Measurement of Mental Workload in Clinical Medicine: A Review Study

AUTHORS

Aidan Byrne 1 , *

1 Clinical Skills and Simulation, School of Medicine, Cardiff University, Cardiff, UK

How to Cite: Byrne A. Measurement of Mental Workload in Clinical Medicine: A Review Study, Anesth Pain Med. 2011 ; 1(2):90-94. doi: 10.5812/kowsar.22287523.2045.

ARTICLE INFORMATION

Anesthesiology and Pain Medicine: 1 (2); 90-94
Published Online: September 26, 2011
Article Type: Review Article
Received: August 8, 2011
Revised: August 10, 2011
Accepted: August 15, 2011
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Abstract

Background: Measures of mental workload are now commonly used in industries to identify sources of error and to improve performance.

Objectives: This study aimed to review the evidence for the use of this technique within medicine.

Patients and Methods: We used search engines and the internet to identify experimental studies that included a measure of mental workload in medical practitioners or trainees/students. Studies that aimed to measure mental “stress” as a disorder, or “productivity” were excluded. Each abstract and then the full paper were appraised prior to inclusion.

Results: Thirty-three studies were identified that matched the inclusion criteria. Although these covered a variety of settings, common methods were identifiable. The results support the concept of mental workload measurement as an important factor in medical performance.

Conclusions: The limited number of studies and the variety of definitions and measurement techniques used in these studies, make direct comparisons difficult. However, the utility of this methodology in medical education appears to have been established, and guidelines for further research methods are proposed.

Keywords

Workload Patient simulation Medical errors

Copyright © 2011, Iranian Society of Regional Anesthesia and Pain Medicine (ISRAPM). This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/) which permits copy and redistribute the material just in noncommercial usages, provided the original work is properly cited.

2. Published Evidence

A wide variety of medical specialities have used these techniques, including anesthesia (9-23), surgery (24-29), general medicine (30, 31), emergency medicine (32, 33), intensive care (34), radiology (35), ward staff (36, 37), primary care physicians (31, 38), and medical students (39).

The settings for the studies were also diverse, including simulators (9-14, 17, 19, 24-29, 34, 36, 37, 39), operating theatres (15, 16, 18, 20-23), outpatient/ambulatory care centres (30, 31, 38), emergency rooms (32, 33), general wards (36, 37), and radiology reporting rooms (35). However, most were small-scale studies with an average of 29 subjects (range, 9–116), and an average of 158 procedures (range, 9–2053). The workload associated with the primary task was defined by non-standardized measures such as task analysis (20, 22), number of patients seen (30, 31), or number of knots tied (24, 25). Other studies used an “unloaded” period to compare to the main task studied (12, 39). The use of widely accepted and validated measures of primary task workload would make comparison between studies easier; however, such measures were used in a minority of the studies (26, 27). The methods used to measure the primary task workload were equally diverse and used data from simulators (14), rating of videotapes by trained observers (11, 35), observed counts of procedures completed (11), clinical record-keeping (10), reaction times (9), time-in-motion studies (16), and the number of observed errors (25). An objective measure of workload was used in 14 studies and also varied widely in method. These included time taken for the subject to respond to a change in a visual stimulus (16, 20-22, 26), heart rate derived from the electrocardiogram (22), accuracy of 2-number mental arithmetic (15), response time to a tactile stimulus (12, 39), accuracy of the clinical record (10), skin conductance (33), and eye-blink rate (24).

Subjective workload was measured with either the NASA TLX form (9, 11, 14, 17, 19, 25, 26, 28, 32-37), the Borg workload score (20-22), or other paper-based, unvalidated forms (13, 15, 23, 24, 38). The conclusions of these studies suggest that mental workload is reduced by using speech-input records compared to written records (13), with experience (15, 21, 23, 26, 27, 29, 30), using a mixed graphical-numeric interface (11), using drug administration devices that provide feedback (14), with an improved electronic interface (17, 37), with the addition of instruction to training, (25) with increased practice (28), and with digital rather than hard-copy x-rays (35). Workload was increased with fatigue (30, 31), number of patients seen (30, 31, 38), dissatisfaction (30, 31), poor self-rating of performance (30, 31), poor observer rating of performance (30), laparoscopic compared with open surgery (24), during an anesthetic crisis (10), during induction of anesthesia (15, 16, 20-22), with more difficult cases (15, 23, 26, 38), with increased administrative tasks (38), when students are present (22), and when using transesophageal echo during anesthesia (20).

3. Practical Considerations

The relevance of mental workload to performance has already been identified, both as a theoretical possibility and as part of published studies (4, 6, 7, 26). It shares many concepts with Cognitive Load Theory (40), which emphasizes the need to recognize the limited cognitive abilities of learners when designing educational processes. The principles guiding the measurement of mental workload are well established and there is a very wide range of literature describing such studies in non-medical environments (1, 3, 5, 6). For example, the effects of using a mobile phone while driving have been measured using mental workload techniques (41).

It is also recommended that multiple measures be used, and that primary workload be included. This is both because currently available methods are not adequate to use in isolation, and because workload can vary in unexpected ways (1, 6, 8). For example, in a previous study, the heart rate of a subject was found to be raised during the “normal” phase of a simulation (in anticipation of a problem) only to fall toward normal during the simulated problem (42), presumably because the problem was not as bad as anticipated. In the same way, the mental workload of novices may be lower than more experienced trainees because they have yet to appreciate the difficulties facing them; this is termed “unconscious incompetence” (43). The principle difficulty faced by researchers is the establishment of standardized measures of mental workload and their normal ranges so that valid comparisons can be made between subject groups. Primary task measures are relatively easy to define in procedural skills, for example, the number of knots tied per minute by a laparoscopic trainer. However, it may also be possible to define a range, for example, of history-taking tasks with defined levels of complexity, in the same way that the workload associated with a variety of airway procedures has already been established (23). Objective measures of workload are more difficult to define, as theoretical approaches to the problem emphasize that workload has multiple aspects that may be measured separately (1). These aspects are often linked to specific neurological processes. For example, it is recognized that it is possible to a watch a monitor (visual task) while also listening to a conversation (auditory task). It is also possible to watch a monitor (sensory) and run through possible diagnoses (cognitive). However, it is difficult or impossible to listen to two conversations (auditory-auditory), watch two different monitors (visual-visual), or run through diagnoses and calculate a drug dose at the same time (cognitive-cognitive).

It is therefore crucial that the objective measure chosen is appropriate to the task chosen. For example, subjects asked to calculate drug dosages (cognitive), may not show any change in reaction time to a warning light (visual), because different resources are being used. More appropriate measures, for example, would be the response to a pattern stimulus (visual) while performing laparoscopic surgery (visuo-motor), (27) or response to a tactile stimulus (monitoring) during anesthesia (monitoring) (12). Physiological measures such as heart rate have fewer problems in that they are less resource-specific. For example, subjects are likely to sweat more whether overloaded by visual, auditory, or cognitive tasks. However, these tasks may themselves be more intrusive and subject to physical effects (21, 22). For example, the increased heart rate of a subject performing chest compression during resuscitation is unlikely to be entirely due to increased mental workload. Further, the complex medical environment makes it inappropriate to directly transfer techniques used in other environments such as aviation, for example, as pilots work in a standardized, constrained environment where monitors may be placed in fixed locations. In contrast, even in anesthesia, which is often compared to aviation, staff move between rooms and often use a variety of equipment in different settings (15). Subjective measurements are less complex in that a simple questionnaire can be used. The NASA TLX (44) questionnaire has been validated in other areas and is freely available for non-commercial use. The Borg Workload Scale (45) has also been used in a minority of studies, but is less well validated. Clinicians may feel that the reduction of clinical practice to a set of numbers is inappropriate. We agree with others (46) that expertise alone is not the hallmark of a competent doctor but “rather the manner in which individuals choose to approach their work. ” Measured mental workload is only one aspect of performance; however, it may provide vital insights into ways to make medicine safer. Recent research has already linked measured mental workload with clinical outcomes (47).

4. Conclusions

Mental workload is a concept that may be used as a method of assessment, to determine the effect of training, and perhaps also as a component of performance assessment. Further studies should include, as a minimum, a measure of the primary workload, an objective measure of workload, and a measure of subjective workload. Studies should avoid the use of new methods that have not yet been validated, unless used in addition to an established method for comparative purposes. In particular, subjective workload should use the NASA TLX score as this has been widely validated in other fields and has been used in the majority of studies reviewed in this paper. Whenever possible, additional techniques should be included so that comparisons between measurement techniques can be made. For example, in a study of primary task workload in an outpatient department, primary workload could be measured in terms of the number of patients seen, case difficulty rating, and observer rating.

It must also be recognized that mental workload should be evaluated as a single aspect of medical performance, and not confused with the concepts of competence or effective practice.

Acknowledgements

Footnotes

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