Which clinical decisions benefit from automation? A task complexity approach☆
Introduction
There is a need to improve clinical decision-making in order to reduce practice variation, preventable medical errors and support the delivery of evidence-based medicine [1], [2]. Information technology has been recognised as a key enabler in the improvement of clinical decision quality [3]. However, the decision to use technology in support of clinical decision making is itself often empirical rather than guided by sound theoretical principles. It thus remains unclear when technology should be brought in to support decision-making for complex clinical problems. Not surprisingly, information-technology initiatives have provided mixed results and the uptake of decision aids by clinicians has been slow [3], [4], [5].
The decision to select one task over another for computational support should be based on some principled methods [6]. The effectiveness of instructional aids and decision support systems (DSS) is predicated on their usefulness in addressing the specific problems that lead to sub-optimal decisions [7]. The objective of our study is to design a framework for the rational selection of clinical tasks for automation using a cognitive task complexity approach and to investigate its potential benefits.
Section snippets
Theoretical framework
Task complexity affects information use and has been shown to be one of the important determinants of decision-making efficiency [8]. Based on a cognitive engineering approach, Woods postulated that all tasks contain three essential components such as products, required acts, and information cues [9]. He has argued that complexity describes the relationships between acts and information cues as task inputs and has used these components to derive three dimensions of task complexity: component
Choice of the domain
Prescribing is an activity central to clinical practice [11]. The prescribing task in critical care is accomplished under time pressure and with limited diagnostic information. Infection is a common presenting problem and antimicrobials are among the most frequently prescribed drugs. Therefore antibiotic use has long been considered an important target for analysis of decision-making [11]. Several studies demonstrated significant level of antibiotic misuse ranging at university medical centres
Prescribing effort and quality
We have constructed a prescribing decision tree that incorporates the two main management strategies (‘treat first’ and ‘test first and treat after’) for suspected VAP (Fig. 3). It is structured around the prescribing sub-goals: (1) control of potentially treatable infection; (2) prevention/delay of antibiotic resistance; (3) reduction of drug-related adverse effects; (4) prevention of super-infection with multi-resistant microorganisms [15]. This decision tree allows the calculation of the
Discussion and conclusions
We argue that prescribing is a complex clinical task because it involves: (a) integration of complex information from a variety of sources; (b) incomplete or imperfect information; (c) the presence of uncertainty and time pressure; (d) a complex interaction between the clinician and the patient with different utilities and values to the alternatives in the decision.
Such high complexity is a general risk factor in clinical decision making. It has, for example, been suggested that clinicians
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This paper was presented at the MIE2002.