Appl Clin Inform 2012; 03(01): 52-63
DOI: 10.4338/ACI-2011-01-RA-0002
Research Article
Schattauer GmbH

Reduction in Clinical Variance Using Targeted Design Changes in Computerized Provider Order Entry (CPOE) Order Sets

Impact on Hospitalized Children with Acute Asthma Exacerbation
B. R. Jacobs
1   Children’s National Medical Center, Washington, DC
,
K. W. Hart
2   Cincinnati Children‘s Hospital Medical Center, Cincinnati, OH
,
D. W. Rucker
3   Siemens Medical Solutions, Malvern, PA
› Author Affiliations
Further Information

Correspondence to:

Brian R. Jacobs, MD
Vice President & Chief Medical Information Officer (CMIO)
Executive Director, Center for Pediatric Informatics
Children’s National Medical Center
111 Michigan Avenue, NW
Washington, DC 20010
Phone: (202) 4 76 39 69   
Fax: (202) 4 76 59 88   

Publication History

received: 02 August 2011

accepted: 22 January 2012

Publication Date:
16 December 2017 (online)

 

Summary

Objectives: Unwarranted variance in healthcare has been associated with prolonged length of stay, diminished health and increased cost. Practice variance in the management of asthma can be significant and few investigators have evaluated strategies to reduce this variance. We hypothesized that selective redesign of order sets using different ways to frame the order and physician decision-making in a computerized provider order entry system could increase adherence to evidence-based care and reduce population-specific variance.

Patients and Methods: The study focused on the use of an evidence-based asthma exacerbation order set in the electronic health record (EHR) before and after order set redesign. In the Baseline period, the EHR was queried for frequency of use of an asthma exacerbation order set and its individual orders. Important individual orders with suboptimal use were targeted for redesign. Data from a Post-Intervention period were then analyzed.

Results: In the Baseline period there were 245 patient visits in which the acute asthma exacerbation order set was selected. The utilization frequency of most orders in the order set during this period exceeded 90%. Three care items were targeted for intervention due to suboptimal utilization: admission weight, activity center use and peak flow measurements. In the Post-Intervention period there were 213 patient visits. Order set redesign using different default order content resulted in significant improvement in the utilization of orders for all 3 items: admission weight (79.2% to 94.8% utilization, p<0.001), activity center (84.1% to 95.3% utilization, p<0.001) and peak flow (18.8% to 55.9% utilization, p<0.001). Utilization of peak flow orders for children ≥8 years of age increased from 42.7% to 94.1% (p<0.001).

Conclusions: Details of order set design greatly influence clinician prescribing behavior. Queries of the EHR reveal variance associated with ordering frequencies. Targeting and changing order set design elements in a CPOE system results in improved selection of evidence-based care.


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Conflict of Interest Statement

Donald Rucker, MD is an employee of the Siemens Corporation but does not have any conflicts of interest to report in regards to this research. Brian Jacobs, MD and Kim Ward Hart declare that they have no conflicts of interest in this research.

  • References

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  • 2 Kuperman GJ, Bobb A, Payne TH, Avery AJ, Gandhi TK, Burns G. et al. Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc 2007; 14: 29-40.
  • 3 van Doormaal JE, van den Bemt PM, Zaal RJ, Egberts AC, Lenderink BW, Kosterink JG. et al. The influence that electronic prescribing has on medication errors and preventable adverse drug events: an interrupted time-series study. J Am Med Inform Assoc 2009; 16: 816-825.
  • 4 Bates DW, Teich JM, Lee J, Seger D, Kuperman GJ, Ma’luf N. et al. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc 1999; 6 (04) 313-321.
  • 5 Payne TH, Savarino J, Marshall R, Hoey CT. Use of a clinical event monitor to prevent and detect medication errors. Proc AMIA Symp 2000: 640-644.
  • 6 Cunningham TR, Geller ES, Clarke SW. Impact of electronic prescribing in a hospital setting: a process-focused evaluation. Int J Med Inform 2008; 77: 546-554.
  • 7 Abboud PA, Ancheta R, McKibben M. Jacobs BR Impact of workflow-integrated corollary orders on aminoglycoside monitoring in children. Health Informatics J 2006; 12: 187-187.
  • 8 Bogucki B, Jacobs BR, Hingle J. Computerized reminders reduce the use of medications during shortages. J Am Med Inform Assoc 2004; 11 (04) 278-280.
  • 9 Potts AL, Barr FE, Gregory DF, Wright L, Patel NR. Computerized physician order entry and medication errors in a pediatric critical care unit. Pediatrics 2004; 113: 59-63.
  • 10 Teich JM, Merchia PR, Schmiz JL, Kuperman GJ, Spurr CD, Bates DW. Effects of computerized physician order entry on prescribing practices. Arch Intern Med 2000; 160 (18) 2741-2747.
  • 11 Lehmann CU, Kim GR. Computerized provider order entry and patient safety. Pediatr Clin North Am 2006; 53: 1169-1184.
  • 12 Luft HS, Bunker JP, Enthoven AC. Should operations be regionalized? The empirical relation between surgical volume and mortality. N Engl J Med 1979; 301 (25) 1364-1369.
  • 13 Wennberg JE. Understanding geographic variations in health care delivery. N Engl J Med 1999; 340 (01) 52-53.
  • 14 Wells RD, Dahl B, Nilson B. Comparison of the levels of quality of inpatient care delivered by pediatrics residents and by private, community pediatricians at one hospital. Acad Med 1998; 73 (02) 192-197.
  • 15 Christakis DA, Cowan CA, Garrison MM, Molteni R, Marcuse E, Zerr DM. Variation in inpatient diagnostic testing and management of bronchiolitis. Pediatrics 2005; 115 (04) 878-884.
  • 16 Wahlstrom R, Hummers-Pradier E, Lundborg CS, Muskova M, Lagerlov P, Denig P. et al. Variations in asthma treatment in five European countries--judgement analysis of case simulations. Fam Pract 2002; 19 (05) 452-460.
  • 17 Meade MO, Jacka MJ, Cook DJ, Dodek P, Griffith L, Guyatt GH. Survey of interventions for the prevention and treatment of acute respiratory distress syndrome. Crit Care Med 2004; 32 (04) 946-954.
  • 18 Bero LA, Grilli R, Grimshaw JM, Harvey E, Oxman AD, Thomson MA. Closing the gap between research and practice: an overview of systematic reviews of interventions to promote the implementation of research findings. The Cochrane Effective Practice and Organization of Care Review Group Bmj 1998; 317 7156 465-468.
  • 19 O’Connor RD, O’Donnell JC, Pinto LA, Wiener DJ, Legorreta AP. Two-year retrospective economic evaluation of three dual-controller therapies used in the treatment of asthma. Chest 2002; 121 (04) 1028-1035.
  • 20 Sanders DL, Aronsky D. Biomedical informatics applications for asthma care: a systematic review. J Am Med Inform Assoc 2006; 13 (04) 418-427.
  • 21 Eccles M, McColl E, Steen N, Rousseau N, Grimshaw J, Parkin D. et al. Effect of computerised evidence based guidelines on management of asthma and angina in adults in primary care: cluster randomised controlled trial. Bmj 2002; 325 7370 941.
  • 22 Massie J, Efron D, Cerritelli B, South M, Powell C, Haby MM. et al. Implementation of evidence based guidelines for paediatric asthma management in a teaching hospital. Arch Dis Child 2004; 89 (07) 660-664.
  • 23 McCowan C, Neville RG, Ricketts IW, Warner FC, Hoskins G, Thomas GE. Lessons from a randomized controlled trial designed to evaluate computer decision support software to improve the management of asthma. Med Inform Internet Med 2001; 26 (03) 191-201.
  • 24 Tierney WM, Overhage JM, Murray MD, Harris LE, Zhou XH, Eckert GJ. et al. Can computer-generated evidence-based care suggestions enhance evidence-based management of asthma and chronic obstructive pulmonary disease? A randomized, controlled trial. Health Serv Res 2005; 40 (02) 477-497.
  • 25 Sarrell EM, Mandelberg A, Cohen HA, Kahan E. Compliance of primary care doctors with asthma guidelines and related education programs: the employment factor. Isr Med Assoc J 2002; 4 (06) 403-406.
  • 26 Kwan-Gett TS, Lozano P, Mullin K, Marcuse EK. One-year experience with an inpatient asthma clinical pathway. Arch Pediatr Adolesc Med 1997; 151 (07) 684-689.
  • 27 Nilasena DS, Lincoln MJ. A computer-generated reminder system improves physician compliance with diabetes preventive care guidelines. Proc Annu Symp Comput Appl Med Care 1995: 640-645.
  • 28 Bobb AM, Payne TH, Gross PA. Viewpoint: controversies surrounding use of order sets for clinical decision support in computerized provider order entry. J Am Med Inform Assoc 2007; 14 (01) 41-47.
  • 29 Starmer J, Lorenzi N, Pinson CW. The Vanderbilt EvidenceWeb –developing tools to monitor and improve compliance with evidence-based order sets. AMIA Annu Symp Proc 2006: 749-53.
  • 30 Starmer J, Waitman LR. Orders and evidence-based order sets –Vanderbilt’s experience with CPOE ordering patterns between 2000 and 2005. AMIA Annu Symp Proc. 2006: 1108.
  • 31 Kuperman GJ, Gibson RF. Computer physician order entry: benefits, costs, and issues. Ann Intern Med 2003; 139 (01) 31-39.
  • 32 Wright A, Sittig DF. Automated development of order sets and corollary orders by data mining in an ambulatory computerized physician order entry system. AMIA Annu Symp Proc 2006: 819-823.
  • 33 Miller RA, Waitman LR, Chen S, Rosenbloom ST. The anatomy of decision support during inpatient care provider order entry (CPOE): empirical observations from a decade of CPOE experience at Vanderbilt. J Biomed Inform 2005; 38 (06) 469-485.
  • 34 Chisolm DJ, McAlearney AS, Veneris S, Fisher D, Holtzlander M, McCoy KS. The role of computerized order sets in pediatric inpatient asthma treatment. Pediatr Allergy Immunol 2006; 17 (03) 199-206.
  • 35 Cincinnati Children’s Hospital Medical Center Evidence-Based Clinical Practice Guidelines. (Accessed June 5, 2008, at http://www.cincinnatichildrens.org/svc/alpha/h/health-policy/ev-based/default.htm )
  • 36 Wennberg JE. Practice variations and health care reform: connecting the dots. Health Aff. (Millwood) 2004; Suppl Web Exclusives:VAR140-4.
  • 37 Tu SW, Musen MA, Shankar R, Campbell J, Hrabak K, McClay J. et al. Modeling guidelines for integration into clinical workflow. Medinfo 2004; 11 Pt 1 174-178.
  • 38 Deming WE. The New Economics. Cambridge, MA: MIT Press; 2000
  • 39 Tversky A, Kahneman D. The framing of decisions and the psychology of choice. Science 1981; 211 4481 453-458.
  • 40 Tversky A, Kahneman D. Judgment under Uncertainty: Heuristics and Biases. Science 1974; 185 4157 1124-1131.

Correspondence to:

Brian R. Jacobs, MD
Vice President & Chief Medical Information Officer (CMIO)
Executive Director, Center for Pediatric Informatics
Children’s National Medical Center
111 Michigan Avenue, NW
Washington, DC 20010
Phone: (202) 4 76 39 69   
Fax: (202) 4 76 59 88   

  • References

  • 1 Bates DW, Kuperman GJ, Rittenberg E, Teich JM, Fiskio J, Ma’luf N. et al. A randomized trial of a computer-based intervention to reduce utilization of redundant laboratory tests. Am J Med 1999; 106 (02) 144-150.
  • 2 Kuperman GJ, Bobb A, Payne TH, Avery AJ, Gandhi TK, Burns G. et al. Medication-related clinical decision support in computerized provider order entry systems: a review. J Am Med Inform Assoc 2007; 14: 29-40.
  • 3 van Doormaal JE, van den Bemt PM, Zaal RJ, Egberts AC, Lenderink BW, Kosterink JG. et al. The influence that electronic prescribing has on medication errors and preventable adverse drug events: an interrupted time-series study. J Am Med Inform Assoc 2009; 16: 816-825.
  • 4 Bates DW, Teich JM, Lee J, Seger D, Kuperman GJ, Ma’luf N. et al. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc 1999; 6 (04) 313-321.
  • 5 Payne TH, Savarino J, Marshall R, Hoey CT. Use of a clinical event monitor to prevent and detect medication errors. Proc AMIA Symp 2000: 640-644.
  • 6 Cunningham TR, Geller ES, Clarke SW. Impact of electronic prescribing in a hospital setting: a process-focused evaluation. Int J Med Inform 2008; 77: 546-554.
  • 7 Abboud PA, Ancheta R, McKibben M. Jacobs BR Impact of workflow-integrated corollary orders on aminoglycoside monitoring in children. Health Informatics J 2006; 12: 187-187.
  • 8 Bogucki B, Jacobs BR, Hingle J. Computerized reminders reduce the use of medications during shortages. J Am Med Inform Assoc 2004; 11 (04) 278-280.
  • 9 Potts AL, Barr FE, Gregory DF, Wright L, Patel NR. Computerized physician order entry and medication errors in a pediatric critical care unit. Pediatrics 2004; 113: 59-63.
  • 10 Teich JM, Merchia PR, Schmiz JL, Kuperman GJ, Spurr CD, Bates DW. Effects of computerized physician order entry on prescribing practices. Arch Intern Med 2000; 160 (18) 2741-2747.
  • 11 Lehmann CU, Kim GR. Computerized provider order entry and patient safety. Pediatr Clin North Am 2006; 53: 1169-1184.
  • 12 Luft HS, Bunker JP, Enthoven AC. Should operations be regionalized? The empirical relation between surgical volume and mortality. N Engl J Med 1979; 301 (25) 1364-1369.
  • 13 Wennberg JE. Understanding geographic variations in health care delivery. N Engl J Med 1999; 340 (01) 52-53.
  • 14 Wells RD, Dahl B, Nilson B. Comparison of the levels of quality of inpatient care delivered by pediatrics residents and by private, community pediatricians at one hospital. Acad Med 1998; 73 (02) 192-197.
  • 15 Christakis DA, Cowan CA, Garrison MM, Molteni R, Marcuse E, Zerr DM. Variation in inpatient diagnostic testing and management of bronchiolitis. Pediatrics 2005; 115 (04) 878-884.
  • 16 Wahlstrom R, Hummers-Pradier E, Lundborg CS, Muskova M, Lagerlov P, Denig P. et al. Variations in asthma treatment in five European countries--judgement analysis of case simulations. Fam Pract 2002; 19 (05) 452-460.
  • 17 Meade MO, Jacka MJ, Cook DJ, Dodek P, Griffith L, Guyatt GH. Survey of interventions for the prevention and treatment of acute respiratory distress syndrome. Crit Care Med 2004; 32 (04) 946-954.
  • 18 Bero LA, Grilli R, Grimshaw JM, Harvey E, Oxman AD, Thomson MA. Closing the gap between research and practice: an overview of systematic reviews of interventions to promote the implementation of research findings. The Cochrane Effective Practice and Organization of Care Review Group Bmj 1998; 317 7156 465-468.
  • 19 O’Connor RD, O’Donnell JC, Pinto LA, Wiener DJ, Legorreta AP. Two-year retrospective economic evaluation of three dual-controller therapies used in the treatment of asthma. Chest 2002; 121 (04) 1028-1035.
  • 20 Sanders DL, Aronsky D. Biomedical informatics applications for asthma care: a systematic review. J Am Med Inform Assoc 2006; 13 (04) 418-427.
  • 21 Eccles M, McColl E, Steen N, Rousseau N, Grimshaw J, Parkin D. et al. Effect of computerised evidence based guidelines on management of asthma and angina in adults in primary care: cluster randomised controlled trial. Bmj 2002; 325 7370 941.
  • 22 Massie J, Efron D, Cerritelli B, South M, Powell C, Haby MM. et al. Implementation of evidence based guidelines for paediatric asthma management in a teaching hospital. Arch Dis Child 2004; 89 (07) 660-664.
  • 23 McCowan C, Neville RG, Ricketts IW, Warner FC, Hoskins G, Thomas GE. Lessons from a randomized controlled trial designed to evaluate computer decision support software to improve the management of asthma. Med Inform Internet Med 2001; 26 (03) 191-201.
  • 24 Tierney WM, Overhage JM, Murray MD, Harris LE, Zhou XH, Eckert GJ. et al. Can computer-generated evidence-based care suggestions enhance evidence-based management of asthma and chronic obstructive pulmonary disease? A randomized, controlled trial. Health Serv Res 2005; 40 (02) 477-497.
  • 25 Sarrell EM, Mandelberg A, Cohen HA, Kahan E. Compliance of primary care doctors with asthma guidelines and related education programs: the employment factor. Isr Med Assoc J 2002; 4 (06) 403-406.
  • 26 Kwan-Gett TS, Lozano P, Mullin K, Marcuse EK. One-year experience with an inpatient asthma clinical pathway. Arch Pediatr Adolesc Med 1997; 151 (07) 684-689.
  • 27 Nilasena DS, Lincoln MJ. A computer-generated reminder system improves physician compliance with diabetes preventive care guidelines. Proc Annu Symp Comput Appl Med Care 1995: 640-645.
  • 28 Bobb AM, Payne TH, Gross PA. Viewpoint: controversies surrounding use of order sets for clinical decision support in computerized provider order entry. J Am Med Inform Assoc 2007; 14 (01) 41-47.
  • 29 Starmer J, Lorenzi N, Pinson CW. The Vanderbilt EvidenceWeb –developing tools to monitor and improve compliance with evidence-based order sets. AMIA Annu Symp Proc 2006: 749-53.
  • 30 Starmer J, Waitman LR. Orders and evidence-based order sets –Vanderbilt’s experience with CPOE ordering patterns between 2000 and 2005. AMIA Annu Symp Proc. 2006: 1108.
  • 31 Kuperman GJ, Gibson RF. Computer physician order entry: benefits, costs, and issues. Ann Intern Med 2003; 139 (01) 31-39.
  • 32 Wright A, Sittig DF. Automated development of order sets and corollary orders by data mining in an ambulatory computerized physician order entry system. AMIA Annu Symp Proc 2006: 819-823.
  • 33 Miller RA, Waitman LR, Chen S, Rosenbloom ST. The anatomy of decision support during inpatient care provider order entry (CPOE): empirical observations from a decade of CPOE experience at Vanderbilt. J Biomed Inform 2005; 38 (06) 469-485.
  • 34 Chisolm DJ, McAlearney AS, Veneris S, Fisher D, Holtzlander M, McCoy KS. The role of computerized order sets in pediatric inpatient asthma treatment. Pediatr Allergy Immunol 2006; 17 (03) 199-206.
  • 35 Cincinnati Children’s Hospital Medical Center Evidence-Based Clinical Practice Guidelines. (Accessed June 5, 2008, at http://www.cincinnatichildrens.org/svc/alpha/h/health-policy/ev-based/default.htm )
  • 36 Wennberg JE. Practice variations and health care reform: connecting the dots. Health Aff. (Millwood) 2004; Suppl Web Exclusives:VAR140-4.
  • 37 Tu SW, Musen MA, Shankar R, Campbell J, Hrabak K, McClay J. et al. Modeling guidelines for integration into clinical workflow. Medinfo 2004; 11 Pt 1 174-178.
  • 38 Deming WE. The New Economics. Cambridge, MA: MIT Press; 2000
  • 39 Tversky A, Kahneman D. The framing of decisions and the psychology of choice. Science 1981; 211 4481 453-458.
  • 40 Tversky A, Kahneman D. Judgment under Uncertainty: Heuristics and Biases. Science 1974; 185 4157 1124-1131.