The role of knowledge facilitators and inhibitors: lessons from airline reservations systems.

Strategic frameworks seeking to explain how an organisation may generate superior performance are numerous. Earlier approaches centred on the competitive position of an organisation within its industry, with subsequent attention focused on an organisation's core competences. More recently, research has concentrated on knowledge and organisational learning. By reference to a study of airline-developed computer reservation systems (CRSs), this article explores the strategic importance of information in creating knowledge to generate superior performance. By examining developments in the use, management and control of information derived from CRSs, evidence is presented to explain how CRS-owning airlines have circumvented regulatory controls and increasingly competition to sustain competitive advantage through the development of their information and knowledge systems. This research demonstrates the need for organisations to develop 'knowledge facilitators' that foster the creation of new knowledge. Equally, managers must develop 'knowledge inhibitors' that help to sustain competitive advantage by limiting the abilities of competitors to create knowledge themselves.

Competing with CRS-generated information in the airline industry.

For over 20 years, computer reservation systems (CRSs) have been cited as a source of competitive advantage for the airlines that developed them. This paper reviews the developments that have given rise to such advantage, emphasising the use of CRS-generated information as a competitive tool within the airline industry. New evidence is presented suggesting that the control, dissemination and manipulation of CRS data by owning airlines continued to allow them to capitalise on their investment at the expense of competitors during the 1990s.

Development of process control methodology for tracking the quality and safety of pain, agitation and sedation management in critical care units

Objective: To develop sedation, pain, and agitation quality measures using process control methodology and evaluate their properties in clinical practice. Design: A Sedation Quality Assessment Tool was developed and validated to capture data for 12-hour periods of nursing care. Domains included pain/discomfort and sedation-agitation behaviors; sedative, analgesic, and neuromuscular blocking drug administration; ventilation status; and conditions potentially justifying deep sedation. Predefined sedation-related adverse events were recorded daily. Using an iterative process, algorithms were developed to describe the proportion of care periods with poor limb relaxation, poor ventilator synchronization, unnecessary deep sedation, agitation, and an overall optimum sedation metric. Proportion charts described processes over time (2 monthly intervals) for each ICU. The numbers of patients treated between sedation-related adverse events were described with G charts. Automated algorithms generated charts for 12 months of sequential data. Mean values for each process were calculated, and variation within and between ICUs explored qualitatively. Setting: Eight Scottish ICUs over a 12-month period. Patients: Mechanically ventilated patients. Interventions: None. Measurements and Main Results: The Sedation Quality Assessment Tool agitation-sedation domains correlated with the Richmond Sedation Agitation Scale score (Spearman [rho] = 0.75) and were reliable in clinician-clinician (weighted kappa; [kappa] = 0.66) and clinician-researcher ([kappa] = 0.82) comparisons. The limb movement domain had fair correlation with Behavioral Pain Scale ([rho] = 0.24) and was reliable in clinician-clinician ([kappa] = 0.58) and clinician-researcher ([kappa] = 0.45) comparisons. Ventilator synchronization correlated with Behavioral Pain Scale ([rho] = 0.54), and reliability in clinician-clinician ([kappa] = 0.29) and clinician-researcher ([kappa] = 0.42) comparisons was fair-moderate. Eight hundred twenty-five patients were enrolled (range, 59-235 across ICUs), providing 12,385 care periods for evaluation (range 655-3,481 across ICUs). The mean proportion of care periods with each quality metric varied between ICUs: excessive sedation 12-38%; agitation 4-17%; poor relaxation 13-21%; poor ventilator synchronization 8-17%; and overall optimum sedation 45-70%. Mean adverse event intervals ranged from 1.5 to 10.3 patients treated. The quality measures appeared relatively stable during the observation period. Conclusions: Process control methodology can be used to simultaneously monitor multiple aspects of pain-sedation-agitation management within ICUs. Variation within and between ICUs could be used as triggers to explore practice variation, improve quality, and monitor this over time