Is primary care a patient-safe setting? Prevalence, severity, nature, and causes of adverse events: numerous and mostly avoidable.

Knowing the frequency and characteristics of adverse events (AEs) is key to implementing actions that can prevent their occurrence. However, reporting systems are insufficient for this purpose and epidemiological studies are also required. Currently, the reviewing of clinical records is the gold standard method for knowing the frequency and characteristics of AEs. Research on AEs in a primary care setting has been limited and primarily focuses on specific types of events (medication errors, etc.) or patients. Large studies that search for any kind of AE in all patients are scarce. This study aimed to estimate the prevalence of AEs in the primary care setting and their characteristics. SETTING: all 262 primary health-care centres in the Madrid region (Spain) during the last quarter of 2018. DESIGN: cross-sectional descriptive study. Eligible population: subjects over 18 years of age who attended medical consultation over the last year (N = 2 743 719); a randomized sample stratified by age. MAIN OUTCOMES: age, sex, occurrence of an AE, number of consultations in the study period, avoidability, severity, place of occurrence, type of event, and contributory factors. The clinical records were reviewed by three teams, each composed of one doctor and one nurse trained and with expertise in patient safety. The SPSS software package (version 26) was used for the statistical analyses. The evaluators reviewed 1797 clinical records. The prevalence of AEs over the study period was 5.0% [95% confidence interval (CI): 4.0%‒6.0%], with higher values in women (5.7%; 95% CI: 4.6%‒6.8%;P = 0.10) and patients over 75 years of age (10.3%; 95% CI: 8.9%‒11.7%; P < 0.001). The overall occurrence per hundred consultations was estimated to be 1.58% (95% CI: 1.28%‒1.94%). Of the detected AEs, 71.3% (95% CI: 62.1%‒80.5%) were avoidable. Additionally, 60.6% (95% CI: 50.7%‒70.5%) were categorized as mild, 31.9% (95% CI: 22.4%‒41.4%) as moderate, and 7.4% (95% CI: 2.1%‒12.7%) as severe. Primary care was the occurrence setting in 76.6% (95% CI: 68.0%‒85.2%) of cases. The overall incidence of AEs related to medication was 53.2% (95% CI: 50.9%‒55.5%). The most frequent types of AEs were prescription errors (28.7%; 95% CI: 19.5%‒37.9%), followed by drug administration errors by patients (17.0%; 95% CI: 9.4%‒24.6%), and clinical assessment errors (11.7%; 95% CI: 5.2%‒18.2%). The most common contributory factors were those related to the patient (80.6%; 95% CI: 71.1%‒90.1%) and tasks (59.7%; 95% CI: 48.0%‒71.4%). A high prevalence of AEs (1 in 66 consultations) was observed, which was slightly higher than that reported in similar studies. About 3 out of 4 such events were considered to be avoidable and 1 out of 13 was severe. Prescription errors, drug administration errors by patients, and clinical assessment errors were the most frequent types of AEs. Graphical Abstract.

[Descriptive analysis of medication errors notified by Primary Health Care: Learning from errors]. / Análisis descriptivo de los errores de medicación notificados en atención primaria: aprendiendo de nuestros errores.

INTRODUCTION AND OBJECTIVES: Aim of this study is to determine the setting, causes, and the harm of medication errors (ME) which are notified by Primary Health Care. MATERIAL AND METHODS: Setting: Primary Care Regional Health Service of Madrid. 2016. DESIGN: Descriptive and cross-sectional study. PARTICIPANTS: All ME (1,839) which were notified by Primary Care Centres by notification system of safety incidents between January 1st 2016 and November 17th 2016. MAIN MEASUREMENTS: Setting, real harm, potential harm, and cause of error. These items were classified by one researcher. Concordance was checked with another researcher. RESULTS: Just under half (47%) (95% CI: 44.8%-49.3%) of ME occurred in Primary Care Centre, 26.5% (95% CI: 24.5%-28.6%) of ME were patient medication errors, and 27.5% (95% CI: 24.1%-30.8%) of ME were potential severe harm errors. Prescribing errors were the cause of most ME in Primary Care Centre [27.4% (95% CI: 24.4%-30.4%)]. Communication between patients and doctors were the cause of most patient medication errors [66% (95% CI: 61.8%-70.2%)]. Patient mistakes and forgetfulness were also causes of patient medication errors. CONCLUSIONS: Half of all mediation errors hppened at Primary Care Center while one quarter of them were patient medication errors. One quarter of all ME were potential severe harm errors. The main causes were prescribing errors, failure of communication between patients and doctors, and patient mistakes and forgetfulness. Prescribing aid systems, communication improvements and patients aids should be implemented.

[Concordance and usefulness of a stratification system for clinical decision making]. / Concordancia y utilidad de un sistema de estratificación para la toma de decisiones clínicas.

OBJECTIVES: 1) To analyse concordance between the level of risk classification using the Adjusted Groups Morbidity (GMA) tool and the assigned level of intervention by general practitioners (GP). 2) To study the usefulness of the GMA tool as an aid in electronic medical records (EMR) for decision making. DESIGN: Cross-sectional observational study of concordance. LOCATION: Primary Care. Madrid Health Service. PARTICIPANTS: Twenty eight GPs. A sample of 840 patients assigned to participating GPs was selected by disproportionate stratified random sampling (0.65 kappa, 0.125 precision, 5% positive rate, 95% confidence level). MAIN MEASUREMENTS: Weighted Cohen Kappa index for the degree of concordance between the GMA tool and the GPs. The usefulness of the tool was assessed using an ad hoc developed questionnaire. RESULTS: Kappa weighted index obtained was 0.60 (95%CI: 0.55-0.65). In 3% of cases the disagreement was maximum. The GPs found that the grouping tool had been useful in 76% of cases. CONCLUSION: Moderate strength/good concordance; incorporating a grouping tool in the EMR helps as a reminder for taking more proactive/integrated decisions based on social and health needs of people with chronic diseases.

Validation of a Reduced Set of High-Performance Triggers for Identifying Patient Safety Incidents with Harm in Primary Care: TriggerPrim Project.

OBJECTIVE: The aim of the study was to construct and validate a reduced set of high-performance triggers for identifying adverse events (AEs) via electronic medical records (EMRs) review in primary care (PC). METHODS: This was a cross-sectional descriptive study for validating a diagnostic test. The study included all 262 PC centers of Madrid region (Spain). Patients were older than 18 years who attended their PC center over the last quarter of 2018. The randomized sample was n = 1797. Main measurements were as follows: ( a ) presence of each of 19 specific computer-identified triggers in the EMR and ( b ) occurrence of an AE. To collect data, EMR review was conducted by 3 doctor-nurse teams. Triggers with statistically significant odds ratios for identifying AEs were selected for the final set after adjusting for age and sex using logistic regression. RESULTS: The sensitivity (SS) and specificity (SP) for the selected triggers were: ≥3 appointments in a week at the PC center (SS = 32.3% [95% confidence interval {CI}, 22.8%-41.8%]; SP = 92.8% [95% CI, 91.6%-94.0%]); hospital admission (SS = 19.4% [95% CI, 11.4%-27.4%]; SP = 97.2% [95% CI, 96.4%-98.0%]); hospital emergency department visit (SS = 31.2% [95% CI, 21.8%-40.6%]; SP = 90.8% [95% CI, 89.4%-92.2%]); major opioids prescription (SS = 2.2% [95% CI, 0.0%-5.2%]; SP = 99.8% [95% CI, 99.6%-100%]); and chronic benzodiazepine treatment in patients 75 years or older (SS = 14.0% [95% CI, 6.9%-21.1%]; SP = 95.5% [95% CI, 94.5%-96.5%]).The following values were obtained in the validation of this trigger set (the occurrence of at least one of these triggers in the EMR): SS = 60.2% (95% CI, 50.2%-70.1%), SP = 80.8% (95% CI, 78.8%-82.6%), positive predictive value = 14.6% (95% CI, 11.0%-18.1%), negative predictive value = 97.4% (95% CI, 96.5%-98.2%), positive likelihood ratio = 3.13 (95% CI, 2.3-4.2), and negative likelihood ratio = 0.49 (95% CI, 0.3-0.7). CONCLUSIONS: The set containing the 5 selected triggers almost triples the efficiency of EMR review in detecting AEs. This suggests that this set is easily implementable and of great utility in risk-management practice.