Prediction of medical device performance using machine learning techniques: infant incubator case study

Measurement of non-invasive cardiac output during cycling exercise in ischemic stroke inpatients: A pilot study

BACKGROUND

Cardiac dysfunction accompanies acute ischemic stroke and affects the effective implementation of early rehabilitation interventions. There is a lack of reference hemodynamic data on cardiac function in the subacute phase of ischemic stroke.

OBJECTIVE

In this study, we aimed to identify appropriate cardiac parameters for exercise training utilizing a pilot study.

METHODS

We used a transthoracic electrical bioimpedance non-invasive cardiac output measurement (NICOM) device to monitor cardiac function in real time for two groups [i.e., subacute ischemic stroke inpatients group (n= 10) and healthy control group (n= 11)] using a cycling exercise experiment. The parameters of both groups were compared to highlight the cardiac dysfunction in the subacute phase in patients with ischemic stroke.

RESULTS

We considered stroke volume index (SVI) and systemic vascular resistance index (SVRi) as the primary outcomes, and there was significant intragroup difference (stroke group: P< 0.001; control group: P< 0.001, using one-way ANOVA) and significant intergroup difference at each individual time segment (P< 0.01, using independent t-test). Among the secondary outcomes, i.e., cardiac index (CI), ejection fraction (EF), end-diastolic volume (EDV), and cardiac contraction index (CTI), we found significant intergroup differences in CI, EF, and CTI scores (P< 0.01, using independent t-test). Significant interaction with respect to time and group were seen only in the SVRi and CI scores (P< 0.01, using two-way ANOVA). There was no significant inter- or intra-group differences in EDV scores.

CONCLUSION

SVRI, SVI, and CI values highlight cardiac dysfunction in stroke patients the most. At the same time, these parameters suggest that cardiac dysfunction in stroke patients may be closely related to the increased peripheral vascular resistance caused by infarction and the limitation of myocardial systolic function.

Effect evaluation of new dressing URGOTULRANGE in the treatment of pressure injury

BACKGROUND

Pressure injury (PI) is a local injury of the skin and/or soft tissue located at the bone caused by medical or other equipment and is common in long-term bedridden patients.

OBJECTIVE

To investigate the clinical effect of Urgotul foam dressing in the treatment of stage 3 ∼ 4 PI and deep tissue PI.

METHODS

A total of 38 patients with stage 3 ∼ 4 PI and deep tissue PI admitted to Jinan Central Hospital from January 2016 to December 2018 were selected and randomly divided into a control group (dressing change plus silver ion cream dressing) and an observation group (dressing change plus Urgotul Absorb non-border foam dressing), with 19 cases in each group. After 4 weeks of treatment, the pain intensity during dressing change and the treatment efficacy for PI wounds were compared between the two groups.

RESULTS

There were no differences in gender (P= 0.740), age (P= 0.130), single wound area (P= 0.673), consultation department (P= 0.972), stage (P= 0.740), presence of undermining (P= 0.721), deep tissue PI (P= 0.721), and systemic antibiotic therapy (P= 1.000) between the two groups, which were comparable. The treatment effect of the observation group was better than that of the control group (P= 0.003), and the pain score of the observation group was lower than that of the control group (P< 0.001).

CONCLUSION

Urgotul Absorb non-border foam dressing has a good effect in the treatment of stage 3 ∼ 4 PI and deep tissue PI and can relieve patients' pain, and is thus worth promoting.

Application of a rehabilitation management strategy based on symptom management theory in postoperative functional exercises in patients with lower extremity arteriosclerosis obliterans

BACKGROUND

Lower extremity arteriosclerosis obliterans (ASO) is the most common occlusive disease of the peripheral blood vessels.

OBJECTIVE

To explore the application effect of symptom management-based rehabilitation strategy in postoperative functional exercises in patients with lower extremity ASO.

METHODS

The researchers selected 136 patients that underwent lower extremity ASO surgery for the first time in their department from January to September 2020. Patients were divided into a control group (n= 68) and an experimental group (n= 68). The control group implemented routine discharge rehabilitation education and continuous nursing. On this basis, the experimental group applied the symptom management theory to the rehabilitation management strategy to compare the degree of pain, the ankle-brachial index, self-care ability and quality of life between the two groups before and after the intervention.

RESULTS

Three months (P= 0.045) and six months (P=0.013) after discharge, the experimental group's degree of pain was significantly lower than that of the control group. At one month (P= 0.019), three months (P= 0.003) and six months (P= 0.000) after discharge, the experimental group recovered significantly better than the control group. At six months after discharge, the self-care ability, mood status and physical pain of the experimental group were significantly higher than in the control group (P< 0.05).

CONCLUSION

The rehabilitation management strategy, which is based on symptom management theory, can effectively improve the symptoms, quality of life and self-efficacy of ASO patients in continuous care. This nursing strategy is worthy of clinical promotion.

Treatment prediction with machine learning in prostate cancer patients

There are various treatment modalities for prostate cancer, which has a high incidence. In this study, it is aimed to make predictions with machine learning in order to determine the optimal treatment option for prostate cancer patients. The study included 88 male patients diagnosed with prostate cancer. Independent variables were determined as Gleason scores, biopsy, PSA, SUVmax, and age. Prostate cancer treatments, which are dependent variables, were determined as hormone therapy(n = 30), radiotherapy(n = 28) and radiotherapy + hormone therapy(n = 30). Machine learning was carried out in the Python with SVM, RF, DT, ETC and XGBoost. Metrics such as accuracy, ROC curve, and AUC were used to evaluate the performance of multi-class predictions. The model with the highest number of successful predictions was the XGBoost. False negative rates for hormone therapy, radiotherapy, and radiotherapy + hormone therapy treatments were, respectively, 12.5, 33.3, and 0%. The accuracy values were computed as 0.61, 0.83, 0.83, 0.72 and 0.89 for SVM, RF, DT, ETC and XGBoost, respectively. The three features that had the greatest influence on the treatment model prediction for prostate cancer with XGBoost were biopsy, Gleason score (3 + 3), and PSA level, respectively. According to the AUC, ROC and accuracy, it was determined that the XGBoost was the model that made the best estimation of prostate cancer treatment. Among the variables biopsy, Gleason score, and PSA level are identified as key variables in prediction of treatment.

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 mechanical ventilators for post-market surveillance purposes

BACKGROUND

Mechanical ventilators are medical devices used in intensive care units when patients are in need of mechanical aid to facilitate the process of breathing. As the function of breathing is the exchange of gases, the mechanical ventilator takes over that function while the patient is incapable to spontaneous breathing. As these devices are used to maintain the life of patents, their performance must be ensured and there cannot be significant deviations in the volumes and pressure of gases they introduce to the patient. 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 mechanical ventilators for post-market surveillance purposes.

METHOD

The method was developed on the basis of metrology characteristics of mechanical ventilators and evaluation of their vital safety and performance parameters. In addition to the evaluation of essential safety and visual integrity of mechanical ventilators, their performance in terms of volume of oxygen delivered to the patient as well as the flow and pressure of the delivered gas is evaluated.

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 mechanical ventilators as a method used during PMS contributes to significant improvement in devices' accuracy and reliability.

CONCLUSION

A standardized approach in conformity assessment testing of mechanical ventilators 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.

A novel method for conformity assessment testing of patient monitors for post-market surveillance purposes

BACKGROUND

Patient monitors are medical devices used to monitor vital parameters such as heart rate, respiratory rate, blood pressure, blood oxygen saturation, and body temperature during inpatient treatment. As such, patient monitors provide physicians with information necessary to adjust the treatment as well as evaluate the overall status and recovery of the patient. Measurements made by intrinsic sensors of patient monitors must be compliant and provide reliable readings in order to ensure safety and optimal quality of care to the patients.

OBJECTIVE

This paper proposes a novel method for conformity assessment testing of patient monitors in healthcare institutions for post-market surveillance purposes.

METHOD

The method was developed on the basis of metrology characteristics of sensors used to monitor vital parameters observed by patient monitors and evaluation of their vital safety and performance parameters. In addition to the evaluation of essential safety and visual integrity of patient monitors, their performance in terms of accuracy of the readings is evaluated.

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 patient monitors as a method used during PMS contributes to significant improvement in devices' accuracy and reliability.

CONCLUSION

A standardized approach in conformity assessment testing of patient monitors 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.

A novel method for conformity assessment testing of dialysis machines for post-market surveillance purposes

BACKGROUND

Dialysis machines are used regularly in healthcare practice. They are classified as a type of medical device with moderate and high risk therefore significant requirements are placed on their safety and performance every time they are used on patients. 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 evidence-based method for conformity assessment testing of dialysis machines for post-market surveillance purposes.

METHOD

The novel method is developed according to the International Organisation of Legal Metrology (OIML) guidelines and is to be used for the purpose of conformity assessment testing of Dialysis machines with respect to their metrological characteristics during PMS.

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 dialysis machines as a method used during PMS contributes to significant improvement in devices' accuracy and reliability.

CONCLUSION

A standardized approach in conformity assessment testing of dialysis machines 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.

A novel method for conformity assessment testing of infusion and perfusion pumps for post-market surveillance purposes

BACKGROUND

Introduction of fluids, medicaments and nutrients into the human body during hospitalization is fundamental for treatment and healing of patients. Fluids are introduced by means of infusion pumps while nutrients and medicaments are introduced by perfusion pumps. It is of vital importance for these devices to deliver exact amounts of the aforementioned substances as significant deviations can result in severe patient harm. Therefore it is important to effectively monitor their performance and prevent failures.

OBJECTIVE

This paper proposes a novel method for conformity assessment testing of infusion and perfusion pumps for post-market surveillance purposes.

METHOD

The method was developed on the basis of metrology characteristics of the devices. In addition to the evaluation of essential safety and visual integrity of infusion and perfusion pumps, their performance in terms of delivered volumes was assessed and monitored.

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 infusion and perfusion pumps as a method used during PMS contributes to significant improvement in devices' accuracy and reliability.

CONCLUSION

A standardized approach in conformity assessment testing of infusion and perfusion pumps 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.

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

BACKGROUND

Monitoring cardiac parameters is the fundamental aspect of every diagnostic process and is facilitated by electrocardiography (ECG) devices. This way, continuous state-of-the-art performance of ECG devices can be ensured. 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 ECG devices for post-market surveillance purposes.

METHOD

The method was developed on the basis of International Organisation of Legal Metrology (OIML) guidelines and applied in healthcare institutions from 2018 to 2021.

RESULTS

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

CONCLUSION

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

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.

A novel method for conformity assessment testing of anaesthesia machines for post-market surveillance purposes

BACKGROUND

Anaesthesia machines, as moderate to high-risk medical devices intended for use on patients during surgical procedures must be safe and reliable with traceable performance every time they are used in healthcare practice. 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 evidence-based method for conformity assessment testing of anaesthesia machines for post-market surveillance purposes.

METHOD

The novel method is developed according to the International Organisation of Legal Metrology (OIML) guidelines and is to be used for the purpose of conformity assessment testing of anaesthesia machines with respect to their technical and metrological characteristics during PMS.

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 anaesthesia machines as a method used during PMS contributes to significant improvement in devices' accuracy and reliability.

CONCLUSION

A standardized approach in conformity assessment testing of anaesthesia machines 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.

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

BACKGROUND

Defibrillators are medical devices (MDs) used in the most critical situations, hence their performance must be ensured at all times. This requires defibrillators to be subjected to regular performance assessments after they have been placed on the market. 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 evidence-based method for conformity assessment testing of defibrillators.

METHOD

The proposed method is developed in accordance with the International Organisation of Legal Metrology (OIML) guidelines and is intended to be used for conformity assessment testing of defibrillators for post-market surveillance purposes.

RESULTS

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

CONCLUSION

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

A novel method for conformity assessment testing of therapeutic ultrasounds for post-market surveillance purposes

BACKGROUND

Therapeutic ultrasounds are medical devices used for treatment of conditions such muscle spasms, joint contractures or general muscle pain. Their function relies in the delivery of ultrasonic pulses that generate heat in tissue thus relieving the symptoms of aforementioned conditions. Accuracy of the delivered pulses directly affects the quality and effectiveness of the treatment and has to be ensured throughout the utilization of the therapeutic ultrasound in practice. 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 therapeutic ultrasounds for post-market surveillance purposes.

METHOD

The method was developed based on metrology characteristics of therapeutic ultrasounds and includes visual, electrical safety and performance inspections of therapeutic ultrasounds to ensure that both safety and treatment reliability are achieved.

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 therapeutic ultrasounds as a method used during PMS contributes to significant improvement in devices' accuracy and reliability.

CONCLUSION

A standardized approach in conformity assessment testing of therapeutic ultrasounds 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.

Machine learning based orthodontic treatment planning for mixed dentition borderline cases suffering from moderate to severe crowding: An experimental research study

BACKGROUND

Pedodontists and general practitioners may need support in planning the early orthodontic treatment of patients with mixed dentition, especially in borderline cases. The use of machine learning algorithms is required to be able to consistently make treatment decisions for such cases.

OBJECTIVE

This study aimed to use machine learning algorithms to facilitate the process of deciding whether to choose serial extraction or expansion of maxillary and mandibular dental arches for early treatment of borderline patients suffering from moderate to severe crowding.

METHODS

The dataset of 116 patients who were previously treated by senior orthodontists and divided into two groups according to their treatment modalities were examined. Machine Learning algorithms including Multilayer Perceptron, Linear Logistic Regression, k-nearest Neighbors, Naïve Bayes, and Random Forest were trained on this dataset. Several metrics were used for the evaluation of accuracy, precision, recall, and kappa statistic.

RESULTS

The most important 12 features were determined with the feature selection algorithm. While all algorithms achieved over 90% accuracy, Random Forest yielded 95% accuracy, with high reliability values (kappa = 0.90).

CONCLUSION

The employment of machine learning methods for the treatment decision with or without extraction in the early treatment of patients in the mixed dentition can be particularly useful for pedodontists and general practitioners.

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.

Cost supervision mining from EMR based on artificial intelligence technology

BACKGROUND

To effectively monitor medical insurance funds in the era of big data, the study tries to construct an inpatient cost rationality judgement model by designing a virtuous cycle of inpatient cost supervision information system and exploring a complete set of inpatient cost supervision methods.

OBJECTIVE

To lay the foundation for applying artificial intelligence (AI) technology in medical insurance cost control supervision and provide feasible paths and available tools for medical insurance cost control managers.

METHODS

By way of collecting and cleaning electronic medical record (EMR) data from 2016 to 2018 of a city in East China, focusing on basic patient information and cost information, and using a combination of machine learning modeling and information system construction, the study tries to form a feasible inpatient cost supervision method and operation path.

RESULTS

The set of the regulatory method, applied in nursing homes of a city in East China, is compelling. The accuracy rates of rationality judgement in different main diseases are stable up to 80%, the false positive rate is steady within 10%, and rehabilitation fee days of hospitalization, and the number of complications are important factors affecting the rationality of the inpatient cost.

CONCLUSION

The model construction and optimization method combining machine learning and information system can make practical cost rationality judgement on medical institution's inpatient cost data, which can directly reflect the key influencing factors of relevant inpatient costs, and achieve the effect of guiding medical behavior and improving the efficiency of medical insurance fund use.

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.

Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

The advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations' expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment's preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment's preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.

A Systematic Review of Medical Equipment Reliability Assessment in Improving the Quality of Healthcare Services

Medical equipment highly contributes to the effectiveness of healthcare services quality. Generally, healthcare institutions experience malfunctioning and unavailability of medical equipment that affects the healthcare services delivery to the public. The problems are frequently due to a deficiency in managing and maintaining the medical equipment condition by the responsible party. The assessment of the medical equipment condition is an important activity during the maintenance and management of the equipment life cycle to increase availability, performance, and safety. The study aimed to perform a systematic review in extracting and categorising the input parameters applied in assessing the medical equipment condition. A systematic searching was undertaken in several databases, including Web of Science, Scopus, PubMed, Science Direct, IEEE Xplore, Emerald, Springer, Medline, and Dimensions, from 2000 to 2020. The searching processes were conducted in January 2020. A total of 16 articles were included in this study by adopting Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA). The review managed to classify eight categories of medical equipment reliability attributes, namely equipment features, function, maintenance requirement, performance, risk and safety, availability and readiness, utilisation, and cost. Applying the eight attributes extracted from computerised asset maintenance management system will assist the clinical engineers in assessing the reliability of medical equipment utilised in healthcare institution. The reliability assessment done in these eight attributes will aid clinical engineers in executing a strategic maintenance action, which can increase the equipment's availability, upkeep the performance, optimise the resources, and eventually contributes in providing effective healthcare service to the community. Finally, the recommendations for future works are presented at the end of this study.