Occupational health risk assessment at healthcare institutions: Developing a semi-quantitative risk method

Abstract

BACKGROUND:

Environmental hazards in healthcare institutions affect the quality of patient care as well as personnel and patient safety.

OBJECTIVE:

The aim of this study was to develop and apply a semi-quantitative risk assessment method to calculate occupational health risk levels with regard to the sensitivities of healthcare institutions.

METHODS:

The present research was conducted in three phases. In phases 1 and 2, the model was developed using a review of different risk assessment methods, extracting expert opinions (N = 10) through semi-structured interviews, and using the fuzzy analytical hierarchy process (FAHP). In phase 3, in order to validate the proposed method, one of the five public hospitals was randomly selected and a case study comprising 6 sections was performed.

RESULTS:

A total of 43 health risks were identified and evaluated using the present method, 41.86% of which were at very high levels, 16.27% at high levels, 30.23% at substantial ones, 9.3% at medium and 2.32% at low levels. The highest health risks were found in paraclinical and operating room wards.

CONCLUSION:

To overcome the shortcomings of the proposed health risk assessment methods, a semi-quantitative method was used in the present study to more accurately calculate the risk levels in the healthcare institutions and also calculate the risk level of each hospital unit. The proposed semi-quantitative method can be used as a tool for assessing occupational health risks as a key element of risk management. In addition, by focusing on an appropriate framework for occupational health risk assessment, specialists in the organization will be able to take significant and effective steps to implement an efficient risk management system.

Keywords

Healthcare institutions risk assessment semi-quantitative method occupational health

1. Introduction

A large workforce in the health sector is providing services and there are many different hazards in such work environments [1]. Occupational Health and Safety (OHS) is an important issue among healthcare staff [2]. The aim of the OHS management system is to control the risks and improve the employees’ health [3]. It is necessary to provide safety for effective management by establishing an appropriate risk analysis method [4]. Risk analysis involves determining consequences and their probabilities. The results of risk analyses are the basis for risk assessment. Occupational Health Risk Assessment (OHRA) is a tool for controlling health risks [5]. Risk assessment methods and their matrices are classified into three categories: quantitative, qualitative, and semi-quantitative [6]. Most matrices have qualitative or semi-quantitative values. Qualitative methods are based on qualitative or subjective descriptions, and less detailed information is required for their development and use. In qualitative methods, consequence of severity, exposure probability, and risk levels are described through qualitative terms such as high, medium, and low. Semi-quantitative risk methods use qualitative data, but the values proposed for risk ranking are numerical, and risk score can be obtained using a certain formula [7]. It should be noted that the numbers used in quantitative methods are usually determined by expert judgments and experiences, and mainly without quantitative data. They are only valuable compared to each other. Quantitative risk methods use data to determine the values of severity and probability of consequences and generate risk level values in specific numerical units. As explained in the US National Risk Assessment Standard (ISO 31010/ANSI Z690.3), “in the absence of sufficient information”, analyses may be incomplete or impossible. Two measures of consequence severity and probability of occurrence are typically used for risk assessment [8]. In quantitative methods, the lack of sufficient information may result in incomplete or impossible analyses; hence, semi-quantitative assessments can be useful because risk quantification is really difficult. However, qualitative interpretations are too subjective. A combination of both quantitative and qualitative methods can be a solution to increase the advantages and reduce the disadvantages. In health risk assessment methods, the relative importance of the risk criteria is not taken into account, and their significance is considered the same. When the weight of each criterion is taken into account, a more accurate risk assessment can be performed [9]. In this study, efforts were done to develop a semi-quantitative health risk assessment method in order to help calculate the risk levels more precisely with respect to the weights of the criteria. The health risk assessment methods are examined in the literature review section.

2. Literature review

In the 1980s, the Health and Safety Executive (HSE) in England introduced a tool called control of substances hazardous to health (COSHH) which is essential for chemicals risk assessment. Most businesses use substances or products that are mixtures of substances for which we can implement the COSHH essential tool to prevent or reduce workers’ exposure to hazardous substances [10]. Industrial countries and international organizations frequently issued guidelines for health risk assessment in the early 1980s [11]. In this section, various models are briefly described. The Australian model is a qualitative one developed in 2011. It is used for chemical, physical and dust risk assessment. The method is suitable for small and medium-sized industries and has a wide range and good applicability [12]. The risk levels in this method can be assessed using a manual diagram method used for chemical, physical and dust risk assessment [5].

The Romanian model is also a qualitative one developed in 1998. Like the Australian model, this one is used for chemical, physical and dust risk assessment and has a wide range, but in this model, it is difficult to judge the probability of the occurrence of an event [13]. The Singaporean model is a semi-quantitative one developed for chemicals and dust risk assessment in 2005. This method eliminates the defects of other quantitative and qualitative methods used for chemicals and dust risk assessment and calculates the risk at three levels [14].

The International Council on Mining and Metals (ICMM) model is a qualitative model and developed in 2009. This model is used for chemical, physical and dust risk assessment and applies a matrix method to assess risk levels. Although it has a wide range and can be used in various industries, the model overcalculates the risk level in quantitative scoring [15].

The Environmental Protection Agency (EPA) model is a quantitative method developed in 1980 by EPA in Washington for chemicals risk assessment. It can provide a quantitative assessment of carcinogenic and non-carcinogenic hazards, but it is difficult to distinguish between multi-hazard levels in this method [16].

The “Health Risk Rating” (HRR) semi-quantitative method was developed by US American Industrial Hygiene Association (AIHA). In this method, health effect rating and exposure rating are used for hazard level calculation. It is a scoring method in which the exposure probability criterion is an estimate of the exposure level associated with Occupational Exposure Limit (OEL) [17]. ANSI Z 590.3-2011 standard was introduced by US American Society of Safety Engineers in which the severity and probability of occurrence are combined in a matrix and the risk value is calculated. This method has not been specifically designed for health risk assessment but can be modified and used to assess the hazards of chemical, physical and biological substances [18]. The hospital electronic tool introduced by Occupational Safety and Health Administration (OHSA) in the US is mainly used as a checklist to identify hazards [19]. The present study aimed to develop and apply a semi-quantitative model to calculate risk levels with regard to the hazards and sensitivities of the healthcare institutions.

3. Materials and methods

In this applied research, the data were collected through the field method and interviews with experts. The semi-quantitative method presented in this study was used to assess and describe the risk level in the healthcare institutions. In semi-quantitative methods, the risk level can be categorized into different types such as low, medium, high and very high. The number of risk levels can vary from 3 to 10. Semi-quantitative methods consist of two parts: risk criteria (probability of exposure to harmful agents, consequences of exposure, etc.) and risk scoring system. Different scales are used to describe and determine the score of risk criteria. To introduce and implement the proposed semi-quantitative method, the following phases were performed, as shown in Fig. 1.

Phase 1: Identification of risk criteria and determination of the weights of the criteria

Step 1: Identification of risk criteria

In this stage, the risk criteria were selected to calculate risk levels based on previous studies and experts’ opinions through a semi-structured interview. To determine the risk criteria, a number of semi-structured interviews with industrial health experts were conducted by the researchers. The semi-structured interview method has been used in various studies in the OHS field [17–19]. The specialists were identified and selected through non-random sampling. The individuals with more than 10 years of work experience in a hospital setting were selected as the study samples. The advantage of using a non-random method is that only the specialists with sufficient experience are selected [20]. The selected individuals included hospital OHS experts (N = 3), safety and health inspectors (N = 4), and academic specialists (N = 3). Each interview lasted about 30 to 60 minutes. The researcher collected the data after each face-to-face interview. Following the content analysis of the information obtained from the interviews, three risk criteria were considered for this method, which included the probability of exposure to hazardous factors, the exposure duration, and the severity of consequence.

Fig. 1.

Study phases.

The probability of exposure to hazardous factors: To rate this criterion, control measures for any potential hazard are required to be assessed directly or indirectly. In direct assessments, the exposure level needs to be measured and compared with the standards. In indirect assessments, documents of recent measurements can be used.

Based on the experts’ opinion and according to the sensitivity of the hospital environment to determine the probability of exposure to hazardous biological factors in operating rooms, isolation rooms, Intensive Care Units (ICUs) and other units, environmental measurements had to be initially performed. The numbers of bacteria and fungi were then compared based on CFU/m³ within the range recommended by the WHO (the standard range was <100 CFU/m³ 3 for bacteria and <50 CFU/m³ for fungi) [20]. If the number of bacteria or fungi is lower than 50% of the standard limit, the probability criterion would be scored LOW. In case it is 50%–100% of the standard limit, the criterion would be scored ‘Medium’, and the score ‘High’ would be given if the bacteria and fungi count is higher than the standard limit.

To determine the likelihood of exposure to harmful chemical agents such as benzene or furfural resulting from surgical fumes or formaldehyde in pathological laboratories, the exposure probability was scored after doing the measurement and comparison with standard limits. Besides, the probability of exposure to blood-borne pathogens and possible pathogens in the patients’ body fluids in this method and in the present study (as suggested by the experts) needed to be always ‘High’ because the blood of all patients admitted to the hospital could not be analyzed for the presence or absence of blood pathogens. Table 1 presents the scales and ranks of each exposure level.

Table 1

Risk rating table for risk criteria

Risk criteria
	Description	Risk rating regardless of the criterion weight	Risk rating given the criterion weight (PoE × 0.391)
Possibility of Exposure (PoE) criteria	Exposure rate lower than 50% of standard limit	2	0.782
	50%–100% of standard limit	3	1.173
	Above standard	5	1.955
	Description	Risk rating regardless of the criterion weight	Risk rating given the criterion weight (DE × 0.170)
Duration of Exposure	Once a year	1	0.170
(DoE) criteria	Several times a year	2	0.34
	Short periods several times a month	3	0.51
	2–8 hours on average during shift work	4,5	0.85
	Over 8 hours of exposure (within overtime and shiftwork)	6,7	1.19
	Description	Risk rating regardless of the criterion weight	Risk rating given the criterion weight (SC × 0.439)
Severity of Consequences (SoC) criteria	Exposure at this level does not harm the personnel	1	0.439
	Health effects are reversible and not a threat to one’s life	2	0.878
	Undesirable health effects that are permanent or temporary but have little effect on one’s quality of life and life expectancy	3	1.317
	Health effects that result in moderate limitation or disability and thus can cause a change in one’s job and lifestyle	4,5	2.195
	Health effects that are usually permanent and can significantly decrease quality of life or life expectancy	6,7	3.073

Duration of exposure: The exposure duration criterion was set at 5 levels: ‘Very low’, ‘Low’, ‘Medium’, ‘High’, and ‘Very high’ (exposure for over 3 working hours during overtime and shiftwork) (Table 1). Long-term exposure to hazardous occupational factors at the shiftwork of the healthcare institutions staff could aggravate exposure effects and increase the risk of health effects as well.

Severity of consequence: The severity of consequence criterion was considered at very low, low, medium, high and very high levels. Table 1 presents the value and rank of each severity of consequence level.

Step 2: Determining the weights of the criteria using a fuzzy analytical hierarchy process

In this study, the weights of the risk criteria were calculated using the fuzzy analytical hierarchy process (FAHP). FAHP is one of the multi-criteria decision-making methods [21] that enables simple and easy paired comparisons and weighting of the criteria [22]. In general, it is difficult to estimate experts’ opinions with precise numerical values under uncertainty, as decision-making results are strongly dependent on imprecise and ambiguous subjective judgments, thus necessitating the use of fuzzy logic in multi-criteria decision-making techniques [23,24]. For these reasons, the use of fuzzy multi-criteria decision-making methods has recently increased [25]. There are several ways to apply the fuzzy analytical hierarchy process [26]. The FAHP proposed by Chang was used in the present research. In this method, a matrix is formed and the criteria are compared two by two by an expert, and the scoring is done based on a verbal scale (Table 2). In the next step, the verbal scale is replaced with its corresponding fuzzy numbers. If there was more than one expert, the geometric mean would be used to aggregate the experts’ opinions [27]. After aggregating the experts’ opinions, the weights of the criteria were calculated using the FAHP process, and the fuzzy mean was converted to definite weights and normalized through the defuzzification method. In the present study, 10 experts’ opinions were used for weighting the criteria. Table 3 shows the weights of the criteria. For more information on the implementation of the FAHP method and the carried out computations, see Bashikçi et al. and Chang et al. [24,27].

Phase 2: Determining the risk scoring system and risk level

The risk score is determined by the combination of risk factors. When there are three or four risk factors, the risk scoring system must be carefully considered and selected. The ANSI Z590.3 as a guideline for addressing occupational hazards and risks recommended that in cases three factors were used, the risk number could be calculated by multiplying the factors. However, Manuele stated that when the results were obtained by multiplying severity, probability, and frequency of exposure, severity would reduce to 1 at the final risk score, and the risk level would be calculated with less precision [11].

Table 2

Verbal scale and corresponding triangular fuzzy numbers

Verbal scale	Corresponding triangular fuzzy number
Absolutely important	(9, 9, 9)
Intermediate	(7, 8, 9)
Very important	(6, 7, 8)
Intermediate	(5, 6, 7)
Important	(4, 5, 6)
Intermediate	(3, 4, 5)
Weak	(2, 3, 4)
Intermediate	(1, 2, 3)
Equally important	(1, 1, 1)

Table 3

Weight of risk criteria

Risk criteria	Fuzzy weight			Normal weight	Rank
	L	M	U
PoE	0.2495	0.3575	0.5064	0.391	2
DE	0.1870	0.2564	0.3575	0.170	3
SC	0.2758	0.3861	0.5400	0.439	1

If the risk scoring system proposed by ANSI Z590.3 is used and the specified criteria weights are considered, the risk formula will be as follows: $\begin{eqnarray}\displaystyle \text{Risk Number}=(0.391\times PoE)\times (0.170\times DoE)\times (0.439\times SoC). & & \displaystyle\end{eqnarray}$ (1) However, if the risk scoring system proposed by Manuele is used and the specified criteria weights are taken into account, the risk formula will be as follows: $\begin{eqnarray}\displaystyle \text{Risk Number}=((0.391\times PoE)+(0.170\times DoE))\times (0.439\times SoC). & & \displaystyle\end{eqnarray}$ (2) Both scoring systems were used in this study and the results were compared. In this semi-quantitative method, 5 risk levels (low, medium, significant, high and very high) were considered to describe the results.

If combining different levels of the three risk criteria, we would have 75 scenarios and 75 risk numbers. According to the scenarios and based on the experts’ experiences, the risk levels were determined as shown in Table 4. After designing this semi-quantitative method, a case study was conducted to validate it.

Table 4

Risk levels

Color	Calculated risk	Risk levels (risk classification)	Recommended response
Green	<2	Low risk	To manage by routine procedures
Yellow	2 < RN < 3	Medium risk	Action plan to be developed and implemented within 6 months
Orange	3 < RN < 4	Substantial risk	Action plan to be developed and implemented within 4 months
Brown	4 < RN < 5	High risk	Existing workplaces/processes
Red	RN ≥ 5	Very high risk	Short-term measures must be quickly implemented to minimize risks while longer-term urgent action plan must be developed and implemented within 2 months to manage the risks according to the Hierarchy of Controls:
			1. Elimination of hazard
			2. Substitution of hazard
			3. Engineering control of hazard
			4. Administrative control of hazard
			5. Personal protective equipment to control exposure to hazard
			New workplaces/processes
			Control measures to mitigate consequence must be reviewed according to the Hierarchy of Controls prior to implementation

Phase 3: Case study implementation

In this phase, we validated the proposed method. One of the five university hospitals was randomly selected and a case study was performed. The selected hospital had several wards and 600 active beds. To identify and assess the potential hazards, various units were identified and assessed after the identification of the study scope and objectives team formation. The steps are presented in the following section.

Step 1: Establishing the context and creating an assessment team

The assessment scope and objectives were first determined in this study. The scope and objectives of risk assessment, also known as risk context, needed to be identified. This step was very important since it determined the direction, tone, and expectations of the project. Once the context was determined, a cross-functional group comprising the people familiar with the process under assessment who had sufficient knowledge of risk assessment and hazard identification was established. To create a positive collaboration, the subjects were selected from inside and outside of the organization. In this study, the team consisted of 6 people, including 3 health and safety experts and 3 hospital accreditation officers, all of whom were aware of the assessment scope and objectives and were also able to communicate with each other in order to focus on the assessment process and pursue its goals.

Step 2: Identifying unit hazards

The most important stage of managing workplace risks is hazard identification, without which risk assessment and control will be impossible. There are several ways to identify hazards, but a systematic approach is more reliable and comprehensive. It is not possible to assess the risks threatening all the individuals exposed to; so it is necessary to identify the groups with the same exposure rate. In this study, observational or qualitative methods were used to identify the groups with the same exposure. All the important tasks performed by the people of similar jobs were first determined to more accurately identify the hazards threatening those at risk of exposure. The hospital e-tool developed by the OHSA was then used to identify the hazards and prepare a preliminary list. The list was completed by interviewing and discussing with section supervisors. In the end, the expert team reviewed the information gathered and finalized the completed list of possible major hazards through brainstorming.

Step 3: Performing risk assessment

Once the list of hazards was completed, the assessment team used Table 1 to score each criterion. The risk score and level of each hazard were determined for all units using Formulas (1) and (2).

4. Results

In the present study, a semi-quantitative method was provided to the healthcare institutions and was used to calculate the risk level of each hospital unit. The results of the case study showed that the semi-quantitative method could be well used to assess the risk levels of different hospital units. The use of measured data along with experts’ knowledge would lead to improved subjective judgments of exposure probability. Furthermore, using weighted criteria could greatly improve the results of risk analysis based on the proposed method compared to other assessment approaches.

In this research, the hazards related to emergency departments, laundries, labs, clinical departments, operating rooms, ICUs, and isolation rooms were identified and assessed based on the mentioned process. Table 5 shows the identified hazards and the risk number for each. The risk numbers were calculated and compared using formulas (1) and (2), proposed by ANSI and Manuele, respectively. Considering the number of the assessed hazards in the 6 hospital units (n = 43), we concluded that in 90.69% of the cases, the risk number calculated by ANSI’s proposed scoring system was lower than the one by Manuele, and in 9.31% of the cases the risk numbers calculated by the two scoring systems were the same. According to the results, chemical and biological factors had a higher rank.

Table 5
Risk number values for different unit hazards

Units Hazard PoE DoE SoC Risk number based on Manuele method Risk level Risk number based on ANSI Z590.3 Risk level

Emergency department Blood-borne pathogens 1.955 1.19 2.195 6.903 Very high 5.107 High

Bodily fluids 1.955 1.19 2.195 6.903 Very high 5.107 High

Air-borne pathogen (Bacillus sp bacteria) 1.955 1.19 1.317 4.142 High 3.064 Substantial

Air-borne pathogen (Staphylococcus bacteria) 1.955 1.19 1.317 4.142 High 3.064 Substantial

Air-borne pathogen (Mold) 1.955 1.19 1.317 4.142 High 3.064 Substantial

Operating room hazards Isoflurane (anesthetic gas) 1.173 0.51 1.317 2.217 Medium 0.788 Low

1,3-butadiene 1.955 0.51 3.073 7.575 Very high 3.064 Substantial

Benzene 1.955 0.51 3.073 7.575 Very high 3.064 Substantial

Furfural 1.955 0.51 2.195 5.411 Very high 2.189 Medium

Blood-borne pathogens 1.955 0.51 2.195 5.411 Very high 2.189 Medium

Bodily fluids 1.955 0.51 2.195 5.411 Very high 2.189 Medium

Air-borne pathogen (Bacillus sp bacteria) 1.955 0.51 1.317 3.246 Substantial 1.313 Low

Air-borne pathogen (Staphylococcus bacteria) 1.955 0.51 1.317 3.246 Substantial 1.313 Low

Air-borne pathogen (Mold) 1.955 0.51 1.317 3.246 Substantial 1.313 Low

ICUs and isolation room hazards Blood-borne pathogens 1.955 0.85 2.195 6.157 Very high 3.648 Substantial

Bodily fluids 1.955 0.85 2.195 6.157 Very high 3.648 Substantial

Air-borne pathogen (Bacillus sp bacteria) 1.955 0.85 1.317 3.694 Substantial 2.189 Medium

Air-borne pathogen (Staphylococcus bacteria) 1.955 0.85 1.317 3.694 Substantial 2.189 Medium

Air-borne pathogen (Mold) 1.955 0.85 1.317 3.694 Substantial 2.189 Medium

Clinical services hazards Blood-borne pathogens 1.955 0.85 2.195 6.157 Very high 3.648 Substantial

Air-borne pathogen (Bacillus sp bacteria) 1.955 0.85 1.317 3.694 Substantial 2.189 Medium

Air-borne pathogen (Staphylococcus bacteria) 1.955 0.85 1.317 3.694 Substantial 2.189 Medium

Air-borne pathogen (Mold) 1.955 0.85 1.317 3.694 Substantial 2.189 Medium

X-ray 1.955 0.85 2.195 6.157 Very high 3.648 Substantial

Units	Hazard	PoE	DoE	SoC	Risk number based on Manuele method	Risk level	Risk number based on ANSI Z590.3	Risk level
Emergency department	Blood-borne pathogens	1.955	1.19	2.195	6.903	Very high	5.107	High
	Bodily fluids	1.955	1.19	2.195	6.903	Very high	5.107	High
	Air-borne pathogen (Bacillus sp bacteria)	1.955	1.19	1.317	4.142	High	3.064	Substantial
	Air-borne pathogen (Staphylococcus bacteria)	1.955	1.19	1.317	4.142	High	3.064	Substantial
	Air-borne pathogen (Mold)	1.955	1.19	1.317	4.142	High	3.064	Substantial
Operating room hazards	Isoflurane (anesthetic gas)	1.173	0.51	1.317	2.217	Medium	0.788	Low
	1,3-butadiene	1.955	0.51	3.073	7.575	Very high	3.064	Substantial
	Benzene	1.955	0.51	3.073	7.575	Very high	3.064	Substantial
	Furfural	1.955	0.51	2.195	5.411	Very high	2.189	Medium
	Blood-borne pathogens	1.955	0.51	2.195	5.411	Very high	2.189	Medium
	Bodily fluids	1.955	0.51	2.195	5.411	Very high	2.189	Medium
	Air-borne pathogen (Bacillus sp bacteria)	1.955	0.51	1.317	3.246	Substantial	1.313	Low
	Air-borne pathogen (Staphylococcus bacteria)	1.955	0.51	1.317	3.246	Substantial	1.313	Low
	Air-borne pathogen (Mold)	1.955	0.51	1.317	3.246	Substantial	1.313	Low
ICUs and isolation room hazards	Blood-borne pathogens	1.955	0.85	2.195	6.157	Very high	3.648	Substantial
	Bodily fluids	1.955	0.85	2.195	6.157	Very high	3.648	Substantial
	Air-borne pathogen (Bacillus sp bacteria)	1.955	0.85	1.317	3.694	Substantial	2.189	Medium
	Air-borne pathogen (Staphylococcus bacteria)	1.955	0.85	1.317	3.694	Substantial	2.189	Medium
	Air-borne pathogen (Mold)	1.955	0.85	1.317	3.694	Substantial	2.189	Medium
Clinical services hazards	Blood-borne pathogens	1.955	0.85	2.195	6.157	Very high	3.648	Substantial
	Air-borne pathogen (Bacillus sp bacteria)	1.955	0.85	1.317	3.694	Substantial	2.189	Medium
	Air-borne pathogen (Staphylococcus bacteria)	1.955	0.85	1.317	3.694	Substantial	2.189	Medium
	Air-borne pathogen (Mold)	1.955	0.85	1.317	3.694	Substantial	2.189	Medium
	X-ray	1.955	0.85	2.195	6.157	Very high	3.648	Substantial

Table 5 (Continued).

Units	Hazard	PoE	DoE	SoC	Risk number based on Manuele method	Risk level	Risk number based on ANSI Z590.3	Risk level
Lab hazards	Formaldehyde	1.955	0.85	2.195	6.157	Very high	3.648	Substantial
	Toluene	1.173	0.85	2.195	4.440	High	2.189	Medium
	Xylene	0.782	0.85	2.195	3.582	Substantial	1.459	Low
	Blood-borne pathogens	1.955	0.85	2.195	6.157	Very high	3.648	Substantial
	Air-borne pathogen (Bacillus sp bacteria)	1.955	0.85	1.317	3.694	Substantial	2.189	Medium
	Air-borne pathogen (Staphylococcus bacteria)	1.955	0.85	1.317	3.694	Substantial	2.189	Medium
	Air-borne pathogen (Mold)	1.955	0.85	1.317	3.694	Substantial	2.189	Medium
	Bleach or sodium hypochlorite	1.955	0.85	2.195	6.157	Very high	3.648	Substantial
	Methanol	0.782	0.85	1.317	2.149	Medium	0.875	Low
	Ethanol	0.782	0.85	1.317	2.149	Medium	0.875	Low
Laundry hazards	Blood-borne pathogens	1.955	1.19	2.195	6.903	Very high	5.107	Very High
	Bodily fluids	1.955	1.19	2.195	6.903	Very high	5.107	Very High
	Air-borne pathogen (Bacillus sp bacteria)	1.955	1.19	1.317	4.142	High	3.064	Substantial
	Air-borne pathogen (Staphylococcus bacteria)	1.955	1.19	1.317	4.142	High	3.064	Substantial
	Air-borne pathogen (Mold)	1.955	1.19	1.317	4.142	High	3.064	Substantial
	Noise	1.173	1.19	1.317	3.112	Substantial	1.838	Low
	Excessive heat	1.173	1.19	0.878	2.075	Medium	1.226	Low
	Poor lighting	0.782	1.19	0.878	1.731	Low	0.817	Low
	Bleach or sodium hypochlorite	1.955	1.19	2.195	6.903	Very high	5.107	Very high

The risk of exposure to blood-borne pathogens and the pathogens found in the patients’ body fluids and hospital air and also the risk of adverse effects of exposure to benzene, furfural, 1,3-butadiene, sodium hypo chloride, formaldehyde, toluene, and x-rays were high and substantial. So it was necessary to take appropriate control measures to modify the risks. Figure 2 shows the comparisons between the 4 different units in terms of risk levels. At very high and high risk levels, paraclinical units, operating rooms, ICUs, isolation rooms, and emergency rooms had higher risk levels respectively and needed to receive priority in terms of control measures and risk reduction. A histogram of the percentage of risk frequencies in terms of risk rating levels was also depicted (Fig. 3). Using a histogram can be very helpful in making better decisions about resource allocation to control and reduce risks. Figure 3 shows that in the hospital studied, 2.32% of the risks were at a low level, 9.30% were at a medium level, 30.23% were at a significant one, and 16.27% and 41.86% were at high and very high levels, respectively.

Fig. 2.

Comparison of different risk levels in the different hospital units.

Fig. 3.

Frequency percentage of risk levels in the hospital.

5. Discussion

According to this study, medical wards, especially paraclinical wards and operating rooms, were found to be very important for risk assessment, the main reason for which was the presence of hazardous materials and equipment and the high probability of personnel and patients exposure to biological agents and harmful radiation. Among the hazardous factors, chemical and biological ones were in a higher rank. These results are consistent with the ones obtained by Omidvari et al. One of the challenges in periodic health risk assessments of hospitals is the lack of a sensitive, valid, and simple method [28]. In this study, various methods were examined to develop a semi-quantitative method, and the following steps were taken to develop it:

1. Increasing the sensitivity of the model by considering the weight of risk criteria.

In order to assess safety hazards in the study by Taherkhani et al., the risk criteria were weighted using the FAHP process. They suggested that using the weight of risk criteria could improve the assessment results [29]. This was not taken into account in the study by Samantra et al. and the relative importance of each criterion was considered equal. In this regard, it is not in line with the present study [30]. In addition, the ICMM qualitative model and the semi-quantitative HRR method did not consider the relative importance of risk criteria in calculating the overall risk level either [15,17].

2. Considering the results of the assessments and measurements carried out to determine the exposure probability parameter for more accurate decision making and scoring by evaluators.

In this study, in order to less rely on the experts’ subjective judgments and calculate the risk levels more precisely, measurements of the exposure probability along with the experts’ comments were used. Healthcare institutions are highly sensitive due to their type of activities. Hazards such as airborne pathogens and other biological hazards can affect not only the staff but the patients as well. Therefore, risk levels need to be more carefully calculated and control measures must be taken more severely. Direct measurements or the use of measurement documentation may provide more detailed information on the probability of exposure to a hazardous factor and do not have a subjective nature. Thus, the risk levels will be calculated more accurately. In the study by Samantra et al., to calculate the exposure probability, the standard limit of exposure to hazardous factors was taken into account, the results of which were in line with those of the present study [30]. In the ICMM qualitative method and the HRR semi-quantitative method, the standard exposure limit was taken to calculate the exposure probability [15,17]. Expert comments need to be taken into account in addition to the standard limit to determine the probability of exposure, because although the composition and concentration of bioaerosols in the air are measured when performing risk assessment, they are highly unstable in the air. So their underlying concentration is constantly changing. Surface sampling data are short-lived and there is little data on their cumulative doses. In addition, there is no way to have all bioaerosols sampled properly.

3. Examining the risk scoring system in semi-quantitative methods, comparing them, and selecting an appropriate scoring system.

To calculate the risk levels, Samantra et al. used exposure consequence, exposure probability, and duration of exposure criteria. In this regard, their study is in line with the present one, but using the ANSI Z590.3 scoring system, Samantra et al. multiplied the three risk factors to calculate the risk number [30]. The ANSI Z590.3 proposed that the risk number could be calculated through criteria multiplication. But if the results were obtained by multiplying severity, probability and frequency of exposure, the severity criterion would be discounted in the final risk score, and the risk level would be calculated less accurately. In this study, the risk level was calculated using this scoring system and the results showed that severity was discounted in the final risk score. In other words, in 90.69% of cases, the risk number calculated by the proposed ANSI Z590.3 scoring system was lower than the one by Manuele. Furthermore, if there were three criteria in the equation regardless of their weights, each would have a weight of 33% of the final risk score and the risk level would be calculated [11].

Like the Australian and Romanian models, the ICMM has a wide range and is suitable for risk assessment of chemical, physical and dust hazards. The scope of this study was not only chemical and physical hazards, but biological ones were also taken into account. In this regard, the present method is not consistent with other models.

6. Limitations and recommendations for future research

The present study aimed to develop and apply a semi-quantitative model to calculate risk levels with regard to the hazards and sensitivities of the healthcare institutions. The results showed that this method could be used as a suitable tool for assessing occupational health risks but it should be noted that in this method and other health risk assessment ones, there might always be parameter- and modeling- related uncertainties or those related to the non-holistic nature of analyses. In such conditions, the risk level may not be calculated as accurately and in accordance with actual conditions. Further studies are needed to assess a health risk method with higher accuracy in uncertainty conditions. The present study did not consider ergonomic and psychological risks because the effects of psychological hazards are very different from other ones and there is a wide range of individual abilities to give physiological responses to psychological hazards. Therefore, it is necessary to use other methods for such risks. It has also been suggested to use the performed measurements when calculating the exposure probability but measurement processes may be costly to the organization. However, since healthcare institutions have high income and their sensitivity is also higher than that of other industries, the process of monitoring risk factors and assessing health risks must be continually performed to reach acceptable risk levels.

7. Conclusion

The proposed semi-quantitative method can be used as a tool for assessing occupational health risks as a key element of risk management. In addition, focusing on an appropriate framework for occupational health risk assessment will enable OHS specialists in organizations to take significant and effective steps to implement an effective and efficient risk management system.

Footnotes

Acknowledgements

The authors thank the Occupational Health Department, Research Deputy of Iran University of Medical Sciences, Student Research and Ethical Committee (Code of ethics IR.IUMS.FMD.REC 1398.9511139002).

Conflict of interest

The authors declare that there is no conflict of interest.

Funding

The study received no funding.

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