Clinical and economic justification for managerial decisions on replacing corroded dental instruments

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INTRODUCTION

Infection control within health care organizations remains one of the most critical challenges in contemporary health care. This multifactorial task encompasses technical and organizational components, as well as the influence of individual biological characteristics of patients [1–5]. Given the large cohort of dental patients in outpatient settings—environments with high exposure to airborne viral and bacterial pathogens—it is essential to anticipate the potential for cross-transmission among all individuals present. Any breach of infection prevention and control protocols contributes to outbreaks of health care–associated infections (HAIs)²³⁴ [6, 7]. According to the World Health Organization, these infections impose the greatest annual socioeconomic burden across all countries⁵ [8–11]. The cumulative global cost amounts to hundreds of billions of dollars [12, 13].

Effective mitigation of HAIs is achievable only through prevention based on validated and approved protocols for chemical disinfection, pre-sterilization cleaning, and sterilization of instruments. These protocols require strict adherence to infection control regulations within health care facilities. The specific procedures for chemical disinfection and sterilization depend on the profile of medical care and the operational characteristics of the dental organization¹²³ [14, 15].

Chemical disinfection is an effective and low-cost antimicrobial measure. However, over time, pathogenic microorganisms develop resistance to newly introduced disinfectants, which is typically countered by increasing solution concentrations and implementing more rigorous disinfection protocols. Deviations from established disinfection procedures must also be noted. At the same time, it is well known that chemical disinfectants are toxic and require strict adherence to handling instructions by personnel using them [11, 16]. Taken together, these factors compromise the integrity of dental instrument surfaces, leading to corrosion and rust, which prevent adequate repeat disinfection and ultimately necessitate disposal of the instruments [16–20].

Corroded instruments are difficult to clean thoroughly of organic and inorganic contaminants. Subsequent chemical disinfection and autoclave sterilization of such instruments cannot achieve complete, reliable microbial decontamination [14–16].

In various industrial sectors, anticorrosive and surface-strengthening coatings are widely used and may effectively restore corroded dental instruments [18–22]. A notable example includes metal–ceramic coatings produced via electrospark alloying of dental instruments using TiC–NiCr–based SHS electrodes [17]. These coatings enhance microhardness and corrosion resistance of treated surfaces.

In recent years, advanced sterilization methods using supercritical and subcritical carbon dioxide have been actively investigated to achieve high-level decontamination without compromising the physicochemical properties of instruments. These methods have demonstrated effective pathogen inactivation and material preservation [23–25]. TiC–NiCr metal–ceramic anticorrosive coatings that are resistant to aggressive disinfectants are considered a promising solution for increasing service life and economic efficiency [26].

AIM

The work aimed to provide a clinical and economic rationale for restoring corroded dental instruments rather than disposing of them.

METHODS

The innovative decision-making model for managing replacement of corrosion-affected dental instruments integrates a mathematical framework, an algorithm, and software implemented in the Python programming environment [27]. The software enables users, through an intuitive graphical interface, to enter initial parameters and calculate the maximum economically justified restoration cost as a percentage of the instrument’s original value (see Fig. 1).

 

Fig. 1. User-interface dialog window for determining the maximum restoration cost for dental instruments.

 

As shown in Fig. 1, the input parameters include disposal costs (expressed as a percentage of the instrument’s original value), depreciation or salvage value reduction (likewise expressed as a percentage of original value), and the instrument’s service life extension following restoration. Managerial decisions regarding replacement are based on the following mathematical model:

$\frac{\Delta P\left(t\right)+Y\left(t\right)}{T}\le \frac{P\left(0\right)+\Delta P\left(0\right)+Z\left(t\right)}{\tau }$

where ΔP(t) is the reduction in salvage value of an instrument of age t when transitioning from step t to t+1 (rubles); Y(t) is the restoration cost of an instrument of age t (rubles); T is the service life after restoration (procedures); P(0) is the purchase price of a new instrument (rubles); ΔP(0) is the reduction in salvage value of a new instrument when transitioning from step t to t+1 (rubles); Z(t) is the disposal cost of an instrument of age t (rubles); τ is the standard service life of a new instrument (days).

When developing this decision-making model for replacing corrosion-affected dental instruments, we assumed that decisions regarding continued use, restoration, or replacement are based on visual inspection by a designated commission, results of corrosion-loss testing, and the premise that restoration extends service life while reducing salvage value.

Thus, in this work, the optimality criterion for managerial decision-making regarding instrument replacement is defined as the total cost per unit of additional service life gained after restoration. This total cost includes expenditures associated with restoration—specifically, application of a strengthening anticorrosive coating—and the reduction in salvage value of the instrument.

RESULTS AND DISCUSSION

The practical implementation of the innovative decision-making model developed in the Python programming environment for managing replacement of corrosion-affected dental instruments is illustrated in Fig. 2.

 

Fig. 2. Practical implementation of the innovative managerial decision-making model for replacing corrosion-affected dental instruments in the Python programming environment.

 

For the user-defined inputs (see Fig. 2), the output generated according to formula (1) is shown in Fig. 3.

 

Fig. 3. Output of the software used to determine the maximum allowable restoration cost for dental instruments.

 

According to the modeling results, the user should opt for restoration if the restoration cost does not exceed 49.5% of the instrument’s original value (see Fig. 3).

If disposal costs amount to 60% of the instrument’s original value, the reduction in salvage value is 40%, and the post-restoration service life increases by 300%, then restoration is economically justified—provided that restoration costs do not exceed 560.0% of the instrument’s original value (see Fig. 4).

 

Fig. 4. Output of the Python-based decision support software.

 

Fig. 5 demonstrates software functional testing in Python when negative values are entered (Fig. 5, a) and when incorrect or illogical data are provided (see Fig. 5, b), along with an analysis of the outputs.

 

Fig. 5. Software functional testing: a, negative input values; b, incorrect user-entered data; c, analysis of the outputs.

 

As shown in Fig. 5, negative values entered by the user do not impair software operability. However, when clearly illogical or absurd data are entered, the software displays an error message prompting the user to provide valid inputs, as illustrated in Fig. 5, b.

The proposed software allowed for clinical and economic justification and prioritization of managerial decisions regarding the replacement of corrosion-affected dental instruments. Ranking was based on restoration cost and salvage value expressed as percentages of the instrument’s original value, as well as service life extension after restoration expressed as a percentage of the standard service life (see Supplement 1).

The clinical and economic feasibility matrix for restoring corrosion-affected dental instruments, presented in Supplement 1, enables managerial decision-making on instrument replacement based on the following parameters: disposal costs; restoration costs and reduction in salvage value expressed as percentages of the instrument’s original value; and service life extension following restoration expressed as a percentage of the standard service life.

For example, if disposal costs represent 10% of the original value, restoration costs equal 80%, and the reduction in salvage value is estimated at 15%, then restoration is advisable provided that the post-restoration service life increases by at least 76% of the standard service life. This can be seen in row 14 of Table 1. Similarly, the clinical and economic feasibility of managerial decisions regarding replacement of corrosion-affected instruments can be determined for other combinations of parameters and conditions of subsequent instrument use.

Fig. 6 presents the outputs of the Python-based decision support software, which corroborate the findings described above.

 

Fig. 6. Output of the Python-based decision support software.

 

Analysis of Supplement 1 and the results generated by the developed software show that for end users (dental practices), restoration of dental instruments—when it yields a substantial extension of subsequent service life—is economically more advantageous than purchasing new instruments. Supplement 1 specifies the maximum allowable restoration costs, expressed as a percentage of the purchase price of a new instrument P(0). These values define the upper restoration-cost threshold at which restoration remains more cost-effective than replacement based on the decision criterion derived from formula (1).

CONCLUSION

An innovative managerial decision-making model was developed that incorporates a mathematical framework represented by formula (1); an algorithm; Python-based software; and a clinical and economic feasibility matrix for restoring corrosion-affected dental instruments. The optimality criterion for such managerial decision-making was formulated.

A decision problem was formulated and solved to determine whether corrosion-affected dental instruments should be replaced, based on a comparison between: (1) total restoration costs using electrospark alloying with SHS electrodes, normalized per unit of service life and including both restoration expenses and the reduction in salvage value; and (2) total costs of purchasing new instruments, including the purchase cost, depreciation during the initial decision period, and disposal costs for corroded instruments.

The developed software and the clinical and economic feasibility matrix presented in Supplement 1 demonstrate that restoration of dental instruments—when it substantially extends their subsequent service life—is economically more advantageous than purchasing new corrosion-prone instruments.

The Python-based software enables users to determine the economic feasibility of restoring an instrument for any combination of restoration costs, salvage value, disposal costs, and post-restoration service life extension, in accordance with the established decision-making criterion. Moreover, the software identifies threshold parameter values that influence the managerial decision once the relevant input factors are known.

ADDITIONAL INFORMATION

Appendix 1. Matrix of clinical and economic feasibility for restoring corroded dental instruments

doi: 10.17816/dent686560-4363816

Author contributions: S.N. Kerasov, M.S. Galstyan: writing―original draft; E.V. Kostyrin, P.M. Bazhin: conceptualization; S.A. Arutyunov: investigation. All the authors approved the final version of the manuscript for publication and agreed to be accountable for all aspects of the work, ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Ethics approval: Not applicable.

Funding sources: No funding.

Disclosure of interests: The authors have no relationships, activities, or interests for the last three years related to for-profit or not-for-profit third parties whose interests may be affected by the content of the article.

Statement of originality: No previously published material (text, images, or data) was used in this work.

Data availability statement: All data generated during this study are available in this article.

Generative AI: No generative artificial intelligence technologies were used to prepare this article.

Provenance and peer review: This paper was submitted unsolicited and reviewed following the standard procedure. The peer review process involved two external reviewers, a member of the editorial board, and the in-house scientific editor.

 

¹ Order of the Ministry of Health of the Russian Federation No. 1108n of November 29, 2021, “On Approval of the Procedure for Conducting Preventive Measures, Identifying and Recording Cases of Health Care–Associated Infections in a Medical Organization, and the Nomenclature of Health Care–Associated Infectious Diseases Subject to Identification and Recording in a Medical Organization.” Available at: https://base.garant.ru/403336489/. Accessed on: April 27, 2025.

² SanPiN 3.3686–21 “Sanitary and Epidemiological Requirements for the Prevention of Infectious Diseases” (approved by Resolution of the Chief State Sanitary Physician of the Russian Federation No. 4 of January 28, 2021). Available at: https://docs.cntd.ru/document/573660140. Accessed on: April 27, 2025.

³ Federal Law of the Russian Federation No. 492-FZ of December 30, 2020, “On Biological Safety in the Russian Federation.” Available at: http://www.consultant.ru/document/cons_doc_LAW_372000/. Accessed on: April 27, 2025.

⁴ Federal Service for the Oversight of Consumer Protection and Welfare of the Russian Federation. On Measures to Improve the Prevention of Health Care–Associated Infections. Available at: https://www.rospotrebnadzor.ru/about/info/news/news_details.php?ELEMENT_ID=7576. Accessed on: April 27, 2025.

⁵ World Health Statistics 2024. In: World Health Organization [Internet]. 2024. Available at: https://whodc.mednet.ru/en/main-publications/epidemiologiya-i-statistika/4063.html. Accessed on: April 27, 2025.

About the authors

Stefan N. Kerasov

Russian University of Medicine; Bauman Moscow State Technical University

Email: stenley007@mail.ru
ORCID iD: 0009-0004-3144-2781
SPIN-code: 7994-8670
Russian Federation, Moscow; Moscow

Evgeny V. Kostyrin

Bauman Moscow State Technical University

Email: mauntain76@mail.ru
ORCID iD: 0000-0003-2569-1146
SPIN-code: 1012-2883

Dr. Sci. (Economics), Associate Professor

Russian Federation, Moscow

Mariam S. Galstyan

Russian University of Medicine

Author for correspondence.
Email: galstyan_mariam@mail.ru
ORCID iD: 0000-0002-3372-5775
SPIN-code: 3814-7044
Russian Federation, 5 Kedrova st, Moscow, 117292

Sergey A. Arutyunov

Russian University of Medicine

Email: sa.arutyunov@rambler.ru
ORCID iD: 0009-0005-7605-5715
SPIN-code: 4682-1730
Russian Federation, 5 Kedrova st, Moscow, 117292

Pavel M. Bazhin

Peoples’ Friendship University of Russia

Email: bazhin@ism.ac.ru
ORCID iD: 0000-0003-1710-3965
SPIN-code: 8117-0070

Dr. Sci. (Engineering), Professor

Russian Federation, Moscow

References

Supplementary files