Evaluation of Healthcare Waste Treatment Methods Using Multiple Criteria ^†

Abstract

Hospitals generating waste daily need to identify efficient, cost-effective, and sustainable alternatives to conventional disposal methods. Therefore, we formulated a mathematical model to evaluate emerging healthcare waste treatment (HCW) methods by developing a multiple-criteria framework. The framework enables an evidence-based evaluation that supports better HCW management-related decisions in resource-limited settings. Results found that ozone is the optimal method for hospitals with a waste profile mostly made up of infectious and pathological wastes. Methods such as promession, for stricter environmental priorities, and superheated steam, for better performance on all evaluation criteria, may be preferred if hospital priorities shift.

1. Introduction

In the Philippines, healthcare waste (HCW) management remains a pressing issue as waste generation has increased significantly, particularly since the COVID-19 pandemic [1]. Metro Manila alone produces approximately 47 metric tons of medical waste per day, with 56% classified as potentially infectious [2]. This issue has made the country’s limited infrastructure and weak regulatory enforcement more ineffective compared with other nations’ robust systems [3]. To address this gap, the study evaluates emerging treatment methods, namely, plasma, ozone, superheated steam, and promession, as sustainable alternatives to traditional systems. This study will apply operations research tools to evaluate these emerging HCW treatment methods based on multiple evaluation criteria.

2. Literature Review

2.1. Healthcare Wastes

Referred to as waste generated by healthcare facilities, research establishments, and laboratories, HCWs pose a serious worldwide hazard, especially in developing countries in which there are limited proper disposal resources. Although 75 to 90% of HCWs are non-hazardous, the remaining present serious health risks to patients, workers, and nearby communities [4]. Comprising about 15% of total waste, hazardous HCWs include sharp, infectious, pathological, pharmaceutical, cytotoxic, and chemical waste [5].

2.2. Healthcare Waste Treatment Methods

Currently, HCW treatment practices still rely on common methods such as autoclaving, microwave treatment, chemical disinfection, and incineration. Each has its respective advantages and limitations, such as being incompatible in treating certain waste types, producing harmful emissions, and demanding higher operational costs [6]. Furthermore, because stricter environmental regulations are being implemented, switching to emerging waste treatment methods is considered. These include plasma, which does not generate harmful emissions when decomposing but is energy intensive and costly, and ozone, which is a safe and environmentally friendly method but needs strict exposure control. Another is the superheating method, which, although it does not use chemicals during waste degradation and is energy efficient, lacks practical use in healthcare [7]. Promession is also used as a quick decomposition process for anatomical waste [8].

2.3. Evaluation Methods

Decisions between HCW treatment methods consider multiple factors beyond functionality, including environmental impacts, expenses, safety, and practicality. Multiple assessment tools have been developed to aid decision-making. Cost–benefit analysis is prevalent but neglects health and environmental impacts, and while life cycle assessment has wider perspectives despite its drawbacks with complex data and systems, it is still effectively applied [9,10]. Sustainability assessment technologies consider technical, social, and economic attributes but are highly reliant on expert opinions [11]. Multi-criteria decision analysis provides systematic tradeoffs but is limited by subjectivity [12].

3. Methodology

Through a review of the related literature, we defined the problem and identified emerging HCW treatment alternatives. This led to the selection and weighting of evaluation criteria, which guided data collection and parameter setting. These steps preceded the formulation, solution, and validation of the mathematical model, which served as the basis for the study’s conclusions and recommendations.

3.1. Evaluation Criteria

Evaluation criteria were selected based on the literature review, highlighting key dimensions for assessing the performance of HCW treatment methods.

3.1.1. Environmental Impact and Social Acceptability

Environmental performance is one consideration in HCW management, because methods must minimize ecological harm. Social acceptability is equally relevant because technology must be aligned with societal values and address the problem of noise, odor, and visual impact to garner the support of the public and long-term acceptance [11]. Fuzzy set qualitative comparative analysis is used to attribute six environmental influence indicators and eight indicators of social acceptability, with 0–1 scores [13]. The authors identified five equally weighted environmental influence indicators, pollution control, resource efficiency, emissions management, ecological impact, and regulatory compliance, and four equally weighted indicators of social acceptability, cultural acceptability, community acceptance, health and safety perception, and transparency and feedback. Equally weighted indicators scored 0.00, 0.33, 0.50, 0.67, or 1.00, and the weighted average was calculated thereafter (Table 1).

Table 1. Environmental impact and social accessibility.

Environmental Impact Score	Social Accessibility Score	Description
0.75–1.00	0.71–1.00	Most indicators are met
0.34–0.74	0.34–0.70	Some indicators show adequate performance, but improvements are needed
0.17–0.33	0.17–0.33	Many conditions are not being adequately met
0.00–0.16	0.00–0.16	Major shortcomings with little to no compliance

3.1.2. Energy Consumption, Handling Safety, and Space Consumption

Energy consumption is an important factor in HCW treatment, as it affects both cost and environmental impact [11,12]. One study supplied benchmark data on electricity consumption across different methods, including incineration, plasma melting, and steam or microwave sterilization with landfill, as shown in Table 2 [14].

Table 2. Energy consumption, handling safety, and space consumption rubric.

Score	Energy Consumption (kWh/ton)	Handling Safety	Space Consumption (m2)
1.0	0 to 100	Minimal emissions, no chemical use, no sharps, heat, microbial, or radiation exposure	Less than 12
0.9	100 to 150	Minimal emissions and chemical use, no sharps, heat, microbial, or radiation exposure	12 to 16
0.8	150 to 200	Minimal emissions and chemical use, minimal sharps, heat, microbial, or radiation exposure	16 to 20
0.7	200 to 250	Moderate emissions, minimal chemical use, minimal sharps, heat, microbial, or radiation exposure	20 to 24
0.6	250 to 300	Moderate emissions and chemical use, minimal sharps, heat, microbial, or radiation exposure	26 to 30
0.5	300 to 350	Moderate emissions and chemical use, moderate sharps, heat, microbial, or radiation exposure	30 to 40
0.4	350 to 400	Hazardous emissions, moderate chemical use, moderate sharps, heat, microbial, or radiation exposure	40 to 50
0.3	400 to 450	Hazardous emissions and chemical use, moderate sharps, heat, microbial, or radiation exposure	50 to 60
0.2	450 to 500	Hazardous emissions and chemical use, hazardous sharps, heat, microbial, or radiation exposure	60 to 80
0.1	500 to 550	Extremely hazardous emissions and chemical use, hazardous sharps, heat, microbial, or radiation exposure	80 to 100
0.0	At least 550	Does not meet safety standards	Needs expansion

Guaranteeing the safety of hospital staff and surrounding communities during HCW treatment is also essential. Research highlights the necessity of assessing risks such as chemical exposure and operational hazards [11]. The safety rubric in Table 2 incorporates additional risks identified by previous research, including microbial, chemical, thermal, radiation, and sharp-related hazards, and evaluates how effectively each treatment method protects human health [15].

Since methods such as incineration and plasma demand large spaces for equipment and safe operation, space is another critical factor for hospitals with inadequate infrastructure [6]. This study adopts a facility type A–2 design to develop a generalizable rubric for space requirements, scoring treatment methods from 1.0 to 0.0 [16].

3.2. Optimization Model

3.2.1. Objective Function

The objective function is used to improve the score of each HCW treatment method. It figures out the best option by adding up the method’s performance on evaluation criteria and its effectiveness on different types of waste. Here, Z is the total score in relation to choosing a certain treatment method; $X_i$ is the treatment method i; P is the proportion of priority for waste compatibility; $R_w$ is the proportion of waste type w; $E_{iw}$ is the effectiveness of treatment method i in treating waste type w; $W_j$ is the weight for criterion j; and lastly, $A_{ij}$ is evaluation score of method i with respect to criterion j. The term $X_i$ ensures that only the selected treatment method contributes to the final score. The equation captures the total score generated by the treatment method’s effectiveness and its performance on the evaluation criteria, while the objective function seeks to maximize this value. Ultimately, this formulation identifies the HCW treatment method that yields the highest combined effectiveness and evaluative performance.

$$Max Z=\sum _i \{X_i\times \left[\left(P\times \sum _w (R_w\times E_{iw})\right)+\left(\left(1-P\right)\times \sum _j (W_j\times A_{ij})\right)\right]\}$$

(1)

3.2.2. Constraints

By setting the sum of the decision variables to 1, the constraint ensures that only one treatment method is chosen. Since $X_i$ is a binary variable, restricting its total to 1 guarantees that only one method is chosen while all others are excluded.

$${\sum }_i X_i=1$$

(2)

The second limit ensures that the cost of the chosen method, including both the implementation cost and the cost of processing all waste types, does not go over the hospital’s budget. In this equation, $N_w$ is the number of kilograms for waste type w; $S_i$ is cost per kilogram to process waste with treatment method i; and B represents the hospital’s budget. The left-hand side of the equation represents the total cost associated with selecting a certain method. This constraint requires that these total costs remain within the available budget, ensuring financial feasibility.

$$\sum _i X_i\times [C_i+(\sum _w N_w\times S_i)] \le B$$

(3)

This constraint ensures that the chosen method is effective and meets the minimum compatibility requirements. Here, $E_{iw}$ is the effectiveness of treatment method i in treating waste type w, and D is the minimum waste compatibility threshold. The left-hand side of the equation captures the overall waste compatibility performance of the chosen treatment method. The right-hand side is the minimum required threshold the chosen method must meet.

$$X_i\times \sum _w (R_w\times E_{iw})\ge X_i\times D ; \forall i$$

(4)

The next limit is on the time it takes to implement the chosen method, where T_i is the implementation time for the method, and $T_{max}$ is the maximum allowable time given by the hospital. The left-hand side captures the total time needed for the selected method. This constraint makes sure that the total time it takes to implement the method does not go over the hospital’s maximum allowed time.

$$X_i\times T_i \le T_{max} ; \forall i$$

(5)

The last constraint guarantees that the treatment method’s capacity to process is adequate to manage the total waste produced by the hospital. Here, $K_i$ is the capacity of the method in processing waste. The left-hand side represents the capacity supplied by the chosen method. The right-hand side is the amount of waste that the hospital has, which serves as the required capacity.

$$\sum _i X_i \ast K_i \ge \sum _w N_w$$

(6)

3.3. Data Source

Hypothetical data were generated through prior studies that reflect realistic values and were used to construct and validate the model. The hypothetical values used for evaluation are shown in Table 3.

Table 3. Effectiveness of methods in treating waste type, waste capacity, and processing cost.

Method		Infectious	Pathological	Pharmaceutical	Cytotoxic	Chemical	Capacity (kg)	Processing Cost (USD/kg)
Ozone	0.1	1.0	0.8	1.0	0.7	0.7	10,800	0.007
Plasma	0.1	0.9	0.7	0.8	0.9	0.8	5000	0.08
Superheated steam	0.8	0.9	0.3	1.0	0.7	0.7	5900	0.33
Promession	0.1	0.8	1.0	0.7	0.1	0.1	4000	0.06

On top of the data provided above, the minimum waste compatibility threshold will be set to 0.75, and the weight of priority on waste compatibility will be set to 0.6. In addition, the hospital budget is set to Php 1,500,000, and the time limit set by the hospital is 24 months. Moreover, the weights that will be set for each criterion, specifically, environmental, energy, handling, space, and social acceptability, will be 0.30, 0.15, 0.25, 0.10, and 0.20, respectively. It is important to note that these thresholds and weights can be adjusted based on the hospital or the government (Table 4 and Table 5).

Table 4. Evaluation score of different methods for the criteria, implementation cost, and time.

Method		Energy	Handling	Space	Social	Cost (USD)	Time
Ozone	0.75	0.4	0.7	0.9	0.75	320,200	10 months
Plasma	0.9	0.2	0.9	0.4	0.65	1,500,000	24 months
Superheated steam	0.75	0.4	1.0	0.9	0.8	500,000	12 months
Promession	0.95	0.5	1.0	0.5	0.5	425,000	18 months

Table 5. Proportion and weight of waste type in hospital.

	Sharp	Infectious	Pathological	Pharmaceutical	Cytotoxic	Chemical
Proportion	0.1	0.4	0.3	0.11	0.05	0.04
Amount (kg)	500	1500	2000	550	250	200

3.4. Model Solution

The model was coded, solved, and validated using the Excel Solver add-in. The use of Excel formulas made the model easy to compute, replicate, and evaluate results based on a criterion.

4. Results and Discussion

We used a multi-criteria optimization model to evaluate four emerging HCW treatment methods: ozone, plasma, superheated steam, and promession, based on waste compatibility and five key criteria: environmental impact, energy use, handling safety, space, and social acceptability. Cost and implementation time were included as hospital constraints. Due to limited access to local hospital data, hypothetical values were used, consistent with prior studies that adopt similar simulation approaches. The values used approximate real-life data based on the literature. This approach aligns with methodologies used in prior healthcare waste management studies, many of which similarly rely on literature-based or assumed values to simulate practical decision-making scenarios [10]. Other values, such as the hospital’s profile, priority weights, and criteria weights, are dependent on the hospital or government. The model identified ozone as the optimal method, achieving a performance score of 0.7618, meeting the 0.75 compatibility threshold, and remaining within budget and time limits, as shown in Table 6. Although promession scored 0.7402, ozone offered better overall balance, especially in waste compatibility, making it the most optimal option. These results also showed how the model enables data-driven decisions between near-equal alternatives.

Table 6. Model results.

Method	Objective Function	Waste Compatibility Score	Total Cost	Implementation Time
Ozone	0.7618	0.803	USD 320,235	10 months

5. Conclusions and Recommendations

We assessed environmental impact, energy use, handling safety, space, and social acceptability under budget, time, and compatibility limitations. The developed model identified ozone as the optimal choice. However, if the hospital’s priorities shift, alternative approaches can be used, such as promession for stricter environmental priorities and superheated steam for better performance across all evaluation criteria. This presents the model’s flexibility in adapting to multiple decision contexts and providing the tradeoffs among the options. Thus, making it a practical tool for hospital decision-making. To improve relevance, future research is necessary to explore the effect of additional factors such as maintenance, repairs, and potential upgrades. It also is necessary to consider a more objective way of evaluating emissions in the rubric.