scholarly journals Path Analysis of Hospitalization Expenses of 2,164 Appendicitis Patients at County-Level Public Hospitals in Anhui Province, China

2020 ◽  
Author(s):  
Junjun Xue ◽  
Heng Wang ◽  
Niannian Li ◽  
Yuhuan Ling ◽  
Chen Qian ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Path Analysis ◽  
Average Length ◽  
Anhui Province ◽  
Public Hospitals ◽  
County Level ◽  
Operative Duration ◽  
Average Length Of Stay ◽  
Factors Influencing ◽  
Controllable Factors

Abstract Background and objective Appendicitis resection is one of the most common surgical procedures. Hospitalization expenses are a major determinant of appendicitis treatment. This study explored the factors influencing hospitalization expenses of appendicitis patients in Anhui province and provided a scientific basis for reasonably controlling medical expenses. Methods A multi-stage random cluster sampling method was used to collect case information on 2,164 patients who underwent appendicitis surgery at 6 county-level public hospitals in Anhui province, China. Path analysis was used to study the factors influencing hospitalization expenses of appendicitis patients. Results The average length of stay was 5.62 ± 2.64 days, with a median of 5 days; the average hospitalization expenses were 6,109.60 ± 2,109.44 CNY, with a median of 5,511.93 CNY. The direct effect of length of stay was 0.535. Surgical grades, surgical methods, and chronic appendicitis directly affected hospitalization expenses, with direct effects of 0.149, 0.081, and -0.037; surgical costs, anesthesia costs, disease outcomes, age, acute simple appendicitis, and operative duration not only directly affected hospitalization expenses, but also indirectly affected hospitalization expenses, the total effects were 0.283, 0.045, 0.200, 0.202, -0.162, and 0.062, respectively. The total number of surgeons and assistants only indirectly affected hospitalization expenses, with indirect effect of 0.020. Conclusions The length of stay is the most important factor affecting hospitalization expenses. Based on controlling the average length of stay, combined with shortening operative duration, conducting health education, strengthening controllable factors, and other comprehensive measures can effectively reduce the economic burden of patients and hospitals.

scholarly journals Path Analysis of Hospitalization Expenses of 2,164 Appendicitis Patients at County-Level Public Hospitals in Anhui Province, China

2020 ◽  
Author(s):  
Junjun Xue ◽  
Heng Wang ◽  
Niannian Li ◽  
Yuhuan Ling ◽  
Chen Qian ◽  
...  
Keyword(s):  
Path Analysis ◽  
Average Length ◽  
Anhui Province ◽  
Public Hospitals ◽  
Path Coefficient ◽  
County Level ◽  
Chronic Appendicitis ◽  
Factors Influencing ◽  
Controllable Factors ◽  
Surgical Methods

Abstract Background and objectiveAppendicitis resection is one of the most common surgical procedures in China. Hospitalization expenses are a major determinant of appendicitis treatment. This study explored the factors influencing hospitalization expenses of appendicitis surgery patients in Anhui province and provided a scientific basis for reasonably controlling medical expenses.MethodsA multi-stage random cluster sampling method was used to collect case information on 2,164 patients who underwent appendicitis surgery at 6 county-level public hospitals in Anhui province, China. Path analysis was used to study the factors influencing hospitalization expenses of appendicitis surgery patients.ResultsThe average length of stay (LOS) was 5.62 ± 2.64 days, with a median of 5 days; the average hospitalization expenses were 6,109.60 ± 2,109.44 CNY, with a median of 5,511.93 CNY. The direct effect of LOS was 0.535, which was the most important direct factor affecting hospitalization expenses, and the remaining path coefficient was 0.699. surgical grades, surgical methods, and chronic appendicitis directly affected hospitalization expenses, with direct effects of 0.149, 0.081, and -0.037; surgical costs, anesthesia costs, disease outcomes, age, acute simple appendicitis, and operative duration (OD) not only directly affected hospitalization expenses, but also indirectly affected hospitalization expenses through the LOS, and the total effects were 0.283, 0.045, 0.200, 0.202, -0.162, and 0.062, respectively. The total number of surgeons and assistants only indirectly affected the hospitalization expenses through the LOS, and the indirect effect was 0.020.ConclusionsThe LOS is the most important factor affecting hospitalization expenses. Controlling hospitalization expenses is an effective method of reducing the economic burden of patients undergoing appendicitis surgery and decreasing hospital medical expenditures. Based on controlling the average LOS, combined with other comprehensive measures such as decreasing the OD and health education, strengthening controllable factors, and effectively managing the unreasonable increase in hospitalization expenses.

The Health Sector Evolution Plan and the Technical Efficiency of Public Hospitals in IranBackground: Iranian public hospitals have been excessively changing during the healthcare reform since 2014. This study aimed to examine the technical efficiency of p

2020 ◽  
Author(s):  
Edris KAKEMAM ◽  
Hossein DARGAHI
Keyword(s):  
Length Of Stay ◽  
Technical Efficiency ◽  
Healthcare Reform ◽  
Average Length ◽  
Health Sector ◽  
Public Hospitals ◽  
Occupancy Rate ◽  
Bed Occupancy ◽  
Tobit Regression ◽  
Average Length Of Stay

Background: Iranian public hospitals have been excessively changing during the healthcare reform since 2014. This study aimed to examine the technical efficiency of public hospitals during before and after the implementation of Health Sector Evolution Plan (HSEP) and to determine whether, and how, efficiency is affected by various factors. Methods: Forty-two public hospitals were selected in Tehran, Iran, from 2012 to 2016. Data envelopment analysis was employed to estimate the technical and scale efficiency sample hospitals. Tobit regression was used to relate the technical efficiency scores to seven explanatory variables in 2016, the last year. Results: Overall, 24 (57.1%), 26 (61.9%), 26 (61.9%), 24 (57.1%) and 21 (50%) of the 42 sample hospitals ran inefficiently in 2012 to 2016, with average technical efficiency of 0.859, 0.836, 0.845, 0.905 and 0.934, respectively. The average pure technical efficiency in sample hospitals increased from 0.860 in 2010 (before the HSEP) to 0.944 in 2012 (after the HSEP). Tobit regression showed that average length of stay had a negative impact on technical efficiency of hospitals. In addition, bed occupancy rate, ratio of beds to nurses and ratio of nurses to physicians assumed a positive sign with technical efficiency. Conclusion: Despite government support, public hospitals operated relatively inefficien. Managers can enhance technical efficiency by increasing bed occupancy rate through shortening the average length of stay, proportioning the number of doctors, nurses, and beds along with service quality assurance.

The Problem of Capacity Management in Greek Public Hospitals

2017 ◽  
Vol 1 (2) ◽  
pp. 414
Author(s):  
Christos Tsitsakis ◽  
Persefoni Polychronidou ◽  
Anastasios Karasavvoglou
Keyword(s):  
Length Of Stay ◽  
Average Length ◽  
National Health System ◽  
Healthcare Services ◽  
Capacity Management ◽  
Point Of View ◽  
Public Hospitals ◽  
Theory Of Constraints ◽  
Healthcare Organizations ◽  
Average Length Of Stay

<p class="AbstractText">One of the most important problems that the users of Greek National Health System face, is the long waiting lists. Αrather superficial explanationof this phenomenon is usually refer the increasing demand for healthcare services, ignoring that the problem is mainly a problem of capacity management, which is associated with the occupancy rate of specific wards of a hospital, and the average length of stay.</p><p class="AbstractText">The theory of constraints can apply successfully to healthcare organizations, to solve problems of capacity management, reducing the inpatient length of stay and increasing the satisfaction from the offering services as has been proved by international research.</p><p class="AbstractText">In this paper, we study the problem from this point of view. Our qualitative research revealed that there is a bottle-neck in the normal flow of patients, because of the delays in the imaging departments of the hospitals.</p>The increase of the capacity of the imaging departments would offer a feasible solution to the problem.

scholarly journals Australian hospital services: An overview

2002 ◽  
Vol 25 (1) ◽  
pp. 2 ◽  
Author(s):  
S.J. Duckett
Keyword(s):  
Length Of Stay ◽  
Private Health Insurance ◽  
Average Length ◽  
Admission Rate ◽  
Public Hospitals ◽  
State Governments ◽  
Hospital Services ◽  
Private Hospitals ◽  
Average Length Of Stay ◽  
Australian Hospital

Hospital services in Australia are provided by public hospitals (about 75% of hospitals, two-thirds of separations) and private hospitals (the balance). Australians use about one bed day per person per year, with an admission rate of about300 admissions per thousand population per annum. Provision rates for public hospitals have declined significantly (by 40%) over the last 20 years but separation rates have increased. Average length of stay for overnight patients has been stable but, because the proportion of same day patients has increased dramatically, overall length of stay has declined from around seven days in the mid 1980s to around four days in the late 1990s. Overall, the Commonwealth and state governments each meet about half the costs of public hospital care, private health insurance meets about two-thirds of the costs of private hospitals.

scholarly journals Length of stay comparisons for private and public hospitals

2000 ◽  
Vol 23 (3) ◽  
pp. 162 ◽  
Author(s):  
Jennifer Badham ◽  
Jason Brandrup
Keyword(s):  
Length Of Stay ◽  
Public Hospital ◽  
Average Length ◽  
Public Hospitals ◽  
Private Hospitals ◽  
The Public ◽  
Average Length Of Stay ◽  
Private And Public

This analysis uses average length of stay as a proxy for efficiency, to compare the Australian private and public hospitalsectors. We conclude that private hospitals are more efficient than public hospitals in providing the range of care providedby private hospitals. However, public hospitals are more efficient in handling the casemix of the public hospital sector.The picture is more complicated when particular types of care (such as obstetric and psychiatric) are excluded.

scholarly journals Evaluation of Selected Ken-Drgs in Greek Public Hospitals: The Degree to Which they Reflect Actual Expenditure and Average Length of Stay

2014 ◽  
Vol 17 (7) ◽  
pp. A602
Author(s):  
O. Siskou ◽  
P. Galanis ◽  
D. Kaitelidou ◽  
M. Kalogeropoulou ◽  
E. Kouli ◽  
...  
Keyword(s):  
Length Of Stay ◽  
Average Length ◽  
Public Hospitals ◽  
Average Length Of Stay

scholarly journals Colonization and Infection With MRSA and CRKP and Its Result in an Increased Mortality Rate Within the Intensive Care and High-Dependency Units in Barbados

2020 ◽  
Vol 41 (S1) ◽  
pp. s173-s174
Author(s):  
Keisha Gustave
Keyword(s):  
Intensive Care ◽  
Length Of Stay ◽  
High Mortality ◽  
Average Length ◽  
Cohort Analysis ◽  
Health Concern ◽  
Queen Elizabeth Hospital ◽  
Surveillance Program ◽  
Average Length Of Stay ◽  
High Dependency

Background: Methicillin-resistant Staphylococcus aureus(MRSA) and carbapenem-resistant Klebsiella pneumoniae (CRKP) are a growing public health concern in Barbados. Intensive care and critically ill patients are at a higher risk for MRSA and CRKP colonization and infection. MRSA and CRKP colonization and infection are associated with a high mortality and morbidly rate in the intensive care units (ICUs) and high-dependency units (HDUs). There is no concrete evidence in the literature regarding MRSA and CRKP colonization and infection in Barbados or the Caribbean. Objectives: We investigated the prevalence of MRSA and CRKP colonization and infection in the patients of the ICU and HDU units at the Queen Elizabeth Hospital from 2013 to 2017. Methods: We conducted a retrospective cohort analysis of patients admitted to the MICU, SICU, and HDU from January 2013 through December 2017. Data were collected as part of the surveillance program instituted by the IPC department. Admissions and weekly swabs for rectal, nasal, groin, and axilla were performed to screen for colonization with MRSA and CRKP. Follow-up was performed for positive cultures from sterile isolates, indicating infection. Positive MRSA and CRKP colonization or infection were identified, and patient notes were collected. Our exclusion criteria included patients with a of stay of <48 hours and patients with MRSA or CRKP before admission. Results: Of 3,641 of persons admitted 2,801 cases fit the study criteria. Overall, 161 (5.3%) were colonized or infected with MRSA alone, 215 (7.67%) were colonized or infected with CRKP alone, and 15 (0.53%) were colonized or infected with both MRSA and CRKP. In addition, 10 (66.6%) of patients colonized or infected with MRSA and CRKP died. Average length of stay of patients who died was 50 days. Conclusions: The results of this study demonstrate that MRSA and CRKP cocolonization and coinfection is associated with high mortality in patients within the ICU and HDU units. Patients admitted to the ICU and HDU with an average length of stay of 50 days are at a higher risk for cocolonization and coinfection with MRSA and CRKP. Stronger IPC measures must be implemented to reduce the spread and occurrence of MRSA and CRKP.Funding: NoneDisclosures: None

scholarly journals Temporal Changes in Central-Line–Associated Bloodstream Infection Time Between Events, 2017–2018 Versus 2015–2016

2020 ◽  
Vol 41 (S1) ◽  
pp. s403-s404
Author(s):  
Jonathan Edwards ◽  
Katherine Allen-Bridson ◽  
Daniel Pollock
Keyword(s):  
Length Of Stay ◽  
Patient Care ◽  
Average Length ◽  
Bloodstream Infections ◽  
Central Line ◽  
Significant Shift ◽  
Care Hospital ◽  
Average Length Of Stay ◽  
Period 2 ◽  
Hospital Bed

Background: The CDC NHSN surveillance coverage includes central-line–associated bloodstream infections (CLABSIs) in acute-care hospital intensive care units (ICUs) and select patient-care wards across all 50 states. This surveillance enables the use of CLABSI data to measure time between events (TBE) as a potential metric to complement traditional incidence measures such as the standardized infection ratio and prevention progress. Methods: The TBEs were calculated using 37,705 CLABSI events reported to the NHSN during 2015–2018 from medical, medical-surgical, and surgical ICUs as well as patient-care wards. The CLABSI TBE data were combined into 2 separate pairs of consecutive years of data for comparison, namely, 2015–2016 (period 1) and 2017–2018 (period 2). To reduce the length bias, CLABSI TBEs were truncated for period 2 at the maximum for period 1; thereby, 1,292 CLABSI events were excluded. The medians of the CLABSI TBE distributions were compared over the 2 periods for each patient care location. Quantile regression models stratified by location were used to account for factors independently associated with CLABSI TBE, such as hospital bed size and average length of stay, and were used to measure the adjusted shift in median CLABSI TBE. Results: The unadjusted median CLABSI TBE shifted significantly from period 1 to period 2 for the patient care locations studied. The shift ranged from 20 to 75.5 days, all with 95% CIs ranging from 10.2 to 32.8, respectively, and P < .0001 (Fig. 1). Accounting for independent associations of CLABSI TBE with hospital bed size and average length of stay, the adjusted shift in median CLABSI TBE remained significant for each patient care location that was reduced by ∼15% (Table 1). Conclusions: Differences in the unadjusted median CLABSI TBE between period 1 and period 2 for all patient care locations demonstrate the feasibility of using TBE for setting benchmarks and tracking prevention progress. Furthermore, after adjusting for hospital bed size and average length of stay, a significant shift in the median CLABSI TBE persisted among all patient care locations, indicating that differences in patient populations alone likely do not account for differences in TBE. These findings regarding CLABSI TBEs warrant further exploration of potential shifts at additional quantiles, which would provide additional evidence that TBE is a metric that can be used for setting benchmarks and can serve as a signal of CLABSI prevention progress.Funding: NoneDisclosures: None

scholarly journals Biased and unbiased estimation of the average length of stay in intensive care units in the Covid-19 pandemic

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Nathanael Lapidus ◽  
Xianlong Zhou ◽  
Fabrice Carrat ◽  
Bruno Riou ◽  
Yan Zhao ◽  
...  
Keyword(s):  
Intensive Care ◽  
Length Of Stay ◽  
Average Length ◽  
Parametric Method ◽  
Estimation Methods ◽  
Unbiased Estimation ◽  
Bed Occupancy ◽  
Average Length Of Stay ◽  
Discharged Patients ◽  
First Admission

Abstract Background The average length of stay (LOS) in the intensive care unit (ICU_ALOS) is a helpful parameter summarizing critical bed occupancy. During the outbreak of a novel virus, estimating early a reliable ICU_ALOS estimate of infected patients is critical to accurately parameterize models examining mitigation and preparedness scenarios. Methods Two estimation methods of ICU_ALOS were compared: the average LOS of already discharged patients at the date of estimation (DPE), and a standard parametric method used for analyzing time-to-event data which fits a given distribution to observed data and includes the censored stays of patients still treated in the ICU at the date of estimation (CPE). Methods were compared on a series of all COVID-19 consecutive cases (n = 59) admitted in an ICU devoted to such patients. At the last follow-up date, 99 days after the first admission, all patients but one had been discharged. A simulation study investigated the generalizability of the methods' patterns. CPE and DPE estimates were also compared to COVID-19 estimates reported to date. Results LOS ≥ 30 days concerned 14 out of the 59 patients (24%), including 8 of the 21 deaths observed. Two months after the first admission, 38 (64%) patients had been discharged, with corresponding DPE and CPE estimates of ICU_ALOS (95% CI) at 13.0 days (10.4–15.6) and 23.1 days (18.1–29.7), respectively. Series' true ICU_ALOS was greater than 21 days, well above reported estimates to date. Conclusions Discharges of short stays are more likely observed earlier during the course of an outbreak. Cautious unbiased ICU_ALOS estimates suggest parameterizing a higher burden of ICU bed occupancy than that adopted to date in COVID-19 forecasting models. Funding Support by the National Natural Science Foundation of China (81900097 to Dr. Zhou) and the Emergency Response Project of Hubei Science and Technology Department (2020FCA023 to Pr. Zhao).

scholarly journals Times of change in complex environments - Evaluating hospital co-payments in Palestine refugee camps

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
S V Valente de Almeida ◽  
H Ghattas ◽  
G Paolucci ◽  
A Seita
Keyword(s):  
Length Of Stay ◽  
Policy Change ◽  
Negative Binomial ◽  
Negative Impact ◽  
Average Length ◽  
Lessons Learned ◽  
Policy Impact ◽  
Average Length Of Stay ◽  
Ex Post ◽  
The Impact

Abstract We measure the impact introducing a of 10% co-payment component on hospitalisation costs for Palestine refugees from Lebanon in public and private hospitals. This ex-post analysis provides a detailed insight on the direction and magnitude of the policy impact in terms of demand and supply for healthcare. The data was collected by the United Nations Relief and Works Agency for Palestine Refugees in the Near East and include episode level information from all public, private and Red Crescent Hospitals in Lebanon, between April 2016 and October 2017. This is a complete population episode level dataset with information from before and after the policy change. We use multinomial logit, negative binomial and linear models to estimate the policy impact on demand by type of hospital, average length of stay and treatment costs for the patient and the provider. After the new policy was implemented patients were 18% more likely to choose a (free-of-charge) PRCS hospital for secondary care, instead of a Private or Public hospital, where the co-payment was introduced. This impact was stronger for episodes with longer stays, which are also the more severe and more expensive cases. Average length of stay decreased in general for all hospitals and we could not find a statistically significant impact on costs for the provider nor the patient. We find evidence that the introduction of co-payments is hospital costs led to a shift in demand, but it is not clear to what extent the hospitals receiving this demand shift were prepared for having more patients than before, also because these are typically of less quality then the others. Regarding costs, there is no evidence that the provider managed to contain costs with the new policy, as the demand adapted to the changes. Our findings provide important information on hospitalisation expenses and the consequences of a policy change from a lessons learned perspective that should be taken into account for future policy decision making. Key messages We show that in a context of poverty, the introduction of payment for specific hospital types can be efficient for shifting demand, but has doubtable impact on costs containment for the provider. The co-payment policy can have a negative impact on patients' health since after its implementation demand increased at free-of-charge hospitals, which typically have less resources to treat patients.

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