While many errors can be detected and corrected by use of human knowledge and inspection, these represent weak error reduction strategies. In 1995, Leape et al.13
demonstrated that almost half of all medication errors were intimately linked with insufficient information about the patient and drug. Similarly, when people are asked to detect errors by inspection, they routinely miss many.21
It has recently been demonstrated that computerized physician order entry systems that incorporate clinical decision support can substantially reduce medication error rates as well as improve the quality and efficiency of medication use. In 1998, Bates et al.20
found in a controlled trial that computerized physician order entry systems resulted in a 55 percent reduction in serious medication errors. In another time series study,22
this group found an 83 percent reduction in the overall medication error rate, and a 64 percent reduction even with a simple system. Evans et al.23
have also demonstrated that clinical decision support can result in major improvements in rates of antibiotic-associated adverse drug events and can decrease costs. Classen et al.24
have also demonstrated in a series of studies that nosocomial infection rates can be reduced using decision support.
Another class of clinical decision support is computerized alerting systems, which can notify physicians about problems that occur asynchronously. A growing body of evidence suggests that such systems may decrease error rates and improve therapy, thereby improving outcomes, including survival, the length of time patients spend in dangerous conditions, hospital length of stay, and costs.25–27
While an increasing number of clinical information systems contain data worthy of generating an alert message, delivering the message to caregivers in a timely way has been problematic. For example, Kuperman et al.28
documented significant delays in treatment even when critical laboratory results were phoned to caregivers. Computer-generated terminal messages, e-mail, and even flashing lights on hospital wards have been tried.29–32
A new system, which transmits real-time alert messages to clinicians carrying alphanumeric pagers or cell phones, promises to eliminate the delivery problem.33,34
It is now possible to integrate laboratory, medication, and physiologic data alerts into a comprehensive real-time wireless alerting system.
Shabot et al.33,34
have developed such a comprehensive system for patients in intensive care units. A software system detect alerts and then sends them to caregivers. The alert detection system monitors data flowing into a clinical information system. The detector contains a rules engine to determine when alerts have occurred.
For some kinds of alert detection, prior or related data are needed. When the necessary data have been collected, alerting algorithms are executed and a decision is made as to whether an alert has occurred (Figure 1). The three major forms of critical event detection are critical laboratory alerts, physiologic “exception condition” alerts, and medication alerts. When an alert condition is detected, an application formats a message and transmits it to the alphanumeric pagers of various recipients, on the basis of a table of recipients by message type, patient service type, and call schedule. The message is sent as an e-mail to the coded PIN (personal identification number) of individual caregivers' pagers or cell phones. The message then appears on the device's screen and includes appropriate patient identification information (Figure 2).
Alert detection system. Three major forms of critical event detection occur—critical laboratory alerts, physiologic “exception condition” alerts, and medication alerts.
Wireless alerting system. In the Cedars-Sinai system, alerts are initially detected by the clinical system, then sent to a server, then via the Internet, then sent over a PageNet transmitter to a two-way wireless device.
Alerts are a crucial part of a clinical decision support system,35
and their value has been demonstrated in controlled trials.27,35
In one study, Rind et al.27
alerted physicians via e-mail to increases in serum creatinine in patients receiving nephrotoxic medications or renally excreted drugs. Rind et al. reported that when e-mail alerts were delivered, medications were adjusted or discontinued an average of 21.6 hours earlier than when no e-mail alerts were delivered. In another study, Kuperman et al.35
found that when clinicians were paged about “panic” laboratory values, time to therapy decreased 11 percent and mean time to resolution of an abnormality was 29 percent shorter.
As more and different kinds of clinical data become available electronically, the ability to perform more sophisticated alerts and other types of decision support will grow. For example, medication-related, laboratory, physiologic data can be combined to create a variety of automated alerts. (Table 1 shows a sample of those currently included in the system used at Cedars-Sinai Medical Center, Los Angeles, California.) Furthermore, computerization offers many tools for decision support, but because of space limitations we have discussed only some of these; Among the others are algorithms, guidelines, order sets, trend monitors, and co-sign forcers. Most sophisticated systems include an array of these tools.
Table 1 Sample of Wireless Alerts Currently in Use at Cedars-Sinai Medical Center, Los Angeles, California