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Master's Programme in Health Informatics Spring Semester 2016 Degree thesis, 30 Credits

Mapping acute coronary syndrome registries to SNOMED CT: A comparative study between Malaysia and Sweden

Author: ‘Ismat Mohd Sulaiman

Author: ‘Ismat Mohd Sulaiman Main supervisor: Sabine Koch, Health Informatics Centre, Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet Co-supervisor: Daniel Karlsson, Department of Biomedical Engineering, Linköping University Examiner: Stefano Bonacina, Health Informatics Centre, Department of Learning, Informatics, Management and Ethics (LIME), Karolinska Institutet

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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, 1st June2016

__________________________________________________________

‘Ismat Mohd Sulaiman

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Mapping acute coronary syndrome registries to SNOMED CT: A comparative study between Malaysia and Sweden

Abstract

Background: Malaysia and Sweden planned to encode its acute coronary syndrome registries with SNOMED CT as part of their effort towards reusing data for research and reporting. Since, similar-purposed patient registries often contain similar data, these data should be mapped to the same code despite differing languages. This study then aims to discover the terminological similarities and differences in both maps, and explore the reproducibility and reusability of these maps.

Objective: To induce the mapping approach taken by the mappers by analyzing the resulting maps, and understand the reasons for similarities and differences found.

Methods: A summative content analysis combining a quantitative followed by qualitative analysis was conducted. From the Malaysia’s NCVD-ACS and Sweden’s RIKS-HIA registry forms, distributions of headings, variables and values were studied. Data items with equivalent meaning (EDIs) were paired and categorized into match, mismatch, and non-comparable. Emerging themes from each paired EDIs were seen as factors that contributed to the similarities and differences between the maps.

Results: The registries, mappings, and EDIs shared similar distribution. The matching EDIs occurred mostly in pre-coordinated SNOMED CT expressions. Mismatches occurred due to challenges arising from the mappers themselves, limitations in SNOMED CT, and complexity of the registries. Non-comparable EDIs were due the use of other coding system, unmapped and uncoded data items, as well as a few requests for a new SNOMED CT concept.

Discussion: Variations in the mapping was discovered which would lead to inconsistencies and prevent the aim for semantic interoperability. To reduce the variations, three recommendations were made: (i) development of a mapping guideline specific for patient registries; (ii) openly share maps; and (iii) establish collaboration between scientific research and SNOMED CT community. In turn, the maps not only become more consistent, but also reproducible and reusable.

Keywords: SNOMED CT, reference terminology, registries, semantic, cardiology.

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Acknowledgement

In the name of God, most beneficent, most merciful. Praise to Him that I have completed this thesis as part of my Master’s Programme in Health Informatics. Through this journey, not only did I learn about the subject matter, but I have also connected with so many passionate people. I am humbled to be this close than just having a “six degrees of separation”. These relationships are my rizq (provision) that I will cherish.

My everlasting awe goes to my supervisors and mentors: Sabine Koch and Daniel Karlsson in guiding me with their vast experience in their respective field; the late Joseph Franke for engaging in the same vision even when we’ve never met; and Dr. Md. Khadzir Sheikh Ahmad for the constant faith in me and the rizq that awaits.

My salute goes to the Swedish and Malaysian team who are filled with hard working professionals as an informatician, IT engineer, clinician, and researcher. You are faced with hope and challenges, yet you are determined to confront it, and doing it in ways that you know best.

My heart goes to my family, whom have supported me physically and mentally to allow me to continue my quest: My parents who have taught me about the pursuit of knowledge and practice (‘Ilm and ‘Amal); Mama and my in-laws who have taught me about sacrifices and relief; My siblings who have encouraged me from afar and always willing to lend their hand in their own beautiful way. I’ve grown up and will continue to grow because of you.

Last but not least, my partner in life and my three kenits (little ones). Thank you Rusydan for being the stake of the house filling it with more than love, keeping us safe and secure; the experience has changed us for the better. My dear Arishah, Nashrah and Luqman, you are my penghibur hati (entertainer), my penyejuk hati (comfort), and my himmah (willpower, motivation, and courage). Remember that to be good at something, it is going to be hard, but He will ease us if we keep our niat (intention) pure and true.

For knowledge and practice,

‘Ismat Mohd Sulaiman 1st June 2016 Karolinska Institutet, Stockholm, Sweden

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Table of Contents

Glossary ................................................................................................................................... 5

List of Figures ......................................................................................................................... 6

List of Tables ........................................................................................................................... 7

1. Introduction ................................................................................................................... 8

1.1 Patient Registry ...................................................................................................... 8

1.2 SNOMED CT ...................................................................................................... 11

1.3 Previous studies on mapping to SNOMED CT ........................................... 15

1.4 Problem description .......................................................................................... 17

1.5 Aims and objectives ........................................................................................... 17

1.6 Research question .............................................................................................. 17

2. Methods ....................................................................................................................... 18

2.1 Materials ............................................................................................................... 18

2.2 Data preparation ................................................................................................ 20

2.3 Data analysis ........................................................................................................ 23

2.4 Ethical consideration ......................................................................................... 23

3. Results .......................................................................................................................... 24

3.1 Distributions of data items – quantitative results ....................................... 24

3.2 Qualitative results .............................................................................................. 28

4. Discussion ................................................................................................................... 36

4.1 Analysis of the findings ...................................................................................... 36

4.2 Recommendations ............................................................................................. 40

4.3 Strengths and limitations .................................................................................. 42

4.4 Future directions/research ............................................................................... 43

5. Conclusion .................................................................................................................. 44

References ............................................................................................................................ 45

Appendices ........................................................................................................................... 49

Appendix 1a: NCVD-ACS Registry Form ................................................................. 50

Appendix 1b: RIKS-HIA Registry Form ..................................................................... 53

Appendix 2: Equivalent data items (EDIs) ................................................................. 57

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Glossary

Controlled vocabulary: A list of standardized terms grouped into a single concept and given a unique code.

Electronic health record: An electronic record to represent a patient care used by healthcare professionals. Here, this term is interchangeable with electronic medical record (EMR) and electronic patient record (EPR).

Map: A documentation of the registry’s data items and its assigned codes from the chosen coding system.

Mapping process: The process to assign the codes to the registry’s data items.

Ontology: Philosophically, it is the study of what exists in the real world. From a computer science point of view, real world entities are referred to as concepts and described by its properties and relationships to other concepts.

Patient registry system: A system that collects information about a group of patients with a common condition for one or more purposes. This term is often interchangeable with clinical registry, disease registry, quality registry, etc.

Pre-coordinated expression: In SNOMED CT, it is the expression which contains a single concept with a single code.

Post-coordinated expression: In SNOMED CT, it is the expression which contains a string of concepts with multiple codes composed in an approved SNOMED CT compositional grammar.

Reference terminology: A reference terminology for clinical data is a set of concepts and relationships that provides a common reference point for comparison and aggregation of data about the entire health care process, recorded by multiple different individuals, systems, or institutions.

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List of Figures Figure 1.1: Duplication of data entry occurs when entering a similar patient record into multiple systems. ACS=Acute coronary syndrome; PCI=Percutaneous coronary intervention; CABG=Coronary artery bypass graft. ....................................................................9

Figure 1.2: An example of comparing the conventional method to communicate between systems versus using a reference clinical terminology standard like SNOMED CT. ........ 10

Figure 1.3: SNOMED CT as a controlled vocabulary and reference terminology using "chest pain" as an example. .................................................. Error! Bookmark not defined.

Figure 1.4: Illustration of the poly-hierarchical relationships for |Chest pain (finding)|. Note that “..”) means there are other concepts in between. Instead of the horizontal structure shown here, these relationships are often illustrated in a bottom-up tree structure. In addition, the relationships with concepts below (children concepts) are not shown in this diagram. ...................................................................................................................... 13

Figure 2.1: The four steps to prepare data for comparative analysis. ................................... 20

Figure 2.2: An example of the registries' information structure in UML class diagram. .. 21

Figure 2.3: Identifying the data items in both registries. .......................................................... 22

Figure 3.1: Comparison of the percentage of distribution for each data items (heading, variable and values) between the registry form, its mapping, and EDIs. .............................. 24

Figure 3.2: Distribution of matches, mismatches, and non-comparables for all EDIs (n=101), coded EDIs (n=72), and only SNOMED CT coded EDIs (n=42).......................... 26

Figure 3.3: Detailed comparison and varying distribution of non-comparable data items for all EDIs (n=68), coded EDIs (n=39), and only SNOMED CT coded EDIs (n=9) between NCVD-ACS and RIKS-HIA map. .................................................................................. 27

Figure 3.4: Hierarchical illustration that shows the shared parent concept |Identification code (observable entity)| for Study ID 2. .................................................................................... 31

Figure 3.5: Hierarchical Illustration that shows the child-parent concept for Study ID 16. ............................................................................................................................................................... 31

Figure 3.6: Schematic diagram of |Blood pressure finding (finding)| concept which was defined by |Blood pressure (observable entity). ........................................................................ 32

Figure 3.7: Hierarchical comparison of |Thrombolytic agent not administered because contraindicated (situation)| used by NCVD-ACS and |Thrombolysis contraindicated (situation)| used by RIKS-HIA. ........................................................................................................ 32

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List of Tables Table 1.1: SNOMED CT pre- and post-coordinated expression for angina and chronic angina respectively. .............................................................................................. 13

Table 2.1: Categories for equivalent data items (EDI). .............................................. 22

Table 3.1: Distribution of data items (heading, variable and value) between the registry forms, its maps, and the EDIs. ................ Error! Bookmark not defined.

Table 3.2: Comparing the approach to map data items between the different mappers. ............................................................................................................................... 25

Table 3.3: Example of lexically matching terms between data items and SNOMED CT descriptions. .................................................................................................................. 28

Table 3.4: Example of inferencing and utilising medical knowledge to derive the meaning of data items and assign SNOMED CT expression. ................................... 29

Table 3.5: Example of a single variable represented by multiple questions with an attempt at post-coordinate it. ......................................................................................... 30

Table 3.6: Example of a single value represented by multiple answers. ................. 30

Table 3.7: Example of a mismatch as a result of cultural or language differences. ................................................................................................................................................ 33

Table 3.8: An example that shows uncoded headings and variables with a corresponding coded value. ............................................................................................. 34

Table 3.9: Example of new requests for SNOMED CT concept and code paired against coded data items. .................................................................................................. 35

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1. Introduction Accurate health information starts at the point of care. The information documented is often reused for secondary purposes such as for research and reporting. This creates a need to ensure that the information captured, stored and transferred for secondary use must be accurate.

The vision for seamless exchange of information between isolated electronic health systems meets many challenges. One such system is the patient registry which relies on medical records as their data source (1). There is also a need to reuse existing patient registry systems without continuously creating a new one (2,3). To overcome the challenges, the current recommendation is to use communication standards to facilitate the exchange of information between these systems.

The following explains the initial steps taken by two different nations trying to achieve the same goal for a more effective and efficient data transfer.

1.1 Patient Registry Patient registry systems (or registries henceforth) are systems that collect information about a group of patients with a common condition for one or more purposes (4). The term “patient registry” is often interchangeable with other names to indicate its purposes, such as clinical registry, quality registry, disease registry, and outcome registry.

Registries are mainly used as a basis for observational studies for clinical and public health research. They are used to understand the distribution of a disease(s); the course of a disease(s); treatments and options; and related outcomes. They are also being used to monitor and measure the quality of service. Only recently, a patient registry was used for a new method in an experimental study called registry-based randomized clinical trial (5).

Registries with the same purpose often have similar data items because they refer to data standards developed by international clinical societies. For example, the acute coronary syndrome (ACS) registries worldwide are often built with reference to established cardiology data standards: European’s Cardiac Audit and Registration Data Standards (CARDS) (6); American College of Cardiology Clinical Data Standards (7); and Australia’s National Variable for ACS (8).

Duplication of data entry Among the challenges of implementing registries in the clinical setting is the time and resources needed for data entry. When a patient fulfils the inclusion criteria in a registry, required data in medical records are searched, sometimes transcribed into a paper-based registry form, and then re-entered into the electronic registry system. If the same patient fulfils the criteria in multiple

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registries, there may be duplicated data to be re-entered into all of these registries (Figure 1.1).

Another challenge is the inability to reuse and collate data from existing registries (9–11). This results in an increasing number of new registries developed with a slightly different purpose but collects overlapping information with existing registries. As more stakeholders require information from the same healthcare provider, data entry became a burden.

Duplication of data entry occurs when the same data are entered multiple times. The workflow and resulting workload increase the risk for human transcribing error. It costs registry owners time, resources, money, and most importantly the quantity and quality of registry data.

Figure 1.1: Duplication of data entry occurs when entering a similar patient record into multiple systems. ACS=Acute coronary syndrome; PCI=Percutaneous coronary intervention; CABG=Coronary artery bypass graft.

Seamless data transfer One of the proposed solutions is to enable seamless or ‘automated’ data transfer from EHR-to-registry or registry-to-registry. The foreseen benefit has raised interest in many registry owners and national institutions such as United States, Europe, Sweden, and Malaysia (12–15).

However, the proposal is faced with challenges. One factor is due to the complexity of the EHR which consists of data mainly in narrative and unstructured form (13). On-going studies are being conducted to enable natural language processing of unstructured data into a structured format for clinical research and clinical decision support systems (16). Even in structured form, sharing between registries is a challenge because they may not share common terms, information model, and even language (11).

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Another factor is the multiple ways to represent the same clinical information model. For example, in trying to capture “past history of myocardial infarction”, system A may structure it as Past medical history > Myocardial infarction; system B may structure it as Past medical history > Myocardial infarction > Yes; and system C may give a single statement in a check box. Recent studies proposed to standardize the clinical information model (17,18) and introduce ontology to this model (19–22). An ontology-based information model would define each information entity with specific properties and its relationships to other information entities in a logical manner.

Conventional way to exchange data A conventional way for two legacy systems to transfer or exchange data is to develop an interface layer. This interface layer would map data from one system to another. A simple example would be, mapping “chest pain” in one system to “bröstsmärta” in another system, and to many other terming variations.

The problem with this conventional method is that as the number of systems to interact increases, the number of interfaces between system increases exponentially (23). Using the formula (N^2-N)/2, sharing data from 3 sources may need 3 interfaces; while 5 sources may need 10 interfaces (Figure 1.2). The amount of work and associated cost is tremendous. This is the effect of non-interoperable systems.

Figure 1.2: An example of comparing the conventional method to communicate between systems versus using a reference clinical terminology standard like SNOMED CT.

Using standards to support interoperability Health informatics standards are specifications that set the common ground for health information systems. In theory, instead of the conventional way, each system would use a common health informatics standard. In other words, rather

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than create 10 bidirectional maps and interface layer for 5 systems to communicate, there would only be 5 (Figure 1.2).

When the systems are able to exchange information without additional intervention, they are interoperable (24,25). Syntactic interoperability is achieved when the information (message) are received in the valid computational grammar. Semantic interoperability is achieved when information (message) received is understood and usable without needing further interpretation (26).

Standards in patient registries Several national projects have been developed to oversee the full interoperability between multiple systems concerning registries. Some examples are the Patient Registry Initiative (PARENT) in Europe (15); the National Cardiovascular Research Infrastructure (NCRI) project in United States (14); the Nationella Programmet för Datainsamling (NPDi) project in Sweden (13); and the Malaysian Health Data Warehouse (MyHDW) project (12).

These projects share a common theme, which is the implementation of one type of standards – a clinical terminology standard as one of the many components to support semantic interoperability.

1.2 SNOMED CT SNOMED CT is a clinical terminology standard aiming to support semantic interoperability (27,28). It contains a collection of clinical terms and synonyms, indexed and structured in a computer-processable way, so that the clinical meaning is consistent when referenced by different sources. This definition describes some of the characteristics of SNOMED CT which concern registries (29).

Characteristics of SNOMED CT First, SNOMED CT acts as a controlled vocabulary, where clinical terms that carry the same meaning are standardized and grouped into a single concept and given a unique code (30). This is useful to limit the variations of documenting the same clinical concept. At the same time, it is flexible enough for practitioners to record clinical information due to available synonyms and the possibility to expand this lists to suit local context. Each concept in SNOMED CT is given a human readable name and a computer processable code. For example, the concept |Chest pain (finding)| is given a code 29857009 (Figure 1.3).

Second, SNOMED CT acts as a reference terminology to ensure the clinical meaning is maintained when data are transferred, collated and analysed from different systems or institutions (31). Regardless how a clinical term is spelled or worded in different legacy systems, data with the same meaning would be referenced to the same SNOMED CT code. Therefore, a receiving system would be able to understand and use the data that was sent meaningfully (Figure 1.3).

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Figure 1.3: SNOMED CT as a controlled vocabulary and reference terminology using "chest pain" as an example.

Third, SNOMED CT is built upon some ontological foundation (32). This means that the concepts are structured and linked in a logical manner. This characteristic helps to group and aggregate data for reporting, as well as a useful reasoning mechanism within a clinical decision support system. For example, |Chest pain (finding)| IS A type of |Pain (finding)| with a FINDING SITE |Trunk structure (body structure)|. When implemented properly, finding patients presented with |Pain (finding)| could also return patients encoded with |Chest pain (finding)|. This linkages or relationships is shown in the following Figure 1.4 and Figure 1.5.

Figure 1.4: Illustration of the poly-hierarchical relationships for |Chest pain (finding)|. Instead of the horizontal structure shown here, these relationships are often illustrated in a bottom-up tree structure.

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Figure 1.5: SNOMED CT Design as depicted in the SNOMED CT Starter Guide, 2014 (28).

SNOMED CT expressions In SNOMED CT, a concept can be represented in a pre-coordinated or post-coordinated expression. Pre-coordinated expression contains a single concept with a single code. Post-coordinated expression contains a string of concepts with multiple codes composed in an approved SNOMED CT compositional grammar. Table 1.1 shows an example of representing angina using pre-coordinated expression and chronic angina using post-coordinated expression.

Table 1.1: SNOMED CT pre- and post-coordinated expression for angina and chronic angina respectively. SNOMED CT expressions

Pre-coordinated expression for angina

Full expression: 194828000 |Angina (disorder)| Coded expression: 194828000 Human-readable expression:

|Angina (disorder)|

Post-coordinated expression for chronic angina:

Full expression: 194828000 |Angina (disorder)|: 408731000 |Temporal context (attribute)| = 90734009 |Chronic (qualifier value)|

Coded expression: 194828000: 408731000 = 90734009 Human-readable expression:

|Angina (disorder)|: |Temporal context (attribute)| = |Chronic (qualifier value)|

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Implementing SNOMED CT in patient registries SNOMED CT is gaining recognition due to its comprehensiveness to represent clinical concepts (33,34). Currently, it has more than 300,000 concepts used in healthcare. Lee et al. (35) found the wide range of use involving 36 medical specialties between 2001 to 2012. The registry community was attracted by SNOMED CT’s characteristics and its potential benefits. Malaysia and Sweden have taken a national approach to encode their registries with SNOMED CT.

Malaysia and Sweden The Malaysian Health Data Warehouse (MyHDW) project aims to provide a comprehensive view for health managers to plan their national healthcare (36). It will receive data from various sources such as the patient registries (including hospital discharge registries), pharmaceutical information systems and EHR systems for reporting and research. The foundation to enable semantic interoperability in this project is the use of standards such as International Classification of Diseases (ICD), SNOMED CT and LOINC.

The Swedish Nationella Programmet för Datainsamling (NPDi) project aims to ease the data collection for registry owners by extracting data from the EHRs (37). The need is profound when there are more than 100 registries to be filled from EHR systems that are implemented in almost 100% of Sweden’s healthcare facilities (38,39). To enable semantic interoperability, similar standards were proposed.

Mapping patient registries to SNOMED CT Developing a SNOMED CT encoded registry takes a two-step process: mapping followed by coding. Mapping is a process to assign SNOMED CT concept and code to the registry’s data items. The result is a one-to-one map which will then be used by computers to bind the two and encode the patient registry.

While the coding process takes on an ‘automated’ approach, the mapping process remains a human labour. It is assumed that human intervention is more reliable, and may be the only option due to the lack of technology and skill to do an automated mapping. Full automation may be possible in the future with current developments in clinical natural language processing (16,40,41). Even then, the initial stage always requires mapping with human intervention and validation.

The mapping process from registries to SNOMED CT There are two known approaches to create a map: the parallel approach and the verification approach (42). The parallel approach involves two terminologists that map the same dataset independently. The results are then compared in a review session to discuss and finalize on the mismatches. The session is done with a team and may involve the registry owners. The verification approach involves

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one terminologist to map a dataset followed by a second terminologist to review the resulting map. The final review process is done as the parallel approach.

The mapping results can be documented in a simple spreadsheet or in a dedicated software application. The map would contain the registry’s data items paired with the matching SNOMED CT concept and/or code. Additional metadata may be included such as requests for new concepts and commentaries.

Multi-professional collaboration between terminologists and clinician/registry owner are often needed to help clarify clinical meanings, describe the clinical context and workflow, and final validation of the map. Previous studies also encouraged using terminologists with experience in using the registry or have come from similar clinical background.

1.3 Previous studies on mapping to SNOMED CT Currently, there is no specific guideline on mapping legacy systems to SNOMED CT. Because of this, the scientific community has shared their mapping method using structured EHR templates, but these were based on single centre experience (43–46). Since registries are in a structured format, the proposed method perhaps can be reused.

The studies have similar recommendations such as identifying the variables/questions and values/answers; referring to the templates to provide clear context and meaning; and using pre-coordinated expression before considering post-coordination. A different recommendation was seen by Rasmussen & Rosenbeck (47) followed by two other studies (43,44) which promoted a Clinical finding hierarchy to represent variables; whereas SNOMED CT editorial guidelines recommended Observable entity hierarchy. These recommendations should be closely examined because any similarities or differences will impact on the consistency of the mapping result.

Apart from SNOMED CT, these study also found the need for a common reference information model to support semantic interoperability. Projects like the Clinical Information Model Initiative (CIMI) and SemanticHealthNet aims to address the issue of binding clinical terminologies like SNOMED CT to an EHR information model. Some studies also explored on designing the information model according to ontological foundations (21,22,48). These solutions may work well when developing a new system, but it is an arguable solution for legacy systems. Migrating legacy system to a new information model may introduce errors, information loss, and jeopardize the reusability of past data for comparison with new data.

In another aspect, Rector et al. (48) proposed a method to bind codes to data structures. The difference with other studies previously mentioned is that it

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looks at binding data to codes syntactically for data to be processed reliably; not on how to bind the codes accurately to the meaning.

Comparative studies on mapping results were conducted by Karlsson et al. (49) and Andrews et al. (50) which used Krippendorff α statistical measurement. A higher value would indicate a higher disagreement or dissimilarity between different mappings coming from the same dataset. While Karlsson’s team focused on finding a better statistical method, Andrews’ team on the other hand was concerned with the mapping approach between the mappers. Moreover, Karlsson’s team used a mocked dataset, while Andrews’ team used the Rare Disease Clinical Research Network (RDCRN) dataset.

It is concerning that Andrew’s study revealed a poor level of similarity between 3 different coding companies to map the same structured dataset. Interestingly, the variations occurred despite them having a high degree of confidence in their own result. This study was published in 2007, with no recent studies to match. With increased SNOMED CT use, lessons learnt from implementations, and changes in SNOMED CT itself, the current situation perhaps have a different outcome.

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1.4 Problem description One would expect that data with the same meaning should be mapped to the same SNOMED CT code. Yet, previous studies have shown some similarities and differences in the mapping approach and the varying results between individual mappers. Variations in the maps would defeat the aim for semantic interoperability. Inconsistent results will impact numerous stakeholders particularly registry owners in making inaccurate decisions, and may result in patient harm and poor policy making.

There is a knowledge gap on which mapping techniques contributed to the similarities and differences in the mapping result. Identifying and understanding the reason behind it may help in reducing the variations and support accurate information exchange.

1.5 Aim and objectives The aim is to compare existing registry-to-SNOMED CT of similar-purposed registries from two different countries. To do so, the study will:

a. Investigate the distribution of data items in the registries and maps for areas of similarities and differences;

b. Conduct a retrograde analysis or backward induction on the maps to induce the mapping approach taken by the mappers;

c. Identify the reasons for similarities and differences found.

1.6 Research question The research questions are:

1. What are the terminological similarities and differences between the Swedish and the Malaysian registry-to-SNOMED CT maps?

2. What are the implications of different mapping results on reproducibility and reusability of the mapping?

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2. Methods This study was an applied research designed and simplified based on the hermeneutic methodology (51). Hermeneutic methodology focusses on the meaning and understanding of textual materials to reflect everyday life experiences or practices. However, the textual materials used in this study is already in structured form, hence the interpretation was simplified. By comparing similar materials, this study tried to understand what was actually happening – whether clinical concepts with shared meanings also have shared codes; how it happened; and why or why not.

This study was also based on the semiotic theory at semantic level, which is the study of how a concept (referred as a sign in this theory) is given its meaning and interpreted by the recipient in its own culture or context (52). Here, context plays an essential role which affects the how one defines the meaning of a concept. Comparing the result from two different countries sharing the same terminology may be of relevance to this theory.

A summative content analysis was used to support this study design (53,54). In this approach, documents were analysed using a mixed method of quantitative followed by a qualitative analysis. The foreseen benefit was to support one another’s findings. The quantitative findings identified areas of similarities and differences (aim a) that could be explained by the qualitative findings (aim b). The qualitative findings were able to be generalized due to the distribution pattern identified in the quantitative findings.

2.1 Materials The study data were acquired from 2 types of documents which originated from a single type of registry: the registry form and the registry-to-SNOMED CT map. For comparison, at least 2 registries were needed.

2.1.1 Inclusion criteria Choosing the registries to be compared was based on these criteria:

(i) the registries must share a similar purpose to ensure that they collect the same information;

(ii) the registries must have been mapped to SNOMED CT; (iii) the mapping process preferably used (or was depended on) the same

version of SNOMED CT International Edition.

The last criterion was made optional because versioning has been a known challenge when implementing SNOMED CT due to its biannual updates (55).

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2.1.2 Acute coronary syndrome (ACS) registries The acute coronary syndrome (ACS) registries from Sweden and Malaysia were chosen. Both registries collect patients’ information admitted to hospitals for acute coronary syndrome. Both registries had undergone the mapping process.

Specifically, they were:

1. RIKS-HIA: Register of Information and Knowledge about Swedish Heart Intensive Care Admissions (56).

2. NCVD-ACS: National Cardiovascular Disease registry – the Acute Coronary Syndrome (57).

Registry forms The registry forms represent the interface layer for the registry system. It was used to provide context and meaning by looking at how the information is structured and related to one another. NCVD-ACS (Appendix 1a) form uses UK English; while RIKS-HIA (Appendix 1b) form uses the Swedish language.

The version used in this study and the mapping process was clarified. The same NCVD-ACS form version 1.20 was used in both situations. However, RIKS-HIA’s form versions were slightly different. RIKS-HIA form version 2014 was used in this study but RIKS-HIA version 2012 was used during the mapping process. Only 2 data items were removed in the 2014 version, which was “IV heparin” and “Fragmin sc” for patient’s medication before arrival; but no new data items were added which did not affect the study results.

Registry-to-SNOMED CT Maps The maps documented the coding system and the SNOMED CT codes to represent the registry’s data items. It was created through a mapping process, which is the process to assign the codes to the registry’s data items.

The maps were obtained from its respective owners: Region Örebro County, Nationella programmet för datainsamling (NPDi), Sveriges Kommuner och Landsting (SKL) for RIKS-HIA map; and Health Informatics Centre, Planning Division, Ministry of Health Malaysia for NCVD-ACS map. Both documents were in Microsoft Excel format.

Both maps were created in 2012 but was released about 6 months apart. The SNOMED CT International version used during the mapping process also differs by one year.

Sweden released their map on 15 August 2013. They used SNOMED CT Swedish Edition released on 30 May 2013 which was the translated version of SNOMED CT International edition released on 31 January 2013. There were no local SNOMED CT codes used in their map. A locally built tool to browse their national edition was used during the mapping.

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Malaysia’s map was released on 20 March 2014. They used SNOMED CT International Edition 31 January 2014 provided in the IHTSDO Workbench. This software was accessible for all International Health Terminology Standards Development Organisation (IHTSDO) country members. Because of this difference, data items which were requested as new concepts were analysed in more detail if the underlying issue were due to the use of different SNOMED CT versions.

2.2 Data preparation Prior to analysis, the study data was prepared in 4 steps as illustrated in Figure 2.1.

Figure 2.1: The four steps to prepare data for comparative analysis.

2.2.1 Understand the registries’ information structure A general understanding of the information structure for both registries was required to identifying data items in the registries. Figure 2.2 describes both information structure that began with the registry’s name, then the form’s name, followed by the sections, variables, and values.

A single registry contained several forms. A single form contained several sections. Each section contained several variables (or variables or questions). However, variables may contain sub-variables. One variable may have several possible values.

NCVD-ACS registry consisted of 3 forms: notification form, 30-days follow-up form, and 1-year follow-up form. Only data items from the notification form were included in this study. This form was divided into sections based on clinical

I. Understand the registries' information structure

II. Identify data items in the registry form • Label which data items are headings, variables, values

III. Identify equivalent data items (EDIs) • Pair data items that share the same meaning • Document what each data items was mapped to

IV. Categorize the EDIs • Categorize EDIs into Match, Mismatch, and Non-comparable for analysis

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documentation structure: patient’s demography, clinical history, laboratory investigations, medications and inpatient outcome. It was meant to be filled on or after discharge.

RIKS-HIA registry also consisted of 3 forms but were based on patient flow: on-admission form, in-ward form, and on-discharge form. All 3 forms were included in this study. The sections were then ordered based on local workflow, where each section reflects different data entry points. The form was meant to be filled immediately after EHR documentation by different healthcare professionals.

Figure 2.2: An example of the registries' information structure in UML class diagram.

2.2.2 Identify data items in the registry form Data items can then be easily identified (Figure 2.3). They were the sections headings, variables, and values. Each type was assigned a reference number.

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Figure 2.3: Identifying the data items in both registries.

2.2.3 Identify equivalent data items (EDI) Equivalent data items (EDI) were paired data items that came from both registries with shared meaning. For example, in Figure 2.3, “Smoking Status” and “Rökning” asks about the patient’s smoking history. They share the same meaning, thus were paired together. EDIs were identified and tabulated in Microsoft Excel.

Several validation steps were done. To avoid missed data items, RIKS-HIA’s data items were first reviewed against NCVD-ACS and vice versa. To ensure the accuracy of meaning, a Swedish domain expert who had experience in using RIKS-HIA was sought. She translated RIKS-HIA’s data items to English and confirmed the EDI’s meaning. Finally, a quantitative validation was done to ensure consistency.

Once the EDIs were tabulated, their respective maps were referred to identify the coding system. If SNOMED CT was used, the study then identifies if they were in pre- or post-coordinated expression. Either way, the full SNOMED CT expression in human readable form and its code (SCTID) was documented alongside the EDIs.

Please refer to Appendix 2 for the full list of EDI and what it was mapped to.

2.2.4 Categorize equivalent data items (EDI) Coded and uncoded EDIs were compared and categorized into three: Match, mismatch, and non-comparable. Table 2.1 explains the different categories.

Table 2.1: Categories for equivalent data items (EDI). Category Description/Meaning Match When the EDIs, or paired data items from both registries with shared meanings,

were assigned the same SNOMED CT concept/expression in the maps.

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Mismatch When the EDIs, or paired data items from both registries with shared meanings, were assigned to different SNOMED CT concept/expression in the map but are still meaningfully related. Reason for mismatch can be further categorised into: - Subsumption: When one concept was broader or narrower than the other. - Siblings: When both concepts shared a parent concept. - Defining relationship: When one concept was used to define another

concept. - Duplicate concept: When the concept was thought to be a duplicate of

another. - Complex information structure: When the information structure

contributed to differing post-coordinated expression, even though the underlying information need was thought to be the same.

Non-comparable

When mappings cannot be compared. The reasons may be due to: - one or both used a different coding system - one or both were not mapped and thus not coded - one or both were mapped but not coded - non-existing concept: When there was a request for a new concept.

2.3 Data analysis As mentioned, this study used combined both quantitative and qualitative analysis. Quantitative analysis was expected to show distribution patterns of similarities and differences. The qualitative analysis was conducted to understand how and why these patterns occurred.

The quantitative analysis started with comparing the distribution of different data items between the registries, its mappings, and the EDIs. The intention was to discover how well the EDI would represent its respective maps and registries. Next was to compare the distribution of coded and uncoded data between the maps. The intention was to discover the mapping approach taken by each mapper. Last was to compare the distribution of coded and uncoded EDIs in the match, mismatch, and non-comparable categories. The intention was to discover areas that contributed to each category.

The qualitative content analysis was conducted on the EDIs and what it was mapped to. The intention was to understand the underlying reason which contributed to the match, mismatch, and non-comparable data items (Table 2.1). It’s SNOMED CT expression were analysed by looking at the hierarchical position, concept model, and compositional grammar. The matching category may suggest a recommended approach to be taken. The mismatch and non-comparable categories may suggest approaches and mistakes to avoid.

2.4 Ethical consideration Ethical approval was not necessary because it did not involve patient’s data. Pre-study interviews were conducted face-to-face with registry owners and mappers. An informed consent was obtained from all agreed interviewees prior to the interview via emails.

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3. Results The results are demonstrated in sequence of quantitative and qualitative findings.

3.1 Distributions of data items – quantitative results 3.1.1 Distribution of data items: headings, variables, and values Figure 3.1 illustrates a similar distribution pattern of headings, variables and values between the two registries. It also gave a glimpse of the types of data item that were included in the maps. NCVD-ACS map excluded all headings, half of its variables, and a quarter of its values. This results in only 68% of the form’s data items (216 of 314 data items) to be represented in the map. In contrast, RIKS-HIA map had included almost half of its heading, all of its variables, and had additional values compared to what was originally in the form. Further details are tabled out in section 3.1.2.

Figure 3.1: Comparison of the percentage of distribution for each data items (heading, variable and values) between the registry form, its mapping, and EDIs.

[CELLRANGE]

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0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Form (n=314)

Map (n=216)

EDI (n=101)

Form (n=450)

Map (n=454)

EDI (n=101)

NC

VD-A

CS

RIK

S-H

IA

Values Variable Headings

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3.1.2 Distribution in the maps Not all types of data items were assigned a code. Table 3.2 compares the distribution of coded and uncoded data items in the maps. For coded data items, both mappers mainly used pre-coordinated SNOMED CT expression – 75% of data items for NCVD-ACS and 49% of data items for RIKS-HIA. Only NCVD-ACS mapped a few of its data items using post-coordinated SNOMED CT expression (7% of 216 data items); while RIKS-HIA had none.

A prominent difference in RIKS-HIA’s approach was the use of additional coding system other than SNOMED CT (27% of its data items), while NCVD-ACS did not. RIKS-HIA indicated the use of Anatomical Therapeutic Chemical (ATC) code for drug values and International Classification of Disease 10th Edition (ICD10) for diagnosis.

Another difference was the uncoded data items seen in RIKS-HIA (255 uncoded data items), thus making it not comparable to NCVD-ACS. These data items represent the nonspecific date (“Date/Datum”) and time (“Time/Kl”), negations (“No/Nej”), “unknowns” and “others/övrigt” found in the registries. NCVD-ACS mappers however had excluded these data item from their map. The study also found additional “unknown” in the map that was not in the RIKS-HIA form. This explained the reason for NCVD-ACS having a much lesser percentage of data items in the map compared to RIKS-HIA as previously discussed in 3.1.1 (Table 3.1).

Table 3.1: Comparing the approach to map data items between the different mappers. Data items in the maps NCVD-ACS RIKS-HIA

n = 216 % n = 454 (n=199 ¤)

% (n=199)

SNOMED CT - Pre-coordinated expression - Post-coordinated expression

163 15

75 7

97 0

49 0

Other coding system - ATC¥ - ICD10† - Mix £

0 0 0

0 0 0

45 1 6

23 1 3

Uncoded - Request for new concept - Other reasons

38

(not in map) €

18 -

50

255 €

25 -

¥ ATC = Anatomical Therapeutic Chemical † ICD10-SE for Sweden £ Mix SNOMED CT and ICD10 ¤ Total number of uncoded data items other than requests for new concept € Data items that were in the map but were left uncoded, and thus not comparable. Some reasons include negations (“No/Nej”), “unknown”, “others/övrigt”, “Date/Datum”, “Time/Kl” ; while other reasons were not apparent and analysed qualitatively.

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3.1.3 Distribution of EDIs Both registries shared 101 data items of equivalent meaning (EDI). This number represents 32% (101 out of 314) of NCVD-ACS’ registry’s data items, and 22% (101 out of 450) of RIKS-HIA’s registry’s data items. Only 19% of the total EDIs can actually be shared due to the use of common coding system and codes. These EDIs were further reduced into coded EDIs and SNOMED CT coded EDIs (Figure 3.2).

Coded EDIs meant that at least one or both data items were assigned to any coding system, which totalled to 72 EDIs. This sub-category excluded paired data items that were both uncoded. Bear in mind that the EDIs were derived from the registry forms. Some of these EDIs were not in the map, and therefore were non-coded. There were also EDIs that were in the map but were left non-coded. These occurrences have been explained in the section 3.1.2.

On the other hand, if at least one or both data items that were mapped to SNOMED CT were analysed, a total of 42 EDIs remained. Therefore, a SNOMED CT coded data item may be paired with a non-coded data item.

Figure 3.2 also shows that the variability between each sub-category only occurs in the non-comparable EDIs. Figure 3.3 then shows the variability in details which pre- and post-coordinated SNOMED CT expression, requests for new SNOMED CT concept, and non-coded data items for headings, variables, and negations.

Match; 19; 19%

Mismatch; 14; 14% Non-

comparable; 68;

67%

All EDIs (n=101)

Match; 19; 26%

Mismatch; 14; 20%

Non-comparable; 39;

54%

Coded EDIs (n=72)

Match; 19; 45%

Mismatch; 14; 33%

Non-comparable; 9; 22%

SNOMED CT coded EDIs (n=42)

Figure 3.2: Distribution of matches, mismatches and non-comparables for all EDIs (n=101), coded EDIs (n=72), and only SNOMED CT coded EDIs (n=42).

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Figure 3.3: Detailed comparison and varying distribution of non-comparable data items for all EDIs (n=68), coded EDIs (n=39), and only SNOMED CT coded EDIs (n=9) between NCVD-ACS and RIKS-HIA map.

Note: UC=Uncoded; NewC=New concept requested; ATC=Anatomical Therapeutic Chemical; SCT=SNOMED CT; PostC=Post-coordinated SNOMED CT expression; PreC=Pre-coordinated SNOMED CT expression.

PreC; 34

PreC; 2

PreC; 34

PreC; 2

PreC; 7

PreC; 2

PostC; 1 PostC; 1

ATC; 28 ATC; 28

ICD; 1 ICD; 1 ATC+SCT; 1 ATC+SCT; 1

UC NewC; 3

UC NewC; 3

UC NewC; 3

UC NewC; 3

UC NewC; 1

UC NewC; 3

UC Heading; 2 UC Heading; 5

UC Heading; 3

UC Heading; 3

UC Variable; 27 UC Variable; 27



UC Negation; 1 UC Negation; 1

0

10

20

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40

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70

NCVD-ACS RIKS-HIA NCVD-ACS RIKS-HIA NCVD-ACS RIKS-HIA

All (n=68) Coded (n=39) SNOMED CT coded (n=9)

Non-comparables

UC Negation

UC Variable

UC Heading

UC NewC

ATC+SCT

ICD

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PreC

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3.2 Qualitative results The qualitative results were based on the EDIs with matched codes, mismatched codes and non-comparable data items.

3.2.1 EDIs with matched codes Previous distribution results showed that all 19 EDIs with matched codes were assigned a pre-coordinated SNOMED CT expression. Three recurring themes were identified that contributes to consistent mapping results. These were: lexical match, meaningful inference, and use of background clinical knowledge.

Lexical match Both maps contained data items that match the lexicon of the assigned SNOMED CT description. Table 3.3 shows an example where the terms matched one of the descriptions in SNOMED CT. This makes it easier for mappers to search, select from the search results, and assign the code.

Table 3.2: Example of lexically matching terms between the values and SNOMED CT descriptions. NCVD-ACS RIKS-HIA Study ID 40 Data item & structure:

Clinical diagnosis at admission (heading)

Acute coronary syndrome stratum (variable)

STEMI (value)

Diagnos (heading) Infarkttyp (variable) STEMI (value)

Mapped to: 401314000|Acute ST segment elevation myocardial infarction (disorder)| Other descriptions: • akut hjärtinfarkt med ST-höjning • Acute ST segment elevation myocardial infarction • STEMI - ST elevation myocardial infarction

Theme: Lexical match Lexical match

Inferencing Where there were no lexical matches, inferencing was seen to derive the meaning for the data items. Inferences were made for data items with either limited or extensive information.

In Table 3.4, data values for Study ID 14 in both registries had very little information content (Yes and Ja), making it impossible to derive any meaning to assign an appropriate code. But by looking at the information provided along with the information structure, the heading and the variable, the intended meaning or information need can be understood. In these cases, both data values actually tried to capture the presence of past history of myocardial infarction in the patient.

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In Study ID 27, the NCVD-ACS data value had a long clinical expression providing extensive information. The mapper however made an inference that the actual information need was to capture the presence of ST segment elevation. This was deemed sufficient regardless the missing expression on the height of the elevation and the leads affected. It was identified that the disregarded information was already captured elsewhere in the NCVD-ACS registry form. A comment section in the map also noted that the additional information was only a definitional guide for clinical users when entering the data.

Background clinical knowledge Study ID 12 is an example of utilising background clinical knowledge to assign SNOMED CT code. Both mappers assigned |Diabetes mellitus (disorder)| by inferencing the variable, but had eliminated the heading which indicated the disease is a past medical history or tidigare sjukdomar. With medical knowledge, one understands that Diabetes mellitus is a continuous chronic disease, hence no “past” medical history. Furthermore, SNOMED CT does not have a clinical concept for “past history of Diabetes Mellitus”. This knowledge helped in determining the actual clinical meaning despite how the data item was structured in the registry.

Table 3.3: Example of inferencing and utilising medical knowledge to derive the meaning of data items and assign SNOMED CT expression. NCVD-ACS RIKS-HIA Study ID 14 Data item & structure:

Past medical history (heading) Myocardial infarction history

(variable) Yes (value)

Tidigare sjukdomar (heading) Tidigare hjärtinfarkt (variable) Ja (value)

Mapped to: 399211009|History of myocardial infarction (situation)

Theme: Inference, limited information Inference, limited information

Study ID 27 Data item & structure:

Electrocardiography (ECT) (heading) ECG abnormalities type (variable) ST segment elevation ≥1 mm

in ≥2 contiguous limb leads (value)

Beslutsgrundande EKG och ankomststatus (heading)

EKG STT (variable) ST-höjning (value)

Mapped to: 76388001|ST segment elevation (finding)|

Theme: Inference, extensive information Lexical match Study ID 12 Data item & structure:

Past medical history (heading) Diabetes (variable) Yes (value)

Tidigare sjukdomar (heading) Diabetis (variable) Ja (value)

Mapped to: 73211009 |Diabetes mellitus (disorder)|

Theme: Knowledge; Knowledge;

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Inference, limited information Inference, limited information

3.2.2 Mismatches Three themes emerged that contributed to 14 mismatched EDIs. Some mismatches had multiple overlapping themes. The first was contributed by complex data items found in the registries (8 EDIs). The second was contributed by the inadequate pre-coordinated SNOMED CT expressions to represent the variables (6 EDIs). The third was due to mistakes made by the mappers including one that violates SNOMED CT editorial guidelines (8 EDIs).

Complex data items in the registries Complex data items were seen when multiple information need were contained in a single data item. Table 3.5 showed an example where a single variable was represented by multiple questions (Study ID 33), and Table 3.6 showed an example where a single value was represented by multiple answers (Study ID 44). Both were found in NCVD-ACS registries. Since there was no pre-coordinated SNOMED CT expression to represent such clinical concept, the mappers attempted to post-coordinate.

Table 3.4: Example of a single variable represented by multiple questions with an attempt at post-coordinate it. Study ID 33

Note: “Peak CK-MB” can be broken into peak and CK-MB. NCVD-ACS Data item & structure:

Baseline investigation (heading) Peak CK-MB (variable)

Mapped to: 365768006 |Finding of cardiac enzyme levels (finding)|; 255587001 |Peak (qualifier value)|; 12016004 |Creatine kinase isoenzyme, MB fraction (substance)|

Theme: Compositional error; Choice of hierarchy

Table 3.5: Example of a single value represented by multiple answers. Study ID 44

Note: The data value “Not given-contraindicated” can be broken into medication was not given and fibrinolytic therapy was contraindicated as the reason.

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Limitation in SNOMED CT The mappers were challenged with inadequate pre-coordinated SNOMED CT concepts to represent certain variables. Variables or questions should be represented from either the Observable entity hierarchy or the Evaluation procedure under the Procedure hierarchy. Post-coordinating was then attempted by NCVD-ACS mappers, but this approach was further complicated by unsupported compositional grammar to link concepts from Observable entity/Evaluation procedure with other hierarchies (Study ID 33 in Table 3.6).

Mapping mistakes There were two editorial mistakes made by the mappers. The first mistake was using SNOMED CT unapproved concept arising from the Attribute hierarchy (Study ID 19, RIKS-HIA’s data value). This hierarchy was not suitable for any clinical data items, but is used to model SNOMED CT concepts. The second mistake was the inconsistent use of SNOMED CT hierarchy to represent its variables. Both maps used SNOMED CT concepts from the Observable entity and Clinical finding hierarchy interchangeably.

Mismatched with a degree of equivalence Even when mismatched occur in data items thought to have similar meanings, some degree of equivalence was found between the assigned SNOMED CT code. The degree of equivalence can be further categorized into sibling, subsumed, and defining concepts.

Sibling concepts are concepts that shared the same parent. An example is seen in Study ID 2 (Figure 3.4), where |Identification number (observable entity)| used by NCVD-ACS and |Patient-related identification code (observable entity)| used by RIKS-HIA shared the parent concept |Identification code (observable entity)|.

Subsumed concepts are concepts that are the child of another concept. For example, in Study ID 16 (Figure 3.5), |History of cerebrovascular accident) (situation)| used by RIKS-HIA is a child concept of |History of cerebrovascular disease (situation)| used by NCVD-ACS.

Figure 3.4: Hierarchical illustration that shows the shared parent concept |Identification code (observable entity)| for Study ID 2.

Figure 3.5: Hierarchical Illustration that shows the child-parent concept for Study ID 16.

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Defining concepts are concept that are used in another concept’s model. For example, in Study ID 24 (, |Blood pressure (observable entity)| used by NCVD-ACS was the defining concept for |Blood pressure finding (finding)| used by RIKS-HIA via |Interpret (attribute)|.

Figure 3.6: Schematic diagram of |Blood pressure finding (finding)| concept which was defined by |Blood pressure (observable entity).

Mismatched without a degree of equivalence This study also found concepts that were thought to have a degree of equivalence, but are not reflected in the SNOMED CT concept model. Study ID 46 identified that both registries tried to capture that thrombolysis was contraindicated in the patient. When the SNOMED CT concepts were compared, the hierarchical lineage was too far apart to be related to each other as seen in Figure 3.7. It is possible that the concept could have duplicated meanings.

Figure 3.7: Hierarchical comparison of |Thrombolytic agent not administered because contraindicated (situation)| used by NCVD-ACS and |Thrombolysis contraindicated (situation)| used by RIKS-HIA.

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Cultural or language difference Language difference has also affected the result of the mapping. Study ID 25 in Table 3.7 showed that Malaysia uses the term “Height” while Sweden uses “Längd/Length” as part of the body mass index (BMI) calculation. In terms of cultural usage, these terms may be synonymous. But since both terms have a corresponding SNOMED CT concept and code, it is possible that the mappers chose one with a lexical match rather than consider the equal translation used in another language. This unconscious choice and unawareness may have caused the mismatch.

Table 3.6: Example of a mismatch as a result of cultural or language differences. NCVD-ACS RIKS-HIA Study ID 25 Data item & structure:

Clinical presentation & examination (heading)

Anthropometric (variable) Height (variable)

Klinisk Bakgrund (heading) Längd (variable)

Mapped to: 50373000 |Body height measure (observable entity)|

248334005 |Length of body (observable entity)|

Theme: Cultural/language difference

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3.2.3 Non-comparables In the previous quantitative analysis (Figure 3.3), the study has shown the varying distribution of non-comparable EDIs. EDIs that fall into this category were due to either one or both data items with:

i. Different coding system; ii. Data items that were not included in the map (not mapped), and

therefore not coded; iii. Data items that were included in the map but were not assigned a code

(not coded); iv. Data items that were requested as a new SNOMED CT concept, and

therefore were not coded (new request)

The use of different coding system (i) and the cause of unmapped data items (ii) were described in section 3.1.2. This section looked into the reasons for uncoded data items (iii) and items requested as new concepts (iv) as seen in section 3.1.3.

Uncoded variables with a corresponding coded value Some of the headings and variables were not comparable because they were not coded, but its values were assigned a matching code. Table 3.8 shows an example in RIKS-HIA Study ID 8 & 9 were not comparable because of the non-coded heading and non-coded variable but in the end, have a coded value. This value was assigned a SNOMED CT concept and code that included information from the headings and variables. In other words, the headings and variables were translated as the context and was inferred in the value. A similar pattern can be seen in NCVD-ACS with an uncoded sub-variable, but have a coded value.

Table 3.7: An example that shows uncoded headings and variables with a corresponding coded value. NCVD-ACS RIKS-HIA Study ID 8 Data item & structure:

Status before event (heading) Past medical history (variable)

Tidigare sjukdomar (heading)

Mapped to: 417662000 |History of clinical finding in subject (situation)|

Uncoded


Status before event (heading) Past medical history (variable) Hypertension (variable)

Tidigare sjukdomar (heading) Hypertoni (variable)

Mapped to: Uncoded Uncoded


Status before event (heading) Past medical history (variable) Hypertension (variable) Yes (value)

Tidigare sjukdomar (heading) Hypertoni (variable) Ja (value)

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Mapped to: 38341003 |Hypertensive disorder, systemic arterial (disorder)|

Requests for a new SNOMED CT concept and code It was interesting to find four data items that were requested for a new SNOMED CT concept was paired with a coded data item. Two examples are shown in Table 3.9, each one from NCVD-ACS and RIKS-HIA.

Table 3.8: Example of new requests for SNOMED CT concept and code paired against coded data items. NCVD-ACS RIKS-HIA Study ID 51 Data item & structure:

Invasive therapeutic procedures (heading)

Did patient undergo cardiac catheterization on this admission (variable)

Yes (value)

Revaskularisering (heading) Akut cor angio utan åtgärd

(variable) Ja (value)

Mapped to: 41976001 |Cardiac catheterization (procedure)|

New concept requested


Baseline investigation (heading) Lipid profile (fasting) (variable) HDL-C (variable)

Laboratorieuppgifter (heading) HDL (variable)

Mapped to: New concept requested 28036006 |High density lipoprotein cholesterol measurement (procedure)|

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4. Discussion This study has shown the terminological similarities and differences in the Malaysian and Swedish acute coronary syndrome registry to SNOMED CT maps. The registries shared 101 equivalent data items (EDIs) consisting of headings, variables, and values. About half of the EDIs (42%) were mapped to SNOMED CT. Consecutively, only half of those EDIs (45%) had matching codes.

In return, the study induced the methods that contributed to the similarities, and identified the causes that contributed to the differences found. This study did not try to show if the data items were assigned the right code. Rather, it intends to understand how and why the maps differ.

4.1 Analysis of the findings Here, the implications and lessons learnt from the similarities and differences will be discussed.

4.1.1 Implications from the similarities The need to initiate a mapping guideline The methods that contributed to the terminological similarities were derived from the matching codes. Previous studies had reiterated the same methods. First, the use of mappers with domain clinical knowledge was strongly advocated by Wade & Rosenbloom (58). Second, inferencing based on the actual information need and contextual consideration was recommended by Lee et al. (45). To help with the context, Lee et al. and Andrews et al. (50) stressed in providing adequate information during the mapping process based on the information structure found in the forms or templates. This indicates that these methods should be strongly considered as a guideline for future mapping activities specifically for registry-to-SNOMED CT mapping.

What seemed lacking in previous studies was how lexical matching was done manually. Instead, there is more interest in describing lexical matching for developing an automated mapping (46) and SNOMED CT browsers (20,59). As lexical matching is a natural way to start in a manual mapping process, it may seem uncritical to explain or the results of this step are regarded to depend on the browsing tool. Nevertheless, the author believes that this step should not be underestimated because it may significantly prevent inconsistencies. Further research is encouraged to discover the optimal way to do lexical matching manually irrespective of the tool to achieve a more consistent result.

The choice of pre- or post-coordinated SNOMED CT expressions Both the Malaysian and Swedish team preferred pre-coordinated SNOMED CT expression over post-coordinated ones, which strengthens the findings in previous studies (45,46). This choice may be dependent on the skills of the mapping and technical teams, as well as the technology to support it. Pre-

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coordinated expressions would be an easier approach; whereas post-coordinated expressions require a higher SNOMED CT skill, along with a higher technological requirement to validate the grammar, store, retrieve and reuse the information for analysis and reporting.

On the other hand, post-coordination was suggested to give a more expressive power to the user and coverage to the clinical domain (60). Rosenbloom et al. suggests its usefulness for data entry, but also agree the greater risk for inaccuracy due to unnoticeable duplication of meaning. This can occur for it is possible to create the same clinical expression in different ways by different users, even though unintended. Data encoded with post-coordinated expression may differ algorithmically, and therefore cannot be merged to mean the same thing. Therefore, there must be some control measures both at SNOMED CT, mapping, and technology level to reduce this risk if post-coordination is used.

Furthermore, previous variability studies had also shown poorer semantic results when using post-coordination (46,50). This is not acceptable for clinical research nor patient registries which rely on achieving accurate information at the point of care to derive a high quality conclusion. It seems that pre-coordinated expression may be more suitable for research and reporting for now, but more work is needed to achieve the same target when using post-coordinated expressions.

4.1.2 Implications from the differences The differences found in this study were derived from the mismatches and non-comparable EDIs. These inconsistencies may be due to two reasons. It may be that the data items were not equivalent after all and had been defined wrongly by the author. Or worse, the inconsistencies may mean that the mapping is inaccurate.

Taking these possibilities into consideration, the study then looked at individual EDIs qualitatively to understand possible challenges faced by the mappers that would contribute to inconsistent mapping. The challenges can originate internally – the mappers themselves; from SNOMED CT – due to certain terminological limitations; and from the forms – due to complex data structures. Being aware of these challenges would help mappers to generate a more consistent mapping result.

The need to improve mapping activities Since similar-purposed registries often refers to international registry data standards, it is obvious that mapping the source would reduce mapping variability. Local mappers can then adopt these registry data standards equipped with SNOMED CT codes and expand the mapping according to local needs. Encouraging reusability and reproducibility at international level can save time

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and resources in the long run. Furthermore, this approach may be able to address the cultural/language issues that contributed to mismatches as seen in this study.

This top-down approach is a common way to introduce some control measure instead of producing maps at local or national level separately. However, this model of engagement may affect IHTSDO’s licensing structure where a fee is incurred when using SNOMED CT. On the other hand, it could also be an opportunity to advocate the wide-spread use of SNOMED CT. A probable solution would be making the international registry data standard mapped to SNOMED CT openly available, but licensing would be required only for those that implement SNOMED CT in their systems.

These maps should be shared in the open community for evaluations and further improvement. When mapping alone, mapping exercises may seem sufficient (50); but when the maps by different mappers were compared as in this study, inconsistencies were apparent. Instead of looking at who’s right or wrong, improvement means striving to reduce the inconsistencies. This study had also made an interesting discovery where one team had found an appropriate SNOMED CT concept and code while another team could not.

Benefits for shared mappings are many and was encouraged by Lee et al. (45). When maps are openly accessible, the community can address mapping mistakes through feedback and subsequently improve the mappers’ skills. The community can aid in finding appropriate concepts, thus reducing requests for new concepts, as well as reducing potential duplicated concepts created at international and national level. Moreover, the mapping process becomes more efficient by allowing others to adopt and adapt existing maps.

Some may also argue whether there is a need to have consistent mapping at international level. For local or national data exchange, it is possible that the inconsistent mapping results found in this study could be dismissed because the same mapping team would be used locally. This is a reasonable justification to a certain extent because each nation has different health policies and epidemiological distribution.

However, as the amount of mapping work increases, the team would eventually grow and the mapping work may become distributed. In the end, mapping results could still be inconsistent nationally. The fact is, the European Medical Agencies already realized the over-development of new registries and instead recommended reuse and expansion of existing registries to reduce duplication of data entry and data collection (2). SNOMED CT could facilitate this work by being a reference terminology, but the problem of inconsistency could probably be the same, if not worse, if mapping efforts continue to be done in silos.

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Improve SNOMED CT Mapping exercise is a good way to find gaps in SNOMED CT content and guidelines. Even though SNOMED CT is the most comprehensive terminology by far, it is not complete. Mappers may identify relevant clinical concepts that have not been represented in SNOMED CT. Possible duplication of a SNOMED CT concept can also be identified as evidenced in this study by a pair of mismatched EDI without a degree of equivalence.

In addition, requests for a new concept can be managed more effectively and efficiently when engaged with registry mappers. The current workflow is tedious but necessary due to the rigorous quality measures needed to avoid unambiguous and duplicated concepts. Local institutions need to submit to their national release centres for screening before being submitted to IHTSDO. To be added to the SNOMED CT international edition, the requested concept must be used by at least two or more member countries. Through registry mappers, IHTSDO would realize that the requested concept is used by more than a single member country, thus making the role of national release centres lighter. The new concept could then be released earlier for the benefit of registry owners and mappers.

Improve the registries The registries’ data structure has shown to affect the mapping results, as expected by Spisla & Lundberg (11). Complex data structures had resulted in attempts for post-coordination with limited success. In addition, the lack of common information structure also brings the question of when to map to SNOMED CT and where to bind the code. The scientific community has yet to find a definitive answer.

Registry owners who are aware of the issues described in this study could improve their method when developing a new registry. First of all, reusing existing international clinical data standards that has been mapped to SNOMED CT would be helpful. Secondly, collaborating with SNOMED CT mappers early in the development process is also encouraged. And lastly, simplifying the data structures is needed especially when pre-coordination is preferred. Overall, registry development should also include reusability and reproducibility for consideration.

Unfortunately, the approach for migrating from a legacy registry would be different. Registry owners may be hesitant to change the structure of their legacy registries based on ontological foundations (22,48). Once the data structure change, there is risk that the old data cannot be collated or compared with the new data. This is often done to generate trending reports which reflect years of healthcare improvement activities. The scientific community need to explore a

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better approach, or a solution for this before change can be accepted by registry owners with legacy systems.

Improve the use of SNOMED CT Using multiple terminology standards has been advocated as the way forward for semantic interoperability. There were indications to combine multiple coding system in a single SNOMED CT expression both at international and national level.

At international level, terminology-to-terminology mapping are being conducted and made available by different organisations such as the Unified Medical Language Systems (UMLS) (61) and IHTSDO (62). Gaps found in one terminology standard can then be filled by another. For example, LOINC may help in providing the codes for variables or questions missing in SNOMED CT’s Observable entity/Evaluation procedure hierarchy; and vice versa. Instead of developing new standards, concerted effort should focus on improving current standards with complementing rather than competing aims.

At national level, Malaysia and Sweden are working towards the same implementation strategy. Malaysia is building its capacity to use LOINC along with SNOMED CT and ICD10. Sweden is currently using an additional code system for laboratory investigations called Nomenclature for Properties and Units (NPU). These decisions will affect future mapping projects and revisions.

4.1.3 Summary of analysis It is evident that mappers play an important role to bridge the communication between clinical research societies who develop clinical ideas and IHTSDO who creates the clinical terminology. Engaging clinical researchers through mapping activities would improve SNOMED CT content based on current needs in various clinical domains. It allows better understanding on the practicality of using SNOMED CT for research. In addition, the research community would be encouraged to use standards that are implementable.

4.2 Recommendations Based on the analysis above, this study proposes 3 initial recommendations to reduce the mapping variability, summarized in Figure 4.1. The aim is for data reusability and reproducibility to support semantic interoperability when using patient registries.

I: Develop a specific mapping guideline for patient registries A guideline may reduce the varying approaches to map patient registries to SNOMED CT. It is obvious that variations in the method would lead to varying mapping results, thus preventing the aim for semantic interoperability and increasing resources and energy. The need is greater as more registries are being

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mapped to SNOMED CT. This guideline can also help those developing new registries. However, a guideline alone cannot guarantee consistent information.

II: Openly share maps Transparency invites community involvement and open evaluation. Local mapping skills, the resulting maps, and registries can be improved in this manner. Malaysia and Sweden needed more than a year to develop the internal skills to map to SNOMED CT, build relationships with clinical stakeholders, and produce the map separately. Yet, the registries they were working on had shared almost half of its data items. Furthermore, there were requests for new SNOMED CT concepts, but it was in fact possible to code as proven by the other team. These findings present an opportunity to minimize duplicated efforts for a reusable and reproducible maps.

III: Collaborate between clinical research and SNOMED CT community Reducing duplicated efforts calls for collaboration at a higher level with multidisciplinary action between clinical research societies and the SNOMED CT community. The benefits would be for all parties: SNOMED CT – by facilitating the development of SNOMED CT content and recognizing internationally used clinical concepts; IHTSDO and National release centres – by facilitating in requests management process; registry owner – by allowing their interest and implementation needs known; and especially the mappers – by maximizing their resources and skills.

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Figure 4.1: Summary of the findings, its implications, and the recommendations of this study.

4.3 Strengths and limitations Strengths This study conducted a content analysis of the maps for a more objective approach. Instead, an alternative method could have been an interview with the mappers. Interviews, however, may generate reporting bias where the intended approach rather than the actual approach is reported. Furthermore, interviews would not have been able to study specific areas of differences seen in the mismatches and non-comparable data items as this study had found. The chosen method had allowed the results to speak for themselves.

The chosen registries were also seen as good candidates for a comparative study. The result had shown similar distribution pattern of headings, variables, and values between the two registry forms. Future studies that intend to employ similar method may consider this as a criterion for choosing the materials for comparison.

In addition, the language difference adds an interesting perspective since SNOMED CT is supposed to be language neutral. Even though SNOMED CT is believed to have provided the concepts of “height” and “length” unambiguously, this study had shown a glimpse of how different languages and cultures may affect

I. Develop a specific mapping guideline for patient registries II. Openly share maps III. Collaborate between clinical research and SNOMED CT community

Recommendations

1. Need for a mapping guideline 2. Choosing pre- or post-coordination 3. Need to improve mapping activities 4. Need to improve the registries 5. Need to improve SNOMED CT 6. Need to improve the use of SNOMED CT

Implications

Similarities: • Distribution of headings, variables, and values

• Methods: lexical match, inferencing, clinical background knowledge

• Preference for pre-coordination Differences: • Mapping mistakes • Culture/language differences • Complex data item in the registries • Limited pre-coordinated concept to represent variables

• Limited way to post-coordinate using SNOMED CT compositional grammar

• Managing requests for new concept • Use of other coding system • Mismatches with and without a degree of equivalence

• Unmapped and uncoded issues

Findings

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semantic interoperability. To the author’s knowledge, there is no previous study that uses semiotic methods when conducting SNOMED CT translations when SNOMED CT has already been translated into four languages.

Limitations The study also had several limitations. To begin with, the qualitative study was only limited to the EDIs. The study may have missed issues specific to NCVD-ACS or RIKS-HIA. But since the aim was to find common ground, limiting to studying the EDI was a viable option. Peculiarities arising from specific registries must be handled locally; while this study focused on data items shared internationally through two IHTSDO member countries.

Also, the different browsing tools used to search SNOMED CT may partly contribute to the mismatches as noted by Chiang et al. (63). But regardless of the tool used, the author believes that mappers would need to navigate and become expert of their own tools. Instead of using a single tool, mappers should look into the system features and mapping techniques that would enable them to search the concept that they need. Therefore, training the mappers is essential.

Lastly, the author may be biased when pairing the data items as equivalent in meaning. The author was involved in the development of the Malaysian maps which may have interpreted the data items based on the Malaysian context, language, and culture. The author attempted to reduce this conscious bias by validating the EDIs with a Swedish domain expert.

4.4 Future directions/research The future direction will focus on reducing mapping variability to increase consistency and enable semantic interoperability. An immediate step would be to develop a mapping guideline for registries to SNOMED CT. This guideline should also address the issue of inclusion and exclusion of registry data items in the map; a general matching technique; and ways to bind the code.

This method may lead to future research on the effectiveness of using SNOMED CT as a reference terminology and an enabler for semantic interoperability between registries. In addition, another possibility is to compare the benefits of a conventional registry versus a SNOMED CT encoded patient registry. One reason for this is to explore the use of SNOMED CT’s ontological foundation in patient registries especially for analysis and reporting.

This study also raised a new question on how to manage the cultural differences when using a common terminology. When applying semiotic theory, the same clinical terms can be interpreted differently in different culture, or the same clinical concept can be called differently. This study has shown a glimpse of how the same concept uses different terms with its own distinct codes. Although this

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may not be an issue with the terminology standard in itself, it is a problem for data sharing, which mappers will need to address.

5. Conclusion This study has motivated on how reusing accurate information would need a consistent mapping result. However, variations were seen when comparing the maps that would jeopardize the effort towards semantic interoperability. Although there were common techniques that contributed to consistent mapping results such as lexical matching, inferencing, and use of mappers with clinical knowledge; the majority were differences that resulted in inconsistencies. It was apparent that the results were mostly dependent on the mappers.

Therefore, to assist the mappers and subsequently reduce the variations, the study recommended to: (i) develop a specific mapping guideline for patient registries; (ii) openly share maps to encourage reproducibility and reusability; and (iii) collaborate between the scientific research and SNOMED CT community. Future studies will focus on discovering and developing a mapping method aim to reduce the variations in similar-purposed registries. This can further lead to exploring the benefits of implementing SNOMED CT in registries, such as the analytical benefits of SNOMED CT encoded registry and registry-to-registry data transferability.

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Appendices

1a : NCVD-ACS registry

1b : RIKS-HIA registry

2 : Equivalent Data Items

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Appendix 1a: NCVD-ACS Registry Form

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Appendix 1b: RIKS-HIA Registry Form

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Appendix 2: Equivalent data items (EDIs)

NCVD-ACS RIKS-HIA

Study ID

Ref. No.

Data item type Term

Coding

Human readable expression

Rating code-code

Ref. No.

Data item type Term

Coding

Human readable expression

1 B

Data element Date of Admission

variable 9 A

Data element Ankomstdatum PreC

405799000 |Time of arrival at hospital (observable entity)|

2 1.3

Data element

Section 1: Demographics > Identification card number PreC

396278008 |Identification number (observable entity)| 0 B

Data element Patient ID PreC

422549004 |Patient-related Identification code (observable entity)|

3 2.0 Heading

Section 2: Status before event

heading 9 6.0

Heading Riskfaktorer

heading

4 2.1

Data element

Section 2: Status before event > Smoking status

variable 9 6.1

Data element Riskfaktorer > Rökning

variable

5 2.1.1 Data value

Section 2: Status before event > Smoking status > Never PreC

266919005 |Never smoked tobacco (finding)| 1 6.1.0

Data value

Riskfatorer > Rökning > Aldrig rökare PreC -"-

6 2.1.2 Data value

Section 2: Status before event > Smoking status > Former (quit more than 30 days) PreC

8517006 |Ex-smoker (finding)| 1 6.1.1

Data value

Riskfatorer > Rökning > Ex-rök more than 1 mån PreC -"-

7 2.1.3 Data value

Section 2: Status before event > Smoking status > Current (any tobacco within last 30 days) PreC

77176002 |Smoker (finding)| 1 6.1.2

Data value

Riskfatorer > Rökning > Rökare PreC -"-

8 2.3

Data element

Section 2: Status before event > Past medical history PreC

417662000 |History of clinical finding in subject (situation)| 9 8.0

Heading Tidigare sjukdomar

heading

9 2.3.b

Data element

Section 2: Status before event > Past medical history > Hypertension

variable 9 8.2

Data element

Tidigare sjukdomar > Hypertoni

variable

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10 2.3.b Data value

Section 2: Status before event > Past medical history > Hypertension > Yes PreC

38341003 |Hypertensive disorder, systemic arterial (disorder)| 1 8.2

Data value

Tidigare sjukdomar > Hypertoni > Ja PreC -"-

11 2.3.c

Data element

Section 2: Status before event > Past medical history > Diabetes

variable 9 8.1

Data element

Tidigare sjukdomar > Diabetes

variable

12 2.3.c Data value

Section 2: Status before event > Past medical history > Diabetes > Yes PreC

73211009 |Diabetes mellitus (disorder)| 1 8.1

Data value

Tidigare sjukdomar > Diabetes > Ja PreC -"-

13 2.3.e

Data element

Section 2: Status before event > Past medical history > Myocardial infarction history

variable 9 7.1

Data element

Tidigare sjukdomar > Tidigare hjärtinfarkt

variable

14 2.3.e Data value

Section 2: Status before event > Past medical history > Myocardial infarction history > Ja PreC

399211009 |History of myocardial infarction (situation)| 1 7.1

Data value

Tidigare sjukdomar > Tidigare hjärtinfarkt > Ja PreC -"-

15 2.3.l

Data element

Section 2: Status before event > Premorbid or past medical history > Cerebrovascular disease

variable 9 8.3

Data element

Tidigare sjukdomar > Tidigare stroke (ej TIA)

variable

16 2.3.l Data value

Section 2: Status before event > Premorbid or past medical history > Cerebrovascular disease > Yes PreC

308064009 |History of cerebrovascular disease (situation)| 0 8.3

Data value

Tidigare sjukdomar > Tidigare stroke (ej TIA) > Ja PreC

275526006 |History of cerebrovascular accident (situation)|

17 3.1.a

Data element

Section 3: Onset > Date of onset of ACS Symptoms PreC

298059007 |Date of onset (observable entity)| 0 3.2.1

Data element

Prehospitala uppgifter > Symtomdebut > Datum PreC

410671006 |Date (attribute)|

18 3.2.b

Data element

Section 3: Onset > Time of onset of ACS Symptoms PreC

405795006 |Time of symptom onset (observable entity)| 1 3.2.2

Data element

Prehospitala uppgifter > Symtomdebut, KI PreC

405795006 |Time of symptom onset (observable entity)|

19 3.2.a Data eleme

Section 3: Onset > Date patient presented PreC

406543005 |Date of visit (observable entity)| 9 4.1.1

Data eleme

Ankomstuppgifter > Ankomst till akuten,

newConcept

of 70

nt nt Datum

20 3.2.b

Data element

Section 3: Onset > Time patient presented PreC

441968004 |Time of patient arrival in healthcare facility (observable entity)| 9 4.1.2

Data element

Ankomstuppgifter > Ankomst till akuten, KI

newConcept

21 3.2

Data element

Section 3: Onset > Was patient transferred from another centre?

variable 9 1.0

Heading överflyttad patient

heading

22 3.3.2 Data value

Section 3: Onset > Was patient transferred from another centre > No

negation 9 1.1.0

Data value överflyttad från > Nej

negation

23 4.2

Data element

Section 3: Onset > Heart rate at presentation PreC

364075005 |Heart rate (observable entity)| 1 2.5

Data element

Beslutsgrundande EKG och ankomststatus > Hjärtfrekvens PreC -"-

24 4.3

Data element

Section 4: Clinical presentation & examination > Blood pressure at presentation PreC

75367002 |Blood pressure (observable entity)| 0 2.6

Data element

Beslutsgrundande EKG och ankomststatus > Blodtryck (Syst/Diast) PreC

392570002 |Blood pressure finding (finding)|

25 4.4.a

Data element

Section 4: Clinical presentation & examination > Anthropometric > Height PreC

50373000 |Body height measure (observable entity)| 0 5.1

Data element

Klinisk bakgrund > Längd PreC

248334005 |Length of body (observable entity)|

26 4.4.b

Data element

Section 4: Clinical presentation & Examination > Anthropometric > Weight PreC

27113001 |Body weight (observable entity)| 1 5.2

Data element Klinisk bakgrund > Vikt PreC -"-

27 5.1.1 Data value

Section 5: Electrocardiography (ECT) > ECG abnormalities type > ST segment elevation ≥1 mm in ≥2 contiguous limb leads PreC

76388001 |ST segment elevation (finding)| 1 2.4.2

Data value

Beslutsgrundande EKG och ankomststatus > EKG STT > ST-höjning PreC -"-

28 5.1.2 Data value

Section 5: Electrocardiography (ECT) > ECG abnormalities type PreC

76388001 |ST segment elevation (finding)| 1 2.4.2

Data value

Beslutsgrundande EKG och ankomststatus > EKG STT > ST-höjning PreC -"-

of 70

> ST segment elevation ≥1 mm in ≥2 contiguous frontal/chest leads

29 5.1.3 Data value

Section 5: Electrocardiography (ECT) > ECG abnormalities type > ST segment depression ≥0.5 mm in ≥2 contiguous leads PreC

26141007 |ST segment depression (finding)| 1 2.4.3

Data value

Beslutsgrundande EKG och ankomststatus > EKG STT > ST-sänkning PreC -"-

30 5.1.4 Data value

Section 5: Electrocardiography (ECT) > ECG abnormalities type > T-wave inversion PreC

59931005 |Inverted T wave (finding)| 1 2.4.4

Data value

Beslutsgrundande EKG och ankomststatus > EKG STT > Patol. T-neg PreC -"-

31 ** Data value

Section 5: Electrocardiography (ECT) > ECG abnormalities type > Left bundle branch block PreC

164909002 |Electrocardiographic left bundle branch block (finding)| 1 2.2.3

Data value

Beslutsgrundande EKG och ankomststatus > EKG QRS > Vänstergrenblock PreC -"-

32 ** Data value

Section 5: Electrocardiography (ECT) > ECG abnormalities type > Right bundle branch block PreC

164907000 |Electrocardiographic right bundle branch block (finding)| 1 2.2.5

Data value

Beslutsgrundande EKG och ankomststatus > EKG QRS > Högergrenblock PreC -"-

33 6.1

Data element

Section 6: Baseline investigations > Peak CK-MB

PostC

365768006 |Finding of cardiac enzyme levels (finding)| 255587001 |Peak (qualifier value)| 12016004 |Creatine kinase isoenzyme, MB fraction (substance)| 0 13.1.3

Data element

Laboratorieuppgifter > Infarktmarkör > CKMB PreC

104613001 |Creatine kinase MB measurement (procedure)|

34 6.3.a

Data element

Section 6: Baseline investigations > Peak troponin > TnT

PostC

365768006 |Finding of cardiac enzyme levels (finding)|255587001 |Peak (qualifier value)|102682001 0 13.1.1

Data element

Laboratorieuppgifter > Infarktmarkör > Troponin T PreC

121871002 |Troponin T measurement (procedure)|

of 70

|Troponin T (substance)|

35 6.3.b

Data element

Section 6: Baseline investigations > Peak troponin > TnI

PostC

365768006 |Finding of cardiac enzyme levels (finding)| 255587001 |Peak (qualifier value)| 102683006 |Troponin I (substance)| 0 13.1.2

Data element

Laboratorieuppgifter > Infarktmarkör > Trop. I PreC

121870001 |Troponin I measurement (procedure)|

36 6.4

Data element

Section 6: Baseline investigations > Lipid profile (fasting) > Total cholesterol PreC

365793008 |Finding of cholesterol level (finding)| 0 13.3

Data element

Laboratorieuppgifter > Kolesterol PreC

121868005 |Total cholesterol measurement (procedure)|

37 6.4.b

Data element

Section 6: Baseline investigation > Lipid profile (fasting) > HDL-C

newConcept 9 13.5

Data element

Laboratorieuppgifter > HDL PreC

28036006 |High density lipoprotein cholesterol measurement (procedure)|

38 6.5

Data element

Section 6: Baseline investigations > Fasting blood glucose

PostC

365812005 |Finding of blood glucose level (finding)| 47429007 |Associated with (attribute)| 16985007 |Fasting (finding)| 0 13.8

Data element

Laboratorieuppgifter > P-Glucos PreC

33747003 |Glucose measurement, blood (procedure)|

39 6.6

Data element

Section 6: Baseline investigation > Left ventricular ejection fraction PreC

250908004 |Left ventricular ejection fraction (observable entity)| 9 12.4

Data element

Utrednignar och bhandlingar > Vänsterkammarfunktion (LVEF)

variable

40 7.1.1 Data value

Section 7: Clinical diagnosis at admission > Acute coronary syndrom stratum > STEMI PreC

401303003 |Acute ST segment elevation myocardial infarction (disorder)| 1 20.1.1

Data value

Diagnos > Infarkttyp > STEMI PreC -"-

41 7.1.2 Data value

Section 7: Clinical diagnosis at admission > Acute coronary syndrom PreC

401314000 |Acute non-ST segment elevation myocardial infarction 1 20.1.2

Data value

Diagnos > Infarkttyp > NSTEMI PreC -"-

of 70

stratum > NSTEMI (disorder)|

42 8.0 Heading

Section 8: Fibrinolytic therapy

heading 9 10.2

Data element

Revaskularisering > Trombolys

variable

43 8.1.1 Data value

Section 8: Fibrinolytic therapy > Fribrinolytic therapy status > Given at this centre

PostC

414051007 |Drug administration observations (finding)| 278307001 |On admission (qualifier value)| 0 10.1.1

Data value

Revaskularisering > Reprefusionsbehandling > Trombolys PreC

32912007 |Thrombolysis of coronary artery by intravenous infusion (procedure)|

44 8.1.3 Data value

Section 8: Fibrinolytic therapy > Fribrinolytic therapy status > Not given - proceeded directly to primary angioplasty

PostC

371900001 |Medication not administered (situation)|41339005 |Coronary angioplasty (procedure)| 0 10.1.2

Data value

Revaskularisering > Reprefusionsbehandling > Primär PCI PreC

415070008 |Percutaneous coronary intervention (procedure)|

45 9.2.1.a.1.1

Data value

Section 9: Invasive therapeutic procedures > Did patient undergo cardiac catheterization on this admission at your centre > Yes PreC

414089002 |Emergency percutaneous coronary intervention (procedure)| 0 10.1.2

Data value

Revaskularisering > Reprefusionsbehandling > Primär PCI PreC

415070008 |Percutaneous coronary intervention (procedure)|

46 8.1.6 Data value

Section 8: Fibrinolytic therapy > Fribrinolytic therapy status > Not given - contraindicated PreC

373148008 |Thrombolytic agent not administered because contraindicated (situation)| 0 10.3

Data value

Revaskularisering > Trombolys kontrindikation > Ja PreC

390910005 |Thrombolysis contraindicated (situation)|

47 8.2.1 Data value

Section 8: Fibrinolytic therapy > Fibrinolytic therapy drug used > Streptokinase PreC

319810009 |Streptokinase (product)| 9 10.2.1

Data value

Revaskularisering > Trombolys > Streptokinas ATC

48 8.2.2 Data value

Section 8: Fibrinolytic therapy > Fibrinolytic therapy drug used > Others (t-PA) PreC

27638005 |Alteplase (product)| 9 10.2.2

Data value

Revaskularisering > Trombolys > Altilyse ATC

49 ** Data Section 8: Fibrinolytic PreC 108998004 |Reteplase 9 10.2.3 Data Revaskularisering > ATC

of 70

value therapy > Fibrinolytic therapy drug used > Others (r-PA)

(product)| value Trombolys > Rapilysin

50 ** Data value

Section 8: Fibrinolytic therapy > Fibrinolytic therapy drug used > Others (TNK t-PA) PreC

127967007 |Tenecteplase (product)| 9 10.2.4

Data value

Revaskularisering > Trombolys > Metalyse ATC

51 9.1 Data value

Section 9: Invasive therapeutic procedures > Did patient undergo Cardiac catheterization on this admission > Yes PreC

41976001 |Cardiac catheterization (procedure)| 9 10.1.4

Data value

Revaskularisering > Reperfusionbehandling > Akut cor angio utan åtgärd

newConcept

52 9.9 Data value

Section 9: Invasive therapeutic procedures > Did patient undergo CABG on this admission > Yes PreC

414088005 |Emergency coronary artery bypass graft (procedure)| 1 10.1.3

Data value

Revaskularisering > Reperfusionsbehandling > Akut CABG PreC

53 10.a

Data element

Section 10: Pharmacological therapy > Given pre admission PreC

281379000 |Pre-admission (qualifier value)| 9 9.0

Heading

Medicin vid ankomsten

heading

54 10.c

Data element

Section 10: Pharmacological therapy > Given after discharge PreC

307169000 |Post admission (qualifier value)| 9 16.0

Heading

Mediciner vid utskrivningen

heading

55 10.a.1

Data element

Section 10: Pharmacological therapy > Given pre admission > ASA

variable 9 9.4

Data element

Medicin vid ankomsten > ASA

variable

56 10.a.1.1

Data value

Section 10: Pharmacological therapy > Given pre admission > ASA > Yes PreC

7947003 |Aspirin (product)| 9 9.4

Data value

Medicin vid ankomsten > ASA > Ja ATC

57 10.c.1

Data element

Section 10: Pharmacological therapy > Given after discharge > ASA

variable 9 9.4

Data element

Mediciner vid utskrivningen > ASA

variable

58 10.c.1.1

Data value

Section 10: Pharmacological therapy > PreC

7947003 |Aspirin (product)| 9 16.4

Data value

Mediciner vid utskrivningen > ASA > ATC

of 70

Given after discharge > ASA > Yes

Ja

59 ** Data value

Section 10: Pharmacological therapy > Given pre admission > ADP antagonist > Clopidogrel > Yes PreC

108979001 |Clopidogrel (product)| 9 9.5.1

Data value

Medicin vid ankomsten > Clopidogrel (Plavix) > Ja ATC

60 ** Data value

Section 10: Pharmacological therapy > Given after discharge > ADP antagonist > Clopidogrel > Yes PreC

108979001 |Clopidogrel (product)| 9 16.5.1

Data value

Mediciner cvid utskrivningen > Clopidogrel (Plavix) > Ja ATC

61 ** Data value

Section 10: Pharmacoogical therapy > Given pre admission > ADP antagonist > Ticagrelor

newConcept 9 9.5.4

Data value

Medicin vid ankomsten > Ticagrelor (Brilique) > Ja ATC

62 ** Data value

Section 10: Pharmacoogical therapy > Given after discharge > ADP antagonist > Ticagrelor > Yes

newConcept 9 16.5.4

Data value

Mediciner cvid utskrivningen > Ticagrelor (Brilique) > Ja ATC

63 10.4 Data value

Section 10: Pharmacological therapy > Given pre admission > Unfractionated heparin > Yes

PostC

84812008 |Heparin (product)|; 127489000 |Has active ingredient (attribute)|; 96382006 |Unfractionated heparin (substance)| 9 11.1.1

Data value

Medicinering > iv/sc antikagulantia > IV heparin

ATC + SCT

431215000 |Administration of substance via intravenous route (procedure)| + ATC code

64 10.a.6

Data element

Section 10: Pharmacological therapy > Given pre admission > Beta-blocker

variable 9 9.7

Data element

Medicin vid ankomsten > Betablockerare

variable

65 10.a.6.1

Data value


33252009 |beta-Blocking agent 9 9.7

Data value

Medicin vid ankomsten > ATC

of 70

Given pre admission > Beta-blocker > Yes

(product)| Betablockerare > Ja

66 10.c.6

Data element

Section 10: Pharmacological therapy > Given after discharge > Beta-blocker

variable 9 16.7.1

Data element

Mediciner cvid utskrivningen > Betablockerare

variable

67 10.c.6.1

Data value

Section 10: Pharmacological therapy > Given after discharge > Beta-blocker > Yes PreC

33252009 |beta-Blocking agent (product)| 9 16.7.1

Data value

Mediciner cvid utskrivningen > Betablockerare > Ja ATC

68 10.a.7

Data Element

Section 10: Pharmacological therapy > Given pre admission > ACE inhibitor

variable 9 9.1

Data element

Medicin vid ankomsten > ACE-hämmare

variable

69 10.a.7.1

Data value

Section 10: Pharmacological therapy > Given pre admission > ACE inhibitor > Yes PreC

41549009 |Angiotensin-converting enzyme inhibitor agent (product)| 9 9.1

Data value

Medicin vid ankomsten > ACE-hämmare > Ja ATC

70 10.c.7

Data element

Section 10: Pharmacological therapy > Given after admission > ACE inhibitor

variable 9 16.1.1

Data element

Mediciner cvid utskrivningen > ACE-hämmare

variable

71 10.c.7.1

Data value

Section 10: Pharmacological therapy > Given after admission > ACE inhibitor > Yes PreC

41549009 |Angiotensin-converting enzyme inhibitor agent (product)| 9 16.1.1

Data value

Mediciner cvid utskrivningen > ACE-hämmare > Ja ATC

72 10.a.8

Data element

Section 10: Pharmacological therapy > Given pre admission > Angiotensin II receptor blocker

variable 9 9.2

Data element

Medicin vid ankomsten > A2-blockerare

variable

73 10.a.8.1

Data value

Section 10: Pharmacological therapy > Given pre admission > Angiotensin II receptor PreC

96308008 |Angiotensin II receptor antagonist (product)| 9 9.2

Data value

Medicin vid ankomsten > A2-blockerare > Ja ATC

of 70

blocker > Yes

74 10.c.8

Data element

Section 10: Pharmacological therapy > Given after discharge > Angiotensin II receptor blocker

variable 9 16.2.1

Data element

Mediciner vid utskrivningen > A2-blockerare

variable

75 10.c.8.1

Data value

Section 10: Pharmacological therapy > Given after discharge > Angiotensin II receptor blocker > Yes PreC

96308008 |Angiotensin II receptor antagonist (product)| 9 16.2.1

Data value

Mediciner vid utskrivningen > A2-blockerare > Ja ATC

76 10.a.9

Data element

Section 10: Pharmacological therapy > Given pre admission > Statin

variable 9 9.13

Data element

Medicin vid ankomsten > Statiner

variable

77 10.a.9.1

Data value

Section 10: Pharmacological therapy > Given pre admission > Statin > Yes PreC

96302009 |Hydroxymethylglutaryl-coenzyme A reductase inhibitor (product)| 9 9.13

Data value

Medicin vid ankomsten > Statiner > Ja ATC

78 10.c.9.1

Data element

Section 10: Pharmacological therapy > Given after discharge > Statin

variable 9

16.14.1

Data element

Mediciner vid utskrivningen > Statiner

variable

79 10.c.9.1

Data value

Section 10: Pharmacological therapy > Given after discharge > Statin > Yes PreC

96302009 |Hydroxymethylglutaryl-coenzyme A reductase inhibitor (product)| 9

16.14.1

Data value

Mediciner vid utskrivningen > Statiner > Ja ATC

80 10.a.10.1

Data element

Section 10: Pharmacological therapy > Given pre admission > Other lipid lowering agent

variable 9 16.15

Data element

Mediciner vid utskrivningen > övriga lipid sänkare

variable

81 10.10 Data value

Section 10: Pharmacological therapy > Given after discharge > Other lipid lowering agent

variable 9 16.15

Data element

Medicin vid ankomsten > övriga lipid sänkare

variable

of 70

82 ** Data value

Section 10: Pharmacological therapy > X (added in mapping) PreC

83750004 |Bile acid sequestrant antilipemic agent (product)| 9

16.15.1

Data value

Mediciner vid utskrivningen > Ezetrol ATC

83 ** Data value

Section 10: Pharmacological therapy > X (added in mapping) PreC

108602006 |Fibrate antihyperlipidemic (product)| 9

16.15.2

Data value

Mediciner vid utskrivningen > Fibrater ATC

84 10.a.11

Data element

Section 10: Pharmacological therapy > Given pre admission > Diuretics

variable 9 9.11

Data element

Medicin vid ankomsten > Diuretika

variable

85 10.a.11.1

Data value

Section 10: Pharmacological therapy > Given pre admission > Diuretics > Yes PreC

30492008 |Diuretic (product)| 9 9.11

Data value

Medicin vid ankomsten > Diuretika > Ja ATC

86 10.c.1

Data element

Section 10: Pharmacoogical therapy > Given after discharge > Diuretics

variable 9 16.12

Data element

Mediciner vid utskrivningen > Diuretika

variable

87 10.c.1.1

Data value

Section 10: Pharmacoogical therapy > Given after discharge > Diuretics > Yes PreC

30492008 |Diuretic (product)| 9 16.12

Data value

Mediciner vid utskrivningen > Diuretika > Ja ATC

88 10.a.12

Data element

Section 10: Pharmacological therapy > Given pre admission > Calcium antagonist

variable 9 9.7

Data element

Medicin vid ankomsten > Ca-hämmare

variable

89 10.a.12.1

Data value

Section 10: Pharmacological therapy > Given pre admission > Calcium antagonist > Yes PreC

48698004 |Calcium channel blocking agent (product)| 9 9.7

Data value

Medicin vid ankomsten > Ca-hämmare > Ja ATC

90 10.c.12.1

Data element

Section 10: Pharmacological therapy > Given after discharge > Calcium antagonist

variable 9 16.8

Data element

Mediciner vid utskrivningen > Ca-hämmare

variable

91 10.c.1 Data Section 10: PreC 48698004 |Calcium 9 16.8.1 Data Mediciner vid ATC

of 70

2.1 value Pharmacological therapy > Given after discharge > Calcium antagonist > Yes

channel blocking agent (product)|

value utskrivningen > Ca-hämmare > Ja

92 10.a.13

Data element

Section 10: Pharmacological therapy > Given pre admission > Oral hypoglycaemic agent

variable 9 9.9

Data element

Medicin vid ankomsten > Diabetesbeh. Per oral

variable

93 10.a.13.1

Data value

Section 10: Pharmacological therapy > Given pre admission > Oral hypoglycaemic agent > Yes PreC

346597008 |Oral hypoglycemic (product)| 9 9.9

Data value

Medicin vid ankomsten > Diabetesbeh. Per oral > Tabletbeh ATC

94 10.c.13

Data element

Section 10: Pharmacological therapy > Given after discharge > Oral hypoglycaemic agent

variable 9 16.10

Data element

Mediciner cvid utskrivningen > ASA > Diabetesbeh. Per oral

variable

95 10.c.13.1

Data value

Section 10: Pharmacological therapy > Given after discharge > Oral hypoglycaemic agent > Yes PreC

346597008 |Oral hypoglycemic (product)| 9 16.10

Data value

Mediciner cvid utskrivningen > ASA > Diabetesbeh. Per oral > Tabletbeh ATC

96 10.a.14

Data element

Section 10: Pharmacoogical therapy > Given pre admission > Insulin

variable 9 9.8

Data element

Medicin vid ankomsten > Diabetesbehandling insulin

variable

97 10.a.14.1

Data value

Section 10: Pharmacoogical therapy > Given pre admission > Insulin > Yes PreC

39487003 |Insulin product (product)| 9 9.8

Data value

Medicin vid ankomsten > Diabetesbehandling insulin > Ja ATC

98 10.c.14

Data element

Section 10: Pharmacological therapy > Given after discharge > Insulin

variable 9 16.9

Data element

Mediciner cvid utskrivningen > ASA > Diabetesbehandling insulin

variable

99 10.c.14.1

Data value


39487003 |Insulin product (product)| 9 16.9

Data value

Mediciner cvid utskrivningen > ASA > ATC

of 70

Given after discharge > Insulin > Yes

Diabetesbehandling insulin > Ja

100

11.2.1.a

Data element

Section 11: In-hospital clinical outcomes > Outcome > Discharged > Date PreC

442864001 |Date of discharge (observable entity)| 1 21.0

Data element

Utskrivning > Utskrivningsdatum PreC -"-

101 11.3

Data element

Section 11: In-hospital clinical outcomes > Final diagnosis at discharge PreC

89100005 | Final diagnosis (discharge) (contextual qualifier)(qualifier value)| 9 20.0

Data element Diagnos > Diagnos 1-5

ICD10

0 = Mismatch; 1 = Matching; 9 = Non-comparable ** = Additional concept found/added in mapping, not found in the registry -“- = The same SNOMED CT expression and code assigned

Cardiology registries to SNOMED CT mapping: A comparative ... · penyejuk hati (comfort), and my ....

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Transcript of Cardiology registries to SNOMED CT mapping: A comparative ... · penyejuk hati (comfort), and my ....