In this section we analyse healthcare collections in Ireland with a focus on demographic data, as it is the most well represented data category, with 28 of the 75 catalogues explicitly mentioning they cover demographic data, the largest of any specific category. The consistent representation of demographic data is therefore essential to support interoperability between health services.
The analysis was done based upon the Health Information and Quality Authority (HIQA) Catalogue of national health and social care data collections . HIQA is an independent authority established to drive high-quality and safe care for people using health and social care services in Ireland. HIQA’s role is to develop standards, inspect and review these services and support informed decisions on how services are delivered. Towards this goal, HIQA has published the “Catalogue of national health and social care data collections” (version 3). The aim of this third version of the Catalogue is to enable all stakeholders (including the general public, patients and service users, clinicians, researchers, and healthcare providers) to readily access information about health and social care data collections in Ireland. The catalogue consists of a comprehensive list of national health and social care data collections. These are national repositories of routinely collected health and social care data in the Republic of Ireland.
The catalogue lists 75 of these collections, and for each collection provides data in terms of title, managing organisation, description/summary, data providers, available data dictionaries, data content (i.e. a breakdown of the type of data collected) etc. In order to structure the analysis, HIQAs National standard demographic dataset and guidance for use in health and social care settings in Ireland was used . This provides guidelines on a set of concepts and properties for describing demographic data, such as related to name, date of birth, contact details, address, etc. (see Fig. 2).
4.1 Methodology for Analysis of Health Catalogue
The methodology consisted of three main phases. In the first phase the data dictionaries given in the data dictionary field were analysed. The second phase the different named concepts were extracted from the data field. The third phase consisted of a harmonisation process, to identify a set of classifications for the different concepts identified.
4.2 Results of Analysis
The analysis began with the collections with associated data dictionaries, 39 had “no”, “not available”, or “not available online”. Of the remaining 36, 15 provided links (such as www.noca.ie) with no obvious way to access the data dictionary or required a password, 3 had broken links, 5 mentioned resources that could not be located (e.g. Under revision as part of HRB LINK project) and so these were discounted. The remaining 13 data dictionaries were mostly pdf documents, such as the Ambulatory Care Report (ACR), Cardiac First Responder (CFR), Patient Care Report (PCR), Patient Treatment Register (PTR) standards, as well as EUROCATFootnote 9, and heartwatch. The Irish Mental Health Care provides an excel file.
Secondly all 75 collections “data content” field was analysed. Typical examples of this type of data (without a corresponding data dictionary) is “Name, address, date of birth, gender, District Electoral Division (DED), HSE area, Local Health Office (LHO) area, task force area, date commenced on methadone, type of methadone treatment, prescribing doctor, dispensing clinic, date and reason for discontinuation of methadone, client photograph and client signature.”, although the range of data concepts covered is highly varied, reflecting the nature of the health services. From this analysis a matrix of collections against listed data concepts was created (such as name, HSE area, so on) and a tick was given for a data concept if it is present in a collection. Due to the wide range of data concepts, a process of harmonisation took place to identify classes for data concepts, either taken the name of the data concept directly, i.e. “Name”, or deriving an appropriate class for a set of data concepts, e.g. “Patient” and “Person”, based on our analysis of openEHR and FHIR, and also the HIQA schema.
Figure 3 gives the count for each concept explicitly referenced in the data content field, with gender and date of birth (dob) being the most represented. It should be noted that it is expected that more data related to the person class is included in the data collections, as often they refer to “demographic data, for example...” listing then one or two examples of the type of demographic data (this explains the high number of gender and dob). Figure 4 shows number of properties for both Name and Contact Details, both are classes directly related to Person. There are three Object Properties (associations) that relate Person to these classes.
Through this process over 50 potential classes have been identified within the Irish health domain, ranging from (not an exhaustive list): person, name, contact details, patient, patient infant, patient pregnant, disabled person, address, location, medical/clinical information/assessment, treatment, therapy, prescription, observations, test, results, diagnosis, event, incident, injury, death, paediatric mortality, service, procedure, operation, product, device, vehicle,vaccination/ immunisations, disease, infection, staff, practitioner, admission, child admission unit, legal status, approved centre, etc. Each class has two or more data concepts taken from the analysis.
4.3 Demographic Data Alignments with Standards
Table 1 gives a very high level overview of some of the data concepts and properties identified in the above standards with respect to demographics. As can be seen, the 13606 demographics package includes concepts such as Person, Postal Address, gender, name, gender and birth time. It does not explicitly have a concept Patient, although it does have an “entity role relationship”, so the potential exists to model a patient relationship between person entities in a similar fashion as OpenEHRs demographic information model, in which a Patient can be defined using the “Party_Relationship” held between a Person and an Organisation. The OpenEHR Person concept is more detailed than 13606-1 allowing a person to have a contact, and also a “Party_Identity” which can be broken down into elements for first name, last name, etc. The OpenEHR CKM provides more detailed archetypes for defining demographics (e.g. the DEMOGRAPHIC-CLUSTER archetypes), these cover the concepts found in our analysis of the Irish Health domain. Nonetheless, the concepts are scattered across the different archetypes. The FHIR ontology on the other hand covers all the required concepts and each can be found explicitly defined in the ontology. Given the focus of FHIR on information exchange, its adherence to semantic web principles, we therefore believe FHIR is the most suitable approach for managing Irish healthcare data going forward.