Mercy E. Edoho, M. Ekpenyong, Aliu B. Momodu, Geoffery Joseph
Access to clinical data is critical for advancing translational research; but regulatory constraints and policies surrounding the use of clinical data often challenge data access and sharing. Mixed medical datasets (structured and unstructured) are increasingly dominating the clinical information space, hence, demanding AI-driven techniques such as Natural Language Processing-to reorganize them for effective usage. This paper excavates the HMDB (Human Metabolome Database), for efficient knowledge mining, supported by diversely certified oncology physicians and pharmacists' contributions. We propose a novel taxonomy for knowledge representation and establish a universe of discourse for disease clustering and prediction. Excavated data include metabolites and their respective concentration values, age, gender, as well as gene and protein sequences, of normal and abnormal patients. These data were then merged to form an AI-ready 'Omic' technology datasets. Preliminary results reveal that the proposed AI-ready datasets would aid precision oncology research by adding quality analysis to the present HMDB, and for explaining the variations in concentration values of cancer patients.
{"title":"Mining the Human Metabolome for Precision Oncology Research","authors":"Mercy E. Edoho, M. Ekpenyong, Aliu B. Momodu, Geoffery Joseph","doi":"10.1145/3418094.3418123","DOIUrl":"https://doi.org/10.1145/3418094.3418123","url":null,"abstract":"Access to clinical data is critical for advancing translational research; but regulatory constraints and policies surrounding the use of clinical data often challenge data access and sharing. Mixed medical datasets (structured and unstructured) are increasingly dominating the clinical information space, hence, demanding AI-driven techniques such as Natural Language Processing-to reorganize them for effective usage. This paper excavates the HMDB (Human Metabolome Database), for efficient knowledge mining, supported by diversely certified oncology physicians and pharmacists' contributions. We propose a novel taxonomy for knowledge representation and establish a universe of discourse for disease clustering and prediction. Excavated data include metabolites and their respective concentration values, age, gender, as well as gene and protein sequences, of normal and abnormal patients. These data were then merged to form an AI-ready 'Omic' technology datasets. Preliminary results reveal that the proposed AI-ready datasets would aid precision oncology research by adding quality analysis to the present HMDB, and for explaining the variations in concentration values of cancer patients.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126328990","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}
Xia Wu, Yulong Qi, Fei Feng, Guanxun Cheng, N. Zhang
To determine if pharmacokinetic information derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could improve the diagnostic value of breast disease. A total of thirty-six female patients (range, 30-70 years, mean 48.1±12.6 years) confirmed BIRADS 3~6 underwent DCE-MRI were retrospectively recruited to this study. Modified two-compartment Tofts model and Brix mode were used to obtain relevant tracer kinetic parameters. Mann-Whitney U-test was used to compare the parameters between the BIRADS 3 and 4, BIRADS 4 and 5, BIRADS 5 and 6, respectively. A P value of less than 0.05 was considered to indicate a significant difference. In evaluating significance between BIRADS 3 and BIRADS 4, the statistical analysis showed that Brix-kep (P=0.014), ABrix (P<0.001), TTP (P=0.022) were independent factors associated with the discrepancy. All kinetic parameters we calculated were independent factors associated with the discrepancy between BIRADS 4 and BIRADS 5. ABrix (AUC=0.915) do have a good discriminative power between BIRADS 3 and 4. Tofts-kep (AUC= 0.952), ABrix (AUC= 0.990) do have a good discriminative power between BIRADS 4 and 5.The numeric values of Brix-kep, Brix-kel, ABrix, Tofts-ktrans, Tofts-kep and Slope were higher in high grades (BIRADS 5 and BIRADS 6) than in low grades (BIRADS 3 and BIRADS 4) tumors. ABrix have a good discriminative power between BIRADS 3 and 4. Tofts-kep, ABrix do have a good discriminative power between BIRADS 4 and 5. No significant difference between BIRADS 5 and 6.
{"title":"Correlation Between Pharmacokinetic Analysis of DCE-MRI and Breast Image Reporting and Data System (BIRADS) Classification","authors":"Xia Wu, Yulong Qi, Fei Feng, Guanxun Cheng, N. Zhang","doi":"10.1145/3418094.3418106","DOIUrl":"https://doi.org/10.1145/3418094.3418106","url":null,"abstract":"To determine if pharmacokinetic information derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) could improve the diagnostic value of breast disease. A total of thirty-six female patients (range, 30-70 years, mean 48.1±12.6 years) confirmed BIRADS 3~6 underwent DCE-MRI were retrospectively recruited to this study. Modified two-compartment Tofts model and Brix mode were used to obtain relevant tracer kinetic parameters. Mann-Whitney U-test was used to compare the parameters between the BIRADS 3 and 4, BIRADS 4 and 5, BIRADS 5 and 6, respectively. A P value of less than 0.05 was considered to indicate a significant difference. In evaluating significance between BIRADS 3 and BIRADS 4, the statistical analysis showed that Brix-kep (P=0.014), ABrix (P<0.001), TTP (P=0.022) were independent factors associated with the discrepancy. All kinetic parameters we calculated were independent factors associated with the discrepancy between BIRADS 4 and BIRADS 5. ABrix (AUC=0.915) do have a good discriminative power between BIRADS 3 and 4. Tofts-kep (AUC= 0.952), ABrix (AUC= 0.990) do have a good discriminative power between BIRADS 4 and 5.The numeric values of Brix-kep, Brix-kel, ABrix, Tofts-ktrans, Tofts-kep and Slope were higher in high grades (BIRADS 5 and BIRADS 6) than in low grades (BIRADS 3 and BIRADS 4) tumors. ABrix have a good discriminative power between BIRADS 3 and 4. Tofts-kep, ABrix do have a good discriminative power between BIRADS 4 and 5. No significant difference between BIRADS 5 and 6.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129410190","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}
Ling Yan, Zuojian Zhou, Xuhao Sun, Yihua Song, Yamei Bai
Atrial fibrillation is closely related to hypertension. In view of the few studies on the combination of atrial fibrillation and blood pressure, an atrial fibrillation sphygmomanometer which can measure both atrial fibrillation and blood pressure simultaneously is developed. Meanwhile, a cloud platform for atrial fibrillation early warning service and a discriminant model for atrial fibrillation are built. Supported by mobile Internet, Internet of Things and cloud computing, with atrial fibrillation and hypertension as the research object, an atrial fibrillation model was established by mixing circulating neural network (RNN) and short-term memory network (LSTM). Then we applied the model to MIT-BIH Atrial Fibrillation Database and results verified that the accuracy is as high as 98.9%. Finally, to make the system more comprehensive, we developed patient-side and physician-side APPs, including atrial fibrillation recognition, physician teleservice and health care recommendations, and doctors monitor patient synopsis in real time and provide personalized medical services.
{"title":"Research and Design of Atrial Fibrillation Early Warning Service System Based on Mobile Internet","authors":"Ling Yan, Zuojian Zhou, Xuhao Sun, Yihua Song, Yamei Bai","doi":"10.1145/3418094.3418119","DOIUrl":"https://doi.org/10.1145/3418094.3418119","url":null,"abstract":"Atrial fibrillation is closely related to hypertension. In view of the few studies on the combination of atrial fibrillation and blood pressure, an atrial fibrillation sphygmomanometer which can measure both atrial fibrillation and blood pressure simultaneously is developed. Meanwhile, a cloud platform for atrial fibrillation early warning service and a discriminant model for atrial fibrillation are built. Supported by mobile Internet, Internet of Things and cloud computing, with atrial fibrillation and hypertension as the research object, an atrial fibrillation model was established by mixing circulating neural network (RNN) and short-term memory network (LSTM). Then we applied the model to MIT-BIH Atrial Fibrillation Database and results verified that the accuracy is as high as 98.9%. Finally, to make the system more comprehensive, we developed patient-side and physician-side APPs, including atrial fibrillation recognition, physician teleservice and health care recommendations, and doctors monitor patient synopsis in real time and provide personalized medical services.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131494593","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}
Shibabroto Banerjee, Pourush Sood, S. Ghose, P. Das
Photoplethysmography is a non-invasive and low-cost modality for assessing blood oxygen and volume variations. It is used extensively by physicians for basic monitoring tasks. These signals, however, as prior work has shown, have a plethora of interesting features and can be used for the diagnosis of several cardiovascular diseases. In this article, we aim to detect Coronary Artery Disease (CAD) using Photoplethysmographic signal features. We outline a simple signal processing method to extract these features. Machine learning-based approaches are then used to train classifiers that detect the presence of cardiac distress based on these features. We observe that the proposed method is effective in detecting CAD in a MIMIC-III, a benchmark data set. The technique can be used for low-cost monitoring of early signs of cardiac diseases.
{"title":"Coronary Artery Disease Classification from Photoplethysmographic Signals","authors":"Shibabroto Banerjee, Pourush Sood, S. Ghose, P. Das","doi":"10.1145/3418094.3418116","DOIUrl":"https://doi.org/10.1145/3418094.3418116","url":null,"abstract":"Photoplethysmography is a non-invasive and low-cost modality for assessing blood oxygen and volume variations. It is used extensively by physicians for basic monitoring tasks. These signals, however, as prior work has shown, have a plethora of interesting features and can be used for the diagnosis of several cardiovascular diseases. In this article, we aim to detect Coronary Artery Disease (CAD) using Photoplethysmographic signal features. We outline a simple signal processing method to extract these features. Machine learning-based approaches are then used to train classifiers that detect the presence of cardiac distress based on these features. We observe that the proposed method is effective in detecting CAD in a MIMIC-III, a benchmark data set. The technique can be used for low-cost monitoring of early signs of cardiac diseases.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116036145","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}
The research community has been noticing the importance of the online forums in healthcare in understanding the nuances of health-related problems. Breast Cancer is the most common malignance among women worldwide and its survival rate is steadily increasing thanks to early diagnosis and timely and effective treatment. Still how they manage the disease and maintain the quality of daily life are worth understanding. And it is in this context that the online posts of patients with breast cancer, discussing several topics, become patient generated content. The multi-label nature with these posts arising out of the combined contents of these posts can bring out a multitude of divulging conclusions. The resulting classification issues of these online posts under various categories of topics is examined in the present work. A semi-supervised multi-label classification, followed by refinement of multiple assignments of labels based on fuzzy logic and a neighborhood technique is proposed in the paper. Multiplicity of labels, occurs during the assignment of labels to clusters based on proximity. While extending the refined label set to a greater number of unlabeled posts clustered on the basis of proximity, it is observed that the proposed method could bring out more information on the description of posts. The results thus convey that the most discussed topic in the posts is about diagnosis, along with tangential reference to adverse drug effect, presumably to offer support in terms of information or viewpoint. The results show how the diverse nature of multiple labels can be effectively harnessed to draw conclusion from the potential of social media posts of patients' experience in critical health problems.
{"title":"Multi-label Topic Classification of Patient Generated Content in a Breast-cancer Community Forum","authors":"B. Athira, S. M. Idicula, Josette F. Jones","doi":"10.1145/3418094.3418132","DOIUrl":"https://doi.org/10.1145/3418094.3418132","url":null,"abstract":"The research community has been noticing the importance of the online forums in healthcare in understanding the nuances of health-related problems. Breast Cancer is the most common malignance among women worldwide and its survival rate is steadily increasing thanks to early diagnosis and timely and effective treatment. Still how they manage the disease and maintain the quality of daily life are worth understanding. And it is in this context that the online posts of patients with breast cancer, discussing several topics, become patient generated content. The multi-label nature with these posts arising out of the combined contents of these posts can bring out a multitude of divulging conclusions. The resulting classification issues of these online posts under various categories of topics is examined in the present work. A semi-supervised multi-label classification, followed by refinement of multiple assignments of labels based on fuzzy logic and a neighborhood technique is proposed in the paper. Multiplicity of labels, occurs during the assignment of labels to clusters based on proximity. While extending the refined label set to a greater number of unlabeled posts clustered on the basis of proximity, it is observed that the proposed method could bring out more information on the description of posts. The results thus convey that the most discussed topic in the posts is about diagnosis, along with tangential reference to adverse drug effect, presumably to offer support in terms of information or viewpoint. The results show how the diverse nature of multiple labels can be effectively harnessed to draw conclusion from the potential of social media posts of patients' experience in critical health problems.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128964591","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}
Cross-sectional research aimed to assess the prevalence and identifying the association between sodium intake, sleep quality and the Quality of Life (QoL) among hill tribes in Chiang Rai Province. Stratified Random Sampling was used to recruit 2,831 participants from eight hill tribes group. The prevalence of sodium intake more than 2,000 mg/day found that 90.67%. The Pittsburgh Sleep Quality Index (PSQI) reported that half of them (50.6%) had poor sleep quality and most of them had a medium level of quality of life 72.5% (2,054 persons). The univariate analysis found that four factors were significantly associated with sodium intake: Adding sauce, adding seasoning powder, PSQI, QOL respectively. The multiple logistic regression analysis revealed the only low quality of life can predict sodium consumption. This research shows that they should have to receive intervention to reduce sodium intake and emphasize hill tribe population of the low quality of life to find health care alternatives.
{"title":"The Association of Sodium Intake on Sleep Quality and Quality of Life of Hill Tribes in Chiang Rai Province, Northern Thailand","authors":"Phatcharin Winyangkul, Anusara Pongjanta, Watcharapong Ruankham","doi":"10.1145/3418094.3418113","DOIUrl":"https://doi.org/10.1145/3418094.3418113","url":null,"abstract":"Cross-sectional research aimed to assess the prevalence and identifying the association between sodium intake, sleep quality and the Quality of Life (QoL) among hill tribes in Chiang Rai Province. Stratified Random Sampling was used to recruit 2,831 participants from eight hill tribes group. The prevalence of sodium intake more than 2,000 mg/day found that 90.67%. The Pittsburgh Sleep Quality Index (PSQI) reported that half of them (50.6%) had poor sleep quality and most of them had a medium level of quality of life 72.5% (2,054 persons). The univariate analysis found that four factors were significantly associated with sodium intake: Adding sauce, adding seasoning powder, PSQI, QOL respectively. The multiple logistic regression analysis revealed the only low quality of life can predict sodium consumption. This research shows that they should have to receive intervention to reduce sodium intake and emphasize hill tribe population of the low quality of life to find health care alternatives.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125620650","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}
Cardiovascular disease results great life and economics threat to the patients and their family members around the world. Optical coherence tomography (OCT) image not only obtains higher resolution and faster image modality to assess coronary vessels, but also provides safer guidance for micro scale medical interventions. Deep learning network frameworks have been considered as a promising approach for pathology feature classification and segmentation of computerized diagnosis. In compare with universal dataset for object recognition, atherosclerotic coronary plaque of different patients usually involves different pathology character and formation modality. Limited samples and weakly labeling always cause plaque mis-classification and evaluation. In order to improve recognition accuracy, a novel regional based fully convolutional network with deformable deep networks has been developed to realize plaque object recognition for OCT images. The deformable convolution and regional of interest pooling can adapt to the anchor scales and offsets change to improve classification precision for different pathology character and formation modality of different patients. Recognition experiments from atherosclerotic coronary plaque image have validated the effectiveness and performance of proposed method.
{"title":"Deformable Deep Network Atherosclerotic Coronary Plaque Recognition of Oct Imaging","authors":"Chaoyu Sun, Hai Huang, Zhaoliang Wan","doi":"10.1145/3418094.3418107","DOIUrl":"https://doi.org/10.1145/3418094.3418107","url":null,"abstract":"Cardiovascular disease results great life and economics threat to the patients and their family members around the world. Optical coherence tomography (OCT) image not only obtains higher resolution and faster image modality to assess coronary vessels, but also provides safer guidance for micro scale medical interventions. Deep learning network frameworks have been considered as a promising approach for pathology feature classification and segmentation of computerized diagnosis. In compare with universal dataset for object recognition, atherosclerotic coronary plaque of different patients usually involves different pathology character and formation modality. Limited samples and weakly labeling always cause plaque mis-classification and evaluation. In order to improve recognition accuracy, a novel regional based fully convolutional network with deformable deep networks has been developed to realize plaque object recognition for OCT images. The deformable convolution and regional of interest pooling can adapt to the anchor scales and offsets change to improve classification precision for different pathology character and formation modality of different patients. Recognition experiments from atherosclerotic coronary plaque image have validated the effectiveness and performance of proposed method.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124447772","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}
Myat Su Yin, P. Haddawy, Benedikt W. Hosp, P. Sa-ngasoongsong, Thanwarat Tanprathumwong, Madereen Sayo, Supawit Yangyuenpradorn, A. Supratak
Arthroscopic shoulder surgery is an advanced orthopedic surgical procedure, which is particularly challenging due to the complex anatomy of the shoulder, and tight spaces for navigation, which also limits the view from the arthroscope. In carrying out arthroscopy, the ability to quickly and effectively navigate through the joint to reach a desired location is essential. Novices often experience confusion in trying to triangulate the information from arthroscopy output with the background knowledge of anatomy while orienting and navigating the instruments. In this paper, we report on the results of the first cadaveric eye-tracking study of arthroscopic surgery in which we investigate differences in perception between experts and novices. Novices' perception is analyzed with cognitive load analysis throughout the procedure and specifically, during the portions of the procedure in which subjects are observed to be confused. In investigating such portions, the gaze data analysis is supplemented with head rotations and acceleration information from gyroscope and accelerometer sensors from the eye tracker. We also use the gathered eye tracking metrics to construct a model to classify subjects into expert/novice. We find statistically significant relations between head movement as well as pupil diameter and periods of confusion. We identify a subset of the metrics that we use to build a simple classifier that is able to distinguish between novices and experts with accuracy of 84%.
{"title":"A Study of Expert/Novice Perception in Arthroscopic Shoulder Surgery","authors":"Myat Su Yin, P. Haddawy, Benedikt W. Hosp, P. Sa-ngasoongsong, Thanwarat Tanprathumwong, Madereen Sayo, Supawit Yangyuenpradorn, A. Supratak","doi":"10.1145/3418094.3418135","DOIUrl":"https://doi.org/10.1145/3418094.3418135","url":null,"abstract":"Arthroscopic shoulder surgery is an advanced orthopedic surgical procedure, which is particularly challenging due to the complex anatomy of the shoulder, and tight spaces for navigation, which also limits the view from the arthroscope. In carrying out arthroscopy, the ability to quickly and effectively navigate through the joint to reach a desired location is essential. Novices often experience confusion in trying to triangulate the information from arthroscopy output with the background knowledge of anatomy while orienting and navigating the instruments. In this paper, we report on the results of the first cadaveric eye-tracking study of arthroscopic surgery in which we investigate differences in perception between experts and novices. Novices' perception is analyzed with cognitive load analysis throughout the procedure and specifically, during the portions of the procedure in which subjects are observed to be confused. In investigating such portions, the gaze data analysis is supplemented with head rotations and acceleration information from gyroscope and accelerometer sensors from the eye tracker. We also use the gathered eye tracking metrics to construct a model to classify subjects into expert/novice. We find statistically significant relations between head movement as well as pupil diameter and periods of confusion. We identify a subset of the metrics that we use to build a simple classifier that is able to distinguish between novices and experts with accuracy of 84%.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121473987","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}
M. Ekpenyong, S. Udoh, Mercy E. Edoho, Ifiok J. Udo, E. Udo, Temitope Fakiyesi, S. B. Oyong
Incorporating evidence-based healthcare practice would improve patients' response and safety and make patients partners in current healthcare practice. This partnership is certain to offer patients the opportunity to guide safety initiatives through data access by clinicians and encourage evidence-based healthcare while alleviating potential medical errors. In this paper, we promote a collaborative model that integrates interrelated concepts for responsive healthcare services that target patient-centred healthcare--with healthcare providers and relevant stakeholders in the loop. The implementation strategies for fulfilling the desired healthcare outcomes as well as design implications are also provided. The model is expected to offer transformative impact that would drive our weak healthcare system for improved healthcare and complement the huge dearth in healthcare services. The outcome is shared prosperity and health, and a mainstream of the people into healthcare decision making for informed policy planning and implementation.
{"title":"Hybrid Collaborative Model for Evidence-Based Healthcare Practice","authors":"M. Ekpenyong, S. Udoh, Mercy E. Edoho, Ifiok J. Udo, E. Udo, Temitope Fakiyesi, S. B. Oyong","doi":"10.1145/3418094.3418105","DOIUrl":"https://doi.org/10.1145/3418094.3418105","url":null,"abstract":"Incorporating evidence-based healthcare practice would improve patients' response and safety and make patients partners in current healthcare practice. This partnership is certain to offer patients the opportunity to guide safety initiatives through data access by clinicians and encourage evidence-based healthcare while alleviating potential medical errors. In this paper, we promote a collaborative model that integrates interrelated concepts for responsive healthcare services that target patient-centred healthcare--with healthcare providers and relevant stakeholders in the loop. The implementation strategies for fulfilling the desired healthcare outcomes as well as design implications are also provided. The model is expected to offer transformative impact that would drive our weak healthcare system for improved healthcare and complement the huge dearth in healthcare services. The outcome is shared prosperity and health, and a mainstream of the people into healthcare decision making for informed policy planning and implementation.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130104186","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}
This study aims to identify the effect between organizational learning and service quality in public hospitals. The theoretical framework is derived based on Behavioral Theory of Firm by Cyert and March (1963) and Donabedian's Healthcare Quality Model. Service quality is discussed as endogenous construct. The Schwandt's organizational learning system model is evaluated as exogenous construct. The unit of analysis consists of clinical and clinical support departments with a sample size of 221 from 23 public hospitals in Peninsula Malaysia. Patients' perception on the departments' service quality is evaluated, and organizational learning and organizational innovativeness are assessed by the department heads in public hospitals. Patients' response data from 884 were aggregated to match the 221 responses of the department head. Structural equation modeling with AMOS and SPSS was applied to analyze data. Findings reveal that all hypotheses are supported. Organizational innovativeness shows a partial mediating effect between learning activity and service quality, and between performing actions and service quality. Service quality provides the organizational performance feedback to improve the learning activity for the human resource development. Thus, this study implies that the need for human resource development is imperative in contributing towards service performance to patients in the public hospitals.
{"title":"Behavioural Understanding of Hospital Service Quality: Organisational Learning and Organisational Innovativeness","authors":"Thilageswary Arumugam, S. Arumugam","doi":"10.1145/3418094.3418104","DOIUrl":"https://doi.org/10.1145/3418094.3418104","url":null,"abstract":"This study aims to identify the effect between organizational learning and service quality in public hospitals. The theoretical framework is derived based on Behavioral Theory of Firm by Cyert and March (1963) and Donabedian's Healthcare Quality Model. Service quality is discussed as endogenous construct. The Schwandt's organizational learning system model is evaluated as exogenous construct. The unit of analysis consists of clinical and clinical support departments with a sample size of 221 from 23 public hospitals in Peninsula Malaysia. Patients' perception on the departments' service quality is evaluated, and organizational learning and organizational innovativeness are assessed by the department heads in public hospitals. Patients' response data from 884 were aggregated to match the 221 responses of the department head. Structural equation modeling with AMOS and SPSS was applied to analyze data. Findings reveal that all hypotheses are supported. Organizational innovativeness shows a partial mediating effect between learning activity and service quality, and between performing actions and service quality. Service quality provides the organizational performance feedback to improve the learning activity for the human resource development. Thus, this study implies that the need for human resource development is imperative in contributing towards service performance to patients in the public hospitals.","PeriodicalId":192804,"journal":{"name":"Proceedings of the 4th International Conference on Medical and Health Informatics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122234884","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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