ABSTRACT
Background: The complex intensive care unit (ICU) admission decision process has numerous non-linear relationships involving multiple factors. To better describe and analyse this process, exploration of novel techniques to clearly delineate the importance and interrelationships of factors is warranted. Network analysis (NA), based on graph theory, attempts to identify patterns of connections within a network and may be useful in this regard. Objectives: To identify patterns of ICU decision-making pertaining to patients referred for admission to ICU and to identify key factors, their distribution, connection and relative importance. The secondary aim was to compare subgroups as per decision outcomes and case labels. Methods: NA was performed using Gephi software package as a secondary analysis on a dataset generated from a previous study on ICU admission decision-making process using a 20-questions game approach. The data were standardised and coded up to a quaternary level for this analysis. Results: The coding process generated 31 nodes and 964 edges. Regardless of the measure used (centrality, prestige, authority and hubs), properties of the acute illness, progress of the acute illness and properties of comorbidities emerged consistently as among the most important factors and their relative rankings differed. Using different measures allowed important factors to emerge differentially. The six subgroups that emerged from the modularity measure bore little resemblance to traditional factor subgroups. Differences were noted in the subgroup comparisons of decision outcomes and case prognoses. Conclusion: The use of NA with its various measures has facilitated a more comprehensive exploration of the ICU admission decision, allowing us to reflect on the process. Further studies with larger datasets are needed to elucidate the exact role of NA in decision-making processes. Contributions of the study: We performed a novel analysis of a complex decision-making process that allowed for comparison with traditional analytic methods. It allowed for identification of key factors, their distribution, connection and relative importance. This may subsequently allow for reflection on difficult decision-making processes, thereby leading to more appropriate outcomes. Moreover, this may lead to new considerations in developing decision support systems such as the formulation of pro-forma data-capture tools (e.g. referral forms). Further, the way factors have been traditionally subgrouped may need to be reconsidered, with different subgroups being partitioned to better reflect their connection. This study offers a good basis for more advanced future studies in this area to use a new variety of analytical tools.
ABSTRACT
Background: Deciding to admit a patient into the intensive care unit (ICU) is a high-stakes, high-stress, time-sensitive process. Elucidating the complexities of these decisions can contribute to a more efficient, effective process. Objectives: To explore physicians' strategic thought processes in ICU triage decisions and identify important factors. Methods: Practitioners (N=29) were asked to decide on ICU referrals of two hypothetic cases using a modified '20 Questions' approach. Demographic data, decisions when full information was available, feedback on questions, rating of factors previously identified as important and influence of faith and personality traits were explored. Results: Of the 735 questions asked, 95.92% were patient related. There were no significant differences in interview variables between the two cases or with regard to presentation order. The overall acceptance rate was 68.96%. Refusals were associated with longer interview times (p=0.014), as were lower ICU bed capacity (p=0.036), advancing age of the practitioner (p=0.040) and a higher faith score (p=0.004). Faith score correlated positively with the number of questions asked (p=0.028). There were no significant correlations with personality trait stanines. When full information was available, acceptances for Case A decreased (p=0.003) but increased for Case B (p=0.026). The net reclassification improvement index was -0.138 (p=0.248). Non-subspecialists were more likely to change their decisions (p=0.036). Conclusion: Limiting information to what is considered vital by using a '20 Questions' approach and allowing the receiving practitioner to create the decision frame may assist with ICU admission decisions. Practitioners should consider the metacognitive elements of their decision-making. Contributions of the study: The study used a novel approach to explore physicians' decision-making process for admitting a patient to the intensive care unit (ICU). Understanding the main factors that influence the decision-making process will allow for streamlining the referral process, more effective selection of patients most likely to benefit from ICU treatment, and prevent inappropriate admissions into the ICU. The findings can also help to improve data capture tools and encourage practitioners to critically reflect on their decision-making processes.
ABSTRACT
BACKGROUND: The aim was to evaluate a non-operative approach to the management of haemobilia. METHODS: This was a retrospective analysis of patients presenting over 10 years with haemobilia. All patients had upper gastrointestinal endoscopy, abdominal ultrasonography and digital subtraction angiography. Superselective coil and/or Gelfoam embolization was done as close as possible to the bleeding site. Completion angiography was performed routinely to confirm adequate embolization. RESULTS: There were 23 patients with liver trauma and six with inflammatory conditions. All patients required resuscitation with fluids and blood transfusion, and had the haemobilia controlled successfully by angiographic embolization. There was one death from fulminant hepatic sepsis. CONCLUSION: This series attests to the efficacy of a non-operative approach to haemobilia using radiological diagnosis and intervention.
