The reporting of the research was made according to the consolidated criteria for reporting qualitative research (COREQ) checklist [13] and the manuscript was written according to the SQUIRE 2.0 guidelines [14].
Context
The study was conducted in the surgical department at the National Institute of Oncology (NIO), which is an academic anticancer center in Rabat (Morocco, North Africa) with approximately 300 major abdominal surgeries, including 50 liver resections per year. The RCA was performed using a standardized reporting tool that was developed from the ALARM framework [12, 15] by the local multidisciplinary team to ensure that critical contributory factors are considered during Morbidity and Mortality reviews (MMRs). The tool has been used locally since July 2019 for weekly MMRs that are dedicated to investigating severe postoperative complications and near misses. Therefore, participants in the study were familiar with the tool. The MMR reporting tool consists of 50 questions (Q) selected from a large set of examples proposed by the French High Authority for Health https://www.has-sante.fr/upload/docs/application/pdf/2017-07/dir152/2017-alarm-commente.pdf. Each question investigates one of the contributory factors related to the six following ALARM categories: “Patient”, “Tasks”, “Individual staff”, “Team”, “Work environment”, and “Management and Institutional context”. This latter was obtained from the merge of two categories “Organizational and management factors” and “Institutional context factors”, as it was suggested by Vincent et al ([12, 15]). Answers incriminating a contributing factor (“yes” or “no” depending on the context) are referred to as “triggered answer” or “triggered contributory factor”, indifferently. A “refuted” option or a “non-applicable (NA)” option (when information is judged lacking) is offered otherwise. Justifications and comments regarding triggered contributory factors, recovery factors, and corrective measures are included in the final report of the MMR. The set of 50 questions of the MMR reporting tool and their ALARM categorization is presented in Appendix 1.
Interventions
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Design
In the current study, an aggregate RCA (AggRCA) based on the ALARM framework [12] was used as a method to identify the main patterns of contributory factors associated with severe complications after liver resection. A pattern was defined as a regular sequence of factors contributing to the predefined outcome (vs. single root cause [5]).
In order to limit data overwriting, we chose to analyze aggregated data from independent RCAs of single cases, rather than making a root cause analysis directly from a summary of the cohort.
All the cases of severe complications after elective liver resection that were consecutively performed at an academic surgical department between January 1st, 2018 and December 31st, 2019 were included. Severe complications were defined as complications grade > IIIa according to the Clavien-Dindo classification within the first 90 postoperative days (PODs) [16, 17].
In order to overcome selection and disponibility biases associated with voluntary reporting of adverse events [18], cases were identified from a prospective electronic database including all liver resections performed at the department,
Research team and participant selection
The research team included the surgeon in charge of the liver surgery program at the NIO (BA), a surgical resident (LO), and a research fellow that acted as a third party (HK). Six clinicians (4 surgeons, 2 intensivists) and 2 nurses, were purposively selected among surgical and intensive care staff given their involvement in the management of liver resections and their experience with Mortality and Morbidity reviews (MMRs). Characteristics and roles of participants and research team members are detailed in Table 1.
Table 1
Characteristics and roles of the research team and study participants
Initials, Credentials | Age, Gender | Specialty (subspeciality), Current position at the NIO | Experience in the specialty; Experience at the NIO** | Roles in the Aggregate Root Cause Analysis (RCA) process |
Step 1 Event storyline | Step 2 Single-case RCAs | Step 3 Step 2 consolidation | Step 4 Focus group Step 3 validation | Step 5 Aggregate RCA |
HK* | 26 years, Female | MD student, Research fellow | NA; 24 months | Production | NA | Participation | Co-facilitation | Participation |
LO*, MD | 27 years, Female | Surgery, Resident | 2 years; 18 months | Production | Participation | Participation | Participation | Participation |
BA*, MD | 41 years, Male | Surgery (hepatobiliary), Attending physician, MMR coordinator | 10 years; 25 months | Validation | Participation | Participation | Facilitation | Participation |
GA, MD | 36 years, Male | Anesthesiology & Intensive care, Attending physician, MMR coordinator | 7 years; 59 months | Validation | Participation | NA | Participation | NA |
EB, MD | 37 years, Male | Anesthesiology & Intensive care, Attending physician | 7 years; 31 months | NA | Participation | NA | Participation | NA |
MA, MD | 40 years, Male | Surgery (colorectal), Attending physician | 10 years; 20 months | NA | Participation | NA | Participation | NA |
SA, MD | 39 years, Male | Surgery (colorectal, peritoneal surface), Attending physician, Head of the OR | 8 years, 64 months | NA | Participation | NA | Participation | NA |
AM | 38 years, Male | Nurse, Head nurse | 13 years, 157 months | NA | NA | NA | Participation | NA |
AS | 29 years, Female | Nurse, Patient care coordinator | 6 years, 27 months | NA | NA | NA | Participation | NA |
* Research team **At the end of the study |
MD, medical doctor; NA, not applicable; NIO, National Institute of Oncology; OR, operative room |
Aggregate RCA process
The AggRCA was conducted through a five-step process over the period from December 2019 to March 2020. The MMR reporting tool was used for data collection and aggregation.
Step 1: Event storyline. For each case, a storyline depicting the timeframe of the perioperative care was created. Data were collected from the electronic database and completed from the patients’ respective hard copy files: case history, physical examination, results and/or copies of documented preoperative medical imaging, pre-anesthetic consultation reports, treatment plan decisions, procedure reports, monitoring, and complication diagnosis and management. Interviews with staff members were conducted in case of missing information to obtain the most comprehensive case reports.
Step 2. Single-case RCAs
Anonymized storylines were emailed to the six participating clinicians individually for review. Each participant was asked to fill out the MMR reporting tool for every single case independently to determine potential contributory factors, recovery factors, and corrective measures. A deadline was set for two weeks after receipt of the storylines.
Step 3. Consolidation of single-case RCAs (workshop):
For each question, the research team consolidated the respondents’ answers into a single option: “Triggered”, ”Refuted” and “NA”. Consolidation was based on the congruence of responses between at least three (half) of the respondents, unless a justification that was presented brought unique insights: individual staff perception (stress, fatigue, moral support) and/or specific perspective of the context of events (direct involvement in the management in the ICU or the OR). Conflicting justifications were identified and deferred to the next step for resolution. Recovery factors and corrective measures were pooled and rephrased using verbs.
Step 4. Validation of single-case RCAs (focus group)
All the participants were gathered one month after the individual analysis was completed. The consolidation process and results were presented. Conflicting justifications were discussed and settled on a case by case basis until a consensus was reached for all triggered contributory factors, recovery factors, and corrective measures.
Step 5. Aggregate RCA. Combinations of triggered contributory factors across the single cases were visualized according to the ALARM categories. Validated data from single cases were aggregated to obtain a distribution (percentage) of triggered contributory factors and their respective categories among the whole cohort.
A network of relationships (contribution to harm and/or failure to prevent harm) was established between the contributory factors that were triggered in more than half of the cases. The main patterns were suggested from the network analysis and refined from the integration of less frequent contributory factors, as well as insights from recovery factors and corrective measures.
Statistical analysis
Aggregated data from single-case answers were summarized into descriptive statistics tables, including median, percentages, standard deviation, and quartiles when appropriate. Analyses were performed using Google sheet.