Feasibility of semiautomated surveillance of healthcare-associated Staphylococcus aureus bloodstream infections using hospital electronic health records in Victoria, Australia

Discussion

Our study provides a current snapshot of the extent to which hospitals in Victoria, Australia are engaged in the use of EHRs and the extent to which EHR data are used to support HAI surveillance. Vast heterogeneity was described in EHR functionalities, how routine care data were captured, and local approaches to reduce the workload of HAI surveillance. None of the sites described using AS of HAI. While hospital IPC staff involved in local SABSI surveillance identified the EHR as an important source of data for clinical care and diagnostic results, the use of EHR reports to identify events was not described. Participants described hybrid solutions to access clinical information during staged roll-outs of EHR within healthcare organisations, resulting in multiple clinical documentation processes and definitive sources of information, issues with data quality related to EHR system design and disrupted clinical workflows, and lack of intuitiveness when trying to find data on EHR. Potential solutions to address poor data quality related to missing, incomplete or inaccurate documentation are to improve user interfaces for clinicians and reduce excessive data entry requirements.14

Few Australian hospitals have achieved the highest stage of digital maturity as measured by the HIMSS EMRAM.13 14 In Victoria, healthcare organisations have the flexibility to choose their EHR vendor.20 As seen in our study, EHR systems, level of adoption and maturity of systems vary between hospitals. Implementation of EHR is performed within healthcare organisations, and while organisations can implement the same EHR functionalities, each may use this resource differently, which creates value that can be described as ‘non-imitable’ and ‘non-mobile’.21 The heterogeneity between local sites in use, reporting formats and structures, and non-standardisation of data elements reported in fixed fields complicates data extraction and limits interoperability between EHR systems.3 22 This limits the ability to share surveillance methods across networks of hospitals.11

None of our study participants described algorithms applied to their EHR data to support reduction in manual workload of HAI surveillance. The main disadvantage of a manual approach to chart review and case ascertainment is that review is required for a large number of SABSIs to identify a relatively small number of HAIs, making it inefficient and resource and time intensive.22 23 Use of AS offers the promise of improvements in accuracy of measurement and standardisation of processes. Gains in efficiency have the potential to support IPC staff and alleviate current workload, expand the breadth of surveillance, and focus efforts on quality improvement and IPC initiatives.3 As Australian healthcare organisations look at implementing AS for HAIs, a concern is that the process of locally translating surveillance definitions into electronic rules may lead to algorithms that vary between hospitals and consequently lead to unexpected discrepancies in data reported by different hospitals. Currently, there are no available solutions identified that escape the need for local integration and validation.

We propose that offering Australian hospitals an externally validated and standardised surveillance approach with consensus in case definitions and standardised EHR data elements can partially reduce the work effort required for local AS of HAI implementation. This approach has been successful overseas.24 This will allow hospitals to focus on the integration of the data elements to their local systems and use test data sets to pilot the performance of these rules.

Our evaluation confirmed that key core data elements of our proposed SABSI model algorithm are available in EHRs, suggesting that SABSI definitions may require modest modifications to make AS feasible. Further steps would be to incorporate these data elements into an algorithm and perform validation. Since algorithm-based HAI surveillance was first described in 2004,25 the majority of studies have applied a semiautomated AS approach for the identification of surgical site infections, central-line bloodstream infections and other HAIs, generally reporting high sensitivity (>0.8) but variable specificity (0.4–1.0).26 However, there are increasingly promising reports on the feasibility and performance of AS of bloodstream infections, such as using the hospital-onset bacteraemia detection algorithm developed by the PRAISE (Providing a Roadmap for Automated Infection Surveillance in Europe) network.22 24 27 Semi-AS, allowing for case review prior to finalisation, is reported as associated with higher acceptance from clinicians unfamiliar with HAI AS than fully AS.11 Given the heterogeneity in EHRs used in Victorian hospitals, we feel that application of a semiautomated approach is also more likely to be feasible for local EHR teams to implement as there is potentially room for adaptation.

Our findings highlighted that some required data may be missing or not easily extractable from Victorian hospital EHRs. These are recognised barriers to AS implementation in healthcare organisations.28 We also acknowledge that the focus of our research was on using EHR to support case identification; however, the utility of EHR also extends to providing data on risk factors. To ensure the availability of such high-quality data in EHR, hospitals would need to review how data are currently captured on their EHRs and invest in engaging clinicians to improve clinical documentation in EHRs despite the challenges of working in a time-poor clinical environment.14

Encouraging Australian healthcare organisations to transition to HAI AS requires a commitment to a national surveillance approach which currently does not exist in Australia.29 In addition, a roadmap to large-scale implementation of AS at a national level is needed. This roadmap can be used to guide future steps towards implementation, including designing solutions for AS and practical guidance checklists.22 Surveillance definitions will need to be revised to align with available structured EHR data elements.

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