A10.1 Low Uncertainty Group sensitivity analysis
In the main analysis we set the level of consistency at 0.8 in our initial analysis. However, by setting the level of consistency lower, the degree to which cases displaying a given recipe lead to the outcome is lower and we might expect that different combinations may lead to the outcome.
A10.1.1 High Uncertainty Group - Low Admission Rate (HUG-LAR)
In the HUG-LAR group, the number of recipes consistently leading to the outcome was four (Table A10.1). In recipe 1, having high co-location AND not high use of HIT was not seen at the 0.8 consistency level. From a theoretical perspective this was consistent with the hypothesis, but may indicate that HIT is not necessary for high performance when co-location was present. This raises the question of how interchangeable information strategies might be with each other. The presence of an observation room was also consistently a recipe for high performance at the lower consistency (0.7) level. In recipe 3, high guideline use and not high co-location may indicate that there are cases where guidelines can play a role in care, potentially in those individuals with simpler clinical presentation of high uncertainty conditions. Since their use is in combination with not high co-location, this would support the idea that certain cases do not require high interdependence. Finally, similar to the
previous combination, having an emergency residency program and not having high co-location, may allow for less complex cases to be treated without the need for high interdependence. While this is not supportive of the theory, in practice this is often the case in the ED.
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Table A10.1 Simplified Recipes for achieving high performance (HUG - LAR) at 0.7
Recipe
Feature 1 2 3 4
EM Residency X
Observation Unit X
Screen All Patients
High IT user x
High guideline user X
High Co-location X x x
Raw coverage 0.333 0.274 0.390 0.119 Unique coverage 0.146 0.090 0.153 0.019 Consistency 0.835 0.932 0.880 0.900 Overall solution consistency 0.848
Overall solution coverage 0.694
Note. Upper-case X indicates causal condition is present; Lower-case X indicates causal condition is absent
Abbreviation: HUG High Uncertainty Group
A10.1.2 High Uncertainty Group - High Performance (HUG - LAR and LRR)
For the HUG when we applied the sensitivity analysis by lowering the criteria from 0.8 to 0.7 we found four high performance recipes (Table A10.3). Recipe 1 consisted of Screen all patients AND High IT user. Recipe 2 is an ED with an observation unit; Recipe 3 no EM residency and high Guideline use and recipe 4 High Guideline use and not a high Co-locator. Consistency scores were mostly high for the individual recipes, however overall consistency was low (72.7%). For overall coverage, the set of 4 recipes represent a majority (88.2%) of fuzzy set membership in high
performance. Considering individual recipe coverage, recipe 1 (not screening all patients and not high HIT use) was the most empirically relevant recipe with raw coverage of 34.4% and relative (unique) coverage of 12.6%.
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Table A10.2 Simplified Recipes for achieving high performance (HUG - LAR and LRR) at 0.7
Recipe
Feature 1 2 3 4
EM Residency x
Observation Unit X
Screen All Patients x
High IT user x
High guideline user X X
High Co-location x
Raw coverage 0.344 0.265 0.483 0.376 Unique coverage 0.126 0.074 0.045 0.024 Consistency 0.882 0.956 0.900 0.900 Overall solution consistency 0.882
Overall solution coverage 0.727
Note. Upper-case X indicates causal condition is present; Lower-case X indicates causal condition is absent
Abbreviation: HUG High Uncertainty Group
We found some similarity in the recipes for overall performance at the 0.8 and 0.7 level, however only one recipe (Recipe 5 in the 0.8 level and Recipe 2 at the 0.7 level) exactly the same. This recipe was for observation unit alone. As mentioned above, there is evidence to suggest that observation units can improve certain patient outcomes (Ross et al., 2012). Recipe 1 at the 0.7 level bears some similarity to Recipe 1 at the 0.8 level mark – both have Not screening all patients AND High HIT use. In practical terms, not screening all patients may allow more autonomy to the clinician and
performance will therefore vary on the level of clinical experience and clinical uncertainty across the patient population. The use of screening and high HIT use together may be a common finding in practice as they have overlapping technology platforms and both can be incorporated in ED work flow. However the combination of both may limit a clinicians ability to make decisions if the criteria are set to high, leading to high admissions, or alternatively, patients not being admitted, resulting in early discharge and possibly early return. From a theoretical point of view this finding is less supportive of the IP theory than at the 0.8 level which included High co-location. Based on the
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theory, we would have expected both high HIT use AND screening all patients to produce high performance in this group, since one is a high information processing strategy and the other a high IP mechanism. Recipe 3 bears some similarity to recipe 6 at the 0.8 level, in that both recipes have a combination of high guideline use AND no EM residency. Recipe 6 however, incorporates High co- location which is supported by the theory under conditions of high uncertainty. As mentioned above, from a practical point of view, guidelines are potentially being applied for cases of lower uncertainty in the Hug group, leading to better performance, which is supported in the literature (Grimshaw et al., 2004). However guidelines often do not provide consistent improvement and require reinforcement. One such example where guidelines need reinforcement and may be less effective is with high turnover, which is may occur in EM residency programs, were residents rotate through. This may offer an explanation for the combination of not having an EM residency program and High guideline use with high performance. On the other hand, this combination may represent an ED which has less EM trained-physicians or experienced physicians, and were guidelines have higher use. Recipe 4 is not supported by the theory, since the theoretical model places co-location as the most efficient IP strategy under high uncertainty. However in practice as was mentioned above, this combination may reflect an ED with a less severe population to which a simple guideline may apply.
A10.2 Low Uncertainty Group sensitivity analysis
A10.2.1 Low Uncertainty Group - Low Admission Rate (LUG-LAR)
No recipes were found which met the 0.7 threshold for high performance measured as low admission rate.
A10.2.2 Low Uncertainty Group - Low 72-hour Return Rate (LUG-LRR)
Two types of sensitivity analysis were conducted for this measure. The first used a lower consistency rate (0.7) and the second expanded the return rate period from 3 to 30 days.In the low injury group, it is possible that complications may develop more slowly due to the nature of the injury or treatment
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thereof. For example, a wound that was sutured in the ED may become infected over the course of several days with the patient only returning after the 3-day period.
When the 30 day return rate was used as the high performance measure, several recipes were found to result in high performance (Table A8.4). These recipes were all different to the two recipes found for low 3-day rates (7.2.4). Recipe 1 was High guideline user, no EM residency, not a high co- locator. Recipe 2 consisted of EM residency, not a high HIT user, a high co-locator while Recipe 3 combined high HIT user and not a high co-locator. Recipe 4 and 5 each consisted of a single measure. In Recipe 4 the measure was not screening all patients, while Recipe 5 consisted of only having an observation unit.
Consistency scores were high for all the individual recipes however relative to the 3-day return rate the overall consistency was low (73.2%). For overall coverage, the set of 5 recipes represent a majority (94.2%) of fuzzy set membership in high performance. Considering individual recipe coverage, recipe 4 (not screening all patients) was the most empirically relevant recipe with raw coverage of 39.4% and relative (unique) coverage of 18%.
Table A10.3 Simplified Recipes for achieving high performance (LUG Low 30-day Rates)
Recipe
Feature 1 2 3 4 5
EM Residency x X
Observation Unit X
Screen All Patients x
High IT user x X
High guideline user X
High Co-location x X x
Raw coverage 0.335 0.067 0.211 0.394 0.247 Unique coverage 0.103 0.018 0.03 0.18 0.089 Consistency 0.931 0.93 0.956 0.952 0.961 Overall solution consistency 0.732
Overall solution coverage 0.942
Note. Upper-case X indicates causal condition is present; Lower-case X indicates causal condition is absent. Abbreviation: HUG High Uncertainty Group; LUG Low Uncertainty Group.
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Recipe 1 (High guideline user AND no EM residency AND not a high co-locator) aligns with the study’s theoretical and practical approach. The low uncertainty task is matched by a mechanism supported by rules and protocols (high guideline user). However in this case we see that having an information processing mechanism such as an EM residency and strategy such as high co-location which are more suited to high uncertainty tasks, may have conflicted with the application of rules or guidelines and so their absence may facilitate the use of guidelines and support high performance in this group. Both Recipe 2 (EM residency AND not a high HIT user AND a high co-locator) and Recipe 3 (high HIT user and not a high co-locator) present one of the high information processing strategies and the non-presence of another. This raises the question of how the various information lowering and information processing strategies relate with each other. The theory put forward by Galbraith presents the strategies and mechanisms as hierarchical i.e. there is a linear relationship such that as the uncertainty increases the focus on information processing moves from quantity to
efficiency. However based on the recipes we found, this may not be the case. For example as is the case in recipe 3, the presence of two high information processing strategies may not be symbiotic if one is efficient on its own. In fact, the presence of both may lead to underutilization of resources, if for example patient volume or clinical complexity is not reaching a significant level in a particular ED.
The findings in recipe 4 - (Not screening all patients) is supportive of our theoretical framework, which hypothesized that goal-setting is better suited to high task uncertainty than low task
uncertainty. Practically speaking, if the admission criteria are too strict, patients may be under-treated and incorrectly discharged. These patients may then return at a later time. The presence of an
observation unit for high performance in recipe 5 is supportive of our theoretical framework and in practice, as with the high uncertainty group, gives clinicians time to reflect on changes in a patient’s condition and make a disposition decision with more clinical confidence.
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A10.2.3 Low Uncertainty Group - High Performance (LUG - LAR and LRR)
No recipes were found which met the 0.8 threshold for high performance measured as low admission rate and low 72-hour return rate. The same result was found when the threshold was set lower to 0.7. in the sensitivity analysis. The lack of recipes may be due to the low number of admissions in this group, which limits the number of EDs with high performance in this group.
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