Appl Clin Inform 2013; 04(01): 144-152
DOI: 10.4338/ACI-2012-12-RA-0055
Research Article
Schattauer GmbH

Provider Use of and Attitudes Towards an Active Clinical Alert

A Case Study in Decision Support
J. Feblowitz
1   Division of General Internal Medicine, Brigham & Women’s Hospital, Boston, MA, USA
2   Partners HealthCare, Boston, MA, USA
3   Harvard Medical School, Boston, MA, USA
,
S. Henkin
1   Division of General Internal Medicine, Brigham & Women’s Hospital, Boston, MA, USA
2   Partners HealthCare, Boston, MA, USA
,
J. Pang
1   Division of General Internal Medicine, Brigham & Women’s Hospital, Boston, MA, USA
2   Partners HealthCare, Boston, MA, USA
,
H. Ramelson
1   Division of General Internal Medicine, Brigham & Women’s Hospital, Boston, MA, USA
2   Partners HealthCare, Boston, MA, USA
3   Harvard Medical School, Boston, MA, USA
,
L. Schneider
1   Division of General Internal Medicine, Brigham & Women’s Hospital, Boston, MA, USA
3   Harvard Medical School, Boston, MA, USA
,
F. L. Maloney
2   Partners HealthCare, Boston, MA, USA
,
A. R. Wilcox
4   University of Louisville School of Medicine, Louisville, KY
,
D.W. Bates
1   Division of General Internal Medicine, Brigham & Women’s Hospital, Boston, MA, USA
2   Partners HealthCare, Boston, MA, USA
3   Harvard Medical School, Boston, MA, USA
5   Department of Health Policy and Management, Harvard School of Public Health, Boston, MA
,
A. Wright
1   Division of General Internal Medicine, Brigham & Women’s Hospital, Boston, MA, USA
2   Partners HealthCare, Boston, MA, USA
3   Harvard Medical School, Boston, MA, USA
› Institutsangaben
Weitere Informationen

Correspondence to:

Adam Wright, Ph.D.
Brigham and Women’s Hospital
1620 Tremont St.
Boston, MA 02115
617–525–9811

Publikationsverlauf

received: 26. Dezember 2012

accepted: 12. März 2013

Publikationsdatum:
19. Dezember 2017 (online)

 

Summary

Background: In a previous study, we reported on a successful clinical decision support (CDS) intervention designed to improve electronic problem list accuracy, but did not study variability of provider response to the intervention or provider attitudes towards it. The alert system accurately predicted missing problem list items based on health data captured in a patient’s electronic medical record.

Objective: To assess provider attitudes towards a rule-based CDS alert system as well as heterogeneity of acceptance rates across providers.

Methods: We conducted a by-provider analysis of alert logs from the previous study. In addition, we assessed provider opinions of the intervention via an email survey of providers who received the alerts (n = 140).

Results: Although the alert acceptance rate was 38.1%, individual provider acceptance rates varied widely, with an interquartile range (IQR) of 14.8%-54.4%, and many outliers accepting none or nearly all of the alerts they received. No demographic variables, including degree, gender, age, assigned clinic, medical school or graduation year predicted acceptance rates. Providers’ self-reported acceptance rate and perceived alert frequency were only moderately correlated with actual acceptance rates and alert frequency.

Conclusions: Acceptance of this CDS intervention among providers was highly variable but this heterogeneity is not explained by measured demographic factors, suggesting that alert acceptance is a complex and individual phenomenon. Furthermore, providers’ self-reports of their use of the CDS alerting system correlated only modestly with logged usage.

Citation: Feblowitz J, Henkin S, Pang J, Ramelson H, Schneider L, Maloney FL, Wilcox AR, Bates DW, Wright A. Provider use of and attitudes towards an active clinical alert. A case study in decision support. Appl Clin Inf 2013; 4: 144–152

http://dx.doi.org/10.4338/ACI-2012-12-RA-0055


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Conflicts of Interest

The authors declare that they have no conflicts of interest in this research.

  • References

  • 1 Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, Sam J, Haynes RB. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. Jama 2005; 293 (10) 1223-1238.
  • 2 Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ (Clinical research ed. 2005; 330 7494 765.
  • 3 Asch SM, McGlynn EA, Hogan MM, Hayward RA, Shekelle P, Rubenstein L, Keesey J, Adams J, Kerr EA. Comparison of quality of care for patients in the Veterans Health Administration and patients in a national sample. Ann Intern Med 2004; 141 (12) 938-945.
  • 4 Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. A roadmap for national action on clinical decision support. J Am Med Inform Assoc 2007; 14 (02) 141-145.
  • 5 Bates DW, Cohen M, Leape LL, Overhage JM, Shabot MM, Sheridan T. Reducing the frequency of errors in medicine using information technology. J Am Med Inform Assoc 2001; 8 (04) 299-308.
  • 6 Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR, Samsa G, Hasselblad V, Williams JW, Musty MD, Wing L, Kendrick AS, Sanders GD, Lobach D. Effect of clinical decision-support systems: a systematic review. Annals of internal medicine 2012; 157 (01) 29-43.
  • 7 Wright A, Maloney F, Feblowitz JC. Clinician attitudes toward and use of electronic problem lists: a thematic analysis. BMC Med Inform Decis Mak 2011; 11: 36.
  • 8 Reisman Y. Computer-based clinical decision aids. A review of methods and assessment of systems. Medical informatics = Medecine et informatique 1996; 21 (03) 179-197.
  • 9 Wright AP J, Feblowitz JC, Maloney FL, Wilcox AR, Ramelson HZ, Schneider LI, Bates DW. A method and knowledge base for automated infrence of patient problems from structured data in an electronic medical record. J Am Med Inform Assoc 2011; 18 (06) 859-867.
  • 10 Wright A, Pang J, Feblowitz JC, Maloney FL, Wilcox AR, McLoughlin KS, Ramelson H, Schneider L, Bates DW. Improving Electronic Problem List Completeness Through Clinical Decision Support: A Randomized, Clinical Trial. J Am Med Inform Assoc 2012; 19 (04) 555-561.
  • 11 Harris P, Taylor R, Thielke R, Payne J, Gonzalez N, Conde J. Research electronic data capture (REDCap) -A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009; 42 (02) 377-381.
  • 12 Ash J, Sittig DF, Campbell EM, Guappone KP, Dykstra RH. Some Unintended Consequences of Clinical Decision Support Systems. AMIA Annu Symp Proc 2007; 2007: 26-30.
  • 13 Phansalkar S, van der Sijs H, Tucker AD, Desai AA, Bell DS, Teich JM, Middleton B, Bates DW. Drug-drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc. 2012 Sept 25 [Epub].
  • 14 Holmes C, Brown M, Hilaire DS, Wright A. Healthcare provider attitudes towards the problem list in an electronic health record: a mixed-methods qualitative study. BMC Med Inform Decis Mak 2012; 12: 127.

Correspondence to:

Adam Wright, Ph.D.
Brigham and Women’s Hospital
1620 Tremont St.
Boston, MA 02115
617–525–9811

  • References

  • 1 Garg AX, Adhikari NK, McDonald H, Rosas-Arellano MP, Devereaux PJ, Beyene J, Sam J, Haynes RB. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. Jama 2005; 293 (10) 1223-1238.
  • 2 Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ (Clinical research ed. 2005; 330 7494 765.
  • 3 Asch SM, McGlynn EA, Hogan MM, Hayward RA, Shekelle P, Rubenstein L, Keesey J, Adams J, Kerr EA. Comparison of quality of care for patients in the Veterans Health Administration and patients in a national sample. Ann Intern Med 2004; 141 (12) 938-945.
  • 4 Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. A roadmap for national action on clinical decision support. J Am Med Inform Assoc 2007; 14 (02) 141-145.
  • 5 Bates DW, Cohen M, Leape LL, Overhage JM, Shabot MM, Sheridan T. Reducing the frequency of errors in medicine using information technology. J Am Med Inform Assoc 2001; 8 (04) 299-308.
  • 6 Bright TJ, Wong A, Dhurjati R, Bristow E, Bastian L, Coeytaux RR, Samsa G, Hasselblad V, Williams JW, Musty MD, Wing L, Kendrick AS, Sanders GD, Lobach D. Effect of clinical decision-support systems: a systematic review. Annals of internal medicine 2012; 157 (01) 29-43.
  • 7 Wright A, Maloney F, Feblowitz JC. Clinician attitudes toward and use of electronic problem lists: a thematic analysis. BMC Med Inform Decis Mak 2011; 11: 36.
  • 8 Reisman Y. Computer-based clinical decision aids. A review of methods and assessment of systems. Medical informatics = Medecine et informatique 1996; 21 (03) 179-197.
  • 9 Wright AP J, Feblowitz JC, Maloney FL, Wilcox AR, Ramelson HZ, Schneider LI, Bates DW. A method and knowledge base for automated infrence of patient problems from structured data in an electronic medical record. J Am Med Inform Assoc 2011; 18 (06) 859-867.
  • 10 Wright A, Pang J, Feblowitz JC, Maloney FL, Wilcox AR, McLoughlin KS, Ramelson H, Schneider L, Bates DW. Improving Electronic Problem List Completeness Through Clinical Decision Support: A Randomized, Clinical Trial. J Am Med Inform Assoc 2012; 19 (04) 555-561.
  • 11 Harris P, Taylor R, Thielke R, Payne J, Gonzalez N, Conde J. Research electronic data capture (REDCap) -A metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009; 42 (02) 377-381.
  • 12 Ash J, Sittig DF, Campbell EM, Guappone KP, Dykstra RH. Some Unintended Consequences of Clinical Decision Support Systems. AMIA Annu Symp Proc 2007; 2007: 26-30.
  • 13 Phansalkar S, van der Sijs H, Tucker AD, Desai AA, Bell DS, Teich JM, Middleton B, Bates DW. Drug-drug interactions that should be non-interruptive in order to reduce alert fatigue in electronic health records. J Am Med Inform Assoc. 2012 Sept 25 [Epub].
  • 14 Holmes C, Brown M, Hilaire DS, Wright A. Healthcare provider attitudes towards the problem list in an electronic health record: a mixed-methods qualitative study. BMC Med Inform Decis Mak 2012; 12: 127.