Pub Date : 2024-04-03DOI: 10.3389/fdgth.2024.1359771
Saskia Neumann, Christoph M. Bauer, Luca Nastasi, Julia Läderach, Eva Thürlimann, A. Schwarz, J.P.O. Held, C. A. Easthope
Introduction Wearables are potentially valuable tools for understanding mobility behavior in individuals with neurological disorders and how it changes depending on health status, such as after rehabilitation. However, the accurate detection of gait events, which are crucial for the evaluation of gait performance and quality, is challenging due to highly individual-specific patterns that also vary greatly in movement and speed, especially after stroke. Therefore, the purpose of this study was to assess the accuracy, concurrent validity, and test–retest reliability of a commercially available insole system in the detection of gait events and the calculation of stance duration in individuals with chronic stroke. Methods Pressure insole data were collected from 17 individuals with chronic stroke during two measurement blocks, each comprising three 10-min walking tests conducted in a clinical setting. The gait assessments were recorded with a video camera that served as a ground truth, and pressure insoles as an experimental system. We compared the number of gait events and stance durations between systems. Results and discussion Over all 3,820 gait events, 90.86% were correctly identified by the insole system. Recall values ranged from 0.994 to 1, with a precision of 1 for all measurements. The F1 score ranged from 0.997 to 1. Excellent absolute agreement (Intraclass correlation coefficient, ICC = 0.874) was observed for the calculation of the stance duration, with a slightly longer stance duration recorded by the insole system (difference of −0.01 s). Bland–Altmann analysis indicated limits of agreement of 0.33 s that were robust to changes in walking speed. This consistency makes the system well-suited for individuals post-stroke. The test–retest reliability between measurement timepoints T1 and T2 was excellent (ICC = 0.928). The mean difference in stance duration between T1 and T2 was 0.03 s. We conclude that the insole system is valid for use in a clinical setting to quantitatively assess continuous walking in individuals with stroke.
{"title":"Accuracy, concurrent validity, and test–retest reliability of pressure-based insoles for gait measurement in chronic stroke patients","authors":"Saskia Neumann, Christoph M. Bauer, Luca Nastasi, Julia Läderach, Eva Thürlimann, A. Schwarz, J.P.O. Held, C. A. Easthope","doi":"10.3389/fdgth.2024.1359771","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1359771","url":null,"abstract":"Introduction Wearables are potentially valuable tools for understanding mobility behavior in individuals with neurological disorders and how it changes depending on health status, such as after rehabilitation. However, the accurate detection of gait events, which are crucial for the evaluation of gait performance and quality, is challenging due to highly individual-specific patterns that also vary greatly in movement and speed, especially after stroke. Therefore, the purpose of this study was to assess the accuracy, concurrent validity, and test–retest reliability of a commercially available insole system in the detection of gait events and the calculation of stance duration in individuals with chronic stroke. Methods Pressure insole data were collected from 17 individuals with chronic stroke during two measurement blocks, each comprising three 10-min walking tests conducted in a clinical setting. The gait assessments were recorded with a video camera that served as a ground truth, and pressure insoles as an experimental system. We compared the number of gait events and stance durations between systems. Results and discussion Over all 3,820 gait events, 90.86% were correctly identified by the insole system. Recall values ranged from 0.994 to 1, with a precision of 1 for all measurements. The F1 score ranged from 0.997 to 1. Excellent absolute agreement (Intraclass correlation coefficient, ICC = 0.874) was observed for the calculation of the stance duration, with a slightly longer stance duration recorded by the insole system (difference of −0.01 s). Bland–Altmann analysis indicated limits of agreement of 0.33 s that were robust to changes in walking speed. This consistency makes the system well-suited for individuals post-stroke. The test–retest reliability between measurement timepoints T1 and T2 was excellent (ICC = 0.928). The mean difference in stance duration between T1 and T2 was 0.03 s. We conclude that the insole system is valid for use in a clinical setting to quantitatively assess continuous walking in individuals with stroke.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"175 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140748336","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 : 2024-04-02DOI: 10.3389/fdgth.2024.1345451
Tina Damalas, Eamon Penney, Theresa Cullen, Aaron Dibner-Dunlap, Cecelia English, Jacob Gomez, Amanda Sapp, Sara Selig, S. Sutermaster
Recent improvements in the accessibility of mapping tools and an increased recognition of the importance of leveraging data to inform public health operations has led to enthusiasm among public health departments to rapidly evolve their ability to analyze and apply data to programs. As the COVID-19 pandemic made evident, many health department data systems have been neglected for decades and data literacy among staff low. Significant federal dollars have been allocated to local health departments to modernize health systems. This case study recounts the effort to equip the Pima County Health Department with a highly sophisticated “COVID-19 Vaccines Solutions Dashboard” in 2021–2022, quantifying community vulnerability in the midst of the COVID-19 pandemic and shares key successes and challenges in process and outcomes that can guide other such dashboard initiatives. The experience informed the development of Pima' County Health Department's Data & Informatics Team as well as efforts to cultivate a more robust data culture throughout the department. Many health departments around the United States are in a similar position, and these lessons learned are widely applicable.
{"title":"Pima County COVID-19 vaccine solutions dashboard project: lessons learned","authors":"Tina Damalas, Eamon Penney, Theresa Cullen, Aaron Dibner-Dunlap, Cecelia English, Jacob Gomez, Amanda Sapp, Sara Selig, S. Sutermaster","doi":"10.3389/fdgth.2024.1345451","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1345451","url":null,"abstract":"Recent improvements in the accessibility of mapping tools and an increased recognition of the importance of leveraging data to inform public health operations has led to enthusiasm among public health departments to rapidly evolve their ability to analyze and apply data to programs. As the COVID-19 pandemic made evident, many health department data systems have been neglected for decades and data literacy among staff low. Significant federal dollars have been allocated to local health departments to modernize health systems. This case study recounts the effort to equip the Pima County Health Department with a highly sophisticated “COVID-19 Vaccines Solutions Dashboard” in 2021–2022, quantifying community vulnerability in the midst of the COVID-19 pandemic and shares key successes and challenges in process and outcomes that can guide other such dashboard initiatives. The experience informed the development of Pima' County Health Department's Data & Informatics Team as well as efforts to cultivate a more robust data culture throughout the department. Many health departments around the United States are in a similar position, and these lessons learned are widely applicable.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"47 13","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140755265","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 : 2024-03-28DOI: 10.3389/fdgth.2024.1359776
T. Unger, R. de Sousa Ribeiro, M. Mokni, T. Weikert, J. Pohl, A. Schwarz, J.P.O. Held, L. Sauerzopf, B. Kühnis, E. Gavagnin, A.R. Luft, R. Gassert, O. Lambercy, C. Awai Easthope, J.G. Schönhammer
Clinical assessment of upper limb sensorimotor function post-stroke is often constrained by low sensitivity and limited information on movement quality. To address this gap, recent studies proposed a standardized instrumented drinking task, as a representative daily activity combining different components of functional arm use. Although kinematic movement quality measures for this task are well-established, and optical motion capture (OMC) has proven effective in their measurement, its clinical application remains limited. Inertial Measurement Units (IMUs) emerge as a promising low-cost and user-friendly alternative, yet their validity and clinical relevance compared to the gold standard OMC need investigation.In this study, we conducted a measurement system comparison between IMUs and OMC, analyzing 15 established movement quality measures in 15 mild and moderate stroke patients performing the drinking task, using five IMUs placed on each wrist, upper arm, and trunk.Our findings revealed strong agreement between the systems, with 12 out of 15 measures demonstrating clinical applicability, evidenced by Limits of Agreement (LoA) below the Minimum Clinically Important Differences (MCID) for each measure.These results are promising, suggesting the clinical applicability of IMUs in quantifying movement quality for mildly and moderately impaired stroke patients performing the drinking task.
{"title":"Upper limb movement quality measures: comparing IMUs and optical motion capture in stroke patients performing a drinking task","authors":"T. Unger, R. de Sousa Ribeiro, M. Mokni, T. Weikert, J. Pohl, A. Schwarz, J.P.O. Held, L. Sauerzopf, B. Kühnis, E. Gavagnin, A.R. Luft, R. Gassert, O. Lambercy, C. Awai Easthope, J.G. Schönhammer","doi":"10.3389/fdgth.2024.1359776","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1359776","url":null,"abstract":"Clinical assessment of upper limb sensorimotor function post-stroke is often constrained by low sensitivity and limited information on movement quality. To address this gap, recent studies proposed a standardized instrumented drinking task, as a representative daily activity combining different components of functional arm use. Although kinematic movement quality measures for this task are well-established, and optical motion capture (OMC) has proven effective in their measurement, its clinical application remains limited. Inertial Measurement Units (IMUs) emerge as a promising low-cost and user-friendly alternative, yet their validity and clinical relevance compared to the gold standard OMC need investigation.In this study, we conducted a measurement system comparison between IMUs and OMC, analyzing 15 established movement quality measures in 15 mild and moderate stroke patients performing the drinking task, using five IMUs placed on each wrist, upper arm, and trunk.Our findings revealed strong agreement between the systems, with 12 out of 15 measures demonstrating clinical applicability, evidenced by Limits of Agreement (LoA) below the Minimum Clinically Important Differences (MCID) for each measure.These results are promising, suggesting the clinical applicability of IMUs in quantifying movement quality for mildly and moderately impaired stroke patients performing the drinking task.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"31 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140372083","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 : 2024-03-26DOI: 10.3389/fdgth.2024.1377165
Rawan S. Abdulsadig, Esther Rodriguez-Villegas
Class imbalance is a common challenge that is often faced when dealing with classification tasks aiming to detect medical events that are particularly infrequent. Apnoea is an example of such events. This challenge can however be mitigated using class rebalancing algorithms. This work investigated 10 widely used data-level class imbalance mitigation methods aiming towards building a random forest (RF) model that attempts to detect apnoea events from photoplethysmography (PPG) signals acquired from the neck. Those methods are random undersampling (RandUS), random oversampling (RandOS), condensed nearest-neighbors (CNNUS), edited nearest-neighbors (ENNUS), Tomek’s links (TomekUS), synthetic minority oversampling technique (SMOTE), Borderline-SMOTE (BLSMOTE), adaptive synthetic oversampling (ADASYN), SMOTE with TomekUS (SMOTETomek) and SMOTE with ENNUS (SMOTEENN). Feature-space transformation using PCA and KernelPCA was also examined as a potential way of providing better representations of the data for the class rebalancing methods to operate. This work showed that RandUS is the best option for improving the sensitivity score (up to 11%). However, it could hinder the overall accuracy due to the reduced amount of training data. On the other hand, augmenting the data with new artificial data points was shown to be a non-trivial task that needs further development, especially in the presence of subject dependencies, as was the case in this work.
{"title":"A comparative study in class imbalance mitigation when working with physiological signals","authors":"Rawan S. Abdulsadig, Esther Rodriguez-Villegas","doi":"10.3389/fdgth.2024.1377165","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1377165","url":null,"abstract":"Class imbalance is a common challenge that is often faced when dealing with classification tasks aiming to detect medical events that are particularly infrequent. Apnoea is an example of such events. This challenge can however be mitigated using class rebalancing algorithms. This work investigated 10 widely used data-level class imbalance mitigation methods aiming towards building a random forest (RF) model that attempts to detect apnoea events from photoplethysmography (PPG) signals acquired from the neck. Those methods are random undersampling (RandUS), random oversampling (RandOS), condensed nearest-neighbors (CNNUS), edited nearest-neighbors (ENNUS), Tomek’s links (TomekUS), synthetic minority oversampling technique (SMOTE), Borderline-SMOTE (BLSMOTE), adaptive synthetic oversampling (ADASYN), SMOTE with TomekUS (SMOTETomek) and SMOTE with ENNUS (SMOTEENN). Feature-space transformation using PCA and KernelPCA was also examined as a potential way of providing better representations of the data for the class rebalancing methods to operate. This work showed that RandUS is the best option for improving the sensitivity score (up to 11%). However, it could hinder the overall accuracy due to the reduced amount of training data. On the other hand, augmenting the data with new artificial data points was shown to be a non-trivial task that needs further development, especially in the presence of subject dependencies, as was the case in this work.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"103 51","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140380634","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 : 2024-03-06DOI: 10.3389/fdgth.2024.1265846
Tracy Milner, Matthew R. G. Brown, Chelsea Jones, Ada W. S. Leung, S. Brémault-Phillips
Mild Cognitive Impairment (MCI) poses a challenge for a growing population worldwide. Early identification of risk for and diagnosis of MCI is critical to providing the right interventions at the right time. The paucity of reliable, valid, and scalable methods for predicting, diagnosing, and monitoring MCI with traditional biomarkers is noteworthy. Digital biomarkers hold new promise in understanding MCI. Identifying digital biomarkers specifically for MCI, however, is complex. The biomarker profile for MCI is expected to be multidimensional with multiple phenotypes based on different etiologies. Advanced methodological approaches, such as high-dimensional statistics and deep machine learning, will be needed to build these multidimensional digital biomarker profiles for MCI. Comparing patients to these MCI phenotypes in clinical practice can assist clinicians in better determining etiologies, some of which may be reversible, and developing more precise care plans. Key considerations in developing reliable multidimensional digital biomarker profiles specific to an MCI population are also explored.
{"title":"Multidimensional digital biomarker phenotypes for mild cognitive impairment: considerations for early identification, diagnosis and monitoring","authors":"Tracy Milner, Matthew R. G. Brown, Chelsea Jones, Ada W. S. Leung, S. Brémault-Phillips","doi":"10.3389/fdgth.2024.1265846","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1265846","url":null,"abstract":"Mild Cognitive Impairment (MCI) poses a challenge for a growing population worldwide. Early identification of risk for and diagnosis of MCI is critical to providing the right interventions at the right time. The paucity of reliable, valid, and scalable methods for predicting, diagnosing, and monitoring MCI with traditional biomarkers is noteworthy. Digital biomarkers hold new promise in understanding MCI. Identifying digital biomarkers specifically for MCI, however, is complex. The biomarker profile for MCI is expected to be multidimensional with multiple phenotypes based on different etiologies. Advanced methodological approaches, such as high-dimensional statistics and deep machine learning, will be needed to build these multidimensional digital biomarker profiles for MCI. Comparing patients to these MCI phenotypes in clinical practice can assist clinicians in better determining etiologies, some of which may be reversible, and developing more precise care plans. Key considerations in developing reliable multidimensional digital biomarker profiles specific to an MCI population are also explored.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140077735","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 : 2024-03-01DOI: 10.3389/fdgth.2024.1368666
Carol Maher, Ben Singh
{"title":"Editorial: Insights in digital health communication: 2023","authors":"Carol Maher, Ben Singh","doi":"10.3389/fdgth.2024.1368666","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1368666","url":null,"abstract":"","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"99 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140086820","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 : 2024-02-19DOI: 10.3389/fdgth.2024.1259409
Ahmad Rayan, S. Al-Ghabeesh, Mirna Fawaz, Amal Behar, Amina Toumi
The aim of the study is to assess the experiences, barriers, and expectations regarding current patient monitoring systems among intensive care unit nurses at one university hospital.A qualitative exploratory study approach was adopted to test the research questions.Intensive care unit personnel placed a high value on practical criteria such as user friendliness and visualization while assessing the present monitoring system. Poor alarm handling was recognized as possible patient safety hazards. The necessity of high accessibility was highlighted once again for a prospective system; wireless, noninvasive, and interoperability of monitoring devices were requested; and smart phones for distant patient monitoring and alert management improvement were required.Core comments from ICU personnel are included in this qualitative research on patient monitoring. All national healthcare involved parties must focus more on user-derived insights to ensure a speedy and effective introduction of digital health technologies in the ICU. The findings from the alarm control or mobile device studies might be utilized to train ICU personnel to use new technology, minimize alarm fatigue, increase medical device accessibility, and develop interoperability standards in critical care practice.
{"title":"Experiences, barriers and expectations regarding current patient monitoring systems among ICU nurses in a University Hospital in Lebanon: a qualitative study","authors":"Ahmad Rayan, S. Al-Ghabeesh, Mirna Fawaz, Amal Behar, Amina Toumi","doi":"10.3389/fdgth.2024.1259409","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1259409","url":null,"abstract":"The aim of the study is to assess the experiences, barriers, and expectations regarding current patient monitoring systems among intensive care unit nurses at one university hospital.A qualitative exploratory study approach was adopted to test the research questions.Intensive care unit personnel placed a high value on practical criteria such as user friendliness and visualization while assessing the present monitoring system. Poor alarm handling was recognized as possible patient safety hazards. The necessity of high accessibility was highlighted once again for a prospective system; wireless, noninvasive, and interoperability of monitoring devices were requested; and smart phones for distant patient monitoring and alert management improvement were required.Core comments from ICU personnel are included in this qualitative research on patient monitoring. All national healthcare involved parties must focus more on user-derived insights to ensure a speedy and effective introduction of digital health technologies in the ICU. The findings from the alarm control or mobile device studies might be utilized to train ICU personnel to use new technology, minimize alarm fatigue, increase medical device accessibility, and develop interoperability standards in critical care practice.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"19 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139958381","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 : 2024-02-16DOI: 10.3389/fdgth.2024.1321485
Benyamine Abbou, Boris Kessel, M. Ben Natan, R. Gabbay-Benziv, Dikla Dahan Shriki, Anna Ophir, Nimrod Goldschmid, Adi Klein, Ariel Roguin, M. Dudkiewicz
Healthcare organizations operate in a data-rich environment and depend on digital computerized systems; thus, they may be exposed to cyber threats. Indeed, one of the most vulnerable sectors to hacks and malware is healthcare. However, the impact of cyberattacks on healthcare organizations remains under-investigated.This study aims to describe a major attack on an entire medical center that resulted in a complete shutdown of all computer systems and to identify the critical actions required to resume regular operations.This study was conducted on a public, general, and acute care referral university teaching hospital.We report the different recovery measures on various hospital clinical activities and their impact on clinical work.The system malfunction of hospital computers did not reduce the number of heart catheterizations, births, or outpatient clinic visits. However, a sharp drop in surgical activities, emergency room visits, and total hospital occupancy was observed immediately and during the first postattack week. A gradual increase in all clinical activities was detected starting in the second week after the attack, with a significant increase of 30% associated with the restoration of the electronic medical records (EMR) and laboratory module and a 50% increase associated with the return of the imaging module archiving. One limitation of the present study is that, due to its retrospective design, there were no data regarding the number of elective internal care hospitalizations that were considered crucial.The risk of ransomware cyberattacks is growing. Healthcare systems at all levels of the hospital should be aware of this threat and implement protocols should this catastrophic event occur. Careful evaluation of steady computer system recovery weekly enables vital hospital function, even under a major cyberattack. The restoration of EMR, laboratory systems, and imaging archiving modules was found to be the most significant factor that allowed the return to normal clinical hospital work.
{"title":"When all computers shut down: the clinical impact of a major cyber-attack on a general hospital","authors":"Benyamine Abbou, Boris Kessel, M. Ben Natan, R. Gabbay-Benziv, Dikla Dahan Shriki, Anna Ophir, Nimrod Goldschmid, Adi Klein, Ariel Roguin, M. Dudkiewicz","doi":"10.3389/fdgth.2024.1321485","DOIUrl":"https://doi.org/10.3389/fdgth.2024.1321485","url":null,"abstract":"Healthcare organizations operate in a data-rich environment and depend on digital computerized systems; thus, they may be exposed to cyber threats. Indeed, one of the most vulnerable sectors to hacks and malware is healthcare. However, the impact of cyberattacks on healthcare organizations remains under-investigated.This study aims to describe a major attack on an entire medical center that resulted in a complete shutdown of all computer systems and to identify the critical actions required to resume regular operations.This study was conducted on a public, general, and acute care referral university teaching hospital.We report the different recovery measures on various hospital clinical activities and their impact on clinical work.The system malfunction of hospital computers did not reduce the number of heart catheterizations, births, or outpatient clinic visits. However, a sharp drop in surgical activities, emergency room visits, and total hospital occupancy was observed immediately and during the first postattack week. A gradual increase in all clinical activities was detected starting in the second week after the attack, with a significant increase of 30% associated with the restoration of the electronic medical records (EMR) and laboratory module and a 50% increase associated with the return of the imaging module archiving. One limitation of the present study is that, due to its retrospective design, there were no data regarding the number of elective internal care hospitalizations that were considered crucial.The risk of ransomware cyberattacks is growing. Healthcare systems at all levels of the hospital should be aware of this threat and implement protocols should this catastrophic event occur. Careful evaluation of steady computer system recovery weekly enables vital hospital function, even under a major cyberattack. The restoration of EMR, laboratory systems, and imaging archiving modules was found to be the most significant factor that allowed the return to normal clinical hospital work.","PeriodicalId":504480,"journal":{"name":"Frontiers in Digital Health","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139961776","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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