This paper outlines the journey of a large Australian academic health service in relation to the acquisition,
installation and roll out of the REDCap platform (RCP) for the betterment of clinical review (clinical audit)
and research data collection. The main aims of the acquisition of the platform were to facilitate data
collection and management for audit and research across the organization in a more sustainable way than
had previously been possible. We found the platform to be easily installed and maintained. There was rapid
uptake of the platform by a range of health service stakeholders across the audit, research and operational
domains. We were also able to successfully integrate data from our corporate clinical data environment,
The REASON Discovery Platform R
(REASON) into selected REDCap “applications” using the Dynamic
Data Pull (DDP) functionality it provides. In summary the acquisition and installation of REDCap at our
health service has been hugely successful and has provided a great facility for use by a large number of
organizational stakeholders going forwards into the future.
An Adaptive Technique in Electronic Health Record for Clinical Decision Makin...ijtsrd
Cloud computing is a collection of several computer resources that consists of both software and hardware. It is a type of service that is delivered over the internet and can be accessible from anywhere. 1 The data and services can be accessed through the internet. 4 These services are managed by the third party over the internet. They eventually provide access to the servers and resources. Health records consist of patient’s data regarding health. This data is usable by both the hospitals and patients. 6 8 This can be eventually used to track the medical history of patients. Data Visualization is a graphical depiction of the data. It implicates producing images that advertise the link among the data that the users view. Hence, they are used for clinical decision making. In this paper we will be discussing how cloud can be used to maintain health records electronically. Meghana Prakash | Vignesh S "An Adaptive Technique in Electronic Health Record for Clinical Decision Making Based on Data Visualization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30699.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30699/an-adaptive-technique-in-electronic-health-record-for-clinical-decision-making-based-on-data-visualization/meghana-prakash
The document discusses the potential applications of blockchain technology in healthcare and medicine. It outlines how blockchain could be used to improve healthcare information technology systems, enable health information exchange between different organizations, and create secure and decentralized personal health records for patients. The document also categorizes different types of existing healthcare IT systems and discusses how blockchain may intersect with efforts to digitally transform clinical care, public health, and consumer health.
Clinical Narrative And Structured Data In The Ehr Venus And Mars Live In Harm...Nick van Terheyden
For nearly two decades healthcare technology has attempted to impose new documentation methods that are more suited to database management but do not meet the needs of the busy practicing physician. Conventional wisdom is that documents are bad and discrete data is good but historically clinicians have resisted efforts to establish structured data entry methodologies trying to replace the clinician preferred method of data capture – dictation. Clinical Document Architecture for Common Document Types (CDA4CDT) offers a bridge between the two opposing worlds of clinical documentation creating semantically interoperable data while retaining the precise clinical content contained in free flowing narrative
The document discusses challenges in managing healthcare data to balance storage costs with compliance and access requirements. It notes that increased use of electronic medical records and imaging systems has led to exponential growth in healthcare data. This data must be stored and managed for long periods as required by regulations while controlling rising storage costs. The presented solution proposes implementing tiered storage with lower-cost systems to reduce storage expenses while also consolidating data management functions into a single archiving appliance to decrease management overhead and floor space usage. This integrated approach aims to help healthcare organizations meet data retention mandates cost-effectively.
Understanding the Need of Data Integration in E Healthcareijtsrd
This paper discusses the current scenario of e healthcare and different dimensions of Big data in healthcare and the importance of data integration in e health care and the challenges associated with data integration and associated uses of data integration with respect to different use cases which might be helpful to physician's decision making because the data driven decision making involves combination of heterogeneous data which includes Electronic Health Record containing different types of data and connected healthcare organization in order to provide value based connected healthcare which would be useful to primary healthcare center located at different location because patients suddenly expect their healthcare experiences to be as exceptional and as transparent as those of retail or banking, and physician's have to scramble to adjust to these new expectations due to lack of data integrity. Mrs. Shashi Rekha. H. | Dr. Chethana Prakash. M | Dr. K. Thippeswamy "Understanding the Need of Data Integration in E- Healthcare" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31007.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31007/understanding-the-need-of-data-integration-in-e-healthcare/mrs-shashi-rekha-h
The document discusses the challenges healthcare providers face in managing the large amount of medical imaging data being generated. There has been exponential growth in medical imaging data due to factors like an aging population, improved technology, defensive medicine practices, and data retention requirements. This massive amount of data strains healthcare organizations' storage capabilities and makes accessing and sharing images between providers difficult. The document introduces cloud services as a potential solution that could help hospitals better manage resources and provide improved access and sharing of medical images.
Improve Patient Care and Reduce IT Costs with Vendor Neutral Archiving and Cl...EMC
This white paper discusses how vendor neutral archiving (VNA) combined with cloud storage on the EMC Atmos platform can help healthcare organizations improve patient care and reduce IT costs. By breaking down PACS silos and providing secure access to medical images from any device, VNA and cloud storage reduce storage and archive costs while enabling images to be accessed at the point of care. A case study is presented of how one healthcare network leveraged this approach to improve medical imaging workflows.
Vendor Neutral Archives can reduce costs and demands upon system administration while enhancing patient care.
For more information, please visit us at:http://www.carestream.com/vue-vendor-neutral-archiving.html
An Adaptive Technique in Electronic Health Record for Clinical Decision Makin...ijtsrd
Cloud computing is a collection of several computer resources that consists of both software and hardware. It is a type of service that is delivered over the internet and can be accessible from anywhere. 1 The data and services can be accessed through the internet. 4 These services are managed by the third party over the internet. They eventually provide access to the servers and resources. Health records consist of patient’s data regarding health. This data is usable by both the hospitals and patients. 6 8 This can be eventually used to track the medical history of patients. Data Visualization is a graphical depiction of the data. It implicates producing images that advertise the link among the data that the users view. Hence, they are used for clinical decision making. In this paper we will be discussing how cloud can be used to maintain health records electronically. Meghana Prakash | Vignesh S "An Adaptive Technique in Electronic Health Record for Clinical Decision Making Based on Data Visualization" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30699.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/30699/an-adaptive-technique-in-electronic-health-record-for-clinical-decision-making-based-on-data-visualization/meghana-prakash
The document discusses the potential applications of blockchain technology in healthcare and medicine. It outlines how blockchain could be used to improve healthcare information technology systems, enable health information exchange between different organizations, and create secure and decentralized personal health records for patients. The document also categorizes different types of existing healthcare IT systems and discusses how blockchain may intersect with efforts to digitally transform clinical care, public health, and consumer health.
Clinical Narrative And Structured Data In The Ehr Venus And Mars Live In Harm...Nick van Terheyden
For nearly two decades healthcare technology has attempted to impose new documentation methods that are more suited to database management but do not meet the needs of the busy practicing physician. Conventional wisdom is that documents are bad and discrete data is good but historically clinicians have resisted efforts to establish structured data entry methodologies trying to replace the clinician preferred method of data capture – dictation. Clinical Document Architecture for Common Document Types (CDA4CDT) offers a bridge between the two opposing worlds of clinical documentation creating semantically interoperable data while retaining the precise clinical content contained in free flowing narrative
The document discusses challenges in managing healthcare data to balance storage costs with compliance and access requirements. It notes that increased use of electronic medical records and imaging systems has led to exponential growth in healthcare data. This data must be stored and managed for long periods as required by regulations while controlling rising storage costs. The presented solution proposes implementing tiered storage with lower-cost systems to reduce storage expenses while also consolidating data management functions into a single archiving appliance to decrease management overhead and floor space usage. This integrated approach aims to help healthcare organizations meet data retention mandates cost-effectively.
Understanding the Need of Data Integration in E Healthcareijtsrd
This paper discusses the current scenario of e healthcare and different dimensions of Big data in healthcare and the importance of data integration in e health care and the challenges associated with data integration and associated uses of data integration with respect to different use cases which might be helpful to physician's decision making because the data driven decision making involves combination of heterogeneous data which includes Electronic Health Record containing different types of data and connected healthcare organization in order to provide value based connected healthcare which would be useful to primary healthcare center located at different location because patients suddenly expect their healthcare experiences to be as exceptional and as transparent as those of retail or banking, and physician's have to scramble to adjust to these new expectations due to lack of data integrity. Mrs. Shashi Rekha. H. | Dr. Chethana Prakash. M | Dr. K. Thippeswamy "Understanding the Need of Data Integration in E- Healthcare" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31007.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31007/understanding-the-need-of-data-integration-in-e-healthcare/mrs-shashi-rekha-h
The document discusses the challenges healthcare providers face in managing the large amount of medical imaging data being generated. There has been exponential growth in medical imaging data due to factors like an aging population, improved technology, defensive medicine practices, and data retention requirements. This massive amount of data strains healthcare organizations' storage capabilities and makes accessing and sharing images between providers difficult. The document introduces cloud services as a potential solution that could help hospitals better manage resources and provide improved access and sharing of medical images.
Improve Patient Care and Reduce IT Costs with Vendor Neutral Archiving and Cl...EMC
This white paper discusses how vendor neutral archiving (VNA) combined with cloud storage on the EMC Atmos platform can help healthcare organizations improve patient care and reduce IT costs. By breaking down PACS silos and providing secure access to medical images from any device, VNA and cloud storage reduce storage and archive costs while enabling images to be accessed at the point of care. A case study is presented of how one healthcare network leveraged this approach to improve medical imaging workflows.
Vendor Neutral Archives can reduce costs and demands upon system administration while enhancing patient care.
For more information, please visit us at:http://www.carestream.com/vue-vendor-neutral-archiving.html
The document discusses the National Health Data Warehouse of Bangladesh, which integrates health data from different government organizations using DHIS2 and OpenMRS software. It provides an overview of the features of the data warehouse, including reporting and data mining capabilities. However, it also notes some issues that need to be addressed like a lack of skilled workforce, incomplete coverage of health organizations, and data security/privacy risks of integrating identifiable patient records. Recommendations are provided to improve performance, security and inclusion of private healthcare data in the future.
White paper explores Intel’s latest SSD technology, new Carestream solutions, the impact for PACS, and a look at the future of medical imaging data, access, storage and analysis.
Revenue opportunities in the management of healthcare data delugeShahid Shah
Healthcare data is hard to deal with and getting even harder and more expensive. In this presentation, Shahid Shah covers why:
* Healthcare data is going from hard to nearly impossible to manage.
* Applications come and go, data lives forever.
* Data integration is notoriously difficult, even in the best of circumstances, and requires sophisticated tools and attention to detail.
And, then talks about how new techniques are needed to store and manage healthcare data.
Whitepaper : The Bridge From PACS to VNA: Scale Out Storage EMC
This whitepaper discusses how a vendor-neutral archive (VNA) for image archive and management requires a phased storage approach due to the capital and operational expenditures involved. The EMC Isilon scale-out approach provides a simple, predictable, and manageable path from PACS (Picture Archiving and Communications System) to VNA.
White paper examines the unstructured data management challenges healthcare organizations face and how the Hitachi Data Systems solution employs metadata to address the data storm.
The healthcare industry has traditionally been slow to adopt new technologies due to concerns about privacy and security of patient data. However, cloud computing is now being widely adopted in healthcare due to its benefits of lower costs, rapid deployment, and access to data from any location. Common uses of the cloud in healthcare include electronic medical records, patient portals, telemedicine, clinical research, and big data analytics. Patient portals in particular allow patients to access their health information anytime from any device and interact with providers through secure messaging.
This document provides information about HL7 standards and two experts, Dr. Supachai Parchariyanon and Dr. Nawanan Theera-Ampornpunt. It discusses Dr. Parchariyanon's background and interests in standards and interoperability. It then outlines the topics to be covered, including an introduction to standards and interoperability, what HL7 is, HL7 Version 2 and 3, the Reference Information Model, and Clinical Document Architecture.
Cloud eHealth in Medical Imaging & RadiologyCarestream
The document discusses how cloud-based infrastructure can support various healthcare workflows. It provides two case studies:
1) Maasstad Hospital in the Netherlands used the cloud to archive radiology images and consolidate clinical data to avoid managing storage itself.
2) Imadis, a French teleradiology company, used the cloud to enable a dedicated reading workflow and ensure performance, security, and 24/7 support for emergency readings.
The cloud infrastructure allows customization of workflows while outsourcing complexity to cloud providers and avoiding large capital investments. This flexible model is positioned to support diverse healthcare needs going forward.
A Real-World Solution for Patient-Centric WorkflowCarestream
Vendor Neutral Archives can reduce costs and demands upon system administration while resolving enterprise clinical workflow challenges.
For more information, please visit: http://www.carestream.com/vna
THE TECHNOLOGY OF USING A DATA WAREHOUSE TO SUPPORT DECISION-MAKING IN HEALTH...ijdms
This paper describes the technology of data warehouse in healthcare decision-making and tools for support
of these technologies, which is used to cancer diseases. The healthcare executive managers and doctors
needs information about and insight into the existing health data, so as to make decision more efficiently
without interrupting the daily work of an On-Line Transaction Processing (OLTP) system. This is a
complex problem during the healthcare decision-making process. To solve this problem, the building a
healthcare data warehouse seems to be efficient. First in this paper we explain the concepts of the data
warehouse, On-Line Analysis Processing (OLAP). Changing the data in the data warehouse into a
multidimensional data cube is then shown. Finally, an application example is given to illustrate the use of
the healthcare data warehouse specific to cancer diseases developed in this study. The executive managers
and doctors can view data from more than one perspective with reduced query time, thus making decisions
faster and more comprehensive
The document discusses public health informatics standards and the Public Health Information Network (PHIN) framework. It outlines how PHIN aims to advance interoperability between public health organizations through selecting relevant data standards, describing minimum IT capabilities, and developing standardized software applications. It then summarizes Houston Department of Health and Human Services' (HDHHS) data systems integration project, which will develop a web portal integrating various applications using PHIN recommendations to facilitate data sharing.
Baptist Health: Solving Healthcare Problems with Big DataMapR Technologies
Editor’s Note: Download the complimentary MapR Guide to Big Data in Healthcare for more information: https://mapr.com/mapr-guide-big-data-healthcare/
There is no better example of the important role that data plays in our lives than in matters of our health and our healthcare. There’s a growing wealth of health-related data out there, and it’s playing an increasing role in improving patient care, population health, and healthcare economics.
Join this webinar to hear how Baptist Health is using big data and advanced analytics to address a myriad of healthcare challenges—from patient to payer—through their consumer- centric approach.
MapR Technologies will cover broader big data healthcare trends and production use cases that demonstrate how to converge data and compute power to deliver data-driven healthcare applications.
Imaging in the Cloud: A New Era for RadiologyCarestream
A look at how cloud computing is helping the medical imaging industry. The cloud is changing old mindsets, and allowing technologies, such as a vendor-neutral archive (VNA), to make health facilities more efficient and provide higher quality care.
“The Zato Health software platform for data
liquidity and system interoperability will accelerate benefits to patients, providers, and payers from next generation medical record processing, automated coding, and reporting of quality measures leveraging the uniquely efficient and cost effective architecture of IBM POWER8 servers.”
Ramathibodi Hospital welcomed a study group from Nakhon Si Thammarat Municipal Hospital on November 20, 1960. Ramathibodi Hospital is affiliated with Mahidol University and was established in 1965 and began operations in 1969. Its vision is to be a world-class medical institution and its mission is to provide education, research, academic services, and healthcare to benefit society. The document then provides details on Ramathibodi's organization, services, facilities and informatics division.
Building an Intelligent Biobank to Power Research Decision-MakingDenodo
This presentation belongs to the workshop: "Building an Intelligent Biobank to Power Research Decision-Making", from ISBER 2015 Annual Meeting by Lori A. Ball (Chief Operating Officer, President of Integrated Client Solutions at BioStorage Technologies, Inc), Brian Brunner (Senior Manager, Clinical Practice at LabAnswer) and Suresh Chandrasekaran (Senior Vice President at Denodo).
The workshop cover three different topic areas:
- Research sample intelligence: the growing need for Global Data Integration (Biobank Sample and Data Stakeholders).
- Building a research data integration plan and cloud sourcing strategy (data integration).
- How data virtualization works and the value it delivers (a data virtualization introduction, solution portfolio and current customers in Life Sciences industry).
The biomedical R&D environment is increasingly dependent on data meta-analysis and bioinformatics to support research advancements. The integration of biorepository sample inventory data with biomarker and clinical research information has become a priority to R&D organizations. Therefore, a flexible IT system for managing sample collections, integrating sample data with clinical data and providing a data virtualization platform will enable the advancement of research studies. This workshop provides an overview of how sample data integration, virtualization and analytics can lead to more streamlined and unified sample intelligence to support global biobanking for future research.
IRJET- A Framework for Disease Risk PredictionIRJET Journal
This document presents a framework for disease risk prediction using machine learning techniques. It proposes using a convolutional neural network model for disease prediction. The system architecture includes an admin module for dataset management and file conversion, and a disease risk prediction module for prediction. The admin module allows uploading medical data, converting files to a compatible format, and performing predictions. The disease risk prediction module takes a test set, generates compatible files, and uses those files and a convolutional neural network for prediction. The goal is to develop an automated disease prediction system to help predict disease and improve healthcare.
The document describes the development of metadata and data standards for the health domain in India by the Health MDDS Domain Committee. The committee was formed to promote interoperability across health IT systems. It identified over 1000 common data elements across 39 health entities. It defined the data elements and established 111 code directories derived from global clinical coding standards. The standards are intended to enable integration and information exchange between existing fragmented health IT systems in India.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
SURVEY OF OPEN SOURCE HEALTH INFORMATION SYSTEMS hiij
Due to the Health Information Technology for Economic and Clinical Health Act (HITECH), the US
medical industry has been given a directive to transition to electronic health records. Electronic Health
Records will enhance efficiency and quality of patient care. In this paper, open-source health information
systems are surveyed.These systems include electronic medical records, electronic health records and
personal health record systems. Their functionality, implementation technologies used, and security
features are discussed.
Survey of open source health information systemshiij
Due to the Health Information Technology for Economic and Clinical Health Act (HITECH), the US
medical industry has been given a directive to transition to electronic health records. Electronic Health
Records will enhance efficiency and quality of patient care. In this paper, open-source health information
systems are surveyed.These systems include electronic medical records, electronic health records and
personal health record systems. Their functionality, implementation technologies used, and security
features are discussed.
The document discusses the National Health Data Warehouse of Bangladesh, which integrates health data from different government organizations using DHIS2 and OpenMRS software. It provides an overview of the features of the data warehouse, including reporting and data mining capabilities. However, it also notes some issues that need to be addressed like a lack of skilled workforce, incomplete coverage of health organizations, and data security/privacy risks of integrating identifiable patient records. Recommendations are provided to improve performance, security and inclusion of private healthcare data in the future.
White paper explores Intel’s latest SSD technology, new Carestream solutions, the impact for PACS, and a look at the future of medical imaging data, access, storage and analysis.
Revenue opportunities in the management of healthcare data delugeShahid Shah
Healthcare data is hard to deal with and getting even harder and more expensive. In this presentation, Shahid Shah covers why:
* Healthcare data is going from hard to nearly impossible to manage.
* Applications come and go, data lives forever.
* Data integration is notoriously difficult, even in the best of circumstances, and requires sophisticated tools and attention to detail.
And, then talks about how new techniques are needed to store and manage healthcare data.
Whitepaper : The Bridge From PACS to VNA: Scale Out Storage EMC
This whitepaper discusses how a vendor-neutral archive (VNA) for image archive and management requires a phased storage approach due to the capital and operational expenditures involved. The EMC Isilon scale-out approach provides a simple, predictable, and manageable path from PACS (Picture Archiving and Communications System) to VNA.
White paper examines the unstructured data management challenges healthcare organizations face and how the Hitachi Data Systems solution employs metadata to address the data storm.
The healthcare industry has traditionally been slow to adopt new technologies due to concerns about privacy and security of patient data. However, cloud computing is now being widely adopted in healthcare due to its benefits of lower costs, rapid deployment, and access to data from any location. Common uses of the cloud in healthcare include electronic medical records, patient portals, telemedicine, clinical research, and big data analytics. Patient portals in particular allow patients to access their health information anytime from any device and interact with providers through secure messaging.
This document provides information about HL7 standards and two experts, Dr. Supachai Parchariyanon and Dr. Nawanan Theera-Ampornpunt. It discusses Dr. Parchariyanon's background and interests in standards and interoperability. It then outlines the topics to be covered, including an introduction to standards and interoperability, what HL7 is, HL7 Version 2 and 3, the Reference Information Model, and Clinical Document Architecture.
Cloud eHealth in Medical Imaging & RadiologyCarestream
The document discusses how cloud-based infrastructure can support various healthcare workflows. It provides two case studies:
1) Maasstad Hospital in the Netherlands used the cloud to archive radiology images and consolidate clinical data to avoid managing storage itself.
2) Imadis, a French teleradiology company, used the cloud to enable a dedicated reading workflow and ensure performance, security, and 24/7 support for emergency readings.
The cloud infrastructure allows customization of workflows while outsourcing complexity to cloud providers and avoiding large capital investments. This flexible model is positioned to support diverse healthcare needs going forward.
A Real-World Solution for Patient-Centric WorkflowCarestream
Vendor Neutral Archives can reduce costs and demands upon system administration while resolving enterprise clinical workflow challenges.
For more information, please visit: http://www.carestream.com/vna
THE TECHNOLOGY OF USING A DATA WAREHOUSE TO SUPPORT DECISION-MAKING IN HEALTH...ijdms
This paper describes the technology of data warehouse in healthcare decision-making and tools for support
of these technologies, which is used to cancer diseases. The healthcare executive managers and doctors
needs information about and insight into the existing health data, so as to make decision more efficiently
without interrupting the daily work of an On-Line Transaction Processing (OLTP) system. This is a
complex problem during the healthcare decision-making process. To solve this problem, the building a
healthcare data warehouse seems to be efficient. First in this paper we explain the concepts of the data
warehouse, On-Line Analysis Processing (OLAP). Changing the data in the data warehouse into a
multidimensional data cube is then shown. Finally, an application example is given to illustrate the use of
the healthcare data warehouse specific to cancer diseases developed in this study. The executive managers
and doctors can view data from more than one perspective with reduced query time, thus making decisions
faster and more comprehensive
The document discusses public health informatics standards and the Public Health Information Network (PHIN) framework. It outlines how PHIN aims to advance interoperability between public health organizations through selecting relevant data standards, describing minimum IT capabilities, and developing standardized software applications. It then summarizes Houston Department of Health and Human Services' (HDHHS) data systems integration project, which will develop a web portal integrating various applications using PHIN recommendations to facilitate data sharing.
Baptist Health: Solving Healthcare Problems with Big DataMapR Technologies
Editor’s Note: Download the complimentary MapR Guide to Big Data in Healthcare for more information: https://mapr.com/mapr-guide-big-data-healthcare/
There is no better example of the important role that data plays in our lives than in matters of our health and our healthcare. There’s a growing wealth of health-related data out there, and it’s playing an increasing role in improving patient care, population health, and healthcare economics.
Join this webinar to hear how Baptist Health is using big data and advanced analytics to address a myriad of healthcare challenges—from patient to payer—through their consumer- centric approach.
MapR Technologies will cover broader big data healthcare trends and production use cases that demonstrate how to converge data and compute power to deliver data-driven healthcare applications.
Imaging in the Cloud: A New Era for RadiologyCarestream
A look at how cloud computing is helping the medical imaging industry. The cloud is changing old mindsets, and allowing technologies, such as a vendor-neutral archive (VNA), to make health facilities more efficient and provide higher quality care.
“The Zato Health software platform for data
liquidity and system interoperability will accelerate benefits to patients, providers, and payers from next generation medical record processing, automated coding, and reporting of quality measures leveraging the uniquely efficient and cost effective architecture of IBM POWER8 servers.”
Ramathibodi Hospital welcomed a study group from Nakhon Si Thammarat Municipal Hospital on November 20, 1960. Ramathibodi Hospital is affiliated with Mahidol University and was established in 1965 and began operations in 1969. Its vision is to be a world-class medical institution and its mission is to provide education, research, academic services, and healthcare to benefit society. The document then provides details on Ramathibodi's organization, services, facilities and informatics division.
Building an Intelligent Biobank to Power Research Decision-MakingDenodo
This presentation belongs to the workshop: "Building an Intelligent Biobank to Power Research Decision-Making", from ISBER 2015 Annual Meeting by Lori A. Ball (Chief Operating Officer, President of Integrated Client Solutions at BioStorage Technologies, Inc), Brian Brunner (Senior Manager, Clinical Practice at LabAnswer) and Suresh Chandrasekaran (Senior Vice President at Denodo).
The workshop cover three different topic areas:
- Research sample intelligence: the growing need for Global Data Integration (Biobank Sample and Data Stakeholders).
- Building a research data integration plan and cloud sourcing strategy (data integration).
- How data virtualization works and the value it delivers (a data virtualization introduction, solution portfolio and current customers in Life Sciences industry).
The biomedical R&D environment is increasingly dependent on data meta-analysis and bioinformatics to support research advancements. The integration of biorepository sample inventory data with biomarker and clinical research information has become a priority to R&D organizations. Therefore, a flexible IT system for managing sample collections, integrating sample data with clinical data and providing a data virtualization platform will enable the advancement of research studies. This workshop provides an overview of how sample data integration, virtualization and analytics can lead to more streamlined and unified sample intelligence to support global biobanking for future research.
IRJET- A Framework for Disease Risk PredictionIRJET Journal
This document presents a framework for disease risk prediction using machine learning techniques. It proposes using a convolutional neural network model for disease prediction. The system architecture includes an admin module for dataset management and file conversion, and a disease risk prediction module for prediction. The admin module allows uploading medical data, converting files to a compatible format, and performing predictions. The disease risk prediction module takes a test set, generates compatible files, and uses those files and a convolutional neural network for prediction. The goal is to develop an automated disease prediction system to help predict disease and improve healthcare.
The document describes the development of metadata and data standards for the health domain in India by the Health MDDS Domain Committee. The committee was formed to promote interoperability across health IT systems. It identified over 1000 common data elements across 39 health entities. It defined the data elements and established 111 code directories derived from global clinical coding standards. The standards are intended to enable integration and information exchange between existing fragmented health IT systems in India.
International Journal of Computational Engineering Research(IJCER)ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
SURVEY OF OPEN SOURCE HEALTH INFORMATION SYSTEMS hiij
Due to the Health Information Technology for Economic and Clinical Health Act (HITECH), the US
medical industry has been given a directive to transition to electronic health records. Electronic Health
Records will enhance efficiency and quality of patient care. In this paper, open-source health information
systems are surveyed.These systems include electronic medical records, electronic health records and
personal health record systems. Their functionality, implementation technologies used, and security
features are discussed.
Survey of open source health information systemshiij
Due to the Health Information Technology for Economic and Clinical Health Act (HITECH), the US
medical industry has been given a directive to transition to electronic health records. Electronic Health
Records will enhance efficiency and quality of patient care. In this paper, open-source health information
systems are surveyed.These systems include electronic medical records, electronic health records and
personal health record systems. Their functionality, implementation technologies used, and security
features are discussed.
This document discusses factors to consider when evaluating a clinical information system (CIS), including:
- Who is involved in choosing, implementing, and revising a CIS
- Factors to consider before implementing a CIS such as costs and failure rates
- How a CIS should be structured and updated
- Companies that design clinical decision support systems
- Security, access controls, and costs including implementation, support personnel, and purchasing options.
- How users should be educated on a system and updates through various learning methods.
The Air Force Medical Service (AFMS) provides healthcare to over 2.6 million beneficiaries annually through 239 clinics worldwide. To address challenges like lack of data integration and strategic alignment, the AFMS implemented the Health Service Data Warehouse Project. This project integrated data through acquisition, transformation, and management over 12 months. As a result, data can now be accessed in near real-time to promote clinical improvements and reduce costs. The AFMS follows characteristics of high performing organizations by using analytics to improve business functions and clearly communicating their mission of providing quality healthcare globally.
This document provides an overview of hospital management systems and the benefits of web-based systems. It discusses that web-based systems allow for simultaneous access to data from various points and integration of all parties. The document then reviews characteristics of web-based systems like multiple autonomous components and points of control/failure. Benefits of a hospital management web-based system include improved patient care through increased access to records, improved cost control through standardized processes, and increased security of patient information.
This summary provides an overview of a proposed clinical database project for cleft patients in the East of England region:
The project aims to develop a collaborative web-based clinical database from an existing in-house database to standardize clinical data coding and provide an audit trail for cleft patients over a 20-year care pathway. The database will utilize SNOMED CT terminology and allow clinicians to track patient diagnoses, assessments, and outcomes over time to improve treatment. The database will require PHP, mySQL, and Apache resources and follow a MVC framework to separate the data, interface, and control layers to facilitate maintenance. The database aligns with UK clinical guidelines and aims to collect standardized data on approximately 1620 active cleft
A Proposed Security Architecture for Establishing Privacy Domains in Systems ...IJERA Editor
Information and communication technology (ICT) are becoming a natural part in healthcare. Instead of keeping patient information inside a written file, you can find all information stored in an organized database as well defined files using a specific system in almost every hospital. But those files sometimes got lost or information was split up in files in different hospitals or different departments so no one could see the whole picture from this point we come up with our idea. One of this paper targets is to keep that information available on the cloud so doctors and nurses can have an access to patient record everywhere, so patient history will be clear which helps doctors in giving the right decision. We present security architecture for establishing privacy domains in e-Health bases. In this case, we will improve the availability of medical data and provide the ability for patients to moderate their medical data. Moreover, e-Health system in cloud computing has more than one component to be attacked. The other target of this paper is to distinguish between different kinds of attackers and we point out several shortcomings of current e-Health solutions and standards, particularly they do not address the client platform security, which is a crucial aspect for the overall security of systems in cloud. To fill this gap, we present security architecture for establishing privacy domains in e-Health infrastructures. Our solution provides client platform security and appropriately combines this with network security concepts.
This document discusses a Laboratory Information Management System (LIMS) for Family Aids Care and Educational Services (FACES) in Kenya. FACES provides care for over 100,000 HIV/AIDS patients through various departments including its laboratory department. The laboratory department currently faces challenges in collecting, managing, and disseminating test samples and results. The objective of this project is to design and develop a working prototype of a LIMS to address these challenges. The LIMS will be developed using a waterfall methodology based on user requirements gathered through interviews and observations at the laboratory.
This document provides an overview of data mining applications in healthcare. It discusses how electronic health records have increased the amount of patient data available and how healthcare organizations are now using data mining and predictive analytics to optimize efficiency and quality. The document outlines several common uses of data mining in healthcare, such as predictive medicine, fraud detection, and measuring treatment effectiveness. It also describes some common data mining algorithms like decision trees and neural networks that are applied in healthcare. Finally, the document discusses future opportunities for data mining in healthcare like improved data sharing and more integrated web mining tools.
In light of Cloud Computing System CDA Generation and Integration for Health ...IJAEMSJORNAL
Theoretical Successful sending of Electronic Health Record enhances tolerant security and nature of care, however it has the essential of interoperability between Health Information Exchange at various doctor's facilities. The Clinical Document Architecture (CDA) created by HL7 is a center record standard to guarantee such interoperability, and engendering of this archive configuration is basic for interoperability. Lamentably, clinics are hesitant to receive interoperable HIS because of its organization fetched with the exception of in a modest bunch nations. An issue emerges notwithstanding when more healing facilities begin utilizing the CDA archive arrange on the grounds that the information scattered in various reports are difficult to oversee. In this paper, we portray our CDA report era and incorporation Open API benefit in light of distributed computing, through which doctor's facilities are empowered to advantageously create CDA archives without purchasing restrictive programming. Our CDA archive combination framework incorporates various CDA records per tolerant into a solitary CDA report and doctors and patients can peruse the clinical information in sequential request. Our arrangement of CDA report era and joining depends on distributed computing and the administration is offered in Open API. Engineers utilizing distinctive stages along these lines can utilize our framework to improve interoperability.
1) The document proposes a system to clean and structure unstructured medical data from electronic health records into a standardized format called a Care Record Summary (CRS) to enable efficient analysis of big healthcare data.
2) The system implements medical, medication, test, and allergy information from electronic health records using international CDA standards up to the entry level within a CRS to ensure interoperability.
3) A CRS model suitable for Korea's healthcare system is designed to facilitate sharing and analysis of clinical data between hospitals for improved care.
IRJET- A Core Medical Treatment System forEmergency Management using CloudIRJET Journal
This document proposes a core medical treatment system using cloud computing to improve emergency management. The system would store patients' medical histories in the cloud to be accessible from any healthcare facility. This would allow doctors to access vital patient information during emergencies even if the history is from a different healthcare provider. The system would use encryption algorithms like Twofish to securely store private patient records in the cloud. This would help provide timely treatment, identify pre-existing conditions, and save more lives during medical emergencies by giving doctors access to full patient histories.
Big Data Risks and Rewards (good length and at least 3-4 references .docxtangyechloe
Big Data Risks and Rewards (good length and at least 3-4 references everything in APA 7 format)
When you wake in the morning, you may reach for your cell phone to reply to a few text or email messages that you missed overnight. On your drive to work, you may stop to refuel your car. Upon your arrival, you might swipe a key card at the door to gain entrance to the facility. And before finally reaching your workstation, you may stop by the cafeteria to purchase a coffee.
From the moment you wake, you are in fact a data-generation machine. Each use of your phone, every transaction you make using a debit or credit card, even your entrance to your place of work, creates data. It begs the question: How much data do you generate each day? Many studies have been conducted on this, and the numbers are staggering: Estimates suggest that nearly 1 million bytes of data are generated every second for every person on earth.
As the volume of data increases, information professionals have looked for ways to use big data—large, complex sets of data that require specialized approaches to use effectively. Big data has the potential for significant rewards—and significant risks—to healthcare. In this Discussion, you will consider these risks and rewards.
To Prepare:
Review the Resources and reflect on the web article
Big Data Means Big Potential, Challenges for Nurse Execs
.
Reflect on your own experience with complex health information access and management and consider potential challenges and risks you may have experienced or observed.
By Day 3 of Week 5
Post
a description of at least one potential benefit of using big data as part of a clinical system and explain why. Then, describe at least one potential challenge or risk of using big data as part of a clinical system and explain why. Propose at least one strategy you have experienced, observed, or researched that may effectively mitigate the challenges or risks of using big data you described. Be specific and provide examples.
By Day 6 of Week 5
Respond
to at least
two
of your colleagues
* on two different days
, by offering one or more additional mitigation strategies or further insight into your colleagues’ assessment of big data opportunities and risks.
Click on the
Reply
button below to reveal the textbox for entering your message. Then click on the
Submit
button to post your message.
*Note:
Throughout this program, your fellow students are referred to as colleagues.
Michea Discussion ( in APA 7 format and at least 2-3 references)
With the fast growing pace of technological advancement in the health care sector, daily operations of the institution helps generate millions of data that over time needs proper channels of transmission, storage, processing, assimilation and utilization. Following from the vast amount of data generated, some of its benefits includes but is not limited to functioning as a pattern discovery aid with relation to the amount of variance or similarity in .
This document discusses several existing architectures for analyzing healthcare-based big data. It describes architectures that use Hadoop and Apache Storm for batch and stream-based computing. The architectures gather data from various sources like medical records, devices, and social media. The data is stored in Hadoop Distributed File System and processed using MapReduce or Apache Storm topologies. The architectures aim to analyze large, complex healthcare data to provide clinical insights, predict diseases, and improve public health.
Open Insights Harvard DBMI - Personal Health Train - Kees van Bochove - The HyveKees van Bochove
In this talk, the Personal Health Train concept will be introduced, which enables running personalized medicine workflows as trains visiting data stations (e.g. hospital records, primary care records, clinical studies and registries, patient-held data from e.g. wearable sensors etc.) The Personal Health Train is a very powerful concept, which is however dependent on source medical data to be coded with appropriate metadata on consent, license, scope etc. of the data, and the data itself to be encoded using biomedical data standards, which is an ever growing field in biomedical informatics. In order to realize the Personal Health Train biomedical data will need to be FAIR, i.e. adopt the FAIR Guiding Principles. This talk will cover the emerging GO-FAIR international movement, and provide examples of how several European health data networks currently are adopting open standards based stacks, to enable routine health care data to be come accessible for research.
Health institution requires quality data and information management to function effectively and efficiently. It is an understatement to say that many organizations, institutions or government agencies have become critically dependent on the use of database system for their successes especially in the hospital. This work aims at developing an improved hospital information management system using a function-based approach. An efficient HIMS that can be used to manage patient information and its administration is presented in this work. This is with the goal of eradicating the problem of improper data keeping, inaccurate reports, wastage of time in storing, processing and retrieving information faced by the existing hospital information system in order to improve the overall efficiency of the health institution. The system was developed with Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), Hypertext Preprocessor (PHP), and My Structured Query Language (MySQL). The new system was tested using data collected from Renewal Clinic, Ibadan, Nigeria was used as case study were the data for the research was collected and the system was tested. The system provides a vital platform of information storage and retrieval in hospitals.
Innovation in Enterprise Imaging: Clinical Context is What's NextTodd Winey
Clinicians have one word for what they want from your next generation enterprise imaging solutions. Context. A recent study in the Journal of Digital Imaging suggests that nearly 60% of radiology orders have no mention of important chronic conditions, calling it “an alarming lack of communication” that “may negatively impact interpretation quality.” Imaging orders such as “chest pain” or “lower abdominal pain,” for example, are essentially context free, giving clinicians little information to work with. Access to a complete clinical history behind those orders can help clinicians provide richer input for more accurate diagnoses and more effective care plans, along with results of the imaging study.
IRJET- A Survey on Big Data Frameworks and Approaches in Health Care SectorIRJET Journal
This document discusses how big data frameworks and approaches can benefit the healthcare sector. It first introduces common big data tools like Hadoop, MapReduce, and Apache Sqoop that can be used to analyze large amounts of healthcare data from sources like electronic health records. It then discusses how data warehouse platforms like OpenEHR and EHR4CR can be used to store this healthcare data. The document proposes a framework to retrieve and analyze electronic health records using big data systems. Potential applications of this approach mentioned include improved healthcare analysis, prognosis, report management, and follow-up systems. In conclusion, the integration of big data technologies with healthcare data platforms and sources can provide benefits like personalized care, disease prediction, and support for clinical decision
Role of Cloud Computing in Healthcare Systemsijtsrd
The healthcare industry is complex because it is so vast in terms of the processes involved and the amount of private and sensitive information it needs to deal with. The industry’s complexity often leads to two major challenges - increased operational cost including data storage cost and difficulty in building a self sufficient health ecosystem. Technology has always been the savior that workaround for overcoming major healthcare industry challenges. One such technology is cloud computing. It has been in use in the healthcare industry for several years and continuously evolving with industry changes. Cloud computing is transforming the healthcare industry at different levels with features like collaboration, scalability, reach ability, efficiency, and security.The on demand computing feature of the cloud adds value, especially when healthcare institutes and care providers need to deploy, access and handle network information at the drop of a hat. With the rise in demand for data based security, there needs to be a shift in the creation, usage, better storage, collaboration, and sharing of healthcare data techniques. It is where cloud computing leaves no stone unturned Healthcare is one such sector that has been at the forefront of adopting cloud technology. Healthcare providers are coming to realize the true potential of cloud solutions across the globe.According to the BBC research report, estimated global spending by stakeholders in the industry on cloud computing is expected to be around 35 billion dollars by 2022. It is anticipated that the CAGR of cloud services and solutions will maintain a trajectory of 15 rise and the size of the Cloud powered healthcare market is to be around 55 billion dollars by the year 2025. Nidhi Prasad | Mahima Chaurasia "Role of Cloud Computing in Healthcare Systems" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-3 , April 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49488.pdf Paper URL: https://www.ijtsrd.com/computer-science/other/49488/role-of-cloud-computing-in-healthcare-systems/nidhi-prasad
Similar to THE 4 R’S – REASON, REDCAP, REVIEW AND RESEARCH -IN A LARGE HEALTHCARE ORGANIZATION (20)
A PRACTICAL APPROACH TO PREDICTING DEPRESSION: VERBAL AND NON-VERBAL INSIGHTS...hiij
While global standards have been established for diagnosing depression, the reliance on expert judgement
and observation remains a challenge. This study delves into a potential approach of efficient data
collection to increase the practicability of machine learning models in accurately predicting depression
based on a comprehensive analysis of verbal and non-verbal cues exhibited by individuals.
Health Disparities: Differences in Veteran and Non-Veteran Populations using ...hiij
Introduction: This study investigated self-reported health status, health screenings, vision problems, and
vaccination rates among veteran and non-veteran groups to uncover health disparities that are critical for
informed health system planning for veteran populations.
Methods: Using public-use data from the National Health Interview Survey (2015-2018), this study adopts
an ecologic cross-sectional approach to conduct an in-depth analysis and visualization of the data assisted
by Generative AI, specifically ChatGPT-4. This integration of advanced AI tools with traditional
epidemiological principles enables systematic data management, analysis, and visualization, offering a
nuanced understanding of health dynamics across demographic segments and highlighting disparities
essential for veteran health system planning.
Findings: Disparities in self-reports of health outcomes, health screenings, vision problems, and
vaccination rates were identified, emphasizing the need for targeted interventions and policy adjustments.
Conclusion: Insights from this study could inform health system planning, using epidemiological data
assessment to suggest enhancements for veteran healthcare delivery. These findings highlight the value of
integrating Generative AI with epidemiological analysis in shaping public health policy and health
planning.
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
HEALTH DISPARITIES: DIFFERENCES IN VETERAN AND NON-VETERAN POPULATIONS USING ...hiij
Introduction: This study investigated self-reported health status, health screenings, vision problems, and
vaccination rates among veteran and non-veteran groups to uncover health disparities that are critical for
informed health system planning for veteran populations.
Methods: Using public-use data from the National Health Interview Survey (2015-2018), this study adopts
an ecologic cross-sectional approach to conduct an in-depth analysis and visualization of the data assisted
by Generative AI, specifically ChatGPT-4. This integration of advanced AI tools with traditional
epidemiological principles enables systematic data management, analysis, and visualization, offering a
nuanced understanding of health dynamics across demographic segments and highlighting disparities
essential for veteran health system planning.
Findings: Disparities in self-reports of health outcomes, health screenings, vision problems, and
vaccination rates were identified, emphasizing the need for targeted interventions and policy adjustments.
Conclusion: Insights from this study could inform health system planning, using epidemiological data
assessment to suggest enhancements for veteran healthcare delivery. These findings highlight the value of
integrating Generative AI with epidemiological analysis in shaping public health policy and health
planning.
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
BRIEF COMMENTARY: USING A LOGIC MODEL TO INTEGRATE PUBLIC HEALTH INFORMATICS ...hiij
The COVID-19 pandemic has been a watershed moment in public health surveillance, highlighting the
crucial role of data-driven insights in informing health actions and policies. Revisiting key concepts—
public health, epidemiology in public health practice, public health surveillance, and public health
informatics—lays the foundation for understanding how these elements converge to create a robust public
health surveillance system framework. Especially during the COVID-19 pandemic, this integration was
exemplified by the WHO efforts in data dissemination and the subsequent global response. The role of
public health informatics emerged as instrumental in this context, enhancing data collection, management,
analysis, interpretation, and dissemination processes. A logic model for public health surveillance systems
encapsulates the integration of these concepts. It outlines the inputs and outcomes and emphasizes the
crucial actions and resources for effective system operation, including the imperative of training and
capacity development.
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
AUTOMATIC AND NON-INVASIVE CONTINUOUS GLUCOSE MONITORING IN PAEDIATRIC PATIENTShiij
Glycated haemoglobin does not allow you to highlight the effects that food choices, physical activity and
medications have on your glycaemic control day by day. The best way to monitor and keep track of the
immediate effects that these have on your blood sugar levels is self-monitoring, therefore the use of a
glucometer. Thanks to this tool you have the possibility to promptly receive information that helps you to
intervene in the most appropriate way, bringing or keeping your blood sugar levels as close as possible to
the reference values indicated by your doctor. Currently, blood glucose meters are used to measure and
control blood glucose. Diabetes is a fairly complex disease and it is important for those who suffer from it
to check their blood sugar (blood sugar) periodically throughout the day to prevent dangerous
complications. Many children newly diagnosed with diabetes and their families may face unique challenges
when dealing with the everyday management of diabetes, including treatments, adapting to dietary
changes, and the routine monitoring of blood glucose. Many questions may also arise when selecting a
blood glucose meter for paediatric patients. With current blood glucose meters, even with multiple daily
self-tests, high and low blood glucose levels may not be detected. Key factors that may be considered when
selecting a meter include accuracy of the meter; size of the meter; small sample size required for testing;
ease of use and easy-to-follow testing procedure; ability for alternate testing sites; quick testing time and
availability of results; ease of portability to allow testing at school and during leisure time; easyto- read
numbers on display; memory options; cost of meter and supplies. In this study we will show a new
automatic portable, non-invasive device and painless for the daily continuous monitoring (24 hours a day)
of blood glucose in paediatric patients.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
BRIEF COMMENTARY: USING A LOGIC MODEL TO INTEGRATE PUBLIC HEALTH INFORMATICS ...hiij
The COVID-19 pandemic has been a watershed moment in public health surveillance, highlighting the
crucial role of data-driven insights in informing health actions and policies. Revisiting key concepts—
public health, epidemiology in public health practice, public health surveillance, and public health
informatics—lays the foundation for understanding how these elements converge to create a robust public
health surveillance system framework. Especially during the COVID-19 pandemic, this integration was
exemplified by the WHO efforts in data dissemination and the subsequent global response. The role of
public health informatics emerged as instrumental in this context, enhancing data collection, management,
analysis, interpretation, and dissemination processes. A logic model for public health surveillance systems
encapsulates the integration of these concepts. It outlines the inputs and outcomes and emphasizes the
crucial actions and resources for effective system operation, including the imperative of training and
capacity development.
INTEGRATING MACHINE LEARNING IN CLINICAL DECISION SUPPORT SYSTEMShiij
This review article examines the role of machine learning (ML) in enhancing Clinical Decision Support
Systems (CDSSs) within the modern healthcare landscape. Focusing on the integration of various ML
algorithms, such as regression, random forest, and neural networks, the review aims to showcase their
potential in advancing patient care. A rapid review methodology was utilized, involving a survey of recent
articles from PubMed and Google Scholar on ML applications in healthcare. Key findings include the
demonstration of ML's predictive power in patient outcomes, its ability to augment clinician knowledge,
and the effectiveness of ensemble algorithmic approaches. The review highlights specific applications of
diverse ML models, including moment kernel machines in predicting surgical outcomes, k-means clustering
in simplifying disease phenotypes, and extreme gradient boosting in estimating injury risk. Emphasizing
the potential of ML to tackle current healthcare challenges, the article highlights the critical role of ML in
evolving CDSSs for improved clinical decision-making and patient care. This comprehensive review also
addresses the challenges and limitations of integrating ML into healthcare systems, advocating for a
collaborative approach to refine these systems for safety, efficacy, and equity.
Health Informatics - An International Journal (HIIJ)hiij
Healthcare Informatics: An International Journal is a quarterly open access peer-reviewed journal that Publishes articles which contribute new results in all areas of the health care.
The journal focuses on all of aspect in theory, practices, and applications of Digital Health Records, Knowledge Engineering in Health, E-Health Information, and Information Management in healthcare, Bio-Medical Expert Systems, ICT in health promotion and related topics. Original contributions are solicited on topics covered under the broad areas such as (but not limited to) listed below:
The Proposed Guidelines for Cloud Computing Migration for South African Rural...hiij
It is now overdue for the hospitals in South African rural areas to implement cloud computing technologies in order to access patient data quickly in an emergency. Sometimes medical practitioners take time to attend patients due to the unavailability of kept records, leading to either a loss of time or the reassembling of processes to recapture lost patient files. However, there are few studies that highlight challenges faced by rural hospitals but they do not recommend strategies on how they can migrate to cloud computing. The purpose of this paper was to review recent papers about the critical factors that influence South African hospitals in adopting cloud computing. The contribution of the study is to lay out the importance of cloud computing in the health sectors and to suggest guidelines that South African rural hospitals can follow in order to successfully relocate into cloud computing.The existing literature revealed that Hospitals may enhance their record-keeping procedures and conduct business more effectively with the help of the cloud computing. In conclusion, if hospitals in South African rural areas is to fully benefit from cloud-based records management systems, challenges relating to data storage, privacy, security, and the digital divide must be overcome.
SUPPORTING LARGE-SCALE NUTRITION ANALYSIS BASED ON DIETARY SURVEY DATAhiij
While online survey systems facilitate the collection on copious records on diet, exercise and other healthrelated data, scientists and other public health experts typically must download data from those systems
into external tools for conducting statistical analyses. A more convenient approach would enable
researchers to perform analyses online, without the need to coordinate additional analysis tools. This
paper presents a system illustrating such an approach, using as a testbed the WAVE project, which is a 5-
year childhood obesity prevention initiative being conducted at Oregon State University by health scientists
utilizing a web application called WavePipe. This web application has enabled health scientists to create
studies, enrol subjects, collect physical activity data, and collect nutritional data through online surveys.
This paper presents a new sub-system that enables health scientists to analyse and visualize nutritional
profiles based on large quantities of 24-hour dietary recall records for sub-groups of study subjects over
any desired period of time. In addition, the sub-system enables scientists to enter new food information
from food composition databases to build a comprehensive food profile. Interview feedback from novice
health science researchers using the new functionality indicated that it provided a usable interface and
generated high receptiveness to using the system in practice.
AN EHEALTH ADOPTION FRAMEWORK FOR DEVELOPING COUNTRIES: A SYSTEMATIC REVIEWhiij
The document summarizes a systematic literature review on factors influencing adoption of eHealth technologies in developing countries. The review analyzed 29 papers published between 2009-2021. Key findings included:
- Widely used frameworks for eHealth adoption in developing countries were TAM, UTAUT, and TOE, but these did not fully capture all relevant factors.
- Additional factors identified included socio-demographic, technological, information, socio-cultural, organizational, governance, ethical/legal, and financial dimensions.
- The review proposed a novel, context-specific eHealth adoption framework for developing countries with eight dimensions addressing the above factors.
PGx Analysis in VarSeq: A User’s PerspectiveGolden Helix
Since our release of the PGx capabilities in VarSeq, we’ve had a few months to gather some insights from various use cases. Some users approach PGx workflows by means of array genotyping or what seems to be a growing trend of adding the star allele calling to the existing NGS pipeline for whole genome data. Luckily, both approaches are supported with the VarSeq software platform. The genotyping method being used will also dictate what the scope of the tertiary analysis will be. For example, are your PGx reports a standalone pipeline or would your lab’s goal be to handle a dual-purpose workflow and report on PGx + Diagnostic findings.
The purpose of this webcast is to:
Discuss and demonstrate the approaches with array and NGS genotyping methods for star allele calling to prep for downstream analysis.
Following genotyping, explore alternative tertiary workflow concepts in VarSeq to handle PGx reporting.
Moreover, we will include insights users will need to consider when validating their PGx workflow for all possible star alleles and options you have for automating your PGx analysis for large number of samples. Please join us for a session dedicated to the application of star allele genotyping and subsequent PGx workflows in our VarSeq software.
- Video recording of this lecture in English language: https://youtu.be/RvdYsTzgQq8
- Video recording of this lecture in Arabic language: https://youtu.be/ECILGWtgZko
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Storyboard on Acne-Innovative Learning-M. pharm. (2nd sem.) CosmeticsMuskanShingari
Acne is a common skin condition that occurs when hair follicles become clogged with oil and dead skin cells. It typically manifests as pimples, blackheads, or whiteheads, often on the face, chest, shoulders, or back. Acne can range from mild to severe and may cause emotional distress and scarring in some cases.
**Causes:**
1. **Excess Oil Production:** Hormonal changes during adolescence or certain times in adulthood can increase sebum (oil) production, leading to clogged pores.
2. **Clogged Pores:** When dead skin cells and oil block hair follicles, bacteria (usually Propionibacterium acnes) can thrive, causing inflammation and acne lesions.
3. **Hormonal Factors:** Fluctuations in hormone levels, such as during puberty, menstrual cycles, pregnancy, or certain medical conditions, can contribute to acne.
4. **Genetics:** A family history of acne can increase the likelihood of developing the condition.
**Types of Acne:**
- **Whiteheads:** Closed plugged pores.
- **Blackheads:** Open plugged pores with a dark surface.
- **Papules:** Small red, tender bumps.
- **Pustules:** Pimples with pus at their tips.
- **Nodules:** Large, solid, painful lumps beneath the surface.
- **Cysts:** Painful, pus-filled lumps beneath the surface that can cause scarring.
**Treatment:**
Treatment depends on the severity and type of acne but may include:
- **Topical Treatments:** Such as benzoyl peroxide, salicylic acid, or retinoids to reduce bacteria and unclog pores.
- **Oral Medications:** Antibiotics or oral contraceptives for hormonal acne.
- **Procedures:** Such as chemical peels, extraction of comedones, or light therapy for more severe cases.
**Prevention and Management:**
- **Cleanse:** Regularly wash skin with a gentle cleanser.
- **Moisturize:** Use non-comedogenic moisturizers to keep skin hydrated without clogging pores.
- **Avoid Irritants:** Such as harsh cosmetics or excessive scrubbing.
- **Sun Protection:** Use sunscreen to prevent exacerbation of acne scars and inflammation.
Acne treatment can take time, and consistency in skincare routines and treatments is crucial. Consulting a dermatologist can help tailor a treatment plan that suits individual needs and reduces the risk of scarring or long-term skin damage.
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Selective alpha1 blockers are Prazosin, Terazosin, Doxazosin, Tamsulosin and Silodosin majorly used to treat BPH, also hypertension, PTSD, Raynaud's phenomenon, CHF
Receptor Discordance in Breast Carcinoma During the Course of Life
Definition:
Receptor discordance refers to changes in the status of hormone receptors (estrogen receptor ERα, progesterone receptor PgR, and HER2) in breast cancer tumors over time or between primary and metastatic sites.
Causes:
Tumor Evolution:
Genetic and epigenetic changes during tumor progression can lead to alterations in receptor status.
Treatment Effects:
Therapies, especially endocrine and targeted therapies, can selectively pressure tumor cells, causing shifts in receptor expression.
Heterogeneity:
Inherent heterogeneity within the tumor can result in subpopulations of cells with different receptor statuses.
Impact on Treatment:
Therapeutic Resistance:
Loss of ERα or PgR can lead to resistance to endocrine therapies.
HER2 discordance affects the efficacy of HER2-targeted treatments.
Treatment Adjustment:
Regular reassessment of receptor status may be necessary to adjust treatment strategies appropriately.
Clinical Implications:
Prognosis:
Receptor discordance is often associated with a poorer prognosis.
Biopsies:
Obtaining biopsies from metastatic sites is crucial for accurate receptor status assessment and effective treatment planning.
Monitoring:
Continuous monitoring of receptor status throughout the disease course can guide personalized therapy adjustments.
Understanding and managing receptor discordance is essential for optimizing treatment outcomes and improving the prognosis for breast cancer patients.
Congestive Heart failure is caused by low cardiac output and high sympathetic discharge. Diuretics reduce preload, ACE inhibitors lower afterload, beta blockers reduce sympathetic activity, and digitalis has inotropic effects. Newer medications target vasodilation and myosin activation to improve heart efficiency while lowering energy requirements. Combination therapy, following an assessment of cardiac function and volume status, is the most effective strategy to heart failure care.
THE 4 R’S – REASON, REDCAP, REVIEW AND RESEARCH -IN A LARGE HEALTHCARE ORGANIZATION
1. Health Informatics - An International Journal (HIIJ) Vol.4, No.3/4, November 2015
DOI: 10.5121/hiij.2015.4402 15
THE 4 R’S – REASON, REDCAP, REVIEW AND
RESEARCH - IN A LARGE HEALTHCARE
ORGANIZATION
Christopher Bain1, 2
, Annie Gilbert3
, Bismi Jomon3
, Robin Thompson3
, David
Kelly3
and Chris Mac Manus4
1
Faculty of Information Technology, Monash University. Clayton, Vic. Australia
2
Health Information Services, Mercy Hospital for Women. Heidelberg, Vic. Australia
3
Applications and Knowledge Management Department, Alfred Health.
Melbourne. Vic. Australia
4
Ozescribe. Glen Iris, Vic, Australia
ABSTRACT
This paper outlines the journey of a large Australian academic health service in relation to the acquisition,
installation and roll out of the REDCap platform (RCP) for the betterment of clinical review (clinical audit)
and research data collection. The main aims of the acquisition of the platform were to facilitate data
collection and management for audit and research across the organization in a more sustainable way than
had previously been possible. We found the platform to be easily installed and maintained. There was rapid
uptake of the platform by a range of health service stakeholders across the audit, research and operational
domains. We were also able to successfully integrate data from our corporate clinical data environment,
The REASON Discovery Platform R
(REASON) into selected REDCap “applications” using the Dynamic
Data Pull (DDP) functionality it provides. In summary the acquisition and installation of REDCap at our
health service has been hugely successful and has provided a great facility for use by a large number of
organizational stakeholders going forwards into the future.
KEYWORDS
Hospital, REDCap, REASON, audit, research
1.INTRODUCTION
There are a number of challenges facing large healthcare organizations, particularly those where
research and audit are key agenda items for the organization.
One such challenge is how to balance the operational needs of functional systems and data
collections, often tailored to highly specialized health service delivery areas (eg - cardiology
versus obstetrics), with a corporate need to save money and provide good information technology
(IT) governance, often through standardisation and consolidation. This can lead to tensions
between somewhat autonomous business areas and the centralised corporate functions of
information management (IM) and IT. In this case study we will examine how our organization
set about managing this tension - particularly, but not only, in relation to clinical audit and
research needs.
Our health service, Alfred Health (AH) [1], has 3 main hospital campuses and several smaller
satellite facilities (including psychiatric outreach clinics and a specialised sexual health centre)
under its control, as well as many ambulatory services. It also provides state-wide referral
services in the areas of adult organ transplantation, adult burns and adult trauma.
2. Health Informatics - An International Journal (HIIJ) Vol.4, No.3/4, November 2015
16
The original setting for this work is the creation of a Health Informatics (HI) department at the
health service in early 2011. The department was charged with assuming responsibility for the
technical development and management of the corporate data and reporting infrastructure, whilst
another separate key business unit was charged with the responsibility for delivering data and
reporting off the infrastructure. In the mid part of 2011, HI along with Health Information
Services (HIS), Information Technology Services (ITS) and the Australian Centre for Health
Innovation was brought under a single new business division – the Information Development
Division (IDD). It was the vision of the new divisional head to continue to develop this technical
infrastructure as part of a broader plan. After several corporate restructures, this work is now
being continued in the recently created Applications and Knowledge Management (AKM)
Department.
2. BACKGROUND
Often the "big data" paradigm is viewed as a very modern phenomenon, in part driven by recent
changes in technology that support it, and in part by the vast array of machine generated data (eg -
from medical devices) now available to us for leveraging. It could be argued however that
healthcare, with its large numbers of patients and decades of detailed history about them in
variety of formats (in many cases), has been operating in such a paradigm for many years. How
well it has done in efficiently handling such large volumes of data is a separate question however.
Although working with, and making sense of, “big data” in the more modern sense of the term, is
not without its dangers [2], the benefits of its use are thought to be significant [3]. Some of the
purported advantages of the “big data” paradigm are the ability to mine data sets for patterns,
identify uncommon events and to unearth interesting or valuable insights
As previously described in the literature [4], we have constructed a platform in this paradigm
called The REASON Discovery Platform®
. We have also previously described some of the actual
and potential value from the platform [5-9], including the development of a web based cohort
identification tool [10], a prototype image analysis platform [11] and automated data collation
and transmission to clinical registries [12-14]
It can be difficult for readers to get a full appreciation of what is being attempted through the
construction of the REASON platform as the means to address this technical need. There are 2
United States (US) based examples outlined below, that allow the reader to get a sense of the
amount of work done to date to establish this infrastructure, and the direction of travel of the
platform.
One US initiative is “Informatics for Integrating Biology and the Bedside” (i2b2), although this
operates under a different kind of governance model to our platform, and arguably has a broader
reach [15]. One of the primary aims of the i2b2 Centre is in “developing a scalable computational
framework to address the bottleneck limiting the translation of genomic findings and hypotheses
in model systems relevant to human health”. This initiative is well known internationally and
there are even competitions to analyse data provided from the platform.
The REASON platform however, is most closely aligned to the Stanford – based STRIDE
(Stanford Translational Research Integrated Database Environment). STRIDE “is a research
and development project at Stanford University to create a standards-based informatics platform
supporting clinical and translational research.” [16]. There has been evidence published in the
international literature pertaining to the benefits to health care processes, and patients, of such a
platform [17]. In order to augment the scope and benefits of REASON, as well as to deal with the
3. Health Informatics - An International Journal (HIIJ) Vol.4, No.3/4, November 2015
17
issue of excessive application diversity, a corporate decision was made to acquire the REDCap
Platform (RCP) [18-19].
The RCP (Figure 1) is an open-source, secure web application that can be used for data capture
and analysis in different ways. This application is developed by Vanderbilt University and is
available to institutional partners at no cost. AH has been a member of the REDCAP consortium
based at Vanderbilt since early 2014. With minimal effort and training, any user can access this
application to build and manage online data collection instruments and analyse them at their own
pace. The potential advantage of using this application is that the data is stored centrally and
access is secured and controlled by active directory login [20]. The real benefit to organisations
around the use of the RCP is that provides a pathway to reduce the use of standalone legacy
applications like Microsoft Access 97 databases. In addition it is easy to access and use with a
range of different end-point devices like iPads, Motion tablets and desktop PC’s.
Figure 1. Map based view of IP addresses of users accessing AH REDCap Platform (RCP)
In this article, through a case study approach, we will examine how our organisation has dealt
with the big data deluge, and the issue of excessive numbers of software applications and
standalone databases, through the use of the RCP.
3. METHOD
3.1. Pre-Acquisition of the RCP
At AH there are 7000 or more employees, 3 main hospitals and multiple satellite sites, several
attached internationally known research facilities and a close affiliation with several universities -
most notably Monash University. In addition the health service contributes to about 90 clinical
registries. As a result there are a multitude of needs when it comes to data collection,
management and analysis
As a direct result of this, and also driven by suboptimal information and technology governance
in the past, multiple point information solutions have developed over time, often run
4. Health Informatics - An International Journal (HIIJ) Vol.4, No.3/4, November 2015
18
independently by individual departments. This has had a number of adverse effects - for example
there are now many thousands of standalone Microsoft Access databases across the organization
which, whilst filling a need and providing benefits to the immediate users, also introduce risks to
the organization such as incompatibilities between old software environments - eg MS Access 97
(officially out of support 11 years ago) [21] and newer operating systems such as Windows 7. A
scan of the 4 key organizational file servers at the time of writing revealed 13,925 MS Access
databases on these servers alone, 92% of which were actually older MS Access versions (Access
97, XP or 2002). This scan does not include the MS Access databases solely located on hardware
end points like PC’s. In some cases these standalone systems gave matured to be proxy electronic
medical records (EMRs), and in one case such a system needed to be urgently decommissioned
and replaced due to a key man risk being realised.
One of the principles used by the IDD in order to try to meet these needs was to aim to use
platform level solutions where possible, to address multiple stakeholder needs in a cost effective,
extensible and sustainable way. It is in this context, and in the setting of REASON having been
established, that when the relevant leaders became aware of the existence of REDCap, and it use
in a large research facility across town, that investigations about the acquisition of the RCP for
AH commenced
3.2. RCP Acquisition and Pilot
We became aware that the Murdoch Children’s Research Institute (MCRI) [22] had used
REDCap for several years. We did a virtual introduction with the key contact there (using the
contact details kept by the REDCap consortium) in order to explore this. We then met the
REDCap administrator at the MCRI and were immediately impressed both by what we saw, and
how the MCRI were using and running REDCap. We felt that many elements of their model
would be fairly easy to establish at AH, and that the platform was eminently suited to addressing
some core data and information needs at AH.
A technical investigation was then performed in conjunction with the other IDD Departments to
assess the technical requirements needed to host the platform, and the ease of acquiring suitable
hardware. One this was done, the Executive Director of the IDD approved the acquisition of
REDCap and signed off on the REDCap consortium agreement (March 2014) so that a pilot of
the platform could commence.
A pilot was run in the organisation from mid-2014. This pilot focused on 3 key research and audit
projects – the Patient Experience Survey (PES), the Limiting IV Chloride to Reduce Acute
Kidney Injury (LICRA) clinical trial [23] and the Severe Asthma Clinic Survey. In the section
(Section 4) that follows we will focus on the results of some specific pieces of work using
REDCap in the setting of the REASON platform and our corporate strategies in this area.
3.3. Operationalization and Advanced Usage of the RCP
There were 3 key evaluation principles underpinning the pilot of the RCP at AH. These were:
• could the platform be successful installed and connected to other hospital systems (eg -
Active Directory [20] for authentication)
• could users be trained to successfully create REDCap applications and collect data in
them and
• could the users then get access to that data for their desired purpose.
5. Health Informatics - An International Journal (HIIJ) Vol.4, No.3/4, November 2015
19
It was fairly clear after 6 months of usage of the platform that all of these criteria had been easily
fulfilled. As a result, a formal decision was made to operationalise the system and make it part of
the technology landscape at AH. In turn, several key undertakings were performed to facilitate
this operationalisation
• responsibility for the platform was established in the AKM department
• the Dynamic Data Pull (DDP) [18] functionality of REDCap was instantiated
• the routine nightly upload of all REDCap Database (DB) data into REASON was
established
• access requests for REDCap were set us as a usable service in the IT service catalogue,
and
• more powerful and robust hardware was sought to support the expected growth in usage
of the RCP.
In addition, towards the middle of 2015, a formal disaster recovery approach for REDCap was
designed and implemented, over and above the routine REDCap DB backups.
4. RESULTS
In this section of the paper we will examine the outcome of the RCP pilot, and some of the
achievements with the system since it was subsequently formally operationalized at AH.
4.1. The Patient Experience Survey
As described above, the Patient Experience Survey (PES) was one of the original projects created
on the AH RCP as part of the pilot.
PES (Figure 2) is about capturing patient experience across the health service. The study
captured all areas of the patient experience including some descriptive features of the patients
themselves, the area of the hospital they were in, the service they attended or utilised, and their
feedback about the quality of the service or care that they received. There are now more than
3,500 patient responses, from the three different campuses, which were collected by volunteers in
the PES REDCap “application”.
Figure 2. Patient Experience Survey (PES) in the AH RCP Instance
6. Health Informatics - An International Journal (HIIJ) Vol.4, No.3/4, November 2015
20
With the recent progress in technology and infrastructure at AH, it ought soon be possible that
each and every area of the organisation will have wireless connectivity, and hence feedback could
be collected from patients or carers any physical location across the various health service
campuses.
4.2. The LICRA Study
The LICRA study is a research study being run by the Anaesthesia Department at AH [22]. The
study aims to test the impact of a strategy of peri-operative chloride-restriction through IV fluid
therapy on incidence of acute kidney injury after cardiothoracic surgery. There was an existing
paper based case report form (CRF) that was replaced by a development in REDCap by the
anaesthetics research team as part of a pilot study for LICRA. There were 13 data collection
instruments and a total of 560 fields as part of this LICRA pilot study. It took approximately 1
hour per record to complete the paper based data collection instruments.
The AKM Department subsequently conducted an analysis of the fields to be collected for the
LICRA study and ascertained that many of these were already collected or generated in the
Cerner Millennium environment [24], AH’s EMR System. As such, these fields were extracted
and available from the REASON Discovery Platform which receives much of the Millennium
data routinely on a daily basis. An extract was written to retrieve these files from REASON, and
combine it with the additional data entered through REDCap, to provide a consolidated dataset
for the researcher. After extensive testing, the number of data collection instruments in REDCap
was reduced to 8 with only 249 fields. Part of the validation process highlighted some manual
error occurred in the original data collection approach, either due to transcription problems, or
updates to the EMR post CRF completion (Figure 3). Providing a combined electronic dataset in
this way resulted in a 50% reduction in data entry time, and an improvement in data quality. The
final dataset is being analysed in separate statistics package.
All projects that have data capture requirements for registry submissions at AH, and have data
collection gaps identified or need the facilitation of data collection supported, could consider
REDCap to support this process using a similar approach to what we have outlined here, so only
data that is not already in existence electronically is then collected – rather than re-collecting or
re-recording data already present in other hospital systems.
Figure 3. LICRA project in the AH RCP Instance
7. Health Informatics - An International Journal (HIIJ) Vol.4, No.3/4, November 2015
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4.3. Key Technical Achievements
Several projects are currently using the Dynamic Data Pull (DDP) functionality of REDCap to
validate the patient details - in the Team and Patient Assessment System (TAPAS) project in
General Medicine for example, plus it will also be used in some upcoming nursing audit projects,
including the re-creation of the Point of Care (POC) audit [7-8] in REDCap. The DDP feature
allows data to be imported into the data collection instrument from an external source. It operates
by communicating between web services via HTTP/HTTPS. In our case, the patient first name,
last name, gender and date of birth are retrieved when the patient’s medical record number is
entered into the relevant REDCap collection instrument.
4.4. Overall REDCap Usage at AH
As can be seen in Figure 4 and Tables 1 and 2, the RCP provides very a comprehensive suite of
information pertaining to the use of the local RCP instance. This information has been very
valuable in managing the platform and in getting a clear picture of the uptake of the RCP across
the health service.
Figure 4. AH RCP Instance Statistics
At the time of writing there were about 120 projects (“applications”) on the RCP and 130 users
with access to the platform, and all were active -as opposed to inactive or suspended users. Users
are people with security accounts that allow them then to access the platform and potentially
develop REDCap projects, and this number does not include end-users who may complete
surveys, for themselves or on behalf of others, that are housed on the platform.
When viewed from an "event log" perspective (Table 1) there had been over 35,000 events
recorded by the system in the last month as at the time of writing.
Table 1. Event based usage of the AH RCP Instance.
Parameter Value
Total logged events 138,045
- Past 30 minutes 22
- Today 1,418
- Past 7 days 8,208
- Past 30 days 35,505
8. Health Informatics - An International Journal (HIIJ) Vol.4, No.3/4, November 2015
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There were also more than 15,700 data fields being used across 470 data instruments in the AH
instance of the RCP (Table 2) at the time of writing, as well as over 13,000 records of different
kinds across the 120 plus projects.
Table 2. Record and content based usage of the AH RCP Instance.
Parameter Value
Data collection instruments 470
Fields on all data collection instruments 15,774
Records 13,035
Survey responses 7,095
Survey participants (in participants list) 79
Survey invitations sent 124
- Responded 38
- Not responded 86
Calendar events 65
Schedules generated 19
Projects created from a project template 16
Records with data imported from source system via DDP 79
Total data values imported from source system via DDP 440
5. DISCUSSION
5.1. An Overview
The pilot of the RCP at AH was successfully completed in December 2014. The uptake of
REDCap in the organisation has been predominantly via word of mouth, rather than active
advertising. In all cases there has been positive interest and engagement with the projects
developed to date on the RCP - 75% of which are surveys and 25% are data collection
instruments In terms of the purposes of the projects, 40% are for operational support, 40% for
quality improvement and 18% are for research. The potential advantage of using this application
is that the data is stored centrally and access is secured and controlled by active directory logins.
The data collected through this tool is also uploaded on a nightly basis to the REASON platform
for fast and flexible reporting and research purposes.
The work has been so successful that now the AH Ethics Committee (EC) has also approved the
RCP as an appropriate organisational wide data collection method wherever suitable. The AKM
Business Services team then govern the process to create and release projects into production, and
they also train the relevant super users. In addition we have implemented a support model where
this team review the applications for REDCap use, ensure EC approval is obtained where required
(eg- for project requests not coming through the AH EC), provide locations for the online training
and assist in design, and implementation, of REDCap applications where required. We have
found that with minimal effort and training, any user can use this application to build and manage
online data collection instruments and analyse the data from them (at a high level) at their own
pace. The user is not subject to other IT processes or beholden to other IT priorities. The
challenge with this is where the users are too busy to create their own survey, although contractor
REDCap developer services are available to them if they can find funding. Enabling users to be
able to develop their own surveys and data collection instruments has been a very positive
process. Non-technical users are able to develop and utilise the online designer, branching logic
and calculated fields easily.
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One of the real benefits to our organisation in using the RCP is a reduction in the number of
standalone legacy applications like MS Access databases, and its ease of use with a range of
different types of devices like iPads and Motion tablets. It is now much easier to acquire and
leverage “big data” whilst simultaneously preventing further growth in the numbers of standalone
applications around the business. In addition, because of the RCP, there is now also a
standardised approach supported for capturing quality of care data separately to the EMR,
ensuring that the EMR’s focus is the actual clinical care of patients and the workflow and
decision support around that care. Finally, findings from research conducted in REDCap can be
used as an impetus to further research, and cross collaboration between research teams can be
easily facilitated.
AKM are also hoping to expand our use of REDCap to include more advanced functionality
including API’s and utilising the Plugin and Hook functions. Technical training about some of
these RCP capabilities will be addressed at the US REDCap conference being held in late 2015.
5.2. Lessons Learned
There have been cultural challenges within the organisation to be addressed through this process,
where there is the perception of data, knowledge and control are lost if a central standardised
methodology is used for data collection. This is being addressed by individual meetings and
discussion, and future REDCap seminars to promote success stories where REDCap has been
utilised. It is also critical that REDCap is not perceived as a replacement to data capture that is
required in the EMR. A policy is being developed that patient data is captured, that relates to
patient care, may only be captured in REDCap temporarily and only under agreed circumstances,
with the aim to migrate the functionality to the EMR (Cerner Millennium) environment as soon as
it can be made to happen.
There have always been a large number of audits - clinical and non-clinical - going on in our
health service at any given point in time. This is especially true since the Australian government
instituted a new set of national standards for hospitals [25]. One of the effects of the combination
of the instantiation of REDCap and the creation of the REASON platform is that setting up and
collecting data for an audit is now more streamlined than ever. It also means however that there
are now - completely predictable - governance requirements to he addressed around who gets to
create audits on the platform and under what circumstances. Good independent governance,
including assessing the need for a piece of work, exists for research but this is not necessarily so
for clinical audit activities. One of the lessons of this work, which in part an inevitable
consequence of the success of REDCap, is that there now needs to be greater consideration given
to the governance around audits - most particularly clinical audits - as they are now easier than
ever to create and run.
Another key lesson of the work, although not sometime that is that surprising, as that the success
of the REDCap platform at AH has meant that it now needs some ongoing human resources to
run it as a business service. Whilst we already have found and used existing skilled labour from
within our current workforce to act in superuser- system expert roles, this has been to the
detriment of other work streams at times. We ate currently discussing whether we can access a
permanent dedicated staff member to act in this system expert role.
5.3. Future Work
As outlined in the section above on learnings, there is now work to be done at AH to consider the
process by which audits - especially clinical audits - are approved; as well as work to be done to
10. Health Informatics - An International Journal (HIIJ) Vol.4, No.3/4, November 2015
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ensure that AH RCP data collections are reused where possible, and that the same basic data
elements are not re-collected in slightly different ways in subsequent audits.
Another of the next steps in this work program is to further expand the awareness of the
capabilities of the RCP platform amongst health service staff. To this end we recently held 2
internal sessions about the platform, the successes it has supported, and the future opportunities it
provides. Key business users of the system such as the Director in charge of nursing quality spoke
at the session about their positive experiences with the platform and running projects (eg - clinical
audit activities) on it.
One of the additional steps we have taken, with an eye to the future, is to embed a link to the RCP
instance at AH within a broader intranet portal (Figure 5) that contains a number of other web
based applications created or managed by the AKM department.
Figure 5. AKM Intranet Portal – Link to REDCap
Recently an organisation wide program around recognising excellence was supported by an in-
house built REDCap survey to allow the electronic nomination of teams or individuals to
potentially receive recognition awards. This piece of RCP development can of course be reused in
subsequent years, and can also serve as a robust permanent repository of the history of
nominations and the reason for nomination, over time.
6. CONCLUSIONS
The acquisition, piloting and ongoing operationalisation of REDCap at AH has been an
overwhelming success. Evidence of this is the vast number of projects being created and hosted
on the platform, and the ongoing support we had from various business stakeholders for the
platform. We recently held 2 seminars about the use of the platform at AH and the opportunities
it provides for multiple stakeholders. These sessions were well supported by experienced and
potential users of REDCap alike.
Next steps with the platform include more activities like the combined use of REASON and
REDCap to support large research studies and audits – especially where existing data is already
captured in REASON, thus saving researchers in particular, much time and money, as per the
model used in the LICRA study. This in turn saves public monies that can better spent on more
direct care activities
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Whilst in this case study we have described the benefits of REDCap in our context – that of a
very large Australian healthcare organization – we believe that REDCap or platforms like it hold
significant promise for smaller healthcare organizations that undertake research and audit
activities. The vast number of members of the REDCap consortium internationally is also a
testament to this.
ACKNOWLEDGEMENTS
The authors would like to thank the IDD teams that have assisted with the work on the AH RCP,
and in particular the infrastructure team and the service delivery team. We would also like to
thank Dr Ethan Gershon for his support of the acquisition of the RCP when it was first raised as a
possibility for AH, and Mr Emilio Pozo for his subsequent support. Finally we would like to
thank the dozens of clinicians, business users and researchers who have seen the unique
opportunity presented by REDCap and the REASON platform, and who have worked with us to
make these initiatives a success. In particular we would like to acknowledge Suzanne Corcoran
(Consumer Participation), Pam Ingram (Nursing Quality), Shirley Leong (Infection Prevention)
and Dr David Mc Ilroy (Lead Anaesthesia clinician on the LICRA Study).
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