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.

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.