Ju Huang 1 , Min Zhang 2 , Xin Liu 3 Show Affiliations »
Abstract
OBJECTIVES: We studied the characteristics of patient and visitor violence (PVV) and the workload of doctors and nurses, and identified the correlation between the incidence of PVV and healthcare worker (HCW) workload in China. DESIGN: Cross-sectional study. SETTING: 288 public health institutions in a city in northern China. PARTICIPANTS: Data on 87 998 HCWs were extracted from the 2015 database of the Medical Quality and Safety Notification System. MEASURES: The data included characteristics of the healthcare institution, types of services provided, information about each complaint and reported PVV incidents by hospital level. Pearson correlation analysis and multiple linear regression modelling were used to identify the correlation between the incidence of PVV and HCW workload. RESULTS: Three types of institutions were identified as being at particularly high risk for PVV: tertiary hospitals, specialised hospitals and institutions in a downtown district. Incidence rates of PVV at tertiary hospitals, specialised hospitals and institutions in a downtown district were 16.6%, 17.6% and 22.8%, respectively. All three types of institutions had a high inpatient workload (admissions per doctor per day): 0.15, 0.17 and 0.12, respectively. After controlling for scale, type and location, it was found that the overall incidence rate of PVV increased with increasing outpatient workload (β=0.236, p<0.01). CONCLUSIONS: PVV poses a significant challenge to public health institutions in China, and the high workload of HCWs likely contributes to higher risk of PVV. Prevention and intervention of PVV may be targeted to HCWs at specific institutions. The patient complaint notification system needs improvement and could be, for example, a better source of PVV information for future interventions. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
OBJECTIVES: We studied the characteristics of patient and visitor violence (PVV) and the workload of doctors and nurses, and identified the correlation between the incidence of PVV and healthcare worker (HCW) workload in China. DESIGN: Cross-sectional study. SETTING: 288 public health institutions in a city in northern China. PARTICIPANTS : Data on 87 998 HCWs were extracted from the 2015 database of the Medical Quality and Safety Notification System. MEASURES: The data included characteristics of the healthcare institution, types of services provided, information about each complaint and reported PVV incidents by hospital level. Pearson correlation analysis and multiple linear regression modelling were used to identify the correlation between the incidence of PVV and HCW workload. RESULTS: Three types of institutions were identified as being at particularly high risk for PVV: tertiary hospitals, specialised hospitals and institutions in a downtown district. Incidence rates of PVV at tertiary hospitals, specialised hospitals and institutions in a downtown district were 16.6%, 17.6% and 22.8%, respectively. All three types of institutions had a high inpatient workload (admissions per doctor per day): 0.15, 0.17 and 0.12, respectively. After controlling for scale, type and location, it was found that the overall incidence rate of PVV increased with increasing outpatient workload (β=0.236, p<0.01). CONCLUSIONS: PVV poses a significant challenge to public health institutions in China, and the high workload of HCWs likely contributes to higher risk of PVV. Prevention and intervention of PVV may be targeted to HCWs at specific institutions. The patient complaint notification system needs improvement and could be, for example, a better source of PVV information for future interventions. © Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
Entities: Disease
Gene
Species
Keywords:
healthcare workers; occupational safety and health; patient and visitor violence; workload
Mesh： See more »
Year: 2020
PMID： 32354778 PMCID： PMC7217091 DOI： 10.1136/bmjopen-2019-034605
Source DB: PubMed Journal: BMJ Open ISSN： 2044-6055 Impact factor: 2.692