What’s New in the Patient Safety World

January 2016

Successful E-Prescribing Alerts in Renal Disease

 

 

Last month we noted how alerts for opioids were largely ignored and failed to reduce adverse events related to opioids, at least in the ED setting (see our December 2015 What's New in the Patient Safety World column “Opioid Alert Fatigue”).

 

In contrast, e-prescribing alerts may be more successful in reducing adverse events in patients with renal disease. So many drugs are eliminated via the kidneys and their dosages or dosing intervals need to be modified in patients with impaired renal function. But busy clinicians often fail to consider “renal” dosing so incorporating into e-prescribing or CPOE systems background assessment of renal function and its impact on specific drugs and alerting clinicians is a logical step. A prior study showed that a real-time computerized decision support system for prescribing drugs in patients with renal insufficiency appeared to result in improved dose and frequency choices (Chertow 2001). More recently Awdishu and colleagues (Awdishu 2015) developed a clinical decision support (CDS) tool that detected scenarios in which drug discontinuation or dosage adjustment for 20 medications was recommended for adult patients with impaired renal function in the ambulatory and acute settings. Their algorithms were applied both when one of these medications was initially prescribed and later if a change in renal function was noted during monitoring if the patient was already on one of these medications. Prescribing orders were appropriately adjusted 17% of the time in those receiving the alerts vs 5.7% of the time in those where the alert was not visible.

 

Also the prospective alerts (those applied at the time the medication was initially prescribed) were much more likely to be heeded (leading to appropriate adjustment of dose) than those delivered after a patient was already on one of these medications (55.6% vs 10.3%). We’ve noted in multiple previous columns that when an alert is activated for a drug they have already prescribed, physicians seldom stop that drug but they are more likely to heed such alerts at the time of original prescribing.

 

Actually, the Awdishu study is a good news/bad news one. The bad news is that the overall prescribing at appropriate doses for patients with impaired renal function was quite low. The good news is that such prescribing was at least moderately improved with the clinical decision support tools and alerts.

 

Another recent study piloted a clinical decision support system (CDSS) for renal risk drugs in a primary care setting (Hellden 2015). The tool used the Cockcroft-Gault formula (ClCG) to estimate renal function (the article discussion discusses use of the ClCG vs. the eGFR). If the EMR identified a patient via the ClCG, a window on the computer screen would pop up with identification color-coded by level of renal function and a list of that patient’s medications that could be impacted by that level of renal dysfunction. Clicking on each listed medication would provide evidence-based short texts and recommendations about possible dose or dosing interval adjustments. The system was piloted with a small group of primary care practitioners and was well-received. Physicians liked its simplicity, speed and possibilities of choice.

 

Renal dosing of medications is only one aspect of managing patients with chronic kidney disease (CKD). Clinical decision support systems also have the capability to help better manage multiple other aspects of care of the CKD patient. Several years ago recommendations were made for CDSS to support such management in CKD patients (Patwardhan 2009). Such systems could help identify patients as having CKD, stage the CKD, diagnose the primary cause, establish a co-management (PCP and nephrologist) plan, manage comorbid conditions, monitor progression of CKD, help plan for permanent vascular access, and help implementation of a patient education plan including identification of reasons for noncompliance.

 

We expect use of such clinical decision support systems will become more widespread in the future to help manage patients with CKD.

 

 

 

References:

 

 

Chertow GM, Lee J, Kuperman GJ, et al. Guided Medication Dosing for Inpatients With Renal Insufficiency. JAMA 2001; 286(22): 2839-2844

http://jama.jamanetwork.com/article.aspx?articleid=194455

 

 

Awdishu L, Coates CR, Lyddane A, et al. The impact of real-time alerting on appropriate prescribing in kidney disease: a cluster randomized controlled trial. JAMIA 2015; First published online: 28 November 2015

http://jamia.oxfordjournals.org/content/early/2015/11/27/jamia.ocv159

 

 

Hellden A, Al-Aieshy F, Bastholm-Rahmner P, et al. Development of a computerised decisions support system for renal risk drugs targeting primary healthcare

BMJ Open 2015; 5: e006775 doi:10.1136/bmjopen-2014-006775

http://bmjopen.bmj.com/content/5/7/e006775.full

 

 

Patwardhan MB, Kawamoto K, Lobach D, et al. Recommendations for a Clinical Decision Support for the Management of Individuals with Chronic Kidney Disease. Clin J Am Soc Nephrol. 2009 Feb; 4(2): 273–283

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2637586/

 

 

 

 

 

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