Hospital efficiency and utilization of high-technology medical equipment: A panel data analysis

Positive Effect of a Financial Incentive on Radiologist Compliance With Quality Metric Placement in Knee Radiography Reports

PURPOSE

Ongoing quality improvement (QI) processes in the authors' department include the insertion of a Kellgren-Lawrence (KL) osteoarthritis grading template in knee radiography reports to decrease unnecessary MRI. However, uniform adoption of this grading system is lacking. Department-wide financial incentives were instituted to improve compliance with QI metrics. The purpose of this study was to evaluate the effect of a financial incentive on KL grading system use and to compare compliance rates of musculoskeletal (MSK) radiologists with those of general radiologists who were not financially incentivized to use KL grading.

METHODS

Percentages of all knee radiography reports containing KL grading with standardized follow-up recommendations were determined by querying the departmental radiology database before and after the introduction of the new quality-based financial incentive. Preincentive compliance rates for MSK and general radiologists were compared with an adoption period and two separate 6-month postincentive periods.

RESULTS

In total, 52,673 reports were retrospectively analyzed for KL grading use (41,670 reports interpreted by MSK radiologists and 11,003 interpreted by general radiologists). Increase in compliance was greatest among MSK radiologists' reports during the incentivized adoption period (from 36.1% to 53.2%). This improvement was sustained among MSK radiologists and averaged 62.7% during the most recently studied postimplementation period. A lesser degree of improvement in compliance was observed in nonincentivized general radiologists' reports (from 19.3% to 27.5%); during the postimplementation follow-up period, their compliance decreased to 26.5%.

CONCLUSIONS

The introduction of a financial incentive resulted in significantly increased adoption of QI practices with sustained improvement among incentivized MSK radiologists compared with nonincentivized general radiologists.

Medical equipment effectiveness evaluation model based on cone-constrained DEA and attention-based bi-LSTM

This study constructs a composite indicator system covering the core dimensions of medical equipment input and output. Based on this system, an innovative cone-constrained data envelopment analysis (DEA) model is designed. The model integrates the advantages of the analytic hierarchy process (AHP) with an improved criterion importance through intercriteria correlation (CRITIC) method to determine subjective and objective weights and employs game theory to obtain the final combined weights, which are further incorporated as constraints to form the cone-constrained DEA model. Finally, a bidirectional long short-term memory (Bi-LSTM) model with an attention mechanism is introduced for integration, aiming to provide a novel and practical model for evaluating the effectiveness of medical equipment. The proposed model has essential reference value for optimizing medical equipment management decision-making and investment strategies.

Measurement and characteristics of the temporal-spatial evolution of China's healthcare services efficiency

BACKGROUND

Healthcare services efficiency (HSE) is directly related to the healthcare demands of the general public and also plays an essential role in the country's coordinated economic and social development.

METHODS

In this study, the stochastic frontier approach (SFA)-Malmquist model was applied to measure the HSE of 31 Chinese provinces based on panel data from 2010-2020. Then, kernel density estimation, Markov chain, and exploratory spatial data analysis were adopted to study the temporal-spatial dynamic evolution characteristics of the HSE.

RESULTS

The study found that China's HSE showed an average value of approximately 0.841, indicating room for improvement. The HSE varied significantly across regions, presenting an "East > Central > West" distribution layout. The TFP of healthcare services in China grew by 1.6% per year, driven mainly by technological progress of 1.8% per year. The trend of the HSE shifting to a high level in China was significant, but its evolution exhibited stability of maintaining the original state, and it was harder to achieve leapfrog transfer. The temporal-spatial evolution of the HSE was also significantly affected by geospatial factors, with a clear spatial spillover effect and spatial agglomeration characteristics. Provinces with high-level HSE exhibited positive spatial spillover effects, while provinces with low-level HSE had negative spatial spillover effects. Thus, the "club convergence" phenomenon of "high efficiency concentration, low efficiency agglomeration, high levels of radiation, and low levels of suppression" was formed in the spatial distribution.

CONCLUSIONS

The results indicate that countermeasures should be taken to improve the HSE in China. Theoretical support for the improvement of HSE is provided in this paper.

3D Ultrasonic Brain Imaging with Deep Learning Based on Fully Convolutional Networks

Compared to magnetic resonance imaging (MRI) and X-ray computed tomography (CT), ultrasound imaging is safer, faster, and more widely applicable. However, the use of conventional ultrasound in transcranial brain imaging for adults is predominantly hindered by the high acoustic impedance contrast between the skull and soft tissue. This study introduces a 3D AI algorithm, Brain Imaging Full Convolution Network (BIFCN), combining waveform modeling and deep learning for precise brain ultrasound reconstruction. We constructed a network comprising one input layer, four convolution layers, and one pooling layer to train our algorithm. In the simulation experiment, the Pearson correlation coefficient between the reconstructed and true images was exceptionally high. In the laboratory, the results showed a slightly lower but still impressive coincidence degree for 3D reconstruction, with pure water serving as the initial model and no prior information required. The 3D network can be trained in 8 h, and 10 samples can be reconstructed in just 12.67 s. The proposed 3D BIFCN algorithm provides a highly accurate and efficient solution for mapping wavefield frequency domain data to 3D brain models, enabling fast and precise brain tissue imaging. Moreover, the frequency shift phenomenon of blood may become a hallmark of BIFCN learning, offering valuable quantitative information for whole-brain blood imaging.

Efficiency of Medical Equipment Utilization and Its Associated Factors at Public Referral Hospitals in East Wollega Zone, Oromia Regional State, Ethiopia

Background

The significance of effectively using medical equipment has received widespread public attention. Due to its complex effects on healthcare costs and quality, the relationship between hospital features and medical equipment consumption has drawn increased attention. Therefore, the study aimed to evaluate the efficiency of medical equipment utilization and its associated factors.

Methods

The study was conducted at public referral hospitals in East Wollega, Oromia Regional National State, Ethiopia, in 2021. A cross-sectional study design was mixed with observation and document review. The study included approximately 192 pieces of equipment. Descriptive statistics and Pearson Chi-square (χ ²) were used to identify associations between each independent and dependent variable at p<0.05 to declare level of significance.

Results

Using 95% confidence interval (CI), the level of utilization coefficient was estimated to be 0.49 (0.44-0.55). As a result, 111 pieces of equipment (57.8%) were used efficiently, while 81 (42.2%) were underutilized. The form in which the hospitals received the equipment (χ ²=7.7.2; P=0.005), regular availability (χ ²=19.30; P=0.00), equipment breakdown (χ ²=11.57; P=0.001), the availability of trained staffs operating the equipment (χ ²=26.14; P=0.00), performing preventive maintenance (χ ²=91.54; P=0.00), the availability of spare parts (χ ²=32.36; P=0.00), and the availability of accessories (χ ²=43.91; P=0.00) were statistically significant factors affecting the medical equipment utilization.

Conclusion

On average, the utilization coefficient of medical equipment in the study hospitals was low compared to other study findings, which indicated that 2 out of 5 pieces of medical equipment were under-utilized, which could be significantly affected by the form in which the hospital received the equipment, its regular availability, equipment breakdown, availability of trained staff operating the equipment, performing preventive maintenance, and availability of adequate spare parts and accessories. Every hospital should develop an appropriate strategic framework to manage and utilize the available medical diagnostic equipment based on its level and demand.

Hospital utilization among urban poor in Indonesia in 2018: is government-run insurance effective?

BACKGROUND

An urban poor is a vulnerable group that needs government financing support to access health services. Once they are sick, they will fall deeper into poverty. The study aims to analyze the effectiveness of government-run insurance in hospital utilization in urban poor in Indonesia.

METHODS

The research analyzed the 2018 Indonesian Basic Health Survey data. This cross-sectional survey collected 75,970 participants through stratification and multistage random sampling. Meanwhile, the study employed hospital utilization as an outcome variable and health insurance ownership as an exposure variable. Moreover, the study looked at age, gender, marital status, education, and occupation as control factors. The research employed a binary logistic regression to evaluate the data in the final step.

RESULTS

The results show that someone with government-run insurance is 4.261 times more likely than the uninsured to utilize the hospital (95% CI 4.238-4.285). Someone with private-run insurance is 4.866 times more likely than the uninsured to use the hospital (95% CI 4.802-4.931). Moreover, someone with government-run and private-run insurance has 11.974 times more likely than the uninsured to utilize the hospital (95% CI 11.752-12.200).

CONCLUSION

The study concluded that government-run insurance is more effective than the uninsured in improving hospital utilization among the urban poor in Indonesia. Meanwhile, private-run is more effective than government-run and uninsured in improving hospital utilization among the urban poor in Indonesia. Moreover, the most effective is to combine the kind of health insurance ownership (government-run and private-run).

Regional disparities in hospital utilisation in Indonesia: a cross-sectional analysis data from the 2018 Indonesian Basic Health Survey

OBJECTIVES

Policymakers must ensure that the entire population has equal access to health services, and efforts to minimise inequalities are needed. This study aimed to analyse the regional disparities in hospital utilisation in Indonesia.

DESIGN

A cross-sectional study analysing secondary data from the 2018 Indonesian Basic Health Survey.

SETTING

National-level survey data from Indonesia.

PARTICIPANTS

A total of 629 370 participants were included in the study.InterventionWe employed no interventionPrimary and secondary outcome measuresThe primary outcome was hospital utilisation. Aside from region, we utilise residence type, age, gender, marital status, educational level, occupation, wealth, insurance and travel time as control variables. We used binary logistic regression in the final analysis RESULTS: The respondents in Sumatra were 1.079 times (95% CI 1.073 to 1.085) more likely than those in Papua to use the hospital. Furthermore, compared with the respondents in Papua, those in the Java-Bali region (1.075 times, 95% CI 1.069 to 1.081), Nusa Tenggara (1.106 times, 95% CI 1.099 to 1.113), Sulawesi (1.008 times, 95% CI 1.002 to 1.014) and Kalimantan (1.212 times, 95% CI 1.205 to 1.219) were more likely to use the hospital. However, those in Maluku were less likely than those in Papua to use the hospital (0.827 times, 95% CI 0.820 to 0.835). Six demographic variables (age, gender, marital status, educational level, occupation and wealth) and three other control variables (residence type, insurance and travel time to the hospital) were found to be associated with hospital utilisation.

CONCLUSIONS

Our findings highlight the existence of regional disparities in hospital utilisation in Indonesia.

Policy to expand hospital utilization in disadvantaged areas in Indonesia: who should be the target?

BACKGROUND

The disadvantaged areas are one of the government's focuses in accelerating development in Indonesia, including the health sector. The study aims to determine the target for expanding hospital utilization in disadvantaged areas in Indonesia.

METHODS

The study employed the 2018 Indonesian Basic Health Survey data. This cross-sectional study analyzed 42,644 respondents. The study used nine independent variables: residence, age, gender, marital, education, employment, wealth, insurance, and travel time, in addition to hospital utilization, as a dependent variable. The study employed binary logistic regression to evaluate the data.

RESULTS

The results found that average hospital utilization in disadvantaged areas in Indonesia in 2018 was 3.7%. Urban areas are 1.045 times more likely than rural areas to utilize the hospital (95% CI 1.032-1.058). The study also found age has a relationship with hospital utilization. Females are 1.656 times more likely than males to use the hospital (95% CI 1.639-1.673). Moreover, the study found marital status has a relationship with hospital utilization. The higher the education level, the higher the hospital utilization. Employed individuals have a 0.748 possibility to use the hospital compared with those unemployed (95% CI 0.740-0.757). Wealthy individuals have more chances of using the hospital than poor individuals. Individuals with all insurance types are more likely to utilize the hospital than those uninsured. Individuals with travel times of ≤ 1 h are 2.510 more likely to use the hospital than those with > 1 h (95% CI 2.483-2.537).

CONCLUSION

The specific targets to accelerate the increase in hospital utilization in disadvantaged areas in Indonesia are living in a rural area, being male, never in a union, having no education, being employed, being the poorest, uninsured, and having a travel time of > 1 h. The government should make a policy addressing the problem based on the research findings.

Regional differences in primary healthcare utilization in Java Region-Indonesia

BACKGROUND

Policymakers must understand primary healthcare utilization disparity to minimize the gap because they must seek fair service for every citizen. The study analyzes regional differences in primary healthcare utilization in Java Region-Indonesia.

METHODS

The cross-sectional research analyzes secondary data from the 2018 Indonesian Basic Health Survey. The study setting represented Java Region-Indonesia, and the participants were adults 15 years or more. The survey explores 629,370 respondents. The study used primary healthcare utilization as an outcome variable and province as the exposure variable. Moreover, the study employed eight control variables (residence, age, gender, education, marital, employment, wealth, and insurance). The study evaluated data using binary logistic regression in the final step.

RESULTS

People in Jakarta are 1.472 times more likely to utilize primary healthcare than those in Banten (AOR 1.472; 95% CI 1.332-1.627). People in Yogyakarta are 1.267 times more likely to use primary healthcare than those in Banten (AOR 1.267; 95% CI 1.112-1.444). In addition, people in East Java are 15% less likely to utilize primary healthcare than those in Banten (AOR 0.851; 95% CI 0.783-0.924). Meanwhile, direct healthcare utilization was the same between West Java, Central Java, and Banten Province. They are sequentially starting from the minor primary healthcare utilization: East Java, Central Java, Banten, West Java, Yogyakarta, and Jakarta.

CONCLUSION

Disparities between regions exist in the Java Region-Indonesia. They are sequentially starting from the minor primary healthcare utilization: East Java, Central Java, Banten, West Java, Yogyakarta, and Jakarta.

The Role of Government-run Insurance in Primary Health Care Utilization: A Cross-Sectional Study in Papua Region, Indonesia, in 2018

Health development in the Papua region often lags behind other areas of Indonesia. The study aims to analyze the role of government-run insurance in primary health care utilization in the Papua region, Indonesia. The study examined 17,879 Papuan. The study used primary health care utilization as an outcome variable and health insurance ownership as an exposure variable. The study also employed nine control variables: province, residence, age, gender, marital status, education, employment, wealth, and travel time to primary health care. The research employed data using binary logistic regression in the final analysis. The results show that Papuans with government-run insurance were three times more likely to utilize primary health care than uninsured Papuans (AOR 3.081; 95% CI 3.026-3.137). Meanwhile, Papuan with private-run insurance were 0.133 times less likely to utilize primary health care than uninsured Papuans (AOR 0.133; 95% CI 0.109-0.164). Moreover, Papuans who have two types of health insurances (government-run and private-run) were 1.5 times more likely to utilize the primary health care than uninsured Papuan (AOR 1.513; 95% CI 1.393-1.644). The study concluded that government-run insurance increases the chance of primary health care utilization in the Papua region, Indonesia. Government-run insurance has the most prominent role compared to other health insurance categories.

Hospital utilization in Indonesia in 2018: do urban-rural disparities exist?

Background

The government must ensure equality in health services access, minimizing existing disparities between urban and rural areas. The referral system in Indonesia is conceptually sound. However, there are still problems of uneven service access, and there is an accumulation of patients in certain hospitals. The study aims to analyze the urban–rural disparities in hospital utilization in Indonesia.

Methods

The study used secondary data from the 2018 Indonesian Basic Health Survey. This cross-sectional study gathered 629,370 respondents through stratification and multistage random sampling. In addition to the kind of home and hospital utilization, the study looked at age, gender, marital status, education, occupation, wealth, and health insurance as control factors. The research employed multinomial logistic regression to evaluate the data in the final step.

Results

According to the findings, someone who lives in an urban region has 1.493 times higher odds of using outpatient hospital services than someone in a rural area (AOR 1.493; 95% CI 1.489–1.498). Meanwhile, someone who lives in an urban region has 1.075 times higher odds of using an inpatient facility hospital than someone who lives in a rural one (AOR 1.075; 95% CI 1.073–1.077). Furthermore, someone living in an urban region has 1.208 times higher odds than someone who lives in a rural area using outpatient and inpatient hospital services simultaneously (AOR 1.208; 95% CI 1.204–1.212).

Conclusion

The study concluded there were urban–rural disparities in hospital utilization in Indonesia.

Exploring the nexus of equality and efficiency in healthcare

Purpose

This study aims to explore the nexus of equality and efficiency by considering public hospitals' development dynamics, capacity and technology indicators.

Design/methodology/approach

Data was collected from the Ministry of Health Public Hospital Almanacs from 2014 to 2017. The Gini index (GI) is used to estimate the inequality of distribution of hospital performance indicators. A bias-corrected efficiency analysis is calculated to obtain efficiency scores of public hospitals for the year 2017. A path analysis is then constructed to better identify patterns of causation among a set of development, equality and efficiency variables.

Findings

A redefined path model highlights that development dynamics, equality and efficiency are causally related and health technology (path coefficient = 0.57; t = 19.07; p < 0.01) and health services utilization (path coefficient = 0.24; t = 8; p < 0.01) effects public hospital efficiency. The final path model fit well (X2/df = 50.99/8 = 6; RMSEA = 0.089; NFI = 0.95; CFI = 0.96; GFI = 0.98; AGFI = 0.94). Study findings indicate high inequalities in distribution of health technologies (GI > 0.85), number of surgical operations (GI > 0.70) and number of inpatients (GI > 0.60) among public hospitals for the years 2014–2017.

Originality/value

Study results highlight that, hospital managers should prioritize equal distribution of health technology and health services utilization indicators to better orchestrate equity-efficiency trade-off in their operations.

Socioeconomic Disparities in Hospital Utilization Among Female Workers in Indonesia: A Cross-Sectional Study

BACKGROUND

The study aims to analyze the relationship between socioeconomic and hospital utilization among female workers in Indonesia.

METHODS

The study analyzed secondary data from the 2018 Indonesian Basic Health Survey. The study gathered 161 186 female workers through stratification and multistage random sampling. As control factors, the study looked at age, marital status, education, occupation, and health insurance, in addition to the categories of socioeconomic and hospital utilization. The study used binary logistic regression to evaluate the data in the final step.

RESULTS

The result shows female workers with poorer wealth status are 1.142 times more likely than the most impoverished female workers to utilize the hospital (AOR 1.142; 95% CI 1.135-1.148). Female workers with median wealth status are 1.509 times more likely than the poorest female workers to take advantage of the hospital (AOR 1.509; 95% CI 1.501-1.517). Female workers with wealthier wealth status are 1.808 times more likely than the poorest female workers to use the hospital (AOR 1.808; 95% CI 1.799-1.817). The wealthiest female workers are 2.399 times more likely than the poorest female workers to utilize the hospital (2.399; 95% CI 2.387-2.411).

CONCLUSION

The study concluded a relationship between socioeconomic status and hospital utilization among female workers in Indonesia. The better the socioeconomic, the better the hospital utilization.

Machine Learning-Based Hospital Discharge Prediction for Patients With Cardiovascular Diseases: Development and Usability Study

Background

Effective resource management in hospitals can improve the quality of medical services by reducing labor-intensive burdens on staff, decreasing inpatient waiting time, and securing the optimal treatment time. The use of hospital processes requires effective bed management; a stay in the hospital that is longer than the optimal treatment time hinders bed management. Therefore, predicting a patient’s hospitalization period may support the making of judicious decisions regarding bed management.

Objective

First, this study aims to develop a machine learning (ML)–based predictive model for predicting the discharge probability of inpatients with cardiovascular diseases (CVDs). Second, we aim to assess the outcome of the predictive model and explain the primary risk factors of inpatients for patient-specific care. Finally, we aim to evaluate whether our ML-based predictive model helps manage bed scheduling efficiently and detects long-term inpatients in advance to improve the use of hospital processes and enhance the quality of medical services.

Methods

We set up the cohort criteria and extracted the data from CardioNet, a manually curated database that specializes in CVDs. We processed the data to create a suitable data set by reindexing the date-index, integrating the present features with past features from the previous 3 years, and imputing missing values. Subsequently, we trained the ML-based predictive models and evaluated them to find an elaborate model. Finally, we predicted the discharge probability within 3 days and explained the outcomes of the model by identifying, quantifying, and visualizing its features.

Results

We experimented with 5 ML-based models using 5 cross-validations. Extreme gradient boosting, which was selected as the final model, accomplished an average area under the receiver operating characteristic curve score that was 0.865 higher than that of the other models (ie, logistic regression, random forest, support vector machine, and multilayer perceptron). Furthermore, we performed feature reduction, represented the feature importance, and assessed prediction outcomes. One of the outcomes, the individual explainer, provides a discharge score during hospitalization and a daily feature influence score to the medical team and patients. Finally, we visualized simulated bed management to use the outcomes.

Conclusions

In this study, we propose an individual explainer based on an ML-based predictive model, which provides the discharge probability and relative contributions of individual features. Our model can assist medical teams and patients in identifying individual and common risk factors in CVDs and can support hospital administrators in improving the management of hospital beds and other resources.

Assessing and coping with the financial burden of computed tomography utilization in Limbe, Cameroon: a sequential explanatory mixed-methods study

BACKGROUND

There has been a significant increase in computed tomography (CT) utilization over the past two decades with the major challenges being a high exposure to ionizing radiation and rising cost. In this study we assess the risk of financial hardship after CT utilization and elaborate on how users adapt and cope in a sub-Saharan context with user fee for services and no national health insurance policy.

METHODS

We carried out a sequential explanatory mixed methods study with a quantitative hospital-based survey of CT users followed by in-depth interviews of some purposively selected participants who reported risk of financial hardship after CT utilization. Data was summarized using frequencies, percentages and 95% confidence intervals. Logistic regression was used in multivariable analysis to determine predictors of risk of financial hardship. Identified themes from in-depth interviews were categorized. Quantitative and qualitative findings were integrated.

RESULTS

A total of 372 participants were surveyed with a male to female sex ratio of 1:1.2. The mean age (standard deviation) was 52(17) years. CT scans of the head and facial bones accounted for 63% (95%CI: 59-68%) and the top three indications were suspected stroke (27% [95%CI: 22-32%]), trauma (14% [95%CI: 10-18%]) and persistent headaches (14% [95%CI: 10-18%]). Seventy-two percent (95%CI: 67-76%) of the respondents reported being at risk of financial hardship after CT utilization and predictors in the multivariable analysis were a low socioeconomic status (aOR: 0.19 [95%CI: 0.10-0.38]; p < 0.001), being unemployed or retired (aOR: 11.75 [95%CI: 2.59-53.18]; p = 0.001) and not having any form of health insurance (aOR: 3.59 [95%CI: 1.31-9.85]; p = 0.013). Coping strategies included getting financial support from family and friends, borrowing money and obtaining discounts from the hospital administration and staff.

CONCLUSION

No health insurance ownership, being unemployed or retired and a low socioeconomic status are associated with financial hardship after CT utilization. Diverse coping strategies are utilized to lessen the financial burden, some with negative consequences. Minimizing out-of-pocket payments and/or the direct cost of CT can reduce this financial burden and improve CT access.

The effect of different classification of hospitals on medical expenditure from perspective of classification of hospitals framework: evidence from China

Background

Different classification of hospitals (COH) have an important impact on medical expenditures in China. The objective of this study is to examine the impact of COH on medical expenditures with the hope of providing insights into appropriate care and resource allocation.

Methods

From the perspective of COH framework, using the Urban Employee Basic Medical Insurance (UEBMI) data of Chengdu City from 2011 to 2015, with sample size of 488,623 hospitalized patients, our study empirically analyzed the effect of COH on medical expenditure by multivariate regression modeling.

Results

The average medical expenditure was 5468.86 Yuan (CNY), the average expenditure of drug, diagnostic testing, medical consumables, nursing care, bed, surgery and blood expenditures were 1980.06 Yuan (CNY), 1536.27 Yuan (CNY), 500.01 Yuan (CNY), 166.23 Yuan (CNY), 221.98 Yuan (CNY), 983.18 Yuan (CNY) and 1733.21 Yuan (CNY) respectively. Patients included in the analysis were mainly elderly, with an average age of 86.65 years old. Female and male gender were split evenly. The influence of COH on total medical expenditures was significantly negative (p < 0.001). The reimbursement ratio of UEBMI had a significantly positive (p < 0.001) effect on various types of medical expenditures, indicating that the higher the reimbursement ratio was, the higher the medical expenditures would be.

Conclusions

COH influenced medical expenditures significantly. In consideration of reducing medical expenditures, the government should not only start from the supply side of healthcare services, but also focus on addressing the demand side.

The efficiency analysis and spatial implications of health information technology: A regional exploratory study in China

The new adoption of healthcare information technology is costly, and effects on healthcare performance can be questionable. This nationwide study in China investigated the efficient performance of healthcare information technology and examined its spatial correlation. Panel data were extracted from the Annual Investigation Report on Hospital Information in China and the China Health Statistics Yearbook for 2007 through 2015 (279 observations). Stochastic frontier analysis was employed to estimate the technical efficiency of healthcare information technology performance and related factors at the regional level. Healthcare information technology performance was positively associated with electronic medical records, total input, and cost of inpatient stay, while picture archiving and communication systems and net assets were negatively related. Local Indicators of Spatial Association showed that there existed significant spatial autocorrelation. Governmental policies would best make distinctions among different forms of healthcare information technology, especially between electronic medical records and picture archiving and communication systems. Policies should be formulated to improve healthcare information technology adoption and reduce regional differences.

Investigation of Relationship Between Spatial Distribution of Medical Equipment and Preventable Mortality

The aim of the study is to investigate the relationship between the spatial distribution of the selected medical equipment and the preventable mortality rate in the regions of the Slovak Republic. The main analytical approach is carried out through the cluster analysis based on a Euclidean distance technique in order to get similarity of the administrative divisions in form of a district and a pseudot2 approach aimed at the determination of a number of the districts in a cluster. A number of medical equipment had a rising tendency from the year 2008. The most extreme position according to a localisation distribution of the computed tomographs and the magnetic resonance imaging scanners is held by the Košice IV District at the level of 7.50630. From an angle of view of the preventable mortality, the Piešťany District holds the most extreme position peaking at the level of 10.97969 for the female sex and the Kežmarok District with the value of 9.44088. The study has the significant dissemination outputs for health policy interventions, especially to draw up regional health plans for computed tomography and magnetic resonance imaging deployment, mainly in locations with a high preventable mortality rate for both sexes.

Efficiency Measurement in Health Facilities: A Systematic Review in Low- and Middle-Income Countries

BACKGROUND

Limited healthcare resources in low- and middle-income countries (LMICs) have led policy-makers to improve healthcare efficiency. Therefore, it is essential to understand how efficiency has been measured in the LMIC setting.

OBJECTIVE

This paper reviews methodologies used for efficiency studies in health facilities in LMICs.

METHODS

We searched MEDLINE, Embase, Global Health, EconLit and ProQuest Dissertations and Theses databases to Week 6 in 2018. We included all types of quantitative analysis studies relating to the measurement of the efficiency of services at health facilities in LMICs. We extracted data from eligible studies, and assessed the validity for each study. Because of the substantial heterogeneity of the studies, results were presented narratively.

RESULTS

A total of 137 papers were eligible for inclusion. These articles covered a wide range of health facility types, with more than half of the studies relating to hospitals. Our systematic review showed that there is an increasing trend in efficiency measurements in LMICs using various methods. Most studies employed data envelopment analysis as an efficiency measurement method. The studies typically included physical inputs and health services as outputs. Sixty-one percent of the studies analysed the contextual variables of the health facility efficiency.

CONCLUSION

This review highlights the potential for methodological improvement and policy impacts in efficiency measurements.