Introducing the spinal invasiveness score to quantify spine surgery wait lists: a stakeholder analysis

Introduction

Canadian spine care delivery is currently in a crisis, largely due to inordinate patient wait times (1,2). There are a multitude of factors contributing to the system’s inefficiencies, and ultimately, this system needs to undergo considerable optimization to address this critical issue of access to spine care.

In the worlds of engineering and business, to optimize any system, that system must first be subject to “operational analysis” (3). Operation analysis entails quantification of operational metrics or key performance indicators (KPIs), which typically fall into two categories: quality and quantity (4). In the case of surgery, quality is relatively fixed at a baseline high standard given that surgeons are regulated by an external authority as well as by their immediate institutional colleagues both formally and informally (5). Furthermore, our system is not having a crisis with respect to quality; the crisis is access to timely care, which is a consequence of quantity-related issues (6). As such, quantity-based KPIs are most pertinent when discussing the main issues currently plaguing the delivery of spine care in Canada. Common quantity-related KPIs for surgery are wait times, throughput rate, operating room (OR) time and length of stay (LOS) (7).

For total joint arthroplasty (TJA), these KPIs are easily quantifiable because there are only two commonly analyzed procedure types: total knee arthroplasty (TKA) and total hip arthroplasty (THA). As a result, wait times are typically calculated using a simple count-based approach. For example, if a hospital performs approximately 20 TKAs per month and has 60 patients awaiting surgery, the estimated wait time for a TKA is roughly three months.

In contrast, “spine surgery” encompasses dozens of distinct procedures that vary substantially in complexity, operative time, and resource requirements. Complex spine surgeries such as scoliosis corrections are resource-intensive, limited to a small number of specialized hospitals, and therefore associated with long wait times (8). Conversely, less invasive procedures such as lumbar discectomy require fewer resources and can be performed in ambulatory centers, resulting in much shorter waits (9). This wide procedural heterogeneity underscores the need to differentiate spine surgeries rather than treating them as a single, undifferentiated category when calculating and managing waitlists.

One method of doing so would be to have a separate set of KPIs for the dozens of types of spine procedures, which would be laborious to analyze. As such, we need an alternative method that can quantify and encompass all spine procedures that will make calculation of KPIs such as wait times and throughput rate straightforward enough for systems performing operational analyses and subsequent optimizations.

Accordingly, we propose the use of the Spinal Invasiveness Score (SIS) to categorize spine surgeries. The SIS is a validated and well-established method for objectively scoring spine surgeries and is a predictor of pertinent parameters, including hospital resource consumption and clinical variables such as OR time, LOS and clinical variables like blood loss (10-12). Compared to other surgical invasiveness indices that often require granular intraoperative data or are restricted to specific sub-populations, such as the Adult Spinal Deformity invasiveness score, SIS offers superior operational usability. It relies on straightforward procedural parameters that are readily identifiable at the time of surgical booking, making it highly practical for proactive, system-wide waitlist management. The SIS score quantifies invasiveness by assigning points per vertebral level of decompression, arthrodesis, and instrumentation from an anterior and/or posterior approach. The SIS score is established across six parameters:

• Anterior decompression (Ad): 1 point per disc space;
• Anterior instrumentation (Ai): 2 points for first disc space, 1 for each subsequent disc space;
• Anterior fusion (Af): 2 points for the first disc space, 1 for each subsequent disc space;
• Posterior decompression (Pd): 1 point per laminectomy or disc space;
• Posterior instrumentation (Pi): 1 point per level instrumented;
• Posterior fusion (Pf): 1 point per level fused.

By using the SIS score, each spine surgery performed in a health system can be quantified using an objective scale in a manner that encompasses all possible spine procedures. Subsequently, we hypothesize that this scoring system could potentially be used to calculate all pertinent quantity-related KPIs.

The aim of our study is to determine the perceptions of key stakeholders across Canada regarding the adoption of the SIS score as the basis for performing quantity-related operational analysis, such as waitlists, on our health system with respect to spine surgery care delivery. We present this article in accordance with the COREQ reporting checklist (available at https://asj.amegroups.com/article/view/10.21037/asj-2026-1-0002/rc).

Methods

Study design

A qualitative descriptive approach and thematic analysis technique was used in this study to determine the preferred method to quantify surgical waitlists by spine surgeons. A modified version of the consolidated criteria for reporting qualitative research (COREQ) was used to guide our study design, survey and results reporting (13).

Data collection was completed using a survey following a case presentation with surgeons and hospital stakeholders. The case presentation (Appendix 1) was developed iteratively by the research team, drawing upon established SIS literature and the clinical expertise of the principal investigator. Prior to study initiation, the case scenario was reviewed for clinical accuracy and face validity by a pilot group of two independent spine surgeons and healthcare administrators. The case presentation illustrated how a SIS-based model could restructure a provincial spine surgery waitlist. The scenario used three representative hospitals in a Canadian province to contrast the current count-based waitlist with an SIS-stratified approach: (I) a tertiary centre capable of all levels of invasiveness, (II) a secondary hospital performing moderately extensive cases, and (III) an ambulatory site limited to day procedures. In each example a case was assigned a SIS and grouped into tiers (SIS <2, SIS 2–11, SIS >11), which were mapped to minimally, moderately, and highly invasive procedures with corresponding typical OR times and venue assignments. Participants were shown how a tiered SIS framework could change the interpretation of wait times and throughput compared with the existing system. Participants were then asked to indicate their preferred method and comment on perceived benefits, limitations, and barriers to implementation.

Participant selection

Potential participants, including spine surgeons and hospital administrators, were recruited to take part in this study. Inclusion criteria required participants to be either independently practicing spine surgeons or hospital administrators directly involved in the management, scheduling, or oversight of spine surgery delivery within a Canadian healthcare facility. Individuals who did not meet these professional criteria, such as medical trainees or those practicing outside of Canada, were excluded. The case study in Appendix 1 provided fundamental background, showcasing the current waitlist method for spine surgery versus the SIS method. The survey then asked participants to select a preferred waitlist method based on the presented case study. Personal identifying information was not collected in the survey. Each participant was surveyed once. The survey was conducted online through the Google Forms platform and consisted of a combination of multiple-choice and open-ended questions designed to capture both quantitative preferences and qualitative reasoning. Appendix 2 summarizes the survey questions for the hospital administrators and spine surgeons respectively, which consisted of a combination of multiple-choice and open-ended questions.

A snowball recruitment strategy was used to identify potential participants. Initial participants within the principal investigator’s professional network who met the criteria were contacted via a secure email connection. In-person conversations were also employed by the principal investigator (J.T.), a practicing spine surgeon, to recruit initial participants from within their immediate professional network. Those who participated were then asked if they knew any other individuals who met the criteria and if they were willing to refer them to the study. Although the principal investigator had prior professional relationships with several of the initial participants, participation remained strictly voluntary and responses were anonymized. With the snowball recruitment strategy the methodology successfully captured a geographically and professionally diverse cohort. Respondents represented four different Canadian provinces (Ontario, Manitoba, Alberta, and British Columbia) and included representation from both academic teaching hospitals (57%) and non-academic community centers (43%).

Data collection

All surveys were conducted virtually across Canada and were overseen by principal investigator (J.T.) and research assistant (G.S.), both male researchers between April and September 2024. Since the surveys were administered digitally, there was no single physical location for data collection; participants completed the surveys from their respective institutional or home workspaces. Prior to survey administration, informed consent was obtained from all participants. Participants were provided with an informational briefing detailing the study’s purpose and the voluntary, anonymized nature of their participation. The survey took approximately 15 minutes to complete. Given the asynchronous digital format, no field notes were recorded and transcripts were not generated or returned to participants for correction.

Data analysis

The survey results were analyzed using thematic analysis to understand the perceptions of key stakeholders regarding the adoption of the SIS score for waitlist calculations in spine surgery care delivery. Qualitative data was organized using Microsoft Excel. Two independent researchers (J.T. and G.S.) systematically coded each response to identify meaningful data segments relevant to the research question, focusing on preferences for the waitlist calculation method and barriers to adopting the SIS score. Any discrepancies in initial coding between the researchers were resolved through discussion and mutual consensus. The initial codes were reviewed and collated into potential themes that emerged inductively from the data representing broader patterns of meaning across the dataset. These themes were then refined to ensure they accurately represented the coded data and the entire dataset, with descriptive labels assigned to each theme. Data collection and analysis proceeded iteratively until thematic saturation was reached. Saturation was determined to have been achieved with the final sample size of 21 participants, as no new substantive themes or codes emerged during the analysis of the final survey responses. Participants did not provide further feedback on the findings after their initial survey submission.

Ethical considerations

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was reviewed and granted an exemption by the Research Improvements Through Harmonization in Manitoba (RITHIM) Research Ethics Board and informed consent was obtained from all individual participants.

Results

In total, 21 stakeholders (11 spine surgeons and 10 hospital administrators) participated in this study out of an initial 34 individuals contacted, yielding a response rate of 62%. No individual dropped out or refused to participate. The participant cohort successfully captured a geographically and professionally diverse national perspective, representing four Canadian provinces (Ontario, Manitoba, Alberta, and British Columbia). Surgeons had a median of 12 years in practice, and administrators averaged 8 years in healthcare management roles. Furthermore, the sample included representation from both academic teaching hospitals (57%) and non-academic community centers (43%). Seventeen of the 21 participants (81%) preferred the SIS method for calculating surgical wait times over their current system of reporting by the surgeon’s office (4 participants, 19%). Full breakdown of preferences between surgeons and administrators for the SIS-based waitlist approach can be seen in Figure 1.

Figure 1 Stakeholder preferences for waitlist management calculation methods. Among the 21 participants, 17 preferred the SIS-based approach (including 9 surgeons and 8 administrators), while 4 preferred the current system (including 2 surgeons and 2 administrators). SIS, Spinal Invasiveness Score.

The aspects of the SIS method that participants highlighted as beneficial included its ability to provide a more accurate representation of wait times and better differentiation of complex cases such as those involving deformity or multi-level surgeries. Key perceived barriers to SIS adoption included difficulty in real-time data capture (n=4), the upfront cost of implementation (n=3), and concerns over the integration of revision surgeries and other unique cases into the SIS system (n=3). A full breakdown of benefits and barriers to adopting the SIS-based waitlist approach can be seen in Figure 2 and Table 1.

Figure 2 Frequency of mentioned benefits and barriers in adopting the SIS-based waitlist approach. SIS, Spinal Invasiveness Score.

Benefits of the SIS method

Subcategorizing waitlists

Participants appreciated SIS for its ability to quantify procedural complexity, which facilitates the subcategorization of waitlists. One respondent noted: “The SIS framework finally gives us a more detailed picture of waitlists that accounts for procedure complexity, allowing for more accurate prioritization of patients. This is a game-changer for addressing long delays for more complex cases like deformity surgeries, which have a disproportionately long wait time compared to simpler procedures.” Another respondent emphasized that by quantifying complexity objectively, hospitals could implement tailored approaches to backlog management: “Complex cases are often sidelined because they are viewed as too time-consuming. SIS would allow a hospital to build OR schedules that accommodate these cases without delaying simpler procedures unnecessarily.”

Triaging cases

The SIS system was seen as a valuable tool for efficient triaging of surgical cases, addressing the long-standing issue of resource misallocation. Respondents emphasized the potential for smarter resource distribution, particularly for complex, time-intensive surgeries. For instance, spine deformity cases or multi-level fusions can now be scheduled on designated “long OR days”, while simpler procedures like discectomies can be triaged to ambulatory settings with shorter turnaround times. One hospital administrator shared: “The SIS score introduces clarity into triaging decisions. Hospitals can structure OR time to meet the demands of complex surgeries without creating bottlenecks for less invasive procedures.” Another participant added that the SIS could improve patient communication: “Patients waiting for multi-level procedures often feel forgotten. With SIS, we can provide better transparency about why certain surgeries take longer and where they stand in the queue.”

Accurate wait time calculation

The SIS approach supports optimal scheduling and resource distribution by distinguishing between various types of spine procedures, such as instrumented versus non-instrumented surgeries, anterior versus posterior approaches, and single-level versus multi-level operations. Participants noted that SIS allows for data-driven decision-making, enabling hospitals to allocate resources more efficiently. By identifying categories with disproportionately long wait times, such as revision surgeries or complex deformity cases, healthcare systems can implement tailored interventions, such as allocating specific OR blocks or investing in additional staff training. One respondent summarized the benefits succinctly: “The SIS method doesn’t just tell us how long the waitlist is; it tells us why certain cases are delayed and where to focus our efforts. This makes waitlist management a proactive process instead of a reactive one.”

Barriers to SIS adoption

Difficulty in capturing SIS

Several stakeholders expressed concerns regarding the feasibility of capturing SIS scores for each surgery in real time, particularly in systems with limited administrative support or outdated electronic health records (EHRs). One hospital administrator highlighted: “The SIS method is excellent in theory, but we lack the tools to implement it efficiently. Manual data entry isn’t sustainable, and most hospitals don’t have automated systems sophisticated enough to capture SIS scores seamlessly.”

Cost of implementation

Stakeholders identified upfront costs as a significant barrier to integrating SIS into hospital workflows. These costs include staff training, EHR upgrades, and system-wide workflow adjustments. Participants expressed concerns about securing funding to implement SIS, particularly in resource-limited healthcare settings. One administrator shared: “Hospitals are already operating on tight budgets. While SIS would yield long-term benefits, the initial costs, such as both financial and logistical, are daunting.”

Complexity in case factors

While SIS is effective in quantifying procedural invasiveness, participants emphasized that it does not fully capture the complexity of certain cases, such as elevated body mass index (BMI), revision surgeries, and significant comorbidities, including diabetes or rheumatoid arthritis, which add layers of complexity to perioperative care. One participant noted: “Revision cases and comorbidities like BMI are critical variables that add complexity but aren’t fully captured by SIS. We need to incorporate these factors to make the model truly comprehensive.” Several respondents suggested that incorporating supplementary variables or modifiers into the SIS framework could enhance its utility.

Discussion

In this national qualitative survey of Canadian spine surgery stakeholders, a vast majority of participants preferred using the SIS to calculate and manage spine surgery waitlists over the current surgeon-reported approach. Preference was also consistent across roles, with a majority of surgeons and hospital administrators favouring an SIS-based model. Respondents emphasized three practical advantages: (I) enhanced prioritization by quantifying procedural complexity, (II) improved triage and scheduling (such as aligning high-complexity cases with long OR blocks while routing low-complexity cases to ambulatory settings), and (III) greater transparency about why certain cases wait longer and where bottlenecks occur. A frequency analysis of coded responses corroborated these themes and the two principal barriers, difficulty of real-time data capture and up-front implementation costs, while also noting concerns about representing special populations, such as obesity, revisions, and multimorbidity.

The original index described by Mirza et al. [2008] demonstrated that each incremental point was associated with longer operative duration and higher blood loss, with the index explaining a large share of variance in both outcomes, far more than individual patient factors (10). Subsequent work has shown that higher invasiveness scores independently predict surgical site infection and can be adapted for registry or administrative data while retaining predictive value (11,14). Incorporating invasiveness into predictive models also improves forecasting of LOS and discharge disposition over standard calculators (15). While prior literature has firmly established the clinical and predictive validity of SIS, such as forecasting operative time, blood loss, and complications, our study uniquely demonstrates its perceived operational utility among frontline decision-makers. Together, these studies support the core intuition voiced by participants: a standardized invasiveness score captures clinically meaningful heterogeneity that directly maps to operational demand. Specifically, our findings expand upon the existing literature by showing that both surgeons and administrators view SIS as a practically viable tool for equitable case prioritization, while also highlighting the real-world systemic barriers, such as IT infrastructure and upfront costs, that must be overcome prior to adoption.

Addressing the most common comment from respondents, “why not just use operative time?”, we note that while OR duration is a valuable measure, it is retrospective, variable across surgeons and teams, and confounded by intraoperative events. In contrast, SIS is prospective, standardized, and known at the time of booking. It is also strongly correlated with operative time and blood loss in empirical studies, but unlike OR time, it offers a reproducible way to benchmark across surgeons and institutions (10,14). For this reason, our findings suggest SIS and OR time could potentially be viewed as complementary: SIS may provide the complexity signal needed for fair prioritization and throughput monitoring, while OR time supplies the capacity signal necessary for block management.

Our study illustrates how stakeholders perceive SIS could potentially be operationalized to inform scheduling and venue assignment. In the case scenario described in Appendix 1, cases were stratified into tiers: SIS >11 directed to Health Sciences Centre for highly invasive procedures, SIS 2–11 to Concordia for moderately extensive cases, and SIS <2 to Maples for day surgery. This tiered mapping provides a practical framework to match surgical complexity with institutional capacity, ensuring that high-SIS cases are accommodated without displacing simpler cases that can be completed efficiently in ambulatory settings. Evidence supports this logic, as outpatient lumbar discectomy, laminectomy, and anterior cervical discectomy and fusion (ACDF) have demonstrated safety and efficiency in appropriately selected patients (16-18).

Beyond venue assignment, stakeholder feedback suggests SIS could enable complexity-adjusted throughput and simulation-based planning. Rather than counting cases equally, future operational models could allow leaders to monitor “SIS points cleared” per block/day/quarter, making performance comparisons fair across teams and institutions with different case mixes. Discrete-event simulation (DES) is an operations-research model that represents care delivery as a series of time-stamped events, such as case start, case end and recovery, thus allowing bottlenecks and policy changes to be tested in silico before altering real-world workflows. Theoretically, incorporating SIS into DES would allow for rapid “what-if” testing of scheduling policies, such as reserving long blocks for SIS >11, batching low-SIS cases, changing venue splits between inpatient and ambulatory sites, and reading out effects on median and tail wait times, overtime, LOS, and cancellations before implementation. This approach is supported by operations research showing DES can improve OR throughput and reduce delays in surgical services and orthopedics (3,7,19,20). While testing such systems is beyond the scope of this qualitative perception study, future research could explore whether an SIS-weighted simulator parameterized with local OR logs and stratifying cases into SIS tiers (<2, 2–11, >11) would let administrators forecast the impact of different policies on wait times and cancellations, choose strategies that free capacity where it matters most, and maximize throughput without compromising safety or staff well-being.

Despite its promise, SIS adoption faces several challenges. Participants highlighted barriers including feasibility of real-time data capture, limited IT infrastructure, and upfront costs related to EHR integration and training. These concerns align with broader evidence: lack of integration into clinical workflows is a common reason why prediction tools fail to gain traction, with training and IT costs are often the largest drivers of implementation expense (21-23). A practical solution is to auto-compute SIS directly from structured booking fields or procedural codes, minimizing manual burden (10). Invasiveness indices have already been adapted to registry and coded data with strong validity (14). To address the incomplete representation of BMI, revisions, and comorbidities, SIS could be supplemented with simple modifiers for operational forecasting, a direction supported by evidence linking these factors to increased risk in invasive lumbar surgery (12). Finally, prospective validation of scheduling policies through SIS-weighted DES using local OR data is recommended prior to broad rollout.

A minority of respondents questioned whether SIS could be harmonized across provinces, given current jurisdictional variability in wait-time reporting. This is a familiar Canadian challenge, as provinces apply pan-Canadian definitions differently in practice. Establishing SIS as a national complexity definition with standardized reporting conventions, while allowing local flexibility in implementation, could balance comparability with responsiveness to provincial realities (24,25).

Finally, the findings of this study must be interpreted in the context of its limitations. The sample size is limited to 21 participants, and the use of a snowball recruitment strategy initiated from the principal investigator’s professional network introduces a risk of selection bias. This method may overrepresent respondents already sympathetic to the SIS framework. However, this bias is partially mitigated by the geographic and institutional diversity of the final cohort, which included stakeholders from four provinces across both academic and community hospital settings, ensuring the feedback was not restricted to a single homogenous group. Furthermore, while this study focused on surgeons and hospital administrators as the primary decision-makers for waitlist prioritization and operational scheduling, it is limited by the exclusion of other essential stakeholders. The perspectives of patients, anesthesiologists, and nursing staff are critical to the holistic evaluation of surgical triage and were not captured in this cohort. Future research should incorporate these multidisciplinary viewpoints to fully assess the systemic impact and feasibility of widespread SIS adoption. Additionally, a fundamental limitation of the SIS framework is that it focuses exclusively on procedural invasiveness, inherently omitting patient-specific factors, such as socioeconomic barriers, individual pain levels, and overall waitlist tolerance, that also critically influence equitable case prioritization and patient outcomes.

Conclusions

The SIS represents a promising framework for complexity-adjusted waitlist management in spine surgery that warrants further operational testing. Our findings show strong stakeholder support for its adoption, citing improved prioritization, triage, and transparency. Supported by extensive literature linking invasiveness to operative time, blood loss, complications, and resource use, stakeholders perceive that SIS offers advantages over relying solely on OR time and has potential applications across scheduling, simulation, and benchmarking. Implementation will require thoughtful integration into EHR workflows, investment in automation and training, and national harmonization, but with these steps, SIS could serve as a valuable tool for future implementation studies aimed at improving equity and efficiency in Canadian spine care.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the COREQ reporting checklist. Available at https://asj.amegroups.com/article/view/10.21037/asj-2026-1-0002/rc

Data Sharing Statement: Available at https://asj.amegroups.com/article/view/10.21037/asj-2026-1-0002/dss

Peer Review File: Available at https://asj.amegroups.com/article/view/10.21037/asj-2026-1-0002/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://asj.amegroups.com/article/view/10.21037/asj-2026-1-0002/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was reviewed and granted an exemption by the Research Improvements Through Harmonization in Manitoba (RITHIM) Research Ethics Board and informed consent was obtained from all individual participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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