Why are we not using existing data for healthcare-associated infection surveillance?

ssi

This is a guest post by Dr Gabriel Birgand (bio below), a researcher at Imperial College London…

Surveillance is an essential component in any infection control programme. In UK, the surveillance of infections associated with some procedures (e.g. certain orthopaedic procedures) is mandated by Public Health England. This surveillance requires time and represents either a full time job (i.e. dedicated nurses doing the data collection and follow-up of patients undergoing surgery) or additional work (i.e. surveillance of catheter-associated urinary tract infection by infection control nurses). This process is time-consuming, expensive and dependent on the rigour of the person in charge of the surveillance. Despite proven value, the involved nature of the method makes these manual HCAI surveillance systems difficult cumbersome to deliver. Moreover, hospitals often struggle to recruit and retain nursing staff dedicated to surveillance meaning that reporting is frequently incorporated into other posts, which interrupts other clinical duties and may reduce the detail of reporting.

In 2013, Carina King at the Imperial College London investigated whether routinely collected electronic hospital data can be exploited for HCAI surveillance within the NHS. The key findings were primarily the emerging importance of automated and syndromic surveillance in infection surveillance, but the lack of investigation and application of these tools within the NHS. In principle, routine administrative data can be used to generate novel surveillance tools for healthcare-associated infections. We are working on applying this in the field of SSIs, with the following strengths and weaknesses:

Strengths

  • Firstly, this type of system may improve the coverage of surveillance including a broader spectrum of infection, i.e. all types of surgical specialties, which is difficult with a manual system.
  • Secondly, the tool will facilitate rapid reporting and feedback of recent HCAI cases and rates to clinical and infection control teams, giving an early indication of abnormal rates to review promptly.
  • Thirdly, the robust methodology will allow benchmarking between specialties.
  • Finally, syndromic surveillance may improve the diagnosis of infection. However, it is important to remember that this system will not fully replace the human work, but allow more efficient use of the time of SSI surveillance personnel, for example, more detailed individual case review.

Weakness

  • The algorithm is based on procedure codes of the hospital and is therefore completely dependent on the rigour of coders. Bad coding = bad syndromic surveillance of SSI.
  • The tools developed will only identify infection for inpatients and/or at their readmission. This is not addressing post-discharge follow-up of surgical patients. This will be more problematic in some surgical specialties (e.g. Caesarian sections, which usually manifest in the community) than in others (e.g. cardiothoracic infrection, which usually manifest in inpatients or as readmissions).
  • Another problem is the feasibility of linking patients administrative and pathology systems in different NHS trusts. Systems for pathology records are heterogeneous from one NHS trust to another and will require tools to be adaptive.

In summary, building a retrospective surveillance system for SSIs (and other HCAIs) based on existing data source may represent an interesting solution. The improvement of coding and the extension of surveillance through patient involvement may help to fill some of the gaps that currently exist in this approach (e.g. adding some sort of post-discharge surveillance). Syndromic surveillance may ultimately help to improve patient care and safety using existing untapped data sources.

Bio

Dr Gabriel Birgand is a pharmacist specialised in infection control. He works part time as a Research Associate and ARC Fellow at the HPRU, NIHR with Prof Alison Holmes, and part time coordinating infection control in the West of France. His PhD is from Université Pierre et Marie Curie, Paris on new tools for the diagnosis and prevention of surgical site infection. His other topic of interest is the assessment of measures to control the spread of multi-drug resistant organisms. His ARC fellowship is titled “Knowledge and perception of antimicrobial resistance among European healthcare professionals”.

Image: Wikipedia.

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5 thoughts on “Why are we not using existing data for healthcare-associated infection surveillance?

  1. Dr Birgand makes some very good points. From simple beginnings to monitor infection incidence, HAI surveillance has grown into a complex system requiring increasing resources to meet the purposes and demands of all those who use the data. It is crucial that epidemiolgical principles are adhered to ensure robust data. At the same time the search for efficiencies must continue.
    Processes which reduce the burden of data collection whilst maintaining, or even improving, data integrity are crucial for the sustainability of HAI surveillance.
    However, the primary objective, reducing the incidence of HAI and improving patient safety and quality, should never be forgotten.

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  2. I like this as I am already coordinating extensive SSI surveillance within my organisation. Certainly linking existing data collection systems will reduce the burden of SSI data collection, albeit this would need to be validated fully. Still a lot of work to be done on SSIs to improve patient safety.

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    • Thanks for your comment Lilian. Sounds like you have a very impressive SSI surveillance system, that we and others can learn from! Completely agree that this ‘semi-automated’ surveillance does not replace conventional methods – it just augments it!

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      • We are very proud of what we have achieved so far John, but acknowledge that we have to remain vigilant. Very happy to share our experiences with others.

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