PC 4 - Design and implementation of structured reporting
PC 4 - Design and implementation of structured reportingWednesday, March 1, 16:00 - 17:30 Room: G Session Type: Professional Challenges Session Topics: Computer Applications, Management/Leadership Digital Evaluation: Open Digital Evaluation for this Session Moderator: W. H. Sommer (Munich/DE) Add session to my schedule In your schedule (remove)
1. To get an overview on different initiatives for structured reporting.
2. To understand pros and cons of different forms of structured reporting, including template-based reporting, decision support and modular structured reporting.
3. To appreciate the wide range of possibilities for quality management which become possible with structured reporting.
4. To become familiar how different institutions integrate structured reporting in the clinical workflow.
The role of the radiologist will evolve during the next years and decades from the interpretation of imaging data by free-text to providing sets of quantitative and mineable data based on these images. In this context, structured reporting has become one of the most discussed topics. When talking about this topic, one should consider the different forms of structured reporting, ranging from the simple introduction of fixed subsection headers in a free-text report on the one hand to complex decision support algorithm and synoptic reporting on the other hand. The form of structured reporting determines the challenges of integration into the workflow as well as the possible goals which can be obtained by the implementation. The aim of this session is to give an overview on different forms of structured reporting and describe possible achievements of each of these forms, such as quality improvement, educational aspects, improvement of referring physicians’ satisfaction and the possibility of data mining. The session presents several ongoing initiatives, such as the ESR/RSNA structured reporting initiative. There will be a special focus on the combination of structured reporting and natural language processing and how this can be implemented into clinical routine for quality management.
1. To get familiar with advantages of modular structured reporting to template-based approach.
2. To know challenges in language independent reporting in radiology.
3. To understand importance of availability of modules for structured description of abnormal findings.
4. To learn about already developed modular structured reporting tools and apps.
5. To understand features of a problem specific flexible structured reporting application to tailor the template to clinical condition of the patient.
Structured reporting is still following the traditional concept of putting the report together through the template library. So, the efforts and applications are based mostly on the template-driven concepts. But in practice, even using the templates the radiologists address a particular part of the text that may be called sub-templates or modules. These modules can be presented in detailed or brief format or even deleted from the final report. The ideal solution is an expandable flexible dynamic template builder where the modules are selected according to the need letting the user to tailor the length and order of the report to the problem of the patient. Currently available template libraries mostly provide templates for normal findings. But the missing component is the structure for description of abnormal findings. But a library of modules is required to let radiologists report their abnormal cases in a similar manner to achieve the best from the data storage and mining in radiological reports. Using structured reporting to store a report technically means to follow a tabular data structure which bears the potential to be codified rather than being stored as a character set. So, the codes may be presented in any pre-defined languages and one of the major advantages of structured reporting is the potential to be language independent. In this presentation, multiple models of structured reporting applications will be shown as examples of modular, language-independent, problem-specific approach.
1. To learn about available options for basic quality control of radiology reporting.
2. To understand challenges of current prevailing practice for consumers of radiology reports and risk management.
3. To appreciate the importance of performing systematic report quality control and reporting compliance control.
4. To become familiar with tools for Q/C as well as structured data generation and presentation, including multimedia reports and dashboard presentation of report content.
Modern radiology reporting quality assurance must ascertain that radiologists understand the hallmarks of a high-quality report and implement proper reporting. A value-added report is the most important and visible work product of our profession, increasingly expected by our patients and referring clinicians. Required specifications may include use of a standard document model, inclusion of required data elements, qualitative and quantitative image descriptors and the use of building blocks of standardized language. Considering the large number of reports routinely issued in a radiology department, systematic, scalable quality control tools are needed to efficiently assess and improve compliance of radiologists. Modern, natural language processing-based methods can check for errors (laterality, gender), check compliance with reporting requirements (Bi-Rads, LungRads, etc.), completeness of reporting, but also help evaluate more elusive concepts such as frequency of self-referral and use of ambiguous language. The lecture presents a novel reporting quality toolbox that can facilitate those assessments while also analysing report generation metrics. Result can be used for change management in the department. In the future, many of these quality assessments can be performed on the fly to allow the radiologist to make immediate adjustments prior to report issuance. Other approaches to increasing the value of reports, such as multimedia reporting and dashboard presentation of report content, are also presented.
1. To learn about the preferences of radiologists and referring physicians concerning text-oriented vs structured reporting.
2. To understand the definition of structured reporting, and the evolution in thinking about the objectives to attain.
3. To become familiar with initiatives to create a framework for structured reporting, including a SWOP analysis.
Since the beginning of this century, a considerable number of surveys have shown that radiologists as well as referring physicians prefer structured reporting (SR) to reporting in free text, at least for complex imaging studies. Comparison of these studies, however, shows a wide variety in the definition of SR. Although the quantitative results of the surveys are very similar, it is possible that they reflect preferences on vastly different forms of reporting, the common factor being that findings should be presented in a predefined, orderly way, in the interest of clarity, coherence and comparability. Several authors have challenged this minimal interpretation. A limited number of studies have also revealed a preference for SR based on an underlying lexicon. Initiatives have been undertaken to create a coherent system of SR templates and a radiology-oriented coded lexicon, the most conspicuous being the ESR/RSNA structured reporting initiative. A new concept in the development of SR is the creation of common data elements (CDE), units of information used in a shared, predefined fashion, that can improve the ability to exchange information among information systems. While in surveys the preference for SR was mainly based on well-chosen, theoretical examples, a few real-life applications of SR have been greeted with enthusiasm by referring clinicians. Many obstacles, however, hinder its acceptance, e.g. the absence of SR-based radiology information systems, the slow introduction of the electronic health record in many countries and, particularly in Europe, the rich variety in languages other than English.