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A Reliable Approach for Applying DICOM Structured Reporting in a. Large-scale Telemedicine Network. Cloves Langendorf Barcellos Junior1,2, Aldo von ...
A Reliable Approach for Applying DICOM Structured Reporting in a Large-scale Telemedicine Network Cloves Langendorf Barcellos Junior1,2, Aldo von Wangenheim1,2, Rafael Andrade2 1 Post-Graduate Program in Computer Science - UFSC (PPGCC) 2 National Institute of Digital Convergence (INCoD) {cloves, awangenh, andrade}@inf.ufsc.br Abstract The need for procedures and methodologies used in clinical information standardization is continuous, requiring the current telemedicine networks to evolve and adapt by using mature standards and guidelines as the basis for clinical documents transmitted through the network. In this work, we present a different approach for large-scale use of DICOM SR in telemedicine networks using structured vocabularies as its foundation, resulting in a reliable, efficient and data mining ready application applied in a real world telemedicine network . This approach is applied in a large-scale network where 60 physicians report daily an average of 400 reports; resulting in more than 80,000 reports written and stored in the last nine months. We conclude that the use of this approach can reduce eventual syntax or conceptual mistakes by using proper structured vocabularies in addition to DICOM SR.

1. Introduction The conception, development and the standardization of medical data are guided by the need to consistently and accurately represent the information distribution, ensuring data storage and recovering in an effective way, and mainly allow the interoperability between different information systems. Most telemedicine networks of nowadays still lacks of interoperability between its Clinical Information System (CIS) and Picture Archiving and Communication System (PACS) interfaces and standardization of the clinical information contained in an examination report, having a direct effect over the quality and efficiency of the diagnostic and reporting writing process. The majority of the current networks still make use of plain text solutions without indexing or linking of information between any clinical standardized vocabulary, making difficult the task of applying data mining techniques over its database.

Developing techniques for the standardization of clinical information without compromising the routine and productivity of any healthcare professional is a common goal of modern telemedicine networks, improving the quality of the clinical information within an examination report. The changing process from non standardized information to a standardized one often suffers resistance. Extensive use of plain text is still required for many real world applications. In clinical environment is no different, especially when these changes affect the way that physicians write their reports. A significant effort on the part of the user is still needed [1]. But the impact of this change can be reduced by using a well structured application capable of provide an usual, fast and reliable interface which the physician would feel comfortable writing his reports without the need to use or even know that is using a complex standardized structure. Currently, there are several CIS and PACS solutions produced with private or free license that don't make use of structured reports. These solutions use mostly plain text as standard format for report writing, ignoring any structured format that would allow interoperability between different CIS solutions. In order to standardize and distribute health information on different equipment from different manufacturers, Digital Imaging and Communications in Medicine (DICOM) [2] was created and is now widely used to manipulate, store, display and transmit medical information. During the past nine years several studies aiming at the development and deployment of the DICOM SR [1] in the physicians and health institutions routine were performed. A systematic survey of the literature showed that most of these studies reflect the difficulty in implementing and applying this extension of the DICOM standard especially in a hospital environment. D. Clunie [13] introduced a DICOM SR object model using the W3C [14] recommendation for XML documents. The development of this object reflects the hierarchical tree structure of a DICOM structured report. Our application makes use of Clunie's object

model as the basis for our XML templates as shown later in methodology section. J. Riesmeier [4] described the prototype of a structured report editor and viewer using DICOM SR. This work was done in 2001, resulting in a functional prototype developed in C++ which extends some features of the well known OFFIS DICOM library, resulting in their incorporation into the application called DICOMscope [15]. The Cyclops Research Group [16] developed a prototype of a structured report editor called Cyclops DICOM Structured Report Editor [17]. This editor was also able to generate templates for different DICOM modalities. Hussein [18] wrote about the challenges when implementing DICOM SR support for PACS workstations. His work resulted in a prototype of an editor and viewer for structured reports integrated with a PACS called CHILLI [19]. In 2007 Arnold Corey [20] described the prototype of a Web-based Reporting System for Onsite-Offsite Clinician Communication. The prototype presented is a web-based application with PACS integration where report issuing is also based on DICOM SR standard, but using only plain text as input. When evaluating these studies is clear that the task of implementing and deploying reports using DICOM structured reporting in a hospital environment is not trivial. Some aspects are common between these works: there is an explicit difficulty in designing a usual interface that would reduce the impact over the physician routine, a second aspect is the lack of flexibility of the prototypes when interacting with other CIS and PACS solutions and at last these prototypes were tested only in controlled and small-scale environments. In this paper we describe a new approach for the use of DICOM SR in telemedicine networks. Our work is applied in Santa Catarina’s Telemedicine Network [3] which is a large-scale telemedicine network with an average of 2,000 examinations sent to its database every day and over one million examinations in its database. We also describe fundamentals, methodology and results obtained during the process of design, development and implementation of the Cyclops Structured Reporting (CSR) application. This application was created from the need to establish a standardized format to attend the necessities of the report writing process and at the same time meet the high demand of a large-scale telemedicine network. The basis for understand how structured vocabularies are used in a large-scale network are described in Section 2. A detailed description of our methodology is presented in Section 3. The results and discussion are

showed in Section 4 and finally in Section 5, we presented the conclusion and future works.

2. Background Before we detail our approach, it's necessary to introduce tools and techniques that are part of this work. These materials are presented in this section.

2.1. DICOM SR In order to standardize and distribute health information on different equipment from different manufacturers, in the late 80's ACR and NEMA together formed the DICOM standard [2]. Today the standard is widely used to manipulate, store, display and transmit medical information. In 2000 the extension DICOM SR [1] was officially launched as part of the DICOM standard, introducing a new concept in structuring and coding of medical records in a standardized way [4]. The DICOM SR provides support for structured writing format by adding precision, clarity and value to a clinical document. Probably the ability to create wellformed structured documents is not the major feature of DICOM SR, but its ability to bind and link the report text with other data types such as DICOM images and waveforms, filling the gap that existed between image systems and textual information [5]. A DICOM SR document basically consists in a sequence of nodes or “Content Items”. These nodes are connected through a set of hierarchical relationships such as “CONTAINS”. For example, an Computed Tomography report contain a node were the clinical findings are described, in this case there’s a relationship of the type “CONTAINS” connecting the DOCUMENT node to the findings node. In other words, the DOCUMENT node contains the FINDINGS node. Essentially, DICOM SR uses an architecture designed to code and exchange clinical information. It uses IOD’s (DICOM objects identifier) which are unique identifiers capable to represent real world objects and entities such as patients, images and reports that are part of a DICOM study.

2.2. Structured Vocabularies Structured vocabularies are collections of terms, organized according to a methodology in which it’s possible to specify relationships between concepts in order to facilitate access to information. Vocabularies are used as a sort of filter between the author's language and the domain terminology. They can also

be considered research assistants helping the user to refine, expand and enrich his research providing more relevant results. Our work uses DeCS [6] and SBC [7] vocabularies as main clinical information descriptors. 2.2.1. DeCS DeCS was created from the MeSH [8] vocabulary in order to allow the use of common terminology in which three languages (English, Portuguese and Spanish) can be used, providing a consistent and unique environment for information retrieval regardless of language. It is a vocabulary containing 30,895 descriptors where 26,225 of those belong to the MeSH. One main difference between the DeCS and MeSH is that DeCS have descriptors related to public health, which are fundamental in the Brazilian healthcare scenario [9]. DeCS makes use of a hierarchical structure in that way, from a specific descriptor it's possible to reach his ancestor, descendant and siblings descriptors. This feature is important when we don't have or don't know the specific descriptor to search for. In that case, similar descriptors are used to reach the relationships in which the specific descriptor may be related. 2.2.2. SBC/CSR Cardiology reports in particular electrocardiograms reports in most cases have the same basic textual structure: "finding description + finding localization". But unfortunately in some cases especially because of software deficiencies, physicians don't have any auxiliary tools or guidelines to follow when writing a report. In order to standardize and support the writing of electrocardiographic reports, in 2009 the Brazilian Society of Cardiology published a document entitled "Guidelines of the Brazilian Society of Cardiology For Reviewing and Writing Electrocardiographic Reports" [7]. The work presented in that article defines guidelines and descriptors for reviewing and writing electrocardiographic reports.

2.3. Santa Catarina's Telemedicine Network In 2005 the state government in association with the Federal University of Santa Catarina, started the project called Santa Catarina Telemedicine Network (RCTM - Acronyms for "Rede Catarinense de Telemedicina" in Portuguese language) [3]. This network has several services to assist the patient’s health. The RCTM has services such as telediagnosis, second opinion, real time and collaborative reporting,

among other services [10]. Currently the network has 287 participating cities and over one million examinations in its database. RCTM users have access to DICOM studies and examinations through the Santa Catarina's Telemedicine and Telehealth System (STT). Through this software they are able to access the stored data since STT is a web-based software that enables examinations visualization, analysis, discussion and large-scale report writing using the DICOM SR standard.

3. Methodology In general terms the DICOM SR standard is flexible, allowing customization in some specific areas. Usually any process of customization or implementation of DICOM SR in a clinical environment consist in three major aspects: the structured report presentation (viewing and printing), the user interface and the data structure used to store the report information as well as the algorithm responsible for handling the data. During the initial evaluation of the textual composition of the clinical reports contained in RCTM, it became clear that the simple transcription of this information to a pre-defined structure would not be enough for indexing and searching purposes, reflecting directly the search quality and linking of information between DICOM studies. Ensuring the information quality for reports and then make complete and accurate search were goals of the first step of our methodology. In order to achieve these goals we build the report information foundation using structured vocabularies such as DeCS and SBC/CSR. When writing a report the physician uses custom or the pre-defined fields "Study Description", "Findings" and "Conclusion" writing in a natural plain text style form in a user-friendly interface. In this model physicians use the interface not only to write the report but also to link and attach one or more descriptors to the final report. At the end, plain text information and descriptors are automatically attached to the DICOM SR file. For electrocardiograms an additional step was necessary since in the RCTM the volume of electrocardiograms is intense and constant, in addition the DeCS descriptor does not cover all possible descriptions and findings on an electrocardiogram report. A detailed study of the reports previously written in plain text style and Guidelines of the Brazilian Society of Cardiology generated a specific process of report writing for electrocardiograms.

We used each descriptor and its description from the guidelines document in addition to several descriptors suggested by a group of experienced cardiologists who work in the RCTM to create an extension of SBC structured vocabulary called SBC/CSR. The SBC/CSR vocabulary was structured from its unique identifier, in that way each identifier has one or more descriptors and a full description. The unique identifier is used when writing a report without making use of plain text style; the physician simply selects which descriptors to link to the report resulting in a completely standardized ECG report without syntax or conceptual mistakes as show later in Figure 4. Figure 1 shows the structure of a SBC/CSR descriptor.

set to "CONTAINS" as showed in Figure 2. Then the XML template header is created and the file is properly encoded.

Figure 2: CT template sample. Figure 1: Structure of a SBC/CSR descriptor. Once established the report foundation we concentrated our efforts in the main application called Cyclops Structured Reporting (CSR). This application is responsible for providing a user-friendly interface for the physician, interpret the input text and correctly encode it in the structured document, connect to the DICOM database and server, communicate with thirdparty software components, render the final report and retrieve the report information. CSR makes use of OFFIS [11] xml2dsr executable binary when generating the DICOM SR file (DSR), in that way it's necessary to compose complete DICOM SR templates without syntactic or semantic mistakes. CSR follows the guidelines established by [1] to compose XML templates for DICOM SR. Each document is represented by a set of containers, sections and relationships. Our interface allows the physician to add or remove containers of the report, all in a transparent manner. The user does not know that he's building a DICOM SR file until he reach the final stage when the HTML version of de DICOM SR file is presented as the final report. The algorithm reads each section of the interface and creates containers in the XML template. After that the information is stored in nodes of "TEXT" type, in these nodes not only the text typed by the physician is stored, but also the selected descriptors such as DeCS and SBC/CSR. Finally, a main container is automatically created containing all the other containers created previously with the relationship type

In CSR, each DICOM modality has its own class that implements specific features for that modality such as which containers a CT report must have or a rule that says that every ECG report must have the patient history attached. While the process of encoding and generation occur at the application level, CSR connects to the database requesting the exam information which the report will be linked to. Among the information requested, we can highlight the patient's personal data, the data from the referring physician and the DICOM study information for the evidence section of the structured document. After being properly generated, coded and checked the file containing the template is submitted for execution by the OFFIS xml2dsr executable binary that generate a valid DICOM SR file. Through this file an HTML version of the report is generated, this version is then made available in the telemedicine network so physicians, assistants and technicians may initiate the proper procedures to the patient whom the examination refers. At last, the content presented in the initial template file is stored in an object-relational database in addition to the already stored exam and procedure information. This step guarantee the information integrity in case of lost of the generated template and DICOM SR files. Figure 3 show the complete report writing process of our methodology from the choice of input interface to the exchange of information between the DICOM server, the CSR, the structured vocabularies and the database using a web server as connector.

or not. With that in mind, we have built a usual interface where physicians only know that they are writing a DICOM report when the final stage of the process is reached. The interface is exactly the same as if there weren't any DICOM SR implementation. Figure 4 shows the writing process of an electrocardiogram report in Portuguese language using only descriptors selection and no plain text input. That approach ensures an output without syntax and conceptual mistakes.

Figure 3: CSR reporting writing process. CSR also provides reading support for DICOM SR, it uses the XML template, the DSR file or the database to retrieve the report information. When a physician decides to make changes in a previous report, the algorithm searches these three possible sources for the latest report written for that examination by the same physician bringing the information to the same interface used before when writing a new report. The physician then make the proper corrections to the report and in sequence the algorithm stores the information as a new report, changing the latest report reference for that examination but keeping the previous versions stored for historical purposes. One of the highlights of the CSR development is its data mining capabilities. When we decided to build its foundations using structured vocabularies, we were not only interested in having a standardized set of information, but also having a reliable platform to perform search. The indexing process occurs already when the physician writes a report, since each selected descriptor represents a link between the report and the structure vocabulary. In case the physician decide to write a report without using any of the available structured vocabularies, we also provide a posterior indexing mechanism using the Apache Lucene [12] application. This indexing alternative makes use of data structures and algorithms such as inverted index, Levenshtein Distance and Cover Density Ranking [13]. Thus we are able to search for every report linked to a specific descriptor or even compare the information within reports indexed with the same descriptor.

4. Results and Discussion Probably the most relevant aspect of any attempt of applying the DICOM SR standard in a clinical environment is reduce the impact of the change in the work routine of the physicians. These professionals need a usual, fast and reliable application for their daily report writing, independent if it uses DICOM SR

Figure 4: Electrocardiogram report being written. Although RCTM has been around for 5 years, the report writing process developed in this work is in operation since August 2010. This process has generated more than 80,000 reports using exclusively the DICOM SR standard and is used by different healthcare professionals such as physicians, technicians and CIS administrators. It supports over 10 different DICOM modalities including ECG, CT, CR, MR and VL. Until May 2011, 60 physicians make daily use of CSR in their reporting routine. Electrocardiogram reports have a special process which allows physicians to only select descriptors when composing a report, for other modalities plain text is allowed but used only in pre-defined containers, keeping the information in a structured form as specified in the DICOM standard. Another key feature of a modern telemedicine network is its capability of providing statistics, especially for government financial planning purposes. Since CSR index each report descriptor we are able to compare the information within reports indexed with the same descriptor, but more important, obtain morbidity statistics of the whole network database or even generate statistics reports over selected

geographical regions or specific population sets. These are powerful information for managers of financial resources. In possession of this information the manager can properly distribute medicines, equipment and healthcare professionals where their expertise is needed. The CSR report writing process proved to be reliable and efficient differing from others in three main aspects: uses a flexible model that can interact with different CIS and PACS interfaces; is exclusive web-based been accessible from any computer, any were, any time and available 24/7; it has data mining features which can be used for administration or health studies purposes; is used in a large-scale telemedicine network with over 2,000 examinations executed per day; makes use of a user-friendly interface reducing the change impact over the physician's routine and it`s available in browser, mobile and tablet platforms. In summary, CSR implementation enables interoperability between different medical systems by offering an efficient mechanism for the distribution and management of medical documents, without the flaws of a system based on plain text documents.

5. Conclusion and Future Work The high demand faced by current telemedicine networks sometimes makes difficult to perform significant changes over the user's routines. But at the same time the need for more precise and standardized information make these changes inevitable. In the specific scenario of clinical report writing, to reduce the impact over the physician's routine when migrating from a plain text input to a structured one, it's important to present a usual, fast and reliable application. This paper presented the CSR application as a solution for applying the DICOM SR standard in telemedicine networks with minimum impact over the physicians' routine and also providing a report writing process for electrocardiograms without syntax or conceptual mistakes and a structured input form for other modalities. We also proved that this approach is applicable and reliable by using it in a large-scale network which is operational 24/7 since August 2010 writing an average of 400 reports daily by 60 different physicians, all using exclusively the CSR application. CSR also proved to be a reliable and practical tool for information retrieval purposes. In a future work the statistic capability of CSR could be further explored and expanded. A future approach for decentralized report writing process could be interesting for largescale telemedicine networks; in that scenario the PACS workstation could have a proper interface of the CSR

application making possible the report writing process to occur first at the workstation and later distributed through the network and then stored in the main database.

6. References [1] D. Clunie, DICOM structured reporting: PixelMed Publishing, 2000. [2] DICOM. (2010, 20/10/2010). Digital Imaging and Communications in Medicine. Available: http://medical.nema.org/ [3] A. Wangenheim, et al., "Ways to implement large scale telemedicine: The Santa Catarina Experience," Latin American Journal of Telehealth, vol. 3, pp. 364-377, 2009. [4] J. Riesmeier, et al., "DICOM Structured Reporting--a prototype implementation," 2001, pp. 795-800. [5] R. Hussein, et al., "DICOM Structured Reporting: Part 1. Overview and Characteristics," Radiographics, vol. 24, pp. 891-896, May 1, 2004 2004. [6] Bireme. DeCS - Descritores em Ciências da Saúde [Online]. Available: http://decs.bvs.br [7] P. C. Pastore CA, Germiniani H, Samesima N, Mano R, et al., "Diretrizes da Sociedade Brasileira de Cardiologia sobre Análise e Emissão de Laudos Eletrocardiográficos (2009)," Arq Bras Cardiol, vol. 93, pp. 1-19, 2009. [8] NLM. MeSH - Medical Subject Headings [Online]. Available: http://www.nlm.nih.gov/mesh/ [9] C. L. BARCELLOS JUNIOR, et al., "Busca Semântica Aplicada a Informações Clínicas," 2008. [10] J. Wallauer, et al., "Building a national telemedicine network," IT Professional, vol. 10, pp. 12-17, 2008. [11] OFFIS. (2005, DCMTK 3.5.4. Available: http://dicom.offis.de/dcmtk.php.en [12] E. Hatcher and O. Gospodnetic, "Lucene in Action (In Action series)," 2004. [13] R. B. CABRAL, et al., "Semantic Information Indexing and Retrieval on Patient Medical Data," in 8th International Information and Telecommunication Technologies Symposium, 2009, pp. 171-174.