The Association between Use of a Clinical Decision Support Tool and Adherence to Monitoring for Medication-Laboratory Guidelines in the Ambulatory Setting

BACKGROUND Stage 2 Meaningful Use criteria require the use of clinical decision support systems (CDSS) on high priority health conditions to improve clinical quality measures. Although CDSS hold great promise, implementation has been fraught with challenges, evidence of their impact is mixed, and the optimal method of content delivery is unknown. OBJECTIVE The authors investigated whether implementation of a simple clinical decision support (CDS) tool was associated with improved prescriber adherence to national medication-laboratory monitoring guidelines for safety (hepatic function, renal function, myalgias/rhabdomyolysis) and intermediate outcomes for antidiabetic (Hemoglobin A(1c); HbA(1c)) and antihyperlipidemic (low density lipoprotein; LDL) medications prescribed within a diabetes registry. METHODS This was a retrospective observational study conducted in three phases of CDS implementation (2008-2009): pre-, transition-, and post-Prescriptions evaluated were ordered from an electronic health record within a multispecialty medical group. Adherence was evaluated within and without applying guideline-imposed time constraints. RESULTS Forty-thousand prescriptions were ordered over three timeframes. For hepatic and renal function, the proportion of prescriptions for which labs were monitored at any time increased from 52% to 65% (p<0.001); those that met time guidelines, from 14% to 21% (p<0.001). Only 6% of required labs were drawn to monitor for myalgias/rhabdomyolysis, regardless of timeframe. Over 90% of safety labs were within normal limits. The proportion of labs monitored at any time for LDL increased from 56% to 64% (p<0.001); those that met time guidelines from 11% to 17% (p<0.001). The proportion of labs monitored at any time for HbA(1c) remained the same (72%); those that met time guidelines decreased from 45% to 41% (p<0.001). CONCLUSION A simple CDS tool may be associated with improved adherence to guidelines. Efforts are needed to confirm findings and improve the timeliness of monitoring; investigations to optimize alerts should be ongoing.

[1]  Jared J Cash Alert fatigue. , 2009, American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists.

[2]  R. Stafford,et al.  Electronic health records and clinical decision support systems: impact on national ambulatory care quality. , 2011, Archives of internal medicine.

[3]  Margaret Williamson,et al.  Computerized clinical decision support for prescribing: provision does not guarantee uptake. , 2010, Journal of the American Medical Informatics Association : JAMIA.

[4]  Yang Liu,et al.  Lack of Association Between Electronic Health Record Systems and Improvement in Use of Evidence‐Based Heart Failure Therapies in Outpatient Cardiology Practices , 2012, Clinical cardiology.

[5]  Bruce W Chaffee,et al.  Future of clinical decision support in computerized prescriber order entry. , 2010, American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists.

[6]  Diane L. Seger,et al.  Research Paper: Impact of Non-interruptive Medication Laboratory Monitoring Alerts in Ambulatory Care , 2009, J. Am. Medical Informatics Assoc..

[7]  David K. Vawdrey,et al.  An Application for Monitoring Order Set Usage in a Commercial Electronic Health Record , 2012, AMIA.

[8]  Christoph U. Lehmann,et al.  Electronic health record-based monitoring of primary care patients at risk of medication-related toxicity. , 2012, Joint Commission journal on quality and patient safety.

[9]  Joan,et al.  Clinical Decision Support Capabilities of Commercially-available Clinical Information Systems , 2022 .

[10]  William Hollingworth,et al.  Implementing an Ambulatory e-Prescribing System: Strategies Employed and Lessons Learned to Minimize Unintended Consequences , 2008 .

[11]  Abha Agrawal,et al.  Adherence to Computerized Clinical Reminders in a Large Healthcare Delivery Network , 2004, MedInfo.

[12]  Dean F. Sittig,et al.  The impact of prescribing safety alerts for elderly persons in an electronic medical record: an interrupted time series evaluation. , 2006, Archives of internal medicine.

[13]  A. Stojadinovic,et al.  Clinical decision support systems: Potential with pitfalls , 2012, Journal of surgical oncology.

[14]  Terry S. Field,et al.  Impact of health information technology interventions to improve medication laboratory monitoring for ambulatory patients: a systematic review , 2010, J. Am. Medical Informatics Assoc..

[15]  H. Mcdonald,et al.  Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. , 2005, JAMA.

[16]  J. Dora,et al.  Standards of Medical Care in Diabetes—2008 , 2008, Diabetes Care.

[17]  Dean F Sittig,et al.  Notification of abnormal lab test results in an electronic medical record: do any safety concerns remain? , 2010, The American journal of medicine.

[18]  S. Zeger,et al.  Longitudinal data analysis using generalized linear models , 1986 .

[19]  Jonathan M. Teich,et al.  Grand challenges in clinical decision support , 2008, J. Biomed. Informatics.

[20]  Emily Beth Devine,et al.  The impact of computerized provider order entry on medication errors in a multispecialty group practice , 2010, J. Am. Medical Informatics Assoc..

[21]  Gillian D Sanders,et al.  Enabling health care decisionmaking through clinical decision support and knowledge management. , 2012, Evidence report/technology assessment.

[22]  K. Gumpper,et al.  What meaningful use means for pharmacy. , 2012, American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists.