support system in prehospital c”>American Journal of Emergency Medicine (2013) 31, 145-153

Original Contribution

Decision support system in prehospital care: a randomized controlled simulation study^?

Magnus Andersson Hagiwara RN, MScN ^a,b,?, Bengt Arne Sjoqvist PhD ^c, Lars Lundberg MD, PhD ^a,d, Bjorn-Ove Suserud RN, PhD ^a,

Maria Henricson RN, CCRN, PhD ^b, Anders Jonsson RN, PhD ^a,d

^aSchool of Health Sciences, University of Boras, Boras, Sweden

^bSchool of Health Sciences, Jonkoping University, Jonkoping, Sweden

^cDepartment of Signals and Systems, Chalmers University of Technology, Gothenburg, Sweden

^dSwedish Armed Forces Centre for Defence Medicine, Gothenburg, Sweden

Received 7 May 2012; revised 20 June 2012; accepted 26 June 2012

Abstract

Introduction: Prehospital emergency medicine is a challenging discipline characterized by a high level of acuity, a lack of clinical information and a wide range of clinical conditions. These factors contribute to the fact that prehospital emergency medicine is a high-risk discipline in terms of Medical errors. Prehospital use of Computerized Decision Support System (CDSS) may be a way to increase patient safety but very few studies evaluate the effect in prehospital care. The aim of the present study is to evaluate a CDSS.

Methods: In this non-blind block randomized, controlled trial, 60 ambulance nurses participated, randomized into 2 groups. To compensate for an expected learning effect the groups was further divided in two groups, one started with case A and the other group started with case B. The intervention group had access to and treated the two simulated patient cases with the aid of a CDSS. The control group treated the same cases with the aid of a regional guideline in paper format. The performance that was measured was compliance with regional prehospital guidelines and On Scene Time (OST).

Results: There was no significant difference in the two group’s characteristics. The intervention group had a higher compliance in the both cases, 80% vs. 60% (pb0.001) but the control group was complete the cases in the half of the time compare to the intervention group (pb0.001).

Conclusion: The results indicate that this CDSS increases the ambulance nurses’ compliance with regional prehospital guidelines but at the expense of an increase in OST.

(C) 2013

^? The authors would like to thank Sparbanksstiftelsen Sjuharad, Sweden, for supporting this study; the personnel of the ambulance organization of South Alvsborg, Sweden; and the personnel of Defence Medicine in Gothenburg.

* Corresponding author. School of Health Sciences, University of Boras,

501 90 Boras, Sweden. Tel.: +46 33 435 47 20; fax: +46 33 435 44 46.

E-mail address: [email protected] (M.A. Hagiwara).

Introduction

Emergency medicine is a challenging discipline charac- terized by a high level of acuity, a lack of clinical information, time pressure, and a wide range of clinical conditions [1]. These factors contribute to the fact that emergency medicine is a high-risk discipline in terms of

0735-6757/$ - see front matter (C) 2013 http://dx.doi.org/10.1016/j.ajem.2012.06.030

medical errors [2]. Patient safety issues and threats to patient safety are poorly investigated in prehospital care [3]. In addition to common threats to patient safety found in regular in-hospital emergency care, the specific Environmental factors encountered in prehospital care [4] may contribute to an increased risk. Errors in decision making and clinical judgment appear to be the dominant threats to patient safety in the prehospital setting [5,6]. The complexity of prehospital care is increasing with more advanced care such as the identification of patient condition, bypass protocols, and therapeutic interventions [6].

The prehospital work is accomplished by protocols and clinical guidelines. Evidence-based guidelines increase the quality of care in the prehospital setting [7,8], but compliance with guidelines is sometimes poor, for example, regarding patients with chest pain [9-11], pediatric patients with asthmatic symptoms [12], patients with exacerbated chronic obstructive pulmonary disease [13], and patients with burns [14].

The reason for the poor compliance in prehospital care is not known. Compliance with guidelines is a complex matter, depending on several factors such as guideline format, implementation strategy, and context [15]. Poor compliance with guidelines among physicians may depend on the usability and design of the guidelines. Paper-based guide- lines are thought to be very time-consuming and not suitable for “just in time use” [16]. Knowledge of the clinician’s cognitive process is important when designing guidelines. Humans are not rational decision makers who analytically compare different options; instead, they use patterns to highlight the most relevant information and to identify typical reactions in a given situation. Decisions in a naturalistic and dynamic environment are characterized by a mix of an intuitive and an analytic approach, where the decision maker uses his or her experience to produce a chain of patterns [17]. One way to make a guideline more suitable for daily clinical practice is to integrate the guideline in a computerized decision support system (CDSS) [16].

There are studies indicating that a CDSS increases compliance with guidelines in hospital settings [18,19], but other studies reveal a more complex picture, with a wide range of effects of CDSS [20]. Systems in which a CDSS was a natural part of the workftow performed better compared with systems where the users actively had to initiate a CDSS. Systems that provide decision support at the time and location of decision making also produced improved results [18,19]. To our knowledge, there are no existing studies that evaluate CDSS in prehospital care [21]. A CDSS constitutes a very complex intervention, which is heavily dependent on the context in which it is going to operate, and for this reason, a thorough scientific evaluation is requested before implemen- tation [22]. Because paper-based DSS have been shown to increase on-scene time (OST) [23], it is likely that the use of CDSS may also increase OST.

The aim of the present study is to evaluate the CDSS

effects on (1) compliance with regional prehospital guide- lines and (2) OST.

Methods

Study setting and population

This nonblind, randomized, controlled trial was con- ducted over 6 days during the period of March 12 to 31, 2011. The ambulance personnel were taking part in a full day of continuous education and were invited at the same time to take part in the present study. They were all employed in an ambulance service district in southwest Sweden. This district has a population of 285 329 inhabitants and was served by 28 109 ambulance missions in 2010. The districts area is estimated to be 2500 square miles, and the main hospital is placed in the middle of the district.

The inclusion criteria were registered nurse employed in an ambulance service district in southwest Sweden (n = 93) and willingness to participate in the study. Sixty of the 93 eligible nurses invited to participate in the study accepted the invitation (Fig. 1).

The trial took place at the Centre for Defence Medicine (CDM) in Gothenburg, Sweden. The CDM incorporates a simulation center with more than 10 years’ experience of medical simulation for educational purposes. Personnel from the CDM operated the manikin during the trial. The Laerdal Stavanger, Norway SimMan 3G was used. This particular patient simulator has previously been evaluated by para- medic students, demonstrating that the simulator has high functional fidelity [24].

The study was approved by the ethical adviser at the University of Boras, and the recommendations of the Swedish Research Council [25] were followed.

Trial design

Ten ambulance nurses were randomized to each of the 6 study days, with a total of 60 participants. The ftowchart of the randomization process is shown in Fig. 1.

To compensate for learning effect, where the results for the case performed second in order were expected to improve compared with the first case, the intervention group was further divided into 2 groups. One of them started with simulation case A (SCA) and the other with simulation case B (SCB). The control group was divided in the same way.

The study was part of a full day of station education. Simulation cases A and B were only used in the intervention/ control study. Both groups received the same training on the simulator manikin (40 minutes), apart from the fact that the intervention group received an additional 20 minutes’ hands- on training on the CDSS. The hands-on training did not include any simulation case. The same 2 instructors provided all the training for the participants, in both the control and the intervention groups.

The CDSS used in the study was designed using the MobiMed 4.0 prehospital eHealth platform from Ortivus AB, Stockholm, Sweden. The CDSS was customized and adapted to the study prerequisites, and regional prehospital

Control group starting with SCB

Control group starting with SCA

Intervention group starting with Simulation Case B (SCB)

Intervention group starting with Simulation Case A (SCA)

Control group N = 30

Intervention group N = 30

to participate study

= 60

Willingness

in the N

Ambulance personnel from southwest Sweden

N = 93

Fig. 1 Flowchart.

guidelines were used during the evaluation. In the design of the CDSS, interest focused on the parts supporting decisions, whereas the parts relating to care documentation were kept to a minimum to resemble the workload imposed on the reference group. The CDSS follows a structure common to all Medical patients and gives advice to the ambulance nurse during the assessment process. The assessment starts with a first survey with an ABCDE assessment followed by a second survey including anamnesis, identification of major symptoms, establishment of field diagnoses, and suggestions to treatment. The algorithm used in the CDSS is schemat- ically described in Fig. 2.

The participants in both groups assessed and treated 2 simulation cases. Simulation case A was a 60-year-old man with severe sepsis. Simulation case B was a 60-year-old woman with moderate dyspnea. The first case (SCA) represents a seriously ill patient where in the ambulance nurses had to identify and treat problems with the airway, breathing, circulation, and disability. The second case (SCB) was of a less acute nature, with minor respiratory distress.

Protocol

The scoring protocol was based on the care described in regional [26] and national [27] prehospital guidelines, which, in turn, comply with the principles of Advanced Medical Life Support (AMLS) [28]. The regional guidelines are validated by a prehospital medical expert panel. Based on these guidelines, a protocol with 35 steps for SCA and 30 steps for SCB was developed (Appendix). The final protocol was approved by the regional prehospital medical control panel.

Each step was given a score, 1 if performed correctly or 0 if not performed or performed incorrectly.

Because the main end point for the study was to measure the compliance with regional prehospital guidelines, steps were rated equally, as the guidelines do not actually rank the steps. Instead, steps regarded as critical decisions and actions for SCA (Chin lift performed, oropharyngeal airway inserted, oxygen administered, intravenous infusion initiated) and SCB (inhalation of salbutamol and ipratropium initiated) were identified and measured with exact times.

Outcome measurements

Both cases and the scoring protocol were tested twice in a pilot study (n = 12). An interrater reliability analysis using the ? statistic was performed during the pilot study to determine consistency between the 2 raters. The interrater reliability for the raters was found to be ? = 0.80. The only change that was made after the pilot study was a change in the maximum time limits for each simulation case. The regional prehospital guidelines suggest a maximum OST of 10 minutes in the assessment of the critically ill patient. Initially, we thought that the participants required 5 minutes of extra time in compensation because of the simulation environment. After the pilot study, it was found that only one extra minute was necessary. As a result, the final time limit for assessment and initial treatment was set at 11 minutes. The start was defined as the moment when the ambulance nurse first saw the patient and stop when the transport was initiated.

The data from the pilot study were also used for a sample size calculation. The result from the pilot study (n = 12)

If further support is needed

Triaging METTS, ADAPT, etc.

Yes

Yes

No

No

Yes

No

No

New ABCDE Assessment?

New ABCDE Assessment?

Yes

New ABCDE Assessment?

Pathway Support?

Detailed Assessment & Field diagnosis?

ABCDE Assessment OK?

Start

Yes

No

Triaging?

No

Guidelines; National & Regional

ABCDE evaluation, treatment, procedures & medications

Yes

Guidelines; National & Regional

Medical Assessment & History SOPQRST, AMPLE

Yes

Guidelines; National & Regional

Support for implemented pathways, fast tracks etc.

Guidelines; National & Regional

Fig. 2 Computerized decision support system main principles and workftow.

showed an effect size of 1.1. With a significance level of P =

.05 and a power of 80%, we estimate that the study needs 15 participants in each group.

The participants were instructed to perform all the assessments and treatment they usually perform on the scene. All steps were accomplished in real time and with real equipment. During the simulation, an Emergency Medical Technician (EMT) played by an experienced ambulance nurse assisted the participants. The performance was measured as the number of steps the participants accomplished correctly in the 2 simulation cases. In SCA, there were 35 possible steps, and in SCB, there were 30 possible steps (Appendix).

All experiments were recorded on digital video, and 2 of the authors (M.H., A.J.) subsequently analyzed the digital videos to determine the final scores for SCA and SCB. The

Table 1 Baseline characteristics of study participants

final scores were based on an average score from the 2 raters for each step in the protocol. After the simulation, the participants were asked to answer a questionnaire with questions on their specialist training, number of years as a nurse, number of years in the ambulance service, experience of simulation, and how they rated their computer skills. The data were used to investigate the correlation between the variables and the performance.

Data analysis

To demonstrate the cohort equity, the independent t test and Pearson ?2 test were used. To compare the number of interventions and the time between CDSS intervention

Intervention group (n = 30)		Control group (n = 30)	Significance
Age (y)	39.33 (7.32)	39.67 (6.70)	NS
Male	19	22	NS
Female	11	8	NS
Total years as nurse	9.13 (5.69)	10.80 (4.31)	NS
Total years as nurse in ambulance	6.20 (3.77)	6.83 (3.75)	NS
Specialist nurse course
Ambulance	21	23	NS
Anesthesia	4	1	NS
Intensive care	1	2	NS
Other	2	0	NS
No specialist course	2	4	NS
Simulation experience (no. of previous simulation sessions)	4.7 (2.15)	5.4 (2.16)	NS
Values are presented as mean (SD). NS, no significant differences between the groups.

Intervention group (n = 30)		Control group (n = 30)	P
No. of adequately performed anticipated interventions
- Chin lift performed	28	19	.005 a
- Oropharyngeal airway inserted	21	20	.783
- Administration of oxygen	30	29	.317
- Intravenous infusion initiated	27	29	.305
Percentage of maximal compliance
- Primary survey	82 (76-88)	53 (41-65)	b.001 a
- Anamnesis	83 (83-100)	50 (33-54)	b.001 a
- secondary survey	70 (57-80)	70 (60-80)	1.000
- Therapeutic interventions	100 (100-100)	100 (100-100)	.579
Total	80 (74-86)	60 (51-66)	b.001 a
Median total time (minutes. seconds)	10.19 (8.42-11.00)	5.40 (4.35-5.39)	b.001 a
Time to chin lift (minutes. seconds)	1.02 (0.42-1.22)	1.05 (0.47-1.23)	.828
Time to oropharyngeal airway (minutes. seconds)	1.51 (1.36-2.09)	1.36 (0.46-3.30)	.611
Time to oxygen (minutes. seconds)	2.46 (2.09-3.44)	2.00 (1.31-2.49)	.007 a
Time to infusion (minutes. seconds)	5.51 (4.58-7.35)	3.39 (3.07-4.30)	b.001 a
Adequately performed anticipated interventions are presented as absolute numbers. Other data are presented as median (interquartile range). a Significance.

groups and control groups and to compare results between groups starting with SCA and groups starting with SCB (Fig. 1), the Mann-Whitney U test was used. The correlation between the number of interventions and education, age, sex, and computer experience was measured using the spearman correlation coefficient. For all other data, descriptive statistics were used. Analyses were performed using SPSS

Table 2 Results in SCA: septic shock

19.0 (SPSS, Inc, Chicago, IL).

Results

The 2 randomized groups had similar baseline character- istics (Table 1). There was a significant difference between the groups when it came to the percentage of maximum compliance (all steps in the protocol) in SCA (P b .001). The intervention group had a median compliance with the regional prehospital guidelines of 80% in comparison with

Table 3 Results in SCB: chronic obstructive pulmonary disease

the control group, which had a median compliance of 60%. There was also a significant difference in compliance between the groups in SCA Phase 1 (primary assessment) favoring the intervention group, and the same result was seen in Phase 2 (anamnesis). There was no significant difference between the groups in phase 3 (secondary survey) or in phase 4 (therapeutic interventions). The same tendency was seen in SCB (Tables 2 and 3)

In SCA, significantly more chin lift maneuvers were performed in the intervention group (28 vs 19, P = .005), but there was no difference between the groups when it came to the insertion of an oropharyngeal airway, the administration of oxygen, and the administration of an infusion. In SCB, there was no significant difference in the administration of 5 mg of salbutamol/0.5 mg of ipratropium, inhaled with oxygen, 7 L/min (Tables 2 and 3).

The control group was significantly faster in the assessment

and treatment of the 2 cases. In SCA, the median total time was

Intervention group (n = 30)		Control group (n = 30)	P
No. of adequately performed anticipated interventions
- Inhalation of salbutamol and ipratropium initiated	24	24	1.000
Percentage of maximal compliance
- Primary survey	100 (83-100)	50 (50-67)	b.001 a
- Anamnesis	83 (75-83)	42 (41-58)	b.001 a
- Secondary survey	70 (60-80)	70 (60-80)	.725
- Therapeutic interventions	100 (50-100)	100 (50-100)	.359
Total	80 (79-83)	60 (53-63)	b.001 a
Median total time (minutes. seconds)	9.38 (8.44-10.58)	5.30 (4.23-6.31)	b.001 a
Time to administration of salbutamol and ipratropium (minutes)	6.46 (5.35-8.32)	3.10 (1.46-3.40)	b.001 a
Adequately performed anticipated interventions are presented as absolute numbers. Other data are presented as median (interquartile range). a Significance.

5 minutes 40 seconds (4.35-5.39 minutes. seconds) compared with the intervention group, who finished in 10 minutes 19 seconds (8.42-11.00 minutes. seconds; P <= .001). The same tendency was found for SCB, where the median time for the control group was 5 minutes 30 seconds (4.23-6.31 minutes. seconds), whereas the intervention group had a median time of 9 minutes 38 seconds (8.44-10.58 minutes. seconds; P <=

.001). For SCA, the control group was significantly faster in starting Oxygen treatment and treatment with an infusion, and in SCB, the control group was faster in starting the administration of 5 mg of salbutamol/0.5 mg of ipratropium, inhaled with oxygen, 7 L/min (Tables 2 and 3).

There were no significant differences in the median compliance between the groups depending on whether they started with SCA or SCB (Table 4).

The individual performance on the 2 simulation scenarios, SCA and SCB, did not correlate significantly with previous computer experience, simulation experience, education, age, or sex.

Discussion

Results

As shown in Tables 2 and 3, a CDSS increases the compliance with guidelines in terms of the primary survey and anamnesis for both SCA and SCB. There was no difference in compliance in the secondary survey and in the therapeutic interventions. There is a previous study of in- hospital emergency medicine that supports the finding of a CDSS effect on guideline compliance [29].

The intervention group showed an increase in OST and an increase in the time to the start of critical interventions such as the administration of oxygen and the infusion and inhalation of salbutamol and ipratropium, compared with the control group.

Intervention Intervention P group starting group starting with SCA with SCB (n = 15) (n = 15)

SCA, septic shock Percentage of 80 (74-83) 83 (80-86) .863 maximum compliance Percentage of 60 (55-66) 60 (54-69) .992 maximum compliance SCB, chronic obstructive pulmonary disease Percentage of 80 (78-80) 80 (77-83) .998 maximum compliance Percentage of 60 (53-67) 63 (60-67) .236 maximum compliance

Data are presented as median (interquartile range).

The primary survey contains some critical assessments and interventions designed to secure vital functions. Of the previously identified critical interventions described in “Methods” for this study, the intervention group had signifi- cantly better compliance regarding the number of chin lifts performed in SCA (Table 2). When performed, there was no difference in terms of the time taken to perform the chin lift between the intervention group and the control group. Apart fromthechinliftprocedure, therewerenosignificantdifferences relating to the number of critical interventions performed between the intervention group and the control group.

The control group was significantly faster in administering oxygen and infusions in SCA. This can be partly explained by the fact that the control group performed fewer interventions during the primary survey than did the intervention group. Moreover, in some cases within the control group, oxygen was administered as the first response to a patient with a relative airway obstruction, rather than performing a chin lift, which would have been a more relevant first action.

In the secondary survey comprising the further examina- tion and evaluation of the effect of interventions performed, there were no significant differences between the groups. In SCB, there was a significant difference between the groups regarding the time before the administration of the recommended drugs (salbutamol 5 mg/ipratropium 0.5 mg). The intervention group recorded an increase in the OST, as compared with the control group. This could be due at least, in part, to the larger number of procedures performed in the primary survey and in recording the anamnesis. In some emergencies, the time spent at the scene is crucial for the patient outcome. This appears to be the case in severe trauma [30,31], but there are studies indicating that a larger number of interventions does not necessarily increase the prehospital time [32]. In Medical emergencies, a thorough assessment has been shown to reduce the total time to definite care [33] and a well-spent OST can also reduce the time from call to final treatment [23]. In the present study, the control group administered 5 mg of salbutamol and 0.5 mg of ipratropium after only 3 minutes 10 seconds, often before a thorough history was taken and auscultation of the lungs. The intervention group started the treatment after 6 minutes 46 seconds. It is possible to question whether it is reasonable to

Table 4 Results in SCA and SCB

start treatment after only a 3-minute assessment.

Assessment with the aid of a CDSS may change the ambulance team’s way of working. In the present study, the ambulance nurses in the CDSS group generally focused more attention on the assessment process. As a result, the partner was more likely to be asked to perform different examinations and treatment steps. It might be a benefit that one person in the team is in charge and that this perhaps enables him/her to obtain a better overall picture of the assessment process. Another possible advantage with the CDSS is that the ambulance nurse starts the documentation, which forms the basis of the patient record, at exactly the same moment that the assessment starts. The need for a quality assessment tool to determine the quality of the ambulance patient record is

highlighted in an Australian study [34]. When the ambulance nurse uses a CDSS and all the assessments are immediately documented, it may improve the quality of the patient record.

Methods

Patient simulation is an established learning tool among paramedic students [24], but it is not well investigated in prehospital research. Simulation has been used in studies with the aim of evaluating different kinds of cognitive aid in hospital settings [35,36], but it can also be used for testing study protocols and to train the researcher [37]. How the results from patient simulation can be translated to patients is, however, unknown. In the present study, efforts were made to create realism. The manikin was of the latest wireless model, and the manikin operators were experienced. The participants performed the assessment steps as in everyday practice. With such high level of realism, it should be possible to measure compliance with recommended care and guidelines. Further studies are needed to draw any firm conclusions relating to patient outcome.

Limitation

Because of the study design, it was not possible to keep the raters blind. This is a limitation of the study, although this is a well-known bias. Studies in which the developer of the CDSS also conducts the trial show better performance in comparison with studies in which a software company alone was the developer [18]. The reasons could be the creation of more applicable software and knowledge of the context but also bias in assessing outcomes. To reduce bias, 2 raters performed the rating, and the final score was an average of their individual scores.

Another possible limitation of this study was the short training on the CDSS (20 minutes). This short introduction may have an inftuence on the time spent in both scenarios. A third possible limitation is the risk of contamination because the study was ongoing for more than 3 weeks. The first 10 participants had a median compliance with the regional prehospital guidelines in SCA (both intervention and control) of 68%, whereas the last 10 had a median compliance of 73%. The result indicates possible contamination from the first study day to the last. However, this contamination is equal for both groups, and a comparison should be possible.

Conclusion

The results of the present study indicate that a CDSS increases the compliance with regional prehospital guide- lines. The main improvement relates to the primary survey and anamnesis. The increased compliance that results from using the CDSS, with a subsequent larger number of procedures and interventions, also increases the OST. Further studies of patients in clinical settings are required.

Acknowledgment

The software company “Ortivus AB” supplied 2 comput- erized decision support systems for the study.

References

1. Franklin A, Liu Y, Li Z, et al. Opportunistic decision making and complexity in emergency care. J Biomed Inform 2011;44(3):469-76.
2. Pham JC, Story JL, Hicks RW, et al. National study on the frequency, types, causes, and consequences of voluntarily reported emergency department Medication errors. J Emerg Med 2011;40(5):485-92.
3. Meisel ZF, Hargarten S, Vernick J. Addressing prehospital patient safety using the science of injury prevention and control. Prehosp Emerg Care 2008;12(4):411-6.
4. Fairbanks RJ, Wireklint Sundstrom B. Forberedd pa att vara oforberedd: en fenomenologisk studie av vardande bedomning och dess larande i ambulanssjukvard. Vaxjo: Vaxjo University Press; 2005.
5. Fairbanks RJ, Crittenden CN, O’Gara KG, et al. Emergency medical services provider perceptions of the nature of adverse events and near- misses in out-of-hospital care: an ethnographic view. Acad Emerg Med 2008;15(7):633-40.
6. Bigham BL, Bull E, Morrison M, et al. Patient safety in emergency medical services: executive summary and recommendations from the Niagara Summit. CJEM: Can J Emerg Med Care 2011;13(1):13-8.
7. Atary JZ, de Visser M, van den Dijk R, et al. Standardised pre-hospital care of Acute myocardial infarction patients: MISSION! guidelines applied in practice. Neth Heart J 2010;18(9):408-15.
8. De Luca A, Toni D, Lauria L, et al. An emergency clinical pathway for stroke patients-results of a cluster randomised trial. BMC health services research; 2009. aviailable from: http://www.mrw.interscience. wiley.com/cochrane/clcentral/articles/630/CN-00681630/frame.html.
9. Figgis K, Slevin O, Cunningham JB. Investigation of paramedics’ compliance with clinical practice guidelines for the management of chest pain. Emerg Med J 2010;27(2):151-5.
10. Woollard M, Smith E, Elwood P. Pre-hospital aspirin for suspected myocardial infarction and acute coronary syndromes: a headache for paramedics? Emerg Med J 2001;18(6):478-81.
11. Rittenberger JC, Beck PW, Paris PM. Errors of omission in the treatment of prehospital chest pain patients. Prehosp Emerg Care 2005;9(1):2-7.
12. Fisher JD, Vinci RJ. prehospital management of Pediatric asthma requiring hospitalization. Pediatr Emerg Care 1995;11(4):217-9.
13. Bosse G, Schmidbauer W, Spies CD, et al. Adherence to guideline- based standard operating procedures in pre-hospital emergency patients with chronic obstructive pulmonary disease. J Int Med Res 2011;39(1):267-76.
14. Taira BR, Singer AJ, Cassara G, et al. Rates of compliance with first aid recommendations in burn patients. J Burn Care Res 2010;31(1):121-4.
15. Gagliardi AR, Brouwers MC, Palda VA, et al. How can we improve guideline use? A Conceptual framework of implementability. Imple- ment Sci 2011;6:26.
16. Latoszek-Berendsen A, Tange H, van den Herik HJ, et al. From clinical practice guidelines to computer-interpretable guidelines. A literature overview. Methods Inf Med 2010;49(6):550-70.
17. Klein G. Naturalistic decision making. Hum Factors 2008;50(3): 456-60.
18. Garg AX, Adhikari NKJ, McDonald H, et al. Effects of computerized Clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005;293(10):1223-38.
19. Kawamoto K, Houlihan CA, Balas EA, et al. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. Br Med J 2005;330(7494): 765-8.
20. Shojania Kaveh G, Jennings A, Mayhew A, et al. The effects of on- screen, point of care computer reminders on processes and outcomes of care. Cochrane Database Syst Rev 2009(3) [serial on the Internet] Available from: http://www.mrw.interscience.wiley.com/cochrane/ clsysrev/articles/CD001096/frame.html.
21. Hagiwara M, Henricson M, Jonsson A, et al. Decision-support tool in prehospital care: a systematic review of randomized trials. Prehosp Disaster Med 2011:1-11.
22. Foy R, Ovretveit J, Shekelle PG, et al. The role of theory in research to develop and evaluate the implementation of patient safety practices. BMJ Qual Saf 2011;20(5):453-9.
23. Woollard M, Pitt K, Hayward AJ, et al. Limited benefits of ambulance telemetry in delivering early thrombolysis: a randomised controlled trial. Emerg Med J 2005;22(3):209-15.
24. Andrea Wyatt FA, Fallows B. Use of simulators in teaching and learning: paramedics’ evaluation of a patient simulator. J Emerg Prim Health Care 2007;5. Available from: http://www.jephc.com/ full_article.cfm?content_id=423.
25. Vetenskapsradet. Forskningsetiska principer. Available at http://www. vr.se/download/18.668745410b37070528800029/HS%5B1%5D.pdf. Accessed 16 January 2009.
26. Sodra Alvsborgs Sjukhus. Behandlingsanvisningar. [cited 2011 0601]; Available from: http://sas.vgregion.se/upload/S%C3%84S/ Ambulanssjukv%C3%A5rd/Behandlingsriktlinjer2011-05-01.pdf.
27. SLAS. Behandlingriktlinjer. [cited 2011 02 01]; Available from: www. flisa.nu/getfile.ashx?cid=116798&cc=3&refid=9.
28. Dalton AL. Advanced medical life support: a practical approach to adult medical emergencies. 3rd edition. Upper Saddle River, NJ: Pearson; 2010.
29. Kwok R, Dinh M, Dinh D, et al. Improving adherence to asthma clinical guidelines and discharge documentation from emergency departments: implementation of a dynamic and integrated elec- tronic decision support system. Emerg Med Australas 2009;21(1): 31-7.
30. Gonzalez RP, Cummings GR, Phelan HA, et al. Does increased emergency medical services prehospital time affect patient mortality in rural motor vehicle crashes? A statewide analysis. Am J Surg 2009;197(1):30-4.
31. Baez AA, Lane PL, Sorondo B, et al. predictive effect of out-of- hospital time in outcomes of severely injured young adult and elderly patients. Prehosp Disaster Med 2006;21(6):427-30.
32. van der Velden MWA, Ringburg AN, Bergs EA, et al. Prehospital interventions: time wasted or time saved? An observational cohort study of management in initial trauma care. Emerg Med J 2008;25(7):444-9.
33. Vermeulen RP, Jaarsma T, Hanenburg FGA, et al. Prehospital diagnosis in STEMI patients treated by Primary PCI: the key to rapid reperfusion. Neth Heart J 2008;16(1):5-9.
34. Smith E, Boyle M, MacPherson J. The development of a quality assessment tool for ambulance patient care records. HIM J 2004;33(4): 112-20.
35. Bould MD, Hayter MA, Campbell DM, et al. Cognitive aid for neonatal resuscitation: a prospective single-blinded randomized controlled trial. Br J Anaesth 2009;103(4):570-5.
36. Coopmans VC, Biddle C. CRNA performance using a handheld, computerized, decision-making aid during critical events in a simulated environment: a methodologic inquiry. AANA J 2008;76(1):29-35.
37. Brindley PG, Dunn WF. Simulation for clinical research trials: a theoretical outline. J Crit Care 2009;24(2):164-7.

Appendix. Scenario details

Scenario A

Primary survey o Level of

consciousness, as observed by distance?

• Spontaneous breathing?
• Patent airway?

Scenario B

• Level of consciousness, as observed by distance?
• Spontaneous breathing?
• Exposing chest
• Inspection of oral o Assessment of cavity breathing rate,

symmetry of breathing, and use of auxiliary breathing muscles

• Airway suction o Checking for radial pulse
• Chin lift o Assessment of body temperature
• Determining proper size of nasal/oral airway
• Inserting nasal/ oral airway
• Assessing effect of inserted nasal/ oral airway
• Placement of trauma mask with oxygen, N10 L/min
• Exposing chest
• Assessment of breathing rate, symmetry of breathing, and use of auxiliary breathing muscles
• Checking for radial pulse
• Checking for carotid pulse
• Assessment of body temperature
• Detailed assessment of level of consciousness
• Assessment of pupil size and reaction to light

Appendix. (continued)

Scenario A

Anamnesis

Scenario B

Secondary survey

Therapeutic o Intravenous (IV) interventions line

• Administration of infusion
• Administration of salbutamol 5 mg and ipratropium 0.5 mg, inhaled along with oxygen, 7 L/min
• IV line

Scenario A: septic shock. Scenario B: chronic obstructive pulmonary disease.

• Repeated overall o Repeated overall examination of examination of physical physical status status after

after interventions interventions

• Pulmonary auscultation
• Electrocardiogram (ECG)
• Top-to-toe examination (signs of edema?)
• Repeated examination of level of consciousness, after inhalation of bronchodilators
• Top-to-toe examination
• Body temperature
• Blood pressure
• Pulse
• Breathing rate
• Oxygene saturation
• Blood pressure
• Pulse
• Breathing rate
• Oxygene saturation
• Body temperature
• Blood glucose
• Pulmonary auscultation
• ECG
• Medical history? o Is the pain stationary or

radiating?

• Last oral intake? o Severity of pain?
  • Duration of pain?
  • Allergies?
  • Present medication?
  • Medical history?
  • Last oral intake?
  • Elimination (urine, feces)
• When did symtoms start?
• What make symptoms worse or better?
• Describe the character of the symptoms
• Present medication?
• What symptoms o What symptoms have have been been observed? observed?
• When did symtoms start?
• Allergies?