Improving the Measurement of Surgical Site Infection (SSI) Risk Stratification and Outcome Detection
ACTION Contract No. 290-2006-00-20, Task Order No. 8
Task 4: Surgeon Focus Group – Final Content Analysis
Participants
Six surgeons in addition to the facilitator participated in the focus group conducted on February 3, 2010, adjunct to the 5th annual Academic Surgical Congress in San Antonio, TX. Participants represented multiple health system types and surgical specialties, as described in Table 1 below:
Table 1. Surgeon Specialties and Health System Affiliations
| Surgeon ID | Surgical Specialty | Health System Type |
|---|---|---|
| Surgeon #01 | General Surgery | Academic / Private |
| Surgeon #02 | General Surgery | Academic |
| Surgeon #03 | General Surgery | VA |
| Surgeon #04 | General Surgery – Trauma/Critical Care focus | Academic / Safety Net |
| Surgeon #05 | General Surgery – Trauma/Critical Care focus | Private |
| Surgeon #06 | Surgical Oncology | Academic / Safety Net |
Data Analysis
Focus group data were collected through audio recordings and notes and observations documented by a qualitative researcher during the focus group session, and subjected to content analysis through review of both written and recorded materials. An inductive approach utilizing an open, heuristic coding process was undertaken in order to identify initial topics mentioned by participants. Individual topics were then further categorized based on the number of participants who conveyed agreement with the concept being discussed. A topic was identified as a theme based on the mention of or agreement with an item by three or more individual participants.
Data were reviewed to the saturation point and discussed with the focus group facilitator and subject expert to ensure the most comprehensive identification of patterns. Topics identified as duplicative were combined into a single occurrence, and themes identified from the comprehensive topic list based on the number of individual mentions. The final themes identified through analysis are categorized and presented in Table 2 below.
Table 2. Themes Identified in Content Analysis of Surgeon Focus Group Data
Models and Metrics |
| Current SSI risk assessment tools are inadequate; current risk adjustment models are inappropriate because of improperly accounting for nonmodifiable factor inclusion. A risk model should be based on patient factors as predictors. |
| The number of variables in the equation (the model) is an issue. Providers may feel frustration at expending effort in tracking nonsignificant factors that don't effect substantive change. |
| Existing metrics need to be adjusted and/or new metrics need to be developed. Providers would like to know which factors are most important and significant. |
| Risk provides a metric (standard) against which to measure outcomes, but current risk models are not specific enough. |
| Factors, Risks, and Rates |
| The specific definition of a factor is important. Variance in the interpretation of measures may significantly affect results (rates). |
| Consider conducting an intervention on one or more risk factor variables based on risk level, instead of a “one size fits all” approach (stratification). |
| Increased awareness of risk factors and/or attention to measurement and tracking improves compliance. |
| Surgery scheduling and timing issues are themselves factors that affect risk. |
| Emergency surgery risks should be considered separately from elective surgery risks. |
| The risk of not operating may outweigh the risk of operating (risk of SSI). |
| The risks for a patient with managed comorbidities, or one who is compliant with medical recommendations, are different from those for one who has unmanaged or undocumented comorbidities or who is not compliant. |
| Feedback needs to be provided/received in timely fashion and at the level of the individual provider. |
| Assessment of rates based on clinical pathways (process factors) is notably different from the assessment of rates based on end results (outcome factors). |
| Rates from data provided by hospitals that pay to participate in the database may not be best reflective of the whole population. |
| Methods of documentation significantly affect reported rates, especially in private settings versus public ones (payer-based). |
Summary
The consensus among participants was that current models for surgical site infection risk assessment were inadequate for their needs. Risk models were found to either inappropriately account for the factors they included or to include an excessive number of factors, such that items of actual significance were obscured. Desire was expressed for the development of new models based on specific patient factors which are identified as significant in affecting risk rates.
Participants agreed that infection rate assessments varied based on items such as whether rates were determined based on process or outcome factors, what methods of documentation were used to report rates between private and public settings, whether or not rate data provided to an analysis database by participant hospitals who pay for inclusion are reflective of all populations, and whether or not there was variance in the interpretation of how risk factor measures were defined.
Likewise, risks were determined to vary with some factors to a degree such that different broad categories of risk might be considered, such as risks for emergency surgery patients versus those for elective surgery patients; risks for a patient with managed comorbidities versus those for patients with poorly managed or undocumented comorbidities; risks for patients in compliance with medical recommendations versus risks for non-compliant patients; and whether in some cases scheduling considerations, operation timing, or the risk to a patient that might result from a delayed operation outweighed the risk of infection resulting from the operation itself.
Finally, participants suggested approaches for improving risk assessment and management, such as giving provider-level feedback in a timely fashion; increasing risk awareness by drawing attention to measurement and tracking of risk factors; and intervening on one or more risk factor variables based on a patient's risk level, instead of taking a “one size fits all” approach to risk.
