Guidelines for the user interface design of electronic medical records in optometry
- Authors: Nathoo, Dina
- Date: 2020
- Subjects: User interfaces (Computer systems) , Medical records -- Data processing , Optometry -- South Africa -- Eastern Cape , System design , Workflow management systems
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: http://hdl.handle.net/10962/148782 , vital:38773
- Description: With the prevalence of digitalisation in the medical industry, e-health systems have largely replaced the traditional paper-based recording methods. At the centre of these e-health systems are Electronic Health Records (EHRs) and Electronic Medical Records (EMRs), whose benefits significantly improve physician workflows. However, provision for user interface designs (UIDs) of these systems have been so poor that they have severely hindered physician usability, disrupted their workflows and risked patient safety. UID and usability guidelines have been provided, but have been very high level and general, mostly suitable for EHRs (which are used in general practices and hospitals). These guidelines have thus been ineffective in applicability for EMRs, which are typically used in niche medical environments. Within the niche field of Optometry, physicians experience disrupted workflows as a result of poor EMR UID and usability, of which EMR guidelines to improve these challenges are scarce. Hence, the need for this research arose, aiming to create UID guidelines for EMRs in Optometry, which will help improve the usability of the optometrists’ EMR. The main research question was successfully answered to produce the set of UID Guidelines for EMRs in Optometry, which includes guidelines built upon from literature and made contextually relevant, as well as some new additions, which are more patient focused. Design Science Research (DSR) was chosen as a suitable approach, and the phased Design Science Research Process Model (DSRPM) was used to guide this research. A literature review was conducted, including EHR and EMR, usability, UIDs, Optometry, related fields, and studies previously conducted to provide guidelines, frameworks and models. The review also included studying usability problems reported on the systems and the methods to overcome them. Task Analysis (TA) was used to observe and understand the optometrists’ workflows and their interactions with their EMRs during patient appointments, also identifying EMR problem areas. To address these problems, Focus Groups (FGs) were used to brainstorm solutions in the form of EMR UID features that optometrists’ required to improve their usability. From the literature review, TAs and FGs, proposed guidelines were created. The created guidelines informed the UID of an EMR prototype, which was successfully demonstrated to optometrists during Usability Testing sessions for the evaluation. Surveys were also used for the evaluation. The results proved the guidelines were successful, and were usable, effective, efficient and of good quality. A revised, final set of guidelines was then presented. Future researchers and designers may benefit from the contributions made from this research, which are both theoretical and practical.
- Full Text:
- Date Issued: 2020
- Authors: Nathoo, Dina
- Date: 2020
- Subjects: User interfaces (Computer systems) , Medical records -- Data processing , Optometry -- South Africa -- Eastern Cape , System design , Workflow management systems
- Language: English
- Type: Thesis , Masters , MCom
- Identifier: http://hdl.handle.net/10962/148782 , vital:38773
- Description: With the prevalence of digitalisation in the medical industry, e-health systems have largely replaced the traditional paper-based recording methods. At the centre of these e-health systems are Electronic Health Records (EHRs) and Electronic Medical Records (EMRs), whose benefits significantly improve physician workflows. However, provision for user interface designs (UIDs) of these systems have been so poor that they have severely hindered physician usability, disrupted their workflows and risked patient safety. UID and usability guidelines have been provided, but have been very high level and general, mostly suitable for EHRs (which are used in general practices and hospitals). These guidelines have thus been ineffective in applicability for EMRs, which are typically used in niche medical environments. Within the niche field of Optometry, physicians experience disrupted workflows as a result of poor EMR UID and usability, of which EMR guidelines to improve these challenges are scarce. Hence, the need for this research arose, aiming to create UID guidelines for EMRs in Optometry, which will help improve the usability of the optometrists’ EMR. The main research question was successfully answered to produce the set of UID Guidelines for EMRs in Optometry, which includes guidelines built upon from literature and made contextually relevant, as well as some new additions, which are more patient focused. Design Science Research (DSR) was chosen as a suitable approach, and the phased Design Science Research Process Model (DSRPM) was used to guide this research. A literature review was conducted, including EHR and EMR, usability, UIDs, Optometry, related fields, and studies previously conducted to provide guidelines, frameworks and models. The review also included studying usability problems reported on the systems and the methods to overcome them. Task Analysis (TA) was used to observe and understand the optometrists’ workflows and their interactions with their EMRs during patient appointments, also identifying EMR problem areas. To address these problems, Focus Groups (FGs) were used to brainstorm solutions in the form of EMR UID features that optometrists’ required to improve their usability. From the literature review, TAs and FGs, proposed guidelines were created. The created guidelines informed the UID of an EMR prototype, which was successfully demonstrated to optometrists during Usability Testing sessions for the evaluation. Surveys were also used for the evaluation. The results proved the guidelines were successful, and were usable, effective, efficient and of good quality. A revised, final set of guidelines was then presented. Future researchers and designers may benefit from the contributions made from this research, which are both theoretical and practical.
- Full Text:
- Date Issued: 2020
Bioinformatics tool development with a focus on structural bioinformatics and the analysis of genetic variation in humans
- Authors: Brown, David K
- Date: 2018
- Subjects: Bioinformatics , Human genetics -- Variation , High performance computing , Workflow management systems , Molecular dynamics , Next generation sequencing , Human Mutation Analysis (HUMA)
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/60708 , vital:27820
- Description: This thesis is divided into three parts, united under the general theme of bioinformatics tool development and variation analysis. Part 1 describes the design and development of the Job Management System (JMS), a workflow management system for high performance computing (HPC). HPC has become an integral part of bioinformatics. Computational methods for molecular dynamics and next generation sequencing (NGS) analysis, which require complex calculations on large datasets, are not yet feasible on desktop computers. As such, powerful computer clusters have been employed to perform these calculations. However, making use of these HPC clusters requires familiarity with command line interfaces. This excludes a large number of researchers from taking advantage of these resources. JMS was developed as a tool to make it easier for researchers without a computer science background to make use of HPC. Additionally, JMS can be used to host computational tools and pipelines and generates both web-based interfaces and RESTful APIs for those tools. The web-based interfaces can be used to quickly and easily submit jobs to the underlying cluster. The RESTful web API, on the other hand, allows JMS to provided backend functionality for external tools and web servers that want to run jobs on the cluster. Numerous tools and workflows have already been added to JMS, several of which have been incorporated into external web servers. One such web server is the Human Mutation Analysis (HUMA) web server and database. HUMA, the topic of part 2 of this thesis, is a platform for the analysis of genetic variation in humans. HUMA aggregates data from various existing databases into a single, connected and related database. The advantages of this are realized in the powerful querying abilities that it provides. HUMA includes protein, gene, disease, and variation data and can be searched from the angle of any one of these categories. For example, searching for a protein will return the protein data (e.g. protein sequences, structures, domains and families, and other meta-data). However, the related nature of the database means that genes, diseases, variation, and literature related to the protein will also be returned, giving users a powerful and holistic view of all data associated with the protein. HUMA also provides links to the original sources of the data, allowing users to follow the links to find additional details. HUMA aims to be a platform for the analysis of genetic variation. As such, it also provides tools to visualize and analyse the data (several of which run on the underlying cluster, via JMS). These tools include alignment and 3D structure visualization, homology modeling, variant analysis, and the ability to upload custom variation datasets and map them to proteins, genes and diseases. HUMA also provides collaboration features, allowing users to share and discuss datasets and job results. Finally, part 3 of this thesis focused on the development of a suite of tools, MD-TASK, to analyse genetic variation at the protein structure level via network analysis of molecular dynamics simulations. The use of MD-TASK in combination with the tools developed in the previous parts of this thesis is showcased via the analysis of variation in the renin-angiotensinogen complex, a vital part of the renin-angiotensin system.
- Full Text:
- Date Issued: 2018
- Authors: Brown, David K
- Date: 2018
- Subjects: Bioinformatics , Human genetics -- Variation , High performance computing , Workflow management systems , Molecular dynamics , Next generation sequencing , Human Mutation Analysis (HUMA)
- Language: English
- Type: text , Thesis , Doctoral , PhD
- Identifier: http://hdl.handle.net/10962/60708 , vital:27820
- Description: This thesis is divided into three parts, united under the general theme of bioinformatics tool development and variation analysis. Part 1 describes the design and development of the Job Management System (JMS), a workflow management system for high performance computing (HPC). HPC has become an integral part of bioinformatics. Computational methods for molecular dynamics and next generation sequencing (NGS) analysis, which require complex calculations on large datasets, are not yet feasible on desktop computers. As such, powerful computer clusters have been employed to perform these calculations. However, making use of these HPC clusters requires familiarity with command line interfaces. This excludes a large number of researchers from taking advantage of these resources. JMS was developed as a tool to make it easier for researchers without a computer science background to make use of HPC. Additionally, JMS can be used to host computational tools and pipelines and generates both web-based interfaces and RESTful APIs for those tools. The web-based interfaces can be used to quickly and easily submit jobs to the underlying cluster. The RESTful web API, on the other hand, allows JMS to provided backend functionality for external tools and web servers that want to run jobs on the cluster. Numerous tools and workflows have already been added to JMS, several of which have been incorporated into external web servers. One such web server is the Human Mutation Analysis (HUMA) web server and database. HUMA, the topic of part 2 of this thesis, is a platform for the analysis of genetic variation in humans. HUMA aggregates data from various existing databases into a single, connected and related database. The advantages of this are realized in the powerful querying abilities that it provides. HUMA includes protein, gene, disease, and variation data and can be searched from the angle of any one of these categories. For example, searching for a protein will return the protein data (e.g. protein sequences, structures, domains and families, and other meta-data). However, the related nature of the database means that genes, diseases, variation, and literature related to the protein will also be returned, giving users a powerful and holistic view of all data associated with the protein. HUMA also provides links to the original sources of the data, allowing users to follow the links to find additional details. HUMA aims to be a platform for the analysis of genetic variation. As such, it also provides tools to visualize and analyse the data (several of which run on the underlying cluster, via JMS). These tools include alignment and 3D structure visualization, homology modeling, variant analysis, and the ability to upload custom variation datasets and map them to proteins, genes and diseases. HUMA also provides collaboration features, allowing users to share and discuss datasets and job results. Finally, part 3 of this thesis focused on the development of a suite of tools, MD-TASK, to analyse genetic variation at the protein structure level via network analysis of molecular dynamics simulations. The use of MD-TASK in combination with the tools developed in the previous parts of this thesis is showcased via the analysis of variation in the renin-angiotensinogen complex, a vital part of the renin-angiotensin system.
- Full Text:
- Date Issued: 2018
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