Decision making methods in Political Economy of health policy in crises: A systematic review for hospitals.

doi:10.21203/rs.3.rs-5007755/v1

Download PDF

Systematic Review

Decision making methods in Political Economy of health policy in crises: A systematic review for hospitals.

https://doi.org/10.21203/rs.3.rs-5007755/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Introduction: we will attempt to fill an important gap that emerged from the preliminary literature review: Do Decision-making methods in hospitals reach homogeneous conclusions about their application?

Methods: For this systematic review, we have searched PubMed, Heal-link, Register PROSPERO and OSF, and forward and backward citations for studies published between databases inceptions and July 30, 2024.

The studies we included based on the inclusion criteria we formulated for the search would have no spatial restriction and were searched in English and French. The search was performed with tools according to the JBI Evidence Synthesis Method. We transformed the results and used KNIME software workflows to extract the results. We classified the studies according to their characteristics and extracted correlations between the variables considered.

Main findings: the decision methods were classified into three categories according to KNIME's cluster analysis: financial crisis, covid-19, personal crisis. Efficiency measurement is prominent among the decision methods. The most used method is DEA. The results of the research were presented using visualization methods.

Discussion: we discussed the heterogeneity among the studies considered found through clustering. We also discussed the extent to which the correlations confirmed the significance and certainty of the questions.

Conclusions: the homogeneity or heterogeneity of the studies demonstrated confirmation of the initial hypothesis: decision making methods in hospitals in crisis do not exhibit homogeneity. The conclusions of the study are important for policy makers and have a strong social impact. We believe that the research will open space for further research in the future.

Other Political Science

Decision making methods

Political Economy

Health policy

crises

The most frequently cited framework for decision making methods developed by Snowden & Boone (2007) demonstrates the inadequacy of a fundamental assumption of organizational theory and practice regarding decision making in complex situations: that in the world there is a certain level of predictability and order. Complex situations require avoiding simplifications and applying complex decision-making methods. The issues in the political economy of health care are many, varied and sometimes obscure (McKinlay, 2022).

Summary of the existing literature search

Decision making methods

The term Decision Analysis was used by Ronald A. Howard, as a practical application of Decision Theory to find tools and methods for the selection of the best decision by a fully informed decision maker (Howard, 1975). In order to make a decision there must be alternative options from which to choose the one that fits the goals, objectives, desires, values of the decision maker (Harris, 1998).

Conventional economic analysis is often used to create a framework in which social goals can be integrated into the health care decision-making process (Jan, Dommers, & Mooney, 2003).

Political Economy of health policy and Decision-making methods

Political economy factors are important in determining the adoption and implementation of health policies. The analysis of the political economy of health provides a way to take into account the political and social realities of health, whether at the community, sub-national, national, regional or global level. (Sparkes, Rivera, Jang, Marten, Rajan, Robb, & Shroff (2022). Political economy is closely related to decision making. The current -third- era of public policy political economy is based on models and methods that have been applied to the study of political science, which are increasingly concerned with public policy issues (John, 2018).

Μέθοδοι Εκτίµησης αποδοτικότητας για τη λήψη αποφάσεων

Methods of Efficiency Assessment for Decision Making

Fountzoula, & Aravossis, (2022) conducted a systematic review on Decision-Making Methods in the Public Sector and listed the most important Decision-Making methods as follows:

- Analytic Hierarchy Process (AHP)
- Analytic Network Process (ANP)
- Technique of Order Preference by Similarity to Ideal Solution (TOPSIS)
- Elimination and Choice Translating Reality (ELECTRE)
- Decision-Making Trial and Evaluation Laboratory (DEMATEL)
- Data Envelopment Analysis (DEA)
- Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEEs)

- VIKOR

Many methods or techniques or models are used complementarily or autonomously for decision making, such as

- ARENA finding factors that may influence the various decisions to be made; or
- SARIMA to predict conditions that will occur in the future based on past data
- Tagushi for solving one-dimensional problems and scenario planning (Tavakoli, Tavakkoli-Moghaddam, Mesbahi, Ghanavati-Nejad, Tajally, 2022).

Απόδοση-Productivity-Efficiency-Effectiveness of hospitals

Hospital productivity, usually measured by total factor productivity (TFP) and labor productivity index, is a measure that deals with finding areas for improvement, which can reduce the cost of healthcare (Ali, Salehnejad, & Mansur, 2019). The efficiency of hospitals is related to the optimal use of resource allocation, which is absolutely necessary in the current conditions in which the costs in health care tend to be inelastic the resources in health care tend to be used uncontrollably (Al-Shammari, 1999).

Crises

Crises and in particular those affecting the global community according to Lomborg (2004) span a wide range: climate crisis, disease, migration and generally any non-normal situation that threatens societies and individuals. Extremely large global crises that have occurred in recent years are the global financial crisis and the covid-19 crisis (Aktar, Alam, & Al-Amin, (2021). Crises that belong to different categories, such as the covid-19 health pandemic and the Russia-Ukraine crisis, share common characteristics because they caused excessive mortality and were difficult to predict, because they surprised most people, had a huge economic impact on the global economy, and affected the world order (Aljifri, 2024). Based on the typology of crises, our study extends to any modern global crisis and the application of decision-making methods to deal with its consequences.

Research motivations

The motivations of the members of the team that carried out this research initially start from the desire to address the challenge of solving unsolved problems, i.e. the problem of the literature gap on decision-making methods in times of crisis, as formulated in our research. Also included in the motivation is the desire to obtain a degree (post-doctoral or doctoral degree) by some of the team members. Certainly, concern for practical problems, such as the one under consideration, strongly motivates research. The desire to shoulder spiritual joy and to serve society by doing this creative work are certainly part of the motivation for research.

Aims of the article

The aim of this scientific article is to investigate the application of decision making methods in hospitals in crisis in terms of heterogeneity, the choice of measured variables, the use of estimation methods and the type of crisis considered and to express the results of the research to make it widely known in the academic world. Also, we intend that by publishing these articles, knowledge and progress of science will be promoted to a greater extent.

Issue under discussion (clear statement of the problem)

The identified gap in the literature concerning the comprehensive view on the application of decision-making methods in hospitals during crises is the one we seek to fill and contribute to knowledge. We did not find such a specialized systematic review in the literature. Examining the homogeneity in the application of such studies is an issue that requires analysis.

Research question

Main research question: do studies applying decision making methods in hospitals during a crisis show homogeneity regarding this application?

Main research hypothesis: The studies that apply decision-making methods in hospitals during a crisis do not show homogeneity regarding this application.

Alternative research hypothesis H1: Studies applying decision-making methods in hospitals in crisis show homogeneity regarding this application.

Secondary research question: Studies applying decision-making methods in hospitals in crisis suggest specific measures to improve the consequences of the crisis.

Table 1

Inclusion and exclusion criteria
Criteria	Include	Exclude
The article based on keyword searches is successfully evaluated in the inclusion process based on inclusion tools. Indicative Boolean operators during the search are listed as follows: ((Analytic Hierarchy Process) OR (AHP) OR (Analytic Network Process) OR (ANP) OR (Technique of Order Preference by Similarity to Ideal Solution) OR (TOPSIS) OR (Elimination and Choice Translating Reality) OR (ELECTRE) OR (Decision-Making Trial and Evaluation Laboratory) OR (DEMATEL) OR (Data Envelopment Analysis) OR (DEA) OR (Preference Ranking Organization Method for Enrichment Evaluations) OR (PROMETHEEs) OR (VIKOR)) AND (hospitals) AND (Efficiency) AND (crisis)	Yes	No
Do the authors explicitly state their aim, and provide evidence on efficiency at sub national, regional, national or international level?	Yes	No
Publication language	English	Non-English
Empirical versus non-empirical	Empirical	Non-empirical
Analyses decision making methods in the health sector versus analyses decision making methods in other sectors (non-health sector)	Health sector	Non-health sector
Publication year	2000–2024
Study design (qualitative, quantitative or mixed methods)	No limit
Study context (Country income classification)	No limit
Publication status (published/grey)	No limit

The studies were searched in English and French. We searched for studies in the following databases and on the following dates:

PubMed (1996 to 30th Aug 2024)
Scopus (1900 to 30th Aug 2024)
Heal-link (1999 to 30th Aug 2024)
Google Scholar (1999 to 30th Aug 2024)

We also included in the search the source for grey literature open Grey (formerly open SINGLE) and the OSF database. The keywords used (with the appropriate Boolean operators, as shown in Table 1 above) were:

Analytic Hierarchy Process
AHP
Analytic Network ProcessANP
Technique of Order Preference by Similarity to Ideal Solution
TOPSIS
Elimination and Choice Translating Reality
ELECTRE
Decision-Making Trial and Evaluation Laboratory
DEMATEL
Data Envelopment Analysis
DEA
Preference Ranking Organization Method for Enrichment Evaluations
PROMETHEEs VIKOR
Hospitals
Efficiency
Crisis

We used The PRISMA 2020 checklist, which is a checklist used in systematic reviews to ensure that all the necessary elements are in place.

The first and supervising researchers are responsible for the overall progress and supervision of the research. They carried out the preliminary actions and organized the members of the research team to proceed with the methodological approach and design of the research. The literature review and inclusion of studies was carried out by the first three researchers.

We followed the approach of the JBI Evidence Synthesis manual (Aromataris, Lockwood, Porritt, Pilla, Jordan, 2024). The inclusion of the studies in our study was performed with a customized tool constructed according to the guidelines of this manual. The inclusion was performed by the second, third and fourth researchers, while the first and the supervising researcher worked in case of disagreement. In some cases, data were requested from the authors of the included studies to fill in missing data from these studies that we needed in our study. Workflows were required to design workflows, and this task was carried out by the first and fourth researchers. Transforming the data to be compatible with KNIME processing, processing the data and extracting the results was performed. Data processing and extraction of results was carried out by the first, second and fourth researchers. The second and the supervising researcher worked for judgment in case of disagreement. The third and fourth researcher worked for publication in a scientific journal.

Categories and Sub-categories of the data

The names of the outcomes we searched for within the included studies, as classified into categories and subcategories are as follows:

Α) Qualitative data

1. Authors

Years

range in time

Number of units

2. Subject of study

Measurement of efficiency/productivity

Risk

Β) Transformed data

11=Productivity

12=Flexibility

13=Efficiency

14=Risk

31= Economic

32= Covid-19

33= Personal crisis

4. Είδος κρίσης

31= Economic

32= Covid-19

33= Personal crisis

We assessed included studies for risk of bias. McGuinness, & Higgins (2021) developed the Robvis risk of bias tool, which estimate risk across multiple studies included in a survey.

Our systematic review takes into account the methodology of van Winden, F.A.A.M. (1988). These researchers argued that the economic theory of political decision making is concerned with the determinants and outcomes of decision making in the political sphere of a society. This sphere is generally considered to be determined by these factors and their relationships that are of direct relevance to the way governments behave. Therefore, the study of classical political economy demonstrates the importance of decision making by political actors and the influence of these decisions on the resulting situation.

Quantitative Analysis

To use the data in KNIME, we transformed the data into numerical form. With its help we performed Correlation Analysis. Pearson's correlation coefficient and p-value significance coefficient were calculated. Figure 1 shows the workflow we designed using KNIME software.

The results of KNIME processing showed a strong indication of heterogeneity of studies. This heterogeneity is due to the variation in the studies, which was identified by clustering the KNIME software. We performed sensitivity analysis to analyze the effect of losing or removing certain data on the results.

The certainty of evidence was investigated for each outcome. For this purpose, homogeneity between the methods used by the included studies was examined. We compared the software that each of the included studies used, based on their methodology.

Systematic review

Based on the inclusion and exclusion criteria, we finally came up with 8 papers using the decision-making methods in hospitals in times of crisis. We constructed a flow diagram to illustrate the process by which we arrived at the included studies.

We identified some themes that summarize the reported studies applying decision making in hospitals in crisis, which were included in our paper. We constructed Fig. 3, which depicts these themes in a Venn diagram.

Improving efficiency

Troisi, De Simone, Vargas, & Franco (2022), study decision making for hospitals in covid-19, using DEA followed by a Panel Regression Analysis. Campanella, Azzolini, Izzi, Pelone et al. (2017) construct a method based on DEA that can be generalized and aims to contain efficiency. Henriques & Gouveia (2022) consider that in order to increase the efficiency of hospitals, collaboration and resource sharing should be implemented.

Ortega-Díaz, Martín (2022) introduce an important dimension: cuts in hospital finances during a crisis increase efficiency but may reduce quality (through an increase in mortality). Special care must be taken when making decisions to ensure that quality is maintained. Berger, Sommersguter-Reichmann, Czypionka (2020) reverse the argument: they consider introducing budget cuts to improve efficiency as a measure to avoid political costs of hospital closure decisions and staff cuts. Service delivery-related factors offset budget cuts.

Ortega-Díaz, Ocaña-Riola, Pérez-Romero, Martín-Martín (2020) examine public, private and PPP hospitals in terms of their positioning on the efficiency frontier created during the financial crisis.Οι Ortega-Díaz, Ocaña-Riola, Pérez-Romero, Martín-Martín (2020) examine the public, private and PPP hospitals in terms of their location at the borders of efficiency created during the financial crisis.

Finding and combating the causes of unconformity

Crises reduce the efficiency of hospitals. Surgery cancellations and reductions in patient attendance at covid-19 reduce the number of efficient hospitals (Henriques & Gouveia, 2022).

Tavakkoli-Moghaddam, Mesbahi, Ghanavati-Nejad, Tajally (2022) apply a completely separate method: they investigate the flow of patients with covid-19, simulate the process in detail, define scenarios based on the Tagushi method and use DEA for scenario efficiency. Thus, they identify and avoid patient bottlenecks.

Decision-making methods in studies

Most studies use the DEA method to assess the efficiency of hospitals in times of crisis and measure efficiency. In some cases, they use the Malmquist index to estimate productivity. Interestingly, most studies span extended periods of time, seeking to assess the change in variables as a function of time. Of particular interest is the case study by Tavakkoli-Moghaddam, Mesbahi, Ghanavati-Nejad, Tajally (2022) which constructs scenarios that identify patient congestion.

However, some studies completely depart from the decision-making methodology for efficiency and related concepts. They refer to other types of crises, such as personal crises, which are part of the typology of crises. Duhem, Berrouiguet, Debien, Ducrocq et al. (2018) analyze the use of the VigilanS method, using an algorithm involved in the decision making for the choice of supportive measures and in particular emergency meetings to avoid the risk of a suicidal episode recurrence.

Table 1

Characteristics of the included studies
Authors	Country-Years	Health sub-sector	measurable concept	Type of crisis	Methods for decision making/ Quantitative methods	Measures against crisis	Conclusions
Mitropoulos, Mitropoulos, Karanikas, Polyzos (2018).	Greece 2009–2012	111 Hospitals	Productivity	Economic	Bootstrapping Regression	Comprehensive reform Improving integration and governance Reduction in expenditure	Comprehensive health system reform will contain expenditure and increase efficiency.
Troisi, De Simone, Vargas, Franco (2022).	Italy	Hospitals 26 regions	FlexibilityEfficiency	Covid-19	DEA (Panel) Regression	Balancing resources to avoid exacerbating demand from non-serious patients	reallocation of resources to Covid-19 patients is often ignored. Flexible triage of patients - avoiding demand for hospital beds.
Campanella, Azzolini, Izzi, Pelone et al. (2017)	Italy 2010	50 Hospitals	Efficiency	Economic	DEA Malmquist Index Luenberger Productivity Indicator (Tobit) Regression	Requires urgent rationalization of resources and cost efficiency	The specific DEA application as a methodological framework can be generalized. Has considerable value for policy managers in crisis
Henriques & Gouveia (2022).	Portugal 2019-20	37 hospitals	Efficiency	covid-19	(Value-based) DEA Robustness analysis	Robustness analysis Reducing the size and changing the structure of the NHS Resource Reallocation	Allocation of resources covid-19 had a negative impact on hospital efficiency Increased inefficiency of inefficient hospitals
Tavakoli, Tavakkoli-Moghaddam, Mesbahi, Ghanavati-Nejad, Tajally, 2022	Iran 2020	1 hospital (case study)	Scenarios Efficiency	Covid-19	ARENA SARIMA Taguchi DEA -	Finding bottlenecks for future patient arrivals and combating them	simulation visualizes the future and helps management in human resource planning, facility procurement and strategic and tactical decisions
Polyzos, 2012	Greece 2009-11	117 Hospitals	Efficiency	Economic	DEA -	It is necessary to reallocate expenditure and to tackle waste of resources with supervision	The onset of the economic crisis has led hospitals to increase their efficiency. Primary health care operation, single provider operation is required
Duhem, Berrouiguet, Debien, Ducrocq, et al., 2018	France	24 psychiatric health units Risk Personal crisis (suicide)	Risk	Personal crisis (suicide)	VigilanS method Algos algorithm	Scheduling appropriate actions (correspondence, meetings) minimises personal crisis	Decision-making with the method can reduce the rates of repeat suicide attempts
Ortega-Díaz & Martín (2022)	Spain	232 General hospitals	Performance Efficiency Quality	Economic	DEA	Selection by DEA of hospitals that will increase efficiency without decreasing quality	To take into account the change in quality and not only in efficiency No evidence of a generalized relationship between efficiency and lack of quality
Berger, Sommersguter-Reichmann, Czypionka (2020)	Austria 2002-15	120 Hospitals	Efficiency	Economic	DEA OLS regression	Political leadership to bear the political cost of not imposing budget constraints	sudden exogenous shocks to public finances increase budgetary pressures on public hospitals by offsetting a pre-existing low budget Budget restraint policies reduce efficiency
Ortega-Díaz, Ocaña-Riola, Pérez-Romero, Martín-Martín (2020)	2009-12	230 Hospitals	Efficiency	Economic	DEA linear mixed model (GLMM)	Investigating the existence and reduction of social and spatial inequalities for efficiency	The crisis has favoured PPP hospitals in terms of efficiency Efficiency of hospitals depends on the wealth of its region
Soroush, Nabilou, Faramarzi, Yusefzadeh (2022)	Iran	1 University Hospital	Efficiency	Covid-19	DEA	Evacuation and redistribution of hospital beds minimizes crisis	DEA Evacuation and redistribution of hospital beds minimizes crisis DEA is a great method for assessing efficiency Policy makers use DEA to maximize hospital efficiency.

Table 2

Methodology variables and correlations with quantitative analysis
Μmeasurable concept	Type of crisis	Methods related to the measured quantities	Crisis response measures (related to)	Quantitative methods
11 = Productivity 12 = flexibility 13 = Efficiency 14 = Risk 15 = Performance 16 = Quality	31 = Economic 32 = Covid-19 33 = Personal crisis	41 = Bootstrapping 42 = DEA 43 = Malmquist Index 44 = Luenberger model 45 = Productivity Indicator 46 = ARENA 47 = SARIMA 48 = Taguchi 49 = VigilanS method	51 = Overall improvement 52 = Reduction of expenditure 53 = Rationalisation of resources 54 = Change of procedures	61 = Regression 62 = Robustness analysis 63 = Algos algorithm 64 = OLS Regression 65 = linear mixed model (GLMM)

Γενικές παρατηρήσεις για τα αποτελέσµατα

The study of the included literature leads us to publications that span the decade of the '10s and the current decade. The studies mainly refer to hospitals. Efficiency and Productivity are examined. The crises examined are the financial and the covid-19 crisis. One study refers to the personal crisis (suicide risk). The quantitative methods are DEA and the Malmquist index. The measures of crisis response revolve around rationalizing costs and increasing efficiency. In the conclusions of the studies, the improvement of efficiency in one way or another is predominant.

It is interesting to note that most of the studies deal with the calculation of efficiency. Few studies using traditional decision-making studies (e.g. AHP, Promethee) as described by Fountzoula, & Aravossis (2022) are used in the studies included in our study. Based on our inclusion criteria, such methods are hardly used in valid databases and are related to decision making in hospitals during a crisis period. Η ανάλυση του risk of bias κατέδειξε ότι οι µελέτες που εντάχθηκαν στην έρευνα µας δεν είχαν υψηλό κίνδυνο. Τα παραγόµενα traffic lights του Robvis risk of bias κατέδειξαν ένα χαµηλό κίνδυνο στην πλειοψηφία των περιπτώσεων για τις µελέτες αυτές. H ανάλυση ευαισθησίας που πραγµατοποιήσαµε συµπληρωµατικά, κατέδειξε ότι η διαφοροποίηση της επιλογής των µεταβλητών δεν επηρεάζει αισθητά τα αποτελέσµατα.

What the integrated studies highlight

At least in the cases of the financial and covid-19 crises, the studies' conclusions point to a restructuring of the operation to achieve higher results with fewer resources. Human resource planning, facility procurement and strategic and tactical decisions (Tavakoli, Tavakkoli-Moghaddam, Mesbahi, Ghanavati-Nejad, Tajally, 2022) and flexible patient triage - avoiding demand for hospital beds (Troisi, De Simone, Vargas, Franco, 2022) are indicative of this direction.

Clustering

With the help of the KNIME software we performed clustering to group the studies into categories according to their characteristics. KNIME software includes a specific knot (K-means) and applies a corresponding workflow to produce the clustering. This clustering is illustrated in Table 3.

Table 3

Clustering using k-means for the included studies.
Cluster number		Cluster 1	Cluster 2	Cluster 3
Number of studies		4	3	1
Cluster name		Decision-making on hospital efficiency	Decision-making on covid-19	Decision-making on avoiding the consequences of human crisis

Percentage distribution of items

In order to study the statistics of the characteristics of the included studies, we constructed a visualized chart of percentages of occurrence of the characteristics in these studies (for measurable concept, type of crisis, etc.). This chart is shown in Fig. 4.

From the statistical analysis of the included studies, we observe that in terms of the type of crisis, the studies referring to the financial crisis and the studies referring to the covid-19 crisis are the most frequently conducted, while only one study refers to the personal crisis. We note that of all the methods we investigated, the DEA method is by far the most used. Related to the extensive use of the DEA method is the suggestion of resource rationalization, which is mentioned in most of the studies in terms of the improvement that can result from the process of coping with a crisis (whether it is a financial crisis or a post-covid-19 crisis).

Correlation Analysis

The correlation analysis we performed for our study shows some significant correlations. These correlations were performed with the help of KNIME software. We calculated the correlation coefficients and p-values. The correlations that fall within acceptable statistical limits (< 0.08) are presented in Table 3.

Table 3: Correlations between the survey variables

Variable 1	Variable 2	Correlation	p-value
Measurement of Efficiency/Productivity	13 = Efficiency - Μeasurable concept	0.6614	0.0523
Risk	13 = Efficiency - Μeasurable concept	-0.6614	0.0523
11 = Productivity - Μeasurable concept	13 = Efficiency - Μeasurable concept	-0.6614	0.0523
13 = Efficiency - Μeasurable concept	14 = Risk - Measurable concept	-0.6614	0.05235
13 = Efficiency - Μeasurable concept	33 = Personal crisis - Type of crisis	-0.6546	0.0781
13 = Efficiency - Μeasurable concept	41 = Bootstrapping (Methods)	-0.6546	0.0781
13 = Efficiency - Μeasurable concept	49 = VigilanS method(Methods)	-0.6546	0.0781
31 = Economic - Type of crisis	32= Covid-19 – Type of crisis	-0.7745	0.0240

We observe that studies measuring Efficiency show high negative correlations with Risk, Bosstrapping and Vigilans methods and studies on personal judgment. These negative correlations are explained because these methods are not used in Efficiency, while personal judgment is referred to a completely different kind of judgment that cannot be correlated with Efficiency.

Pie-charts

We constructed Pie-charts for the percentage distribution of crisis types in the included studies and for the Percentage of the methods used in studies, which is shown in Fig. 5.

Pie-chart shows some important results about the statistics of the studies:

Financial crisis and covid-19 crisis occupy the bulk of the crises dealt with in the relevant studies.
The DEA method is used in a very large number of the studies, while the other methods are used equally.
Resource rationalization is proposed as the main method for dealing with crises (both economic and covid-19 induced).

Summary and interpretation of the main findings

Three categories of studies are grouped by k-means: studies that apply decision making methods to make decisions on the following judgements:

1) financial crisis,

2) covid-19,

3) personal crisis.

We note that these categories are in line with the typology of crises as analyzed in the Farantos & Koutsoukis (2016) model. In particular, we note that we also responded to a personal crisis (risk to the individual), as reported in Boin et al. (2005). This finding and agreement with the typology is important for the conclusions of our study.

We observed that the DEA method is widely used to study the subject during both economic and covid-19 crises. As the most commonly used method of measuring the efficiency of systems and organizations, using the efficient frontier to rank units and suggesting cuts in inputs or increases in outputs, the DEA method seems to have taken up a very large part of the subject. In contrast, classical methods of decision making, such as PROMETHEE or ELECTRA (Fountzoula, & Aravossis, 2022), were not found for this subject in the literature study. While such methods are often used in the field of decision making in different settings, their non-use in hospitals in times of crisis leaves a large scope for further research that can fill this important gap in the literature.

Researchers attach particular importance to the rationalization of resources. Resource rationalization seems to be able to help hospitals in two different situations of financial crises:

In financial crisis, resource rationalization can contain the inputs of inefficient units and make these units as efficient (Campanella, Azzolini, Izzi, Azzolini, Izzi, Pelone et al., 2017).
In the covid-19 crisis, rationalization of resources can direct resources to more serious disease treatment cases freeing hospitals from milder cases (Henriques & Gouveia, 2022).

It is worth noting that increasing efficiency is not a one-way street in an economic crisis, for many authors. Several of them attach particular importance to changing processes, improving the system as a whole and addressing social inequalities.

From the correlations we observed that strong positive correlations appear between studies estimating Productivity/Efficiency and methods of measuring Efficiency. This is expected, but other strong correlations do not appear. Correlation coefficients do appear, but high p-values do not appear. We believe that the relatively small sample size, due to the formulation of the inclusion criteria, is responsible for the lack of correlations. By conducting an extended search, we will be able to find a larger number of valid correlations in future research development. The main question of the investigation is answered in the negative. The analysis of the data from the integrated studies does not show that studies using the decision-making methods in hospitals in crisis are homogeneous. Most p-values are outside the accepted limits, according to the analysis by Veney, Kros, & Rosenthal (2009). In addition to the fact that studies deal with different types of crises, studies also show that they use decision-making methods that measure different sizes. A certain homogeneity appears with regard to the use of Data Envelopment Analysis methods. Indication of errors that limit the validity of the study.

After the preliminary research we carried out, we started our study by setting the inclusion and exclusion criteria, according to the instructions and guides of the JBI Evidence synthesis manual Aromataris, Lockwood, Porritt, Pilla, Jordan, 2024). This course brought us face to face with a difficulty in including papers in our research. Many papers were excluded from the search due to the strictness of the set criteria. We believe that this is a parameter that will be corrected in the final survey.

Suggestions for future research activity in the field

Future research activity in the field could be extended towards the application of 'classical' decision making methods, which at present have been overshadowed by the DEA method. Studies such as ELECTRE or PROMETHEE, which are often used in decision making in more general matters, could be used in the specific issue of decision making in hospitals in times of crisis.

Evaluation of the validity of the results

The validity of the research tools was tested and approved during the development of our research. We checked that the research instruments measured what they were intended to measure. The condition of measuring the measured quantity was verified.

Practical application of the results

The practical application of the research results is useful for the health sector. Political actors, policy makers, hospital administrators will use the results of the research in terms of applying decision making with models to make appropriate decisions in hospitals in crisis situations.

Applying the results of our study to a broader context

The broader context in which the study is set is that of decision making in hospitals in crisis. The study of previous research on the topic under consideration, interacts with and aims to modify the existing framework governing.

Need for further studies to strengthen the result

The need to strengthen the results was presented during the preparation of our research. Broadening the inclusion criteria, treating the studies separately according to the type of crises and finding the different characteristics of the studies according to the type of crises will differentiate and strengthen the results.

Studies on the application of decision-making models in hospitals in times of crisis extend to the conditions of financial crisis, covid-19 crisis and less to personal crisis. Many of the studies revolve around the measurement of Efficiency and the rest around other various measurable variables. The DEA method is used in most of the studies, whether they extend to financial crisis conditions or covid-19 crisis conditions. Resource rationalization dominates the proposals for saving resources from less severe situations and allocating resources to more severe situations, regardless of crisis categories. In many cases, overall system change, and the treatment of social inequalities are suggested more than improving efficiency. Correlations such as the highly negative correlation of Efficiency with Risk and the examination of personal crises (< 0.0781) indicate the heterogeneity of the studies. This heterogeneity is pervasive since different types of judgments are considered when applying decision making methods in hospitals. The central research question was answered in the negative. Our study establishes the need to strengthen future research with additional decision-making methods to be applied in hospitals during crises and leaves room for future expansion.

Ethics approval and consent to participate.

We used deidentified publicly available secondary data which did not require ethical approval.

Consent for publication

Not applicable.

Availability of data and materials

Data is available upon request.

Competing interests

Authors declares that they have no competing interests.

Funding Statement

“The authors declare that there is no funding regarding the publication of this paper.”

“The authors declare that there are no financial or non-financial interests that are directly or indirectly related to the work submitted for publication.”

Helsinki Declaration

Note that a statement on the "Declaration of Helsinki" is not required, because this research is a systematic review and neither patients nor volunteers are involved.

Aktar MA, Alam MM, Al-Amin AQ (2021) Global economic crisis, energy use, CO2 emissions, and policy roadmap amid COVID-19. Sustainable Prod Consum 26:770–781
Ali M, Salehnejad R, Mansur M (2019) Hospital productivity: The role of efficiency drivers. Int J Health Plann Manage 34(2):806–823. 10.1002/hpm.2739Epub 2019 Feb 6. PMID: 30729610
Aromataris E, Lockwood C, Porritt K, Pilla B, Jordan Z (eds) (2024) JBI Manual for Evidence Synthesis. JBI; https://synthesismanual.jbi.global. https://doi.org/10.46658/JBIMES-24-01
Bai X, Ma W, Huang J, Liang C, Zhang X (2024) Performance assessment and improvement of district cooling system: A case study of enhancing energy efficiency of hospital buildings. Case Stud Therm Eng 56:104187
Boin A, Paul’s H, Stern E, Sundelius B (2005) The Politics of CrisisManagement, Public Leadership under Pressure. Cambridge University Press, Cambridge, p, pp 2–4
Campanella P, Azzolini E, Izzi A, Pelone F, De Meo C, La Milia D, Specchia ML, Ricciardi W (2017) Jan-Mar;53(1):46–53 Hospital efficiency: how to spend less maintaining quality? Ann Ist Super Sanita. 10.4415/ANN_17_01_10. PMID: 28361805
Farantos GI, Koutsoukis NS (2015) Integrated Crisis Management: A literature review. In: Kefalaki M, Gagne A-M (eds) International Exchanges on Communication, Management and Marketing. Communication Institute of Greece, pp 35–53. http://coming.gr/wp-content/uploads/2013/11/Book_ICCM20151.pdf
Henriques CO, Gouveia MC (2022) Assessing the impact of COVID-19 on the efficiency of Portuguese state-owned enterprise hospitals. Socio-economic Plann Sci 84:101387
John P (2018) Theories of policy change and variation reconsidered: a prospectus for the political economy of public policy. Policy Sci 51:1–16. https://doi.org/10.1007/s11077-017-9297-x
Lomborg B (ed) (2004) Global crises, global solutions. Cambridge University Press
McGuinness LA, Higgins JP (2021) Risk-of‐bias VISualization (robvis): an R package and Shiny web app for visualizing risk‐of‐bias assessments. Res synthesis methods 12(1):55–61
Mitropoulos P, Mitropoulos I, Karanikas H, Polyzos N (2018) The impact of economic crisis on the Greek hospitals' productivity. Int J Health Plann Manage. ;33(1):171–184. 10.1002/hpm.2410. Epub 2017 Apr 10. PMID: 28393385
Ortega-Díaz MI, Martín JC (2022) How to detect hospitals where quality would not be jeopardized by health cost savings? A methodological approach using DEA with SBM analysis. Health Policy 126(10):1069–1074
Polyzos N (2012) A three-year Performance Evaluation of the NHS Hospitals in Greece. Hippokratia 16(4):350–355 PMID: 23935316; PMCID: PMC3738611
Snowden DJ, Boone ME (2007) A leader's framework for decision making. Harvard Business Rev 85(11):68
Sparkes SP, Rivera PAC, Jang H, Marten R, Rajan D, Robb A, Shroff ZC (2022) Normalizing the political economy of improving health. Bull World Health Organ 100(4):276
Tavakoli M, Tavakkoli-Moghaddam R, Mesbahi R, Ghanavati-Nejad M, Tajally A (2022) Simulation of the COVID-19 patient flow and investigation of the future patient arrival using a time-series prediction model: a real-case study. Med Biol Eng Comput 60(4):969–990. 10.1007/s11517-022-02525-zEpub 2022 Feb 12. PMID: 35152366; PMCID: PMC8853249
Troisi R, De Simone S, Vargas M, Franco M (2022) The other side of the crisis: organizational flexibility in balancing Covid-19 and non-Covid-19 health-care services. BMC Health Serv Res 22(1):1096. 10.1186/s12913-022-08486-1PMID: 36038878; PMCID: PMC9421103
Veney JE, Kros JF, Rosenthal DA (2009) Statistics for health care professionals: working with Excel, vol 9. Wiley
Berger M, Sommersguter-Reichmann M, Czypionka T (2020) Determinants of soft budget constraints: How public debt affects hospital performance in Austria. Soc Sci Med 249:112855. 10.1016/j.socscimed.2020.112855Epub 2020 Feb 20. PMID: 32109755
Ortega-Díaz MI, Ocaña-Riola R, Pérez-Romero C, Martín-Martín JJ (2020) Multilevel Analysis of the Relationship between Ownership Structure and Technical Efficiency Frontier in the Spanish National Health System Hospitals. Int J Environ Res Public Health 17(16):5905. 10.3390/ijerph17165905PMID: 32823922; PMCID: PMC7459985
Soroush F, Nabilou B, Faramarzi A, Yusefzadeh H (2022) A study of the evacuation and allocation of hospital beds during the Covid-19 epidemic: a case study in Iran. BMC Health Serv Res 22(1):864. 10.1186/s12913-022-08286-7PMID: 35790966; PMCID: PMC9254655
van Winden FAAM (1988) The Economic Theory of Political Decision-Making: A Survey and Perspective. In: Van Den Broeck J (ed) Public Choice. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-7784-7_2

The authors declare no competing interests.

Download PDF

Version 1

posted

You are reading this latest preprint version

Decision making methods in Political Economy of health policy in crises: A systematic review for hospitals.

Status:

Version 1

Abstract

Figures

Background of the article

- VIKOR

Methodology

Quantitative Analysis

Results

Systematic review

Improving efficiency

Finding and combating the causes of unconformity

Decision-making methods in studies

Γενικές παρατηρήσεις για τα αποτελέσµατα

What the integrated studies highlight

Clustering

Percentage distribution of items

Correlation Analysis

Pie-charts

Discussion

Summary and interpretation of the main findings

Suggestions for future research activity in the field

Evaluation of the validity of the results

Practical application of the results

Applying the results of our study to a broader context

Need for further studies to strengthen the result

Conclusions

Declarations

References

Additional Declarations

Status:

Version 1