One main concern of the respondents was regarding the findings and observations sec- tion of the structured report as seen in Figure 3.9. A few respondents believed that it is better to display the normal findings first before abnormal findings because they believed that if practitioners read the report they would be very concerned about the abnormal findings and not pay much attention to the normal findings. A few other respondents felt that it is good that the structured report distinguishes normal and ab- normal findings because it forces the radiologists to decide on their findings. However, they believed that there should be more options in reporting the findings and obser- vations in the structured report form especially for special cases like normal variants. This is because, it would be inaccurate to classify normal variant as abnormal since it is actually normal for some people but classifying it as normal without indicating that it is a normal variant would confuse the referring clinician reading the report.
Figure 3.9: The original version of the findings / observations section
Figure 3.10: The revised version of the findings / observations section
Taking into account all of these responses, we have revised the findings / obser- vations section of the structured report page to accommodate their suggestions (see Figure 3.10). In the revised version, the position of abnormal or normal findings will not be an issue anymore because it is now arranged according to the areas that have been observed. Radiologists will have an option to classify the findings as “normal”, “abnormal” or “inconclusive”. If they choose “normal”, they can also indicate if it is a normal variation and if they choose “abnormal”, they can indicate whether it is an incidental finding. The radiologists will also have the option to classify their findings as “inconclusive” for cases where the image was not clear or there were obstructions in examining the relevant areas. However, the radiologists will be required to state their reason for classifying their finding as inconclusive. This forces the radiologists to make decisions on their findings and at the same time giving them flexibility and more options in justifying their classification. It also allows for all findings or observations to be recorded in the report even if the areas are not clearly seen.
The high percentage of participants unable to classify the finding in question nine of this questionnaire as “normal” or “abnormal” proves that it is hard for referring clinicians to make a decision based on just a sentence or paragraph. Even though the sentence or paragraph has enough information such as the measurement of a finding, referring clinician would not be able to make any decision since measurements of or- gans or biospecimens can give different meanings for different patients. Thus, forcing the radiologists to state such findings as normal or abnormal will give meaning to their report and help the referring clinicians in making a decision in diagnosing the patients.
3.5
Chapter Summary
This chapter presents the system architecture model for the medical ultrasound report- ing system. In this system architecture model, a solution was proposed where radiolo- gists are allowed to choose any reporting style that they are most comfortable with and the system will automatically generate a standardised structured version of the report they wrote. The chapter explains each components of the system architecture model together with their purposes. The architecture model uses AUO as a knowledge based together with RST and discourse markers to transform free-form reports to structured form. This chapter also presented the result of the ultrasound reporting system’s inter- face preference survey. This survey was carried out to understand what would attract radiologists to consistently use an ultrasound reporting system. Overall, the radiolo- gists were happy with the proposed system and the interface design of the structured report page. Several issues were raised during the survey especially on the appearance of the findings / observations section. The revised version of the section as a result of the discussion was also presented.
The Knowledge Base for the
Standardised Reporting System
4.1
Introduction
Biomedical systems are an integral part of today’s medical world. Systems such as electronic patient records and clinical decision support systems (CDSS) have played an important role in assisting the works of medical personnel. One area that could ben- efit from the development of biomedical systems is ultrasound reporting. Variations in ultrasound reporting impacts the way a report is interpreted as well as in decision mak- ing. Therefore, it is important that these reports be standardised. In order to achieve this goal, ontologies are used to understand the reports and structure them according to a certain format [94]. They are also used to recognise the relationships between the parts of the text composing the report and to standardise the terminologies used.
Although there exist several biomedical ontologies that cover most of the general concepts in the domain, there are two disadvantages in adopting the whole ontology for reuse in a specific system. First, the ontology will have a lot more classes than required which means that the size will be large. Second, the coverage of the ontology might not be the most optimal since large ontologies have a general domain. In this research for example, using NCIT as its knowledge base would require a large amount of storage because of its size. NCIT contains as many as 118,941 classes and requires more time and effort to process. This would not be efficient since only a small portion of the ontology will be used. On the other hand, to build a new domain specific ontology from scratch will only cause redundancy and requires extra time and manpower. Hence, it is better to select and reuse relevant classes from several ontologies.
This chapter first reviews three established biomedical ontologies that are recog- nised to be potentially suitable for reuse which are FMA, RadLex and SNOMED CT. This is to assess the efficiency of reusing one of these ontologies in the medical ultra- sound reporting system. After arguing against adopting only one whole ontology, we propose a methodology to reuse several biomedical ontologies together with the exist- ing tools that can be used to ease the reuse process. In this chapter, we describe the development of the Abdominal Ultrasound Ontology (AUO) which will serve two pur- poses in this research: (i) it will be used to standardise the development of ultrasound reports and enforce the use of a standard terminology and (ii) to analyse the reports written in Natural Language (English free-text) with the aim of automatically trans- forming them into a structured format. Lastly, the result of using the ontology reuse methodology will be discussed. A large portion of this chapter has been published in the proceedings of the 21st International Conference on Application of Natural Lan- guage to Information Systems [95].