Clinical Decision Support Systems

++

In contrast to clinical reference material, such as continuously updated online journals and medical texts, computerized clinical decision support systems directly assist the clinician in making decisions about a specific patient. Decision support systems do not need to be sophisticated to have significant impact. For example, simple dose-range checking for medications (such as opiates and insulin), drug-drug interaction checking, and drug-allergy checking are conceptually straightforward but can catch a critical source of human error that no amount of personal vigilance will entirely eliminate. Slightly more advanced are systems that analyze clinical data (such as calculating a creatinine clearance) and present guidance based on those data. More sophisticated examples are systems that look for trends in values, such as the rate of fall of the hematocrit or the rising weight of an ICU patient who is accumulating extracellular fluid, where an absolute number may not be noted by the decision support system or clinician, but an alert to the trend may be important and prompt action.

++

Decision support systems are challenging to implement and maintain. The most vexing problem is “alert fatigue.” Studies within and outside health care show that the beneficial effect of an alert, such as a pop-up interaction in a software system, is rapidly extinguished if the alert becomes a routine part of using the system. A familiar clinical example is the minimal attention paid to audible alerts produced by cardiac telemetry systems. If a clinical decision support system provides an “alert” to the drug-drug interaction of two medications routinely used together safely, such as enoxaparin and warfarin, in the same way as to unfamiliar but dangerous interactions, such as theophylline and fluoroquinolones, clinicians become desensitized to the alerts and dismiss the critically important guidance when it does appear. Alert fatigue is a fact of human cognition and cannot be eliminated through training, education, or vigilance. The best clinical systems offer fine-grained tuning of the system’s behavior, such as altering the system’s response by drug and provider specialty, and offer a range of interruptive and noninterruptive support mechanisms. The price of this flexibility is the institutional effort required to design and maintain the system. However, even these measures have yet to demonstrate consistent improvement in the effectiveness of alerts.

++

The most complex decision support systems attempt to aid clinical diagnosis. The application of artificial intelligence to medicine has a long history; however, most diagnostic expert systems have been stand-alone, requiring effort by the clinician outside of their normal workflow and have thus seen limited clinical implementation. Examples of clinical diagnostic systems directly imbedded in an electronic health record are few, but are an area of increasing commercial interest (Table e4–2).

++
Table Graphic Jump Location
Table e4–2. Functional classes and examples of clinical decision support systems. 
Baker DE. Medication alert fatigue: the potential for compromised patient safety. Hosp Pharm. 2009 Jun;44(6):460–1.
Isabel Healthcare Diagnosis Reminder System. http://www.isabelhealthcare.com/
Phansalkar S et al. High-priority drug-drug interactions for use in electronic health records. J Am Med Inform Assoc. 2012 Sep–Oct;19(5):735–43.   [PubMed: 22539083]
Roshanov PS et al. Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ. 2013 Feb 14;346:f657.   [PubMed: 23412440]
Schedlbauer A et al. What evidence supports the use of computerized alerts and prompts to improve clinicians' prescribing behavior? J Am Med Inform Assoc. 2009 Jul–Aug;16(4):531–8.   [PubMed: 19390110]
Seidling HM et al. Patient-specific electronic decision support reduces prescription of excessive doses. Qual Saf Health Care. 2010 Oct;19(5):e15.   [PubMed: 20427312]
Shojania KG et al. Effect of point-of-care computer reminders on physician behaviour: a systematic review. CMAJ. 2010 Mar 23;182(5):E216–25.   [PubMed: 20212028]
Terrell KM et al. Computerized decision support to reduce potentially inappropriate prescribing to older emergency department patients: a randomized, controlled trial. J Am Geriatr Soc. 2009 Aug;57(8):1388–94.   [PubMed: 19549022]
Weingart SN et al. An empirical model to estimate the potential impact of medication safety alerts on patient safety, health care utilization, and cost in ambulatory care. Arch Intern Med. 2009 Sep 14;169(16):1465–73.   [PubMed: 1975240]

Pop-up div Successfully Displayed

This div only appears when the trigger link is hovered over. Otherwise it is hidden from view.