Testing of mechanical ventilators and infant incubators in healthcare institutions

Effect of additional dead space using end-tidal CO2 measurement on ventilating preterm infants: An experimental study

BACKGROUND

Dead space is the part of the airway where no gas exchange takes place. Any increase in dead space volume has a proportional effect on the required tidal volume and thus on the risk of ventilation-induced lung injury. Inserts that increase dead space are therefore not used in small preterm infants. This includes end-tidal CO2 measurement.

OBJECTIVE

The aim of this study was to investigate the effect of the end-tidal CO2 measurement adapter on ventilation.

METHODS

In an experimental setup, an end-tidal CO2 measurement adapter, three different pneumotachographs (PNT-A, PNT-B, PNT-Neo), and a closed suction adapter were combined in varying set-ups. The time required for CO2 elimination by a CO2-flooded preterm infant test lung was measured.

RESULTS

PNT-A prolonged CO2 elimination time by 0.9 s (+3.3%), Neo-PNT by 3.2 s (+11.6%) and PNT-B by 9.0 s (+32.7%). The end-tidal CO2 measurement adapter prolonged the elimination time by an additional second without the pneumotachograph (+3.6%) and in combination with PNT-A (+3.1%) and PNT-Neo (+3.1%). In conjunction with PNT-B, the end-tidal CO2 measurement adapter reduced the elimination time by 0.3 seconds (-1%). The use of a closed suction adaptor increased the CO2 elimination time by a further second with PNT-Neo (+3.1%) and by an additional two seconds with no flow sensor (+6.9%), with PNT-A (+6.4%) and with PNT-B (+5.5%).

CONCLUSION

The flow sensor had the greatest influence on ventilatory effort, while end-tidal CO2 measurement had only a moderate effect. The increased ventilatory effort levied by the CO2 measurement was dependent on the flow sensor selected. The use of closed suctioning more negatively impacted ventilatory effort than did end-tidal CO2 measurement.

Application of prone position ventilation in ventilation strategies for patients with COVID-19

BACKGROUND

Patients with coronavirus disease 2019 (COVID-19) have been shown to die mainly due to disease-induced acute respiratory distress syndrome (ARDS). Prone position ventilation (PPV) is an important ventilation strategy in the management of patients with ARDS.

OBJECTIVE

To investigate the application of PPV in ventilation strategies for patients with COVID-19.

METHODS

Three hundred patients with COVID-19 admitted to the Intensive Care Unit (ICU) of Shanxi Bethune Hospital from January 2020 to June 2021 were retrospectively collected. Based on body position and conscious state, all patients were divided into three groups: intubation prone position group (n= 110), awake prone position group (n= 90) and supine position group (n= 100); The acute physiology and chronic health evaluation II (APACHE-II) scores, blood gas indicators, complications and other relevant clinical indicators were compared among the three groups. One-way ANOVA was used to compare means between multiple groups for quantitative information that conformed to a normal distribution. Repeated measures ANOVA was used for repeated measures data. Component comparisons were made using the Kruskal-Wallis H rank sum test for non-normally distributed quantitative data.

RESULTS

One-way repeated-measures ANOVA main effect analysis showed different effects of different treatments on PaO2 in patients with COVID-19 (F treatment = 256.231, P< 0.05), with the order of awake prone position group > intubation prone position group > supine position group. The effects of the three different treatments on P/F in patients with COVID-19 (F treatment = 311.661, P< 0.05), with the order of awake prone position group > supine position group > intubation prone position group; Moreover, the three treatments had different effects on APACHE II scores in patients with COVID-19 (F treatment = 201.342, P< 0.05), with the order of intubation prone position group > supine position group > awake prone position group.

CONCLUSION

Intubation prone position and awake prone position can improve lung function to some extent in patients with COVID-19, and should be applied as early as possible in patients with COVID-19-induced ARDS.

Predicting medical device failure: a promise to reduce healthcare facilities cost through smart healthcare management

Background

The advancement of biomedical research generates myriad healthcare-relevant data, including medical records and medical device maintenance information. The COVID-19 pandemic significantly affects the global mortality rate, creating an enormous demand for medical devices. As information technology has advanced, the concept of intelligent healthcare has steadily gained prominence. Smart healthcare utilises a new generation of information technologies, such as the Internet of Things (loT), big data, cloud computing, and artificial intelligence, to completely transform the traditional medical system. With the intention of presenting the concept of smart healthcare, a predictive model is proposed to predict medical device failure for intelligent management of healthcare services.

Methods

Present healthcare device management can be improved by proposing a predictive machine learning model that prognosticates the tendency of medical device failures toward smart healthcare. The predictive model is developed based on 8,294 critical medical devices from 44 different types of equipment extracted from 15 healthcare facilities in Malaysia. The model classifies the device into three classes; (i) class 1, where the device is unlikely to fail within the first 3 years of purchase, (ii) class 2, where the device is likely to fail within 3 years from purchase date, and (iii) class 3 where the device is likely to fail more than 3 years after purchase. The goal is to establish a precise maintenance schedule and reduce maintenance and resource costs based on the time to the first failure event. A machine learning and deep learning technique were compared, and the best robust model for smart healthcare was proposed.

Results

This study compares five algorithms in machine learning and three optimizers in deep learning techniques. The best optimized predictive model is based on ensemble classifier and SGDM optimizer, respectively. An ensemble classifier model produces 77.90%, 87.60%, and 75.39% for accuracy, specificity, and precision compared to 70.30%, 83.71%, and 67.15% for deep learning models. The ensemble classifier model improves to 79.50%, 88.36%, and 77.43% for accuracy, specificity, and precision after significant features are identified. The result concludes although machine learning has better accuracy than deep learning, more training time is required, which is 11.49 min instead of 1 min 5 s when deep learning is applied. The model accuracy shall be improved by introducing unstructured data from maintenance notes and is considered the author's future work because dealing with text data is time-consuming. The proposed model has proven to improve the devices' maintenance strategy with a Malaysian Ringgit (MYR) cost reduction of approximately MYR 326,330.88 per year. Therefore, the maintenance cost would drastically decrease if this smart predictive model is included in the healthcare management system.

A novel method for conformity assessment testing of infant incubators for post-market surveillance purposes

BACKGROUND

Premature born infants or infants born sick require immediate medical attention and decreasing the stress imposed onto their body by the environment. Infant incubators provide an enclosed environment that can be controlled to fit the needs of the infant. As such, their performance must be consistent and without significant deviations. The only manner to ensure this is by post-market surveillance (PMS) focused on evaluation of both safety and performance. The new Medical Device Regulation (MDR) defines medical device post-market surveillance (PMS) as performed by independent, third-party, notified bodies more strategically in hope to improve traceability of device performance. However, there is still an apparent gap in terms of standardised conformity assessment testing methods.

OBJECTIVE

This paper proposes a novel method for conformity assessment testing of infant incubators for post-market surveillance purposes.

METHOD

The method was developed based on guidelines for devices providing measurements laid out by the International Organisation of Legal Metrology (OIML). The methodology was validated during a four year period in healthcare institutions of all levels.

RESULTS

The developed method was validated between 2018 and 2021 in healthcare institutions of all levels. The results obtained during validation suggest that conformity assessment testing of infant incubators as a method used during PMS contributes to significant improvement in devices' accuracy and reliability.

CONCLUSION

A standardized approach in conformity assessment testing of infant incubators during PMS, besides increasing reliability of the devices, is the first step in digital transformation of management of these devices in healthcare institutions opening possibility for use of artificial intelligence.

Study on the effective measurement area of an open-ended coaxial probe for the dielectric measurement of biological tissues

BACKGROUND

The dielectric properties of tissues are very important physical factors for the investigation and application of bio-electromagnetism. However, the size of the active sample tissue is usually limited in actual measurement, making it difficult to meet the requirements of the existing high-frequency measurement methods, thus influencing the measurement results.

OBJECTIVE

The present study aimed to systematically investigate the various factors influencing the effective measurement area of the open-ended coaxial probe, including the design size of the probe and the dielectric properties of the object to be measured.

METHODS

The simplified material mixing model, in which several types of materials were set as the material under test (MUT) and the perfect conductor (PEC) was set as the specific material, was used in the simulation to study the effective measurement area of eight types of probes with different sizes for the dielectric measurement of different MUTs. Different concentrations of NaCl solutions and three types of coaxial probes were used in the actual measurement to verify the simulation results.

RESULTS

According to the simulation results, the effective measurement area, especially the effective measurement radius, was closely related to the outer conductor radius of the probe. The effective measurement area of the probe decreased when the outer conductor radius of the probe reduced. Moreover, the change in the effective measurement area of the probe was independent of the MUT when the cross-sectional size of the probe was smaller than a certain threshold value. The experimental results also confirmed this conclusion.

CONCLUSION

According to the research results, the independent variable dimension could be effectively reduced and the modeling difficulty was reduced when the analysis model of the effective measurement area of the probe was established.

Critical Device Reliability Assessment in Healthcare Services

Medical device reliability is the ability of medical devices to endure functioning and is indispensable to ensure service delivery to patients. Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) technique was employed in May 2021 to evaluate existing reporting guidelines on medical device reliability. The systematic searching is conducted in eight different databases, including Web of Science, Science Direct, Scopus, IEEE Explorer, Emerald, MEDLINE Complete, Dimensions, and Springer Link, with 36 articles shortlisted from the year 2010 to May 2021. This study aims to epitomize existing literature on medical device reliability, scrutinize existing literature outcomes, investigate parameters affecting medical device reliability, and determine the scientific research gaps. The result of the systematic review listed three main topics on medical device reliability: risk management, performance prediction using Artificial Intelligence or machine learning, and management system. The medical device reliability assessment challenges are inadequate maintenance cost data, determining significant input parameter selection, difficulties accessing healthcare facilities, and limited age in service. Medical device systems are interconnected and interoperating, which increases complexity in assessing their reliability. To the best of our knowledge, although machine learning has become popular in predicting medical device performance, the existing models are only applicable to selected devices such as infant incubators, syringe pumps, and defibrillators. Despite the importance of medical device reliability assessment, there is no explicit protocol and predictive model to anticipate the situation. The problem worsens with the unavailability of a comprehensive assessment strategy for critical medical devices. Therefore, this study reviews the current state of critical device reliability in healthcare facilities. The present knowledge can be improved by adding new scientific data emphasis on critical medical devices used in healthcare services.

Simple low-cost construction and calibration of accurate pneumotachographs for monitoring mechanical ventilation in low-resource settings

Assessing tidal volume during mechanical ventilation is critical to improving gas exchange while avoiding ventilator-induced lung injury. Conventional flow and volume measurements are usually carried out by built-in pneumotachographs in the ventilator or by stand-alone flowmeters. Such flow/volume measurement devices are expensive and thus usually unaffordable in low-resource settings. Here, we aimed to design and test low-cost and technically-simple calibration and assembly pneumotachographs. The proposed pneumotachographs are made by manual perforation of a plate with a domestic drill. Their pressure-volume relationship is characterized by a quadratic equation with parameters that can be tailored by the number and diameter of the perforations. We show that the calibration parameters of the pneumotachographs can be measured through two maneuvers with a conventional resuscitation bag and by assessing the maneuver volumes with a cheap and straightforward water displacement setting. We assessed the performance of the simplified low-cost pneumotachographs to measure flow/volume during mechanical ventilation as carried out under typical conditions in low-resource settings, i.e., lacking gold standard expensive devices. Under realistic mechanical ventilation settings (pressure- and volume-control; 200–600 mL), inspiratory tidal volume was accurately measured (errors of 2.1% on average and <4% in the worst case). In conclusion, a simple, low-cost procedure facilitates the construction of affordable and accurate pneumotachographs for monitoring mechanical ventilation in low- and middle-income countries.

Post-market surveillance of medical devices: A review

BACKGROUND

Medical devices (MDs) represent the backbone of the modern healthcare system. Considering their importance in daily medical practice, the process of manufacturing, marketing and usage has to be regulated at all levels. Harmonized evidence-based conformity assessment of MDs during PMS relying on traceability of medical device measurements can contribute to higher reliability of MD performance and consequently to higher reliability of diagnosis and treatments.

OBJECTIVE

This paper discusses issues within MD post-market surveillance (PMS) mechanisms in order to set a path to harmonization of MD PMS.

METHODS

Medline (1980-2021), EBSCO (1991-2021), and PubMed (1980-2021) as well as national and international legislation and standard databases along with reference lists of eligible articles and guidelines of relevant regulatory authorities such as European Commission, Food and Drug Administration were searched for relevant information. Journal articles that contain information regarding PMS methodologies concerning stand-alone medical devices. National and international legislation, standards and guidelines concerning the topic.

RESULTS

The search strategy resulted in 2282 papers. Out of those only 24 articles satisfied the eligibility criteria and were finally included in the review. Papers were grouped per categories: medical device registry, medical device adverse event reporting, and medical device performance evaluation. In addition to journal articles, national and international legislation, standards, and guidelines were reviewed to assess the state of PMS in different regions of the world.

CONCLUSION

Although the regulatory framework prescribes PMS of medical devices, the process itself is not harmonized with international standards. Particularly, conformity assessment of MDs, as an important part of PMS, is not measured and managed in a traceable, evidence-based manner. The lack of harmonization within PMS results in an environment of increased adverse events involving MDs and overall mistrust in medical device diagnosis and treatment results.

An additive manufacturing fabricated a split Pitot tube transducer for mechanical ventilator analyzers

Introduction

Frequent calibration of ventilators and anesthesia machines might reduce the risk of death and of sequelae in patients under mechanical ventilation. However, ventilator analyzers might be difficult to purchase due to high cost or even in trade-restrictive scenarios, such as the COVID-19 pandemic. To alleviate this problem, the aim of this article is to present the design and characterization of a Pitot tube transducer fabricated with additive manufacturing (AM), to be used in ventilator analyzers.

Method

A split Pitot tube (SPT) transducer was designed using computer-aided design (CAD) and characterized using finite element method (FEM) simulations. Bernoulli’s equation was used to determine a transducer discharge coefficient. The sensor was fabricated with the acrylonitrile butadiene styrene (ABS) thermoplastic, with the fused deposition modeling after properly configuring the 3D printer.

Results

According to the simulated velocity profile, the transducer does not impose excessive resistance to the flow and the pressure profile revealed that the pressure is constant and stable inside the pressure lines. The characteristic curve of differential pressure (dP) versus flow is quadratic. For the minimal and maximal simulated flows 0.01 to 300 L/min, the corresponding dP values are 6.535·10⁻⁴ Pa and 13.178·10³ Pa.

Conclusion

The split Pitot tube transducer developed here has appropriate characteristics for measuring air flows from mechanical ventilators, and the delivered pressures can be read by commercial electronic sensors. The AM is viable for fabricating the transducer, and the printing time is considerably low compared to that necessary to order a similar part from a sales representative.

Cost-effective vital signs monitoring system for COVID-19 patients in smart hospital

The lack of staffing during COVID-19 pandemic drives hospitals to expand their facilities in non-traditional settings to include centralized communication systems to monitor the vital signs of patients and predictive models to identify their health conditions. In this research, we have developed a microcontroller-based wireless vital signs monitoring system, which is able to measure the body temperature, heart rate, blood oxygen level, respiratory rate and Electrocardiogram of the patients. We managed to obtain a reliable but more affordable vital signs monitor with high mobility that can be implemented in large hospitals. The system satisfies the design considerations of medical centers in terms of size, cost, power consumption and simplicity in implementation. The developed system consists of a set of wearable sensor nodes, wireless communications infrastructure with multiple communications techniques to carry vital data from the patients to the management system that handles the patient’s medical data, and a graphical user interface with a control system that enables the hospital staff to observe the status of all the patients and take the appropriate actions. The system was implemented using 40 sensor nodes, 4 distribution points and one gateway covering a hospital area of approximately 2500 m². The system was tested and the measured percentage of lost packets is found to be less than 3.3% of those sent. During transmission, the current measured from the sensor node was 10.5 mA with a 3.3 V input voltage, which prolonged the operating time of the battery used.

Data automated bag breathing unit for COVID-19 ventilator shortages

BACKGROUND

The COVID-19 pandemic has caused a global mechanical ventilator shortage for treatment of severe acute respiratory failure. Development of novel breathing devices has been proposed as a low cost, rapid solution when full-featured ventilators are unavailable. Here we report the design, bench testing and preclinical results for an 'Automated Bag Breathing Unit' (ABBU). Output parameters were validated with mechanical test lungs followed by animal model testing.

RESULTS

The ABBU design uses a programmable motor-driven wheel assembled for adult resuscitation bag-valve compression. ABBU can control tidal volume (200-800 ml), respiratory rate (10-40 bpm), inspiratory time (0.5-1.5 s), assist pressure sensing (- 1 to - 20 cm H2O), manual PEEP valve (0-20 cm H2O). All set values are displayed on an LCD screen. Bench testing with lung simulators (Michigan 1600, SmartLung 2000) yielded consistent tidal volume delivery at compliances of 20, 40 and 70 (mL/cm H2O). The delivered fraction of inspired oxygen (FiO2) decreased with increasing minute ventilation (VE), from 98 to 47% when VE was increased from 4 to 16 L/min using a fixed oxygen flow source of 5 L/min. ABBU was tested in Berkshire pigs (n = 6, weight of 50.8 ± 2.6 kg) utilizing normal lung model and saline lavage induced lung injury. Arterial blood gases were measured following changes in tidal volume (200-800 ml), respiratory rate (10-40 bpm), and PEEP (5-20 cm H2O) at baseline and after lung lavage. Physiological levels of PaCO2 (≤ 40 mm Hg [5.3 kPa]) were achieved in all animals at baseline and following lavage injury. PaO2 increased in lavage injured lungs in response to incremental PEEP (5-20 cm H2O) (p < 0.01). At fixed low oxygen flow rates (5 L/min), delivered FiO2 decreased with increased VE.

CONCLUSIONS

ABBU provides oxygenation and ventilation across a range of parameter settings that may potentially provide a low-cost solution to ventilator shortages. A clinical trial is necessary to establish safety and efficacy in adult patients with diverse etiologies of respiratory failure.

When a Ventilator Takes Autonomous Decisions without Seeking Approbation nor Warning Clinicians: A Case Series

Background

Complexity and functions of automated medical devices used to support life (eg, ventilators, dialysis machines, monitors, insulin pump with continuous blood glucose monitoring system, etc.) increase over time. Until recently, devices were partially automated by very simple feedback loops, with no or few software dependence (such as the simplest home thermostat). For the last two decades, devices have been increasingly driven by complex algorithms devoted to improve patient's treatment and monitoring as well as users experience.

Methods

We report the unexpected and inappropriate operation of two recent ventilators, associated to potential harmful consequences. We provide both a description of the clinical situations (five ICU patients, archetypal situations) and a test bench analysis.

Results

While set in volume mode, these ventilators activated an algorithm dedicated to limit airway pressure when an increase in airway resistance occurred. In such situations, a pressure-like mode was activated (with decelerating inspiratory flow and set pressure, with target of volume). The main consequences observed were that the tidal volume was no longer guaranteed or delivered and that the pressure limitation operated by the algorithm prevented the airway pressure from reaching the high-pressure alarm threshold.

Conclusion

This led to the silent takeover of commands by the ventilator without clinicians or nurses being aware of it and without any warnings or alarms emission adapted to the severity of the event. Generally speaking, such an algorithm questions the place of automation and its limit when users are not aware of its presence as well as the need for regulation and additional tests before its implementation. Intensivists and respiratory care specialists should remain vigilant regarding the risk of rare but critical events related to unexpected functioning or insufficiently tested equipment during the pre-clinical development phases. They should not neglect misunderstood critical events without having performed sufficient investigations.

Risks of emergency use authorizations for medical products during outbreak situations: a COVID-19 case study

BACKGROUND

The world is facing an unprecedented outbreak affecting all aspects of human lives which is caused by the COVID-19 pandemic. Due to the virus novelty, healthcare systems are challenged by a high rate of patients and the shortage of medical products. To address an increased need for essential medical products, national authorities, worldwide, made various legislative concessions. This has led to essential medical products being produced by automotive, textile and other companies from various industries and approved under the emergency use authorizations or legal concessions of national regulatory bodies. This paper presents a narrative commentary of the available documentation on emergency use authorizations and legal concessions for medical products during COVID-19 pandemic.

METHODOLOGY

The basis for narrative commentary includes scientific articles published in Web of Science, Scopus, PubMed and Embase databases, official publications of international organizations: Food and Drug Agency (FDA), World Health Organisation (WHO), World Bank and United Nations (UN), and national regulatory agency reports in native languages (English, German, Bosnian, and Croatian) published from November 1, 2019 to May 1, 2020. This paper focuses on three types of essential medical products: mechanical ventilators, personal protective equipment (PPE) and diagnostic tests. Evidence-informed commentary of available data and potential identified risks of emergency use authorizations and legal concessions is presented.

DISCUSSION

It is recognized that now more than ever, raising global awareness and knowledge about the importance of respecting the essential requirements is needed to guarantee the appropriate quality, performance and safety of medical products, especially during outbreak situation, such as the COVID-19 pandemic. Emergency use authorizations for production, import and approval of medical products should be strictly specified and clearly targeted from case to case and should not be general or universal for all medical products, because all of them are associated with different risk level.

CONCLUSION

Presented considerations and experiences should be taken as a guide for all possible future outbreak situations to prevent improvised reactions of national regulatory bodies.

An integrated custom decision-support computer aided facility management informative system for healthcare facilities and analysis

This article presents a Computer Aided Facility Management informative system which can output Key Performance Indicators and quantitative parameters about the analysed healthcare facility. The designed system is a self-sufficient application able to manage and analyse digital plans of hospital buildings with no need of third-party plugins or licenses. The system maps hospital’s inner organisation, destinations of use and environmental comforts giving quantitative, qualitative and graphical reports. The core database is linked to other existing hospital databases, so that the system can act as a central control cockpit. Outputs can be used by top-management and decisional staff as a decision-support tool in order to improve hospital’s structure and organisation and to reduce the major workflow risks. Furthermore, many plug-ins and modules have been developed through the years which can be easily linked to the main application thanks to its REST architecture, and which contribute to a complete analysis and management of the healthcare facilities.

Evidence-based medical equipment management: a convenient implementation

Maintenance is a crucial subject in medical equipment life cycle management. Evidence-based maintenance consists of the continuous performance monitoring of equipment, starting from the evidence—the current state in terms of failure history—and improvement of its effectiveness by making the required changes. This process is very important for optimizing the use and allocation of the available resources by clinical engineering departments. Medical equipment maintenance is composed of two basic activities: scheduled maintenance and corrective maintenance. Both are needed for the management of the entire set of medical equipment in a hospital. Because the classification of maintenance service work orders reveals specific issues related to frequent problems and failures, specific codes have been applied to classify the corrective and scheduled maintenance work orders at Careggi University Hospital (Florence, Italy). In this study, a novel set of key performance indicators is also proposed for evaluating medical equipment maintenance performance. The purpose of this research is to combine these two evidence-based methods to assess every aspect of the maintenance process and provide an objective and standardized approach that will support and enhance clinical engineering activities. Starting from the evidence (i.e. failures), the results show that the combination of these two methods can provide a periodical cross-analysis of maintenance performance that indicates the most appropriate procedures.

Graphical abstract

The Outcomes of a Comprehensive Program for Maintenance of Goldmann Applanation Tonometer

PRECIS

We checked 190 tonometers every month and repaired faulty ones. Calibration error (CE) frequency reduced from 23% to 0.6% at 1 year. Tonometers needing one or >1 CE repair differed in survival but not in age.

PURPOSE

The purpose of this study was to report the outcomes of a comprehensive program to maintain calibration status of the Goldmann applanation tonometer.

METHODS

This prospective cohort study was carried out at 2 tertiary eye care referral centers. We included 190 slit-lamp mounted Goldmann applanation tonometers (Model AT 900 C/M). Health care providers (error checking and reporting) and clinical engineers (maintenance) participated. The team carried out CE check once a month, and repair of faulty tonometers, if any, within 24 hours. Failure of tonometer was defined as development of unacceptable CE beyond the third repair. The main outcome measures were the frequency of CE and survival function of the tonometer over 1 year.

RESULTS

The median age of the tonometers was 10.7 (range, 0.2 to 25.1) years. The total number of repairs was 86. The proportion (95% confidence interval) of faulty tonometers reduced from 23.1% (17.7, 29.6) in the first month to 0.6% (0.1, 3.3) at 1 year (P<0.01). The median age of the tonometer did not differ between those needing (n=63, 9.4 y) and not needing (n=127, 10.7 y; P=0.24) repair. All tonometers requiring 1 CE repair (n=49, 25.7%) survived until 1 year. The survival of tonometers requiring >1 CE repair (n=14, 7.3%) was 40% at 1 year.

CONCLUSIONS

Our in-house program maintained 92.6% tonometers error free. Number of repairs rather than age determined the need for replacement/sending back the tonometer to the manufacturer. Our simple and easy to follow maintenance program has the potential for wide application.

Testing of Anesthesia Machines and Defibrillators in Healthcare Institutions

To improve the quality of patient treatment by improving the functionality of medical devices in healthcare institutions. To present the results of the safety and performance inspection of patient-relevant output parameters of anesthesia machines and defibrillators defined by legal metrology. This study covered 130 anesthesia machines and 161 defibrillators used in public and private healthcare institutions, during a period of two years. Testing procedures were carried out according to international standards and legal metrology legislative procedures in Bosnia and Herzegovina. The results show that in 13.84% of tested anesthesia machine and 14.91% of defibrillators device performance is not in accordance with requirements and should either have its results be verified, or the device removed from use or scheduled for corrective maintenance. Research emphasizes importance of independent safety and performance inspections, and gives recommendations for the frequency of inspection based on measurements. Results offer implications for adequacy of preventive and corrective maintenance performed in healthcare institutions. Based on collected data, the first digital electronical database of anesthesia machines and defibrillators used in healthcare institutions in Bosnia and Herzegovina is created. This database is a useful tool for tracking each device's performance over time.