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Appl Clin Inform 2013; 04(04): 476-498
DOI: 10.4338/ACI-2013-06-RA-0041
Research Article
Schattauer GmbH

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

B. Lau
1   Department of Health Services, University of Washington, Seattle, WA
,
C. L. Overby   *
2   Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA
5   Department of Medicine Program in Personalized and Genomic Medicine, University of Maryland, Baltimore, MD
,
H. S. Wirtz
3   Pharmaceutical Outcomes Research and Policy Program, University of Washington, Seattle, WA
,
E. B. Devine
1   Department of Health Services, University of Washington, Seattle, WA
2   Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA
3   Pharmaceutical Outcomes Research and Policy Program, University of Washington, Seattle, WA
4   Department of Surgery, University of Washington, Seattle, WA
› Author Affiliations
Further Information

Publication History

received: 17 June 2013

accepted: 01 October 2013

Publication Date:
19 December 2017 (online)

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Summary

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 A1c; HbA1c) 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 HbA1c remained the same (72%); those that met time guidelines decreased from 45% to 41% (p<0.001).

Conclusions: 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.

Citation: Lau B, Overby CL, Wirtz HS, Devine EB. The association between use of a clinical decision support tool and adherence to monitoring for medication-laboratory guidelines in the ambulatory setting. Appl ClinInf 2013; 4: 476–498

http://dx.doi.org/10.4338/ACI-2013-06-RA-0041

Keywords

Adherence to guidelines - clinical decision support - clinical guidelines - meaningful use - medication laboratory test monitoring

* formerly


 

  • References

  • 1 American Reinvestment and Recovery Act of 2009, Public Law III-5.. [accessed on September 28, 2013]. Available at: http://www.gpo.gov/fdsys/pkg/PLAW-111publ5/pdf/PLAW-111publ5.pdf
    PubMed
  • 2 Centers for Medicare and Medicaid Services.. 2012. Meaningful Use. [accessed on September 28, 2013]. Available at: http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Mean ingful_Use.html
    PubMed
  • 3 Centers for Medicare and Medicaid Services.. 2012. Stage 2. [accessed on September 28, 2013]. Available at: http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Stage_2.html
    PubMed
  • 4 Centers for Medicare and Medicaid Services.. 2012. Stage 1 versus Stage 2 comparison table for eligible professionals. Last updated: August 2012. [accessed on September 28, 2013]. Available at: http://www.cmsgov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/Downloads/Stage1vsStage2CompTablesforEP.pdf.
    PubMed
  • 5 Lobach D. et al. April 2012. Enabling Health Care Decision making Through Clinical Decision Support and Knowledge Management. Evidence Report No. 203. (Prepared by the Duke Evidence-based Practice Center under Contract No. 290–2007–10066-I.) AHRQ Publication No. 12-E001-EF. Rockville, MD: Agency for Healthcare Research and Quality;
  • 6 Romano MJ, Stafford RS. Electronic health records and clinical decision support systems: Impact on national ambulatory care quality. Archives of Internal Medicine 2011; 171 (10) 897-903. [PubMed: 21263077]
    PubMedSearch in Google Scholar
  • 7 Walsh MN. et al. Lack of association between electronic health record systems and improvement in use of evidence-based heart failure therapies in outpatient cardiology practices. Clinical Cardiology 2012; 35 (03) 187-196. [PubMed: 22328100]
    CrossrefPubMedSearch in Google Scholar
  • 8 Wright A. et al. Clinical decision support capabilities of commercially-available clinical information systems. Journal of the American Medical Informatics Association 2009; 16 (05) 637-644. [PubMed: 19567796]
    CrossrefPubMedSearch in Google Scholar
  • 9 Chaffee BW. Future of clinical decision support in computerized prescriber order entry. American Journal of Health-System Pharmacy 2010; 67 (11) 932-935. [PubMed: 20484218]
    PubMedSearch in Google Scholar
  • 10 Eberhardt J, Bilchik A, Stojadinovic A. Clinical decision support: potential with pitfalls. Journal of Surgical Oncology 2012; 105 (05) 502-510. [PubMed: 22441903]
    CrossrefPubMedSearch in Google Scholar
  • 11 Sittig DF, Wright A, Osheroff JA. Grand challenges in clinical decision support. Journal of Biomedical Informatics 2008; 41 (02) 387-392. [PubMed: 18029232]
    CrossrefPubMedSearch in Google Scholar
  • 12 Moxey A. et al. Computerized clinical decision support for prescribing: provision does not guarantee update. Journal of the American Medical Informatics Association 2010; 17 (01) 25-33. [PubMed: 20064798]
    CrossrefPubMedSearch in Google Scholar
  • 13 Cash JJ. Alert fatigue. American Journal of Health-System Pharmacy 2009; 66 (023) 2098-20101. [PubMed: 19923309]
    PubMedSearch in Google Scholar
  • 14 Anderegg SV, Gumpper KF. What meaningful use means for pharmacy. American Journal of Health-System Pharmacy 2012; 69 (10) 890-894. [PubMed: 22555086]
    PubMedSearch in Google Scholar
  • 15 Garg AX. et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes. A systematic review. JAMA 2005; 293 (10) 1223-1228. [PubMed: 15755945]
    CrossrefPubMedSearch in Google Scholar
  • 16 Das M, Eichner J. March 2010. Challenges and barriers to clinical decision support (CDS) design and implementation experienced in the Agency for Healthcare Research and Quality CDS Demonstrations. (Prepared for the AHRQ National Resource Center for Health Information Technology under Contract No. 290–04–0016.) AHRQ Publication No. 10–0064-EF. Rockville, MD: Agency for Healthcare Research and Quality.;
  • 17 Devine EB. et al. The impact of computerized provider order entry on medication errors in a multispecialty group practice. Journal of the American Medical Informatics Association 2010; 17 (01) 78-84. [PubMed: 20064806]
    CrossrefPubMedSearch in Google Scholar
  • 18 EPICare’s Ambulatory EMR®. (Verona, WI) [accessed on September 28, 2013] Available at: http://www.epic.com/software-ambulatory.php
    PubMed
  • 19 Devine EB. et al. August 2008. Implementing an ambulatory e-prescribing system: strategies employed and lessons learned to minimize unintended consequences. In: Henriksen K, Battles JB, Keyes MA, Grady ML. editors. Advances in Patient Safety: New Directions and Alternative Approaches (Vol. 4: Technology and Medication Safety). Rockville (MD): Agency for Healthcare Research and Quality.;
  • 20 Standards of Medical Care in Diabetes-2008 (Executive Summary). Diabetes Care 2008; 31 (Suppl. 01) S5-S11.
    CrossrefPubMedSearch in Google Scholar
  • 21 Standards of Medical Care in Diabetes-2008 (Executive Summary). Diabetes Care 2009; 32 (Suppl. 01) S6-S12
    CrossrefPubMedSearch in Google Scholar
  • 22 Third Report of the National Cholesterol Education Program on Detection, Evaluation and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). (Executive Summary). May 2001. National Cholesterol Education Program, National Heart, Lung, and Blood Institute, National Institutes of Health.; NIH Publication No. 01-3670.
  • 23 Liang KY, Zeger SL. Longitudinal Data Analysis Using Generalized Linear Models. Biometrika 1986; 73 (01) 13-22.
    CrossrefPubMedSearch in Google Scholar
  • 24 Smith DH. et al. The impact of prescribing safety alerts for elderly persons in an electronic medical record. Archives of Internal Medicine 2006; 166 (10) 1098-1104. [PubMed: 16717172]
    CrossrefPubMedSearch in Google Scholar
  • 25 Lo HG. et al. Impact of non-interruptive medication laboratory monitoring alerts in ambulatory care. Journal of the American Medical Informatics Association 2009; 16 (01) 66-71. [PubMed: 18952945]
    CrossrefPubMedSearch in Google Scholar
  • 26 Singh H. et al. Modification of abnormal lab test results in an electronic medical record: do any safety concerns remain?. American Journal of Medicine 2010; 123 (03) 238-244. [PubMed: 20193832]
    CrossrefPubMedSearch in Google Scholar
  • 27 Agrawal A, Mayo-Smith MF. Adherence to computerized clinical reminders in a large healthcare delivery network. Studies in Health Technology Informatics 2004; 107 Pt 1 111-114. [PubMed: 15360785]
    PubMedSearch in Google Scholar
  • 28 Bundy DG. et al. Electronic health record-based monitoring of primary care patients at risk of medication-related toxicity. Jt Comm J Qual Patient Saf 2012; 38 (05) 216-223. [PubMed: 22649861]
    PubMedSearch in Google Scholar
  • 29 Fischer SH, Tjia J, Field TS. Impact of health information technology interventions to improve medication laboratory monitoring for ambulatory patients: a systematic review. Journal of the American Medical Informatics Association 2010; 17: 631-636. [PubMed: 20962124]
    CrossrefPubMedSearch in Google Scholar
  • 30 Cowansage CB. et al. An application for monitoring order set usage in a commercial electronic health record. AMIA Annu Symp Proc 2012: 1184-1190. [PubMed: 23304395]
    PubMed