Pub Date : 2022-01-01DOI: 10.5220/0010814600003123
Carla V. Leite, A. Almeida
This research aims to provide an overview of the existent digital solutions for birth plans’ creation, intending to contribute for the advance of e-health services focused on the perinatal decision-making process. Primary data was found through a web search procedure. Better ranked options complying with the following criteria were included: (a) available online and for free; (b) pregnant people as the target audience; (c) labor and/or birth plan creation features; (d) in English. Four online services were found, and a two part study was conducted: a) a non-exhaustive benchmarking-like analysis of webpages where the digital solutions to create birth plans were provided, according to six dimensions; b) followed by a content analysis of the digital solutions, resulting in 13 categories emerging, that were scored according to their occurrence and completeness. “Consent and Information” category had the lowest score, what is considered critical for the full purpose of a birth plan creation; while, “Freedom”, “Ambience and Equipment”, “People”, “Type of birth” and “Pain management” categories achieved the highest scores. Two solutions were considered particularly incomplete. Results show three solutions based on checklists, and one on visual icons. All solutions were based on a delivery approach, not including interactive or audiovisual components.
{"title":"e-Health Services to Support the Perinatal Decision-making Process: An Analysis of Digital Solutions to Create Birth Plans","authors":"Carla V. Leite, A. Almeida","doi":"10.5220/0010814600003123","DOIUrl":"https://doi.org/10.5220/0010814600003123","url":null,"abstract":"This research aims to provide an overview of the existent digital solutions for birth plans’ creation, intending to contribute for the advance of e-health services focused on the perinatal decision-making process. Primary data was found through a web search procedure. Better ranked options complying with the following criteria were included: (a) available online and for free; (b) pregnant people as the target audience; (c) labor and/or birth plan creation features; (d) in English. Four online services were found, and a two part study was conducted: a) a non-exhaustive benchmarking-like analysis of webpages where the digital solutions to create birth plans were provided, according to six dimensions; b) followed by a content analysis of the digital solutions, resulting in 13 categories emerging, that were scored according to their occurrence and completeness. “Consent and Information” category had the lowest score, what is considered critical for the full purpose of a birth plan creation; while, “Freedom”, “Ambience and Equipment”, “People”, “Type of birth” and “Pain management” categories achieved the highest scores. Two solutions were considered particularly incomplete. Results show three solutions based on checklists, and one on visual icons. All solutions were based on a delivery approach, not including interactive or audiovisual components.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84438662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0010823500003123
Pedro Almir Oliveira, R. Andrade, Pedro de A. Santos Neto, B. Oliveira
The Quality of Life has been studied for a long time, and the World Health Organization defines it as the individual perception about life regarding four major domains: physical, psychological, social, and environmental. The relevance to study QoL lies in the search for strategies able to measure a patient’s well-being. Without these strategies, treatments, and technological solutions that aim to improve people’s QoL would be restricted to physicians’ implicit and subjective perceptions. Thus, there are many instruments for formal QoL assessment (usually questionnaires). However, the use of these instruments is time-consuming, non-transparent, and error-prone. Considering this problem, in this work, we discuss the proposal to use the Internet of Health Things (IoHT) to collect data from smart environments and apply machine learning techniques to infer QoL measures. To achieve this goal, we designed an IoHT platform inspired by the MAPE-K loop. Our literature review has shown that this idea is promising and that there are many open challenges to be addressed.
{"title":"Towards an IoHT Platform to Monitor QoL Indicators","authors":"Pedro Almir Oliveira, R. Andrade, Pedro de A. Santos Neto, B. Oliveira","doi":"10.5220/0010823500003123","DOIUrl":"https://doi.org/10.5220/0010823500003123","url":null,"abstract":"The Quality of Life has been studied for a long time, and the World Health Organization defines it as the individual perception about life regarding four major domains: physical, psychological, social, and environmental. The relevance to study QoL lies in the search for strategies able to measure a patient’s well-being. Without these strategies, treatments, and technological solutions that aim to improve people’s QoL would be restricted to physicians’ implicit and subjective perceptions. Thus, there are many instruments for formal QoL assessment (usually questionnaires). However, the use of these instruments is time-consuming, non-transparent, and error-prone. Considering this problem, in this work, we discuss the proposal to use the Internet of Health Things (IoHT) to collect data from smart environments and apply machine learning techniques to infer QoL measures. To achieve this goal, we designed an IoHT platform inspired by the MAPE-K loop. Our literature review has shown that this idea is promising and that there are many open challenges to be addressed.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79642763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0010968700003123
Hajar Hakkoum, Ibtissam Abnane, A. Idri
Machine learning (ML) has been rapidly growing, mainly owing to the availability of historical datasets and advanced computational power. This growth is still facing a set of challenges, such as the interpretability of ML models. In particular, in the medical field, interpretability is a real bottleneck to the use of ML by physicians. This review was carried out according to the well-known systematic map process to analyse the literature on interpretability techniques when applied in the medical field with regard to different aspects. A total of 179 articles (1994-2020) were selected from six digital libraries. The results showed that the number of studies dealing with interpretability increased over the years with a dominance of solution proposals and experiment-based empirical type. Additionally, artificial neural networks were the most widely used ML black-box techniques investigated for interpretability.
{"title":"A Systematic Map of Interpretability in Medicine","authors":"Hajar Hakkoum, Ibtissam Abnane, A. Idri","doi":"10.5220/0010968700003123","DOIUrl":"https://doi.org/10.5220/0010968700003123","url":null,"abstract":"Machine learning (ML) has been rapidly growing, mainly owing to the availability of historical datasets and advanced computational power. This growth is still facing a set of challenges, such as the interpretability of ML models. In particular, in the medical field, interpretability is a real bottleneck to the use of ML by physicians. This review was carried out according to the well-known systematic map process to analyse the literature on interpretability techniques when applied in the medical field with regard to different aspects. A total of 179 articles (1994-2020) were selected from six digital libraries. The results showed that the number of studies dealing with interpretability increased over the years with a dominance of solution proposals and experiment-based empirical type. Additionally, artificial neural networks were the most widely used ML black-box techniques investigated for interpretability.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90406425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0010985400003123
M. Benchekroun, D. Istrate, V. Zalc, D. Lenne
Although chronic stress is proven to be very harmful to physical and mental well being, its diagnosis is punctual and nontrivial, which calls for reliable, continuous and automated stress monitoring systems that do not yet exist. Wireless biosensors offer opportunities to remotely detect and monitor mental stress levels, enabling improved diagnosis and early treatment. There are different algorithms and methods for wearable stress detection, however, only a few standard and publicly available datasets exist today. In this paper, we introduce a multi-modal high-quality stress detection dataset with details of the experimental protocol. The dataset includes physiological, behavioural and motion data from 74 subjects during a lab study. Different modalities such as electrocardiograms (ECG), photoplethysmograms (PPG), electrodermal activity (EDA), electromyograms (EMG) as well as three axis gyroscope and accelerometer data were recorded. In addition, protocol validation was achieved using both subject’s self-reports and cortisol levels which is considered as gold standard for stress detection.
{"title":"Mmsd: A Multi-modal Dataset for Real-time, Continuous Stress Detection from Physiological Signals","authors":"M. Benchekroun, D. Istrate, V. Zalc, D. Lenne","doi":"10.5220/0010985400003123","DOIUrl":"https://doi.org/10.5220/0010985400003123","url":null,"abstract":"Although chronic stress is proven to be very harmful to physical and mental well being, its diagnosis is punctual and nontrivial, which calls for reliable, continuous and automated stress monitoring systems that do not yet exist. Wireless biosensors offer opportunities to remotely detect and monitor mental stress levels, enabling improved diagnosis and early treatment. There are different algorithms and methods for wearable stress detection, however, only a few standard and publicly available datasets exist today. In this paper, we introduce a multi-modal high-quality stress detection dataset with details of the experimental protocol. The dataset includes physiological, behavioural and motion data from 74 subjects during a lab study. Different modalities such as electrocardiograms (ECG), photoplethysmograms (PPG), electrodermal activity (EDA), electromyograms (EMG) as well as three axis gyroscope and accelerometer data were recorded. In addition, protocol validation was achieved using both subject’s self-reports and cortisol levels which is considered as gold standard for stress detection.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78519304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0010782500003123
Rashmi Burse, M. Bertolotto, G. Mcardle
: Existing lexical auditing techniques for Quality Assurance (QA) of biomedical ontologies exclusively consider lexical patterns of concept names and do not take semantic domains associated with the tokens constituting those patterns into consideration. For many similar lexical patterns the corresponding semantic domains may not be similar. Therefore, not considering the semantic aspect of similar lexical patterns can lead to poor QA of biomedical ontologies. Semantic domain association can be accomplished by using a Biomedical Named Entity Recognition (Bio-NER) system. However, the existing Bio-NER systems are developed with the goal of extracting information from natural language text, like discharge summaries, and as a result do not annotate individual tokens of a clinical concept. Annotating individual tokens of a clinical concept with their semantic domains is important from a QA perspective, since these annotations can be leveraged to gain insight into the type of attributes that should be associated with the concept. In this paper we present an annotator that atomically annotates the tokens of a clinical concept by crafting atomic dictionaries from the sub-hierarchies of Systematized Nomenclature of Medicine (SNOMED). Semantic analysis of lexically similar concepts by atomically annotating semantic domains to the tokens will ensure improved QA of biomedical ontologies.
{"title":"A Novel Atomic Annotator for Quality Assurance of Biomedical Ontologies","authors":"Rashmi Burse, M. Bertolotto, G. Mcardle","doi":"10.5220/0010782500003123","DOIUrl":"https://doi.org/10.5220/0010782500003123","url":null,"abstract":": Existing lexical auditing techniques for Quality Assurance (QA) of biomedical ontologies exclusively consider lexical patterns of concept names and do not take semantic domains associated with the tokens constituting those patterns into consideration. For many similar lexical patterns the corresponding semantic domains may not be similar. Therefore, not considering the semantic aspect of similar lexical patterns can lead to poor QA of biomedical ontologies. Semantic domain association can be accomplished by using a Biomedical Named Entity Recognition (Bio-NER) system. However, the existing Bio-NER systems are developed with the goal of extracting information from natural language text, like discharge summaries, and as a result do not annotate individual tokens of a clinical concept. Annotating individual tokens of a clinical concept with their semantic domains is important from a QA perspective, since these annotations can be leveraged to gain insight into the type of attributes that should be associated with the concept. In this paper we present an annotator that atomically annotates the tokens of a clinical concept by crafting atomic dictionaries from the sub-hierarchies of Systematized Nomenclature of Medicine (SNOMED). Semantic analysis of lexically similar concepts by atomically annotating semantic domains to the tokens will ensure improved QA of biomedical ontologies.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77853036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0010767300003123
Jonas Chromik, Bjarne Pfitzner, Nina Ihde, Marius Michaelis, D. Schmidt, S. Klopfenstein, A. Poncette, F. Balzer, B. Arnrich
: Too many alarms are a persistent problem in today’s intensive care medicine leading to alarm desensitisation and alarm fatigue. This puts patients and staff at risk. We propose a forecasting strategy for threshold alarms in patient monitors in order to replace alarms that are actionable right now with scheduled tasks in an attempt to remove the urgency from the situation. Therefore, we employ both statistical and machine learning models for time series forecasting and apply these models to vital parameter data such as blood pressure, heart rate, and oxygen saturation. The results are promising, although impaired by low and non-constant sampling frequencies of the time series data in use. The combination of a GRU model with medium-resampled data shows the best performance for most types of alarms. However, higher time resolution and constant sampling frequencies are needed in order to meaningfully evaluate our approach.
{"title":"Forecasting Thresholds Alarms in Medical Patient Monitors using Time Series Models","authors":"Jonas Chromik, Bjarne Pfitzner, Nina Ihde, Marius Michaelis, D. Schmidt, S. Klopfenstein, A. Poncette, F. Balzer, B. Arnrich","doi":"10.5220/0010767300003123","DOIUrl":"https://doi.org/10.5220/0010767300003123","url":null,"abstract":": Too many alarms are a persistent problem in today’s intensive care medicine leading to alarm desensitisation and alarm fatigue. This puts patients and staff at risk. We propose a forecasting strategy for threshold alarms in patient monitors in order to replace alarms that are actionable right now with scheduled tasks in an attempt to remove the urgency from the situation. Therefore, we employ both statistical and machine learning models for time series forecasting and apply these models to vital parameter data such as blood pressure, heart rate, and oxygen saturation. The results are promising, although impaired by low and non-constant sampling frequencies of the time series data in use. The combination of a GRU model with medium-resampled data shows the best performance for most types of alarms. However, higher time resolution and constant sampling frequencies are needed in order to meaningfully evaluate our approach.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73090655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0010878900003123
K. Koutsoukos, Chrysostomos Symvoulidis, Athanasios Kiourtis, Argyro Mavrogiorgou, Stella Dimopoulou, D. Kyriazis
: The health industry has evolved significantly through the last years by adapting to the new technologies and exploiting them in order to upgrade the services that provides to the people. In this context, a lot of effort has been focused on converting medical documents to electronic health records and storing them online. However, taking into consideration the current innovations, it is doubtless that there are many limitations when these proposals are applied in a real-life scenario. For this reason, this paper proposes a system that combines electronic data storage and health record exchange between individuals and authenticated medical staff in a secure way. The specific recommendation is being evaluated through the corresponding applications and protocols that are developed and finally, the results exhibit the solutions over existing gaps.
{"title":"Emergency Health Protocols Supporting Health Data Exchange, Cloud Storage, and Indexing","authors":"K. Koutsoukos, Chrysostomos Symvoulidis, Athanasios Kiourtis, Argyro Mavrogiorgou, Stella Dimopoulou, D. Kyriazis","doi":"10.5220/0010878900003123","DOIUrl":"https://doi.org/10.5220/0010878900003123","url":null,"abstract":": The health industry has evolved significantly through the last years by adapting to the new technologies and exploiting them in order to upgrade the services that provides to the people. In this context, a lot of effort has been focused on converting medical documents to electronic health records and storing them online. However, taking into consideration the current innovations, it is doubtless that there are many limitations when these proposals are applied in a real-life scenario. For this reason, this paper proposes a system that combines electronic data storage and health record exchange between individuals and authenticated medical staff in a secure way. The specific recommendation is being evaluated through the corresponding applications and protocols that are developed and finally, the results exhibit the solutions over existing gaps.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73266588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0010893800003123
Anastasios Lamproudis, Aron Henriksson, H. Dalianis
: Research has shown that using generic language models – specifically, BERT models – in specialized domains may be sub-optimal due to domain differences in language use and vocabulary. There are several techniques for developing domain-specific language models that leverage the use of existing generic language models, including continued and domain-adaptive pretraining with in-domain data. Here, we investigate a strategy based on using a domain-specific vocabulary, while leveraging a generic language model for initialization. The results demonstrate that domain-adaptive pretraining, in combination with a domain-specific vocabulary – as opposed to a general-domain vocabulary – yields improvements on two downstream clinical NLP tasks for Swedish. The results highlight the value of domain-adaptive pretraining when developing specialized language models and indicate that it is beneficial to adapt the vocabulary of the language model to the target domain prior to continued, domain-adaptive pretraining of a generic language model.
{"title":"Vocabulary Modifications for Domain-adaptive Pretraining of Clinical Language Models","authors":"Anastasios Lamproudis, Aron Henriksson, H. Dalianis","doi":"10.5220/0010893800003123","DOIUrl":"https://doi.org/10.5220/0010893800003123","url":null,"abstract":": Research has shown that using generic language models – specifically, BERT models – in specialized domains may be sub-optimal due to domain differences in language use and vocabulary. There are several techniques for developing domain-specific language models that leverage the use of existing generic language models, including continued and domain-adaptive pretraining with in-domain data. Here, we investigate a strategy based on using a domain-specific vocabulary, while leveraging a generic language model for initialization. The results demonstrate that domain-adaptive pretraining, in combination with a domain-specific vocabulary – as opposed to a general-domain vocabulary – yields improvements on two downstream clinical NLP tasks for Swedish. The results highlight the value of domain-adaptive pretraining when developing specialized language models and indicate that it is beneficial to adapt the vocabulary of the language model to the target domain prior to continued, domain-adaptive pretraining of a generic language model.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79112081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0010973900003123
A. Sashima
: As personal mobile devices, such as smartphones and smartwatches, are increasingly commoditized, it has become easier to measure individual physiological and physical states and record them continuously. Applying machine learning techniques to the data, we can detect early signs of diseases in older people, such as dementia, and predict probabilities of future disorders. This review paper describes the machine learning technologies in realizing wearable healthcare for older people. First, we survey the literature on machine-learning-driven wearable technologies for the early detection of dementia. Second, we discuss issues of the datasets for constructing ML models. Third, we describe the need for a service framework to collect longitudinal data through continuous monitoring of the user’s health status. Finally, we discuss the socially acceptable implementation of the service framework.
{"title":"Machine-learning-driven Wearable Healthcare for Dementia: A Review of Emerging Technologies and Challenges","authors":"A. Sashima","doi":"10.5220/0010973900003123","DOIUrl":"https://doi.org/10.5220/0010973900003123","url":null,"abstract":": As personal mobile devices, such as smartphones and smartwatches, are increasingly commoditized, it has become easier to measure individual physiological and physical states and record them continuously. Applying machine learning techniques to the data, we can detect early signs of diseases in older people, such as dementia, and predict probabilities of future disorders. This review paper describes the machine learning technologies in realizing wearable healthcare for older people. First, we survey the literature on machine-learning-driven wearable technologies for the early detection of dementia. Second, we discuss issues of the datasets for constructing ML models. Third, we describe the need for a service framework to collect longitudinal data through continuous monitoring of the user’s health status. Finally, we discuss the socially acceptable implementation of the service framework.","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84340658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.5220/0010825100003123
Noemi Stuppia, Federico Sternini, Federica Miola, G. Picci, Claudia Boarini, F. Cabitza, Alice Ravizza
Introduction: Usability evaluation is a core aspect in risk assessment of medical devices, as it aims to ensure the device interface safety, avoiding that usability problems at interface level are not related to harm. Methods: Our research group applied our risk-based approach, international reference standards and guidelines to the usability evaluation of a large family of SaMD. The methodology used for the evaluation is an elaboration of regulatory prescriptions and is composed of a combination of quantitative and qualitative methods. In particular, the usability evaluation is structured in a two-stage evaluation composed by formative and summative evaluation. The formative stage is propaedeutic for the planning of the summative evaluation. The final assessment included the analysis of quantitative data collected through three questionnaires and a
{"title":"Risk-based Comprehensive Usability Evaluation of Software as a Medical Device","authors":"Noemi Stuppia, Federico Sternini, Federica Miola, G. Picci, Claudia Boarini, F. Cabitza, Alice Ravizza","doi":"10.5220/0010825100003123","DOIUrl":"https://doi.org/10.5220/0010825100003123","url":null,"abstract":"Introduction: Usability evaluation is a core aspect in risk assessment of medical devices, as it aims to ensure the device interface safety, avoiding that usability problems at interface level are not related to harm. Methods: Our research group applied our risk-based approach, international reference standards and guidelines to the usability evaluation of a large family of SaMD. The methodology used for the evaluation is an elaboration of regulatory prescriptions and is composed of a combination of quantitative and qualitative methods. In particular, the usability evaluation is structured in a two-stage evaluation composed by formative and summative evaluation. The formative stage is propaedeutic for the planning of the summative evaluation. The final assessment included the analysis of quantitative data collected through three questionnaires and a","PeriodicalId":20676,"journal":{"name":"Proceedings of the International Conference on Health Informatics and Medical Application Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82320840","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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