Changes in the Number of Physicians and Hospital Bed Capacity in Europe.

OBJECTIVES: Our aim was to examine the numbers of practicing physicians and total numbers of hospital beds in European Organisation for Economic Co-operation and Development countries. METHODS: Data analyzed were derived from the "Organisation for Economic Co-operation and Development Health Statistics 2020" database between 1980 and 2018. The selected countries were compared according to the type of healthcare system and geographical location by parametric and nonparametric tests. RESULTS: In 1980, Bismarck-type systems showed an average number of physicians of 2.3 persons/1000 population; in Beveridge-type systems, it was 1.7 persons. By 2018, it leveled out reaching 3.9 persons in both healthcare system types. In 1980, average physician number/1000 was 2.5 persons in Eastern Europe; in Western Europe, it was 1.9 persons. By 2018 this proportion changed with Western Europe having the higher number (3.7 persons; 3.9 persons). In 1980, average number of hospital beds/1000 population was 9.6 in Bismarck-type systems whereas in Beveridge-type systems it was 8.8. By 2018, it decreased to 5.6 in Bismarck-type systems (-42%) and to 3.1 in Beveridge-type systems (-65%). In 1980, the average number of hospital beds/1000 population in Eastern Europe was 10.3; in Western Europe, it was 8.5. By 2018, the difference between the 2 regions did not change. CONCLUSIONS: Although the number of physicians was 33% higher in 1980 in Eastern Europe than in Western Europe, by 2018 the number of physicians was 5% higher in Western Europe. In general, regardless of the healthcare system and geographical location, the proportion of physicians per 1000 population has improved due to a larger decrease in the number of hospital beds.

How to Engage Health Care Workers in the Evaluation of Hospitals: Development and Validation of BSC-HCW1-A Cross-Sectional Study.

Organizations worldwide utilize the balanced scorecard (BSC) for their performance evaluation (PE). This research aims to provide a tool that engages health care workers (HCWs) in BSC implementation (BSC-HCW1). Additionally, it seeks to translate and validate it at Palestinian hospitals. In a cross-sectional study, 454 questionnaires were retrieved from 14 hospitals. The composite reliability (CR), interitem correlation (IIC), and corrected item total correlation (CITC) were evaluated. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used. In both EFA and CFA, the scale demonstrated a good level of model fit. All the items had loadings greater than 0.50. All factors passed the discriminant validity. Although certain factors' convergent validity was less than 0.50, their CR, IIC, and CITC were adequate. The final best fit model had nine factors and 28 items in CFA. The BSC-HCW1 is the first self-administered questionnaire to engage HCWs in assessing the BSC dimensions following all applicable rules and regulations. The findings revealed that this instrument's psychometric characteristics were adequate. Therefore, the BSC-HCW1 can be utilized to evaluate BSC perspectives and dimensions. It will help managers highlight which BSC dimension predicts HCW satisfaction and loyalty and examine differences depending on HCWs' and hospital characteristics.

A systematic review: the dimensions to evaluate health care performance and an implication during the pandemic.

BACKGROUND: The balanced scorecard (BSC) has been implemented to evaluate the performance of health care organizations (HCOs). BSC proved to be effective in improving financial performance and patient satisfaction. AIM: This systematic review aims to identify all the perspectives, dimensions, and KPIs that are vital and most frequently used by health care managers in BSC implementations. METHODS: This systematic review adheres to PRISMA guidelines. The PubMed, Embase, Cochrane, and Google Scholar databases and Google search engine were inspected to find all implementations of BSC at HCO. The risk of bias was assessed using the nonrandomized intervention studies (ROBINS-I) tool to evaluate the quality of observational and quasi-experimental studies and the Cochrane (RoB 2) tool for randomized controlled trials (RCTs). RESULTS: There were 33 eligible studies, of which we identified 36 BSC implementations. The categorization and regrouping of the 797 KPIs resulted in 45 subdimensions. The reassembly of these subdimensions resulted in 13 major dimensions: financial, efficiency and effectiveness, availability and quality of supplies and services, managerial tasks, health care workers' (HCWs) scientific development error-free and safety, time, HCW-centeredness, patient-centeredness, technology, and information systems, community care and reputation, HCO building, and communication. On the other hand, this review detected that BSC design modification to include external and managerial perspectives was necessary for many BSC implementations. CONCLUSION: This review solves the KPI categorization dilemma. It also guides researchers and health care managers in choosing dimensions for future BSC implementations and performance evaluations in general. Consequently, dimension uniformity will improve the data sharing and comparability among studies. Additionally, despite the pandemic negatively influencing many dimensions, the researchers observed a lack of comprehensive HCO performance evaluations. In the same vein, although some resulting dimensions were assessed separately during the pandemic, other dimensions still lack investigation. Last, BSC dimensions may play an essential role in tackling the COVID-19 pandemic. However, further research is required to investigate the BSC implementation effect in mitigating the pandemic consequences on HCO.

Assessment of Countries' Preparedness and Lockdown Effectiveness in Fighting COVID-19.

OBJECTIVES: The aim of this study was to assess the risks in confronting the coronavirus disease 2019 (COVID-19) pandemic and the ongoing lockdown effectiveness in each of Italy, Germany, Spain, France, and the United States using China's lockdown model simulation, and cases forecast until the plateau phase. METHODS: Quantitative and qualitative historical data analysis. Total Risk Assessment (TRA) evaluation tool was used to assess the pre-pandemic stage risks, pandemic threshold fast responsiveness, and the ongoing performance until plateau. The Infected Patient Ratio (IPR) tool was developed to measure the number of patients resulting from 1 infector during the incubation period. Both IPR and TRA were used together to forecast inflection points, plateau phases, intensive care units' and ventilators' breakpoints, and the Total Fatality Ratio. RESULTS: In Italy, Spain, France, Germany, and the United States, an inflection point is predicted within the first 15 d of April, to arrive at a plateau after another 30 to 80 d. Variations in IPR drop are expected due to variations in lockdown timing by each country, the extent of adherence to it, and the number of performed tests in each. CONCLUSIONS: Both qualitative (TRA) and quantitative (IPR) tools can be used together for assessing and minimizing the pandemic risks and for more precise forecasting.