Supporting the diagnosis of childhood brain tumours through structural reports and ontological reasoning

After Leukaemia, childhood brain tumours are the second most common form of cancer. The most accurate way to diagnosis a tumour is via a biopsy, but is not always possible. An alternative is Magnetic Resonance Spectroscopy (MRS) which analyses the chemical make-up of tissue and then used to make a diagnosis. A patient's medical records are an important part of treatment, used to communicate findings from medical images. However, these are written using unclear and ambiguous free text. A solution is to produce records using Structured Reporting. This has been incorporated into the Digital Communications in Medicine (DICOM) Standard for established imaging modalities, but not for MRS. An ontology was modelled to produce DICOM supported Structured Reports for MRS. Also, an algorithm to diagnosis different types of childhood brain cancer using MRS spectra was incorporated, allowing automated diagnostic support. A prototype Structured Reporting application was designed based on the ontology. The ontology was able to produce Structured Reports that successfully diagnosed certain childhood brain tumours based on the MRS readings. Usability testing and the diagnostic aspect of the ontology garnered positive feedback as MRS data is only currently used to diagnosis whether the tissue is cancerous or not

Designing Clinical Data Presentation Using Cognitive Task Analysis Methods

Despite the many decades of research on effective use of clinical systems in medicine, the adoption of health information technology to improve patient care continues to be slow especially in ambulatory settings. This applies to dentistry as well, a primary care discipline with approximately 137,000 practicing dentists in the United States. One critical reason is the poor usability of clinical systems, which makes it difficult for providers to navigate through the system and obtain an integrated view of patient data during patient care. Cognitive science methods have shown significant promise to meaningfully inform and formulate the design, development and assessment of clinical information systems. Most of these methods were applied to evaluate the design of systems after they have been developed. Very few studies, on the other hand, have used cognitive engineering methods to inform the design process for a system itself. It is this gap in knowledge – how cognitive engineering methods can be optimally applied to inform the system design process – that this research seeks to address through this project proposal. This project examined the cognitive processes and information management strategies used by dentists during a typical patient exam and used the results to inform the design of an electronic dental record interface. The resulting 'proof of concept' was evaluated to determine the effectiveness and efficiency of such a cognitively engineered and application flow design. The results of this study contribute to designing clinical systems that provide clinicians with better cognitive support during patient care. Such a system will contribute to enhancing the quality and safety of patient care, and potentially to reducing healthcare costs

The impact of AI on radiographic image reporting – perspectives of the UK reporting radiographer population

Background: It is predicted that medical imaging services will be greatly impacted by AI in the future. Developments in computer vision have allowed AI to be used for assisted reporting. Studies have investigated radiologists' opinions of AI for image interpretation (Huisman et al., 2019 a/b) but there remains a paucity of information in reporting radiographers' opinions on this topic.Method: A survey was developed by AI expert radiographers and promoted via LinkedIn/Twitter and professional networks for radiographers from all specialities in the UK. A sub analysis was performed for reporting radiographers only.Results: 411 responses were gathered to the full survey (Rainey et al., 2021) with 86 responses from reporting radiographers included in the data analysis. 10.5% of respondents were using AI tools? as part of their reporting role. 59.3% and 57% would not be confident in explaining an AI decision to other healthcare practitioners and 'patients and carers' respectively. 57% felt that an affirmation from AI would increase confidence in their diagnosis. Only 3.5% would not seek second opinion following disagreement from AI. A moderate level of trust in AI was reported: mean score = 5.28 (0 = no trust; 10 = absolute trust). 'Overall performance/accuracy of the system', 'visual explanation (heatmap/ROI)', 'Indication of the confidence of the system in its diagnosis' were suggested as measures to increase trust.Conclusion: AI may impact reporting professionals' confidence in their diagnoses. Respondents are not confident in explaining an AI decision to key stakeholders. UK radiographers do not yet fully trust AI. Improvements are suggested

An evaluation of a training tool and study day in chest image interpretation

Background: With the use of expert consensus a digital tool was developed by the research team which proved useful when teaching radiographers how to interpret chest images. The training tool included A) a search strategy training tool and B) an educational tool to communicate the search strategies using eye tracking technology. This training tool has the potential to improve interpretation skills for other healthcare professionals.Methods: To investigate this, 31 healthcare professionals i.e. nurses and physiotherapists, were recruited and participants were randomised to receive access to the training tool (intervention group) or not to have access to the training tool (control group) for a period of 4-6 weeks. Participants were asked to interpret different sets of 20 chest images before and after the intervention period. A study day was then provided to all participants following which participants were again asked to interpret a different set of 20 chest images (n=1860). Each participant was asked to complete a questionnaire on their perceptions of the training provided. Results: Data analysis is in progress. 50% of participants did not have experience in image interpretation prior to the study. The study day and training tool were useful in improving image interpretation skills. Participants perception of the usefulness of the tool to aid image interpretation skills varied among respondents.Conclusion: This training tool has the potential to improve patient diagnosis and reduce healthcare costs

Evaluating the Usability of the Laboratory Information System (LIS) in Coombe Hospital and Hail Hospital

Today, with the rapid evolution of technology, there has also been a rapid development of medical software and systems in hospitals. These systems and software are now being used globally in many hospitals by users of different languages and cultures. Governments and private hospitals pay large sums of money to utilise highly efficient technology. When systems are changed or updated, employees often find it difficult to deal with the characteristics of the new systems. Also, behavioral factors, such as the fear of committing simple errors, might affect system performance and prevent the full utilization of the staff potential. In this research we will measure the usability of the Laboratory Information System (LIS) in two different countries, the Coombe Hospital in Dublin, Ireland and the Hail Hospital in Hail, Saudi Arabia. Two of the most accepted usability models – SUS and QUIS - are used in this research. The comparison of the two hospitals results displayed common weaknesses/strengths as well as differences between two health institutions situated in countries that differ in language and culture. Questionnaires were distributed to both hospitals and interviews were conducted with the employees of each hospital to discuss some of the points about the system. After the analysis of questionnaires and interviews, the search results determined the common system problems for both hospitals. Consequently system problems from the analysis of both survey

M-health review: joining up healthcare in a wireless world

In recent years, there has been a huge increase in the use of information and communication technologies (ICT) to deliver health and social care. This trend is bound to continue as providers (whether public or private) strive to deliver better care to more people under conditions of severe budgetary constraint