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BMC Health Services Research

, 19:830 | Cite as

A systematic review on hospital inefficiency in the Eastern Mediterranean Region: sources and solutions

  • Hamid Ravaghi
  • Mahnaz AfshariEmail author
  • Parvaneh Isfahani
  • Victoria D. Bélorgeot
Open Access
Research article
Part of the following topical collections:
  1. Utilization, expenditure, economics and financing systems

Abstract

Background

Evaluating hospital efficiency is a process to optimize resource utilization and allocation. This is vital due to hospitals being the largest financial cost in a health system. To limit avoidable uses of hospital resources, it is important to identify the sources of hospital inefficiencies and to put in place measures towards their reduction and elimination. Thus, the purpose of this research is to examine the sources of hospital inefficiency in the Eastern Mediterranean Region, and existing strategies tackling this issue.

Methods

In this study, the electronic databases MEDLINE (via PubMed), Web of Science, Embase, Google, Google Scholar, and reference lists of selected articles, were explored. Studies on inefficiency, sources of inefficiency, and strategies for inefficiency reduction in the Eastern Mediterranean region hospitals, published between January 1999 and May 2018, were identified. A total of 1466 articles were selected using the initial criteria. After further reviews based on the inclusion and exclusion criteria, 56 studies were eligible for this study. The chosen studies were conducted in Iran (n = 35), Saudi Arabia (n = 5), Tunisia (n = 5), Jordan (n = 4), Pakistan (n = 2), the United Arab Emirates, Palestine, Iraq, Oman, and Afghanistan (n = 1 each). These studies were analyzed using content analysis in MAXQDA 10.

Results

The analysis showed that approximately 41% of studies used data envelopment analysis (DEA) to measure hospital efficiency. Sources of hospital inefficiency were divided into four categories for analysis: Hospital products and services, hospital workforce, hospital services delivery, and hospital system leakages.

Conclusion

This study has revealed some sources of inefficiency in the Eastern Mediterranean Region hospitals. Inefficiencies are thought to originate from excess workforce, excess beds, inappropriate hospital sizes, inappropriate workforce composition, lack of workforce motivation, and inefficient use of health system inputs. It is suggested that health policymakers and managers use this evidence to develop appropriate strategies towards the reduction of hospital inefficiency.

Keywords

Efficiency Hospitals Eastern Mediterranean countries Systematic review 

Abbreviations

ALS

Average length of stay

BOR

Bed occupancy rate

BTR

Bed turnover rate

DEA

Data Envelopment Analysis

EMR

Eastern Mediterranean Region

FTE

Full Time Employee

HICs

High-income countries

LMICs

Low- and middle-income countries

MoH

Ministry of Health

SFA

Stochastic Frontier Analysis

WHO

World Health Organization

Background

Hospitals are an essential component of health systems, while also being the most costly. They account for 50–80% of total health expenditures [1]. Hospital costs continue to rise due to the development of new technologies. New diagnostic and therapeutic methods are implemented to combat the rising proportion of chronic diseases, the increasing demand for health services, and the subsequent medical errors [2]. This has become a primary challenge and concern for governments [3].

Hospitals in the Eastern Mediterranean Region (EMR) differ in size, proprietorship, assignment, and performance. The total number of hospital beds is estimated to be 740,000 and, except for Lebanon, the majority of hospital beds are in the public sector (80%), with the remaining in private for-profit (18%) and private not-for-profit (2%) hospitals. The range of hospital beds per 10,000 population vary from 3.9 to 32 in 22 countries in the EMR. Hospitals also vary widely in size, location (rural and urban), resources, specialization (general versus specialty hospitals) and organization, as well as their position in the health system (first-level hospitals, secondary care hospitals and large teaching institutions) [4]. A large proportion of hospitals are financed by the government, but out-of-pocket payments are rising due to limited public sector resources [5]. This leads to limited access to health services for vulnerable communities. Private hospitals in the EMR are usually small to medium size and located in capitals and other large cities. These hospitals are not the result of comprehensive health system planning, as such, they can also lead to inequity in access to healthcare. Most countries in the EMR have addressed inequalities by implementing reforms to increase productivity, transparency, and cost flexibility [5, 6, 7]. To facilitate this process and increase hospital efficiency, it is necessary to provide the healthcare sector with additional resources and management tools.

According to Farrell (1957), efficiency is defined as “the firm’s success to produce the maximum feasible amount of output from a given amount of input or producing a given amount of output using the minimum level of inputs where both the inputs and the outputs are correctly measured” [8]. Three different types of efficiency were defined by Farrell: technical efficiency, allocative efficiency, and economic efficiency. Technical efficiency is the ability of a business to gain a maximum output from the specific input. In contrast, allocative efficiency refers to the directing of resources toward products or services with the highest demand. Economic efficiency is allocative efficiency and technical efficiency from a joint unit of cost efficiency. An organization has an economic efficiency Which be efficient in terms of both technical and allocational [8]. In general, different methods have been used to measure hospital efficiency: Data Envelopment Analysis (DEA), Stochastic Frontier Analysis (SFA), and measures of performance, such as Pabon Lasso’s model. DEA is a non-parametric linear programming method used to evaluate the efficiency of decision-making units [8, 9]. SFA is parametric and calculates the difference between the organization’s predicted and expected outputs [10]. Pabon Lasso’s model (1986) assesses hospital performance using three performance indicators: bed occupancy rate (BOR), bed turnover rate (BTR), and average length of stay (ALS) [11].

A decline in hospital efficiency has been observed worldwide. In a global report by the World Health Organization (WHO) published in 2010, 10 sources of hospital inefficiency were identified: (1) underuse or overpricing of generic drugs; (2) use of substandard or counterfeit drugs; (3) inappropriate and ineffective drug use; (4) overuse or oversupply of equipment, investigations and procedures; (5) inappropriate or costly workforce mix, unmotivated worker; (6) inappropriate hospital admissions or length of stay; (7) inappropriate hospital size (low use of infrastructure); (8) medical errors and suboptimal quality of care; (9) waste, corruption and fraud; and (10) inefficient mix or inappropriate level of strategies [12]. However, thus far there has not been a comprehensive review to assess the source of hospital inefficiency in the EMR. This study aims to comprehensively identify the sources of hospital inefficiency in the EMR, and compare these to previously identified sources of hospital inefficiency. This will provide insight into the current condition of healthcare in this region.

According to the aforementioned WHO report, hospital efficiency in the EMR is low, particularly in low and middle-income countries (LMICs) [5]. To increase hospital efficiency in a context of rising costs and limited resources, it is necessary to identify sources of inefficiency and to suggest improvement strategies. Identifying these sources and identifying improvements are the objectives of this study.

Methods

This is a systematic review of existing evidence on hospital inefficiency in the EMR. This study recruited English peer-reviewed articles published between January 1999 and May 2018. To identify relevant articles, a database search was conducted in MEDLINE (via PubMed) (Additional file 1), Web of Knowledge, Embase, Google and Google Scholar. Keywords used included “efficiency”, “productivity”, “inefficiency”, “hospital”, “data envelopment analysis”, “Pabon Lasso”, and “stochastic frontier analysis”. Moreover, the reference lists of selected articles were searched for relevant papers. Economic journals in the field of health economy and efficiency such as the Journal of the Knowledge Economy, the American Journal of Economics and Business Administration, Cost Effectiveness and Resource Allocation, and the International Journal of Economics and Financial Issues were searched individually. An initial review was conducted to determine the scope of the study, and no study published before 1999 was found. Therefore, the review included studies between 1999 and May 2018.

Following the screening of 1087 identified articles, 80 full texts were assessed for eligibility. After assessing these articles, 56 were included in the review. The screening process and search results are shown in the PRISMA Flow Diagram [13] of Fig. 1.
Fig. 1

PRISMA Flow Diagram: Database search and article selection process

A data extraction form with entries for the first author, year of publication, country of study, data collection method, number of hospitals studied, inputs and outputs for efficiency, sources of hospital inefficiency, and factors affecting efficiency, was used to collect data from the selected studies. For higher reliability, two researchers independently extracted data from a randomly selected sample of the chosen articles. Any disagreements were solved by discussion and consensus and, if necessary, by a third reviewer.

Mitton et al.’s fifteen-point scale [14] was used for quality appraisal. The criteria used to assess quality included: literature review and identification of research gaps; research question and design, validity and reliability; data collection; population and sampling; and analysis and reporting of results. These criteria were rated 0 (not present or reported), 1 (present but low quality), 2 (present and mid-range quality), or 3 (present and high quality). Articles were rated independently by two researchers using the article quality rating sheet. Given that the review was qualitative, articles were not removed at this stage, but more weight was given to articles with a quality rating of 10 or above in the data analysis and interpretation of results.

The data were analyzed using qualitative content analysis. Data were coded and managed using MAXQDA 10 for Windows (VERBI GmbH, Berlin, Germany), and themes and subthemes were extracted to identify patterns and relationships between themes.

Results

A total of 56 articles on hospital efficiency in the EMR, published between January 1999 and May 2018, were reviewed. A large number of studies (91%) were published after 2010. The reviewed studies were only conducted in 10 out of 22 EMR countries included in the search. Iran (n = 35) was most represented in the included studies, followed by Saudi Arabia (n = 5) and Tunisia (n = 5), Jordan (n = 4), Pakistan (n = 2), and finally UAE, Palestine, Iraq, Oman, and Afghanistan (n = 1 each).

Overall, 1995 hospitals were examined in these studies; most of them located in Iran (n = 858), Saudi Arabia (n = 573), Tunisia (n = 266), UAE (n = 96), Jordan (n = 72) and Afghanistan (n = 68). Out of 56 reviewed studies, 21 used DEA (37%), 12 used Bayesian SFA (21%), 10 used Pabon Lasso’s model (18%), and four studies used the Malmquist index (7.5%). Moreover, four studies (7.5%) used a hybrid approach by comparing DEA and Pabon Lasso’s model. Finally, five studies (9%) used other methods (the Cobb-Douglas Model, the Lean model, and efficiency and performance indicators).

Calculating efficiency requires input and output variables. In data analysis, the number of workforce, active beds, total costs, hospital size, medical equipment, technological capacity, and budget have been used as input variables (Fig. 2). Total outpatient visits, inpatient admissions and days, number of inpatients, emergency visits, number of surgeries, ratio of major surgeries to total surgeries, total number of medical interventions, BOR, BTR, average length of stay (ALS), number of ambulances, ratio of active beds to fixed beds, hoteling expense (bed-day costs) and employee expense total survival rate, number of discharged patients, number of imaging service users, and number of laboratory test users, were used as output variables (Fig. 3). The input and output selection depends on the objective of the study and efficiency measurement. It is reasonable to consider total costs on the input side; however, few studies have employed hospital hoteling and workforce expenses as output in their evaluation. For example, Hatam [15] used hoteling and workforce expenses and found that most cases had more workforce and hoteling expenses than the similar ones showing significant inefficiency.
Fig. 2

Frequency of input variables used to measure hospital efficiency in EMR countries

Fig. 3

Frequency of output variables used to measure hospital efficiency in EMR countries

Operational definitions for acronyms and terms of input and output measures are given below:
  • Number of active beds: alternative term for ‘available beds’ [16].

  • Number of beds or hospital size: “Hospital beds include all beds that are regularly maintained and staffed and are immediately available for use. They include beds in general hospitals, mental health, and substance abuse hospitals, and other specialty hospitals. Beds in nursing and residential care facilities are excluded” [17].

  • Number of inpatient admissions: Mean number of hospital admissions in a certain hospital per year [16].

  • Number of bed-days: “number of days during which a person is confined to a bed and in which the patient stays overnight in a hospital” [18].

  • Bed occupancy rate (BOR): “The occupancy rate for curative (acute) care beds is calculated as the number of hospital bed-days related to curative care divided by the number of available curative care beds, multiplied by 365”.

  • Bed turnover rate (BTR): the number of times there is change of occupant for a bed during a given time period [17].

  • Average length of stay (ALS): “Average length of stay refers to the average number of days that patients spend in hospital. It is generally measured by dividing the total number of days stayed by all inpatients during a year by the number of admissions or discharges. Day cases are excluded” [17].

  • Day surgery: Day surgery is defined as the release of a patient who was admitted to a hospital for a planned surgical procedure and was discharged the same day [16].

Table 1 provides a summary of the studies reviewed, presenting the type and total number of hospitals examined, the methods used to calculate efficiency, inputs and outputs, and the source of inefficiency.
Table 1

Summary of reviewed studies

Author

Year

Country

Hospital type

Number of hospitals

Method used to calculate efficiency

Input and outputs

Source of inefficiency

Al-Shammari [19]

1999

Jordan

Hospitals of MoH*

15

DEA

Inputs: Numbers of bed-days, physicians, health workforce

Outputs: Numbers of inpatient days, minor operations, major operations

Excess resources

Ramanathan [20]

2005

Oman

Regional and Wilayat hospitals (MoH), Sultan Qaboos University Hospital, Hospital of the Royal Oman Police

20

DEA (Malmquist index)

Inputs: Numbers of beds, physicians, and other medical workforces.

Outputs: Number of visits, in-patient services, surgical operations

Partial utilization of inputs, lack of full compliance with technological changes

Hajialiafzali [21]

2007

Iran

Hospitals affiliated with the Social Security Organization

53

DEA (frontier-based methods)

Inputs: Total numbers of FTE* medical doctors, of FTE nurses, of other FTE workforces, number of beds

Outputs: Numbers of outpatient visits and emergency visits, ratio of major surgeries to total surgeries, total numbers of medical interventions and surgical procedures

Partial utilization of inputs

Hatam [15]

2008

Iran

Hospitals affiliated with the Social Security Organization

18

DEA (frontier-based methods)

Inputs: Numbers of beds, FTE, total expense

Outputs: Patient-days, BOR*, BTR,* ALS*, ratio of available beds to constructed beds, hoteling expense, bed-day costs, workforce costs

Unused beds

Goshtasebi [22]

2009

Iran

MoH hospitals

6

Pabon Lasso

Output: ALS, BOR, BTR

Underutilization of resources, high BOR

Jandaghi [23]

2010

Iran

Public and private hospitals

8

DEA (frontier-based methods)

Inputs: Numbers of physicians, nurses, medical workforce, official workforce, annual costs of hospital

Outputs: Numbers of clinical visits, emergency visits, and bed-days

Excess resources

Hatam [24]

2010

Iran

General public hospitals

21

DEA (frontier-based methods)

Inputs: Numbers of hospital beds, FTE physicians, nurses, and other workforces

Outputs: BOR, patient–day admissions, bed-days, ALS, BTR

Lack of motivation to select inputs to minimize expenses caused by the fact that hospitals are public and therefore do not seek profitability.

Shahhoseini [25]

2011

Iran

Provincial hospitals

12

DEA (frontier-based methods)

Inputs: Numbers of active beds, nurses, physicians, and other professionals

Outputs: Number of surgeries, outpatients visits, BOR, ALS, inpatient days

Excess resources

Ketabi [26]

2011

Iran

Hospitals in Isfahan

23

DEA

Inputs: Average numbers of active beds, medical equipment, workforce (such as doctors, nurses and technicians)

Outputs: BOR (%), ALS, total percentage of survival, performance ratio

Excess medical equipment, workforce and technology for teaching and private hospitals. Teaching hospitals are less efficient because of bureaucratic processes and private hospitals have lower BORs.

Bahadori [27]

2011

Iran

Hospitals affiliated with Urmia University of Medical Sciences

23

Pabon Lasso

Output: ALS, BOR, BTR

Poor performance in BOR and/or BTR in 60.87% of hospitals.

Al-Shayea [28]

2011

Saudi Arabia

Khalid University Hospital

1 (9 departments)

DEA

Inputs: doctors’ total salary, nurses’ total salary

Outputs: Numbers of in-patients, outpatients, bed and average turnover rate

High costs of inputs

Kiadaliri [29]

2011

Iran

General hospitals affiliated with Ahvaz Jondishapour University of Medical Sciences

19

DEA (frontier-based methods)

Inputs: beds, human resources

Outputs: inpatient days, outpatient days, number of surgeries, BOR

Inappropriate hospital sizes

Osmani [30]

2012

Afghanistan

District Hospitals

68

DEA and Tobit regression analysis model

Inputs: Numbers of physicians, midwives, nurses, non-medical workforce, and beds

Outputs: Numbers of outpatient visits, inpatient admissions, and patient days, ALS, BOR, number of hospital beds (proxy for hospital size), bed-physician and outpatient physician ratio, number of physicians

Excess numbers of doctors, nurses, and beds

Farzianpour [31]

2012

Iran

Teaching hospitals of Tehran University of Medical Sciences

16

DEA (frontier-based methods)

Inputs: Numbers of physicians, practicing nurses in health facilities, and active beds

Outputs: Numbers of inpatients, outpatients, ALS

Excess inputs or insufficient outputs

Chaabouni [32]

2012

Tunisia

Public hospitals

10

DEA and The Bootstrap Approach

Inputs: Numbers of physicians, nurses, dentists and pharmacists, other workforces, and beds

Outputs: Numbers of outpatient visits, admissions, post-admission days

High hospital expenditures

Barati Marnani [33]

2012

Iran

Affiliated with Shahid Beheshti University of Medical Sciences

23

Pabon Lasso model and DEA (frontier-based methods)

Pabon Lasso: ALS, BOR, BTR

DEA: Inputs: Numbers of physicians, nurses, other workforces, and active beds

Outputs: BOR, numbers of patients and surgeries

Excess resources

Sheikhzadeh [34]

2012

Iran

Elected public and private hospitals of East Azerbaijani Province

6

DEA (frontier-based methods)

Inputs: Numbers of specialist physicians, general physicians, nurses, residents, medical team workforce with a degree (Bachelor’s), medical team, nonmedical and support workforce, and active beds

Outputs: Numbers of emergency patients, outpatients, and inpatients, average daily inpatients residing in hospital

Excess and inefficient inputs: lack of medical services for the amount of resources used.

Yusefzadeh [35]

2013

Iran

Public hospitals

23

DEA

Inputs: Numbers of active beds, doctors, and other workforces

Outputs: Number of outpatients’ admissions and day-beds

Excess inputs or insufficient outputs

Gholipour [36]

2013

Iran

Obstetrics and gynaecology teaching hospitals

2

Pabon Lasso

Output: ALS, BOR, BTR

Low BOR

Arfa [37]

2013

Tunisia

Public hospitals

101

DEA

Five fixed inputs: Numbers of physicians, dentists, mid-wives, nurses or equivalents, and beds. One variable input: budget

Outputs: Numbers of outpatient visits and admissions

Hospitals are not operating at full capacity

Ajlouni [38]

2013

Jordan

Public hospitals

15

DEA and Pabon-Lasso

Pabon Lasso: ALS, BOR, BTR

DEA: Inputs: Numbers of bed-days, physicians per year, and health workforce per year

Outputs: Patient days, numbers of minor operations and major operations

Poor management, treatment of diseases requiring long patient stays

Abou El-Seoud [39]

2013

Saudi Arabia

Public hospitals that have been reformed to operate under private sector management through the full operating system in Saudi Arabia

20

DEA

Inputs: Numbers of specialists, nurses, allied workforce, and beds

Outputs: Numbers of visits, patient hospital admissions, laboratory tests, and beneficiaries of radiological imaging

Administrative weakness to overcome external environmental factors rather than inability to manage internal operations

Bastani [40]

2013

Iran

Hospitals affiliated to the MoH

139

Four hospital performance indicators

Output: ALS, BOR, BTR

Inappropriate hospital sizes

Younsi [41]

2014

Tunisia

30 public and 10 private hospitals

40

Pabon Lasso

Output: ALS, BOR, BTR

Low bed density which may not match population hospital needs. Hospital bed numbers should be increased or maintained.

Torabipour [42]

2014

Iran

Teaching and non-teaching hospitals of Ahvaz County

12

DEA (Malemquist index)

Inputs: Numbers of nurses, beds, and physicians.

Outputs: Numbers of outpatients and inpatients, ALS, number of major operations

Lack of familiarity of managers with advanced hospital technologies, lack of equipment and inappropriate use of technology in diagnosis, care and treatment.

Syed Aziz Rasool [43]

2014

Pakistan

Non-profit private organization (branches of LRBT hospitals)

16

DEA

Inputs: Numbers of beds, specialists, nurses

Outputs: Numbers of outpatient visits, inpatient admissions, and total numbers of surgeries

Lack of government funds to hospitals run by non-profit organizations.

Pourmohammadi [44]

2014

Iran

All hospitals affiliated with the Social Security Organization

64

The Cobb-Douglas model

Inputs: Numbers of physicians, nurses, other workforces, and active beds

Outputs: Number of outpatients and inpatients

Excess workforce

Mehrtak [45]

2014

Iran

All general hospitals located in Iranian Eastern Azerbijan Province

18

Pabon Lasso and DEA

Pabon Lasso: ALS, BOR, BTR

DEA: Inputs: Numbers of active beds, physicians, nurses, discharged patients

Outputs: Number of surgeries and discharged patients, BOR

Excess inputs: larger hospitals are more efficient than smaller hospitals.

Lotfi [46]

2014

Iran

All hospitals of Ahvaz (8 hospitals affiliated with Jundishapur University of Medical Sciences and 8 non-affiliated hospitals)

16

Pabon Lasso and DEA

Pabon Lasso: ALS, BOR, BTR

DEA: Inputs: Numbers of physicians, nurses, other workforces, and active beds

Outputs: BOR, numbers of patients and surgeries

Underuse of resources, excess hospital inputs

Kalhor [47]

2014

Iran

Hospitals affiliated with Qazvin University

6

Pabon Lasso

Output: ALS, BOR, BTR

Poor managerial decisions

Goudarzi [48]

2014

Iran

Teaching hospitals affiliated with Tehran University of Medical Sciences

12

DEA (frontier-based methods)

Inputs: Numbers of medical doctors, nurses, and other workforces, active beds, and outpatient admissions

Outputs: Number of inpatient admissions

Excess numbers of nurses and active beds

Askari [49]

2014

Iran

Hospitals affiliated with Yazd University of Medical Sciences

13

DEA

Inputs: Numbers of active beds, nurses, physicians, and non-clinical workforce

Outputs: hospitalization admissions, BOR (%), and number of surgeries

High excess inputs, particularly the excess number of nurses.

Adham [50]

2014

Iran

Teaching and non-teaching hospitals

14

Pabon Lasso

Output: ALS, BOR, BTR

Low BOR

Imamgholi [51]

2014

Iran

Hospitals affiliated to Busheher University of Medical Sciences

7

Pabon Lasso

Output: ALS, BOR, BTR

Non-optimal hospital sizes

Shetabi [52]

2015

Iran

Hospitals affiliated to Kermanshah University of Medical Sciences

7

DEA

Inputs: Numbers of active beds, doctors, nurses, and other workforces

Outputs: Numbers of accepted inpatients, outpatients and BOR (%)

Excess inputs

Masoompourb [53]

2015

Iran

Teaching Hospital

1

Pabon Lasso

ALS, BOR, BTR

Decrease in ALS

Chaabouni [54]

2016

Tunisia

Public Hospitals

10

DEA (frontier-based methods)

Inputs: Numbers of physicians, nurses, dentists, pharmacists, and beds, total cost

Outputs: Numbers of outpatient visits, admissions, and post-admission days, price of labor

large hospital sizes

Safdar [55]

2016

Pakistan

A large public hospital

1

DEA

Inputs: Waiting time at the pharmacy, length of waiting line

Outputs: Consultation time at the pharmacy

High waiting times: low efficiency levels (less than 50% efficiency) are associated with high waiting times.

Mohammadi [56]

2016

Iran

Public hospitals

67

Cobb-Douglas production function

Inputs: Human resources (including net working hours of specialized workforce) and bed numbers (including the number of active beds)

Insufficient inputs: Inpatient service production levels were lower than expected in 40% of hospitals. A 10% increase in net working hours of specialized human resources would generate a 8.8% increase in average inpatient service production levels. A 10% increase in the number of active beds would generate a 1.1% increase in average inpatient service production levels.

Mahate [57]

2016

United Arab Emirates

Private and public hospitals in the UAE

96

DEA

Inputs: Numbers of beds, doctors, dentists, nurses, pharmacists and allied health workforce, and administrative workforce

Outputs: Numbers of treated inpatients, outpatients, ALS

Waste of 41 to 52% of inputs during service delivery.

Kalhor [58]

2016

Iran

Tehran city general hospitals

54

DEA

Inputs: Total numbers of FTE medical doctors, and nurses, numbers of supporting medical workforce including ancillary service workforce, and beds

Outputs: Numbers of patient days, outpatient visits, patients receiving surgery, ALS

Ownership type (lower efficiency of university hospitals because of more expenditures)

Kakemam [59]

2016

Iran

Hospitals of public, private, or social security ownership types in Tehran

54

DEA

Inputs: Numbers of active beds, physicians, nurses, and other medical workforces

Outputs: Numbers of outpatient visits, surgeries, and hospitalized days, ALS

Lack of resource optimization. Poor adaptation of the sizes, types of practices, and ownerships of hospitals, affecting their technical efficiency. Approximately 70% of the hospitals were inefficient.

Hassanain [60]

2016

Saudi Arabia

Hospitals affiliated to the MoH

12

Lean

On-time start, room turnover times, percent of overrun cases, average weekly procedure volume and OR utilization

Ppoor hospital infrastructure, old technology, suboptimal management of human resources, the absence of employee engagement, frequent scheduling changes, inefficient process flow

Hamidi [61]

2016

Palestine

22 government hospitals

22

DEA (frontier-based methods)

Inputs: Numbers of beds, doctors, nurses, and non-medical workforce

Outputs: Numbers of admitted patients, hospital days, operations, outpatient visits, ALS

Mismanagement of available resources, shortage of the numbers of doctors and nurses and excess number of non-medical staff

Nabilou [62]

2016

Iran

Hospitals affiliated to Tehran University of Medical Sciences

17

DEA (Malmquist index)

Inputs: Active beds, nurses, doctors and other workforces

Outputs: outpatient admissions, bed-days, number of surgical operations

Due to hospitals’ technological changes, a lack of knowledge of hospital workforce on proper applications of technology for patient treatment became the main cause of low hospital productivity and inefficiency.

Rezaei [63]

2016

Iran

Kurdistan teaching hospitals

12

DEA (frontier-based methods)

Inputs: Numbers of active beds, nurses, physicians, and other workforce members

Outputs: Inpatient admissions

Waste of inputs during service delivery

Farzianpour [64]

2017

Iran

Training and non-training hospitals of Tabriz city

19

DEA

Inputs: Numbers of physicians, total workforce, and active beds

Outputs: Number of outpatients and BOR

Poor management of human and financial resources.

Arfa [65]

2017

Tunisia

Public district hospitals

105

DEA

Inputs: Numbers of physicians, surgical dentists, midwives, nurses and equivalents, and beds, operating budget

Outputs: Outpatient visits in stomatology wards, outpatient visits in emergency wards, outpatient visits in external wards, numbers of admissions, and admissions in maternity wards

Inadequate number of workforce, equipment, beds, and medical supply, health quality and lack of fitting operating budgets: tackling these sources of inefficiency would reduce net user needs and the bypassing of the public district hospitals, to increase their capacity utilization. Social health insurance should be turned into a direct purchaser of curative and preventive care for the public hospitals.

Aly Helal [66]

2017

Saudi Arabia

Public hospitals

270

DEA

Inputs: Numbers of beds, doctors, nurses, and allied medical workforce

Outputs: Numbers of individuals visiting admitted patients, radiography service beneficiaries, laboratory testing beneficiaries, and inpatients

Excess inputs

Mousa [67]

2017

Saudi Arabia

Public hospitals

270

DEA

Inputs: Numbers of physicians, nurses, pharmacists, allied health professionals, beds

Outputs: Numbers of outpatient visits, inpatients, laboratory investigations, X-rays patients, X-rays films, total number of surgical operations

Inadequate resources: some resources should be switched between regions to improve efficiency.

Moradi [68]

2017

Iran

Public hospitals

11

Pabon Lasso

ALS, BOR, BTR

Low number of hospital beds, and need for hospital expansion

Sultan [69]

2017

Jordan

General public hospitals

27

DEA

Inputs: Numbers of beds, physicians, healthcare workforce, administrative workforce

Outputs: Inpatient days, outpatient visits, emergency departments, and ambulances

Diseconomies of scale affect the operational efficiency, poor management, poor productivity in outpatient services and low numbers of physicians.

Kassam [70]

2017

Iraq

Hospitals in Baghdad

3

DEA and Luenberger Productivity Indicator (LPI)

Inputs: Numbers of doctors, nurses, and other health workforces

Outputs: Numbers of outpatients, laboratory tests, radiology tests, sonar tests, emergency visits

The cause of the inefficiencies is undetermined.

Rezaee [71]

2018

Iran

Hospitals affiliated with Kermanshah University of Medical Sciences

15

Pabon Lasso

Output: ALS, BOR, BTR

Excess inputs

Yazan Khalid Abed-Allah Migdadi [72]

2018

Jordan

Public hospitals

15

DEA

Inputs: Numbers of physicians, nurses, and beds

Outputs: ALS, number of Surgeries, BOR

Low BOR

Sajadi [73]

2018

Iran

All hospitals in Isfahan City

54

Cross-sectional descriptive study comparing performance indicators

Outputs: BOR, BTR, bed-days, inpatients visits, number of surgeries in all types of hospitals, outpatient visits in all non-private hospitals, emergency visits in public and social security hospitals, and natural deliveries in public and semi-public hospitals

Inefficient use of limited resources

*BOR bed occupancy rate, BTR bed turnover rate, ALS average length of stay, FTE Full Time Employee, MoH Ministry of Health

Various sources of hospital inefficiency were identified and divided into four themes, each with a set of subthemes: hospital products and services, hospital workforce, hospital services delivery, hospital system leakage (Table 2).
Table 2

Source of inefficiency in Eastern Mediterranean hospitals and strategies for improvement

Source of inefficiency

Common sources of inefficient performance

Proposed actions

Hospital products and services

overuse or supply of equipment, investigations, and procedures

- Inappropriate payment systems (fee-for-service payment mechanisms)

- Misuse or inappropriate use of technology in patient treatment and diagnosis like imaging and lab services due to lack of knowledge and skills of health professional and lack of adopted evidenced-based guidelines.

- Overuse or oversupply of equipment

- Lack of or defective hospital equipment

- Poor standards for use of technologies

-Reform incentive and payment structures, developing appropriate tariff and payment systems (e.g. use capitation or diagnosis-related group mechanism for reimbursement)

-Raising workforce awareness and training workforce and managers about new information systems and technologies

-Raising workforce awareness of energy management through frequent training

-Develop and implement clinical guidelines

Hospital workforce

inappropriate or costly workforce mix

- Lack of or failure to use specialized managers in hospital administration

- Suboptimal use of workforce capabilities, including those of physicians, nurses, paramedics, and support workforce, resulting in excess workforce in some departments

- Inadequate management of hospital resources like workforce

-Recruiting workforce based on hospital needs (both in terms of numbers and specialties required)

-Preventing the recruitment and maintenance of specialist workforce who are not significantly relevant to hospital and patient needs.

-Using work measurement and time management techniques for optimal use of the workforce with respect to the volume of hospital operations

unmotivated workforce

- Lack of motivation due to high workload

- Lack of workforce motivation in the public sector because of inadequate salaries

-Introducing performance-based payments

-Use appropriate incentive, reward and appraisal systems

Hospital services delivery

inappropriate hospital admissions and length of stay

- Inappropriate ALS*, unnecessary admissions, low BORs* and unnecessary referrals to specialists due to inadequate knowledge and training of workforce about best practice.

-Developing and implementing policies to accelerate admission and discharge processes and increase the quality of services

-Developing strategies to reduce ALS*, including full-time presence of physicians and modification of hospital funding policies

-Establishing a two-way electronic referral system, to provide physicians with feedback

-Effective marketing using appropriate customer information, and improving communication and customer loyalty

inappropriate hospital size (low use of infrastructure)

- Inefficient hospital size, lack of scale efficiency and too many hospitals and inpatient beds in some areas, not enough in others

- Suboptimal use of available capacities such as infrastructure and active beds, resulting in excess beds in some departments (lack of planning)

-Modifying hospital size: selecting an efficient size and preventing hospital overdevelopment. if inefficient (downsizing or merging hospitals)

-Making optimal use of hospital beds based on community needs.

-Use of cost analysis and DEA model and other efficiency measurement models for incorporate inputs and output estimation into hospital planning.

-Improving workforce, equipment, and beds based on evidence

-Designing a basic framework for optimal resource allocation by health policymakers

-Diversifying the outputs required for compensating hospital inefficiency

-Redistributing hospital resources among regions

-Training to raise knowledge about efficient admission practice

medical errors and suboptimal quality of care

- Poor care management skills of physicians and other workforces.

- Inadequate managerial skills and lack of training for hospital managers.

- Inadequate skills and training of the hospital workforce.

-Designing on-the-job training courses tailored to workforce roles.

-Using experienced and well-educated managers with management or healthcare management degrees, performance evaluation of hospital managers and provide feedback

-Introducing managers to management techniques and methods of economic analysis

-Improve hygiene standards in hospitals; provide more continuity of care; undertake more clinical audits; monitor hospital performance

Hospital system leakages

waste, corruption and fraud

- Inappropriate suboptimal allocation of funds among hospitals and unclear resource allocation guidance.

- Hospital reliance on public funds and budgets, and lack of competition with other organizations.

-Modifying hospital budget structures

-Improve regulation/governance, including strong sanction mechanisms; assess transparency/vulnerability to corruption; undertake public spending tracking surveys; promote codes of conduct

*BOR bed occupancy rate, BTR bed turnover rate, ALS average length of stay

The most frequent sources of inefficiency in EMR hospitals are excess workforce, excess beds, and inappropriate hospital sizes. Helal et al. [66] investigated the effect of health reforms (privatization) on the efficiency of 270 hospitals in Saudi Arabia and reported a 0.90 average efficiency in 2006 and a 0.92 average efficiency in 2014. The average efficiency of one is considered the best level of performance. Despite a reduction in inputs, outputs increased by 2%. Moreover, there was a 10.1% increase in the number of inpatients from 2006 to 2014. Therefore, reducing excess inputs such as excess workforce, excess beds or/and increasing outputs can be beneficial to hospitals. A 2013 analysis in Saudi Arabia showed that there was a reduction in the number of beds, doctors, nurses, and allied health workforce as inputs. Moreover, there was an increase in the number of inpatients, outpatients, the number of daily laboratory tests and the number daily of radiography services as outputs [39]. The most common strategies proposed in the included studies are: developing health policies for accurate recruitment planning, calculating the required number of beds for each community, and making proper use of hospital beds based on community needs.

Discussion

The purpose of this research was to examine the sources of hospital inefficiency and strategies available to increase hospital efficiency in the EMR. In recent years, there has been an increasing focus on hospital efficiency for health policymakers in developing countries. A total of 56 studies have been conducted on hospital efficiency in the EMR from January 1999 to May 2018. These studies have shown that hospital care is an economic activity requiring adequate funding and budgeting. As such, reducing inputs can improve performance and efficiency [56, 74].

The WHO Regional Office for the EMR classifies countries to there groups: high income countries (six countries), middle income countries (ten countries), and low income countries (six countries). The present research identified 56 articles on hospital efficiency in three high-income countries, five middle-income countries, and two low-income countries. General government expenditure allocated to health in the EMR countries remains between 2 and 16%, a low figure. Regarding hospital service utilization, the overall average bed occupancy rate and length of stays were 60.7% and 4.12 days, respectively, in the Region in 2013. Only a few countries have well-defined and functioning referral networks between hospitals and primary health care facilities, or between hospitals at different levels. Hospitals do not serve geographically defined catchment areas based on national policy mandates. Most countries are entrenched in the historical model of public provision and financing, and there is a mix of funding patterns, including public sector funds (through central government budgets and national insurance funds) and out-of-pocket payments made directly by users. In most countries, there is misalignment between the distribution of hospital beds and high-technology equipment and population health needs [4]. Contextual challenges exist, such as security issues, internal conflict and political volatility in EMR countries, leading to economic problems influencing health policies, health system budgets, and health system efficiency as a result [75, 76].

Some health system challenges are common to all EMR countries: “limited capacity in MoHs for evidence-based policy analysis and formulation and strategic planning through better use of information in adequate capacity to legislate, regulate and enforce rules and regulations” or “most countries lack national medicines policy” [75]. Both this study and the WHO have reported similar findings.

The most common input variables used in these studies were workforces numbers and the number of beds, while the most common output variables were the total number of outpatient visits, admissions and inpatient days. A systematic review of new approaches to measure hospital performance in LMICs in 2015 [77] identified seven key performance indicators. These included total inpatient days; recurrent expenditure per inpatient day; ALS; infection prevention rate; BOR; inpatient days per technical workforce; and unit cost of outpatient care. Seven performance indicators were also identified for high-income countries (HICs): mortality rate from emergency heart attack admissions after 28 days; mortality rate from emergency surgery after 30 days; number of patients on waiting lists; infection rate of methicillin-resistant Staphylococcus aureus per 10,000 bed-days; net profit; probability of workforce leaving within 12 months; and average healthcare commission rating [77].

On average, out-of-pocket payments differ between HICs and LMICs. In HICs, patients rarely pay directly for their care compared to LMICs where direct payment by patients is necessary due to lower insurance coverage. Furthermore, the mortality rate for non-elective admission is not the optimal output indicator for LMICs, as access to healthcare is a significant problem. These explain the differences in outputs between LMICs and HICs [77, 78].

The themes related to inefficiency extracted in this review, and the sources of inefficiency identified in the WHO report 2010 [11], highlight that studies have failed to address the issue of medical drugs. Using drug-related inputs and outputs can provide useful insights into drug-related sources of inefficiency in the EMR. For example, a study in Ethiopia used the cost of drug supply as input [79]. This can provide further insights into how to improve hospital efficiency.

In addition to excess workforce, excess beds and inappropriate hospital sizes, the inefficiency of hospitals in the EMR is also due to inappropriate workforce composition, lack of workforce motivation and inefficient use of health system inputs. According to a WHO report about National Health Accounts published in 2009, 15 to 25% of hospital inefficiency is related to workforce [80]. The workforce is at the core of the health system and accounts for almost half of the total health budget, in the form of wages and other payments [81]. The shortage of human resources is a major obstacle in implementing national healthcare plans, causing ineffective recruitment, inappropriate training, poor supervision, and suboptimal workforce distribution, which can further reduce efficiency [82]. Strategies to increase workforce efficiency focus on assessment and training based on needs, reviews of incentive policies, flexible contracts and performance-based payments [83].

Hospitals can result in lower efficiency if healthcare products and services are not optimal. Hospitals will face higher inputs against the specific output or lower outputs against the specific input. Excessive lengths of hospital stays, unnecessary admissions, and unnecessary referrals to specialists are examples of overuse of healthcare services. Reduced demand for hospital services and low BORs indicate underuse of available services [25, 26, 27, 28, 29, 30, 31, 32]. A WHO report showed that suboptimal use of hospital resources, such as doctors, nurses, and beds, reduce demand for services and thus reduce hospital efficiency [82]. Optimal hospital management plays a vital role in optimizing healthcare services, improving hospital outcomes, and reducing costs [84, 85, 86]. Hospital managers and health policymakers can increase hospital efficiency and productivity through economies of scale. Strategies include optimizing hospital size, providing more products and services, and reducing ALS [38, 84, 85, 86].

Two of the principal sources of inefficiency in the EMR are inappropriate hospital sizes and excess numbers of active beds. These have been analyzed in studies conducted in countries outside the EMR, including in HICs [14, 21, 24, 25, 26, 33, 34, 35, 62]. These studies revealed the significant impact of hospital size and bed numbers on efficiency [87, 88]. The optimal number of active hospital beds typically lies between 200 and 300 beds. Generally, hospitals with less than 200 beds or more than 600 beds have higher costs [89]. According to international standards, a threshold BOR range between 84 and 85% indicates that use of hospital facilities and hospital resources are optimally efficient [90]. Therefore, optimizing hospital sizes and bed numbers can ensure that hospitals respond to population needs thus increasing efficiency. Indeed, it may be necessary for governments to build hospitals of a specific size, to take into account geographical considerations and difficulties accessing healthcare facilities.

The payment system has a vital role in improving hospital efficiency and productivity. In the EMR, payment systems are typically fee-for-service systems. In developed countries payments are often based on performance at clinical and organizational levels, increasing efficiency through performance incentives [91, 92]. Strategies to increase hospital efficiency include developing healthcare policies to implement appropriate payment systems, fair tariffs, and meticulous workforce recruitment plans, calculating required bed numbers for each community, making optimal use of hospital beds based on demand, and developing two-way electronic referral systems.

Conclusion

The results of this study have elucidated numerous sources of hospital inefficiency in the EMR. These sources should be addressed with targeted strategies, to improve hospital performance. Severe resource scarcity and increased costs of healthcare services, particularly in developing countries, require policymakers to ensure maximum use of available resources. Hospitals are highly complex, multidisciplinary social entities, whose performance can be improved through accurate, effective, and timely planning, organization, leadership, and management. Efficiency depends on multiple factors. As such, using various methods to measure hospital efficiency can be an effective strategy for managers and policymakers. Needs-based assessments and training, reviews of incentive policies, flexible contracts, performance-based payments, optimal hospital sizes based on community needs, increased resource availability and preservation of hospital social functions are crucial to increasing hospital efficiency.

Notes

Acknowledgements

Not applicable.

Authors’ contributions

MA and HR designed the research; MA and PI conducted it; MA and PI extracted the data; and MA, HR, VDB, and PI wrote the paper. MA had primary responsibility for final content. All authors read and approved the final manuscript.

Funding

This study had no funding.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Supplementary material

12913_2019_4701_MOESM1_ESM.docx (12 kb)
Additional file 1. Search strategy in Medline via PubMed.

References

  1. 1.
    Velasco-Garrido M, Busse R. Health technology assessment: an introduction to objectives, role of evidence, and structure in Europe. InHealth technology assessment: an introduction to objectives, role of evidence, and structure in Europe 2005. Copenhagen: WHO Regional Office for Europe; 2002. http://www.euro.who.int/observatory/Publications/20020527_16. Accessed 20 Jan 2019Google Scholar
  2. 2.
    Mosadeghrad AM, Esfahani P, Nikafshar M. Hospitals’ efficiency in Iran: a systematic review and meta-analysis of two decades of research. J Payavard Salamat. 2017;11(3):318–31.Google Scholar
  3. 3.
    Parker D, Newbrander W. Tackling wastage and inefficiency in the health sector; 1994.Google Scholar
  4. 4.
    Eastern Mediterranean Regional Office, World Health Organization. Introducing the framework for action for the hospital sector in the Eastern Mediterranean Region. Regional Committee for the Eastern Mediterranean. EM/RC66/5. 2019. http://applications.emro.who.int/docs/RC_Technical_Papers_2019_5_en.pdf?ua=1. Accessed 27 Sept 2019.
  5. 5.
    World Health Organization. Improving hospital performance in the Eastern Mediterranean Region, 2009.Google Scholar
  6. 6.
    Abdullatif AA. Hospital care in WHO Eastern Mediterranean Region; an agenda for change. In: International Hospital Federation Reference Book 2005/2006. Ferney Voltaire: International Hospital Federation; 2005.Google Scholar
  7. 7.
    Pourreza A, Alipour V, Arabloo J, Bayati M, Ahadinezhad B. Health production and determinants of health systems performance in WHO Eastern Mediterranean Region. East Mediterr Health J. 2017;23(5):368–74.PubMedGoogle Scholar
  8. 8.
    Farrell MJ. The measurement of productive efficiency. J R Stat Soc Series A (General). 1957.  https://doi.org/10.2307/2343100.Google Scholar
  9. 9.
    Charnes A, Cooper WW, Rhodes E. Measuring the efficiency of decision making units. Eur J Oper Res. 1978.  https://doi.org/10.1016/0377-2217(78)90138-8.Google Scholar
  10. 10.
    Aigner D, Lovell CK, Schmidt P. Formulation and estimation of stochastic frontier production function models. J Econom. 1977.  https://doi.org/10.1016/0304-4076(77)90052-5.Google Scholar
  11. 11.
    Pabon LH. Evaluating hospital performance through simultaneous application of several indicators; 1986.Google Scholar
  12. 12.
    Chisholm D, Evans DB. Improving health system efficiency as a means of moving towards universal coverage. World health report 2010 background paper, no. 28. http://www.who.int/healthsystems/topics/financing/healthreport/whr_background/en. Accessed 17 July 2018.
  13. 13.
    Liberati A, Altman DG, Tetzlaff J, Mulrow C, Gotzsche PC, Ioannidis JP, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009.  https://doi.org/10.1136/bmj.b2700.PubMedPubMedCentralGoogle Scholar
  14. 14.
    Mitton C, Adair CE, McKenzie E, Patten SB, Perry BW. Knowledge transfer and exchange: review and synthesis of the literature. Milbank Q. 2007.  https://doi.org/10.1111/j.1468-0009.2007.00506.x.PubMedGoogle Scholar
  15. 15.
    Hatam N. The role of Data Envelopment Analysis (DEA) pattern in the efficiency of social security hospitals in Iran. Iran Red Crescent Med J. 2008;10(3):211–7.Google Scholar
  16. 16.
    World Health Organization (WHO). 2015 Global Reference List of 100 Core Health Indicators. 2015. http://apps.who.int/iris/bitstream/10665/173589/1/WHO_HIS_HSI_2015.3_eng.pdf.Google Scholar
  17. 17.
    OECD. Health at a glance: Europe 2018: Organization for economic. Paris: OECD; 2018.Google Scholar
  18. 18.
    OECD Health Data 2001. A comparative analysis of 30 countries; data sources, definitions and methods. Paris: OECD; 2001.Google Scholar
  19. 19.
    Al-Shammari M. A multi-criteria data envelopment analysis model for measuring the productive efficiency of hospitals. Int J Oper Prod Man. 1999;19(9):879–91.Google Scholar
  20. 20.
    Ramanathan R. Operations assessment of hospitals in the Sultanate of Oman. Int J Oper Prod Man. 2005.  https://doi.org/10.1108/01443570510572231.Google Scholar
  21. 21.
    Hajialiafzali H, Moss J, Mahmood M. Efficiency measurement for hospitals owned by the Iranian social security organisation. J Med Syst. 2007.  https://doi.org/10.1007/s10916-007-9051-6.PubMedGoogle Scholar
  22. 22.
    Goshtasebi A, Vahdaninia M, Gorgipour R, Samanpour A, Maftoon F, Farzadi F, et al. Assessing hospital performance by the Pabon lasso model. Iran J Public Health. 2009;38(2):119–24.Google Scholar
  23. 23.
    Jandaghi G, Matin HZ, Doremami M, Aghaziyarati M. Efficiency evaluation of Qom public and private hospitals using data envelopment analysis. Eur J Econ Finance Adm Sci. 2010;22(2):83–91.Google Scholar
  24. 24.
    Hatam N, Moslehi S, Askarian M, Shokrpour N, Keshtkaran A, Abbasi M. The efficiency of general public hospitals in Fars Province, Southern Iran. Iran Red Crescent Med J. 2010;12(2):138.Google Scholar
  25. 25.
    Shahhoseini R, Tofighi S, Jaafaripooyan E, Safiaryan R. Efficiency measurement in developing countries: application of data envelopment analysis for Iranian hospitals. Health Serv Manag Res. 2011.  https://doi.org/10.1258/hsmr.2010.010017.PubMedGoogle Scholar
  26. 26.
    Ketabi S. Efficiency measurement of cardiac care units of Isfahan hospitals in Iran. J Med Syst. 2011.  https://doi.org/10.1007/s10916-009-9351-0.PubMedGoogle Scholar
  27. 27.
    Mohammadkarim B, Jamil S, Pejman H, Seyyed MH, Mostafa N. Combining multiple indicators to assess hospital performance in Iran using the Pabon Lasso model. Australas Med J. 2011.  https://doi.org/10.4066/AMJ.2011.620.Google Scholar
  28. 28.
    Al-Shayea AM. Measuring hospital’s units efficiency: a data envelopment analysis approach. Int J Eng Technol. 2011;11(6):7–19.Google Scholar
  29. 29.
    Ahmadkiadaliri A, Haghparast-Bidgoli H, Zarei A. Measuring efficiency of general hospitals in the south of Iran. World Appl Sci J. 2011;13(6):1310–6.Google Scholar
  30. 30.
    Osmani AR. Technical efficiency of district hospitals in Afghanistan: a data envelopment analysis approach: Chulalongkorn University; 2012.Google Scholar
  31. 31.
    Farzianpour F, Hosseini S, Amali T, Hosseini S, Hosseini SS. The evaluation of relative efficiency of teaching hospitals. Am J Appl Sci. 2012;9(3):392.Google Scholar
  32. 32.
    Chaabouni S, Abednnadher C. Efficiency of public hospitals in Tunisia: a DEA with bootstrap application. Int J Behav Healthc Res. 2012.  https://doi.org/10.1504/IJBHR.2012.051380.Google Scholar
  33. 33.
    Marnani AB, Sadeghifar J, Pourmohammadi K, Mostafaie D, Abolhalaj M, Bastani P. Performance assessment indicators: how DEA and Pabon lasso describe Iranian hospitals’ performance. Health Med. 2012;6(7):791–6.Google Scholar
  34. 34.
    Sheikhzadeh Y, Roudsari AV, Vahidi RG, Emrouznejad A, Dastgiri S. Public and private hospital services reform using data envelopment analysis to measure technical, scale, allocative, and cost efficiencies. Health Promot Perspect. 2012;2(1):28.PubMedPubMedCentralGoogle Scholar
  35. 35.
    Yusefzadeh H, Ghaderi H, Bagherzade R, Barouni M. The efficiency and budgeting of public hospitals: case study of Iran. Iran Red Crescent Med J. 2013;15(5):393.PubMedPubMedCentralGoogle Scholar
  36. 36.
    Gholipour K, Delgoshai B, Masudi-Asl I, Hajinabi K, Iezadi S. Comparing performance of Tabriz obstetrics and gynaecology hospitals managed as autonomous and budgetary units using Pabon Lasso method. Australas Med J. 2013.  https://doi.org/10.4066/AMJ.2013.1903.Google Scholar
  37. 37.
    Arfa C, Sabri B. Appraising the efficiency of public hospitals in Tunisia. Future Healthc. 2013.  https://doi.org/10.1007/s10754-013-9123-8.PubMedGoogle Scholar
  38. 38.
    Ajlouni M, Zyoud A, Jaber B, Shaheen H, Al-Natour M, Anshasi RJ. The relative efficiency of Jordanian public hospitals using data envelopment analysis and Pabon Lasso diagram. Glob J Bus Res. 2013;7(2):59–72.Google Scholar
  39. 39.
    Abouel-Seoud M. Measuring efficiency of reformed public hospitals in Saudi Arabia: an application of data envelopment analysis. Int J Econ Manag Sci. 2013;2(9):44–53.Google Scholar
  40. 40.
    Bastani P, Vatankhah S, Salehi M. Performance ratio analysis: a national study on Iranian hospitals affiliated to ministry of health and medical education. Iran J Public Health. 2013;42(8):876.PubMedPubMedCentralGoogle Scholar
  41. 41.
    Younsi M. Performance of Tunisian public hospitals: a comparative assessment using the Pabon Lasso model. Hosp Res. 2014;3(4):159–66.Google Scholar
  42. 42.
    Torabipour A, Najarzadeh M, Mohammad A, Farzianpour F, Ghasemzadeh R. Hospitals productivity measurement using data envelopment analysis technique. Iran J Public Health. 2014;43(11):1576.PubMedPubMedCentralGoogle Scholar
  43. 43.
    Rasool SA, Saboor A, Raashid M. Measuring efficiency of hospitals by DEA: an empirical evidence from Pakistan. Int Public Health J. 2014.  https://doi.org/10.11591/.v3i2.4684.Google Scholar
  44. 44.
    Pourmohammadi K, Hatam N, Bastani P, Lotfi F. Estimating production function: a tool for Hospital Resource Management. Shiraz E Med J. 2014.  https://doi.org/10.17795/semj23068.
  45. 45.
    Mehrtak M, Yusefzadeh H, Jaafaripooyan E. Pabon Lasso and data envelopment analysis: a complementary approach to hospital performance measurement. Glob J Health Sci. 2014.  https://doi.org/10.5539/gjhs.v6n4p107.
  46. 46.
    Lotfi F, Kalhor R, Bastani P, Zadeh NS, Eslamian M, Dehghani MR, et al. Various indicators for the assessment of hospitals’ performance status: differences and similarities. Iran Red Crescent Med J. 2014;16(4):e12950.Google Scholar
  47. 47.
    Kalhor R, Salehi A, Keshavarz A, Bastani P, Orojloo P. Assessing hospital performance in Iran using the Pabon Lasso model. Asia Pac J Health Manage. 2014;9(2):77.Google Scholar
  48. 48.
    Goudarzi R, Pourreza A, Shokoohi M, Askari R, Mahdavi M, Moghri J. Technical efficiency of teaching hospitals in Iran: the use of stochastic frontier analysis, 1999–2011. Int J Health Policy Manag. 2014;3(2):91.  https://doi.org/10.15171/ijhpm.2014.66.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Askari R, Farzianpour F, Goudarzi R, Shafii M, Sojaei S. Efficiency evaluation of hospitals affiliated with Yazd University of Medical Sciences using quantitative approach of data envelopment analysis in the year 2001 to 2011. Pensee J. 2014;76:416–25.Google Scholar
  50. 50.
    Adham D, Issac B, Sadeghi G, Mohammad P, Hossein A, Salarkhah E. Contemporary use of hospital efficiency indicators to evaluate hospital performance using the Pabon Lasso model. Eur J Bus Soc Sci. 2014;3(2):1–8.Google Scholar
  51. 51.
    Imamgholi S, Khatami Firouzabadi SMA, Goharinezhad S, Fadaei Dehcheshmeh N, Heidarinejad A, Azmal M. Assessing the efficiency of hospitals by using Pabon lasso graphic model. J Res Health. 2014;4(4):890–7.Google Scholar
  52. 52.
    Shetabi HR, Mirbahari SQ, Nasiripour AA, Safi Keykale M, Mohammadi H, Esfandnia A, Safari S, Kazemi M, Mohammadi M. Evluating technical efficiency of Kermanshah city universities by means of data envelopment analysis model. Res J Med Sci. 2015.  https://doi.org/10.3923/rjmsci.2015.53.57.
  53. 53.
    Masoompour SM, Petramfar P, Farhadi P, Mahdaviazad H. Five-year trend analysis of capacity utilization measures in a teaching hospital 2008–2012. Shiraz E-Med J. 2015.  https://doi.org/10.17795/semj21176.
  54. 54.
    Chaabouni S, Abednnadher C. Cost efficiency of Tunisian public hospitals: a Bayesian comparison of random and fixed frontier models. J Knowl Econ. 2016.  https://doi.org/10.1007/s13132-015-0245-8.Google Scholar
  55. 55.
    Safdar KA, Emrouznejad A, Dey PK. Assessing the queuing process using data envelopment analysis: an application in health centres. J Med Syst. 2016.  https://doi.org/10.1007/s10916-015-0393-1.
  56. 56.
    Mohammadi H, Meskarpour-Amiri M. Estimation production function of inpatient services and input productivity: a cross-sectional study of Iran selected public hospitals. Hosp Pract Res. 2016;1(3):91–3.Google Scholar
  57. 57.
    Mahate A, Hamidi S. Frontier efficiency of hospitals in United Arab Emirates: an application of data envelopment analysis. J Hosp Adm. 2015.  https://doi.org/10.5430/jha.v5n1p7.
  58. 58.
    Kalhor R, Amini S, Sokhanvar M, Lotfi F, Sharifi M, Kakemam E. Factors affecting the technical efficiency of general hospitals in Iran: data envelopment analysis. J Egypt Public Health Assoc. 2016.  https://doi.org/10.1097/01.EPX.0000480717.13696.3c.PubMedGoogle Scholar
  59. 59.
    Kakeman E, Forushani AR, Dargahi H. Technical efficiency of hospitals in Tehran. Iran Iran J Public Health. 2016;45(4):494.PubMedGoogle Scholar
  60. 60.
    Hassanain M, Zamakhshary M, Farhat G, Al BA. Use of lean methodology to improve operating room efficiency in hospitals across the Kingdom of Saudi Arabia. Int J Health Plann Manag. 2017.  https://doi.org/10.1002/hpm.2334.PubMedGoogle Scholar
  61. 61.
    Hamidi S. Measuring efficiency of governmental hospitals in Palestine using stochastic frontier analysis. Cost Eff Resour Alloc. 2016.  https://doi.org/10.1186/s12962-016-0052-5.
  62. 62.
    Nabilou B, Yusefzadeh H, Rezapour A, Azar FEF, Safi PS, Asiabar AS, et al. The productivity and its barriers in public hospitals: case study of Iran. Med J Islam Repub Iran. 2016;30:316.PubMedPubMedCentralGoogle Scholar
  63. 63.
    Rezaei S, Zandian H, Baniasadi A, Moghadam TZ, Delavari S, Delavari S. Measuring the efficiency of a hospital based on the econometric Stochastic Frontier Analysis (SFA) method. Electron Physician. 2016.  https://doi.org/10.19082/2025.PubMedPubMedCentralGoogle Scholar
  64. 64.
    Farzianpour F, Emami AH, Foroushani AR, Ghiasi A. Determining the technical efficiency of hospitals in Tabriz City using data envelopment analysis for 2013-2014. Glob J Health Sci. 2016.  https://doi.org/10.5539/gjhs.v9n5p42.Google Scholar
  65. 65.
    Arfa C, Leleu H, Goaied M, van Mosseveld C. Measuring the capacity utilization of public district hospitals in tunisia: using dual data envelopment analysis approach. Int J Health Policy Manag. 2016.  https://doi.org/10.15171/ijhpm.2016.66.PubMedCentralGoogle Scholar
  66. 66.
    Helal SMA, Elimam HA. Measuring the efficiency of health services areas in Kingdom of Saudi Arabia using data envelopment analysis (DEA): a comparative study between the years 2014 and 2006. Int J Health Care Finance Econ. 2017.  https://doi.org/10.5539/ijef.v9n4p172.Google Scholar
  67. 67.
    Mousa W, Aldehayyat JS. Regional efficiency of healthcare services in Saudi Arabia. Middle East Dev J. 2018.  https://doi.org/10.1080/17938120.2018.1443607.Google Scholar
  68. 68.
    Moradi G, Piroozi B, Safari H, Nasab NE, Bolbanabad AM, Yari A. Assessment of the efficiency of hospitals before and after the implementation of health sector evolution plan in Iran based on Pabon Lasso model. Iran J Public Health. 2017;46(3):389.PubMedPubMedCentralGoogle Scholar
  69. 69.
    Sultan WI, Crispim J. Evaluating the productive efficiency of Jordanian public hospitals. Int J Bus Manage. 2016.  https://doi.org/10.5539/ijbm.v12n1p68.Google Scholar
  70. 70.
    Ali AM, Kassam A. Efficiency analysis of healthcare sector. Eng Technol J. 2017;35(5 Part (A) Engineering):509–15.Google Scholar
  71. 71.
    Rezaei S, Hajizadeh M, Bazyar M, Kazemi Karyani A, Jahani B, Karami MB. The impact of health sector evolution plan on the performance of hospitals in Iran: evidence from the Pabon Lasso model. Int J Health Gov. 2018.  https://doi.org/10.1108/IJHG-09-2017-0046.Google Scholar
  72. 72.
    Migdadi YKA-A, Al-Momani HSM. The operational determinants of hospitals’ inpatients departments efficiency in Jordan. Int J Oper Res. 2018;32(1):1–23.  https://doi.org/10.1504/IJOR.2018.091199.CrossRefGoogle Scholar
  73. 73.
    Sajadi HS, Sajadi ZS, Sajadi FA, Hadi M, Zahmatkesh M. The comparison of hospitals’ performance indicators before and after the Iran's hospital care transformations plan. J Educ Health Promot. 2017.  https://doi.org/10.4103/jehp.jehp_134_16.PubMedPubMedCentralGoogle Scholar
  74. 74.
    Goudarzi R, RjabiGilan N, Ghasemi SR, Reshadat S, Askari R, Ahmadian M. Efficiency measurement using econometric stochastic frontier analysis (SFA) method, case study: hospitals of Kermanshah University of Medical Sciences. J Kermanshah Univ Med Sci. 2014;17(10):666–72.Google Scholar
  75. 75.
    Eastern Mediterranean Regional Office, World Health Organization. Health systems in the Eastern Mediterranean Region: situation, challenges and gaps. High Level Expert Meetingon Health Priorities in the Eastern Mediterranean Region1–2March 2012. RDO/WP/12.5. 2012.Google Scholar
  76. 76.
    Blair I, Grivna M, Sharif AA. The “Arab World” is not a useful concept when addressing challenges to public health, public health education, and research in the Middle East. Front Public Health. 2014.  https://doi.org/10.3389/fpubh.2014.00030.
  77. 77.
    Adhikari SR, Sapkota VP, Supakankunti S. A new approach of measuring hospital performance for low- and middle-income countries. J Korean Med Sci. 2015.  https://doi.org/10.3346/jkms.2015.30.S2.S143.PubMedPubMedCentralGoogle Scholar
  78. 78.
    Waheb Y, Kamel L, Mena R. Cost analysis and efficiency indicators for health care: report number 3, summary output for El Gamhuria General Hospital, 1993–1994; 1997.Google Scholar
  79. 79.
    Ali M, Debela M, Bamud T. Technical efficiency of selected hospitals in Eastern Ethiopia. Health Econ Rev. 2017.  https://doi.org/10.1186/s13561-017-0161-7.
  80. 80.
    World Health Organization (WHO). National Health Accounts database. Geneva: WHO; 2009.Google Scholar
  81. 81.
    Hernandez P, Dräger S, Evans DB, Tan-Torres Edejer T, Dal Poz MR. Measuring expenditure for the health workforce: evidence and challenges. World health report 2006 background paper. http://www.who.int/nha/docs/Paper%20on%20HR.pdf. Accessed 7 July 2010.
  82. 82.
    World Health Organization (WHO). The world health report 2006 - working together for health. Geneva: World Health Organization; 2006.Google Scholar
  83. 83.
    Huicho L, Scherpbier RW, Nkowane AM, Victora CG, Multi-Country Evaluation of IMCI Study Group. How much does quality of child care vary between workforce with differing durations of training? An observational multi-country study. Lancet. 2008.  https://doi.org/10.1016/S0140-6736(08)61401-4.Google Scholar
  84. 84.
    Mosadeghrad AM, Esfahani P, Afshari M. Strategies to improve hospital efficiency in Iran: A scoping review. Payesh. 2019;18(1):7–21.Google Scholar
  85. 85.
    Mannion R, Davies HT, Marshall M. Cultural characteristics of “high” and “low” performing hospitals. J Health Organ Manag. 2005;19(6):431–9.  https://doi.org/10.1108/14777260510629689.CrossRefPubMedGoogle Scholar
  86. 86.
    West E. Management matters: the link between hospital organisation and quality of patient care. Qual Health Care. 2001;10(1):40–8.  https://doi.org/10.1136/qhc.10.1.40.CrossRefPubMedPubMedCentralGoogle Scholar
  87. 87.
    Roh C-Y, Jae Moon M, Jung C. Measuring performance of US nonprofit hospitals: do size and location matter? Public Perform Manage Rev. 2010.  https://doi.org/10.2753/PMR1530-9576340102.Google Scholar
  88. 88.
    Yong K, Harris AH. Efficiency of hospitals in Victoria under casemix funding: a stochastic frontier approach. Australia: Centre for Health Program Evaluation; 1999.Google Scholar
  89. 89.
    Giancotti M, Guglielmo A, Mauro M. Efficiency and optimal size of hospitals: results of a systematic search. PLoS One. 2017.  https://doi.org/10.1371/journal.pone.0174533.PubMedPubMedCentralGoogle Scholar
  90. 90.
    Orendi J. Health-care organisation, hospital-bed occupancy, and MRSA. Lancet. 2008;371(9622):1401–2.  https://doi.org/10.1016/S0140-6736(08)60610-8.CrossRefPubMedGoogle Scholar
  91. 91.
    Cylus J, Papanicolas I, Smith PC, editors. Health system efficiency: How to make measurement matter for policy and management [Internet]. Copenhagen: European Observatory on Health Systems and Policies; 2016. (Health Policy Series, No. 46). Available from: https://www.ncbi.nlm.nih.gov/books/NBK436888/ Google Scholar
  92. 92.
    Walker S, Mason AR, Claxton K, Cookson R, Fenwick E, Fleetcroft R, et al. Value for money and the quality and outcomes framework in primary care in the UK NHS. Br J Gen Pract. 2010;60(574):e213–e20.PubMedPubMedCentralGoogle Scholar

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© The Author(s). 2019
corrected publication [2019]

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Authors and Affiliations

  1. 1.Department of Health Service Management, School of Health Management and Information SciencesIran University of Medical SciencesTehranIran
  2. 2.School of Public HealthZabol University of Medical SciencesZabolIran
  3. 3.World Health Organization, Regional Office for the Eastern MediterraneanCairoEgypt

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