Aim To develop a new general wards patient classification tool based on the nursing intensity level that reflects patients’ clinical characteristics and indirect nursing activities. Design A cross‐sectional design was adopted. This methodological study developed a patient classification system to sort general ward patients based on the intensity of their nursing needs and verified the validity and reliability of this classification system. Methods Thirteen experts verified the tools’ content validity. Data collectors and head nurses classified 150 patients from two hospitals with four general wards and various nurse staffing levels. Inter‐rater reliability was analysed. Staff nurses classified 846 patients following the Korean patient classification system on nursing intensity scores that reflected patients’ clinical status. Content validity was verified based on the classification results. Using K‐group cluster analysis, score ranges for four groups were identified. Results The developed tool includes 8 domains, (symptom management, infection control, nutrition and medication, personal hygiene and secretion, activity, sleep and rest, guidance in nursing/emotional support, nursing activity planning and coordination, indirect activity), 24 subdomains, 66 nursing activities and 124 criteria. Inter‐rater reliability showed high agreement.

We investigated associations between full Electronic Medical Record (EMR) system adoption and drug use in healthcare organizations (HCOs) to explore whether EMR system features such as electronic prescribing, medicines reconciliation, and decision support, might be related to drug use by using the relevant nationwide data. Methods: The study design was cross-sectional. Survey data of the level of adoption of EMR systems were collected for the Organization for Economic Cooperation and Development benchmarking information and communication technologies (ICT) study between November 2013 and January 2014, in Korea. Survey respondents were hospital chief information officers and medical practitioners in primary care clinics. From the national health insurance administrative dataset, two outcomes, the rate of antibiotic prescription and polypharmacy with ≥6 drugs, were extracted. Results: We found that full EMR adoption showed a 16.1% lower antibiotic drug prescription than partial adoption including paper-based medical charts in the hospital only (p = 0.041). Between EMR adoption status and polypharmacy prescription, only those clinics which fully adopted EMR showed significant associations with higher polypharmacy prescriptions (36.9%, p = 0.001). Conclusions: The findings suggested that there might be some confounding effects present and sophisticated ICT may provide some benefits to the quality of care even with some mixed results. Although a negative relationship between full EMR system adoption and antibiotic drug use was only significant in hospitals, EMR system functions searching drugs or listing specific patients might facilitate antibiotic drug use reduction. Positive relationships between full EMR system adoption and polypharmacy rate in general hospitals and clinics, but not hospitals, require further research.

Background: This study aimed to compare the difference in healthcare utilization public and private hospitals (by hospital ownership) during Coronavirus disease (COVID-19) pandemic in Republic of Korea. Methods:The study analyzed the national health insurance claim data from January 1, 2019 to December 31, 2021. We performed a panel regression with a fixed effects model using balanced panel data (n=406, time=36). The response variables are the number of inpatients per bed and the proportion of COVID-19 inpatients. The uncertainty was measured by volatility (calculating standard deviation) of daily confirmed cases of COVID-19. An interaction term between volatility and public hospital was included as an explanatory variable. In addition, region population, geospatial accessibility, the number of beds, the ratio of negative pressure isolation beds, and COVID-19 severity were included as control variables. Results: As a result of panel regression analysis, for aggregation of data, (1) the volatility of daily confirmed cases of COVID-19 was not directly related to the number of inpatients per bed. For public hospitals, an increase in the number of inpatients per bed was observed with increasing volatility of daily confirmed cases of COVID-19. (2) For the log-transformed value of the number of inpatients per bed, the time effect of the first wave was -0.488, which was lower than those of the second (-0.328), third (-0.468), and fourth (-0.276) waves. (3) The interaction of volatility and public hospitals was positively associated with the ratio of COVID-19 inpatients. Conclusion:In this study, quantification of uncertainty is proposed to reduce uncertainty of new infectious diseases. Therefore, it is expected to contribute to the policies on health resources.

AimThis study aimed to develop a valid and reliable new intensive care unit nursing classification tool, including direct and indirect nursing activities, by measuring the nursing intensity provided to patients.BackgroundPrior tools primarily examine patients' medical records or disease severity/interactions, systematically failing to reflect comorbidity risk factors.DesignThe Delphi technique was used to test the content validity of the Korean Patient Classification System on Nursing Intensity for Critical Care Nurses (KPCSNIC).MethodsData were collected from four hospitals in two provinces from 26 December 2017 to 30 January 2018. To verify construct validity, staff nurses classified 365 patients, comparing differences by medical department and type of stay. To verify interrater reliability, data collectors and the head nurses of three intensive care units classified 87 patients.ResultsThe KPCSNIC had 8 categories, 44 nursing activities and 105 criteria. Reliability was high (r = .84). Construct validity was verified by revealing differences according to medical department and type of patient. Using total scores, four KPCSNIC groups were identified.ConclusionThe KPCSNIC developed in this study can support staffing for nursing intensity by providing more specific evaluation criteria. Moreover, it reflects nursing intensity, including direct and indirect nursing activities.