**3. Decision Support Systems in Emergency Medicine**

Emergency medicine is one of the most difficult challenges for physicians. Diagnostic and therapeutic choices must be quick, immediate, even if there could be a significant inadequa‐ cy of information. Medical staff has to deal with many types of stressful situations: in-hospi‐ tal emergency departments are often overcrowded [66-68], out-of-hospital emergency situations sometimes carry possible environmental risks. It is not possible to refuse care to anyone and there is also a high legal risk. All these elements can yield a huge stress load for the whole health care team [69-72]. These considerations lead to a request for decision sup‐ port. There are several difficulties for designing systems for this environment and testing them. In a study of 2003, the diagnostic performance of two DSSs planned for an emergency department [73] were compared with expert decision making. Only in one third of the cases, the experts' diagnosis was within the top five diagnosis generated by the software.

We discuss further DSSs for in-hospital emergency medicine and DSSs for pre-hospital and out-of-hospital emergency medicine.

#### **3.1. Decision support systems for in-hospital emergency medicine**

Lastly, a hybrid system for conscious sedation (HSS) with DSS, was presented [65]. This system integrates closed loop sedation with a DSS, offering pop-up menus as smart alarms with several treatment advices for hemodynamic or respiratory adverse events, which need to be confirmed by the anesthetic team by clicking respective touch buttons

Tested on two groups of 50 patients, the detection of critical events was significantly im‐

on a touch screen (Figure 17).

32 Decision Support Systems

proved by the DSS, as shown in Table 3.

**Table 3** Comparison of detecting critical events by the time [65].

**Figure 17.** Pop-up menu for respiratory critical event [65].

**3. Decision Support Systems in Emergency Medicine**

Emergency medicine is one of the most difficult challenges for physicians. Diagnostic and therapeutic choices must be quick, immediate, even if there could be a significant inadequa‐ cy of information. Medical staff has to deal with many types of stressful situations: in-hospi‐ tal emergency departments are often overcrowded [66-68], out-of-hospital emergency situations sometimes carry possible environmental risks. It is not possible to refuse care to anyone and there is also a high legal risk. All these elements can yield a huge stress load for Chest pain and abdominal pain represent principal causes of admission to the emergency room [74]. An example of a DSS for in-hospital emergencies is an artificial neural network designed to diagnose acute myocardial infarction [75]. It was tested in 118 patients, with a sensitivity of 92% and specificity of 96%; without the input of the electrocardiogram, the sensitivity decreased to 86%, while the specificity was 92%. In a later study, the same DSS was tested in 331 patients with anterior chest pain in emergency department [76]. Whereas sensitivity and specificity of physicians in diagnosing myocardial infarction was 77.7% and 84.7%, the DSS values were higher at 97.2% and 96.2%, respectively. This suggests that the system performed better than physicians.

A simple DSS that could be useful in emergency medicine is an artificial neural network designed to detect microembolic Doppler signals [77]; it resulted in a specificity of 56.7% and sensitivity of 73.4%, increasing to 75.9% in patients with mechanical prosthetic car‐ diac valves. However, this study did not test this system in emergency situations. Results about a favorable use of DSS to diagnose pulmonary embolism in emergency medicine in fact are not uniform. For example, the study of Roy et al. tried to evaluate the importance of the introduction of a computer-handled DSS in diagnosing pulmonary embolism com‐ pared with the use of paper guidelines [78]. The software yields a list of tests, specifying which of them is appropriate or inappropriate considering the pre-test probability, en‐ tered before by the physician according to the revised Geneva score; the DSS recom‐ mends as first choice the least invasive investigation among the appropriate tests. The system was compared with paper guidelines in two groups of patients, one with DSS and one without it. In the intervention group (the DSS group), there was an increase of appro‐ priate diagnostic testing by 30.2% while in the other group it only increased by 10.9%. The DSS was used in 80% of cases during real time intervention, suggesting a good per‐ formance in the emergency environment and good acceptance by physicians. A study by Drescher evaluated the impact of the integration of a computerized DSS in a computer‐ ized physician order entry on the frequency of positive CT angiography results for pul‐ monary embolism and the staff's acceptability of such a DSS[79]. The DSS was designed based on a modified Wells score to give diagnostic options to physicians when ordering a CT angiography or D-dimer testing (Figure 18). Although the study showed a superior performance of the DSS rather than physicians in ordering diagnostic investigations, ad‐ herence and acceptability of the DSS were quite low. Probably, the principle reasons were the time needed to enter data and mistrust in the effectiveness of the software.

in real-time, and a computer, that is implemented with three different types of software: the Controller software which records the data from monitor; the Shell software, which protects data by possible corruption and passes to the other software, and the Analysis software, which enables decision support. One important aim of this DSS is avoiding the loss or dam‐ age of information during the transport; this is possible by a special way of transmission of

Decision Support Systems in Medicine - Anesthesia, Critical Care and Intensive Care Medicine

http://dx.doi.org/10.5772/51756

35

Evaluated during a simulation of possible fault scenarios, the performance of each component was good and sufficient to prevent data corruption; decision support was considered useful.

Intensive care medicine represents an equally challenging field for physicians. Patients are usually affected by multiple disease and need to be constantly supervised; often in a state of unconsciousness, patients cannot directly communicate with the practitioner and have to be monitored using a multitude of parameters, producing a significant amount of data. There

We will focus on general DSSs, DSSs for artificial ventilation and DSSs for infections.

**4. Decision Support Systems for Intensive Care Unit**

data that allows the system to know which data are not valid or missed.

**Figure 19.** Basic architecture of iRevive [82].

are significant costs involved.

**Figure 18.** User interface for entering Wells criteria [79].

#### **3.2. Decision support systems for out-of-hospital and pre-hospital emergency medicine**

Decision support systems conceived for out-of hospital and pre-hospital interventions are complex systems designed for receiving and sending a multitude of inputs through a varie‐ ty of platforms and different situations. These systems can interface with different signals, managing a great number of data also at distance. In his review [80], Nangalia describes five basic components of a telemedicine system: the acquisition of data through appropriate sen‐ sors; transmission of data from patient to clinician; ability to combine all data of different sources; decision support for appropriate action and response; storage of data.

One DSS used for pre-hospital emergency care is a system installed in ambulances that pro‐ vides data communication, documentation, triaging and presentation of a checklist [81].

A similar pre-hospital DSS is iRevive, a system which permits rapid acquisition of data in an electronic format [82] through several components: vital sign sensors, wireless patient loca‐ tion and a central command center for discussing and collection of data, all linked by a net‐ work of wireless and hand-held computers. There is a combination of real-time sensor data, procedural data, and geographic data giving real-time decision support at three different hi‐ erarchical levels: at the local site, at the local command center and at the central command center, which is responsible for general coordination (Figure 19).

Another DSS for pre-hospital emergency care delivers real-time decision support during transport of trauma casualties [83]. It consists of a monitor that acquires data from patients in real-time, and a computer, that is implemented with three different types of software: the Controller software which records the data from monitor; the Shell software, which protects data by possible corruption and passes to the other software, and the Analysis software, which enables decision support. One important aim of this DSS is avoiding the loss or dam‐ age of information during the transport; this is possible by a special way of transmission of data that allows the system to know which data are not valid or missed.

**Figure 19.** Basic architecture of iRevive [82].

**Figure 18.** User interface for entering Wells criteria [79].

34 Decision Support Systems

**3.2. Decision support systems for out-of-hospital and pre-hospital emergency medicine**

sources; decision support for appropriate action and response; storage of data.

center, which is responsible for general coordination (Figure 19).

Decision support systems conceived for out-of hospital and pre-hospital interventions are complex systems designed for receiving and sending a multitude of inputs through a varie‐ ty of platforms and different situations. These systems can interface with different signals, managing a great number of data also at distance. In his review [80], Nangalia describes five basic components of a telemedicine system: the acquisition of data through appropriate sen‐ sors; transmission of data from patient to clinician; ability to combine all data of different

One DSS used for pre-hospital emergency care is a system installed in ambulances that pro‐ vides data communication, documentation, triaging and presentation of a checklist [81].

A similar pre-hospital DSS is iRevive, a system which permits rapid acquisition of data in an electronic format [82] through several components: vital sign sensors, wireless patient loca‐ tion and a central command center for discussing and collection of data, all linked by a net‐ work of wireless and hand-held computers. There is a combination of real-time sensor data, procedural data, and geographic data giving real-time decision support at three different hi‐ erarchical levels: at the local site, at the local command center and at the central command

Another DSS for pre-hospital emergency care delivers real-time decision support during transport of trauma casualties [83]. It consists of a monitor that acquires data from patients

Evaluated during a simulation of possible fault scenarios, the performance of each component was good and sufficient to prevent data corruption; decision support was considered useful.
