Surgical Data Science - from Concepts toward Clinical Translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process

Information systems in clinical research : categorization and evaluation of information systems and development of a guide for choosing the appropriate information system

Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2019.The development of information systems used in clinical research is constantly increasing, as their advantages are widely acknowledged. Although many researchers have introduced information systems which can be used during a clinical study’s process, a scarcity of information systems accommodating the complete process has been detected. Based on this finding, twenty-three (23) information systems and ontologies used in clinical research were retrieved, based on certain criteria. The information systems and ontologies were then categorized and evaluated based on categorization and evaluation tools. Finally, the result was the synthesis of the eligible-for-evaluation information systems and the development of a guide for choosing the appropriate information system during each step of a clinical trial; the data provided by each information system were identified. Unfortunately, some information systems and ontologies were excluded from the synthesis due to lack of information regarding the evaluation criteria. Therefore, future research should proceed with retrieving this information and developing a guide which will consider more information systems, especially for conducting observational studies

Front-Line Physicians' Satisfaction with Information Systems in Hospitals

Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

Quantitative imaging in radiation oncology

Artificially intelligent eyes, built on machine and deep learning technologies, can empower our capability of analysing patients’ images. By revealing information invisible at our eyes, we can build decision aids that help our clinicians to provide more effective treatment, while reducing side effects. The power of these decision aids is to be based on patient tumour biologically unique properties, referred to as biomarkers. To fully translate this technology into the clinic we need to overcome barriers related to the reliability of image-derived biomarkers, trustiness in AI algorithms and privacy-related issues that hamper the validation of the biomarkers. This thesis developed methodologies to solve the presented issues, defining a road map for the responsible usage of quantitative imaging into the clinic as decision support system for better patient care

Good Research Practice in Non-Clinical Pharmacology and Biomedicine

This open access book, published under a CC BY 4.0 license in the Pubmed indexed book series Handbook of Experimental Pharmacology, provides up-to-date information on best practice to improve experimental design and quality of research in non-clinical pharmacology and biomedicine

Usability analysis of contending electronic health record systems

In this paper, we report measured usability of two leading EHR systems during procurement. A total of 18 users participated in paired-usability testing of three scenarios: ordering and managing medications by an outpatient physician, medicine administration by an inpatient nurse and scheduling of appointments by nursing staff. Data for audio, screen capture, satisfaction rating, task success and errors made was collected during testing. We found a clear difference between the systems for percentage of successfully completed tasks, two different satisfaction measures and perceived learnability when looking at the results over all scenarios. We conclude that usability should be evaluated during procurement and the difference in usability between systems could be revealed even with fewer measures than were used in our study. © 2019 American Psychological Association Inc. All rights reserved.Peer reviewe

Clinicians’ perspectives and clinical efficacy of a health information technology tool in hospital falls risk assessment and prevention among older persons

Background The expanding ageing population has resulted in a focus on older persons within many healthcare systems. Falls present a growing problem with a significant impact on the community and healthcare system. Identifying falls risk factors and preventing falls have become priorities for many hospital and government policies, yet the evidence for the acceptability and efficacy of such interventions remains limited. Health technology has the potential to influence the field of falls prevention. Within research and clinical use, single and multi-component health technology strategies have been trialled to identify falls risk and prevent falls incidents. These have included sensor systems, video surveillance, and electronic health records. This thesis sought to evaluate the role of health technology in falls risk assessment and prevention, its perceptions by clinicians as end-users, and its effectiveness in reducing falls in hospital. More specifically, the thesis examined clinicians’ perspectives and use of a health information technology tool. This tool incorporated an iPad™ device and automatically generated visual cues to highlight individual patients’ falls risk. Its accuracy and efficacy in identifying and addressing falls risk scenarios, was evaluated compared to a standard screening tool. The aim of this study was to ultimately develop an acceptable and usable tool, in collaboration with clinicians, to deliver effective falls prevention in hospital. Methods Two methodologies and separate analyses were undertaken to complete this thesis: 1) An integrative review collated evidence for the effectiveness and clinicians’ perspectives of health technology use in falls prevention; and 2) an action research study evaluated clinicians’ perspectives on the health information technology tool, and informs its clinical use and efficacy in reducing hospital fall rates. Data was derived from focus group and survey research, with implementation of the health information technology tool occurring over consecutive 12-week periods on two medical wards at a single hospital setting. Both qualitative and quantitative analyses were applied to the data. Results Integrative review evidence, presented for the first time in this thesis, highlighted the lack of robust, consistent evidence for the acceptability and efficacy of health technology measures in falls prevention. The research conducted in this thesis addressed this gap in knowledge by evaluating staff’s attitudes towards the health information technology tool. It evaluated its positive and negative aspects, barriers to use, and recommendations for improvement; alongside its accuracy and effectiveness in reducing fall rates. Overall, clinicians were supportive for incorporating the tool into clinical practice. They perceived it as a useful, timely means of alerting staff and patients to falls risk scenarios, and resulting in better quality of care and understanding of falls risk for patients. Clinicians identified issues with usability and lack of time for tool use, and highlighted potential improvements to tool design. As befitting action research methodology, the health information technology tool has undergone refinement based on clinicians’ feedback. This has resulted in improved technology, clearer functioning of selection keys, colour coding of patients’ falls risk, having an automated trigger for patient education on falls risk, and provision of more iPad™ devices for more efficient use. The falls risk scores for the health information technology tool and standard falls risk in older person screening tool were similar, and did not differentiate between falls-risk and non-risk situations. Both tools had high sensitivity and low specificity for identifying falls-risk scenarios. They had similar rates of completion by clinicians on the wards. Implementation of the intervention tool had mixed outcomes on hospital fall rates. Conclusion This thesis contributed new information to address the knowledge gap on health technology uptake and efficacy in addressing hospital falls risk. Clinicians were willing to use the health information technology tool, and identified benefits to using the tool for themselves and their patients. The intervention tool demonstrated similar acceptability and accuracy to the standard falls risk screening tool. Staff’s concerns about usability are addressed in tool refinement, with active participation of end-users were considered key to improving intervention acceptance and usage, along with maximising useful feedback to further inform tool development. The effect of implementing the intervention tool on fall rates was mixed, highlighting the challenges of identifying and managing falls risk scenarios in hospital settings. The work arising from this thesis informed the development of a hand held android device used in the Ambience Intelligence Geriatric Management (AmbiGEM) system, incorporating printed visual cues with movement sensor alarms that alert clinicians to high-risk patient manoeuvres. Future research directions will involve evaluation of the acceptability and efficacy of the AmbiGEM system, which is currently undergoing clinical trial in two hospitals in South and Western Australia.Thesis (MPhil) -- University of Adelaide, Adelaide Medical School, 201