List of possible variables for estimation of probability.
\r\n\tOver the years, the concept of maintenance became more comprehensive, reducing fault occurrence and increasing industrial system availability. Besides, reliability, safety, and criticality requirements were associated with the system or equipment under analysis. Maintenance strategies or schemes can be classified as corrective (run-to-break), preventive (time-based), and predictive (condition-based maintenance). Corrective maintenance is only performed after an occurrence of a fault. Therefore, it involves unexpected breakdowns, high costs, changes in the production chain, and it could lead to catastrophic events. Preventive maintenance and interventions occur based on a scheduled maintenance plan or the equipment's mean time between failures. Although it is more effective than corrective maintenance, unexpected failure may still occur by preventing most failures. Additionally, the process cost is still high, especially the costs associated with labor, inventory, and unnecessary replacement of equipment or components.
\r\n\tOn the other hand, predictive maintenance analyses the equipment condition so that a possible fault can still be identified at an early stage. Predictive maintenance aims to identify a machine anomaly so that it does not result in a fault. Such maintenance involves advanced monitoring, processing, and signal analysis techniques, which are generally performed non-invasively and, in many cases, in real-time. In the case of machines or processes, these techniques can be developed based on vibration, temperature, acoustic emission, or electrical current signal monitoring. It should be noted that monitoring such signals or parameters to verify the operating condition is called condition monitoring. Condition monitoring aims to observe the machine's current operational condition and predict its future condition, keeping it under a systematic analysis during its remaining life. In this sense, a fault condition can be detected and identified from systematic machine condition monitoring. A diagnosis procedure can be established, whereby properly investigating the fault symptoms and prognosis.
\r\n\t
\r\n\tThis book will aim to merge all these ideas in a single volume, aggregate new maintenance experiences, apply new techniques and approaches, and report field experiences to establish new maintenance processes and management paradigms.
\r\n\t
\n\t\t\t\t
As biomedical technology developed towards ever more complex systems and spread in every clinical practice, so the field of
The main aim of
\n\t\t\t\t
Because of the strong pressure on the health structures to optimize the services provided while lowering the associated costs and reducing the likelihood of adverse events, an organizational approach, in which a Healthcare Risk Management program plays a central role, becomes important.
\n\t\t\tMistakes can be minimized, in fact, by creating organizational systems and using technologies to make it easier to do the right thing. It is clear that patient safety can be increased by means of appropriate procedures aimed at avoiding possible mistakes or correcting those that do happen.
\n\t\t\tIn particular, the potential for biomedical equipment related adverse events needs to be analyzed in order to prevent their occurrence: healthcare structures have to use systematic analytical methods and instruments to manage technological risks to both patients and operators.
\n\t\t\tThe aim of the health organizations is to take care of patients, by providing effective, appropriate and, in particular, safe treatments. The healthcare institutions (such as the clinicians themselves) have to ensure the care, as adequate as possible, of patients, avoiding or at least containing damage caused by human and system errors. Healthcare service activities connote, in fact, with the presence of several hazards that have the potential to harm patients and health operators.
\n\t\t\tCurrently the best known approach is the Healthcare Risk Management program, with which it is possible to identify, assess, mitigate and control healthcare facilities risks, and thus realize the concept of “systemic safety”.
\n\t\t\tOriginally such approaches focused mainly, if not entirely, on the problem of reducing the “Clinical Risk” (Clinical Risk Management, CRM) with the aim of limiting enterprise liability-costs. In fact, over the course of the last several years healthcare institutions and practitioners have experienced a "malpractice crisis" that has led to the increase in jury verdicts, settlement amounts and insurance premiums, as well as dwindling insurance availability due to carrier withdrawals from the medical malpractice market (McCaffrey & Hagg-Rickert, 2010), and consequent increase of risk retention cost.
\n\t\t\tGradually, the focus shifted to clinical problems and thus the term CRM now encompasses strategies to reduce the incidence and magnitude of harm and improve the quality of care (Taylor-Adams, et al., 1999) by focusing on patient safety and patient care related issues, including information gathering systems, loss control efforts, professional liability, risk financing and claims management activities.
\n\t\t\tDealing with clinical risk and patient safety means also dealing with biomedical technologies. In fact, as medical treatments have greatly progressed along with the analogous technological advances in medical equipment (ME), all medical procedures depend, to some extent, on technology to achieve their goals. Despite the (presupposed) inherent safety of MEs (also guaranteed by a plethora of laws and technical standards), device-related adverse events occur every day in hospitals around the world. Some can be very dangerous and occasionally even deadly.
\n\t\t\t\tAn adverse event is (as defined by Medicines and Healthcare Products Regulatory Agency, MHRA) “an event that causes, or has the potential to cause, unexpected or unwanted effects involving the safety of device users (including patients) or other persons”. ME related adverse events can occur for several reasons, ranging from incorrect choice and acquisition of the device, wrong installation, and poor maintenance, to use error and device obsolescence.
\n\t\t\t\tAs stated before, a systemic approach is needed. Such an approach, identified as Medical Equipments Risk Management (MERM), is part of the global Technology Management (Wilkins & Holley, 1998) as practised by the Clinical Engineering Department (CED) within the hospital. The specific activities of the MERM process are, as coded by several international standards (AS/NZS 4360:2004\n\t\t\t\t\t; ISO 14971:2007; ISO 31000:2009) regarding risk management applied to general production processes and specifically to the design and production of medical devices (but addressed to manufacturers, not the users, of medical equipment) as follows:
\n\t\t\t\trisk identification
risk analysis and assessment (including risk prioritization);
planning of actions to mitigate the risk;
tracking of information about the implemented actions;
control and follow up.
All the standards stress that the task of risk assessment, along with risk identification,. is the most important element. This is mainly because all the measures the CED (as well as the healthcare organization as a whole) will take to reduce the level of risk will depend on the results of these two phases: an error in assessment would probably lead to several mistakes (and therefore waste of economical and human resources) in the subsequent phases.
\n\t\t\t\tGiven below is a brief description of the methods available for addressing risk analysis and assessment. However, a thorough analysis of the remaining phases is left to the reader, since they require the active involvement of several lines of professionals, and thus are strongly dependent on the organizational and operational arrangements of the specific healthcare facility.
\n\t\t\t\tRisk identification and risk analysis are processes aimed at identifying the type of hazard and determining the potential severity associated with an identified risk and the probability that a harmful event will occur. Together, these factors establish the “seriousness of a risk” and guide the clinical engineer’s choice of an appropriate “risk treatment” strategy (including preventive maintenance, user training, definition of a renewal plan, etc.).
\n\t\t\t\t\tTechniques for risk identification and assessment are various and dependent on the specific kind of hazard under assessment. In the healthcare sector, two techniques are widely and commonly used: Failure Mode and Effects Analysis and Root Cause Analysis.
\n\t\t\t\t\tFailure Mode and Effect Analysis (FMEA) is a systematic process for identifying potential process and technical failures, with the intent to eliminate them or minimize their likelihood, before they occur, that is in advance of the occurrence of the adverse event related to the analyzed risk (American Society for Healthcare Risk Management [ASHRM], 2002). Initiated in the 1940s by the U.S. Defense Department, FMEA was further developed by the aerospace and automobile industries, but it was only in the late 1960s that it was first applied to healthcare processes. Since then, in the healthcare sector, Failure Mode and Effects Analysis has been developed as a systematic, proactive method for evaluating clinical processes to identify where and how they might fail, and to assess the relative impact (in terms of damage to patients, workers and facilities) of different failures in order to identify the parts of the process that are most in need of change.
\n\t\t\t\t\tThe rationale of FMEA is the acknowledgement that errors are inevitable and predictable, and thus can be anticipated and/or minimized by design.
\n\t\t\t\t\tAs suggested by the name, the focus is on the
All potential failure modes of a system and components of the system;
The effects these failures may have on the system and parts of the system;
How to avoid or reduce the probability of the failures, or mitigate the effects of the failures on the system.
Depicted below is a schematic, step-by-step description of how to conduct the FMEA process:
\n\t\t\t\t\tDefine the FMEA topic.
Write a clear definition of the process to be studied.
Narrow the scope of the review so that it is manageable, and the actions are practical and able to be implemented.
Assemble the Team.
Guarantee the multidisciplinarity of the team by including expert representatives of all affected areas.
Identify the team leader/coordinator.
Prepare a graphic description of the process
Create and verify the flow chart.
Number each process step.
For complex processes, specify the area to focus on.
Identify and create a flow chart of the subprocesses.
Conduct a Hazard Analysis
List all possible/potential failure modes for each process/subprocess.
List all the possible causes of the
Determine the “severity (S)”, “probability (P)” and “detectability (D).”
Determine the Risk Priority Number (RPN = S x P x D).
Determine if the failure mode warrants further action (e.g. RPN > 32).
Actions and Outcome Measures
identify actions or strategies to reduce the
The other widely adopted methodology is Root Cause Analysis (RCA) that aims to assess risks affecting healthcare activities by investigating the adverse events which have occurred. RCA is an analytic tool for performing a comprehensive, system-based review of critical incidents. It includes the identification of the root cause and contributory factors, determination of risk reduction strategies, and development of action plans along with measurement strategies to evaluate the effectiveness of the plans (Canadian Patient Safety Institute [CPSI], 2006). Unlike FMEA, which is a proactive and preventive process, RCA is carried out retrospectively in response to a specific, harmful event.
\n\t\t\t\t\tThe main purpose of the RCA is to uncover the factor(s) that led to and caused the serious preventable adverse event. The preventable adverse event is very often the tip of the iceberg. Conducting and writing an RCA is an opportunity to examine how the systems for providing care function. The more areas investigated, the greater the possibility the system(s) will become better functioning and prevent the next event from occurring.
\n\t\t\t\t\tRCA focuses on the “how” and the “why”, not on the “who”. The goals of a root cause analysis are to determine:
\n\t\t\t\t\twhat happened;
why it happened;
what can be done to reduce the likelihood of recurrence.
A step by step description of the RCA may be depicted as follows:
\n\t\t\t\t\tPlan of action
strategies the organization intends to implement in order to reduce the risk of similar events occurring in the future.
responsibility for implementation, supervision, pilot testing as appropriate, time lines, and strategies for measuring the effectiveness of the actions.
What happened / Facts of the event
Information about the patient
Details of the event
Use of interviews, brain storming, or written description, etc.
Why it happened
Individuate the contributory factors
Identify root causes
Identification of the “Root Causes”
Minimize recurrence/monitoring
Implementation of each specific action that will be measured and communicated
The final goal of both methodologies is to address the commitment of healthcare organizations to reduce the likelihood or severity of adverse events. However, besides their technical, practical and philosophical differences, both present a major fault/drawback when applied to the specific case of medical equipment risk assessment. In fact, the methods themselves require some form of subjective assessment, mainly due to lack of quantitative data on which the assessment could be based. Moreover, to assess the risk related to the entire biomedical technological assets of healthcare facilities would certainly require a more systematic and structured method for collecting and processing data.
\n\t\t\t\t\tA possible solution to this problem could be an adapted implementation of the Risk Map or Risk Matrix (Ruge, 2004; Cox, 2008). A
The figure 1 shows an example of risk matrix, where probability and severity have been split into a range of five values, whereas risk level is categorized into three classes.
\n\t\t\t\t\tThus, the risk assessment problem can be reduced to the estimate of probability and severity values. The estimate of severity does not present any particular concerns: by analyzing equipment design and features (such as, also, the FDA or CE risk classification), device user manual, clinical procedure and medical room in which the ME is used, it should be easy to determine the maximum possible damage the ME could do to the patient (or even to the operator). Moreover, such elements can be easily described by specifically defined numeric variables (for instance, all the considered aspects can be assigned values ranging from 1 to 5, in analogy with the main Risk Matrix axe values) and recorded in the equipment management system used by the CED. Lastly, by defining a computation method (whose complexity can vary from a very simple linear sum up to more complex fuzzy or neural network systems) the severity value can be associated to each ME owned by the healthcare facility.
\n\t\t\t\t\tHowever, the achievement of a robust, objective estimate of probability definitely presents more difficulties. In particular, it would be preferable to take into account only measurable characteristics, thus using easily quantifiable numeric variables.
\n\t\t\t\t\tExample of Risk Matrix
The complexity of estimating probability stems from the fact that probability is dependent on three main different but inter-influenced issues: human factor, medical device functional reliability, medical device design and environmental characteristics (Brueley, 1989; Anderson, 1990; Dillon, 2000; FDA, 1997; FDA, 2000; Samore, et al., 2004). So, estimating the probability value must take into account the evaluation of these three elements. In estimating the human factor element, one must take into account not only those characteristics of the ME, of the process and/or of the environment that may facilitate a human error leading to an adverse event, but also the factors that may make the operator take corrective action for a ME or system failure. The ME functional reliability refers to the potential for device (material and/or functional) failure, potentially leading to an adverse event. Aspects to be considered are those related to the device reliability assessment such as the execution of safety checks, assessment of device obsolescence, and respect of a preventive maintenance plan. Medical device design and environmental characteristics are those related respectively to the possibility of the ME having specific features that could lead to an adverse event without the occurrence of material or functional failure or human error, and to the presence of environmental factors that could cause the ME to fail.
\n\t\t\t\t\tWhen defining the elements to be analyzed on each ME owned by the hospital, two considerations apply:
\n\t\t\t\t\tDefine measurable variables more quantitatively.
Prefer elements (variables) already monitored by the organization and recorded in an information system (such as the ME management system used by the CED)
\n\t\t\t\t\t\tTable 1 shows an example of variables for probability estimation.
\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t
Availability (at point of use) of complete written instructions (e.g., user manual) from the manufacturer | \n\t\t\t\t\t\t\t\tDevice obsolescence | \n\t\t\t\t\t\t\t\tAppropriateness of wiring according to clinical activities and devices | \n\t\t\t\t\t\t\t
Device ergonomics | \n\t\t\t\t\t\t\t\tExistence and respect of a preventive maintenance plan | \n\t\t\t\t\t\t\t\tEnvironmental conditions (noise, temperature, vibrations, electromagnetic interference, etc.) | \n\t\t\t\t\t\t\t
Difficult working conditions (staff shortage, staff shifts, etc.) | \n\t\t\t\t\t\t\t\tResults of safety checks (cfr. IEC or ISO or EN safety standards) | \n\t\t\t\t\t\t\t\tThe device is appropriate for the clinical needs for which it is intended | \n\t\t\t\t\t\t\t
Environmental conditions (noise, temperature, lighting, space, etc.) | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t |
Schedule and records of a training and education program on the use of specific ME and its related risks | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t |
List of possible variables for estimation of probability.
As is done for estimating severity, the last step consists of defining a computation method to elaborate the identified variables. Also, in this case the complexity of the method may vary from a very simple linear sum up to more complex fuzzy or neural network systems.
\n\t\t\t\tNowadays many factors, ranging from the aging of population to the continuous fast-paced technology innovation, as well as the even more critical scarcity of economic resources, emphasize the importance of correct resource allocation at every level of a national health care system. This background adds to the criticality and complexity of decision-making, rendering essential a thorough evaluation which takes into consideration all the areas (health benefits, risks, costs, etc.) where health technology may have an impact..
\n\t\t\tA variety of specific methods and tools are available to support health care and medical decision making, for example Health Technology Assessment (HTA), a standardized methodology that can help decision makers select the most appropriate choice for their specific context.
\n\t\t\tHTA is a multidisciplinary process that systematically examines the technical performance, safety, clinical efficacy, effectiveness, cost, cost-effectiveness ratio, organizational implications, social consequences and legal and ethical considerations of the application of a health technology (EUNEHTA).
\n\t\t\tAdvances in science and engineering | \n\t\t\t\t\t
Intellectual property, especially patent protection | \n\t\t\t\t\t
Aging population | \n\t\t\t\t\t
“Cascade” effects of unnecessary tests, unexpected results, patient or physician anxiety | \n\t\t\t\t\t
Emerging pathogens and other disease threats | \n\t\t\t\t\t
Third-party payment | \n\t\t\t\t\t
Inability of third-party payers to limit coverage | \n\t\t\t\t\t
Financial incentives of technology companies, clinicians, and others | \n\t\t\t\t\t
Clinician specialty training at academic medical centers | \n\t\t\t\t\t
Malpractice avoidance | \n\t\t\t\t\t
Provider competition to offer state-of-the-art technology | \n\t\t\t\t\t
Public demand driven by consumer awareness, direct-to-consumer advertising, and mass media reports | \n\t\t\t\t\t
Strong economies, high employment | \n\t\t\t\t\t
Factors that reinforce the market for health technology (Goodman 2004)
The term “health technology” is quite broad and includes the following categories: drugs, biologics, medical devices, equipment and supplies, medical and surgical procedures, support systems, organizational and managerial systems.
\n\t\t\tHTA may address the direct, intended consequences of technologies as well as their indirect, unintended consequences; its main purpose is to inform technology-related policy-making in health care.
\n\t\t\tHTA is increasingly used in American and European countries to inform decision- and policy-making in the health care sector and several countries have integrated HTA into policy, governance, reimbursement or regulatory processes.
\n\t\t\tAn HTA process is conducted by interdisciplinary groups using explicit analytical frameworks drawing from a variety of methods: given the variety of impacts addressed and the range of methods that may be used in an assessment, several types of experts are needed in HTA.
\n\t\t\t\tDepending upon the topic and scope of assessment, these may include a selection of the following (Goodman, 2004):
\n\t\t\t\tPhysicians, nurses, dentists, and other clinicians
Patients or patient representatives
Epidemiologists
Managers of hospitals, clinics, nursing homes, and other health care institutions
Biostatisticians
Economists
Lawyers
Radiology technicians, laboratory technicians and other health professionals
Social scientists
Ethicists
Decision scientists
Clinical and biomedical engineers
Computer scientists/programmers
Pharmacologists
Librarians/information specialists
According to a recent study there are also significant differences in the practical application of HTA. Whereas in some countries HTA merely studies the clinical effectiveness and perhaps safety and cost-effectiveness of technologies, agencies in other countries apply a broader perspective and also consider other issues, such as ethics, and organizational, social or legal aspects of technology.
\n\t\t\t\tIt is also known that the HTA activities can be carried out at different levels of health-care systems:
\n\t\t\t\tmacro level (international and national - i.e. decision-making within central government institutions)
meso level (administrative level - i.e. regional or provincial health authorities, agencies, primary health-care units or hospitals);
micro level (clinical practice)
At each of these levels, however, these activities should be carried out by a multidisciplinary staff, involving clinicians, clinical engineers, economists, epidemiologists, etc.) and, depending on the object of evaluation, also by specifically qualified professionals from the hospital departments.
\n\t\t\t\t\n\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t|
New technology | \n\t\t\t\t\t\t\tSafety concerns | \n\t\t\t\t\t\t
Changes in old technology | \n\t\t\t\t\t\t\tEthical concerns | \n\t\t\t\t\t\t
New indications for old technology | \n\t\t\t\t\t\t\tEconomic concerns | \n\t\t\t\t\t\t
New findings | \n\t\t\t\t\t\t\tInvestment decisions | \n\t\t\t\t\t\t
Structural/organizational changes | \n\t\t\t\t\t\t\t\n\t\t\t\t\t\t |
Reasons for performing an assessment (Velasco, et al., 2002)
As discussed in the previous paragraph, HTA now represents a multidimensional field of inquiry that increasingly responds to broad social forces such as citizen participation, accelerated technological innovation, and the allocation of scarce resources among competing priorities (Battista, 2006).
\n\t\t\t\tHowever,, this methodology was initially focused and applied on a small scale, concerning (clinical) engineering questions pertaining to a technology’s safety and technical performances, and involving the investigation of one or more properties, impacts, or other features of health technologies or applications.
\n\t\t\t\tThe
In the
The main features characterizing these kinds of technologies can be summarized as follows:
\n\t\t\t\tfast-changing technologies: their development is characterized by a constant flow of incremental product improvements;
device impact on clinical and safety outcome depends on user training and experience that can vary and are hard to evaluate;
the life cycle of a device is often as short as 18–24 months, which is considerably less than, for example, pharmaceuticals;
the clinical application of the technology and potential utility for patients (accuracy or effectiveness) in comparison with the reference standard;
improvement in the operating principle;
state of development of technology (emerging, new, established);
impact on organization (implementation phase, change in the treatment, users’ qualification, IT requirements, etc.);
impact on patient and user safety;
economic aspects (acquisition, maintenance, spare parts, training, etc.);
devices cannot be evaluated by RCTs – hard to blind and randomize. Early evaluation not possible
Representation of Health Technology, Medical Device and Medical Electrical Equipment sets
A further classification of
Functional explorations and therapeutic equipment;
Medical laboratory or clinical chemistry equipment;
Bio-imaging equipment.
The HTA process, while maintaining a uniform and systematic approach, may have to primarily focus on different characteristics because of the different weighting or different evaluation methodologies for the following aspects:
\n\t\t\t\tInnovation
Safety
Technology management
Efficacy
Investment (big ticket technology; high volume purchase; service)
Organization.
\n\t\t\t\t\t
Assessment of innovation for
\n\t\t\t\t\t
The aim of the HTA process, developed within a healthcare facility, is to guide decision- makers on the “correct” acquisition or implementation of a health technology, from different viewpoints:
\n\t\t\t\t\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\t\t\t\t
The evaluation of the technical characteristics of a device can be performed in different ways. A technique based on the European network for Health Technology Assessment (EUnetHTA) model is described below.
\n\t\t\t\t\tThe EUnetHTA proposes an assessment scheme based on a basic unit, called assessment
The
The nature of the elements may vary across domains, since the consequences and implications are understood and studied differently in each domain. The following domains are considered:
\n\t\t\t\t\tHealth problem and current use of technology
Technical specifications
Safety
Clinical effectiveness
Costs and economic evaluation
Ethical analysis
Organizational aspects
Social aspects
Legal aspects.
A
The first task to accomplish in order to carry out the HTA process relates to the identification and definition of each KPI. To do this, the following steps are required:
\n\t\t\t\t\t\n\t\t\t\t\t\t
A thorough literature analysis should be carried out by consulting the most important bibliographical sources such as clinical search engines (Pubmed, Medline, ISI Web of Knowledge, Cochrane Library, etc.), the national and international website of the HTA Agency (INAHTA, HTAi, EUnetHTA, Euroscan) or Institutes (ECRI, FDA, etc.), clinical practice guidelines, grey literature (technical reports from government agencies or scientific research groups, working papers from research groups or committees, white papers, or preprints). Other potential sources of data are manufacturers of the technology, clinicians, nurses, paramedics and patients.
\n\t\t\t\t\tThe search can be performed by using main keywords for the technology in question (for example limiting the research in “abstract/title” OR “topic” fields). The most interesting results of these searches are selected and details investigated in order to intensify and develop the assessment.
\n\t\t\t\t\t\n\t\t\t\t\t\t
The analysis of the literature should therefore lead to the definition of the assessment elements, which are the core of the assessment. They are categorized into “evaluation area”, “macro KPI” and “micro KPI”. In order to make the assessment as objective as possible, the specific characteristics that support the assessment of a single area (and, subsequently, of the whole health technology) must be fully and measurably detailed, therefore objective and “instrumentally” measurable indicators are preferred. Moreover, those KPI that cannot be evaluated a priori should be excluded from the assessment.
\n\t\t\t\t\tTypically, the unit of measurement of KPIs may be:
\n\t\t\t\t\tmetric (e.g. spatial resolution, image uniformity, laser spot size, analytical specificity, etc.)
expressed as a percentage of coverage of the clinical/production/technical needs (e.g. percentage of coverage of analytical test panel; percentage of coverage of nominal “productivity”);
ON/OFF (presence/absence of a specific feature or functionality)
\n\t\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\t
1 | \n\t\t\t\t\t\t\t\tEqual importance of both elements | \n\t\t\t\t\t\t\t\tTwo elements contribute equally | \n\t\t\t\t\t\t\t
3 | \n\t\t\t\t\t\t\t\tModerate importance of one element compared to another | \n\t\t\t\t\t\t\t\tExperience and judgment favor one element over another | \n\t\t\t\t\t\t\t
5 | \n\t\t\t\t\t\t\t\tStrong importance of one element compared to another | \n\t\t\t\t\t\t\t\tAn element is strongly favored | \n\t\t\t\t\t\t\t
7 | \n\t\t\t\t\t\t\t\tVery strong importance of one element compared to another | \n\t\t\t\t\t\t\t\tAn element is very strongly dominant | \n\t\t\t\t\t\t\t
9 | \n\t\t\t\t\t\t\t\tExtreme importance of one element compared to another | \n\t\t\t\t\t\t\t\tAn element is favored by at least one order of magnitude | \n\t\t\t\t\t\t\t
2, 4, 6, 8 | \n\t\t\t\t\t\t\t\tIntermediate values | \n\t\t\t\t\t\t\t\tUsed to reach a compromise between two judgments | \n\t\t\t\t\t\t\t
Saaty scale
\n\t\t\t\t\t\t
After the assessment elements have been identified, it is necessary to define the decision-making framework and in particular to estimate the value of the weight of each element: such activity must involve the whole multidisciplinary evaluation team.
\n\t\t\t\t\tThe definition of the weights, in fact, is a constituent part of the mathematical model of data processing, selected among those available in literature, such as the Analytic Hierarchy Process (AHP), expert systems based on Artificial Neural Network (ANN), and methodologies based on decision Fuzzy logic or Support Vector Machine (SVM).
\n\t\t\t\t\tWith reference to AHP, for example, a structured questionnaire with a series of “pairwise comparisons” between the assessment elements can be used: each team member will be required, therefore, to compare on a qualitative scale e.g., Saaty scale, see table 4) the relative importance of the two compared elements. Finally, after the comparison of all pairs, the weight of each indicator will be calculated.
\n\t\t\t\t\tStep 4 Value of the indicators
\n\t\t\t\t\tThe next step is to assess each technological alternative (the subject of the assessment) on the basis of the mathematical framework so far implemented. For this purpose, we assign values (quantitative or qualitative) to each lowest level KPI (usually a micro KPI, but also macro KPI and, rarely, even an evaluation area), on the basis of available literature data, and technical specifications or expert judgment. These values are then aggregated by the computational model to produce the value and rank of the single health technology.
\n\t\t\t\t\tA) The assessment element ; b) Combination of evaluation areas, macro KPI and micro KPI
\n\t\t\t\t\t\t
The results obtained by aggregating the values can be represented graphically or through numerical reports. In particular, results can be processed to allow, for example:
\n\t\t\t\t\tthe comparison between the technological alternatives in order to show the performance on each evaluation area and/or macro KPI and/or Micro KPI;
the comparison between weights of evaluation areas, macro and micro KPIs
analysis of the evaluation tree with evidence of weighted values for each technological alternative
etc.
Example of graphical representation of comparison of two health technologies
\n\t\t\t\t\t\t
The final HTA report must provide the decision-makers with a clear, understandable summary of the information described above, in order to help them select the most appropriate technology. Moreover, it is essential to follow a standardized scheme, preferably one from a HTA agency or scientific community. However, it cannot be considered acceptable unless it contains the following sections:
\n\t\t\t\t\tdocument summary;
description of the technical characteristics and operating modalities of analyzed technologies;
summary of findings of literature search;
description of the criteria, indicators, macro and micro KPI;
definition of weights;
assigned values and mathematical processing method;
results (e.g., ranking, charts, graphs, etc.)
bibliography
Hundreds to many thousands of medical devices may need to be managed in a healthcare facility, with several million Euros being invested each year for the acquisition of new health technologies and for planned technology replacement, while thousands of maintenance processes per year are required in order to maintain the efficiency of these devices. As evident from the analysis of adverse events occurring during the last few years, serious incidents can often be related to the malfunctioning of medical devices. In particular, a high degree of obsolescence of the technologies, as well as missed, inadequate or improper maintenance, are among the possible causes of failure not attributable to the manufacturer. Therefore, in every healthcare facility, responsibility for the safe management of medical devices should be identified. The CED can provide a relevant contribution to the prevention of adverse events resulting from medical device failures by the technical and clinical assessment of the technologies to be acquired and proper management of maintenance. Different organizational models can be used to manage the above mentioned activities (Italian Ministry of Health, 2009): an internal service with employees of the healthcare facility; a mixed service, with internal control by clinical engineers as well as by means of maintenance contracts with manufacturers and technicians who may either be employees of the healthcare facility or of specialized companies; finally, an external service, with technical assistance entirely outsourced to a “global service” provider. Each of these three models has advantages and disadvantages. The first approach allows timely intervention and a better control of maintenance activities; however it is only justified when there is a sufficiently large quantity of technological equipment in the healthcare facility, and also requires the continuous training of the technical staff: Furthermore, maintenance contracts with manufacturers are still necessary for high-technology equipment. The second model permits flexibility as regards the organizational structure of the healthcare facility, internal control of processes, and a better integration of skills. The last organizational model is often preferred by healthcare facilities that do not yet have a CED; it allows organizational flexibility, but requires a careful selection of a qualified external company and authoritative supervision by the healthcare facility staff, otherwise control of the processes will be progressively lost and the quality of service will deteriorate.
\n\t\t\tMaintenance of medical devices has gradually evolved from the operational repair of out of order equipment to a management function aimed at preventing breakdown and failures, thus reducing risks associated with the use of medical devices, decreasing downtime and contributing to the improvement of diagnostic and therapeutic pathways, where technology is a key determinant. Healthcare facilities should identify responsibilities for maintenance and plan maintenance activities based on a detailed definition of methods, resources (i.e., operators, laboratories, measuring equipment, and maintenance contracts with external suppliers) and tools for supervision of the activities (e.g. dedicated software for the maintenance data management). To ensure adequate quality and safety standards and the rationalization of maintenance activities, a plan for maintenance and safety tests must be implemented, taking into account, for each device, the risks for patients and operators, degree of criticality and function of the device (e.g., therapeutic, diagnostic, or analytical). Within the European Community, preventive maintenance must be planned by the manufacturer prior to marketing the device. The 2007/47/EC Directive states that “the instructions for use must contain... details of the nature and frequency of the maintenance and calibration needed to ensure that the devices operate properly and safely at all times”. Preventive maintenance is of critical importance for ensuring the safe use of devices. Therefore, a preventive maintenance plan for each device must be defined, well documented and available at all operational levels to personnel responsible for maintenance tasks, including daily maintenance. Documentation should include informative documents and specific operating instructions which take into account both mandatory technical regulations and the service and user manuals provided by the manufacturer. Preventive maintenance is particularly relevant for life support devices, equipment for diagnosis and treatment, and devices identified as critical in relation to specific aspects such as the intended use of the device, class of risk, clinical features, type of location in which it is installed (e.g,. operating room, intensive care unit, ward), and presence of backup units. In carrying out the maintenance, the responsible technician must take into account all the maintenance instructions provided mandatorily by the manufacturer. Without affecting the liability of the manufacturer for any original product defects or faults, the person(s) performing maintenance will assume direct responsibility for all events deriving from this action. It is therefore essential that technicians, whether internal or external (see par. 5), have specific and proven experience. Training programs should be planned and preferably technicians should be trained by the manufacturers of the technologies which they maintain. Software for medical use deserves special consideration. Due to the complexity of systems and interactions, software behaviour may not be completely deterministic even when principles of good design practice are respected. Thus, software maintenance, which is usually performed by the manufacturer, should be supervised by the healthcare facility. Safety and performance tests must be periodically performed in order to ensure compliance with the essential safety requirements set by technical standards. The frequency of tests should be established taking into account criticality of device and according to reference guidelines. Particular attention is required in testing devices that can be used for critical applications (e.g., ventilators, anesthesia machines, infusion pumps, defibrillators, electrosurgical units) and for devices emitting or detecting ionizing radiations. Specific procedures and forms for different types of devices should be adopted to examine, measure, and verify the conformity of the device with the current mandatory technical standards and the instructions contained in the user manuals provided by the manufacturer. Dedicated equipment, for which calibration must be regularly performed and documented, should be used to measure parameters specific to each type of technology. Strategies for improving maintenance will only succeed if supervised effectively by external maintenance technicians in order to ensure their compliance with the agreed conditions (see par. 5). All relevant data relating to the life cycle of each device (from acceptance testing to disposal) must be recorded and made available at different operational levels. In order to ensure full traceability of the maintenance processes, preventive and corrective maintenance activities must be documented by detailed technical reports. In particular, preventive maintenance notice should be used to document the regularity of activities. Forms for maintenance requests to the CED must be defined and corrective maintenance notice should contain data useful for the identification of appropriate indicators (e.g. frequency of failures, time of first intervention, time to resolution, average downtime, distribution of failure types, maintenance costs, cost of spare parts), through which the condition of installed medical equipment can be analyzed.
\n\t\t\tEstablishing a complete and reliable inventory of medical equipment and ensuring the quality of the data is a complex task. Several different kinds of events, although rare, can lead to discrepancies between the inventory database and the technologies actually being used in a healthcare facility. These mismatches can be significantly reduced by establishing appropriate procedures and ensuring their strict observance. However, the large number of operators, devices and suppliers, the need to give priority to emergency care and the difficulty in directly and continuously monitoring the use of all devices in the healthcare facility, may inevitably produce such discrepancies. Failure to follow correct procedures for new equipment commissioning, for equipment transfer between departments, or for equipment disposal, are among the many possible events that could cause these mismatches. One possible solution is the use of Radio Frequency IDentification (RFID) tags and asset tracking systems. However, the use of this approach is limited because of ongoing debate about electromagnetic compatibility issues, and because the considerable cost of installation and management of these systems makes them still out of reach of most healthcare facilities. Until an advanced asset tracking solution is lacking in a healthcare facility, alternative strategies need to be implemented to keep the inventory data up-to-date. One way to monitor and update inventory data is through preventive and corrective maintenance or safety tests performed by CED technicians or by external service providers. Finally, it may be necessary to plan periodic inventory checks, which will be carried out independently or collaboratively by the CED and/or by the assets management office. Such controls may also provide an opportunity to remove devices that are no longer in use but are kept in stock and which may represent a source of risk.
\n\t\t\tDuring the last decades, planning health technology acquisitions has become of strategic importance for healthcare organizations, both at the national and at facility level. Such planning is also essential task for the reduction of clinical risk associated with the use of medical devices. The importance of acquisition planning is also determined by the considerable increase in technology investments, which is due to the increase in number and rapid technological evolution of medical devices and systems.
\n\t\t\t\tTherefore, healthcare organizations should define specific methods for planning the acquisition of health technology. Such methods should take into account the obsolescence of devices, the evolution of technical standards, the possibility of improving safety for patients and healthcare operators, the possible availability of innovative technologies for improving clinical performance, as well as considerations about actual or expected clinical needs, economic or technical feasibility, organizational changes, and investment priorities (e.g., innovative technologies vs device renewal). Moreover, the availability of adequate infrastructure, staff and consumables for the equipment must be foreseen in order to ensure full use of the benefits provided by the new technology. The decision to proceed with the acquisition should be conditional on the presence of a detailed clinical, economic and technical assessment with well defined comparative criteria, carried out by qualified and multidisciplinary staff and inspired by the principles of HTA (see par. 2). An equipment replacement plan is aimed at better identification of investment priorities for device renewal and may be based on the definition of a replacement priority value (RPV). RPV is an index which represents synthetically the level of urgency for the replacement of each device, permitting determination of a replacement priority ranking and planning of a progressive replacement of technologies (Fennigkoh, 1992). Variables considered by the RPV computational algorithm may come from different sources, principally the CED database and clinical activities records. Variables must be carefully chosen, according to the organization of the healthcare facility and based on data availability. In fact, the effort needed for collecting new data and keeping it up-to-date must be considered in order to limit the amount of new data to be collected and to make the best use of the data already available. A typical model for computing the RPV is based on the use of component indexes, with each index highlighting the impact on a specific aspect of the device replacement. A coefficient must be assigned to each component index in order to weight its contribution to the RPV. Possible aspects that might be taken into account, by defining specific numeric variables, are obsolescence of the device, maintainability (e.g. cost and availability of spare parts), reliability (e.g. downtime or number of failures), criticality, strategic impact, clinical efficacy, efficiency, clinical risk, potential for performance improvement. For example, the cost of replaced spare parts, the number of technical activities performed by the technicians of the CED, the annual cost of contracts and the cost of technical assistance by external suppliers will be taken into account in the computation of the component index for maintenance costs.
\n\t\t\tA quality assurance requirement for clinical assistance is the implementation of related processes based on the principles of best/good practice standards. In the field of management of medical devices, this concept is fundamental for meeting the need of retaining costs and providing effectiveness in patient care.
\n\t\t\tCEDs are also evaluated as to their ability to implement a policy of Good Management Practice of biomedical technologies (Cheng & Dyro, 2004). Related economic aspects, such as medical equipment maintenance costs, are a critical issue of such management (Table 5).
\n\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\tFinancial | \n\t\t\t\t\t\tInternal processes | \n\t\t\t\t\t\tCustomer satisfaction | \n\t\t\t\t\t\tTraining and continuing education for CE staff | \n\t\t\t\t\t
\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\tStaffing Beds per full-time equivalent employee Service/Acquisition ratio | \n\t\t\t\t\t\tPercent of IPM Complete IPM interval IPM time Repair time | \n\t\t\t\t\t\tAnnual survey | \n\t\t\t\t\t\tTime spent on these activities Certifications obtained | \n\t\t\t\t\t
A balanced performance scorecard for Benchmarking CE departments (Gaev, 2010a)
Clinical engineers play a fundamental role in determining the proper strategy for medical equipment maintenance and in recognizing the best available option for supporting these activities. More specifically, the CED is in charge of setting the expected level of performance, monitoring the quality and integrity of the delivered services, dividing activities between internal and external BMETs, and pursuing the goal of an expense reduction policy. For this reason, before maintaining biomedical technologies, CEDs should plan rational acquisitions, allotting part of the organization budget for service contracts.
\n\t\t\tA service contract is an agreement between a company and a user for the maintenance, in this case, of medical equipment during a specific period of time, usually for a fixed price which may be subject to changes if maintenance activities are performed outside the user’s location. The term “maintenance” typically includes inspection, preventive maintenance and repair. The terms and conditions of the contract usually stipulate the days and hours of service, the types of service, the response time, and which parts to be replaced are replaced free of charge” (Gaev, 2010b). This sort of contract can be extended to include the free loan of biomedical technologies. In this case, prices stated in the service contract are for consumables used for the equipment’s functions, and are increased to include maintenance costs.
\n\t\t\tReasons for having a service contract for a biomedical device are several. The first reason is the impossibility to provide a cost-effective service through in-house CED because of the lack of human and logistical resources. This is particularly common in hospitals where the problem of cost containment is approached with the sole objective of cost cutting and with no other financial or economic performance policy.
\n\t\t\tThe second main cause is that healthcare governance is particularly reluctant to assume responsibility for equipment maintenance, and the belief that original equipment manufacturer (OEM) service contracts represent the “gold standard” is difficult to remove. On the other hand, for certain classes of medical devices (those characterized by high-technological complexity or high consumable costs, such as clinical chemistry analyzers), service contracts seem to be the only realistic solution for accommodating their management costs. The main issues which have to be discussed and negotiated in the drawing up of a service contract are: inspection and preventive maintenance, repair, spare parts, legal and financial aspects.
\n\t\t\tThe term “Inspection and Preventive Maintenance (IPM)” covers all the activities involved in cleaning, lubricating, adjusting, checking for wear, and perhaps replacing components that could cause total breakdown or serious functional impairment of the equipment before the next scheduled inspection (Subhan, 2006).
\n\t\t\tThese activities are well-described in the manufacturer’s service manual and are aimed at avoiding the breakdown of a medical device in use, without any apparent warning of failure. Manufacturers are obliged to explicate preventive maintenance actions to healthcare operators or BMETs, and to suggest the minimum inspection frequency. The definition and respect of a timetable for IPM of all medical equipment is fundamental for reducing risk for patients and users, and preventing excessive repair costs by providing timely interventions; and it should be the CED’s first priority, and should be decided before carrying out preventive maintenance activities.
\n\t\t\tContracts should clearly explain the necessity of making known to all concerned the timetable for the maintenance by external technicians at the beginning of the year, in agreement with the CED and the healthcare personnel. This will allow the organization of clinical activities for healthcare operators and the possibility to enter the whole agenda into the biomedical technology maintenance management system. One other particular observation relates to the availability (at the charge of the contractor) of software update if required for the correct operation of the biomedical instrumentation. The last consideration relates to the possibility for CEDs (according to their competence) to evaluate the IPM requirements of medical equipment (Table 6) and to modify the service intervals recommended by the manufacturer, to obtain a more cost-effective maintenance without adversely affecting patient safety.
\n\t\t\tRepair (corrective maintenance) is a process to restore the physical integrity, safety and/or performance of a device after a failure. Aspects to be considered pertain to economic, safety and logistic concerns. Contracts should explain who can call for technical support: this aspect is fundamental for organizing the internal maintenance process. One possible solution would be for the healthcare personnel to first of all attempt to resolve the problem by telephone (with proper manufacturer’s customer support), and to define an internal procedure for advising the CED of the failure. In this way, the CED can monitor failure resolution time by the manufacturer’s technicians by means of its maintenance management system.
\n\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t\t\n\t\t\t\t\t\t\t | \n\t\t\t\t\t
Electrosurgical unit | \n\t\t\t\t\t\t6 months | \n\t\t\t\t\t\t12 months | \n\t\t\t\t\t
Exam light | \n\t\t\t\t\t\t12 months | \n\t\t\t\t\t\tNo IPM performer | \n\t\t\t\t\t
Physiologic monitor | \n\t\t\t\t\t\t12 months | \n\t\t\t\t\t\t24 months | \n\t\t\t\t\t
Pulse oximeter | \n\t\t\t\t\t\t12 months | \n\t\t\t\t\t\tNo IPM performer | \n\t\t\t\t\t
Variations in IPM intervals for selected equipment, proposed by ECRI Inst. (2010)
Another significant aspect related to maintenance contracts is the definition of “bad-management” of biomedical technologies by healthcare personnel which may cause failure of the equipment. Some manufacturers are reluctant (or do not agree) to repair equipment under contract if abuse or improper use by hospital staff caused the failure. It is essential that the internal training of healthcare staff makes them aware of their responsibility for the correct use of biomedical equipment.
\n\t\t\tMoreover, in the contract, clinical engineers should define a way to evaluate the performance of OEM technicians, and stipulate the right to suspend the service contract in the event of low-quality maintenance work.
\n\t\t\tA common aspect of IPM and repair contracts is the possibility of a partnership for maintenance activities between the OEM technical support and the BMETs (internal or outsourced). Some manufacturers only permit maintenance activities by qualified (and certified by the OEM itself) technicians. Positive results of partnership contracts were showed just a few years ago. A first Italian joint project between OEMs and in-house service was started in 2002 (De Vivo et al., 2004): in-house personnel received adequate training, both generic (basic principles on which devices work) and specific (how to use, repair and maintain a particular model), for maintaining 90 medical devices (mostly monitoring equipment, ventilators and anesthesia units) in shared OEM/internal BMETs maintenance contracts.
\n\t\t\t\n\t\t\t\tFigure 5 summarizes the success of this program. One important effect was the increased awareness of the OEMs about the need for a rational selection of an effective preventive maintenance program in which service procedures and frequencies are based on real world feedback, efficacy of activities are measured and areas needing improvement are identified.
\n\t\t\tClinical Engineers are also in charge of compiling technical reports related to maintenance activities (for instance, by means of an appropriate software system, see par. 6). These data are essential for monitoring the quality of OEM services, and claiming economic and legal penalties. Service contracts should also clearly explain the accuracy level of report writing, to avoid possible future disputes.
\n\t\t\ta) Percentage of in-house repairs (July 2002-March 2004). The number of in-house repairs reached 90% and more after one year and continue to grow as in-house personnel sharpen their required basic skills. b) Number of OEM and in-house repairs (years 1999-2004). The decrease in OEM corrective maintenance was soon significant: as a consequence, OEMs were able to focus their attention on accurate preventive maintenance in order to prevent certain predictable failures. c) Percentage reduction of annual maintenance fees. Significant discounts were obtained based on the percentage of in-house corrective maintenance, justifying the cost related to internal technicians and the energies needed to set up the whole system.
Service contracts should include a specific paragraph on spare parts. OEM contracts usually lack the inclusion of them or any specification of the condition (e.g. new, refurbished) of parts used for maintenance and repair (Gaev, 2010b). It should be the duty of clinical engineers to assess the need for spare parts and include them in the contract, in dedicated annexes.
\n\t\t\tThe economic assessment of service contracts is done using the definition of financial performance indexes. The most common index is the service cost/acquisition cost (S/A) ratio, i.e. the total cost to deliver a service, including parts and labor, divided by the acquisition cost of the equipment. Services delivered by OEMs (or third-party service suppliers) under a full-service contract usually include IPM and repair. The cost of same service delivered by an in-house CED is computed from the amount spent on parts and CED labor (labor hours) multiplied by the “loaded” rate including salary, benefits and other over-head expenses. In-house service is generally less expensive (50 percent less) than full-service OEM contract, even if this estimation varies significantly according to the equipment category. A recent ECRI review shows that imaging and high-tech laboratory equipment has a higher S/A ratio and is thus more costly to maintain than general biomedical equipment, even if this ratio may vary greatly due to institutional (e.g., teaching vs non teaching institution), logistics (e.g., urban vs rural hospital) as well as operational (e.g., low vs high negotiated acquisition price) differences (Gaev, 2010a).
\n\t\t\tParticular consideration should be given to the drawing-up of penalty clauses for the possibility of non-compliant service, the latter defined in terms of technical response time and equipment uptime/downtime. Moreover, competitive benchmarking for service contracts should also take into account fees for service outside of contract work hours, and any minimum charges required for travel time, service time, and work performed outside of the usual contract provisions. However, to make effective the use of penalty clauses, essential tools have to be set in place such as the computerized management of processes, implementation of a contact center (phone or online) for maintenance requests, systematic review of the quality of maintenance activities, failure analyses, and strict control of performance indicators and maintenance costs.
\n\t\tAny action undertaken to improve the management/control of medical devices in a healthcare facility through an efficient and effective organization of maintenance and technology assessment activities, requires the implementation of operating procedures that enable the standardization of CED processes. However, the rapid evolution of health technologies during the last decades and the spread of heterogeneous technologies, besides bringing undeniable clinical improvements, have resulted in a considerable increase in technology investments, with the subsequent need for tools that can aid decision making in acquiring new technologies and managing the existing ones. To achieve the double goal of correctly applying and automating procedures and of implementing a model for the appropriate management of available resources and the proper definition of priorities, a comprehensive and reliable dataset for health technologies as well as an appropriate software tool to support data management will be required. Electronic archives are thus essential for storing all data and all events in the life of the medical devices managed by the CED, from the technology assessment that should always precede their acquisition until their disposal. Such a tool will permit safer documentation and reporting of the maintenance and management activities, sharing of information between the CED and other hospital staff, a dramatic improvement in data search, provision of summary statistics, and the definition of indicators that may contribute to the proper management of health technologies. The organization of this database may vary markedly depending on heterogeneous factors such as healthcare facility organization, technical and administrative management policies, number of devices, and resources dedicated to data management. The opportunity to support and significantly improve the management of medical equipment makes it advisable to implement a solution that can be configured and easily updated according to the evolution of specific needs. The configurable features of the system should include database design, user interfaces, queries, reports and statistics. The possibility to configure the database is useful not only for adding tables and fields, but also for the development of new features and adaptation of the software to the organizational structure of the healthcare facility. Configurable user interfaces should include at least the appropriate forms for inventory, acceptance testing, safety and performance tests, maintenance processes, preventive maintenance plan, maintenance contracts, disposal of devices, and administrative data management. Customizable configurations for different users should be guaranteed, in order to adapt the software according to the role and responsibilities of each user, with different data visualization and operating permissions. System users should be allowed to extract and export data in convenient formats (e.g., spreadsheets) for offline processing. Templates for standard documents (e.g. acceptance testing reports, maintenance reports) must be available and it should be easy to obtain automatically filled in and ready-to-print documents. It should be possible to analyze data with a configurable statistics dashboard. Such a system architecture would be suitable for developing methods for health technologies management and for defining indicators for the implementation of a technology replacement plan, the identification of maintenance priorities, and the optimization of resources allocation. Ultimately, being able to customize the software makes it possible to update the structure and configuration of the system according to the organization and evolution of operational requirements specific to a particular healthcare facility, and also makes it a suitable tool to support the development of processes. This feature is also particularly relevant for the purpose of satisfying the requirements for certification and the standards for national and international accreditation. The configuration should be performed or at least supervised by the CED staff, who best know the specific needs of the organizational context in which the software is to be used. Another advisable solution is to adopt systems that are accessible via the facility’s intranet. Web-based systems that do not require any client-side software installation are useful for sharing information between the different actors involved and can improve the automation of processes for Health Technology Management (HTM). Moreover, with web-based systems it is possible for health operators to access many support features for the management of technologies. They can submit online requests for corrective maintenance, monitor the real-time evolution of submitted requests, search the database for devices, preventive maintenance plan or safety tests, and receive automatic e-mail notifications when certain events occur (e.g., maintenance processes closed by biomed technicians, reminders for scheduled maintenance). This approach also has the advantage that only one data entry is needed (e.g., biomed technicians no longer have to re-enter data that have already been entered by the health operators on the maintenance request form). Obviously, all users should be trained in at least the basic principles of the system. The use of such a system for the management of medical devices can be extended to (or integrated with) the management of other technological facilities, ICT equipment, and other hospital assets.
\n\t\t\tA number of advantages for budget management can be gained by using computerized procedures for online submission of requests from heads of hospital units for the acquisition of new medical devices. Specification of medical device type according to a standard nomenclature system could be required, which would avoid the use of disparate terms for the same equipment. Also, the use of required fields in the electronic request form (e.g., reason for the acquisition, expected benefits, consumables needed) would ensure that all requests contain the essential information for their proper assessment. The medical board, with the support of the CED, would then have the right tools to manage the submitted requests in a uniform manner and make an objective analysis. assign a priority ranking to each request, and finally decide which ones to approve and which to reject. This approach could also be useful for the activation of hospital-based HTA (HB-HTA) processes (see par. 2). Furthermore, the authorization process (i.e., approval by department directors and medical board or medical devices committee) can be automated and differentiated according to the type of acquisition (e.g. property, loan, service, rental, clinical trial). Approval of the request will be automatically notified and immediately available online. The technology renewal plan managed by the CED may be integrated and partially automated in the software by implementing an algorithm for calculating the replacement priority value (see par. 4). Following the approval of requests for new acquisitions and replacement of medical devices, the automation of CED processes would provide valuable support for the management of data and documents relating to the assessment and acquisition of technologies. Information concerning single budget items (e.g., type of acquisition, number of requested devices, allocated budget) and on acquisition progress (i.e., end of the market survey, drafting and issuance of the technical assessment, date of order by the administration, supplier name) can be shared between the CED and the healthcare facility administration, with automatic update of acquisition progress and online availability of documents for each budget item. At all stages, starting from submission of the requests, only a single data entry is needed.
\n\t\t\tIn a computerized system for managing CED’s processes, each medical device has its own inventory record containing the data relevant to its management (e.g., device model, accessories, system configuration, owner hospital unit, location, administrative data). Each device in the inventory must be uniquely identified, and the CED must place an identifying label on it. As stated above, the adoption of a standard medical device nomenclature for model identification is also strongly recommended. If a web-based system is used, health operators will be able to search for inventory records and obtain lists of devices that can be exported onto spreadsheets. For each device in the inventory, the acceptance testing must be registered in the system. The status of the device can be updated automatically and an e-mail notification sent upon completion of testing.. In order to keep the inventory data up-to-date, in addition to routine administrative tasks, periodic inventory checks must also be made. In this regard, mobile units (e.g., PDA) equipped with a tag (e.g. barcode, RFiD) reader, properly configured and synchronized with the CED software system, can be a useful tool. This approach allows easier tracking of devices and verification of equipment location and condition, as well as updating of system components. Another useful feature is the online availability of documents. These could include pre-acquisition documents (e.g., market survey, technical assessment, order form), user and service manuals, acceptance testing documents and training course forms, as well as pictures of system configurations and accessories.
\n\t\t\tMaintenance processes management could exploit the availability of an appropriate software tool. As stated above, a useful feature is the possibility for health operators, in case of failure of a medical device, to request corrective maintenance online. Maintenance activities should then be recorded in the system by CED biomed technicians. CED can enter and update the maintenance plan (i.e., the preventive maintenance activities for which both internal technicians and external maintenance personnel will be appointed) and share it, as well as related information (e.g. maintenance progress, e-mail notification of upcoming preventive maintenance), with all hospital units involved. Health operators should be allowed to retrieve and export lists of maintenance requests. Thousands of safety and performance tests are performed on medical devices each year by the CED. Thus the availability of test reports to health operators is only possible by implementing an automatic upload system. Radiology equipment deserves a particular mention in that it is usually managed by both CED and the Medical Physics Unit. This requires sharing of information on preventive and corrective maintenance and quality controls. Finally, the software tool can also be used to facilitate the management of spare parts. Online access to maintenance documents (i.e., preventive and corrective maintenance activities, safety and performance test reports, administrative documents) is another desirable feature. The availability of such electronic information enables the CED to analyze the history of maintenance processes for each device, to improve monitoring of maintenance activities performed both by CED technicians and by external maintenance personnel, to verify the compliance of suppliers with maintenance contracts, to gather downtime statistics, and to generate summaries of maintenance costs. Finally, algorithms can be defined and implemented to combine device replacement priority value (see par. 4) and maintenance priority rank for immediate identification of the most urgent corrective actions. Automated information sharing can also be helpful for the disposal of devices. The way this feature can be configured depends on the specific organization. For example, CED could be in charge of notifying the hospital unit of device disposal, while the physical removal of the device would be the responsibility of the facility handling service. An automatic e-mail notification of disposal confirmation to the CED would allow an easier tracking of out of order devices, thus reducing inconvenience and risk for patients and health operators.
\nAn important role of rehabilitation is to improve the standing postural balance of the elderly and stroke victims to prevent falls. Recently, motor imagery has been reported to effectively improve standing postural balance and facilitate the effects of neuromodulation techniques. This chapter outlines how motor imagery contributes to the improvement of standing postural balance and reviews how this method can be used in combination with neuromodulation techniques.
Motor imagery is the simulation of motion in the brain without actual motion [1]. According to the PubMed database, 3853 articles on motor imagery were reviewed from 1979 to 2021, and the number is increasing every year. In addition, 1178 articles on rehabilitation using motor imagery were reviewed from 1999 to 2021, and the number of articles in this subtopic is also increasing, indicating that the application of motor imagery in rehabilitation has been attracting attention in recent years (Figure 1). In addition, motor imagery has a common neural basis with actual movement and is considered to be effective in reconstructing motor function. Therefore, clarification of brain activity during motor imagery will enhance the validity of motor function reconstruction by comparing it with brain activity during the actual exercise. In this section, we review the neural basis and mechanisms of motor imagery based on previous studies.
Progression of the number of publications on motor imagery and the number of publications on motor imagery and rehabilitation. The red line shows the number of publications on motor imagery from 1977 to 2021. The blue line shows the number of publications on motor imagery and rehabilitation from 1999 to 2021. Data were collected from the PubMed database (
In 1977, Ingvar and Philipson [2] introduced the first method for displaying the mean blood flow distribution in the brain as a two-dimensional color map in the study of brain function during motor imagery. They used this method to measure and compare regional cerebral blood flow at rest, during motor imagery, and during actual movement. Subjects performed a task in which they were asked to imagine a rhythmical clasping movement of the right hand during motor imagery and then perform a rhythmical movement of the right hand during actual movement. The measurement procedure in this study was the same for all subjects, with the resting state being measured first, followed by the motor imagery, and finally the actual exercise. The results showed that motor imagery increased blood flow in the entire frontal lobe, including the supraorbital region, as well as the parietal and temporal lobe regions. However, the actual movement of the right hand increased blood flow mainly in the central sulcus. Thus, the results of this study suggest that the centers of motor imagery are located in a different region of the cerebrum than the centers that control actual hand movements. However, medical science and technology have made obvious progress since 1977, and new techniques, such as functional magnetic resonance imaging (fMRI) [3] and positron emission tomography (PET) [4], have been used to detect brain activity. Moreover, Hétu et al. [5] performed an activation likelihood estimation (ALE) meta-analysis of 75 studies measuring brain activity during motor imagery using fMRI or PET reported up to 2011. They were the first to examine quantitative maps of structures activated during motor imagery. The results revealed that motor imagery depends on a network that includes motor-related areas, such as frontoparietal and subcortical structures. Therefore, studies from recent years have supported the view that motor imagery and actual movement share a common neural basis. In addition, a study from 2018 [6] that compared brain activity during motor imagery and actual movement in detail reported that there is effective connectivity between motor and cognitive networks. In that study, 20 healthy subjects were tested in a series of finger tapping trials, and electroencephalography (EEG) data throughout the task were validated using dynamic causal modeling. The results demonstrated effective connectivity between the dorsolateral prefrontal cortex (DLPFC) and secondary motor areas (M2), and between primary motor areas (M1) and M2, both during motor imagery and motor execution. Furthermore, DLPFC-premotor cortex (PMC) connectivity was more strongly activated during motor imagery than during actual movement. Additionally, PMC-supplementary motor areas (SMA) connectivity and M1-PMC connectivity were more strongly activated during motor imagery than during actual movement. Thus, in addition to supporting the recent view that motor imagery and actual movement share a common neural basis, the results of that study also suggest that although they share a common neural basis, they are distinct processes. In light of the above, reports on motor imagery are increasing annually, and subsequent studies are expected to elucidate brain activity during motor imagery.
Motor imagery can be divided into two types—muscular sensory imagery (KI) and visual imagery (VI). Because these methods of imagery differ, resulting in differing brain activity and training effects [7], the characteristics of each method must be understood to flexibly introduce motor imagery training in rehabilitation and elicit its effects. Guillot et al. [8] used fMRI to determine whether the neural networks formed by KI and VI are equivalent. In this study, 13 subjects were given a finger movement as a motor imagery task. The results of the comparison between KI and VI showed that movement-related structures and the inferior parietal lobule (IPL) were activated in KI, whereas the occipital lobule and superior parietal lobule (SPL) were mainly activated in VI. Figure 2 shows the results of the evoked responses obtained for KI and VI during the 5 s of the test using a FASTRAK digitizer (Polhemus, Colchester, Vermont, USA), based on the neurophysiological data measured by the magnetoencephalography system. From the figure, it can be seen that KI results in outstanding PMC activity, while VI results in an activated occipital lobe. In addition, Figure 3 shows the power spectra of brain activity measured by EEG for four activities—kinesthetic motor imagery (KMI), visual motor imagery (VMI), motor execution (ME), and visual observation (VO). Panel (a) shows that the normalized power of the KMI and VMI conditions was similar in the alpha and beta frequency bands. Panel (b) demonstrates that the neural networks were similar in KMI and ME due to their high connectivity to regions of interest (ROI) in the sensorimotor cortex. Furthermore, VMI and VO networks were similar, with a large number of networks distributed in the DLPFC and PMC. Moreover, Hétu et al. [5] reported in detail the brain regions that are consistently activated during KI and VI execution. KI showed consistent activation of the SMA, IPL, precentral gyrus (PcG), cerebellum (CB), left inferior frontal gyrus (IFG), supramarginal gyrus (SMG), temporal pole, putamen, anterior insula, right Rolandic operculum, angular gyrus, and pallidum. VI showed consistent activation of the bilateral SMA, left PcG, lingual gyrus, CB, light middle frontal gyrus (MFG), and postcentral gyrus (PocG). When KI and VI were combined, the left PcG, SMA, anterior insula, and bilateral putamen were consistently activated. Therefore, KI is employed during the execution of real movements, while VI is activated in the visual cortex, which processes visual information.
Typical induced responses in KI and VI [
Power spectra and connectivity in ME, KMI, VMI, and VO brain activity [
Improvement of standing postural balance consists of muscle strength [11, 12]; joint range of motion [13]; and somatosensory [14], visual [15], and brain function [16]. Moreover, standing postural balance and fall prevention are correlated, and it has been shown that control of lateral stability is significant for fall prevention interventions [17, 18]. Recently, motor imagery has been attracting attention as an intervention to improve balance function and the effects of motor imagery on standing postural balance have been reported in healthy subjects, the elderly, patients with stroke, and those with Parkinson’s disease, among others. In this section, we review the effects of motor imagery on standing postural balance compared by subject based on previous studies.
In a study on healthy subjects, Jahn et al. [19] examined the activation/deactivation patterns of each brain region during motor imagery using fMRI in imagery tasks of standing posture, walking, running, and supine posture. In this study, 13 healthy adults with an average age of 27.3 years performed the above-mentioned four motor imagery tasks for 20 s each in the supine position with closed eyes. The results showed that different activation/deactivation patterns were detected in the three conditions of standing, walking, and running, respectively. During motor imagery of the standing posture, the thalamus, basal ganglia, and cerebellar mediastinum were activated. During motor imagery of walking, the parahippocampal gyrus and cuneiform gyrus, occipital visual area, and CB were significantly activated. Moreover, during motor imagery of running, the cerebellar vermis and adjacent cerebellar hemispheres in the CB were activated six times more than during motor imagery in the standing and walking conditions, while the parahippocampal gyrus and cuneus gyrus were not activated compared to the walking condition. These results support the concept of hierarchical organization of posture and movement and suggest that motor imagery activates low-intensity CB activity that controls standing postural balance and the sensory-motor control through the thalamus and basal ganglia. In addition, a study has examined the effects of nonphysical training on standing postural balance from the perspective of brain activity [20]. The intervention involved 16 healthy adults with an average age of 27.5 years. The study was conducted under three conditions: (1) a combination of action observation and motor imagery in which the subjects watched a video of a balance task being performed, (2) simple action observation in which they watched a video, and (3) simple motor imagery in which they imagined walking with their eyes closed. Two balance tasks were performed under each condition: static standing and dynamic standing with internal and external perturbations, which were measured four times for static trials and four times for dynamic trials in a randomly determined order. The results showed that the intervention of motor imagery during the dynamic balance task predominantly activated the putamen, CB, SMA, and M1, and the combination of action observation and motor imagery activated the PMC in addition to the brain regions activated in motor imagery alone. However, intervention with action observation did not significantly activate these brain regions. In other words, this study suggests that motor imagery training may be effective in controlling standing posture in the medial and lateral directions.
In a study of older adults, Oh et al. [21] examined the potential for effective training adaptations for fall prevention by assessing static and dynamic balance and fear of falling in older adults who have a history of falls, before and after motor imagery training or task-oriented training. This study included 34 elderly subjects aged 65 years or older, randomly assigned to three groups: a motor imagery (11 subjects), task-oriented training (11 subjects), and a control group (12 subjects). In motor imagery training, the subjects sat in a sitting posture with their eyes closed during a 10-min relaxation period. Then, they imagined movements to protect themselves in the event of a fall for 20 min. In task-oriented training, balance training focusing on daily activities was conducted. The results showed that dynamic balance and fear of falling were significantly improved in the motor imagery group compared to the other two groups. Therefore, motor imagery training for the elderly and those without disease improved balance function, suggesting that it is highly effective as an intervention for fall prevention.
In 2005, a systematic review of seven databases on the effectiveness of motor imagery interventions in stroke patients [22] revealed a significant effect of motor imagery training in the Fugl-Meyer Stroke Assessment (FMA). In another study, a 30-minute motor imagery task of daily activities was performed on the paralyzed upper limbs, and changes in the cortex were verified by fMRI after 10 weeks of intervention [23]. This study revealed significant activation in the bilateral PMC and M1, as well as in the superior parietal lobe of the paralyzed side for flexion or extension movements of the wrist on the paralyzed side. These studies suggest that intervention with motor imagery training is effective for improving function in stroke patients.
In addition, a meta-analysis of balance function in stroke patients was performed in 2016 by extracting randomized controlled trials of motor imagery intervention for gait ability and balance in stroke patients from 12 electronic databases [24]. This study reported that intervention with motor imagery is effective in improving gait performance, but no statistical difference was found concerning balance function. According to Oostra et al. [25], poor motor imagery after stroke is associated with lesions of the left putamen, left ventral premotor cortex (PMv), and long association fibers connecting the parietooccipital region and the DLPFC. In other words, the effect of motor imagery is less clearly defined when the frontoparietal network is impaired. It has also been reported that the effect of motor imagery in stroke patients depends on their ability to maintain and manipulate information in working memory [26]. Moreover, the working memory involves the frontoparietal network [27], and it is highly likely that the basal ganglia and PMC have a strong influence on motor imagery. Additionally, the frontoparietal network has been reported to be the same brain region that is activated in actual movement [5]. Thus, a part of the frontoparietal network that is related to motor imagery as well as the actual movement was impaired, which affected the result that balance function was not significantly improved in the stroke patients. However, many studies have reported statistically significant effects on walking ability and upper limb function, suggesting that screening for motor imagery effectiveness based on lesion localization is necessary.
Parkinson’s disease (PD) presents with movement [28], cognitive [29], and psychiatric symptoms [30]. PD can be clinically classified into a tremor-dominant subtype and a postural instability gait disorder subtype, and it has been reported that balance function is more impaired and the risk of falling is higher in the postural instability gait disorder subtype than in the tremor-dominant subtype [31, 32]. In addition, the severity of the postural instability gait disturbance is a useful indicator of PD severity and prognosis [33], suggesting that improvement in balance function and walking ability may be attributed to a favorable prognosis.
Motor imagery interventions for PD have been reported in many studies. For example, Thobois et al. used PET to compare the brain activity of normal subjects and immobilized PD subjects who imagined continuous hand movements [34]. The results showed that the prefrontal cortex, SMA, superior parietal lobe, IFG, and CB were activated during motor imagery in normal healthy subjects, while M1 activation was only observed during the dominant hand trials. Furthermore, in PD patients, motor imagery of the immobile hand showed a lack of activation in the contralateral primary somatosensory cortex and CB, persistent activation in the SMA, and bilateral activation in the superior parietal lobes. Based on these results, this study reports that PD patients with immobility show abnormal brain activation during motor imagery and that ideal brain activation depends on the state of the imagery hand.
Another symptom of PD is the altered timing of continuous movements. It has been shown that movement timing in internally generated continuous movements is selectively deficient, and the defects can lead to problems in movement planning [35]. This symptom may limit the conduct and potential effectiveness of motor imagery in rehabilitation for PD patients. Therefore, Heremans et al. validated the effects of a goal-directed motor imagery task using visual and auditory cues [36]. The results showed that the motor imagery task with visual cues significantly reduced bradykinesia. Moreover, the results suggest that the effectiveness of motor imagery for restoring function in PD patients can be enhanced by employing VI, while the effectiveness of KI is low. A study of VI intervention in PD patients examined the effects on standing postural balance and walking ability [37]. In this study, VO and motor imagery were administered as VI for 6 weeks. The results showed improved balance function and gait velocity in PD patients with postural instability and gait impairment. Thus, the addition of VI to standing postural balance training in PD patients promoted specific functional reorganization of brain regions involved in motor control and executive-attentional abilities, which is expected to have a long-term effect.
Motor imagery can be easily introduced into clinical practice because it can improve performance without special equipment. However, mental practice using motor imagery is limited in that the quality of the motor imagery being performed is not feedbacked to the subject [38], which causes individual differences in the motor imagery effects [39]. Neuromodulation technology has recently attracted attention as a method to solve this problem. Neuromodulation is a technique used to regulate the nervous system by electrical or scientific measures and is applied for many diseases [40]. Using this technology to provide feedback to the subject on the quality of motor imagery may be effective in improving movement performance. In this section, we review various neuromodulation techniques that facilitate motor imagery.
Neurofeedback is a noninvasive tool for purposeful modulation of human brain function that has the potential to dramatically impact neuroscience and clinical treatment of neuropsychiatric disorders [41]. Boe et al. [42] investigated whether the combined use of neurofeedback during motor imagery tasks could modulate brain activity. In this study, 18 healthy subjects (eight males, ten females, 24.7 ± 3.8 years old) were randomly assigned to a neurofeedback group or a control group. The motor imagery task was a KI activity in which the subject continuously pressed buttons with the ineffective hand. Neurofeedback was based on event-related synchronization/desynchronization (ERS/ERD) in the β-band of the sensorimotor cortex and was provided in real-time during motor imagery from a bar graph on a projector. The results showed that neurofeedback from bilateral sensory-motor cortices increased the contralateral pattern of brain activity associated with motor imagery with each successive session compared to the control group. Thus, this study suggests that the provision of neurofeedback provides significant information about motor imagery training and an opportunity for patients to modulate their own regional brain activation. In addition, neurofeedback approaches have a background of dependence on a single brain imaging modality such as EEG or fMRI. However, a study validated breaking away from this dependency system by reporting the effects of bimodal neurofeedback with simultaneous EEG and fMRI feedback [43]. In this study, the effects of unimodal EEG- and fMRI-neurofeedback were compared with those of bimodal EEG-fMRI-neurofeedback. The results showed that EEG-fMRI-neurofeedback significantly modulated activity in the movement domain compared to the two groups of short-peaked neurofeedback, and specific mechanisms and their additional value were found. Other studies have examined the effects of EEG-fMRI-neurofeedback on motor imagery, and all have shown that neurofeedback can modulate brain activity better than unimodal neurofeedback [44, 45]. In conclusion, neurofeedback during motor imagery can modulate brain activity and improve performance. Furthermore, incorporating multimodal techniques, such as bimodal neurofeedback instead of unimodal neurofeedback, may enhance the effects obtained from motor imagery.
Transcranial electrical stimulation (tES) aims to noninvasively modulate brain function by applying current from the current source [46]. Cranial electro-stimulation therapy (CET), cranial electrotherapy stimulation (CES), and transcranial pulsed current noise stimulation (tRNS) are several methods of tES used as clinical treatment [47]. Several previous studies have reported the use of tES, especially transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation in combination with motor imagery. Moreover, Xie et al. [48] examined the effects of tES on brain activity during motor imagery. The results showed that ERD of μ and β rhythms during a motor imagery task was significantly enhanced by the combined use of tDCS with motor imagery. In addition, a study that examined the modulation of motor learning by transcranial alternating current stimulation [tACS] [49] suggested that 70 Hz tACS enhances motor learning ability by intermodulation activity in the β-wave band. Thus, tDCS and tACS are potential approaches to modulate brain activity during motor imagery and enhance effective functions to improve performance.
Transcranial magnetic stimulation (TMS) is a technique that noninvasively modulates brain activity through the induction of currents by rapidly changing magnetic field pulses [50]. In addition, by reviewing the literature through 2018, guidelines were established for treatment with repetitive transcranial magnetic stimulation (rTMS) in Europe in 2020. The guidelines established rTMS as a clinical treatment modality, although its efficacy has not reached Level A/B evidence. Moreover, rTMS can be divided into high frequency (HF) and low frequency (LF) rTMS. LF rTMS decreases the excitability of the nonaffected hemisphere [51], while HF rTMS increases it [52].
Many previous studies in which motor imagery and rTMS were combined have verified the therapeutic effects on upper limb function in stroke victims [53, 54]. For example, Pan et al. [54] investigated the effects of motor imagery and LF rTMS on upper limb motor function during stroke rehabilitation. They applied 1 Hz rTMS to the M1 of the nonaffected hemisphere; 10 sessions of 30 min were performed during a two-week intervention period. The results showed that upper limb motor function was significantly improved in the group that received motor imagery and LF rTMS in the second and fourth weeks after the intervention compared to the control group (LF rTMS-only group). Moreover, a study in which motor imagery was combined with HF rTMS [55] also revealed a significant improvement in pre- and post-stimulation performance. These results suggest that rTMS can enhance the effects obtained from motor imagery in subjects such as stroke survivors. Finally, it was suggested that there is no difference between high and low rTMS frequencies in terms of performance improvement.
In this chapter, we outlined motor imagery effects that contribute to the improvement of standing postural balance and the effects that can result from use in combination with neuromodulation techniques. We further discussed the consequences for healthy subjects and those with illnesses. Recently, the view that motor imagery constitutes the same neural basis as actual movement is gaining ground, and the main effects of motor imagery in improving standing postural balance have been demonstrated. In addition, neuromodulation technology has the potential to improve the effects of motor imagery and is expected to further contribute to rehabilitation. Thus, the combination of neuromodulation techniques with motor imagery training will be significant in improving the quality of rehabilitation.
This work was supported by the Yuumi Memorial Foundation for Home Health Care and JSPS KAKENHI Grant Number JP20K11173.
The authors declare no conflict of interest.
functional magnetic resonance imaging positron emission tomography activation likelihood estimation dorsolateral prefrontal cortex primary motor areas secondary motor areas premotor cortex supplementary motor areas kinesthetic imagery visual imagery inferior parietal lobule superior parietal lobule motor execution visual observation electroencephalography regions of interest precentral gyrus cerebellum inferior frontal gyrus supramarginal gyrus middle frontal gyrus postcentral gyrus Fugl-Meyer Stroke Assessment ventral premotor cortex Parkinson’s disease transcranial electrical stimulation cranial electro stimulation therapy cranial electrotherapy stimulation transcranial pulsed current stimulation transcranial direct current stimulation transcranial Alternating Current Stimulation transcranial Random Noise Stimulation transcranial magnetic stimulation repetitive transcranial magnetic stimulation high frequency low frequency
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All published Book Chapters are licensed under a Creative Commons Attribution 3.0 Unported License. Monographs are licensed under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license granted to all others. Our Copyright Policy aims to guarantee that original material is published while at the same time giving significant freedom to our Authors. IntechOpen upholds a flexible Copyright Policy meaning that there is no copyright transfer to the publisher and Authors hold exclusive copyright to their work.
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\n\n\n\nIntechOpen publishes different types of publications.
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She holds a degree in Dentistry from the Federal University of Alfenas (UNIFAL), while her specialization and professional improvement in Stomatology took place at Hospital Heliopolis (São Paulo, SP). Her qualifications are: a specialist in Dental Imaging and Radiology, Master in Dentistry (Periodontics) from the University of São Paulo (FORP-USP, Ribeirão Preto, SP), and Doctor (Ph.D.) in Dentistry (Stomatology Clinic) from Hospital São Lucas of the Pontifical Catholic University of Rio Grande do Sul (HSL-PUCRS, Porto Alegre, RS). She held a postdoctoral internship at the Federal University from Jequitinhonha and Mucuri Valleys (UFVJM, Diamantina, MG). She is currently a member of the Brazilian Society for Dental Research (SBPqO) and the Brazilian Society of Stomatology and Pathology (SOBEP). 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