Author: Una Ismaili Main supervisor: MD, PhD, Rong Chen, Department of LIME, Karolinska and

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1 Master's Programme in Health Informatics Spring Semester 2014 Degree thesis, 30 Credits Evaluation of feasibility of openehr and Guideline Definition Language for automatic data transfer between electronic health records and the Auricula quality registry for Atrial fibrillation Una Ismaili Author: Una Ismaili Main supervisor: MD, PhD, Rong Chen, Department of LIME, Karolinska and Cambio Healthcare Systems, Sweden Co supervisor: Konstantinos Kalliamvakos, Cambio Healthcare Systems, Sweden Examiner: Dr. Andrzej Kononowicz, Department of LIME, Karolinska Institutet, Sweden 1

2 Master's Programme in Health Informatics Spring Semester 2014 Degree thesis, 30 Credits Affirmation I hereby affirm that this Master thesis was composed by myself, that the work contained herein is my own except where explicitly stated otherwise in the text. This work has not been submitted for any other degree or professional qualification except as specified; nor has it been published. Stockholm, 2014/05/XX Una, Ismaili

3 Master's Programme in Health Informatics Spring Semester 2014 Degree thesis, 30 Credits Evaluation of feasibility of openehr and Guideline Definition Language for automatic data transfer between electronic health records and the Auricula quality registry for Atrial fibrillation Abstract Background: Currently there is no integration between the electronic health records (EHR) and the atrial fibrillation quality registry (QR) Auricula. This hinders the automatic transfer of information from the EHR to the QR. Objective: The aim is to evaluate if openehr technology and GDL can make it feasible to integrate EHR to the atrial fibrillation QR, Auricula. Methods: The study uses a qualitative approach. It was performed in five steps. The first was to collect information about AF for the guideline where literature and certain criteria were used. In the second step expert feedback was given on the selected elements for the guideline. The third step was the modelling of openehr archetype and the guideline. A semi-structured interview was performed with a cardiology nurse. Content analysis was used to analyze the interview with the expert. Furthermore, the fourth step was to specify computer interpretable CDS rules by using GDL. The last step was the validation of the guideline using mock patient data. Results: The expert feedback provided the study with important data about the information needed for the guideline. The qualitative methods helped answering the questions regarding the problems today without the integration and what could be improved with it. For this one guideline was modelled for the atrial fibrillation registry. The results from the validation of the guideline corresponded with the created mock patient data, a manual check was also performed. Conclusion: The integration between the EHR and QR is theoretically possible with the use of openehr technology and GDL. The validation proved that GDL can model guidelines in the clinical area of atrial fibrillation and that the criteria can be supported. Keywords: openehr, Electronic Health Records, GDL, Atrial Fibrillation

4 Acknowledgements First of all I would like to give my thanks to my supervisor Konstantinos Kalliamvakos for his guidance and support during this thesis. It was a real pleasure working with you and thanks to your patience. You made me feel more secure about myself and in my work. Thanks again for your guidance. I would also like to thank Rong Chen for giving me support and inspiration from your expertise with openehr. Thanks for the opportunity to work with you. Furthermore I would like to thank Sabine Koch for her guidance and insightful feedback for my thesis writing. Your support and helpful advices improved my thesis. I would also like to thank Annika Önnerlöv and Martin Koch for using their valuable time to read the thesis and provide me with their valuable feedback. Finally I would like to thank my family for their love and support and for believing in me.

5 Table of Contents Introduction... 1 Background... 1 Atrial Fibrillation... 3 Auricula... 3 Interoperability... 4 OpenEHR... 4 Guideline Definition Language... 5 Exchanging information between systems... 5 Problem analysis... 6 Aims and objectives... 6 Research question... 7 Method... 7 Study Design... 7 Study approach... 8 Data collection and analysis... 9 Interview with expert Content analysis Software tools Validity and reliability Ethical considerations Results Selection of guideline Selection of text-based rules Modelling of openehr archetypes and the guideline openehr archetypes Terminologies Model of the guidelines Results from the content analysis Research

6 Manual entry Quality registry Model of the integration Specification of GDL rules Basmodulen guideline Validation using mock patient data Discussion Discussion of results Limitations Strengths and weakness of the study Future research Conclusion Appendices Appendix A Archetypes Appendix B GDL rules Appendix C Mock Patient Data Appendix D Execution Logs Appendix E - Interview questions

7 List of Figures Figure 1 process of authoring rules Zhou L et al. (8)... 2 Figure 2 The study framework... 7 Figure 3 The three phases in content analysis Figure 4 The Basmodulen archetype Figure 5 Model over the connections between the used guidelines Figure 6 How the integration could work Figure 7 Condition & Action for registration date Figure 8 The execution from GDL Figure 9 The rule list Figure 10 Thyroid test rule Figure 11 The result of how many patients that have AF Figure 12 Execution outcome List of Tables Table 1 Mock patients for Basmodulen

8 Abbreviations AF AM ADL CDS EACTS EHRA EHR GDL ICT QR RM TM Atrial Fibrillation Archetype Model Archetype Definition Language Clinical Decision Support Endorsed by the European Association for Cardio-Thoracic Surgery European Heart Rhythm Association Electronic Health Record Guideline Definition Language Information and Communication Technology Quality Registry Reference Model Template Model

9 Introduction Background Health informatics is increasing within the health care and information systems are important for the measurement of the quality and the improvement of the health care (1). An information system is interpreted here as a computer-supported system which provides a set of people (users) with information on specified topics of interest in a certain organizational context (2). Electronic health records (EHR) system collects patients health data like medical treatments and their health in general. It is an integrated physical or virtual repository system(3). It can decrease workload, medical errors, cost, improve the client care, the quality of care and also support physicians in their daily practice (4). An EHR s primary purpose is to foster the quality of healthcare and support all stakeholders in the process of healthcare, it is crucial that EHRs themselves adhere to rigid systems of quality assurance and management. (4) (5). The information in the EHR can be shared across different systems, but it can also improve the research field that is beneficial for patients and health professionals by enabling the reuse of data and information(4)(6). Clinical decision support (CDS) is a system that can be delivered through an EHR and it can also be a standalone application. In fact many of the CDS today is standalone and a huge effort is put to actually integrate it within the EHR and the clinical practice. A CDS assists clinicians in their decision making (7) (8). It aids clinicians in monitoring and preventing tasks, drug prescription, diagnosis and management (9)(4). The idea behind CDS is to improve the quality, safety and efficiency in health care (10). CDS has the potential to increase guideline adherence and efficiency (9). Medical knowledge is usually presented as text-based documents like clinical guides and the information needs to be converted to executable CDS rules (11). Figure 1 presents the process of how to author rules from text-based to executable CDS rules. Clinical guidelines are used to support clinicians in their decision making and to improve the quality of patient care (12). 1

10 Figure 1 process of authoring rules Zhou L et al. (8) Quality registry Quality registries (QR) are also important for the improvement of today s healthcare. The creation of the first quality registry (QR) began on the 1800-century. It was already then seen as important to measure and follow up hospitals activities to improve safety and efficiency in the health care (13). Since then this need has evolved and improved. The focus throughout the years has always been on the quality of care (13). A QR is a collection of individual patient data like interventions and the outcome of interventions. It could be background variables (clinical or other) like age, weight, diagnosis and treatments. The data is used to evaluate outcomes particularly for diseases, or conditions (14). This data can be used to analyze and evaluate the health care. QR is mostly used for monitoring, quality development and research in health care (13). The primary purpose with the registries is the continuous improvement and support within the health care sector (13). QR could improve the medical results in some areas, it also could make the health services more effective and also involved the patient more in the care. It also gives the possibility to compare and evaluate different treatment methods, drugs and processes to get the best results (13). The strength with registries is that courses of diseases can be understood by the collected data, for example variations in treatments and the outcomes that might have an influence on the quality of life and the prognosis. 2

11 The feedback gives the opportunity to change the behavior to something more appropriate for the patient, like monitor safety assess effectiveness and to see the care patterns (14). Atrial Fibrillation The last decade s QR has become an important part in Swedish healthcare. Today there are about 80 registries. In Sweden the most prevalent cardiac arrhythmia (heart rhythm disorder) is the atrial fibrillation AF that it is a risk factor for stroke. AF is a growing health concern where the treatments are complex and the condition burdensome (15). Ineffective contractions and chaotic electrical activation is what distinguishes AF. The total estimation of people with AF in Sweden is around 300,000 which correspond to almost 3% of the population. There is a fast going development of pharmacological and non-pharmacological therapies for AF (16). Pharmacological interventions for AF prevention are Warfarin, Novel oral anticoagulants, rate and rhythm control. The non-pharmacological interventions are electric cardioversion, Catheter ablation, left atrial appendage closure, lifestyle advice and patient education (15). The results of the methods are improving rapidly, meaning that more patients will get the same treatment during the coming years. Auricula Auricula is an internet based national QR for patients with AF, and the idea of founding a web based QR for AF started in 2004 (17). Together with Uppsala Clinical Research Center (UCR) this registry was ready to use in Auricula s purpose is to improve the quality of health care so results and health care interventions will have a high quality as possible but also be efficient. It is today used both from hospitals and health care centres. Of approximately 224 care units, 70 are hospitals and about 140 are in primary care, more than patients are registered in this registry. It is supported by Sweden s Local Authorities and Regions and the registry is following the Patient data law and PUL which is the Personal Privacy Act. Auricula QR The QR can map incidences, diagnosis, treatment and the consequences of the AF. With this gathered information there will be a possibility to improve and modernize the treatment for patients with AF (16). To be able to improve and increase the safety of the treatment the registry can monitor the anticoagulants. Some key variables the Auricula 3

12 registry has are; demographic data, date and place the patient sought care, ECG, cardiovascular events, risk factors/concurrent diseases, medical treatments, previous diseases, blood pressure, life quality, body weight and length (18) (17). Interoperability To share data across different systems is known as interoperability. Some of the reasons why interoperability is needed today are due to the fact of saving time and connecting different systems to each other to make improvements on Information Communication Technology (ICT) and to exchange information between these systems (19). Currently there is no integration between the EHR and the QR for atrial fibrillation (AF). There lie potentials with an integration between these systems that could make it possible to transfer data fully automatically and could decrease the manual labour and reduce human errors. Interoperability in health care has shown to be important and successful and is vital for the assurance of the care and for patient safety (20). To make it clear on what is meant by interoperability: The SemanticHealth definition of interoperability (21): Functional and syntactic interoperability: the ability of two or more systems to exchange information (so that it is human readable by the receiver) ; and Semantic interoperability: the ability for information shared by systems to be understood at the level of formally defined domain concepts (so that the information is computer processable by the receiving system). OpenEHR OpenEHR is a flexible open domain-driven platform for e-health systems under development (22)(23). One essential attribute for open source software is the open source concept, which means that the access for the source code is free to use (24). 15 years of research from different projects and standards from around the world is what embodies the openehr (25). OpenEHR have a core design specification that consists of the Reference Model (RM), Archetype Model (AM) and the Template Model (TM). The specifications are described in detail below: Reference Model: RM consists of data types, data structures and EHR models and the classes of RM are the building blocks of archetypes but they also provide basic semantics of the data. The EHR RM has different Entry types that have built-in clinical meaning, Evaluation, Instruction, Observation and Action (22). 4

13 Archetype Model: AM is the design specification for the archetypes, it provides limiting mechanism over RM classes. An archetype can be described as a model that captures clinical information. With the use of the RM, the archetype is a readable specification of how to store patient data and both are keystones for the openehr architecture. They allow clinicians and domain experts to be involved in the design to create standardized clinical content for EHR. It is designed for all imaginable clinical situations (22). The definitions are kept broad and constraints minimal and this is to maximize the interoperability to share and re-use the archetype across many types of healthcare and the broadest range of clinical scenarios (26). Archetype Definition Language (ADL) is a standard that is used to express the specifications. It can also be revived in other formats like structured definitions or mind maps. By design, they provide structure and specify content which means that archetypes can be both clinically meaningful AND interpretable by EHR systems (26). Template Model: TM is grouping the archetypes and also constraining to meet the local requirements. It is the next step towards building clinical models. They contain one or more archetypes and add further constraints required for the use of those archetypes in a particular setting (27). With the archetypes they can build up a template that will fit what is wanted to be examined. Templates define data sets with the help of archetypes and it forms data specific to a particular use e.g. definition of a message or a form of any kind presented. Guideline Definition Language GDL is a formal language for expressing GDL rules and to represent clinical knowledge for computerized decision support (28). It is created based on the openehr Reference Model and Archetype Model providing scalable CDS rules across different electronic health record systems (29). Natural languages, reference terminologies and technical implementations are languages that CDS rules in GDL format will be natural to (28). GDL is at the moment still under development but it allows openehr archetypes to combine with rules. Exchanging information between systems To exchange EHR data between systems that have different suppliers is a challenge. openehr and CEN/ISO have developed an EHR communication that is based on 5

14 archetype methodology. There is very limited research where the experience of this methodology is deployed in EHR systems (30). It has also been challenging for medical informatics research to provide CDS across organizations that are using different types of EHR systems. The lack of commonly shared EHR models and terminology bindings has been recognised as a major barrier to sharing CDS content among different organisations. (29). No research regarding integration particularly between the EHR and the QR was found. Problem analysis The manual labour is a problem today in the care. Nurses are hired to fill out the QR forms for the cardiologists and this job task is taking a lot of their time (31). It is something that is beneficial to reduce for a better health care. A hypothesize is that if a high interoperability and integration between EHR and QR existed this could minimize the problems. This is achieved by important factors leading to a continued good development for QR s: An information infrastructure based on one standard A transfer function from EHR to register that is simple A quick feedback of findings from analysis of registry data(32) OpenEHR technologies could be the solution to the problem mentioned above. With archetypes and templates data forms can be standardized, and it could populate the registry easier with the existing data from the EHR (32). Adjustments, easy modifications and maintenance of the registry could be done. Aims and objectives The primary aim of the study is to evaluate the feasibility integrating EHR and the QR Auricula using openehr technology including archetypes, templates and Guideline Definition Language (GDL). The main objectives are how informatics could improve the integration for a fully automated transfer between the systems and decrease the manual labour. 6

15 The work will be to review existing archetypes and identify the need for new ones and then to create specific templates and finally to model a guideline with GDL facilitating the transformation. The CDS is only a small portion of the thesis, by using GDL a couple of rules are set up to identify patients and extract data from EHR for automatic transfer to Auricula. Research question The specific research questions addressed in this study are: How can openehr technology and GDL improve the transfer of information between EHR and Auricula quality registry for AF? What problems could be solved with the integration between these systems? Method The aims of this study are to assess if the integration using openehr technology can make the data transfer automatic between the EHR and the QR. The report is using an empirical method to gain expert knowledge about the integration of EHR and Auricula QR for AF. Content analysis is used to analyze the collected data. To get a more in-depth analysis over the situation today the choice of method was qualitative methods. Furthermore, the modelled guideline will be presented and the validation of the data will be done by using the CDS workbench tool. The study framework is presented in figure 2. Figure 2 The study framework Study Design There are a lot of different study designs that can be used in a research study. The different designs differ from each other making the choice multiple for researchers and 7

16 their studies (33). Study designs all have different techniques and methods that are used for the collection and analyse of the generated data. It is important to choose a study design that suits the problem description and the aims of the study to get the best results. To answer a research question there are a series of steps that must be done. These necessary steps are defined by the different study designs and this is because they are like a systematic plan (33). For this study a descriptive study design has been selected. It is a study that is identified to describe systematically and accurately the facts and characteristics of a [ ] area of interest (34). This study design allows the research to use both qualitative and quantitative approaches. Both one-time and over-time are interactions that can be done with the respondents. For this study one-time interaction is used where the respondents give feedback and participate in a semi-structured interview. With a descriptive study design the collection of the information takes place before the experiment. The collected data is later included in the experiment (34). According to the aims set in the previous chapter the descriptive study design suits the needs of the study as it gives the possibility to provide a qualitative answer regarding the feasibility of the integration between EHR and the QR. It can also provide answers regarding what problems this integration could solve. The information is firstly collected and the applied to the modelling for the data transfer. Study approach With a qualitative method, it is usually easier to get measurable data of a phenomenon with different backgrounds and from different environments by e.g. semi-structured interviews. It is usually hard to sort the collected data (33). For this study the mixed-method approach is used allowing the research to combine both qualitative and quantitative methods. Johnson Onwuegbuzie describes this method more precisely: Mixed-methods research is formally defined as the class of research where the researcher mixes or combines quantitative and qualitative research techniques, methods, approaches, concepts or language into a single study(35). Taking the aims, objectives and the second research question into considerations a qualitative approach was more appropriate to provide the needed answer. The first research question was better to answer with a quantitative approach regarding the 8

17 modelling with data collected from the interview. By collecting qualitative data from an expert using semi-structured interview the evaluation of the study was possible to examine. The interview with the expert also provides the study with feedback for the selected elements for the modeling of the archetype and the guideline for the AF QR. This qualitative data was important and crucial for the modelling of the archetype and the guideline. It was also important to get feedback on why these elements were significant which could only get done by using a qualitative approach. Subsequent modelling of the acquired knowledge is done by using OpenEHR technology and GDL. Data collection and analysis The first step, as shown in figure 2, was the selection of existing archetypes and guidelines. A literature review was preformed to assemble a collection of information about AF using databases such as PubMed, the online university library of Karolinska Institutet and the search engines Google and Google Scholar. The following keywords were used: Atrial Fibrillation Protocols Diagnosis Guidelines Quality criteria Standards The guideline was analyzed with certain criteria that were based on previous studies for the needs of this study. The following criteria were used: For better future research the guideline needed to be written within the past four years. The guideline needs to be national or international for the generalization of the results. The guideline needs to be in English language. The second step in the study framework was the elicitation of the text-based rules from the selected guideline, which was the European society of cardiology (ECG). ECG 9

18 guidelines were followed due to the reason that the National Board of Health and Welfare in Sweden didn t update their guidelines. Expert feedback from a specialized cardiology nurse was given on the text-based rules. Some of the rules were deselected and new ones were added (presented under section Selected guideline ). The third step was the elicitation of the archetype and guideline. GDL is built upon the openehr RM and archetypes, therefore it needs to use archetypes to be able to operate. In order to locate the archetypes needed for the study the International Repository of Archetypes was used. The required codes to locate the external terminology were searched in the following databases: the Anatomical Therapeutic Chemical (ATC) and the International Classification of Disease (ICD). A semi-structured interview was also performed with the expert to notice possible gaps that could be identified that later was analyzed using content analysis. Interview with expert The questions used for the interview were semi-structured, which means that they are open and the respondents can answer with their own words (36). The medical expert was a specialized cardiology nurse who was selected because of the involvement within the Auricula registry. It was important that the respondent had a significant role in the project involvement for the AF QR. The information about the guideline for the registry needed, to be as up-to-date as possible. Therefore it was important that the respondent worked closely with the Auricula registry. A medical background was preferably due to the importance of the selection of the text-based rules for the guideline. The interview questions were asked in English language, which is not the native language of the respondent or the researcher. There were no difficulties reported in the communication during the interview. A speculation is that the answers could have been answered differently if they were asked in the native language (Swedish) of the respondent. The study was performed at a place where the environment was familiar for the respondent. Some of the questions asked were to clarify how the situation is today without the integration between EHR and Auricula QR, and what problems it hypothetically could solve. The importance of the integration and how it would decrease the manual labour was of interest, and also how it could be in the future and if this integration could make any improvements. A model of today s situation without the integration and how it could 10

19 be if the integration was successfully implemented was presented to the expert to get feedback on possible corrections. Content analysis In this research study the method Content analysis is applied. There are two main directions in the content analysis, quantitative and qualitative. For this study the qualitative direction was chosen. This method is differently interpreted by those who use the method. It can help the researcher to get a clearer picture on how the situation is about specific things. There are some downsides with this method and it is the transcription of the interviews. It takes a long time and can make it hard for the researcher to get finished in time. Another thing is how the researcher will choose to analyze the collected data, there are a lot of interpretations on how to do this. One thing to keep in mind when using content analysis is to always be as objective as possible in the analyze of the data. Content analysis is carried out in three phases which are more described under Study design. They are the preparation phase, the organizing phase and the reporting phase, where the researcher is analyzing the process and the results. These analyzing steps are used to help answering the research question (37). How content analysis is applied To analyze the data collected from the interview the method content analysis was chosen. Because only one interview was performed this method was best suited for the results to become as eligible as possible. The collected data is analyzed through three phases. The first phase is the preparation phase, the second one is the organizing phase and the third is the reporting phase, see figure 3. 11

20 Figure 3 The three phases in content analysis For content analysis there is not a systematic way or set of rules of how to analyze the data, even though there are different steps. The important thing is that the words get classified into categories. In the preparation phase the researcher starts by selecting e.g. words that are of interests. Before that it is important that the researcher decides on what is going to be analyzed and in what detail. Before the analysis begins the researcher needs to decide if the latent content or the manifest content should be analyzed (37). The collected data from the interview was taken in by the researcher with open eyes, which means without preconceptions. Glaser (38) describes this technique as following: The mandate is to remain open to what is actually happening and not to start filtering data through preconceived hypotheses and biases to listen and observe and thereby discover the main concern of the participants in the field and how they resolve this concern. The researcher needs to get familiar with the collected data by reading the interview several times. The researcher read through the transcription of the interviews and analyzed it by using line-by-line (39). This means that each row in the transcription was separately coded. This gives the research an opportunity to stop and think about what each thing is about and how the respondent thinks. Gibbs is saying that (39) What is the most useful about it is the headings you get, the codes you come up with in the end. That you might want to reuse (39). The interview gets transcribed and the parts that are interesting or thought-provoking get highlighted, this step is called open coding. The next step is to organize the data. Short summaries can be noted down on the side. After when the researcher has done this 12

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