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Table 2 Needs of large-scale automated healthcare facility surveillance and potential of federated systems

From: Federated systems for automated infection surveillance: a perspective

Current barriers precluding large-scale automated surveillance

Needs for large-scale automated infection surveillance in healthcare facilities

Potential benefits of federated systems for surveillance

Potential downsides of federated systems for surveillance

Prerequisites for federated systems for surveillance

Case-definitions are not always suitable for fully AS

Changes in definitions, centrally implemented semi-AS, or more complex case detection methods

Enables automated case detection in healthcare facilities by (complex( algorithms, supporting a broader range of definitions that may better align usefulness for local practitioners

Adaptability of algorithms to local situation more difficult to realize

Transparency and explainability of algorithms for case detection; sound methods for algorithm development and validation

Differences in registration procedures of routine care data limits comparability of case definitions across centers and over time

Uniformity when reusing EHR data (FAIR) ensures consistency of case-definition

Improved comparability of surveillance results due to harmonization of source data and improved insight in coding practices

Loss of information with data transformation;

Knowledge and IT capacity for EHR data harmonization

Stakeholder agreement on a minimal data set, data model and terminology;

Willingness and capacity for FAIR data curation;

Robust measures for quality control

Limited flexibility in information needs or granularity of existing national registries currently demands additional infrastructures or additional manual data collection (SARI)

Less resource intensive AS systems that are agile to meet current and possibly changing information needs

Sustainable systems that meet information needs for various purposes and topical issues by performance of various analyses on harmonized data sources at different time points, and different level of details

Methodology may not be accepted if data ownership and good governance are well arranged

Regulatory framework for data access, handling and publication of results;

Transparency of methods

Limited data linkage, due to limitations in interoperability or privacy restrictions

Authorized access to more detailed machine-readable data, allowing for less resource-intensive and more widely implemented surveillance systems

Potentially increased coverage rate of healthcare facilities (more complete information of catchment area); Privacy by design tailored to local situation; Methodology analyzing free text may be supported

Methodology may not be accepted unless validated and well understood;

Lack of access to individual patient-level data on a central level;

Data on rare events or combination of traits can make identifying individuals a risk, new information security issues or risk of hacking may not be excluded

Regulatory framework for data access, handling and publication of results; transparency in technical applications (freely accessible source code and pseudo code);

Technical solution needs to fit the local needs and (regulatory) requirements;

Thorough method validation

Insufficient capacity of IPC and IT in healthcare facilities for development, implementation, and maintenance of AS systems

Reduced workload for development, implementation, and maintenance of AS systems; Interoperable machine readable EHR data allowing exchange of algorithms and scripts

Surveillance systems programmed and maintained at one location;

Easily scalable solution with interoperable, machine-readable data

Software ETL processes necessary every healthcare facility; accountability more complicated; IT capacity at the expense of IP capacity if the importance and shift in IP tasks is not recognized

Technical applications need to be transparent (open source);

Governance framework addressing accountability, enhanced collaboration

Insufficiently broad and thorough knowledge on AS in all participating healthcare facilities hinders development or procurement of methodologically sound and sustainable AS systems

Central support in the development of methodologically sound and sustainable AS systems; easier applicable, not requiring complete knowledge from AS by a large multidisciplinary team within the healthcare facilities

Programme of requirements of a sustainable AS system can be determined collectively within federated community; healthcare facilities are not dependent on individual agreements with vendors

Knowledge in all participating healthcare facilities is required to understand, accept and validate outcomes of federated AS. Potential loss of view on local events without manual assessment

Digital literacy and minimal training in data intelligence for understanding of (maintenance of) algorithms and interpretation of outcomes, for decision making; supervision and oversight of AS methodology by qualified professionals

External validation of locally implemented AS systems difficult (HAI)

Source codes and data handling procedures transparent and easy to review

Surveillance algorithms programmed, validated and maintained at one location

Continuous quality monitoring, maintenance and version control to be performed by coordinating center

Framework for validation; transparency in data processing through whole pipeline from curation to publication of results for quality assurance, and avoidance of gaming in surveillance