Purpose: This study aimed to identify the relationships between medication errors and the factors affecting nurses’ knowledge and behavior in Japan using Bayesian network modeling. It also aimed to identify important factors through scenario analysis with consideration of nursing students’ and nurses’ education regarding patient safety and medications. Methods: We used mixed methods. First, error events related to medications and related factors were qualitatively extracted from 119 actual incident reports in 2022 from the database of the Japan Council for Quality Health Care. These events and factors were then quantitatively evaluated in a flow model using Bayesian network, and a scenario analysis was conducted to estimate the posterior probabilities of events when the prior probabilities of some factors were 0%. Results: There were 10 types of events related to medication errors. A 5-layer flow model was created using Bayesian network analysis. The scenario analysis revealed that “failure to confirm the 5 rights,” “unfamiliarity with operations of medications,” “insufficient knowledge of medications,” and “assumptions and forgetfulness” were factors that were significantly associated with the occurrence of medical errors. Conclusion This study provided an estimate of the effects of mitigating nurses’ behavioral factors that trigger medication errors. The flow model itself can also be used as an educational tool to reflect on behavior when incidents occur. It is expected that patient safety education will be recognized as a major element of nursing education worldwide and that an integrated curriculum will be developed.

Purpose: This study aimed to identify the relationships between medication errors and the factors affecting nurses’ knowledge and behavior in Japan using Bayesian network modeling. It also aimed to identify important factors through scenario analysis with consideration of nursing students’ and nurses’ education regarding patient safety and medications. Methods: We used mixed methods. First, error events related to medications and related factors were qualitatively extracted from 119 actual incident reports in 2022 from the database of the Japan Council for Quality Health Care. These events and factors were then quantitatively evaluated in a flow model using Bayesian network, and a scenario analysis was conducted to estimate the posterior probabilities of events when the prior probabilities of some factors were 0%. Results: There were 10 types of events related to medication errors. A 5-layer flow model was created using Bayesian network analysis. The scenario analysis revealed that “failure to confirm the 5 rights,” “unfamiliarity with operations of medications,” “insufficient knowledge of medications,” and “assumptions and forgetfulness” were factors that were significantly associated with the occurrence of medical errors. Conclusion This study provided an estimate of the effects of mitigating nurses’ behavioral factors that trigger medication errors. The flow model itself can also be used as an educational tool to reflect on behavior when incidents occur. It is expected that patient safety education will be recognized as a major element of nursing education worldwide and that an integrated curriculum will be developed.

Purpose: This study aimed to identify the relationships between medication errors and the factors affecting nurses’ knowledge and behavior in Japan using Bayesian network modeling. It also aimed to identify important factors through scenario analysis with consideration of nursing students’ and nurses’ education regarding patient safety and medications. Methods: We used mixed methods. First, error events related to medications and related factors were qualitatively extracted from 119 actual incident reports in 2022 from the database of the Japan Council for Quality Health Care. These events and factors were then quantitatively evaluated in a flow model using Bayesian network, and a scenario analysis was conducted to estimate the posterior probabilities of events when the prior probabilities of some factors were 0%. Results: There were 10 types of events related to medication errors. A 5-layer flow model was created using Bayesian network analysis. The scenario analysis revealed that “failure to confirm the 5 rights,” “unfamiliarity with operations of medications,” “insufficient knowledge of medications,” and “assumptions and forgetfulness” were factors that were significantly associated with the occurrence of medical errors. Conclusion This study provided an estimate of the effects of mitigating nurses’ behavioral factors that trigger medication errors. The flow model itself can also be used as an educational tool to reflect on behavior when incidents occur. It is expected that patient safety education will be recognized as a major element of nursing education worldwide and that an integrated curriculum will be developed.

Purpose: This study aimed to identify the relationships between medication errors and the factors affecting nurses’ knowledge and behavior in Japan using Bayesian network modeling. It also aimed to identify important factors through scenario analysis with consideration of nursing students’ and nurses’ education regarding patient safety and medications. Methods: We used mixed methods. First, error events related to medications and related factors were qualitatively extracted from 119 actual incident reports in 2022 from the database of the Japan Council for Quality Health Care. These events and factors were then quantitatively evaluated in a flow model using Bayesian network, and a scenario analysis was conducted to estimate the posterior probabilities of events when the prior probabilities of some factors were 0%. Results: There were 10 types of events related to medication errors. A 5-layer flow model was created using Bayesian network analysis. The scenario analysis revealed that “failure to confirm the 5 rights,” “unfamiliarity with operations of medications,” “insufficient knowledge of medications,” and “assumptions and forgetfulness” were factors that were significantly associated with the occurrence of medical errors. Conclusion This study provided an estimate of the effects of mitigating nurses’ behavioral factors that trigger medication errors. The flow model itself can also be used as an educational tool to reflect on behavior when incidents occur. It is expected that patient safety education will be recognized as a major element of nursing education worldwide and that an integrated curriculum will be developed.

Background/Aims: Detailed evaluations of overlapping constipation and gastroesophageal reflux disease (GERD) have not been conducted in Japan. The REACTION-J2 study examined the overlap of these diseases in Japan. Methods: This internet-based survey recruited participants from general public survey panels. Questions included demographic and medical data and assessments based on validated measures for constipation and GERD. Associations between background factors affecting constipation/GERD overlap, disease measures, and treatment were also evaluated. Results: Among 10 000 survey responses received, functional constipation (Rome IV diagnostic criteria) was reported by 439 participants; chronic constipation (Japanese guidelines) by 3804 participants; and subjective constipation symptoms by 2563 participants. The number of participants with constipation/GERD overlap ranged from 73 to 1533 depending on the criteria used. Regardless of the definition used, all GERD groups had significantly higher odds of being constipated than non-GERD participants: the OR (95% CI) for all 9 combinations of definitions ranged between 1.56 (1.21, 2.01) and 2.67 (2.44, 2.92) (all P ≤ 0.001). Straining, hard stools, and sensations of incomplete evacuation and anorectal obstruction/blockage, according to chronic constipation criteria, were common.Participants with constipation/GERD overlap had poorer quality of life (P < 0.001) and worse GERD symptom scores (P < 0.001). The frequency of abnormal stools was highest (P < 0.001) in the constipation/GERD overlap group. In the overlap group, 52.4% and 26.0% used gastric and constipation medication, respectively. Conclusion Individuals with constipation/GERD overlap tend to have worsened symptoms and quality of life.

BACKGROUND/AIMS: Despite improvements in endoscopic hemostasis and pharmacological therapies, upper gastrointestinal (UGI) ulcers repeatedly bleed in 10% to 20% of patients, and those without early endoscopic reintervention or definitive surgery might be at a high risk for mortality. This study aimed to identify the risk factors for intractability to initial endoscopic hemostasis. METHODS: We analyzed intractability among 428 patients who underwent emergency endoscopy for bleeding UGI ulcers within 24 hours of arrival at the hospital. RESULTS: Durable hemostasis was achieved in 354 patients by using initial endoscopic procedures. Sixty-nine patients with Forrest types Ia, Ib, IIa, and IIb at the second-look endoscopy were considered intractable to the initial endoscopic hemostasis. Multivariate analysis indicated that age > or =70 years (odds ratio [OR], 2.06; 95% confidence interval [CI], 1.07 to 4.03), shock on admission (OR, 5.26; 95% CI, 2.43 to 11.6), hemoglobin <8.0 mg/dL (OR, 2.80; 95% CI, 1.39 to 5.91), serum albumin <3.3 g/dL (OR, 2.23; 95% CI, 1.07 to 4.89), exposed vessels with a diameter of > or =2 mm on the bottom of ulcers (OR, 4.38; 95% CI, 1.25 to 7.01), and Forrest type Ia and Ib (OR, 2.21; 95% CI, 1.33 to 3.00) predicted intractable endoscopic hemostasis. CONCLUSIONS: Various factors contribute to intractable endoscopic hemostasis. Careful observation after endoscopic hemostasis is important for patients at a high risk for incomplete hemostasis.
Asian Continental Ancestry Group* ; Emergencies ; Endoscopy ; Hematemesis ; Hemorrhage* ; Hemostasis ; Hemostasis, Endoscopic* ; Humans ; Melena ; Mortality ; Multivariate Analysis ; Peptic Ulcer* ; Risk Factors ; Serum Albumin ; Shock ; Ulcer

Background/Aims: Chronic constipation and lifestyle factors can affect sleep quality. We evaluated the relationship between chronic constipation and sleep in the Japanese population. Methods: This cross-sectional internet-based survey included 3000 subjects with constipation, classified according to sleep status (good/poor).Primary endpoints were Bristol stool form scale (BSFS) score and correlations between sleep disorder criteria of the Pittsburgh Sleep Quality Index (PSQI) and sleep status (good/poor sleep). Secondary endpoints included correlations between quality of life (QOL) and mood, medical, lifestyle, and sleep factors. Results: The proportion of participants with BSFS category 4 (normal stool) was significantly higher in the good sleep group (P < 0.001). Sleep disturbance (P < 0.05), sleep quality, and duration, use of hypnotic medication, and daytime dysfunction of PSQI (all P < 0.001) significantly correlated with poor sleep. In the poor sleep group, QOL was significantly worse and anxiety and depression levels were significantly higher (allP < 0.001) compared with the good sleep group. Anemia and smoking (both P < 0.05), recent body weight increases, and poor eating habits (all P < 0.001) were significantly higher in the poor sleep group. Male sex, onset associated with change in frequency of stools, sensation of incomplete evacuation for at least 25% of defecations, and manual maneuvers to facilitate at least 25% of defecations correlated with poor sleep. Conclusions Subjects with constipation and poor sleep experienced severe symptoms and had poor QOL. These data support the need for a multifocal treatment approach, including lifestyle advice and pharmacotherapy.

Background/Aims: Chronic constipation and lifestyle factors can affect sleep quality. We evaluated the relationship between chronic constipation and sleep in the Japanese population. Methods: This cross-sectional internet-based survey included 3000 subjects with constipation, classified according to sleep status (good/poor).Primary endpoints were Bristol stool form scale (BSFS) score and correlations between sleep disorder criteria of the Pittsburgh Sleep Quality Index (PSQI) and sleep status (good/poor sleep). Secondary endpoints included correlations between quality of life (QOL) and mood, medical, lifestyle, and sleep factors. Results: The proportion of participants with BSFS category 4 (normal stool) was significantly higher in the good sleep group (P < 0.001). Sleep disturbance (P < 0.05), sleep quality, and duration, use of hypnotic medication, and daytime dysfunction of PSQI (all P < 0.001) significantly correlated with poor sleep. In the poor sleep group, QOL was significantly worse and anxiety and depression levels were significantly higher (allP < 0.001) compared with the good sleep group. Anemia and smoking (both P < 0.05), recent body weight increases, and poor eating habits (all P < 0.001) were significantly higher in the poor sleep group. Male sex, onset associated with change in frequency of stools, sensation of incomplete evacuation for at least 25% of defecations, and manual maneuvers to facilitate at least 25% of defecations correlated with poor sleep. Conclusions Subjects with constipation and poor sleep experienced severe symptoms and had poor QOL. These data support the need for a multifocal treatment approach, including lifestyle advice and pharmacotherapy.