Literature DB >> 24454577

The association between use of a clinical decision support tool and adherence to monitoring for medication-laboratory guidelines in the ambulatory setting.

B Lau1, C L Overby, H S Wirtz2, E B Devine.   

Abstract

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.

Entities:  

Keywords:  Adherence to guidelines; clinical decision support; clinical guidelines; meaningful use; medication laboratory test monitoring

Mesh:

Substances:

Year:  2013        PMID: 24454577      PMCID: PMC3885910          DOI: 10.4338/ACI-2013-06-RA-0041

Source DB:  PubMed          Journal:  Appl Clin Inform        ISSN: 1869-0327            Impact factor:   2.342


  19 in total

1.  Clinical decision support systems: potential with pitfalls.

Authors:  John Eberhardt; Anton Bilchik; Alexander Stojadinovic
Journal:  J Surg Oncol       Date:  2012-04-01       Impact factor: 3.454

2.  Future of clinical decision support in computerized prescriber order entry.

Authors:  Bruce W Chaffee
Journal:  Am J Health Syst Pharm       Date:  2010-06-01       Impact factor: 2.637

3.  Electronic health record-based monitoring of primary care patients at risk of medication-related toxicity.

Authors:  David G Bundy; Jill A Marsteller; Albert W Wu; Lilly D Engineer; Sean M Berenholtz; A Harrison Caughey; David Silver; Jing Tian; Richard E Thompson; Marlene R Miller; Christoph U Lehmann
Journal:  Jt Comm J Qual Patient Saf       Date:  2012-05

4.  The impact of computerized provider order entry on medication errors in a multispecialty group practice.

Authors:  Emily Beth Devine; Ryan N Hansen; Jennifer L Wilson-Norton; N M Lawless; Albert W Fisk; David K Blough; Diane P Martin; Sean D Sullivan
Journal:  J Am Med Inform Assoc       Date:  2010 Jan-Feb       Impact factor: 4.497

Review 5.  Computerized clinical decision support for prescribing: provision does not guarantee uptake.

Authors:  Annette Moxey; Jane Robertson; David Newby; Isla Hains; Margaret Williamson; Sallie-Anne Pearson
Journal:  J Am Med Inform Assoc       Date:  2010 Jan-Feb       Impact factor: 4.497

6.  An application for monitoring order set usage in a commercial electronic health record.

Authors:  Cadran B Cowansage; Robert A Green; Alexander Kratz; David K Vawdrey
Journal:  AMIA Annu Symp Proc       Date:  2012-11-03

7.  Electronic health records and clinical decision support systems: impact on national ambulatory care quality.

Authors:  Max J Romano; Randall S Stafford
Journal:  Arch Intern Med       Date:  2011-01-24

Review 8.  Impact of health information technology interventions to improve medication laboratory monitoring for ambulatory patients: a systematic review.

Authors:  Shira H Fischer; Jennifer Tjia; Terry S Field
Journal:  J Am Med Inform Assoc       Date:  2010 Nov-Dec       Impact factor: 4.497

9.  Notification of abnormal lab test results in an electronic medical record: do any safety concerns remain?

Authors:  Hardeep Singh; Eric J Thomas; Dean F Sittig; Lindsey Wilson; Donna Espadas; Myrna M Khan; Laura A Petersen
Journal:  Am J Med       Date:  2010-03       Impact factor: 4.965

10.  Lack of association between electronic health record systems and improvement in use of evidence-based heart failure therapies in outpatient cardiology practices.

Authors:  Mary Norine Walsh; Nancy M Albert; Anne B Curtis; Mihai Gheorghiade; J Thomas Heywood; Yang Liu; Mandeep R Mehra; Christopher M O'Connor; Dwight Reynolds; Clyde W Yancy; Gregg C Fonarow
Journal:  Clin Cardiol       Date:  2012-02-10       Impact factor: 2.882

View more
  5 in total

1.  Noninterruptive Clinical Decision Support Decreases Ordering of Respiratory Viral Panels during Influenza Season.

Authors:  Cameron Escovedo; Douglas Bell; Eric Cheng; Omai Garner; Alyssa Ziman; Sitaram Vangala; Prabhu Gounder; Carlos Lerner
Journal:  Appl Clin Inform       Date:  2020-04-29       Impact factor: 2.342

2.  Medication use and drug-related problems among women at maternity wards-a cross-sectional study from two Norwegian hospitals.

Authors:  J Smedberg; M Bråthen; M S Waka; A F Jacobsen; G Gjerdalen; H Nordeng
Journal:  Eur J Clin Pharmacol       Date:  2016-03-29       Impact factor: 2.953

3.  Development of a clinical decision support system for diabetes care: A pilot study.

Authors:  Livvi Li Wei Sim; Kenneth Hon Kim Ban; Tin Wee Tan; Sunil Kumar Sethi; Tze Ping Loh
Journal:  PLoS One       Date:  2017-02-24       Impact factor: 3.240

4.  Design, effectiveness, and economic outcomes of contemporary chronic disease clinical decision support systems: a systematic review and meta-analysis.

Authors:  Winnie Chen; Kirsten Howard; Gillian Gorham; Claire Maree O'Bryan; Patrick Coffey; Bhavya Balasubramanya; Asanga Abeyaratne; Alan Cass
Journal:  J Am Med Inform Assoc       Date:  2022-09-12       Impact factor: 7.942

5.  Translation of evidence into kidney transplant clinical practice: managing drug-lab interactions by a context-aware clinical decision support system.

Authors:  Zahra Niazkhani; Mahsa Fereidoni; Parviz Rashidi Khazaee; Afshin Shiva; Khadijeh Makhdoomi; Andrew Georgiou; Habibollah Pirnejad
Journal:  BMC Med Inform Decis Mak       Date:  2020-08-20       Impact factor: 2.796

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.