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Abstract
Introduction Adverse events, defined as unintended patient harm contributed to by healthcare, continue to increase morbidity, mortality and cost. Critically ill patients are at high risk of adverse events; however, the optimal approach to detection in this setting is unknown. Numerous approaches have been used, including voluntary reporting, chart reviews and trigger tools. The objective of this systematic review is to gain insight into the capacity of individual methods to detect adverse events in the intensive care unit (ICU), to inform implementation, and to facilitate quality improvement.
Methods and analysis Ovid MEDLINE, Ovid EMBASE, CINAHL, the Cochrane Library and Google Scholar were searched on 2 October 2023 for randomised controlled trials and observational studies evaluating the implementation or ongoing use of one or more systems of detection of adverse events in ICUs (neonatal to adult). Outcomes will include the total number of adverse events identified by detection method per 100 patient days (primary outcome), categories of adverse events, associated harm and whether detection informed quality improvement. A risk of bias assessment will be performed. The results will provide insight into each method’s capacity to detect adverse events in addition to their associated severity.
Ethics and dissemination Ethics approval was not required as patient data will not be collected. A manuscript will be submitted to a peer-reviewed scientific journal.
PROSPERO registration number CRD42024466584.
- Adverse events
- INTENSIVE & CRITICAL CARE
- Quality Improvement
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STRENGTHS AND LIMITATIONS OF THIS STUDY
The search strategy captures all methods to detect adverse events in intensive care units (ICUs) as the optimal approach is not known.
A broad age range (neonatal to adult) is included to minimise the risk of missing methods of detection based on ICU age criteria.
Potential heterogeneity of individual studies in terms of patient characteristics, detection methods and definitions of adverse events.
Potential difficulty standardising the number of adverse events identified to the size and acuity of the ICU.
The assumption that a greater number of adverse events detected can be attributed to the detection method.
Introduction
Over 20 years ago, public attention was brought to the alarming issue of medical error in our healthcare systems by the Institute of Medicine’s To Err is Human.1 Despite this, adverse events, defined as unintended patient harm contributed to by healthcare, remain a serious problem. In fact, the WHO has recently made this a global health priority.2 Canadian healthcare institutions are no exception, as patients continue to experience preventable harm despite increasing attention to healthcare quality and patient safety.3 4
Critically ill patients are at particularly high risk of adverse events.5 6 These are associated with negative outcomes, such as an increase in mortality, increased intensive care unit (ICU) length of stay and higher cost.5 A recent systematic review identified 20 categories of adverse events in intensive care, which were associated with age, disease severity, urgency of admission and the use of certain high-risk devices.6 An estimated one in four critically ill patients experience an adverse event during their ICU stay and adverse event rates may be increasing for reasons that remain unclear.5–7
The detection of adverse events allows early identification of correctable system issues and is an important step towards informing quality improvement initiatives and promoting a culture of safety.8 Several methods of detection of adverse events exist. Many healthcare organisations rely on voluntary reporting however this may lead to an underestimate of adverse events.5 9–11 Other methods include medical record review, morbidity and mortality rounds, review of administrative datasets or malpractice claims and patient interviews.8 In 2000, the Institute for Healthcare Improvement developed the first Trigger Tool with the intention of more efficient and reproducible retrospective detection of adverse events.12
Brunsveld-Reinders et al10 reviewed adult ICU error reporting systems through the lens of the WHO Draft Guidelines for Adverse Event Reporting and Learning Systems,8 and found that none met this international standard. The capacity of each method to detect adverse events in the ICU setting is unknown.
Objectives
The objective of this study is to conduct a systematic review to explore the detection of adverse events among critically ill patients admitted to an ICU, from the neonatal period to adulthood. The number of adverse events (standardised to 100 patient days, where possible), the category of adverse events, the associated level of harm, and whether a quality improvement initiative followed detection will be explored. In addition, studies that directly compared two or more methods of detection will be examined. The results will provide insight into each method’s capacity to detect adverse events in addition to their severity. Evidence in the literature of resulting quality improvement will also be investigated qualitatively.
Methods and analysis
Patient and public involvement
No patient or members of the public will be involved in this systematic review.
Study design
A systematic review of randomised controlled trials, controlled (non-randomised) trials and prospective or retrospective observational studies will be performed.
Study registration
In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this systematic review has been submitted to the International Prospective Register of Systematic Reviews and the registration has been published under the ID CRD42024466584.
Data source and search methods
A search strategy was developed in consultation with a medical librarian. Comprehensive searches were completed on 2 October 2023 in Ovid MEDLINE, Ovid EMBASE, CINAHL and the Cochrane Library (via Wiley). In addition to subscription databases, the first 200 results from Google Scholar were obtained on the same date. To capture all relevant literature related to methods of detecting adverse events in critically ill patients, relevant keywords and controlled vocabulary were carefully selected. Searches will be limited to the English language.
Eligibility criteria
Inclusion criteria for data extraction include the following:
Observational studies or randomised controlled trials published in a peer-reviewed journal.
Assessment of the implementation of a detection method for adverse events, including voluntary reporting, trained observation, trigger tools, patient interviews, educational case rounds and retrospective chart reviews.
The setting included neonatal, paediatric or adult ICUs.
Reporting of at least one outcome related to the measures outlined below.
Exclusion criteria include the following:
Systematic or narrative reviews.
Studies characterising adverse event rates over a defined period without implementation or exploration of a detection system.
Studies implementing a detection method hospital-wide without providing the ICU-specific data.
Studies only assess microbiological testing for nosocomial infection without the implementation of a detection or reporting system.
Conference abstracts/proceedings or unpublished materials.
Outcome measures
The primary outcome will be the total number of adverse events identified by the detection method per 100 patient days. Additional outcomes will include the category of adverse events identified which will facilitate comparison of strengths and weaknesses of specific detection methods. Additionally, the associated level of harm will be measured using the National Coordinating Council for Medication Error Reporting and Prevention Index adapted by the IHI Global Trigger tool,12 and whether the detection method informed a quality improvement initiative, which will be described qualitatively.
Screening and data extraction
Study screening will be performed in duplicate by two reviewers independently and will begin with a title and abstract review. Full texts will then be screened for eligibility criteria by the same two reviewers. Disagreement at each stage of screening will be resolved by discussion or involvement of a third reviewer. Studies will be reviewed, sorted and stored using Covidence software. A PRISMA13 flow diagram demonstrating the screening process will be produced, and exclusions will be captured at the second stage.
Data will be extracted by two reviewers with 20% in duplicate using a standardised extraction form in the Covidence software. Extracted data will include the study type and period/date, event detection method, the setting (location and type of ICU), ICU size and the primary and additional outcomes. Inter-rater reliability will be reported.
Risk of bias assessment
The risk of bias for included studies will be assessed in duplicate for the same 20% of studies using the NIH Study Quality Assessment Tool for observational studies14 which is a three-point tool (ie, good, fair, poor). A weighted kappa will be used to determine inter-rater reliability. The rest of the studies will be assessed for risk of bias by a single reviewer.
Data analysis
The adverse event rate per 100 patient days will be calculated based on the reported size of the unit and the length of the study, to make comparison possible. Studies will be grouped based on five categories of detection methods, namely, incident reporting, trigger tools, trained observation, retrospective chart review or another form of structured review or a direct comparison of two methods. A narrative review will be undertaken to characterise the general themes of the secondary outcome measures.
Discussion
Critically ill patients are particularly prone to adverse events, with estimates as high as 25% of patients experiencing at least one event during their stay.6 Adverse events have a significant negative impact on mortality, length of hospital and ICU stay and healthcare cost.6 Common adverse events in the ICU include drug errors, device malfunction, unintended device removal, falls, nosocomial infections and procedural complications. The ability of a reporting system or detection method to capture this wide variety of clinical data is paramount and is an important step toward quality improvement.
There are numerous studies that report on individual units’ adverse event rates retrospectively, but do not comment on the validity of the method of detection or the change that ensued. The majority rely on voluntary reporting or retrospective chart reviews, strategies that both have limitations and are heavily influenced by safety culture. Voluntary reporting interrupts workflow, requires employee recognition of adverse events, includes a level of bias and comes with an element of fear. Chart review requires trained personnel, can be subjective and is resource heavy.
Brunsveld-Reinders et al10 examined compliance of adverse event reporting systems in ICU with the WHO Draft Guidelines for Adverse Event Reporting and Learning Systems.8 These guidelines have four core concepts:
Reporting systems must enhance safety by learning from failures.
Individuals who report must not suffer negative consequences.
Reporting must lead to a constructive response.
The agency reviewing reports must have the capability to disseminate information and make recommendations.
This systematic review, limited to the adult population, found that none of the incident reporting systems met these international guidelines completely, with emphasis falling on administrative reporting rather than facilitating improvements in patient safety. The surveillance and detection of adverse events allows early identification of correctable system issues. It is an important step towards informing quality improvement initiatives and promoting a culture of safety.15 Just Culture, a safety movement within organisations to take fair actions with those involved with an adverse event, has been touted to increase event reporting.16 A commitment from leadership to implement system-wide changes, education on safety to frontline staff, and an environment of accountability and open communication about errors have been identified as the key components of a Just Culture.16 The relationship between adverse event detection and safety culture is therefore bidirectional.
It is unknown what the progress has been in adverse event detection, across the entire continuum of critical care and whether voluntary reporting systems are yielding better results in terms of the number of adverse events detected or leading to improvement initiatives.
Additionally, there are many other methods of detection of adverse events described that do not rely on voluntary reporting. For example, the Institute for Healthcare Improvement has published an ICU Adverse Event Trigger Tool.17 This approach involves the selection of patient records for a focused review based on specific words or events associated with adverse events. Building on this approach, there are electronic algorithms being developed that use coded data within the patient chart to identify adverse events.18 Other detection methods include safety huddles, patient or staff interviews, mandatory reporting, trained observers or adverse event detection systems within an electronic medical record.19 Although each may have its strengths and weaknesses, the associated number and type of adverse events in the critical care setting detected by each method are not known.
Once a comparison of methods is completed in this systematic review, individual ICUs (or larger networks) will be able to appraise the results through the lens of their individual safety cultures, enabling the selection of the most effective detection method. The goal of this review is to not only determine which methods are most fruitful but also identify which methods are most likely to kickstart an improvement initiative, thereby positively impacting safety culture.
Ethics and dissemination
Ethics approval was not required as patient data will not be collected. The results will be made available in a timely and ethical manner. A detailed manuscript will be submitted to a peer-reviewed scientific journal. Contributing authors will be acknowledged.
Ethics statements
Patient consent for publication
Footnotes
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Contributors JS, SW, JG, JYK and OR contributed to the study protocol development. JS is the guarantor.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests None declared.
Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.
Provenance and peer review Not commissioned; externally peer reviewed.