Table 3 Alternative terms and definitions of data harmonisation interventions

Liu 2010 [1]

Data harmonisation is the process of integrating life-long health data of a person that are distributed in inhomogeneous information systems through identifying, reviewing, matching, redefining and standardising information. This process involves two steps. Firstly, identifying whether all the information necessary for a single electronic platform is available in existing systems, where the information is, and how the information is defined and formatted. And secondly, to make the heterogeneous information recorded by various systems consistent or at least comparable with one another by reviewing, matching, redefining and standardising each data item.

Boyd 2014 [16]

Record linkage is the process of bringing together data relating to the same individual from within and between different datasets. When a unique person-based identifier exists, linkage can be achieved by simply merging datasets on the identifier. However, when a person-based identifier does not exist, then some other form of data matching or record linkage is required for integrating data.

Gill 2001

Hopf 2014

Data linkage can be used to construct a register for a specific geographic area and disease (for example, a district non-communicable disease register). Linkage of routine datasets by unique patient identifiers can provide an opportunity for identifying adverse drug reactions and tracking exposed individuals in real time. Routine data linkage can also enable the creation of exposure cohorts to monitor long-term outcomes and enable a more efficient screening for adverse drug reactions due to an ever-increasing data pool.

Haarbrandt 2016 [28]

Data warehousing is the process of establishing specialised databases by integrating information systems (the authors specifically referred to hospital information systems) to facilitate secondary use of data. Clinical data warehouses are generally built on one of two predominant architectural paradigms: either, data is directly extracted, transformed and loaded from applications systems and databases into a data mart (an integrated view over a defined subject), or it is stored in a centralised data repository from which data marts can be established. Both approaches rely on a process to extract data from sources, transform it appropriately and to load it (or copy it) to a specific database.

Hu et al., 2007 [17]

Jones 2012

Data sharing is based on the need for a more robust method for defining and sharing expected and actual patient outcomes. It must leverage existing informatics tools since a great deal of patient-specific information is already available in medical record systems and billing and administrative systems. One type of data sharing system is an infectious disease informatics (IDI) system. An IDI system should encompass sophisticated algorithms for the automatic detection of emerging disease patterns and the identification of probable threats or events. It should also have advanced computational models that overlay health data for spatial–temporal analysis to support public health professionals’ analysis tasks.

Elysee 2017 [29]

Data interoperability is one of two functionalities of an advanced electronic health record. The first function is health information exchange, which is the ability to electronically share patient-level information among unaffiliated providers across organisational boundaries. The second function is interoperability, which is the ability to produce standardised patient-level health information that can be integrated into unaffiliated health care providers’ electronic health records.

Akhlaq 2016 [15]

Dixon 2010 [33]Esmaeilzadeh 2016 [34]

Esmaeilzadeh 2017 [35]

Fontaine 2010 [36]

Hopf 2014 [38]

Kash 2017 [39]

Mastebroek 2014 [27]

Parker 2016 [42]

Rahurkar 2015 [44]

Rudin 2014 [45]

Sadoughi 2018 [46]

Vest 2012 [48]

Health information exchange (HIE) is a type of health information technology (HIT) intervention. It involves the electronic mobilisation of clinical and administrative data or information within or across data repositories or organisations in a community or region, between various systems as per recognised standards. This is to ensure that the HIE maintains the authenticity and accuracy of the information being exchanged, thereby enabling stakeholders to make informed decisions to enhance healthcare quality and delivery of patients and populations. Sharing clinical data can potentially improve patient safety, care coordination, quality of care and efficiency, facilitate public health efforts and reduce mortality and healthcare costs. Lastly, HIE involves multi-stakeholder organisations that oversee the business, operational and legal issues involved in the exchange of information.