CORR Insights®: Can Surgeons Adequately Capture Adverse Events Using the Spinal Adverse Events Severity System (SAVES) and OrthoSAVES?
Bibliographic record
Abstract
Where Are We Now? The Institute of Medicine has recommended systems for reporting medical errors. However, at a basic level, a standard language and consistent definitions for what constitutes an adverse event (AE) have been lacking [11]. Current terminology, such as “complication,” “AE,” “adverse occurrence,” or “near misses,” are often used interchangeably, but have different meanings (between or within institutions depending on case definition) and varying methods for identification and reporting [10]. In addition, concerns regarding malpractice, professional, and financial implications (personal, practice group, or institutional) of AEs can result in variable reporting of AEs [7]. The inconsistency of AE reporting in surgery reflects the complexity and challenges of this important issue, and currently is a focus of many surgical societies and institutions. Specific to the current article by Chen and colleagues, underreporting of AEs by physicians is one of the evidentiary challenges in patient-safety initiatives [12]. Chen and colleagues prospectively assessed the use of a Spinal Adverse Events Severity System (SAVES) (developed by this writer) for spine and orthopaedic surgery [10, 11]. They demonstrated relative under-reporting by surgeons regarding minor AEs and appropriately questioned the cost-benefit of using third-party reviewers. They also noted that specific training may have improved physician reporting. Where Do We Need To Go? A thorough discussion of the existing body of science on the broader needs, methods, and evaluation of processes for improving patient safety and quality initiatives (QIs) is beyond the scope of this brief commentary. But from my perspective, Chen and colleagues raise several critical questions regarding physician participation and consistent AE reporting across the healthcare sector: (1) As it relates to reporting AEs, is it about education or the broader need for cultural change regarding medical errors? (2) What are the key education components and implementation enablers that address specific system and regional barriers for change? (3) What are the system and regional incentives/drivers for change from the perspective of patients, providers, institution and payer? (4) What is the effectiveness (that is, the impact on reduction of AEs) and cost consequence(s) of current and future interventions? How Do We Get There? For Questions 1-3, the subjective and often-emotional aspects (such as perception of punitive action or professional “shame”) of reporting AEs requires the addition of qualitative research to traditional quantitative methods. Future studies can build on the current study by Chen and colleagues by adding a qualitative component that can add insight into why the surgeons did not feel it was necessary to report the more frequent “minor” AEs. For example, if the prevailing theme from such an exercise was that the more minor events, such as an UTI, were not perceived to have any serious clinical impact, then perhaps education regarding the cumulative financial impact of minor AEs and how it directly affects the surgeons’ practices (such as with respect to bed availability) would result in greater incentive to actively participate [3]. Furthermore, to determine relevant barriers and enablers for implementation of a safety initiative, the regional context requires more explicit exploration. For example, the scenario of reporting a minor event such as UTI will have very a different context in the United States as it pertains to issues such as “never events” and the shifting of the cost burden for such events from payers to providers [2]. This issue is further complicated by variable mechanisms and layers of accountability for a given payer, provider, and institution [8]. Comparatively, in the single-payer context of Canada, other than the need for treatment of a given AE, there is no real personal consequence for the surgeon or immediate actionable consequence for the institution [3]. Consequently, patient safety, QI research, educational efforts, and interventions must take these varying, context specific, factors into consideration and adapt the process to achieve appropriate alignment and incentive across multiple stakeholders, including patients [4]. As demonstrated in previously published studies [6, 13], SAVES functions well when the process of identification and classification of AEs is done by the healthcare team, rather than individual providers or institutional processes [1]. Utilization of the existing healthcare team (doctors, trainees, nurses, allied health providers, and pharmacists) partially addresses concerns regarding cost-benefit of third-party reviewers, but not the cost requirements of data entry and database management. In addition to patient safety and quality of care, AEs have a significant financial impact [3]. Cost-effectiveness analysis (CEA) is the preferred form of health economic assessment, however, large scale CEAs are challenging and costly to execute. Furthermore, the results of CEAs are often difficult to translate to a different regional context. A practical alternative may be cost-consequent analysis which comprises of a disaggregated presentation of relevant variables (including costs and benefits) that provides decision makers with detailed information about the intervention under evaluation, thus allowing different stakeholders to identify components most relevant to their perspective [9]. Regarding outcomes, a recent systematic review [5] demonstrated variable effectiveness in the ability of patient safety initiatives to reduce surgical harm and also implementation feasibility. Continued studies on effectiveness of a given patient safety initiative are paramount, however, studies should also concurrently evaluate the cost consequences.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.148 | 0.062 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".