Consider the following example. A physician orders an X-ray for a patient who has inverted her ankle. When the order is entered into the computer, the physician is reminded of the exact elements of the evidence-based criteria for such an order (the Ottawa Ankle Rules). The order is not registered until the physician acknowledges that the patient either does or does not meet the criteria. Such systems have been shown to decrease inappropriate X-ray orders by as much as 47%.[9]
Similar CPOE systems can be successful in a wide variety of settings, with some of the most compelling uses being the prevention of drug dose and interaction errors. A 1999 report from the Institute of Medicine,[10] revealed the magnitude of ongoing medical error, documenting as many as 7000 annual deaths in the US due to medication errors in hospitals. A more recent Canadian report[11] called for investment in information-technology infrastructures that support the standardized identification, reporting, and tracking of patient safety data. Research that identifies CDSS methods that alert health care providers to errors has become a critical element in improving patient safety.
Disease management systems
The preceding examples of solicited and unsolicited CDSS all centre on isolated transactions that each make up only a small fraction of a patient’s care. Disease management systems are specialized CDSS that help clinicians and patients negotiate complex treatment algorithms for conditions such as asthma, hypertension, diabetes, and hyperlipidemia.[3] Reflecting the changing locus of control in the clinician-patient relationship, many of the systems are designed expressly for the patient. Diabetes systems might be the best example of cases where patient-specific data, such as blood glucose measurements and food intake, are used to generate customized educational modules and detailed dietary recommendations.[12]
How to represent complex clinical guidelines in computer applications is an area of considerable research[13] . While implementing the IF-THEN-ELSE rule illustrated by the Brigham and Women’s Hospital hypokalemia/digoxin example is relatively straightforward, the programming of guidelines with their multiple, often subjective decision points tests the limits of scientific disciplines such as decision analysis and knowledge representation. Nonetheless, there are numerous examples of computerized guidelines in use today.[12]
Integrated information systems
Clearly, to exploit the opportunities for these types of clinical decision support interventions, we must have effective health information systems in place. The Figure provides a blueprint for a health information system that has evolved from one first developed for the US National Library of Medicine in the early 1980s.[14] Note that CDSS is an integral part of this system, alongside laboratory, radiology, and health records. This blueprint can be used not only for large hospitals but also for distributed networks of care sites such as practitioners’ offices.
Evidence of effectiveness
Do clinical decision support systems improve patient care? The question is surprisingly hard to answer. Designing good evaluative trials is difficult and the sheer variety of systems and functions makes comparison complicated. A recent review of 57 randomized controlled trials and 10 systematic reviews evaluated the effectiveness of computer-based delivery of health evidence, including CDSS.[12] The authors found that clinician or patient compliance with evidence-based recommendations improved only a modest amount: from 52% without to 57% with a system. Seven of eight relevant systematic reviews found a positive effect on provider or patient behavior. It is worth noting, however, that this review excluded computer-generated reminders.
Conclusion
In the coming years, provincial and federal health ministries will invest billions of dollars in new health information systems. Computer decision support systems integrated with computerized physician order entry can help make this investment worthwhile by leading to safer, more efficient, and more effective health care.
It is crucial that clinicians be involved in the development and rigorous scientific evaluation of these systems. Clinicians are also best placed to decide how CDSS should be implemented in local care environments. We urge clinicians to identify opportunities for CDSS and to advocate within their health care settings for the development of systems that bring about meaningful improvement of health outcomes.
Competing interests
None declared.