2020
Balthazar, Patricia; Tajmir, Shahein H; Ortiz, Daniel A; Herse, Catherine C; Shea, Lindsey AG; Seals, Kevin F; Cohen-Addad, Dan; Purkayastha, Saptarshi; Gichoya, Judy W
The Artificial Intelligence Journal Club (# RADAIJC): A Multi-Institutional Resident-Driven Web-Based Educational Initiative Journal Article
In: Academic radiology, vol. 27, no. 1, pp. 136–139, 2020.
@article{balthazar2020artificial,
title = {The Artificial Intelligence Journal Club (# RADAIJC): A Multi-Institutional Resident-Driven Web-Based Educational Initiative},
author = {Patricia Balthazar and Shahein H Tajmir and Daniel A Ortiz and Catherine C Herse and Lindsey AG Shea and Kevin F Seals and Dan Cohen-Addad and Saptarshi Purkayastha and Judy W Gichoya},
year = {2020},
date = {2020-01-01},
journal = {Academic radiology},
volume = {27},
number = {1},
pages = {136--139},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purkayastha, Saptarshi; Addepally, Siva Abhishek; Bucher, Sherri
Engagement and Usability of a Cognitive Behavioral Therapy Mobile App Compared With Web-Based Cognitive Behavioral Therapy Among College Students: Randomized Heuristic Trial Journal Article
In: JMIR Human Factors, vol. 7, no. 1, pp. e14146, 2020.
@article{purkayastha2020engagement,
title = {Engagement and Usability of a Cognitive Behavioral Therapy Mobile App Compared With Web-Based Cognitive Behavioral Therapy Among College Students: Randomized Heuristic Trial},
author = {Saptarshi Purkayastha and Siva Abhishek Addepally and Sherri Bucher},
year = {2020},
date = {2020-01-01},
journal = {JMIR Human Factors},
volume = {7},
number = {1},
pages = {e14146},
publisher = {JMIR Publications Inc., Toronto, Canada},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Singh, AK; Guntu, Mounika; Bhimireddy, Ananth Reddy; Gichoya, Judy W; Purkayastha, Saptarshi
Multi-label natural language processing to identify diagnosis and procedure codes from MIMIC-III inpatient notes Journal Article
In: arXiv preprint arXiv:2003.07507, 2020.
@article{singh2020multi,
title = {Multi-label natural language processing to identify diagnosis and procedure codes from MIMIC-III inpatient notes},
author = {AK Singh and Mounika Guntu and Ananth Reddy Bhimireddy and Judy W Gichoya and Saptarshi Purkayastha},
year = {2020},
date = {2020-01-01},
journal = {arXiv preprint arXiv:2003.07507},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Kathiravelu, Pradeeban; Sharma, Ashish; Purkayastha, Saptarshi; Sinha, Priyanshu; Cadrin-Chenevert, Alexandre; Banerjee, Imon; Gichoya, Judy Wawira
Developing and Deploying Machine Learning Pipelines against Real-Time Image Streams from the PACS Journal Article
In: arXiv preprint arXiv:2004.07965, 2020.
@article{kathiravelu2020developing,
title = {Developing and Deploying Machine Learning Pipelines against Real-Time Image Streams from the PACS},
author = {Pradeeban Kathiravelu and Ashish Sharma and Saptarshi Purkayastha and Priyanshu Sinha and Alexandre Cadrin-Chenevert and Imon Banerjee and Judy Wawira Gichoya},
year = {2020},
date = {2020-01-01},
journal = {arXiv preprint arXiv:2004.07965},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Mathur, Varoon; Purkayashtha, Saptarshi; Gichoya, Judy Wawira
Artificial Intelligence for Global Health: Learning From a Decade of Digital Transformation in Health Care Journal Article
In: arXiv preprint arXiv:2005.12378, 2020.
@article{mathur2020artificial,
title = {Artificial Intelligence for Global Health: Learning From a Decade of Digital Transformation in Health Care},
author = {Varoon Mathur and Saptarshi Purkayashtha and Judy Wawira Gichoya},
year = {2020},
date = {2020-01-01},
journal = {arXiv preprint arXiv:2005.12378},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Umoren, Rachel A; Bucher, Sherri; Purkayastha, Saptarshi; Ezeaka, Chinyere; Esamai, Fabian; Mairami, Amsa; Asangansi, Ime; Bresnahan, Brian; Paton, Chris
eHBB/mHBS-DHIS2: Mobile Virtual Reality Provider Training in Helping Babies Breathetextregistered Miscellaneous
2020.
@misc{umoren2020ehbb,
title = {eHBB/mHBS-DHIS2: Mobile Virtual Reality Provider Training in Helping Babies Breathetextregistered},
author = {Rachel A Umoren and Sherri Bucher and Saptarshi Purkayastha and Chinyere Ezeaka and Fabian Esamai and Amsa Mairami and Ime Asangansi and Brian Bresnahan and Chris Paton},
year = {2020},
date = {2020-01-01},
publisher = {American Academy of Pediatrics},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Umoren, Rachel A; Ezeaka, Chinyere; Esamai, Fabian; Kshatriya, Bhavani Agnikula; Avanigadda, Prem; Clopp, Bailey; Ezenwa, Beatrice; Fajolu, Iretiola; Feltner, John; Makokha, Felicitas; others,
Pre-Training Cognitive and Psychomotor Gaps in Healthcare Worker Neonatal Resuscitation Skills for Helping Babies Breathe–A Report from the eHBB/mHBS Study Miscellaneous
2020.
@misc{umoren2020pre,
title = {Pre-Training Cognitive and Psychomotor Gaps in Healthcare Worker Neonatal Resuscitation Skills for Helping Babies Breathe--A Report from the eHBB/mHBS Study},
author = {Rachel A Umoren and Chinyere Ezeaka and Fabian Esamai and Bhavani Agnikula Kshatriya and Prem Avanigadda and Bailey Clopp and Beatrice Ezenwa and Iretiola Fajolu and John Feltner and Felicitas Makokha and others},
year = {2020},
date = {2020-01-01},
publisher = {American Academy of Pediatrics},
keywords = {},
pubstate = {published},
tppubtype = {misc}
}
Banerjee, Imon; Sinha, Priyanshu; Purkayastha, Saptarshi; Mashhaditafreshi, Nazanin; Tariq, Amara; Jeong, Jiwoong; Trivedi, Hari; Gichoya, Judy W
Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications Journal Article
In: arXiv preprint arXiv:2006.13262, 2020.
@article{banerjee2020there,
title = {Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications},
author = {Imon Banerjee and Priyanshu Sinha and Saptarshi Purkayastha and Nazanin Mashhaditafreshi and Amara Tariq and Jiwoong Jeong and Hari Trivedi and Judy W Gichoya},
year = {2020},
date = {2020-01-01},
journal = {arXiv preprint arXiv:2006.13262},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Phillips, Tyler; Yu, Xiaoyuan; Haakenson, Brandon; Goyal, Shreya; Zou, Xukai; Purkayastha, Saptarshi; Wu, Huanmei
AuthN-AuthZ: Integrated, User-Friendly and Privacy-Preserving Authentication and Authorization Proceedings Article
In: 2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), pp. 189–198, IEEE 2020.
@inproceedings{phillips2020authn,
title = {AuthN-AuthZ: Integrated, User-Friendly and Privacy-Preserving Authentication and Authorization},
author = {Tyler Phillips and Xiaoyuan Yu and Brandon Haakenson and Shreya Goyal and Xukai Zou and Saptarshi Purkayastha and Huanmei Wu},
year = {2020},
date = {2020-01-01},
booktitle = {2020 Second IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)},
pages = {189--198},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2019
Nuthakki, Siddhartha; Bucher, Sherri; Purkayastha, Saptarshi
The Development and Usability Testing of a Decision Support Mobile App for the Essential Care for Every Baby (ECEB) Program Conference
International Conference on Human-Computer Interaction Springer, Cham, 2019.
@conference{Nuthakki2019,
title = {The Development and Usability Testing of a Decision Support Mobile App for the Essential Care for Every Baby (ECEB) Program},
author = {Siddhartha Nuthakki and Sherri Bucher and Saptarshi Purkayastha},
year = {2019},
date = {2019-07-26},
pages = {259-263},
publisher = {Springer, Cham},
organization = {International Conference on Human-Computer Interaction},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Purkayastha, Saptarshi; Buddi, Surendra Babu; Nuthakki, Siddhartha; Yadav, Bhawana; Gichoya, Judy W
Evaluating the Implementation of Deep Learning in LibreHealth Radiology on Chest X-Rays Conference
Computer Vision Conference, Springer, Cham, 2019.
@conference{Purkayastha2019,
title = {Evaluating the Implementation of Deep Learning in LibreHealth Radiology on Chest X-Rays},
author = {Saptarshi Purkayastha and Surendra Babu Buddi and Siddhartha Nuthakki and Bhawana Yadav and Judy W Gichoya},
url = {https://scholarworks.iupui.edu/bitstream/handle/1805/18297/paper_258.pdf},
year = {2019},
date = {2019-04-25},
booktitle = {Computer Vision Conference},
pages = {648-657},
publisher = {Springer, Cham},
abstract = {Respiratory diseases are the dominant cause of deaths worldwide. In the US, the number of deaths due to chronic lung infections (mostly pneumonia and tuberculosis), lung cancer and chronic obstructive pulmonary disease has increased. The timely and accurate diagnosis of the disease is highly imperative to diminish the deaths. A chest X-ray is a vital diagnostic tool used for diagnosing lung diseases. Delay in X-Ray diagnosis is run-of-the-mill milieu and the reasons for the impediment are mostly because the X-ray reports are arduous to interpret, due to the complex visual contents of radiographs containing superimposed anatomical structures. A shortage of trained radiologists is another cause of increased workload and thus delay. We integrated CheXNet, a neural network algorithm into the LibreHealth Radiology Information System, which allows physicians to upload Chest X-rays and identify diagnosis probabilities. The uploaded images are evaluated from labels for 14 thoracic diseases. The turnaround time for each evaluation is about 30 seconds, which does not affect clinical workflow. A Python Flask application hosted web service is used to upload radiographs into a GPU server containing the algorithm. Thus, the use of this system is not limited to clients having their GPU server, but instead, we provide a web service. To evaluate the model, we randomly split the dataset into training (70%), validation (10%) and test (20%) sets. With over 86% accuracy and turnaround time under 30 seconds, the application demonstrates the feasibility of a web service for machine learning-based diagnosis of 14-lung pathologies from Chest X-rays.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Respiratory diseases are the dominant cause of deaths worldwide. In the US, the number of deaths due to chronic lung infections (mostly pneumonia and tuberculosis), lung cancer and chronic obstructive pulmonary disease has increased. The timely and accurate diagnosis of the disease is highly imperative to diminish the deaths. A chest X-ray is a vital diagnostic tool used for diagnosing lung diseases. Delay in X-Ray diagnosis is run-of-the-mill milieu and the reasons for the impediment are mostly because the X-ray reports are arduous to interpret, due to the complex visual contents of radiographs containing superimposed anatomical structures. A shortage of trained radiologists is another cause of increased workload and thus delay. We integrated CheXNet, a neural network algorithm into the LibreHealth Radiology Information System, which allows physicians to upload Chest X-rays and identify diagnosis probabilities. The uploaded images are evaluated from labels for 14 thoracic diseases. The turnaround time for each evaluation is about 30 seconds, which does not affect clinical workflow. A Python Flask application hosted web service is used to upload radiographs into a GPU server containing the algorithm. Thus, the use of this system is not limited to clients having their GPU server, but instead, we provide a web service. To evaluate the model, we randomly split the dataset into training (70%), validation (10%) and test (20%) sets. With over 86% accuracy and turnaround time under 30 seconds, the application demonstrates the feasibility of a web service for machine learning-based diagnosis of 14-lung pathologies from Chest X-rays.
Purkayastha, Saptarshi; Allam, Roshini; Maity, Pallavi; Gichoya, Judy W
Comparison of Open-Source Electronic Health Record Systems Based on Functional and User Performance Criteria Journal Article
In: Healthcare informatics research, vol. 25, no. 2, pp. 89-98, 2019.
@article{Purkayastha2019b,
title = {Comparison of Open-Source Electronic Health Record Systems Based on Functional and User Performance Criteria},
author = {Saptarshi Purkayastha and Roshini Allam and Pallavi Maity and Judy W Gichoya},
url = {https://synapse.koreamed.org/DOIx.php?id=10.4258/hir.2019.25.2.89},
doi = {doi.org/10.4258/hir.2019.25.2.89},
year = {2019},
date = {2019-04-01},
journal = {Healthcare informatics research},
volume = {25},
number = {2},
pages = {89-98},
abstract = {Objectives: Open-source Electronic Health Record (EHR) systems have gained importance. The main aim of our research is to guide organizational choice by comparing the features, functionality, and user-facing system performance of the five most popular open-source EHR systems.
Methods: We performed a qualitative content analysis with a directed approach on recently published literature (2012\textendash2017) to develop an integrated set of criteria to compare the EHR systems. The functional criteria are an integration of the literature, meaningful use criteria, and the Institute of Medicine's functional requirements of EHR, whereas the user-facing system performance is based on the time required to perform basic tasks within the EHR system.
Results: Based on the Alexa web ranking and Google Trends, the five most popular EHR systems at the time of our study were OSHERA VistA, GNU Health, the Open Medical Record System (OpenMRS), Open Electronic Medical Record (OpenEMR), and OpenEHR. We also found the trends in popularity of the EHR systems and the locations where they were more popular than others. OpenEMR met all the 32 functional criteria, OSHERA VistA met 28, OpenMRS met 12 fully and 11 partially, OpenEHR-based EHR met 10 fully and 3 partially, and GNU Health met the least with only 10 criteria fully and 2 partially.
Conclusions: Based on our functional criteria, OpenEMR is the most promising EHR system, closely followed by VistA. With regard to user-facing system performance, OpenMRS has superior performance in comparison to OpenEMR.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objectives: Open-source Electronic Health Record (EHR) systems have gained importance. The main aim of our research is to guide organizational choice by comparing the features, functionality, and user-facing system performance of the five most popular open-source EHR systems.
Methods: We performed a qualitative content analysis with a directed approach on recently published literature (2012–2017) to develop an integrated set of criteria to compare the EHR systems. The functional criteria are an integration of the literature, meaningful use criteria, and the Institute of Medicine’s functional requirements of EHR, whereas the user-facing system performance is based on the time required to perform basic tasks within the EHR system.
Results: Based on the Alexa web ranking and Google Trends, the five most popular EHR systems at the time of our study were OSHERA VistA, GNU Health, the Open Medical Record System (OpenMRS), Open Electronic Medical Record (OpenEMR), and OpenEHR. We also found the trends in popularity of the EHR systems and the locations where they were more popular than others. OpenEMR met all the 32 functional criteria, OSHERA VistA met 28, OpenMRS met 12 fully and 11 partially, OpenEHR-based EHR met 10 fully and 3 partially, and GNU Health met the least with only 10 criteria fully and 2 partially.
Conclusions: Based on our functional criteria, OpenEMR is the most promising EHR system, closely followed by VistA. With regard to user-facing system performance, OpenMRS has superior performance in comparison to OpenEMR.
Methods: We performed a qualitative content analysis with a directed approach on recently published literature (2012–2017) to develop an integrated set of criteria to compare the EHR systems. The functional criteria are an integration of the literature, meaningful use criteria, and the Institute of Medicine’s functional requirements of EHR, whereas the user-facing system performance is based on the time required to perform basic tasks within the EHR system.
Results: Based on the Alexa web ranking and Google Trends, the five most popular EHR systems at the time of our study were OSHERA VistA, GNU Health, the Open Medical Record System (OpenMRS), Open Electronic Medical Record (OpenEMR), and OpenEHR. We also found the trends in popularity of the EHR systems and the locations where they were more popular than others. OpenEMR met all the 32 functional criteria, OSHERA VistA met 28, OpenMRS met 12 fully and 11 partially, OpenEHR-based EHR met 10 fully and 3 partially, and GNU Health met the least with only 10 criteria fully and 2 partially.
Conclusions: Based on our functional criteria, OpenEMR is the most promising EHR system, closely followed by VistA. With regard to user-facing system performance, OpenMRS has superior performance in comparison to OpenEMR.
Kasthurirathne, Suranga N; Biondich, Paul G; Grannis, Shaun J; Purkayastha, Saptarshi; Vest, Joshua R; Jones, Josette F
In: Journal of medical Internet research, vol. 21, no. 7, pp. e13809, 2019.
@article{Kasthurirathne2019,
title = {Identification of Patients in Need of Advanced Care for Depression Using Data Extracted From a Statewide Health Information Exchange: A Machine Learning Approach},
author = {Suranga N Kasthurirathne and Paul G Biondich and Shaun J Grannis and Saptarshi Purkayastha and Joshua R Vest and Josette F Jones},
url = {https://www.jmir.org/2019/7/e13809/},
doi = {doi:10.2196/13809},
year = {2019},
date = {2019-01-02},
journal = {Journal of medical Internet research},
volume = {21},
number = {7},
pages = {e13809},
publisher = {JMIR Publications Inc., Toronto, Canada},
abstract = {Background: As the most commonly occurring form of mental illness worldwide, depression poses significant health and economic burdens to both the individual and community. Different types of depression pose different levels of risk. Individuals who suffer from mild forms of depression may recover without any assistance or be effectively managed by primary care or family practitioners. However, other forms of depression are far more severe and require advanced care by certified mental health providers. However, identifying cases of depression that require advanced care may be challenging to primary care providers and health care team members whose skill sets run broad rather than deep.
Objective: This study aimed to leverage a comprehensive range of patient-level diagnostic, behavioral, and demographic data, as well as past visit history data from a statewide health information exchange to build decision models capable of predicting the need of advanced care for depression across patients presenting at Eskenazi Health, the public safety net health system for Marion County, Indianapolis, Indiana.
Methods: Patient-level diagnostic, behavioral, demographic, and past visit history data extracted from structured datasets were merged with outcome variables extracted from unstructured free-text datasets and were used to train random forest decision models that predicted the need of advanced care for depression across (1) the overall patient population and (2) various subsets of patients at higher risk for depression-related adverse events; patients with a past diagnosis of depression; patients with a Charlson comorbidity index of ≥1; patients with a Charlson comorbidity index of ≥2; and all unique patients identified across the 3 above-mentioned high-risk groups.
Results: The overall patient population consisted of 84,317 adult (aged ≥18 years) patients. A total of 6992 (8.29%) of these patients were in need of advanced care for depression. Decision models for high-risk patient groups yielded area under the curve (AUC) scores between 86.31% and 94.43%. The decision model for the overall patient population yielded a comparatively lower AUC score of 78.87%. The variance of optimal sensitivity and specificity for all decision models, as identified using Youden J Index, is as follows: sensitivity=68.79% to 83.91% and specificity=76.03% to 92.18%.
Conclusions: This study demonstrates the ability to automate screening for patients in need of advanced care for depression across (1) an overall patient population or (2) various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors, and past visit history. Furthermore, these results show considerable potential to enable preventative care and can be easily integrated into existing clinical workflows to improve access to wraparound health care services.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: As the most commonly occurring form of mental illness worldwide, depression poses significant health and economic burdens to both the individual and community. Different types of depression pose different levels of risk. Individuals who suffer from mild forms of depression may recover without any assistance or be effectively managed by primary care or family practitioners. However, other forms of depression are far more severe and require advanced care by certified mental health providers. However, identifying cases of depression that require advanced care may be challenging to primary care providers and health care team members whose skill sets run broad rather than deep.
Objective: This study aimed to leverage a comprehensive range of patient-level diagnostic, behavioral, and demographic data, as well as past visit history data from a statewide health information exchange to build decision models capable of predicting the need of advanced care for depression across patients presenting at Eskenazi Health, the public safety net health system for Marion County, Indianapolis, Indiana.
Methods: Patient-level diagnostic, behavioral, demographic, and past visit history data extracted from structured datasets were merged with outcome variables extracted from unstructured free-text datasets and were used to train random forest decision models that predicted the need of advanced care for depression across (1) the overall patient population and (2) various subsets of patients at higher risk for depression-related adverse events; patients with a past diagnosis of depression; patients with a Charlson comorbidity index of ≥1; patients with a Charlson comorbidity index of ≥2; and all unique patients identified across the 3 above-mentioned high-risk groups.
Results: The overall patient population consisted of 84,317 adult (aged ≥18 years) patients. A total of 6992 (8.29%) of these patients were in need of advanced care for depression. Decision models for high-risk patient groups yielded area under the curve (AUC) scores between 86.31% and 94.43%. The decision model for the overall patient population yielded a comparatively lower AUC score of 78.87%. The variance of optimal sensitivity and specificity for all decision models, as identified using Youden J Index, is as follows: sensitivity=68.79% to 83.91% and specificity=76.03% to 92.18%.
Conclusions: This study demonstrates the ability to automate screening for patients in need of advanced care for depression across (1) an overall patient population or (2) various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors, and past visit history. Furthermore, these results show considerable potential to enable preventative care and can be easily integrated into existing clinical workflows to improve access to wraparound health care services.
Objective: This study aimed to leverage a comprehensive range of patient-level diagnostic, behavioral, and demographic data, as well as past visit history data from a statewide health information exchange to build decision models capable of predicting the need of advanced care for depression across patients presenting at Eskenazi Health, the public safety net health system for Marion County, Indianapolis, Indiana.
Methods: Patient-level diagnostic, behavioral, demographic, and past visit history data extracted from structured datasets were merged with outcome variables extracted from unstructured free-text datasets and were used to train random forest decision models that predicted the need of advanced care for depression across (1) the overall patient population and (2) various subsets of patients at higher risk for depression-related adverse events; patients with a past diagnosis of depression; patients with a Charlson comorbidity index of ≥1; patients with a Charlson comorbidity index of ≥2; and all unique patients identified across the 3 above-mentioned high-risk groups.
Results: The overall patient population consisted of 84,317 adult (aged ≥18 years) patients. A total of 6992 (8.29%) of these patients were in need of advanced care for depression. Decision models for high-risk patient groups yielded area under the curve (AUC) scores between 86.31% and 94.43%. The decision model for the overall patient population yielded a comparatively lower AUC score of 78.87%. The variance of optimal sensitivity and specificity for all decision models, as identified using Youden J Index, is as follows: sensitivity=68.79% to 83.91% and specificity=76.03% to 92.18%.
Conclusions: This study demonstrates the ability to automate screening for patients in need of advanced care for depression across (1) an overall patient population or (2) various high-risk patient groups using structured datasets covering acute and chronic conditions, patient demographics, behaviors, and past visit history. Furthermore, these results show considerable potential to enable preventative care and can be easily integrated into existing clinical workflows to improve access to wraparound health care services.
Purkayastha, Saptarshi; Guntu, Mounika; Ravindran, Radhika; Surapaneni, Asha Kiranmayee
Learning Gains of Process Oriented Guided Inquiry Learning in an Online Course Setting Proceedings Article
In: European Conference on e-Learning, pp. 495–XII, Academic Conferences International Limited 2019.
@inproceedings{purkayastha2019learning,
title = {Learning Gains of Process Oriented Guided Inquiry Learning in an Online Course Setting},
author = {Saptarshi Purkayastha and Mounika Guntu and Radhika Ravindran and Asha Kiranmayee Surapaneni},
year = {2019},
date = {2019-01-01},
booktitle = {European Conference on e-Learning},
pages = {495--XII},
organization = {Academic Conferences International Limited},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Nuthakki, Siddhartha; Neela, Sunil; Gichoya, Judy W; Purkayastha, Saptarshi
Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks Journal Article
In: arXiv preprint arXiv:1912.12397, 2019.
@article{nuthakki2019natural,
title = {Natural language processing of MIMIC-III clinical notes for identifying diagnosis and procedures with neural networks},
author = {Siddhartha Nuthakki and Sunil Neela and Judy W Gichoya and Saptarshi Purkayastha},
year = {2019},
date = {2019-01-01},
journal = {arXiv preprint arXiv:1912.12397},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Obidi, Joyce; Villa, Carlos Hipolito; Storch, Emily; Whitaker, Barbee I; Chada, Kinnera; Williams, Alan; Fowler, Stephanie; Schilling, Lisa; Kahn, Michael G; Edlavitch, Stanley A; others,
Trends in RED Blood CELL Transfusions within the Biologics Effectiveness and Safety (BEST) Initiative Network, 2012-2018 Proceedings Article
In: 2019 Annual Meeting, AABB 2019.
@inproceedings{obidi2019trends,
title = {Trends in RED Blood CELL Transfusions within the Biologics Effectiveness and Safety (BEST) Initiative Network, 2012-2018},
author = {Joyce Obidi and Carlos Hipolito Villa and Emily Storch and Barbee I Whitaker and Kinnera Chada and Alan Williams and Stephanie Fowler and Lisa Schilling and Michael G Kahn and Stanley A Edlavitch and others},
year = {2019},
date = {2019-01-01},
booktitle = {2019 Annual Meeting},
organization = {AABB},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Yandrapalli, Bhanu Teja; Jones, Josette; Purkayastha, Saptarshi
Development and Implementation of a Dashboard for Diabetes Care Management in OpenMRS Journal Article
In: arXiv preprint arXiv:1910.11437, 2019.
@article{teja2019development,
title = {Development and Implementation of a Dashboard for Diabetes Care Management in OpenMRS},
author = {Bhanu Teja Yandrapalli and Josette Jones and Saptarshi Purkayastha},
year = {2019},
date = {2019-01-01},
journal = {arXiv preprint arXiv:1910.11437},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Sinha, Priyanshu; Gichoya, Judy W; Purkayastha, Saptarshi
Full training versus fine tuning for radiology images concept detection task for the Image Conference
2019, ISSN: 1613-0073.
@conference{Sinha2019,
title = {Full training versus fine tuning for radiology images concept detection task for the Image},
author = {Priyanshu Sinha and Judy W Gichoya and Saptarshi Purkayastha },
issn = {1613-0073},
year = {2019},
date = {2019-00-00},
journal = {CEUR Workshop Proceedings,(CEUR-WS. org)},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Purkayastha, Saptarshi; Surapaneni, Asha K; Maity, Pallavi; Rajapuri, Anushri S; Gichoya, Judy W
Critical Components of Formative Assessment in Process-Oriented Guided Inquiry Learning for Online Labs Journal Article
In: Electronic Journal of e-Learning, vol. 17, no. 2, 2019.
@article{Purkayastha2019c,
title = {Critical Components of Formative Assessment in Process-Oriented Guided Inquiry Learning for Online Labs},
author = {Saptarshi Purkayastha and Asha K Surapaneni and Pallavi Maity and Anushri S Rajapuri and Judy W Gichoya},
url = {https://files.eric.ed.gov/fulltext/EJ1220140.pdf},
doi = {DOI: 10.34190/JEL.17.2.02},
year = {2019},
date = {2019-00-00},
journal = {Electronic Journal of e-Learning},
volume = {17},
number = {2},
abstract = {In the traditional lab setting, it is reasonably straightforward to monitor student learning and provide ongoing feedback. Such formative assessments can help students identify their strengths and weaknesses, and assist faculty to recognize where students are struggling and address problems immediately. But in an online virtual lab setting, formative assessment has challenges that go beyond space-time synchrony of online classroom. As we see increased enrollment in online courses, learning science needs to address the problem of formative assessment in online laboratory sessions. We
developed a student team learning monitor (STLM module) in an electronic health record system to measure student engagement and actualize the social constructivist approach of Process Oriented Guided Inquiry Learning (POGIL). Using iterative Plan-Do-Study-Act cycles in two undergraduate courses over a period of two years, we identified critical components that are required for the online implementation of POGIL. We reviewed published research on POGIL classroom implementations for the last ten years and identified some common elements that affect learning gains. We present the
critical components that are necessary for implementing POGIL in online lab settings, and refer to this as Cyber POGIL. Incorporating these critical components are required to determine when, how and the circumstances under which Cyber POGIL may be successfully implemented. We recommend that more online tools be developed for POGIL classrooms, which evolve from just providing synchronous communication to improved task monitoring and assistive feedback.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In the traditional lab setting, it is reasonably straightforward to monitor student learning and provide ongoing feedback. Such formative assessments can help students identify their strengths and weaknesses, and assist faculty to recognize where students are struggling and address problems immediately. But in an online virtual lab setting, formative assessment has challenges that go beyond space-time synchrony of online classroom. As we see increased enrollment in online courses, learning science needs to address the problem of formative assessment in online laboratory sessions. We
developed a student team learning monitor (STLM module) in an electronic health record system to measure student engagement and actualize the social constructivist approach of Process Oriented Guided Inquiry Learning (POGIL). Using iterative Plan-Do-Study-Act cycles in two undergraduate courses over a period of two years, we identified critical components that are required for the online implementation of POGIL. We reviewed published research on POGIL classroom implementations for the last ten years and identified some common elements that affect learning gains. We present the
critical components that are necessary for implementing POGIL in online lab settings, and refer to this as Cyber POGIL. Incorporating these critical components are required to determine when, how and the circumstances under which Cyber POGIL may be successfully implemented. We recommend that more online tools be developed for POGIL classrooms, which evolve from just providing synchronous communication to improved task monitoring and assistive feedback.
developed a student team learning monitor (STLM module) in an electronic health record system to measure student engagement and actualize the social constructivist approach of Process Oriented Guided Inquiry Learning (POGIL). Using iterative Plan-Do-Study-Act cycles in two undergraduate courses over a period of two years, we identified critical components that are required for the online implementation of POGIL. We reviewed published research on POGIL classroom implementations for the last ten years and identified some common elements that affect learning gains. We present the
critical components that are necessary for implementing POGIL in online lab settings, and refer to this as Cyber POGIL. Incorporating these critical components are required to determine when, how and the circumstances under which Cyber POGIL may be successfully implemented. We recommend that more online tools be developed for POGIL classrooms, which evolve from just providing synchronous communication to improved task monitoring and assistive feedback.
2018
Asha Kiranmayee Surapaneni Saptarshi Purkayastha, Pallavi Maity
Implementing Guided Inquiry Learning and Measuring Engagement Using an Electronic Health Record System in an Online Setting Conference
European Conference on e-Learning, Academic Conferences International Limited, 2018.
@conference{Purkayastha2018b,
title = {Implementing Guided Inquiry Learning and Measuring Engagement Using an Electronic Health Record System in an Online Setting},
author = {Saptarshi Purkayastha, Asha Kiranmayee Surapaneni, Pallavi Maity},
year = {2018},
date = {2018-11-01},
booktitle = {European Conference on e-Learning},
pages = {481-488},
publisher = {Academic Conferences International Limited},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Parvati Ravindranathan Menon Naliyatthaliyazchayil Saptarshi Purkayastha, Asha Kiranmayee Surapaneni
Improving "Desktop medicine" efficiency using Guided Inquiry Learning in an Electronic Health Records System Conference
Communications in Computer and Information Science, vol 852, Springer, Cham, 2018.
@conference{Purkayastha2018,
title = {Improving "Desktop medicine" efficiency using Guided Inquiry Learning in an Electronic Health Records System},
author = {Saptarshi Purkayastha, Parvati Ravindranathan Menon Naliyatthaliyazchayil, Asha Kiranmayee Surapaneni, Ashwini Kowkutla, Pallavi Maity},
editor = {Stephanidis C. HCI International 2018 \textendash Posters' Extended Abstracts. HCI 2018},
year = {2018},
date = {2018-07-18},
booktitle = {Communications in Computer and Information Science, vol 852},
journal = {Communications in Computer and Information Science, vol 852.},
publisher = {Springer, Cham},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Gichoya, Judy W; Nuthakki, Siddhartha; Maity, Pallavi G; Purkayastha, Saptarshi
Phronesis of AI in radiology: Superhuman meets natural stupidity Journal Article
In: arXiv preprint arXiv:1803.11244, 2018.
@article{gichoya2018phronesis,
title = {Phronesis of AI in radiology: Superhuman meets natural stupidity},
author = {Judy W Gichoya and Siddhartha Nuthakki and Pallavi G Maity and Saptarshi Purkayastha},
year = {2018},
date = {2018-01-01},
journal = {arXiv preprint arXiv:1803.11244},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gichoya, Judy W; Kohli, Marc; Ivange, Larry; Schmidt, Teri S; Purkayastha, Saptarshi
A Platform for Innovation and Standards Evaluation: a Case Study from the OpenMRS Open-Source Radiology Information System Journal Article
In: Journal of digital imaging, pp. 1–10, 2018.
@article{gichoya2018platform,
title = {A Platform for Innovation and Standards Evaluation: a Case Study from the OpenMRS Open-Source Radiology Information System},
author = {Judy W Gichoya and Marc Kohli and Larry Ivange and Teri S Schmidt and Saptarshi Purkayastha},
year = {2018},
date = {2018-01-01},
journal = {Journal of digital imaging},
pages = {1--10},
publisher = {Springer International Publishing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2017
Holden, Richard J; Kulanthaivel, Anand; Purkayastha, Saptarshi; Goggins, Kathryn M; Kripalani, Sunil
Know thy eHealth user: Development of biopsychosocial personas from a study of older adults with heart failure Journal Article
In: International Journal of Medical Informatics, vol. 108, pp. 158-167, 2017, ISSN: 1386-5056.
@article{holden2017know,
title = {Know thy eHealth user: Development of biopsychosocial personas from a study of older adults with heart failure},
author = { Richard J Holden and Anand Kulanthaivel and Saptarshi Purkayastha and Kathryn M Goggins and Sunil Kripalani},
doi = {10.1016/j.ijmedinf.2017.10.006},
issn = {1386-5056},
year = {2017},
date = {2017-12-01},
journal = {International Journal of Medical Informatics},
volume = {108},
pages = {158-167},
publisher = {Elsevier},
abstract = {Background
Personas are a canonical user-centered design method increasingly used in health informatics research. Personas\textemdashempirically-derived user archetypes\textemdashcan be used by eHealth designers to gain a robust understanding of their target end users such as patients.
Objective
To develop biopsychosocial personas of older patients with heart failure using quantitative analysis of survey data.
Method
Data were collected using standardized surveys and medical record abstraction from 32 older adults with heart failure recently hospitalized for acute heart failure exacerbation. Hierarchical cluster analysis was performed on a final dataset of n = 30. Nonparametric analyses were used to identify differences between clusters on 30 clustering variables and seven outcome variables.
Results
Six clusters were produced, ranging in size from two to eight patients per cluster. Clusters differed significantly on these biopsychosocial domains and subdomains: demographics (age, sex); medical status (comorbid diabetes); functional status (exhaustion, household work ability, hygiene care ability, physical ability); psychological status (depression, health literacy, numeracy); technology (Internet availability); healthcare system (visit by home healthcare, trust in providers); social context (informal caregiver support, cohabitation, marital status); and economic context (employment status). Tabular and narrative persona descriptions provide an easy reference guide for informatics designers.
Discussion
Personas development using approaches such as clustering of structured survey data is an important tool for health informatics professionals. We describe insights from our study of patients with heart failure, then recommend a generic ten-step personas development process. Methods strengths and limitations of the study and of personas development generally are discussed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background
Personas are a canonical user-centered design method increasingly used in health informatics research. Personas—empirically-derived user archetypes—can be used by eHealth designers to gain a robust understanding of their target end users such as patients.
Objective
To develop biopsychosocial personas of older patients with heart failure using quantitative analysis of survey data.
Method
Data were collected using standardized surveys and medical record abstraction from 32 older adults with heart failure recently hospitalized for acute heart failure exacerbation. Hierarchical cluster analysis was performed on a final dataset of n = 30. Nonparametric analyses were used to identify differences between clusters on 30 clustering variables and seven outcome variables.
Results
Six clusters were produced, ranging in size from two to eight patients per cluster. Clusters differed significantly on these biopsychosocial domains and subdomains: demographics (age, sex); medical status (comorbid diabetes); functional status (exhaustion, household work ability, hygiene care ability, physical ability); psychological status (depression, health literacy, numeracy); technology (Internet availability); healthcare system (visit by home healthcare, trust in providers); social context (informal caregiver support, cohabitation, marital status); and economic context (employment status). Tabular and narrative persona descriptions provide an easy reference guide for informatics designers.
Discussion
Personas development using approaches such as clustering of structured survey data is an important tool for health informatics professionals. We describe insights from our study of patients with heart failure, then recommend a generic ten-step personas development process. Methods strengths and limitations of the study and of personas development generally are discussed.
Personas are a canonical user-centered design method increasingly used in health informatics research. Personas—empirically-derived user archetypes—can be used by eHealth designers to gain a robust understanding of their target end users such as patients.
Objective
To develop biopsychosocial personas of older patients with heart failure using quantitative analysis of survey data.
Method
Data were collected using standardized surveys and medical record abstraction from 32 older adults with heart failure recently hospitalized for acute heart failure exacerbation. Hierarchical cluster analysis was performed on a final dataset of n = 30. Nonparametric analyses were used to identify differences between clusters on 30 clustering variables and seven outcome variables.
Results
Six clusters were produced, ranging in size from two to eight patients per cluster. Clusters differed significantly on these biopsychosocial domains and subdomains: demographics (age, sex); medical status (comorbid diabetes); functional status (exhaustion, household work ability, hygiene care ability, physical ability); psychological status (depression, health literacy, numeracy); technology (Internet availability); healthcare system (visit by home healthcare, trust in providers); social context (informal caregiver support, cohabitation, marital status); and economic context (employment status). Tabular and narrative persona descriptions provide an easy reference guide for informatics designers.
Discussion
Personas development using approaches such as clustering of structured survey data is an important tool for health informatics professionals. We describe insights from our study of patients with heart failure, then recommend a generic ten-step personas development process. Methods strengths and limitations of the study and of personas development generally are discussed.
Kasiiti, N; Wawira, J; Purkayastha, S; Were, MC
Comparative Performance Analysis of Different Fingerprint Biometric Scanners for Patient Matching. Conference
MedInfo 2017, vol. 245, 2017.
@conference{kasiiti2017comparative,
title = {Comparative Performance Analysis of Different Fingerprint Biometric Scanners for Patient Matching.},
author = {N Kasiiti and J Wawira and S Purkayastha and MC Were},
year = {2017},
date = {2017-11-01},
booktitle = {MedInfo 2017},
journal = {Studies in health technology and informatics},
volume = {245},
pages = {1053--1057},
keywords = {},
pubstate = {published},
tppubtype = {conference}
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Gichoya, Judy Wawira; Alarifi, Mohammad; Bhaduri, Ria; Tahir, Bilal; Purkayastha, Saptarshi
Using cognitive fit theory to evaluate patient understanding of medical images Proceedings Article
In: Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE, pp. 2430-2433, IEEE 2017.
@inproceedings{gichoya2017using,
title = {Using cognitive fit theory to evaluate patient understanding of medical images},
author = { Judy Wawira Gichoya and Mohammad Alarifi and Ria Bhaduri and Bilal Tahir and Saptarshi Purkayastha},
doi = {10.1109/EMBC.2017.8037347},
year = {2017},
date = {2017-07-11},
booktitle = {Engineering in Medicine and Biology Society (EMBC), 2017 39th Annual International Conference of the IEEE},
pages = {2430-2433},
organization = {IEEE},
abstract = {Patients are increasingly presented with their health data through patient portals in an attempt to engage patients in their own care. Due to the large amounts of data generated during a patient visit, the medical information when shared with patients can be overwhelming and cause anxiety due to lack of understanding. Health care organizations are attempting to improve transparency by providing patients with access to visit information. In this paper, we present our findings from a research study to evaluate patient understanding of medical images. We used cognitive fit theory to evaluate existing tools and images that are shared with patients and analyzed the relevance of such sharing. We discover that medical images need a lot of customization before they can be shared with patients. We suggest that new tools for medical imaging should be developed to fit the cognitive abilities of patients.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Patients are increasingly presented with their health data through patient portals in an attempt to engage patients in their own care. Due to the large amounts of data generated during a patient visit, the medical information when shared with patients can be overwhelming and cause anxiety due to lack of understanding. Health care organizations are attempting to improve transparency by providing patients with access to visit information. In this paper, we present our findings from a research study to evaluate patient understanding of medical images. We used cognitive fit theory to evaluate existing tools and images that are shared with patients and analyzed the relevance of such sharing. We discover that medical images need a lot of customization before they can be shared with patients. We suggest that new tools for medical imaging should be developed to fit the cognitive abilities of patients.
Oladiran, Olakunle; Gichoya, Judy; Purkayastha, Saptarshi
Conversion of JPG Image into DICOM Image Format with One Click Tagging Proceedings Article
In: International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, pp. 61-70, Springer, Cham 2017, ISBN: 978-3-319-58466-9.
@inproceedings{oladiran2017conversion,
title = {Conversion of JPG Image into DICOM Image Format with One Click Tagging},
author = { Olakunle Oladiran and Judy Gichoya and Saptarshi Purkayastha},
doi = {10.1007/978-3-319-58466-9_6},
isbn = {978-3-319-58466-9},
year = {2017},
date = {2017-05-14},
booktitle = {International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management},
pages = {61-70},
organization = {Springer, Cham},
abstract = {DICOM images are the centerpiece of radiological imaging. They contain a lot of metadata information about the patient, procedure, sequence of images, device and location. To modify, annotate or simply anonymize images for distribution, we often need to convert DICOM images to another format like jpeg since there are a number of image manipulation tools available for jpeg images compared to DICOM. As part of a research at our institution to customize radiology images to assess cognitive ability of multiple user groups, we created an open-source tool called Jpg2DicomTags, which is able to extract DICOM metadata tags, convert images to lossless jpg that can be manipulated and subsequently reconvert jpg images to DICOM by adding back the metadata tags. This tool provides a simple, easy to use user-interface for a tedious manual task that providers, researchers and patients might often need to do.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
DICOM images are the centerpiece of radiological imaging. They contain a lot of metadata information about the patient, procedure, sequence of images, device and location. To modify, annotate or simply anonymize images for distribution, we often need to convert DICOM images to another format like jpeg since there are a number of image manipulation tools available for jpeg images compared to DICOM. As part of a research at our institution to customize radiology images to assess cognitive ability of multiple user groups, we created an open-source tool called Jpg2DicomTags, which is able to extract DICOM metadata tags, convert images to lossless jpg that can be manipulated and subsequently reconvert jpg images to DICOM by adding back the metadata tags. This tool provides a simple, easy to use user-interface for a tedious manual task that providers, researchers and patients might often need to do.
Addepally, Siva Abhishek; Purkayastha, Saptarshi
Mobile-Application Based Cognitive Behavior Therapy (CBT) for Identifying and Managing Depression and Anxiety Proceedings Article
In: V., Duffy (Ed.): International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management, pp. 3–12, Springer, Cham, 2017, ISBN: 978-3-319-58466-9.
@inproceedings{addepally2017mobile,
title = {Mobile-Application Based Cognitive Behavior Therapy (CBT) for Identifying and Managing Depression and Anxiety},
author = { Siva Abhishek Addepally and Saptarshi Purkayastha},
editor = {Duffy V.},
doi = {10.1007/978-3-319-58466-9_1},
isbn = {978-3-319-58466-9},
year = {2017},
date = {2017-05-14},
booktitle = {International Conference on Digital Human Modeling and Applications in Health, Safety, Ergonomics and Risk Management},
pages = {3--12},
publisher = {Springer, Cham},
abstract = {Mobile technology is a cost effective and scalable platform for developing a therapeutic intervention. This paper discusses the development of a mobile application for people suffering with depression and anxiety. The application which we have developed is similar to a Cognitive Behavior Therapy (CBT) website, which is freely available on the internet. Past research has shown that CBT delivered over the internet is effective in alleviating the depressive symptoms in users. But, this delivery method is associated with some innate drawbacks, which caused user dropout and reduced adherence to the therapy. To overcome these shortfalls, from web based CBT delivery, a mobile application called MoodTrainer was developed. The application is equipped with mobile specific interventions and CBT modules which aim at delivering a dynamic supportive psychotherapy to the user. The mobile specific interventions using this application ensures that the user is constantly engaged with the application and focused to change the negative thought process. We present MoodTrainer as a self-efficacy tool and virtual CBT that is not meant to replace a clinical caregiver. Rather, it is a supportive tool that can be used to self-monitor, as well as a monitoring aid for clinicians.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Mobile technology is a cost effective and scalable platform for developing a therapeutic intervention. This paper discusses the development of a mobile application for people suffering with depression and anxiety. The application which we have developed is similar to a Cognitive Behavior Therapy (CBT) website, which is freely available on the internet. Past research has shown that CBT delivered over the internet is effective in alleviating the depressive symptoms in users. But, this delivery method is associated with some innate drawbacks, which caused user dropout and reduced adherence to the therapy. To overcome these shortfalls, from web based CBT delivery, a mobile application called MoodTrainer was developed. The application is equipped with mobile specific interventions and CBT modules which aim at delivering a dynamic supportive psychotherapy to the user. The mobile specific interventions using this application ensures that the user is constantly engaged with the application and focused to change the negative thought process. We present MoodTrainer as a self-efficacy tool and virtual CBT that is not meant to replace a clinical caregiver. Rather, it is a supportive tool that can be used to self-monitor, as well as a monitoring aid for clinicians.
Purkayastha, Saptarshi; Gichoya, Judy W; Addepally, Siva Abhishek
Implementation of a single sign-on system between practice, research and learning systems Journal Article
In: Applied Clinical Informatics, vol. 8, no. 01, pp. 306-312, 2017, ISSN: 1869-0327.
@article{purkayastha2017implementation,
title = {Implementation of a single sign-on system between practice, research and learning systems},
author = { Saptarshi Purkayastha and Judy W Gichoya and Siva Abhishek Addepally},
doi = {10.4338/ACI-2016-10-CR-0171},
issn = {1869-0327},
year = {2017},
date = {2017-01-01},
journal = {Applied Clinical Informatics},
volume = {8},
number = {01},
pages = {306-312},
publisher = {Schattauer GmbH},
abstract = {Background: Multiple specialized electronic medical systems are utilized in the health enterprise. Each of these systems has their own user management, authentication and authorization process, which makes it a complex web for navigation and use without a coherent process workflow. Users often have to remember multiple passwords, login/logout between systems that disrupt their clinical workflow. Challenges exist in managing permissions for various cadres of health care providers. Objectives: This case report describes our experience of implementing a single sign-on system, used between an electronic medical records system and a learning management system at a large academic institution with an informatics department responsible for student education and a medical school affiliated with a hospital system caring for patients and conducting research.
Methods: At our institution, we use OpenMRS for research registry tracking of interventional radiology patients as well as to provide access to medical records to students studying health informatics. To provide authentication across different users of the system with different permissions, we developed a Central Authentication Service (CAS) module for OpenMRS, released under the Mozilla Public License and deployed it for single sign-on across the academic enterprise. The module has been in implementation since August 2015 to present, and we assessed usability of the registry and education system before and after implementation of the CAS module. 54 students and 3 researchers were interviewed.
Results: The module authenticates users with appropriate privileges in the medical records system, providing secure access with minimal disruption to their workflow. No passwords requests were sent and users reported ease of use, with streamlined workflow.
Conclusions: The project demonstrates that enterprise-wide single sign-on systems should be used in healthcare to reduce complexity like “password hell”, improve usability and user navigation. We plan to extend this to work with other systems used in the health care enterprise.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Background: Multiple specialized electronic medical systems are utilized in the health enterprise. Each of these systems has their own user management, authentication and authorization process, which makes it a complex web for navigation and use without a coherent process workflow. Users often have to remember multiple passwords, login/logout between systems that disrupt their clinical workflow. Challenges exist in managing permissions for various cadres of health care providers. Objectives: This case report describes our experience of implementing a single sign-on system, used between an electronic medical records system and a learning management system at a large academic institution with an informatics department responsible for student education and a medical school affiliated with a hospital system caring for patients and conducting research.
Methods: At our institution, we use OpenMRS for research registry tracking of interventional radiology patients as well as to provide access to medical records to students studying health informatics. To provide authentication across different users of the system with different permissions, we developed a Central Authentication Service (CAS) module for OpenMRS, released under the Mozilla Public License and deployed it for single sign-on across the academic enterprise. The module has been in implementation since August 2015 to present, and we assessed usability of the registry and education system before and after implementation of the CAS module. 54 students and 3 researchers were interviewed.
Results: The module authenticates users with appropriate privileges in the medical records system, providing secure access with minimal disruption to their workflow. No passwords requests were sent and users reported ease of use, with streamlined workflow.
Conclusions: The project demonstrates that enterprise-wide single sign-on systems should be used in healthcare to reduce complexity like “password hell”, improve usability and user navigation. We plan to extend this to work with other systems used in the health care enterprise.
Methods: At our institution, we use OpenMRS for research registry tracking of interventional radiology patients as well as to provide access to medical records to students studying health informatics. To provide authentication across different users of the system with different permissions, we developed a Central Authentication Service (CAS) module for OpenMRS, released under the Mozilla Public License and deployed it for single sign-on across the academic enterprise. The module has been in implementation since August 2015 to present, and we assessed usability of the registry and education system before and after implementation of the CAS module. 54 students and 3 researchers were interviewed.
Results: The module authenticates users with appropriate privileges in the medical records system, providing secure access with minimal disruption to their workflow. No passwords requests were sent and users reported ease of use, with streamlined workflow.
Conclusions: The project demonstrates that enterprise-wide single sign-on systems should be used in healthcare to reduce complexity like “password hell”, improve usability and user navigation. We plan to extend this to work with other systems used in the health care enterprise.
Gichoya, Judy Wawira; Kohli, Marc D; Haste, Paul; Abigail, Elizabeth Mills; Johnson, Matthew S
Proving Value in Radiology: Experience Developing and Implementing a Shareable Open Source Registry Platform Driven by Radiology Workflow Journal Article
In: Journal of digital imaging, vol. 30, no. 5, pp. 602–608, 2017.
@article{gichoya2017proving,
title = {Proving Value in Radiology: Experience Developing and Implementing a Shareable Open Source Registry Platform Driven by Radiology Workflow},
author = { Judy Wawira Gichoya and Marc D Kohli and Paul Haste and Elizabeth Mills Abigail and Matthew S Johnson},
year = {2017},
date = {2017-01-01},
journal = {Journal of digital imaging},
volume = {30},
number = {5},
pages = {602--608},
publisher = {Springer},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Jung, Hye-Young; Gichoya, Judy Wawira; Vest, Joshua R
Providers’ Access of Imaging Versus Only Reports: A System Log File Analysis Journal Article
In: Journal of the American College of Radiology, vol. 14, no. 2, pp. 217–223, 2017.
@article{jung2017providers,
title = {Providers’ Access of Imaging Versus Only Reports: A System Log File Analysis},
author = { Hye-Young Jung and Judy Wawira Gichoya and Joshua R Vest},
year = {2017},
date = {2017-01-01},
journal = {Journal of the American College of Radiology},
volume = {14},
number = {2},
pages = {217--223},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gichoya, J; Haste, P; Mills, A; Kohli, M; Johnson, M
Creating an interventional oncology translational database: early experience at Indiana University Presentation
01.01.2017.
@misc{Gichoya2017c,
title = {Creating an interventional oncology translational database: early experience at Indiana University },
author = { J Gichoya and P Haste and A Mills and M Kohli and M Johnson},
doi = {10.1016/j.jvir.2016.12.996},
year = {2017},
date = {2017-01-01},
journal = {Journal of Vascular and Interventional Radiology},
volume = {28},
number = {2 Supplement},
pages = {S162},
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}
Walker, Marisa; Ge, Weiwei; Gichoya, Judy W; Purkayastha, Saptarshi
Implementing clinical practice guidelines for chronic obstructive pulmonary disease in an EHR system Proceedings Article
In: Healthcare Innovations and Point of Care Technologies (HI-POCT), 2017 IEEE, pp. 148–151, IEEE 2017.
@inproceedings{walker2017implementing,
title = {Implementing clinical practice guidelines for chronic obstructive pulmonary disease in an EHR system},
author = {Marisa Walker and Weiwei Ge and Judy W Gichoya and Saptarshi Purkayastha},
year = {2017},
date = {2017-01-01},
booktitle = {Healthcare Innovations and Point of Care Technologies (HI-POCT), 2017 IEEE},
pages = {148--151},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2016
Gichoya, Judy W.; Kasthurirathne, Suranga; Genereaux, Brad; Karunarathne, Milan; Purkayastha, Saptarshi; Kahn, Charles E
Using HL7 FHIR to Integrate Structured Reporting Reports into an Open Source Medical Records System Conference
SIIM 2016 Scientific Session, Enterprise Imaging 2016.
@conference{genereauxusing,
title = {Using HL7 FHIR to Integrate Structured Reporting Reports into an Open Source Medical Records System},
author = {Judy W. Gichoya and Suranga Kasthurirathne and Brad Genereaux and Milan Karunarathne and Saptarshi Purkayastha and Charles E Kahn},
year = {2016},
date = {2016-07-01},
booktitle = {SIIM 2016 Scientific Session},
series = {Enterprise Imaging},
abstract = {The radiology report is the core tool of communication between radiologists and ordering clinicians. In the current healthcare delivery model, there is interaction with various departments and across multiple providers requiring interchange of the radiology reports. The MRRT (Management of Radiology Report Templates) profile developed by the Integrating the Healthcare Enterprise (IHE) radiology workgroup defines the format of radiology templates and the mechanism to query, retrieve and store the templates. Various studies on the use of MRRT enabled reporting templates postulate a delivery model where there is a central repository of MRRT reports such as the RSNA radiology report template library or a specialty focused repository of reports as proposed by the Society of interventional Radiology (SIR). The MRRT format would be queried by radiologists using a reporting system and loaded as a template that the radiologist can dictate into the fields. The completed report would then be saved into the radiology information system (RIS) and PACS and other applications in the enterprise. Previous work using the MRRT profile described the development of the standard and conversion of available reports from the RSNA report template library to the MRRT profile. Vendors remain in the early phases of adoption of the MRRT standard, and the technical process of how to implement the profile is not documented. Various standards that allow exchange of clinical data include HL7 v1,v2 and v3; CDA (Clinical Document Architecture). FHIR (Fast Health Interoperability Resources) is a standard developed by HL7 to exchange data between clinical applications in a consistent, easy to implement and rigorous mechanism. We describe a technical implementation of MRRT profile that allows exchange of MRRT generated reports with an open-source medical records system (OpenMRS) using actual CT Pulmonary embolism reports. },
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The radiology report is the core tool of communication between radiologists and ordering clinicians. In the current healthcare delivery model, there is interaction with various departments and across multiple providers requiring interchange of the radiology reports. The MRRT (Management of Radiology Report Templates) profile developed by the Integrating the Healthcare Enterprise (IHE) radiology workgroup defines the format of radiology templates and the mechanism to query, retrieve and store the templates. Various studies on the use of MRRT enabled reporting templates postulate a delivery model where there is a central repository of MRRT reports such as the RSNA radiology report template library or a specialty focused repository of reports as proposed by the Society of interventional Radiology (SIR). The MRRT format would be queried by radiologists using a reporting system and loaded as a template that the radiologist can dictate into the fields. The completed report would then be saved into the radiology information system (RIS) and PACS and other applications in the enterprise. Previous work using the MRRT profile described the development of the standard and conversion of available reports from the RSNA report template library to the MRRT profile. Vendors remain in the early phases of adoption of the MRRT standard, and the technical process of how to implement the profile is not documented. Various standards that allow exchange of clinical data include HL7 v1,v2 and v3; CDA (Clinical Document Architecture). FHIR (Fast Health Interoperability Resources) is a standard developed by HL7 to exchange data between clinical applications in a consistent, easy to implement and rigorous mechanism. We describe a technical implementation of MRRT profile that allows exchange of MRRT generated reports with an open-source medical records system (OpenMRS) using actual CT Pulmonary embolism reports.
Kasthurirathne, Suranga N; Dixon, Brian E; Gichoya, Judy; Xu, Huiping; Xia, Yuni; Mamlin, Burke; Grannis, Shaun J
Toward better public health reporting using existing off the shelf approaches: A comparison of alternative cancer detection approaches using plaintext medical data and non-dictionary based feature selection Journal Article
In: Journal of biomedical informatics, vol. 60, pp. 145–152, 2016.
@article{kasthurirathne2016toward,
title = {Toward better public health reporting using existing off the shelf approaches: A comparison of alternative cancer detection approaches using plaintext medical data and non-dictionary based feature selection},
author = { Suranga N Kasthurirathne and Brian E Dixon and Judy Gichoya and Huiping Xu and Yuni Xia and Burke Mamlin and Shaun J Grannis},
year = {2016},
date = {2016-01-01},
journal = {Journal of biomedical informatics},
volume = {60},
pages = {145--152},
publisher = {Elsevier},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2015
Purkayastha, S; Price, A; Biswas, R; Ganesh, AU Jai; Otero, P
From Dyadic Ties to Information Infrastructures: Care-Coordination between Patients, Providers, Students and Researchers Journal Article
In: Yearbook of Medical Informatics, vol. 10, no. 1, pp. 68, 2015.
@article{purkayastha2015dyadic,
title = {From Dyadic Ties to Information Infrastructures: Care-Coordination between Patients, Providers, Students and Researchers},
author = { S Purkayastha and A Price and R Biswas and AU Jai Ganesh and P Otero},
doi = {10.15265/IY-2015-008},
year = {2015},
date = {2015-01-01},
journal = {Yearbook of Medical Informatics},
volume = {10},
number = {1},
pages = {68},
publisher = {Schattauer Publishers},
abstract = {Objective
To share how an effectual merging of local and online networks in low resource regions can supplement and strengthen the local practice of patient centered care through the use of an online digital infrastructure powered by all stakeholders in healthcare. User Driven Health Care offers the dynamic integration of patient values and evidence based solutions for improved medical communication in medical care.
Introduction
This paper conceptualizes patient care-coordination through the lens of engaged stakeholders using digital infrastructures tools to integrate information technology. We distinguish this lens from the prevalent conceptualization of dyadic ties between clinician-patient, patient-nurse, clinician-nurse, and offer the holistic integration of all stakeholder inputs, in the clinic and augmented by online communication in a multi-national setting.
Methods
We analyze an instance of the user-driven health care (UDHC), a network of providers, patients, students and researchers working together to help manage patient care. The network currently focuses on patients from LMICs, but the provider network is global in reach. We describe UDHC and its opportunities and challenges in care-coordination to reduce costs, bring equity, and improve care quality and share evidence.
Conclusion
UDHC has resulted in coordinated global based local care, affecting multiple facets of medical practice. Shared information resources between providers with disparate knowledge, results in better understanding by patients, unique and challenging cases for students, innovative community based research and discovery learning for all.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Objective
To share how an effectual merging of local and online networks in low resource regions can supplement and strengthen the local practice of patient centered care through the use of an online digital infrastructure powered by all stakeholders in healthcare. User Driven Health Care offers the dynamic integration of patient values and evidence based solutions for improved medical communication in medical care.
Introduction
This paper conceptualizes patient care-coordination through the lens of engaged stakeholders using digital infrastructures tools to integrate information technology. We distinguish this lens from the prevalent conceptualization of dyadic ties between clinician-patient, patient-nurse, clinician-nurse, and offer the holistic integration of all stakeholder inputs, in the clinic and augmented by online communication in a multi-national setting.
Methods
We analyze an instance of the user-driven health care (UDHC), a network of providers, patients, students and researchers working together to help manage patient care. The network currently focuses on patients from LMICs, but the provider network is global in reach. We describe UDHC and its opportunities and challenges in care-coordination to reduce costs, bring equity, and improve care quality and share evidence.
Conclusion
UDHC has resulted in coordinated global based local care, affecting multiple facets of medical practice. Shared information resources between providers with disparate knowledge, results in better understanding by patients, unique and challenging cases for students, innovative community based research and discovery learning for all.
To share how an effectual merging of local and online networks in low resource regions can supplement and strengthen the local practice of patient centered care through the use of an online digital infrastructure powered by all stakeholders in healthcare. User Driven Health Care offers the dynamic integration of patient values and evidence based solutions for improved medical communication in medical care.
Introduction
This paper conceptualizes patient care-coordination through the lens of engaged stakeholders using digital infrastructures tools to integrate information technology. We distinguish this lens from the prevalent conceptualization of dyadic ties between clinician-patient, patient-nurse, clinician-nurse, and offer the holistic integration of all stakeholder inputs, in the clinic and augmented by online communication in a multi-national setting.
Methods
We analyze an instance of the user-driven health care (UDHC), a network of providers, patients, students and researchers working together to help manage patient care. The network currently focuses on patients from LMICs, but the provider network is global in reach. We describe UDHC and its opportunities and challenges in care-coordination to reduce costs, bring equity, and improve care quality and share evidence.
Conclusion
UDHC has resulted in coordinated global based local care, affecting multiple facets of medical practice. Shared information resources between providers with disparate knowledge, results in better understanding by patients, unique and challenging cases for students, innovative community based research and discovery learning for all.
Purkayastha, Saptarshi
The Genus of Information Infrastructures: Architecture, Governance & Praxis. PhD Thesis
Norwegian University of Science and Technology, Trondheim, Norway, 2015.
@phdthesis{purkayastha2015genus,
title = {The Genus of Information Infrastructures: Architecture, Governance \& Praxis.},
author = { Saptarshi Purkayastha},
year = {2015},
date = {2015-01-01},
school = {Norwegian University of Science and Technology, Trondheim, Norway},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
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Maheshwari, Manika; Purkayastha, Saptarshi
Designing a drawing-based tool to manage EBRT process in an open-source oncology EMR system Proceedings Article
In: AMIA Symposium 2015 2015.
@inproceedings{maheshwari2015designing,
title = {Designing a drawing-based tool to manage EBRT process in an open-source oncology EMR system},
author = { Manika Maheshwari and Saptarshi Purkayastha},
year = {2015},
date = {2015-01-01},
organization = {AMIA Symposium 2015},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2014
Purkayastha, Saptarshi; Staring, Knut
Special Issue on Theory Driven Interventions: Are We Looking at Social and Organizational Aspects of Health Care Technology in the Field of Medical Informatics? Journal Article
In: International Journal of User-Driven Healthcare (IJUDH), vol. 4, no. 1, pp. 4, 2014.
@article{purkayastha2014special,
title = {Special Issue on Theory Driven Interventions: Are We Looking at Social and Organizational Aspects of Health Care Technology in the Field of Medical Informatics?},
author = { Saptarshi Purkayastha and Knut Staring},
year = {2014},
date = {2014-01-01},
journal = {International Journal of User-Driven Healthcare (IJUDH)},
volume = {4},
number = {1},
pages = {4},
publisher = {IGI-Global},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purkayastha, Saptarshi
OpenScrum: Scrum methodology to improve shared understanding in an open-source community Journal Article
In: 2014.
@article{purkayastha2014openscrum,
title = {OpenScrum: Scrum methodology to improve shared understanding in an open-source community},
author = { Saptarshi Purkayastha},
year = {2014},
date = {2014-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2013
Purkayastha, Saptarshi; Manda, Tiwonge Davis; Sanner, Terje Aksel
A Post-development Perspective on mHealth–An Implementation Initiative in Malawi Proceedings Article
In: System Sciences (HICSS), 2013 46th Hawaii International Conference on, pp. 4217–4225, IEEE 2013.
@inproceedings{purkayastha2013post,
title = {A Post-development Perspective on mHealth--An Implementation Initiative in Malawi},
author = { Saptarshi Purkayastha and Tiwonge Davis Manda and Terje Aksel Sanner},
year = {2013},
date = {2013-01-01},
booktitle = {System Sciences (HICSS), 2013 46th Hawaii International Conference on},
pages = {4217--4225},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Purkayastha, Saptarshi
2052: A Global Forecast for the Next Forty Years Journal Article
In: International Journal of User-Driven Healthcare (IJUDH), vol. 3, no. 1, pp. 88–89, 2013.
@article{purkayastha20132052,
title = {2052: A Global Forecast for the Next Forty Years},
author = { Saptarshi Purkayastha},
year = {2013},
date = {2013-01-01},
journal = {International Journal of User-Driven Healthcare (IJUDH)},
volume = {3},
number = {1},
pages = {88--89},
publisher = {IGI Global},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purkayastha, Saptarshi; Braa, Jørn
Big data analytics for developing countries–using the cloud for operational BI in health Journal Article
In: The Electronic Journal of Information Systems in Developing Countries, vol. 59, no. 1, pp. 1–17, 2013.
@article{purkayastha2013big,
title = {Big data analytics for developing countries--using the cloud for operational BI in health},
author = { Saptarshi Purkayastha and J{\o}rn Braa},
year = {2013},
date = {2013-01-01},
journal = {The Electronic Journal of Information Systems in Developing Countries},
volume = {59},
number = {1},
pages = {1--17},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purkayastha, Saptarshi; Braa, Jørn
Overview, not Overwhelm: Framing Operational BI Tools using Organizational Capabilities Journal Article
In: 2013.
@article{purkayastha2013overview,
title = {Overview, not Overwhelm: Framing Operational BI Tools using Organizational Capabilities},
author = { Saptarshi Purkayastha and J{\o}rn Braa},
year = {2013},
date = {2013-01-01},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Gichoya, Judy; Pearce, Chris; Wickramasinghe, Nilmini
Information architecture considerations in designing a comprehensive tuberculosis enterprise system in Western Kenya. Journal Article
In: Studies in health technology and informatics, vol. 192, pp. 1120–1120, 2013.
@article{gichoya2013information,
title = {Information architecture considerations in designing a comprehensive tuberculosis enterprise system in Western Kenya.},
author = { Judy Gichoya and Chris Pearce and Nilmini Wickramasinghe},
year = {2013},
date = {2013-01-01},
journal = {Studies in health technology and informatics},
volume = {192},
pages = {1120--1120},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2012
Purkayastha, Saptarshi
Design and Implementation of Mobile-Based Technology in Strengthening Health Information System: Aligning mHealth Solutions to Infrastructures Journal Article
In: Mobile Computing Techniques in Emerging Markets: Systems, Applications and Services, pp. 158, 2012.
@article{purkayastha2012design,
title = {Design and Implementation of Mobile-Based Technology in Strengthening Health Information System: Aligning mHealth Solutions to Infrastructures},
author = { Saptarshi Purkayastha},
year = {2012},
date = {2012-01-01},
journal = {Mobile Computing Techniques in Emerging Markets: Systems, Applications and Services},
pages = {158},
publisher = {Information Science Reference},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Purkayastha, Saptarshi; Nehete, Namrata; Purkayastha, Jashodhara
Dyscover—An Orton-Gillingham approach inspired multi-sensory learning application for dyslexic children Proceedings Article
In: Information and Communication Technologies (WICT), 2012 World Congress on, pp. 685–690, IEEE 2012.
@inproceedings{purkayastha2012dyscover,
title = {Dyscover\textemdashAn Orton-Gillingham approach inspired multi-sensory learning application for dyslexic children},
author = { Saptarshi Purkayastha and Namrata Nehete and Jashodhara Purkayastha},
year = {2012},
date = {2012-01-01},
booktitle = {Information and Communication Technologies (WICT), 2012 World Congress on},
pages = {685--690},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Purkayastha, Saptarshi
HIXEn: An integration engine for multi-vocabulary health information using REST & semantic metadata mapping Proceedings Article
In: Information and Communication Technologies (WICT), 2012 World Congress on, pp. 679–684, IEEE 2012.
@inproceedings{purkayastha2012hixen,
title = {HIXEn: An integration engine for multi-vocabulary health information using REST \& semantic metadata mapping},
author = { Saptarshi Purkayastha},
year = {2012},
date = {2012-01-01},
booktitle = {Information and Communication Technologies (WICT), 2012 World Congress on},
pages = {679--684},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Gichoya, Judy; Gamache, Roland E; Vreeman, Daniel J; Dixon, Brian E; Finnell, John T; Grannis, Shaun
An evaluation of the rates of repeat notifiable disease reporting and patient crossover using a health information exchange-based automated electronic laboratory reporting system Proceedings Article
In: AMIA Annual Symposium Proceedings, pp. 1229, American Medical Informatics Association 2012.
@inproceedings{gichoya2012evaluation,
title = {An evaluation of the rates of repeat notifiable disease reporting and patient crossover using a health information exchange-based automated electronic laboratory reporting system},
author = { Judy Gichoya and Roland E Gamache and Daniel J Vreeman and Brian E Dixon and John T Finnell and Shaun Grannis},
year = {2012},
date = {2012-01-01},
booktitle = {AMIA Annual Symposium Proceedings},
volume = {2012},
pages = {1229},
organization = {American Medical Informatics Association},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
2011
Purkayastha, Saptarshi
Towards a contextual insecurity framework: How contextual development leads to security problems in information systems Book Section
In: Proceedings of IRIS 2011, pp. 654–666, Turku Centre for Computer Science, 2011.
@incollection{purkayastha2011towards,
title = {Towards a contextual insecurity framework: How contextual development leads to security problems in information systems},
author = { Saptarshi Purkayastha},
year = {2011},
date = {2011-01-01},
booktitle = {Proceedings of IRIS 2011},
pages = {654--666},
publisher = {Turku Centre for Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {incollection}
}