Other advantages are the electronic prescriptions, the access to databases for evaluation of drug information and a pharmacist team on hand every day of the week. Moreover, the interventions were well accepted by the medical team, for the benefit of hospitalized patients. The need for inclusion of a clinical pharmacist in the multidisciplinary team must be continually emphasized, mainly in high-risk pregnant and postpartum women’s care. However, it is important to clarify that the presence of a clinical pharmacist is not the only solution to prescription error. Due to its complexity, a set of measures must be implemented. Studies recommend the introduction of electronic prescriptions in institutions that still use handwritten prescriptions. In this study, electronic prescriptions were evaluated and errors were found. This system can eliminate particular error types and prevent up to a quarter of errors, but not all [23]. There are errors specific to the electronic prescribing system, such as incorrect product selected. Conclusion In a high-risk obstetric inpatient unit we found 7.0% of prescribing errors, the most common were drug–drug interactions, incorrect frequency and improper dose, with clinical significance of the significant type, occurring mostly in puerperium, among non-breastfeeding women. There was low incidence of serious error. Higher maternal age, higher number of prescribed medications, reasons of hospitalization and non-breastfeeding postpartum women were factors increasing the risk of prescribing errors. Alimentary tract and metabolism drugs were most associated with errors. The rate of clinical pharmacist intervention acceptance was high. The inclusion of a clinical pharmacist in the hospital multidisciplinary team could improve security, contribute to the rationalization of drug therapy and improve the quality of care in a high-risk obstetric inpatient unit. Acknowledgements The authors would like to thank the statistical office of CAISM-UNICAMP for performing this statistical analysis. This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. Funding This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. References 1 Dean B, Schachter M, Vincent C et al.  . Prescribing errors in hospital inpatients: their incidence and clinical significance. Qual Saf Health Care  2002; 11: 340– 4. Google Scholar CrossRef Search ADS PubMed  2 Al-Dhawailie AA. Inpatient prescribing errors and pharmacist intervention at a teaching hospital in Saudi Arabia. Saudi Pharm J  2011; 19: 193– 6. Google Scholar CrossRef Search ADS PubMed  3 Leape LL, Cullen DJ, Clapp MD et al.  . Pharmacist participation on physician rounds and adverse drug events in the intensive care unit. JAMA  1999; 282: 267– 70. 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Google Scholar CrossRef Search ADS PubMed  27 Thompson R, Whennan L, Liang J et al.  . Investigating the frequency and nature of medication-related problems in the Women’s Health Unit of an Australian Tertiary Teaching Hospital. Ann Pharmacother  2015; 49: 770– 66. Google Scholar CrossRef Search ADS PubMed  28 Ross S, Hamilton L, Ryan C et al.  . Who makes prescribing decisions in hospital inpatients? An observational study. Postgrad Med J  2012; 88: 507– 10. Google Scholar CrossRef Search ADS PubMed  29 Smedberg J, Bråthen M, Waka MS et al.  . Medication use and drug-related problems among women at maternity wards-a cross-sectional study from two Norwegian hospitals. Eur J Clin Pharmacol  2016; 72: 849– 57. Google Scholar CrossRef Search ADS PubMed  30 Rohra DK, Das N, Azam SI et al.  . Drug-prescribing patterns during pregnancy in the tertiary care hospitals of Pakistan: a cross sectional study. BMC Pregnancy Childbirth  2008; 15: 8– 24. 31 Bayrampour H, Heaman M. Advanced maternal age and the risk of cesarean birth: a systematic review. Birth  2010; 37: 219– 26. 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

Pharmacist interventions in high-risk obstetric inpatient unit: a medication safety issue

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Oxford University Press
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© 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
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10.1093/intqhc/mzy054
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Abstract

Abstract Objectives The aim of this study was to report number, type and severity of prescribing errors and pharmacist interventions in high-risk pregnant and postpartum women. Design A prospective cross-sectional, observational study. Setting A high-risk obstetric inpatient unit of a Women’s Hospital in Brazil. Participants About 1826 electronic prescriptions for 549 women in the high-risk obstetrics inpatient unit were included. Interventions When the pharmacist detected potential prescribing errors, interventions were suggested. Main Outcome Measures Prescriptions were evaluated by clinical pharmacist to identify the type, frequency and severity of prescribing errors and rate of clinical pharmacist intervention acceptance in a high-risk obstetric inpatient. Results A total of 1826 prescriptions were reviewed with 128 errors (7.0%). The most frequent errors were drug interaction (43.8%), incorrect frequency (21.5%) and improper dose (13.1%). One-hundred and sixty-eight interventions were made by pharmacists, 98.8% of which were accepted by prescribers. Higher maternal age (OR 1.0 (95%CI 1.0–1.1)), higher number of prescribed medications (OR 1.2 (95%CI 1.1–1.3)), obstetric conditions (OR 2.2 (95%CI 1.4–3.3)) and non-breastfeeding postpartum women (OR 3.9 (95% CI 2.5–6.1)) were the independent factors associated with prescribing errors identified through multivariate analysis. Conclusions The most common prescription errors related to drug interactions, incorrect frequency and higher number of prescribed medications. The rate of pharmacist acceptance intervention was high. pharmacist intervention, prescribing error, medication safety, pregnancy, postpartum period Introduction Pharmacists are able to identify errors in the course of routine work [1, 2]. The clinical pharmacist in the inpatient medical team during daily rounds showed a reduction of preventable adverse drug events caused by prescribing errors [3]. Moreover, prescribing errors identified by clinical pharmacy services are associated with decreased costs of care [4]. A prescribing error happens when, as a result of a prescribing decision or prescription writing process, there is an unintentional reduction in the possibility of treatment being effective or an increase in the risk of harm when compared with generally accepted practice [5]. This can be associated with prescribing without taking into account the patient’s clinical status, failure to communicate essential information and/or transcription error [5]. A systematic review of the prevalence, incidence and nature of prescribing errors in hospital inpatients showed that these errors were common and affected 7% of medication orders posing a health public problem [6]. There are numerous gaps in knowledge on the consequences of drug prescription for both the mother and the fetus [7]. Furthermore, rational use of drugs in pregnancy is an essential component of prenatal care for women and their healthcare providers [8]. Studies conducted to describe pharmacist interventions and prescribing errors, especially in the obstetric inpatients unit, are still scarce. One study identified in this area reported prescribing errors intercepted by clinical pharmacists in obstetrics, as well as in pediatrics, in a tertiary hospital in Spain and demonstrated that pharmacist interventions can reduce prescribing errors and improve the quality and efficiency of care provided [9]. Therefore, the aim of this study was to report number, type and severity of prescribing errors and pharmacist interventions at inpatient high-risk pregnant and postpartum women. Patients and methods Study setting and design A prospective cross-sectional study was performed in a high-risk obstetric inpatient unit of a Women’s Hospital, in Brazil (142 beds) from September 2014 to March 2015. This study was approved by the Research Ethics Committee of the University of Campinas (approval number: 26709414.1.0000.5404) and physicians signed an informed consent form authorizing use of data. The Women’s Hospital at the University of Campinas is a teaching hospital where residents, under supervision of a medical professor, are responsible for prescriptions. A pharmaceutical service is provided from 7 am to 7 pm on weekdays. The high-risk obstetric inpatient unit has 20 beds. The obstetric team has 18 medical professors and assistants and 33 residents who work under the supervision of medical professor/assistant. The team changed according to their work schedule. There is, therefore, high staff turnover. Sample size was determined by statistical calculation based on prevalence of 8.4% of prescribing errors confidence level of 95% and acceptable absolute error of 0.015, resulting in a minimum of 1314 prescriptions [10, 11]. The statistical software used was Statistical Analysis System (SAS) 9.4 version for Windows. Eligibility criteria All medical prescriptions for patients in the high-risk obstetric inpatient, done on the weekday mornings. All physicians signined the informed consent. Analysis of medication orders and pharmacist interventions Since March 2003, this hospital has used the electronic prescription system developed in the institution: a barcode system is routinely included for both medication dispensing and administration. Daily, each prescription was accessed electronically and electronic patient records were checked to collect data such as age, characteristics of patients (pregnant women and breastfeeding or non-breastfeeding postpartum women), gestational age and trimester and reasons of hospitalization. Medication orders were evaluated by insertion of each drug name prescribed into the databases Micromedex® [12], Up to Date® [13] and E-lactancia [14], and also by consulting Briggs et al. [15]. In this assessment, prescription data were compared with the database regarding duration of treatment, dosage interval, the dosage, dosage form, route of administration, infusion rate, drug–drug interactions, drug–food interactions and therapeutic duplication. The pharmacist discussed the results of the prescription evaluations in the unit daily. If there are inconsistencies between prescription and literature/database cited (potential prescribing errors), the pharmacist suggested recommendations (pharmacist intervention). Demographics data, period of pregnancy-puerperal cycle, being or not breastfeeding and reasons of hospitalization also were evaluated and categorized for analysis. After medical assessment of published data and its association with women’s clinical condition, the physician accepted or rejected the proposed pharmacist intervention. The severity of the error and impact of the pharmacist intervention were classified. The data collection was performed by two clinical pharmacists, one of them a clinical pharmacist specialist employed by the Brazilian Society of Hospital Pharmacy and Health Services. The data analysis (classification of prescribing errors and pharmacist interventions) was performed by a third professional, a clinical pharmacy Master’s student under the supervision of a PhD professor. This third pharmacist was necessary to avoid bias. Classification of prescribing errors Prescribing errors found by the pharmacist were quantified and classified according to an adaptation of an index developed by the National Coordinating Council for Medication Error Reporting and Prevention (NCCMERP) [16] Also, two categories were included: drug not secure in pregnancy and drug not secure in breastfeeding, in relation to characteristics of the studied inpatient unit. The severity of the error was assessed using a scale proposed by Overhage et al. [17] generating a categorical variable with five possible categories: (1) potentially lethal; (2) serious; (3) significant; (4) minor and (5) no error. The Anatomical Therapeutic Chemical (ATC) classification was used to classify drugs involved in prescribing errors into therapeutic classes [18]. Moreover, these drugs were also classified into high-alert medication (HAMs) or not HAMs. HAMs are defined as medications that, if involved in erroneous medication order, carry a greater risk of significant harm or death [19]. In this study, HAMs were compiled from the Institute for Safer Medication Practices’ HAMs list [20]. Classification of pharmacist interventions The pharmacist interventions were subsequently quantified and classified according to an adaptation of the methods used by Leape et al. [3]. Therefore, pharmacist interventions were categorized. The impact of the pharmacist interventions on women care was assessed using a scale proposed by Overhage et al. [17] generating a categorical variable with six possible categories: (1) extremely significant; (2) very significant; (3) significant; (4) somewhat significant; (5) insignificant and (6) harmful. Pharmacist interventions were also evaluated by acceptance rate (accepted or not accepted). Recommendations were considered as accepted if the physician implemented the change suggested by the pharmacist within 24 h of the intervention. Demographics data, period of pregnancy-puerperal cycle, being or not breastfeeding and reasons of hospitalization also were evaluated and categorized for analysis Data analysis A database was created using Microsoft Excel® to record all data collected from records and prescriptions, including prescribing errors and their details, and pharmacist interventions and their details. This database did not include patients’ names or other identification information. Student’s t-test, chi-square test and Mann–Whitney test were used to compare demographics and reasons of hospitalization with or not prescribing errors. Fisher’s Exact and Kruskal–Wallis tests were performed to verify associations among some variables and prescribing errors/pharmacist interventions; multivariate logistic regression analysis of risk factors for prescribing errors was also performed; 2-sided P-values <0.05 were considered statistically significant (SAS, 9.4 version). All recommendations of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement were followed for this study [21]. RESULTS Physicians performed 1826 prescriptions for 549 women in the high-risk obstetric inpatient unit (3.3 ± 1.8 prescriptions/women). About 128 erroneous medication orders were found (7.0%), responsible for 130 interventions related to errors and 101 patients (18.4%) had at least one erroneous prescription. One-hundred and sixty-eight pharmacist interventions were made related to 128 prescriptions in 121 patients (38 not associated with errors). The acceptance rate of the pharmacist interventions by prescribers was 98.8%. Incidences of prescribing errors and interventions are shown in Table 1. Table 1 Prescribing errors and interventions incidence in high-risk obstetric inpatient unit Parameter  Value  Total number of patients  549  Total number of medical prescriptions  1826  Prescribing errors     Total number of medical prescriptions with      one prescribing error  126    two prescribing errors  2   Total number of patients with prescribing errors  101   Incidence of patients with error (%)  18.4   Incidence of prescription with errors (%)  7.0   Incidence of prescription with errors involving high-risk medications (%)  0.4  Pharmacist interventions     Total number of pharmacist interventions  168   Total number of erroneous medication orders intercepted by pharmacista  128   Total number of pharmacist intervention related with errors  130   Total number of pharmacist intervention not related with errors  38   Total number of patients with pharmacist interventions  121   Incidence of pharmacist interventions accepted (%)  98.8  Parameter  Value  Total number of patients  549  Total number of medical prescriptions  1826  Prescribing errors     Total number of medical prescriptions with      one prescribing error  126    two prescribing errors  2   Total number of patients with prescribing errors  101   Incidence of patients with error (%)  18.4   Incidence of prescription with errors (%)  7.0   Incidence of prescription with errors involving high-risk medications (%)  0.4  Pharmacist interventions     Total number of pharmacist interventions  168   Total number of erroneous medication orders intercepted by pharmacista  128   Total number of pharmacist intervention related with errors  130   Total number of pharmacist intervention not related with errors  38   Total number of patients with pharmacist interventions  121   Incidence of pharmacist interventions accepted (%)  98.8  aTwo prescriptions had two errors. Table 1 Prescribing errors and interventions incidence in high-risk obstetric inpatient unit Parameter  Value  Total number of patients  549  Total number of medical prescriptions  1826  Prescribing errors     Total number of medical prescriptions with      one prescribing error  126    two prescribing errors  2   Total number of patients with prescribing errors  101   Incidence of patients with error (%)  18.4   Incidence of prescription with errors (%)  7.0   Incidence of prescription with errors involving high-risk medications (%)  0.4  Pharmacist interventions     Total number of pharmacist interventions  168   Total number of erroneous medication orders intercepted by pharmacista  128   Total number of pharmacist intervention related with errors  130   Total number of pharmacist intervention not related with errors  38   Total number of patients with pharmacist interventions  121   Incidence of pharmacist interventions accepted (%)  98.8  Parameter  Value  Total number of patients  549  Total number of medical prescriptions  1826  Prescribing errors     Total number of medical prescriptions with      one prescribing error  126    two prescribing errors  2   Total number of patients with prescribing errors  101   Incidence of patients with error (%)  18.4   Incidence of prescription with errors (%)  7.0   Incidence of prescription with errors involving high-risk medications (%)  0.4  Pharmacist interventions     Total number of pharmacist interventions  168   Total number of erroneous medication orders intercepted by pharmacista  128   Total number of pharmacist intervention related with errors  130   Total number of pharmacist intervention not related with errors  38   Total number of patients with pharmacist interventions  121   Incidence of pharmacist interventions accepted (%)  98.8  aTwo prescriptions had two errors. There was an association between the number of prescribed drugs and the type of pharmacist intervention (P = 0.0003; Kruskal–Wallis test). Interventions for the management of drug interactions and therapeutic duplications were common for prescriptions with the highest number of prescribed drugs (mean 7.7 ± 2.5 and 8.0 ± 1.8 drugs, respectively). Prescriptions with interventions for management of improper doses and wrong drug due to adverse effects contained less prescribed drugs (mean 5.5 ± 2.3 and 4.5 ± 1.5 drugs, respectively). Demographics and reasons of women's hospitalization according to occurrence of prescribing errors described in Table 2. Table 2 Demographics and reasons for hospitalization of women according to the occurrence of prescribing errors Characteristics  With prescribing errors  Without prescribing errors  Total  P-value*  Number of patients  101  448  549    Mean age in years (±SD)  29.4 ± 7.5  27.4 ± 6.8  27.8 ± 7.0  0.0083**  Pregnancy-puerperal cycle (n, %)        <0.0001***   Pregnant women—third trimester  36 (35.6)  177 (39.4)  213 (38.8)     Pregnant women—second trimester  22 (21.8)  109 (24.4)  131 (23.9)     Pregnant women—first trimester  6 (5.9)  27 (6.0)  33 (6.0)     Breastfeeding postpartum women  12 (11.9)  110 (24.6)  122 (22.2)     Non-breastfeeding postpartum women  25 (24.8)  25 (5.6)  50 (9.1)    Mean gestational age in weeks (±SD)  27.0 ± 8.0  27.3 ± 8.6  27.2 ± 8.5  0.8**  Reasons of hospitalization (n, %)        0.3***   Fetal and neonatal causes  26 (26.3)  98 (22.0)  124 (22.7)     Pregestational co-morbidities  18 (18.2)  68 (15.3)  86 (15.8)     Obstetric conditions  15 (15.2)  51 (11.4)  66 (12.1)     Labor and postpartum issues  13 (13.1)  91 (20.4)  104 (19.1)     Others  27 (27.2)  139 (30.9)  166 (30.3)    Mean number of causes of hospitalization (±SD)  2.0 ± 1.1  1.9 ± 0.9  1.9 ± 1.0  0.1****  Mean number of prescribed medications (±SD)  7.2 ± 2.5  6.1 ± 2.3  6.4 ± 2.4  <0.0001****  Characteristics  With prescribing errors  Without prescribing errors  Total  P-value*  Number of patients  101  448  549    Mean age in years (±SD)  29.4 ± 7.5  27.4 ± 6.8  27.8 ± 7.0  0.0083**  Pregnancy-puerperal cycle (n, %)        <0.0001***   Pregnant women—third trimester  36 (35.6)  177 (39.4)  213 (38.8)     Pregnant women—second trimester  22 (21.8)  109 (24.4)  131 (23.9)     Pregnant women—first trimester  6 (5.9)  27 (6.0)  33 (6.0)     Breastfeeding postpartum women  12 (11.9)  110 (24.6)  122 (22.2)     Non-breastfeeding postpartum women  25 (24.8)  25 (5.6)  50 (9.1)    Mean gestational age in weeks (±SD)  27.0 ± 8.0  27.3 ± 8.6  27.2 ± 8.5  0.8**  Reasons of hospitalization (n, %)        0.3***   Fetal and neonatal causes  26 (26.3)  98 (22.0)  124 (22.7)     Pregestational co-morbidities  18 (18.2)  68 (15.3)  86 (15.8)     Obstetric conditions  15 (15.2)  51 (11.4)  66 (12.1)     Labor and postpartum issues  13 (13.1)  91 (20.4)  104 (19.1)     Others  27 (27.2)  139 (30.9)  166 (30.3)    Mean number of causes of hospitalization (±SD)  2.0 ± 1.1  1.9 ± 0.9  1.9 ± 1.0  0.1****  Mean number of prescribed medications (±SD)  7.2 ± 2.5  6.1 ± 2.3  6.4 ± 2.4  <0.0001****  SD = Standard deviation. *Comparison between patients with prescribing errors and patients without prescribing errors. **Student’s t-test; ***Chi-square test; ****Mann–Whitney test. Bold: Statistically significant P-values. Table 2 Demographics and reasons for hospitalization of women according to the occurrence of prescribing errors Characteristics  With prescribing errors  Without prescribing errors  Total  P-value*  Number of patients  101  448  549    Mean age in years (±SD)  29.4 ± 7.5  27.4 ± 6.8  27.8 ± 7.0  0.0083**  Pregnancy-puerperal cycle (n, %)        <0.0001***   Pregnant women—third trimester  36 (35.6)  177 (39.4)  213 (38.8)     Pregnant women—second trimester  22 (21.8)  109 (24.4)  131 (23.9)     Pregnant women—first trimester  6 (5.9)  27 (6.0)  33 (6.0)     Breastfeeding postpartum women  12 (11.9)  110 (24.6)  122 (22.2)     Non-breastfeeding postpartum women  25 (24.8)  25 (5.6)  50 (9.1)    Mean gestational age in weeks (±SD)  27.0 ± 8.0  27.3 ± 8.6  27.2 ± 8.5  0.8**  Reasons of hospitalization (n, %)        0.3***   Fetal and neonatal causes  26 (26.3)  98 (22.0)  124 (22.7)     Pregestational co-morbidities  18 (18.2)  68 (15.3)  86 (15.8)     Obstetric conditions  15 (15.2)  51 (11.4)  66 (12.1)     Labor and postpartum issues  13 (13.1)  91 (20.4)  104 (19.1)     Others  27 (27.2)  139 (30.9)  166 (30.3)    Mean number of causes of hospitalization (±SD)  2.0 ± 1.1  1.9 ± 0.9  1.9 ± 1.0  0.1****  Mean number of prescribed medications (±SD)  7.2 ± 2.5  6.1 ± 2.3  6.4 ± 2.4  <0.0001****  Characteristics  With prescribing errors  Without prescribing errors  Total  P-value*  Number of patients  101  448  549    Mean age in years (±SD)  29.4 ± 7.5  27.4 ± 6.8  27.8 ± 7.0  0.0083**  Pregnancy-puerperal cycle (n, %)        <0.0001***   Pregnant women—third trimester  36 (35.6)  177 (39.4)  213 (38.8)     Pregnant women—second trimester  22 (21.8)  109 (24.4)  131 (23.9)     Pregnant women—first trimester  6 (5.9)  27 (6.0)  33 (6.0)     Breastfeeding postpartum women  12 (11.9)  110 (24.6)  122 (22.2)     Non-breastfeeding postpartum women  25 (24.8)  25 (5.6)  50 (9.1)    Mean gestational age in weeks (±SD)  27.0 ± 8.0  27.3 ± 8.6  27.2 ± 8.5  0.8**  Reasons of hospitalization (n, %)        0.3***   Fetal and neonatal causes  26 (26.3)  98 (22.0)  124 (22.7)     Pregestational co-morbidities  18 (18.2)  68 (15.3)  86 (15.8)     Obstetric conditions  15 (15.2)  51 (11.4)  66 (12.1)     Labor and postpartum issues  13 (13.1)  91 (20.4)  104 (19.1)     Others  27 (27.2)  139 (30.9)  166 (30.3)    Mean number of causes of hospitalization (±SD)  2.0 ± 1.1  1.9 ± 0.9  1.9 ± 1.0  0.1****  Mean number of prescribed medications (±SD)  7.2 ± 2.5  6.1 ± 2.3  6.4 ± 2.4  <0.0001****  SD = Standard deviation. *Comparison between patients with prescribing errors and patients without prescribing errors. **Student’s t-test; ***Chi-square test; ****Mann–Whitney test. Bold: Statistically significant P-values. Drug–drug interaction (57 (43.8%)); frequency incorrect (28 (21.5%))—higher than correct (12 (9.2%)) or lower than correct (16 (12.3%)) and improper dose (17 (13.1%))—over-dosage (6 (4.6%)) or under-dosage (11 (8.5%)) were the most common errors, followed by duplication of therapy (14 (10.8%)); wrong drug (6 (4.6%)); wrong duration of treatment (4 (3.1%)); and unintentional omission of drug (4 (3.1%)), which were classified as significant (74 (56.9%)) and serious (56 (43.1%)) errors. A closer look at the ATC sub-therapeutic categories shows that alimentary tract and metabolism (94 (50.3%)) was the drug class most involved with prescribing errors, followed by genitourinary system and sex hormones (35 (18.7%)) and anti-infective for systemic use (19 (10.2%)). The most frequently recorded individual medications associated with an error were metoclopramide (58 (31.0%)), cabergoline (35 (18.7%)) and ranitidine (15 (8.0%)). It is observed that the total number of drugs involved in prescribing errors is higher than the total errors because one error may include one or more drugs involved. The HAMs involved in prescribing errors were enoxaparin, tramadol and promethazine. The associations between the most prescribing errors and pregnancy-puerperal cycle, ATC class and number of medications prescribed are shown in Table 3. Non-breastfeeding women had the highest drug–drug interaction due to improper cabergoline–metoclopramide association. Table 3 Association of prescribing errors and pregnancy-puerperal cycle, reasons for hospitalization, ATC class and number of prescribed medications Variable  Drug–drug interaction (n = 57)  Frequency less than correct (n = 16)  Duplication of therapy (n = 12)  P-value  Pregnancy-puerperal cycle, n = 128        <0.0001*   Pregnant women  10 (17.5)  12 (75.0)  4 (33.3)     Breastfeeding postpartum women  9 (15.8)  4 (25.0)  5 (41.7)     Non-breastfeeding postpartum women  38 (66.7)  0 (0.0)  3 (25.0)    Reasons for hospitalization n = 128        0.0003*   Fetal and neonatal causes  15 (26.3)  0 (0.0)  1 (8.3)     Infections  12 (21.1)  0 (0.0)  1 (8.3)     Obstetric conditions  9 (15.8)  11 (68.8)  7 (58.3)     Labor and postpartum issues  9 (15.8)  1 (6.3)  0 (0.0)     Others  3 (5.2)  0 (0.0)  0 (0.0)    ATC class, n = 187        <0.0001*   Alimentary tract and metabolism  58 (51.3)  9 (53.6)  13 (76.5)     Genitourinary system and sex hormones  34 (30.1)  0 (0.0)  0 (0.0)     Anti-infective for systemic use  1 (0.9)  7 (43.8)  0 (0.0)     Others  20 (17.7)  0 (0.0)  4 (7.5)    Number of prescribed medications, n = 128  7.6 (2.4)  7.1 (2.3)  8.0 (2.0)  0.0136**  Variable  Drug–drug interaction (n = 57)  Frequency less than correct (n = 16)  Duplication of therapy (n = 12)  P-value  Pregnancy-puerperal cycle, n = 128        <0.0001*   Pregnant women  10 (17.5)  12 (75.0)  4 (33.3)     Breastfeeding postpartum women  9 (15.8)  4 (25.0)  5 (41.7)     Non-breastfeeding postpartum women  38 (66.7)  0 (0.0)  3 (25.0)    Reasons for hospitalization n = 128        0.0003*   Fetal and neonatal causes  15 (26.3)  0 (0.0)  1 (8.3)     Infections  12 (21.1)  0 (0.0)  1 (8.3)     Obstetric conditions  9 (15.8)  11 (68.8)  7 (58.3)     Labor and postpartum issues  9 (15.8)  1 (6.3)  0 (0.0)     Others  3 (5.2)  0 (0.0)  0 (0.0)    ATC class, n = 187        <0.0001*   Alimentary tract and metabolism  58 (51.3)  9 (53.6)  13 (76.5)     Genitourinary system and sex hormones  34 (30.1)  0 (0.0)  0 (0.0)     Anti-infective for systemic use  1 (0.9)  7 (43.8)  0 (0.0)     Others  20 (17.7)  0 (0.0)  4 (7.5)    Number of prescribed medications, n = 128  7.6 (2.4)  7.1 (2.3)  8.0 (2.0)  0.0136**  ATC, Anatomical Therapeutic Chemical. *Fisher’s Exact test; **Kruskal–Wallis test (the result considers all types of prescribing error). Bold: Statistically significant P-values. Table 3 Association of prescribing errors and pregnancy-puerperal cycle, reasons for hospitalization, ATC class and number of prescribed medications Variable  Drug–drug interaction (n = 57)  Frequency less than correct (n = 16)  Duplication of therapy (n = 12)  P-value  Pregnancy-puerperal cycle, n = 128        <0.0001*   Pregnant women  10 (17.5)  12 (75.0)  4 (33.3)     Breastfeeding postpartum women  9 (15.8)  4 (25.0)  5 (41.7)     Non-breastfeeding postpartum women  38 (66.7)  0 (0.0)  3 (25.0)    Reasons for hospitalization n = 128        0.0003*   Fetal and neonatal causes  15 (26.3)  0 (0.0)  1 (8.3)     Infections  12 (21.1)  0 (0.0)  1 (8.3)     Obstetric conditions  9 (15.8)  11 (68.8)  7 (58.3)     Labor and postpartum issues  9 (15.8)  1 (6.3)  0 (0.0)     Others  3 (5.2)  0 (0.0)  0 (0.0)    ATC class, n = 187        <0.0001*   Alimentary tract and metabolism  58 (51.3)  9 (53.6)  13 (76.5)     Genitourinary system and sex hormones  34 (30.1)  0 (0.0)  0 (0.0)     Anti-infective for systemic use  1 (0.9)  7 (43.8)  0 (0.0)     Others  20 (17.7)  0 (0.0)  4 (7.5)    Number of prescribed medications, n = 128  7.6 (2.4)  7.1 (2.3)  8.0 (2.0)  0.0136**  Variable  Drug–drug interaction (n = 57)  Frequency less than correct (n = 16)  Duplication of therapy (n = 12)  P-value  Pregnancy-puerperal cycle, n = 128        <0.0001*   Pregnant women  10 (17.5)  12 (75.0)  4 (33.3)     Breastfeeding postpartum women  9 (15.8)  4 (25.0)  5 (41.7)     Non-breastfeeding postpartum women  38 (66.7)  0 (0.0)  3 (25.0)    Reasons for hospitalization n = 128        0.0003*   Fetal and neonatal causes  15 (26.3)  0 (0.0)  1 (8.3)     Infections  12 (21.1)  0 (0.0)  1 (8.3)     Obstetric conditions  9 (15.8)  11 (68.8)  7 (58.3)     Labor and postpartum issues  9 (15.8)  1 (6.3)  0 (0.0)     Others  3 (5.2)  0 (0.0)  0 (0.0)    ATC class, n = 187        <0.0001*   Alimentary tract and metabolism  58 (51.3)  9 (53.6)  13 (76.5)     Genitourinary system and sex hormones  34 (30.1)  0 (0.0)  0 (0.0)     Anti-infective for systemic use  1 (0.9)  7 (43.8)  0 (0.0)     Others  20 (17.7)  0 (0.0)  4 (7.5)    Number of prescribed medications, n = 128  7.6 (2.4)  7.1 (2.3)  8.0 (2.0)  0.0136**  ATC, Anatomical Therapeutic Chemical. *Fisher’s Exact test; **Kruskal–Wallis test (the result considers all types of prescribing error). Bold: Statistically significant P-values. Pharmacist intervention types are shown in Table 4. The majority of pharmacist interventions were very significant (74.4%) and significant (19.6%). There was no extremely significant intervention. Table 4 Pharmacist intervention types classification by the clinical significance of intervention   N (%a)  Intervention type  Very significant  Significant  Somewhat significant  No significance  Total  Drug interactions  51 (40.8)  32 (97.0)  0 (0.0)  0 (0.0)  83 (49.4)  Inappropriate dosage interval  25 (20.0)  0 (0.0)  2 (22.2)  0 (0.0)  27 (16.1)   Higher than correct  12 (9.6)  0 (0.0)  0 (0.0)  0 (0.0)  12 (7.2)   Less than correct  13 (10.4)  0 (0.0)  2 (22.2)  0 (0.0)  15 (8.9)  Drug dosages  17 (13.6)  0 (0.0)  0 (0.0)  0 (0.0)  17 (10.1)   Over-dosage  7 (5.6)  0 (0.0)  0 (0.0)  0 (0.0)  7 (4.2)   Under-dosage  10 (8.0)  0 (0.0)  0 (0.0)  0 (0.0)  10 (5.9)  Duplication of therapy  14 (11.2)  0 (0.0)  0 (0.0)  0 (0.0)  14 (8.3)  Provision of drug information  2 (1.6)  1 (3.0)  6 (66.5)  0 (0.0)  9 (5.3)  Identification of adverse drug event  4 (3.2)  0 (0.0)  1 (11.1)  1 (100)  6 (3.6)  Duration of treatment  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Medication omitted from prescription  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Indication  3 (2.4)  0 (0.0)  0 (0.0)  0 (0.0)  3 (1.8)  Route of administration  1 (0.8)  0 (0.0)  0 (0.0)  0 (0.0)  1 (0.6)  Total  125 (74.4)  33 (19.6)  9 (5.4)  1 (0.6)  168 (100)    N (%a)  Intervention type  Very significant  Significant  Somewhat significant  No significance  Total  Drug interactions  51 (40.8)  32 (97.0)  0 (0.0)  0 (0.0)  83 (49.4)  Inappropriate dosage interval  25 (20.0)  0 (0.0)  2 (22.2)  0 (0.0)  27 (16.1)   Higher than correct  12 (9.6)  0 (0.0)  0 (0.0)  0 (0.0)  12 (7.2)   Less than correct  13 (10.4)  0 (0.0)  2 (22.2)  0 (0.0)  15 (8.9)  Drug dosages  17 (13.6)  0 (0.0)  0 (0.0)  0 (0.0)  17 (10.1)   Over-dosage  7 (5.6)  0 (0.0)  0 (0.0)  0 (0.0)  7 (4.2)   Under-dosage  10 (8.0)  0 (0.0)  0 (0.0)  0 (0.0)  10 (5.9)  Duplication of therapy  14 (11.2)  0 (0.0)  0 (0.0)  0 (0.0)  14 (8.3)  Provision of drug information  2 (1.6)  1 (3.0)  6 (66.5)  0 (0.0)  9 (5.3)  Identification of adverse drug event  4 (3.2)  0 (0.0)  1 (11.1)  1 (100)  6 (3.6)  Duration of treatment  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Medication omitted from prescription  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Indication  3 (2.4)  0 (0.0)  0 (0.0)  0 (0.0)  3 (1.8)  Route of administration  1 (0.8)  0 (0.0)  0 (0.0)  0 (0.0)  1 (0.6)  Total  125 (74.4)  33 (19.6)  9 (5.4)  1 (0.6)  168 (100)  a%Age related to column. Table 4 Pharmacist intervention types classification by the clinical significance of intervention   N (%a)  Intervention type  Very significant  Significant  Somewhat significant  No significance  Total  Drug interactions  51 (40.8)  32 (97.0)  0 (0.0)  0 (0.0)  83 (49.4)  Inappropriate dosage interval  25 (20.0)  0 (0.0)  2 (22.2)  0 (0.0)  27 (16.1)   Higher than correct  12 (9.6)  0 (0.0)  0 (0.0)  0 (0.0)  12 (7.2)   Less than correct  13 (10.4)  0 (0.0)  2 (22.2)  0 (0.0)  15 (8.9)  Drug dosages  17 (13.6)  0 (0.0)  0 (0.0)  0 (0.0)  17 (10.1)   Over-dosage  7 (5.6)  0 (0.0)  0 (0.0)  0 (0.0)  7 (4.2)   Under-dosage  10 (8.0)  0 (0.0)  0 (0.0)  0 (0.0)  10 (5.9)  Duplication of therapy  14 (11.2)  0 (0.0)  0 (0.0)  0 (0.0)  14 (8.3)  Provision of drug information  2 (1.6)  1 (3.0)  6 (66.5)  0 (0.0)  9 (5.3)  Identification of adverse drug event  4 (3.2)  0 (0.0)  1 (11.1)  1 (100)  6 (3.6)  Duration of treatment  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Medication omitted from prescription  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Indication  3 (2.4)  0 (0.0)  0 (0.0)  0 (0.0)  3 (1.8)  Route of administration  1 (0.8)  0 (0.0)  0 (0.0)  0 (0.0)  1 (0.6)  Total  125 (74.4)  33 (19.6)  9 (5.4)  1 (0.6)  168 (100)    N (%a)  Intervention type  Very significant  Significant  Somewhat significant  No significance  Total  Drug interactions  51 (40.8)  32 (97.0)  0 (0.0)  0 (0.0)  83 (49.4)  Inappropriate dosage interval  25 (20.0)  0 (0.0)  2 (22.2)  0 (0.0)  27 (16.1)   Higher than correct  12 (9.6)  0 (0.0)  0 (0.0)  0 (0.0)  12 (7.2)   Less than correct  13 (10.4)  0 (0.0)  2 (22.2)  0 (0.0)  15 (8.9)  Drug dosages  17 (13.6)  0 (0.0)  0 (0.0)  0 (0.0)  17 (10.1)   Over-dosage  7 (5.6)  0 (0.0)  0 (0.0)  0 (0.0)  7 (4.2)   Under-dosage  10 (8.0)  0 (0.0)  0 (0.0)  0 (0.0)  10 (5.9)  Duplication of therapy  14 (11.2)  0 (0.0)  0 (0.0)  0 (0.0)  14 (8.3)  Provision of drug information  2 (1.6)  1 (3.0)  6 (66.5)  0 (0.0)  9 (5.3)  Identification of adverse drug event  4 (3.2)  0 (0.0)  1 (11.1)  1 (100)  6 (3.6)  Duration of treatment  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Medication omitted from prescription  4 (3.2)  0 (0.0)  0 (0.0)  0 (0.0)  4 (2.4)  Indication  3 (2.4)  0 (0.0)  0 (0.0)  0 (0.0)  3 (1.8)  Route of administration  1 (0.8)  0 (0.0)  0 (0.0)  0 (0.0)  1 (0.6)  Total  125 (74.4)  33 (19.6)  9 (5.4)  1 (0.6)  168 (100)  a%Age related to column. Moreover, there was an association between type of pharmacist intervention and the pregnancy-puerperal cycle (P < 0.0001, Fisher’s exact test); 80.0% of prescriptions for non-breastfeeding postpartum women needed intervention for the management of drug interactions. Multivariate logistic regression analysis showed that age (increased age), number of prescribed medications (higher number of drugs), reasons of hospitalization (obstetric conditions) and non-breastfeeding postpartum women are risk factors for prescribing errors (Table 5). The effects of these variables were tested individually and controlled by other covariates (main effects model). Table 5 Multivariate logistic regression analysis of risk factors for prescribing errors (n = 1826 prescriptions) Variable  Odds ratio  95%CI  P-value  Age  1.0  1.0–1.1  <0.0001  Number of prescribed medications  1.2  1.1–1.3  <0.0001  Reasons for hospitalization/co-morbidities         Fetal and neonatal causes  1.3  0.7–2.4  0.4   Pregestational co-morbidities  0.8  0.5–1.3  0.3   Obstetric conditions  2.2  1.4–3.3  0.0003   Others  1.0  0.7–1.6  0.8  Pregnancy-puerperal cycle         Pregnant women—first trimester  1.6  0.9–2.5  0.067   Pregnant women—second trimester  1.1  0.8–1.5  0.5   Breastfeeding postpartum women  0.8  0.6–1.1  0.1   Non-breastfeeding postpartum women  3.9  2.5–6.1  <0.0001  Variable  Odds ratio  95%CI  P-value  Age  1.0  1.0–1.1  <0.0001  Number of prescribed medications  1.2  1.1–1.3  <0.0001  Reasons for hospitalization/co-morbidities         Fetal and neonatal causes  1.3  0.7–2.4  0.4   Pregestational co-morbidities  0.8  0.5–1.3  0.3   Obstetric conditions  2.2  1.4–3.3  0.0003   Others  1.0  0.7–1.6  0.8  Pregnancy-puerperal cycle         Pregnant women—first trimester  1.6  0.9–2.5  0.067   Pregnant women—second trimester  1.1  0.8–1.5  0.5   Breastfeeding postpartum women  0.8  0.6–1.1  0.1   Non-breastfeeding postpartum women  3.9  2.5–6.1  <0.0001  CI, confidence interval. Dependent variable = prescribing error (yes or no); Independent variables = age (quantitative continuous); number of prescribed medications (quantitative discrete); reasons for hospitalization/co-morbidities (categorical—reference: labor and postpartum) and pregnancy-puerperal cycle (categorical—reference: pregnant women—third trimester). Bold: Statistically significant P-values. Table 5 Multivariate logistic regression analysis of risk factors for prescribing errors (n = 1826 prescriptions) Variable  Odds ratio  95%CI  P-value  Age  1.0  1.0–1.1  <0.0001  Number of prescribed medications  1.2  1.1–1.3  <0.0001  Reasons for hospitalization/co-morbidities         Fetal and neonatal causes  1.3  0.7–2.4  0.4   Pregestational co-morbidities  0.8  0.5–1.3  0.3   Obstetric conditions  2.2  1.4–3.3  0.0003   Others  1.0  0.7–1.6  0.8  Pregnancy-puerperal cycle         Pregnant women—first trimester  1.6  0.9–2.5  0.067   Pregnant women—second trimester  1.1  0.8–1.5  0.5   Breastfeeding postpartum women  0.8  0.6–1.1  0.1   Non-breastfeeding postpartum women  3.9  2.5–6.1  <0.0001  Variable  Odds ratio  95%CI  P-value  Age  1.0  1.0–1.1  <0.0001  Number of prescribed medications  1.2  1.1–1.3  <0.0001  Reasons for hospitalization/co-morbidities         Fetal and neonatal causes  1.3  0.7–2.4  0.4   Pregestational co-morbidities  0.8  0.5–1.3  0.3   Obstetric conditions  2.2  1.4–3.3  0.0003   Others  1.0  0.7–1.6  0.8  Pregnancy-puerperal cycle         Pregnant women—first trimester  1.6  0.9–2.5  0.067   Pregnant women—second trimester  1.1  0.8–1.5  0.5   Breastfeeding postpartum women  0.8  0.6–1.1  0.1   Non-breastfeeding postpartum women  3.9  2.5–6.1  <0.0001  CI, confidence interval. Dependent variable = prescribing error (yes or no); Independent variables = age (quantitative continuous); number of prescribed medications (quantitative discrete); reasons for hospitalization/co-morbidities (categorical—reference: labor and postpartum) and pregnancy-puerperal cycle (categorical—reference: pregnant women—third trimester). Bold: Statistically significant P-values. Discussion This study at high-risk obstetric inpatient unit found 7.0% of prescribing errors. Recent studies showed a similar incidence in other medical areas, between 7.1 and 8.8% [2, 6, 11, 22]. The most common prescription errors were drug–drug interactions, incorrect frequency and improper dose. Higher maternal age, higher number of prescribed medications, reasons of and non-breastfeeding postpartum women were factors whom independent increasing the risk of prescribing errors. This study did not focus on the reasons for a prescription error occurring; however, some variables related to patients and prescriptions were studied. It was found that prescriptions containing errors were generally those with a greater number of drugs. Likewise, another study showed that for each additional medication item, the risk of a prescribing error increased by 14% [23]. Drug–drug interaction, incorrect frequency and improper dose were the most common prescribing errors in our study. Errors related to dose and frequency were also more prevalent in others the literature [6, 9, 24]. In a study conducted at the maternity and children’s hospital in Spanish, the prescriptions of obstetric and gynecology inpatients analyzed there was a prevalence of sequential therapy (54.5%) (maintenance of an intravenous medication when it is no longer is necessary), inappropriate dosage interval (16.6%) and dosage error (from 1.5- to 10-fold higher than normal) (6.3%) in the evaluated prescriptions [9]. Drug interactions were the highlight in our study, similarly to the prospective observational study conducted in three medical wards of the public teaching hospital in India which observed 68.2% of errors being caused by drug interactions [25]. In prospective study, including Brazilian pregnant and breastfeeding women admitted to the intensive unit care at the Women’s Hospital demonstrated 175 different combinations of potential drug interaction were identified in 305 prescriptions [26]. Moreover, an association between error type and pregnancy-puerperal cycle was found mainly because drug–drug interaction was strongly present in cases of non-breastfeeding postpartum women prescribing errors (specifically improper cabergoline–metoclopramide association). Alimentary tract and metabolism drugs were the most associated with errors. This result is different from other studies, which found that anti-microbials and cardiovascular system drugs were most frequently involved in errors, probably due to the specific characteristics of the study group [6, 9, 11, 25, 27]. HAMs can be involved in the most serious prescribing errors and we found a low incidence of HAMs involved with prescribing errors in the current study (0.4%). It is known that the nature of prescribing errors is multifactorial such as work environment (heavy workload, interruptions, pressure from the other staff), individual factors (tiredness and stress), task factors (lack of familiarity with medication) and patient factors (complex patient, poor communication with patient) [1, 22, 28]. The average age of 101 women which presented errors in theirs prescriptions was ~30 years, similar age was found in other studies in which women required admission to the obstetrics unit [27, 29]. In the study conducted in a women’s health unit of an Australian teaching hospital which a total of 454 potential medication-related problems were identified over the 5-week period among 241 patients, mean age in years with and without prescribing showed association statistically significant (P = 0.002), similar with our study (P = 0.0083) [27, 29]. In this study, pregnancy-puerperal cycle was associated with a significantly increased likelihood of experiencing with prescribing errors. This occurring given that pregnancy-puerperal cycle (mainly pregnant women in third trimester and breastfeeding postpartum or not breastfeeding) has been associated with number of prescribed medication, increased pregestational co-morbidities and obstetric complications [27, 30, 31]. Pharmacist interventions are essential to avoid drug-related problems, to contribute to the rationalization of drug therapy and to improve the quality of hospital prescribing [9, 11]. The factors may influence in frequency and acceptance interventions were the level of pharmacist’s individual skills, acknowledgment and professionalism with physicians. This study focused on 168 pharmaceutical interventions and 98.9% of the overall interventions were accepted. The results showed values similar in a study conducted by Fernandez-Llamazares et al. [9] in obstetric and gynecology with 89.9 % of the overall 702 interventions being accepted (P < 0.05). The majority of pharmacist interventions performed in this study were classified as ‘very significant’ and ‘significant’ in terms of their impact. These categories were recommendation on patient care for avoiding potential or existing dysfunction in a major organ or avoiding a serious adverse drug interaction or contraindication to use or to improve the quality of life of the patient, respectively. The study by Fernandez-Llamazares et al. [9] who also classified using the Overhage et al. [17] method observed 85.9% ‘significant’ interventions made by clinical pharmacists for obstetric and gynecology patients. The main types of intervention agreed with the main types of prescribing error, as expected. Data collection was done in a high-risk obstetric inpatient unit, where the most prevalent cases are hypertensive syndromes, diabetes associated with pregnancy and preterm labor. However, these most common complications are linked to assistance protocols of conduct that are strongly followed by residents. In the study inpatient unit, only women with newborn complication or fetal deaths are hospitalized and for this reason, these women cannot breastfeed. Breastfeeding women with their babies, who are the majority, do not stay in this sector. This leads to the conclusion that less frequent situations are also more associated with prescribing errors, situation that is very clear among postpartum women with lactation inhibition regardless of fetal death reason, HIV seropositive, use of immunosuppressant or other drugs that contraindicate the breastfeeding. The same to observe to HAM that involved the use of enoxaparin, tramadol and promethazine, medications that are not routinely used among pregnant women, which are also associated with more severe complications and use of a large number of medications. All of this reinforces the importance of pharmacist interventions, especially in the usual same cases in unit. This study is limited due to physicians were aware of the research once they had signed the informed consent; therefore it is hypothesized that they may have paid more attention to prescriptions and have made fewer prescribing errors. By having a high medical staff (professor/assistant), this may also have generated a bias. Thus, the issue of medication reconciliation was not included, and it should be done to identify/avoid mainly omission errors (the incidence of this error could be higher). Because this study was conducted at a single center, the conclusions that may be drawn are limited. As study strength, this research was performed in a teaching hospital with high patient turnover and 20 beds for high-risk pregnant and postpartum women’s care. Other advantages are the electronic prescriptions, the access to databases for evaluation of drug information and a pharmacist team on hand every day of the week. Moreover, the interventions were well accepted by the medical team, for the benefit of hospitalized patients. The need for inclusion of a clinical pharmacist in the multidisciplinary team must be continually emphasized, mainly in high-risk pregnant and postpartum women’s care. However, it is important to clarify that the presence of a clinical pharmacist is not the only solution to prescription error. Due to its complexity, a set of measures must be implemented. Studies recommend the introduction of electronic prescriptions in institutions that still use handwritten prescriptions. In this study, electronic prescriptions were evaluated and errors were found. This system can eliminate particular error types and prevent up to a quarter of errors, but not all [23]. There are errors specific to the electronic prescribing system, such as incorrect product selected. Conclusion In a high-risk obstetric inpatient unit we found 7.0% of prescribing errors, the most common were drug–drug interactions, incorrect frequency and improper dose, with clinical significance of the significant type, occurring mostly in puerperium, among non-breastfeeding women. There was low incidence of serious error. Higher maternal age, higher number of prescribed medications, reasons of hospitalization and non-breastfeeding postpartum women were factors increasing the risk of prescribing errors. Alimentary tract and metabolism drugs were most associated with errors. The rate of clinical pharmacist intervention acceptance was high. The inclusion of a clinical pharmacist in the hospital multidisciplinary team could improve security, contribute to the rationalization of drug therapy and improve the quality of care in a high-risk obstetric inpatient unit. Acknowledgements The authors would like to thank the statistical office of CAISM-UNICAMP for performing this statistical analysis. This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. Funding This research received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. References 1 Dean B, Schachter M, Vincent C et al.  . Prescribing errors in hospital inpatients: their incidence and clinical significance. Qual Saf Health Care  2002; 11: 340– 4. Google Scholar CrossRef Search ADS PubMed  2 Al-Dhawailie AA. Inpatient prescribing errors and pharmacist intervention at a teaching hospital in Saudi Arabia. Saudi Pharm J  2011; 19: 193– 6. Google Scholar CrossRef Search ADS PubMed  3 Leape LL, Cullen DJ, Clapp MD et al.  . Pharmacist participation on physician rounds and adverse drug events in the intensive care unit. JAMA  1999; 282: 267– 70. 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International Journal for Quality in Health CareOxford University Press

Published: Mar 28, 2018

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