ESTRUCTURA PROGRAMÁTICAS DIMENSIÓN: Económico Productiva

Despite the important findings that emerged in this research, the author identified a number of limitations, in particular, relating to the research being based on a single case study i.e. the epilepsy clinic, data collection, research methodology employed and the evaluation study outlined in chapter 5.

The study was based on a single case study and the author observed and evaluated only one service within the epilepsy programme, the epilepsy clinic. The findings would surely be different if the study had taken place at an epilepsy service in a different hospital, or if the study was based on a different chronic disease. The author recognises that the

sociotechnical requirements, system design and deployment issues would differ from those that emerged in this case study. Further studies comparing a different type of setting, end- users and chronic disease could provide valuable insights. As elucidated in the

generalisability discussion, it would be useful to examine how some of the findings identified in part one could be applied to an off-the shelf EPR product which can be customised which is a much more likely scenario than an in-house solution.

Data collection for the research question part one, sociotechnical design and deployment of the epilepsy EPR, was gathered through feedback meetings, workshops, semi-structured interviews, informal conversations, and participant observation. The author validated

interviews and observations that were captured in very busy interview and fieldwork settings through follow-up meetings and informal conversations. In hindsight, it might have been

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advisable to record the interviews (but in terms of observations this could have complicated the issue of obtaining consent with patients). Although the methods listed above are based on one case study, the author drew on established literature to confirm findings.

The data collection period was relatively short in the clinics. For example, for the evaluation study fieldwork was carried out over 4 hours attending the clinic over 18 weeks with

approximately half an hour spent per patient. However the author had spent considerable time carrying out preliminary research in the form of an MSc in this area (see chapter 4). Ideally, the researcher would have liked to return to the case study setting to validate the findings of future enhancements to the EPR.

As highlighted in chapter 1, 2 and 3, healthcare systems and eHealth systems such as EPRs are highly complex and it was not possible to address all aspects of the field of health information that relates to EPRs using a single case study. For example, the author could not cover all issues relating to EPRs and Health Information standards that are needed to successfully deploy EPRs including, for example, Individual Health Identifier’s (particularly relevant in the Irish context), and information governance, etc. In addition, due to the limited scope of a PhD study, the author could only briefly discuss several topics that influence interoperability standards. It was not possible although interesting, to cover in detail essential topics that are required to enable semantic interoperability such as clinical coding.

8.5.1 Limitations of the evaluation study (chapter 5)

There are a number of limitations associated with the evaluation study as outlined in chapter 5. There was a small sample of participants (2 doctors and 2 nurses) for the deployment study and it cannot be claimed that this sample is representative of a larger population.

There was bias in the study as convenience sampling was used. Participants were able to select the patient encounters during which they would use the AED module. This was because organisational practice caused some difficulty in recruiting subjects e.g. first time patients. A patient must have a diagnosis of epilepsy to qualify for entry to the epilepsy EPR. Not all first time patients were considered eligible for the study as they may not have had a formal diagnosis of epilepsy at their first visit or were considered “query” epilepsy following consultation. Hence, patient inclusion was finalised on the day of clinic.

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It is also recognised that entering medication data which is highly structured and can be facilitated by modestly sized drop-down lists is less of a challenge than, for example, entering clinical symptoms or a detailed epilepsy history.

Before the AED module was deployed in this study, UAT was conducted and end-users tested the AED module with test data based on clinical scenarios and they verified that the application worked as intended. This approach was conducive to the concept that

requirements are clarified using working prototypes in clinical routine practice(233) _and

conforms to the concept of “in-use design”.(234) _{Nevertheless, it is recognised that a formal}

usability laboratory could have added benefit.

Given the sociotechnical nature of the design and deployment of the AED module of the epilepsy EPR, it should be noted that the participants in this study were also the domain experts who participated in the design phase of the epilepsy EPR which presents another source of bias. This meant that participants were already enthusiastic and engaged end- users by the time they were involved in the deployment stage which may not be

representative of future users. However, as it was a small team that delivered the epilepsy service, it was not feasible to separate a subset of clinicians to inform the design from those who would use the live system.

Results of our assessment of the EPR deployment were categorised into three themes, namely – human, organisational and technological. However, the boundaries of these themes were sometimes blurred so that a result could equally be classified under human behaviour or organisation workflow.

8.5.2 Limitations of mapping

The HL7 CDA standard is well documented in the literature particularly around the creation, implementation and storage of CDA clinical documents but there are few (if any) published research of mapping from existing relational databases to the HL7 CDA RMIM or HL7 CDA implementation guides. However, the author did draw on literature that was available in relation to the HL7 RIM and mapping as there were some aspects that could overlap. For example, the HL7 CDA model is a subset of the HL7 CDA RIM and the literature around schema matching in the context of HL7 RIM to relational database was useful although the

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direction of mapping was different. For example, Umer et al. (2010) have contributed to the area of mapping and have proposed the development of an automation tool for HL7 RIM-to- Relational database mapping and also recognise that it is also necessary to perform mapping manually given the intricate nature of the HL7 RIM.

Two key issues emerged from the literature regarding the RMIM, mainly the complexity of the RMIM model which required investing significant work effort to understand and interpret the RMIM model accurately; and secondly that mapping between a complex model such as the RMIM and a relational database requires both technical and clinical domain expertise.

This case study involved exporting an epilepsy discharge summary document from secondary to primary care. However, the author acknowledges that it would have been useful to use a second document such as a referral document to further validate the

mapping methodology. It is possible that the medications section of the discharge summary made certain things easier or totally avoided some issues that a different choice might have encountered.

Other limitations included the fact that the mapping was performed manually although the author would argue that this was suitable given that the mapping was based on one use case and mapping was not conducted across multiple databases. However, it must be recognised that manually mapping is time consuming and can be error-prone. Another barrier to conducting the mapping was that the lack of a data dictionary for the epilepsy data requirements which meant that more discussions had to be held with end-users in order to confirm data definitions.