INFECTIOUS DISEASE/EDITORIAL
The Potential for Clinical Decision Support to Improve Emergency Care Adam B. Landman, MD, MS* *Corresponding Author. E-mail: [email protected], Twitter: @landmaad. 0196-0644/$-see front matter Copyright © 2015 by the American College of Emergency Physicians. http://dx.doi.org/10.1016/j.annemergmed.2015.03.006
SEE RELATED ARTICLE, P.
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[Ann Emerg Med. 2015;-:1-2.] Health information technology adoption in emergency departments (EDs) has increased in response to federal policy and incentive payments, with 84% of EDs reporting electronic health record use in 2011.1 However, the benefits of health information technology for health care delivery have not yet been fully realized.2,3 Clinical decision support is a component of health information technology that provides “clinicians with clinical knowledge and patient-related information, intelligently filtered and presented at appropriate times, to enhance patient care.”4 Clinical decision support has reduced medication errors5 and improved adherence to guideline-based care,6 including ED sexual assault prophylaxis recommendations7 and imaging use in mild traumatic brain injury.8 However, few studies have evaluated the effect of clinical decision support on patient outcomes,9 especially in the ED setting. This issue of Annals includes a study by Dean et al,10 who developed a clinical decision support tool to identify ED patients with pneumonia and provided recommendations for severity assessment, disposition, and antibiotic selection in accordance with the Infectious Disease Society of America–American Thoracic Society 2005 and 2007 guidelines. They evaluated the tool’s effect on mortality, patient disposition, and process outcomes in 4 intervention EDs compared with 3 usual care EDs in Utah’s Intermountain Healthcare system. The study found improved adherence to disposition recommendations in hospitals using the clinical decision support tool, but no difference in overall mortality. These study results should be interpreted cautiously, given a trend toward increased mortality in the control group. The increased mortality trend could have been from random fluctuations in annual pneumonia mortality, but it makes the true effect of the clinical decision support tool more difficult to isolate. Although the study did not show a statistically significant effect on overall mortality, it Volume
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highlights key considerations for future clinical decision support implementations and evaluations. Clinical decision support interventions seeking to improve outcomes should be based on best available evidence.11 In this case, the clinical decision support tool was developed according to the Infectious Disease Society of America– American Thoracic Society clinical guidelines, which have subsequently been shown not to improve outcomes for patients with health care–associated pneumonia,12 and therefore we would not expect this clinical decision support intervention to improve overall mortality. Consistent with this latest evidence, in a subgroup analysis, this study observed no effect on health care–associated pneumonia mortality, but found reductions in community-acquired pneumonia mortality. Although the results for the community-acquired pneumonia subgroup are promising, these comparisons were performed post hoc without multiple comparison adjustments and must be confirmed in a future study. For clinical decision support to optimally affect patient outcomes, the clinical decision support intervention must also achieve high levels of adoption by clinicians. In this study, the tool was used for 62.6% of intervention patients with pneumonia. This tool relied on the provider to notice an icon on the electronic tracking board and then to launch the tool. These manual steps may have contributed to the tool’s not being used in more than a third of eligible patients. Seeking ways to more seamlessly incorporate this tool into work flow,4,11 such as automatically opening the clinical decision support tool when the provider accesses the electronic records of a patient with suspected pneumonia, may help improve provider adoption. Although health information technology has had largely positive overall influences,13 it can also introduce negative or unintended consequences, including increased mortality.14 In this study, time to first antibiotic administration was increased in the intervention group. Although this delay did not increase mortality, it could have been caused by the time required to use the electronic tool. Other electronic health record interventions have reported possible patient harm Annals of Emergency Medicine 1
Landman
Potential for Clinical Decision Support to Improve Emergency Care
by extending time to treatment15,16; therefore, the effect of this clinical decision support tool on time to antibiotic administration and patient outcomes should continue to be monitored closely and the clinical decision support optimized as needed. Although the study results of Dean et al10 are not compelling enough to recommend implementation of this pneumonia clinical decision support tool in all EDs, this study affirms clinical decision support’s ability to increase adherence to guidelines. To maximize the ability for clinical decision support to improve patient outcomes and emergency care, clinical decision support tools should be designed in accordance with best available clinical evidence, integrated with work flow to attain high usage rates, and monitored closely before and after implementation for negative or unintended consequences. Rigorous patient outcome studies like this one are critical for assessing clinical decision support’s effect and identifying effective interventions to disseminate to all EDs. Supervising editor: Gregory J. Moran, MD Author affiliations: From the Department of Emergency Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, and the Partners Information Systems, Wellesley, MA. Funding and support: By Annals policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). The author has stated that no such relationships exist.
REFERENCES 1. Jamoom E, Hing E. Progress With Electronic Health Record Adoption Among Emergency and Outpatient Departments: United States, 20062011. Hyattsville, MD: National Center for Health Statistics; 2015. 2. Wears RL. Health information technology and victory. Ann Emerg Med. 2015;65:143-145. 3. Kellermann AL, Jones SS. What it will take to achieve the as-yetunfulfilled promises of health information technology? Health Aff (Millwood). 2013;32:63-68.
2 Annals of Emergency Medicine
4. Osheroff J, Teich J, Levick D, et al. Improving Outcomes With Clinical Decision Support: An Implementer’s Guide. 2nd ed. HIMSS: Chicago, IL; 2012. 5. Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med. 2003;163:1409-1416. 6. Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144:742-752. 7. Britton DJ, Bloch RB, Strout TD, et al. Impact of a computerized order set on adherence to Centers for Disease Control guidelines for the treatment of victims of sexual assault. J Emerg Med. 2013;44: 528-535. 8. Gupta A, Ip IK, Raja AS, et al. Effect of clinical decision support on documented guideline adherence for head CT in emergency department patients with mild traumatic brain injury. J Am Med Inform Assoc. 2014;21:e347-e351. 9. Garg AX, Adhikari NK, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293: 1223-1238. 10. Dean NC, Jones BE, Jones JP, et al. Impact of an electronic clinical decision support tool for emergency department patients with pneumonia. Ann Emerg Med. 2015. http://dx.doi.org/10.1016/ j.annemergmed.2015.02.003. 11. Bates DW, Kuperman GJ, Wang S, et al. Ten commandments for effective clinical decision support: making the practice of evidencebased medicine a reality. J Am Med Inform Assoc. 2003;10: 523-530. 12. Kett DH, Cano E, Quartin AA, et al. Implementation of guidelines for management of possible multidrug-resistant pneumonia in intensive care: an observational, multicentre cohort study. Lancet Infect Dis. 2011;11:181-189. 13. Buntin MB, Burke MF, Hoaglin MC, et al. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff (Millwood). 2011;30: 464-471. 14. Han YY, Carcillo JA, Venkataraman ST, et al. Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics. 2005;116: 1506-1512. 15. Koppel R, Metlay JP, Cohen A, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005;293:1197-1203. 16. Strom BL, Schinnar R, Aberra F, et al. Unintended effects of a computerized physician order entry nearly hard-stop alert to prevent a drug interaction: a randomized controlled trial. Arch Intern Med. 2010;170:1578-1583.
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The Potential for Clinical Decision Support to Improve Emergency Care Adam B. Landman, MD, MS* *Corresponding Author. E-mail: [email protected], Twitter: @landmaad. 0196-0644/$-see front matter Copyright © 2015 by the American College of Emergency Physicians. http://dx.doi.org/10.1016/j.annemergmed.2015.03.006
SEE RELATED ARTICLE, P.
.
---
[Ann Emerg Med. 2015;-:1-2.] Health information technology adoption in emergency departments (EDs) has increased in response to federal policy and incentive payments, with 84% of EDs reporting electronic health record use in 2011.1 However, the benefits of health information technology for health care delivery have not yet been fully realized.2,3 Clinical decision support is a component of health information technology that provides “clinicians with clinical knowledge and patient-related information, intelligently filtered and presented at appropriate times, to enhance patient care.”4 Clinical decision support has reduced medication errors5 and improved adherence to guideline-based care,6 including ED sexual assault prophylaxis recommendations7 and imaging use in mild traumatic brain injury.8 However, few studies have evaluated the effect of clinical decision support on patient outcomes,9 especially in the ED setting. This issue of Annals includes a study by Dean et al,10 who developed a clinical decision support tool to identify ED patients with pneumonia and provided recommendations for severity assessment, disposition, and antibiotic selection in accordance with the Infectious Disease Society of America–American Thoracic Society 2005 and 2007 guidelines. They evaluated the tool’s effect on mortality, patient disposition, and process outcomes in 4 intervention EDs compared with 3 usual care EDs in Utah’s Intermountain Healthcare system. The study found improved adherence to disposition recommendations in hospitals using the clinical decision support tool, but no difference in overall mortality. These study results should be interpreted cautiously, given a trend toward increased mortality in the control group. The increased mortality trend could have been from random fluctuations in annual pneumonia mortality, but it makes the true effect of the clinical decision support tool more difficult to isolate. Although the study did not show a statistically significant effect on overall mortality, it Volume
-,
no.
-
:
-
2015
highlights key considerations for future clinical decision support implementations and evaluations. Clinical decision support interventions seeking to improve outcomes should be based on best available evidence.11 In this case, the clinical decision support tool was developed according to the Infectious Disease Society of America– American Thoracic Society clinical guidelines, which have subsequently been shown not to improve outcomes for patients with health care–associated pneumonia,12 and therefore we would not expect this clinical decision support intervention to improve overall mortality. Consistent with this latest evidence, in a subgroup analysis, this study observed no effect on health care–associated pneumonia mortality, but found reductions in community-acquired pneumonia mortality. Although the results for the community-acquired pneumonia subgroup are promising, these comparisons were performed post hoc without multiple comparison adjustments and must be confirmed in a future study. For clinical decision support to optimally affect patient outcomes, the clinical decision support intervention must also achieve high levels of adoption by clinicians. In this study, the tool was used for 62.6% of intervention patients with pneumonia. This tool relied on the provider to notice an icon on the electronic tracking board and then to launch the tool. These manual steps may have contributed to the tool’s not being used in more than a third of eligible patients. Seeking ways to more seamlessly incorporate this tool into work flow,4,11 such as automatically opening the clinical decision support tool when the provider accesses the electronic records of a patient with suspected pneumonia, may help improve provider adoption. Although health information technology has had largely positive overall influences,13 it can also introduce negative or unintended consequences, including increased mortality.14 In this study, time to first antibiotic administration was increased in the intervention group. Although this delay did not increase mortality, it could have been caused by the time required to use the electronic tool. Other electronic health record interventions have reported possible patient harm Annals of Emergency Medicine 1
Landman
Potential for Clinical Decision Support to Improve Emergency Care
by extending time to treatment15,16; therefore, the effect of this clinical decision support tool on time to antibiotic administration and patient outcomes should continue to be monitored closely and the clinical decision support optimized as needed. Although the study results of Dean et al10 are not compelling enough to recommend implementation of this pneumonia clinical decision support tool in all EDs, this study affirms clinical decision support’s ability to increase adherence to guidelines. To maximize the ability for clinical decision support to improve patient outcomes and emergency care, clinical decision support tools should be designed in accordance with best available clinical evidence, integrated with work flow to attain high usage rates, and monitored closely before and after implementation for negative or unintended consequences. Rigorous patient outcome studies like this one are critical for assessing clinical decision support’s effect and identifying effective interventions to disseminate to all EDs. Supervising editor: Gregory J. Moran, MD Author affiliations: From the Department of Emergency Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, and the Partners Information Systems, Wellesley, MA. Funding and support: By Annals policy, all authors are required to disclose any and all commercial, financial, and other relationships in any way related to the subject of this article as per ICMJE conflict of interest guidelines (see www.icmje.org). The author has stated that no such relationships exist.
REFERENCES 1. Jamoom E, Hing E. Progress With Electronic Health Record Adoption Among Emergency and Outpatient Departments: United States, 20062011. Hyattsville, MD: National Center for Health Statistics; 2015. 2. Wears RL. Health information technology and victory. Ann Emerg Med. 2015;65:143-145. 3. Kellermann AL, Jones SS. What it will take to achieve the as-yetunfulfilled promises of health information technology? Health Aff (Millwood). 2013;32:63-68.
2 Annals of Emergency Medicine
4. Osheroff J, Teich J, Levick D, et al. Improving Outcomes With Clinical Decision Support: An Implementer’s Guide. 2nd ed. HIMSS: Chicago, IL; 2012. 5. Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med. 2003;163:1409-1416. 6. Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144:742-752. 7. Britton DJ, Bloch RB, Strout TD, et al. Impact of a computerized order set on adherence to Centers for Disease Control guidelines for the treatment of victims of sexual assault. J Emerg Med. 2013;44: 528-535. 8. Gupta A, Ip IK, Raja AS, et al. Effect of clinical decision support on documented guideline adherence for head CT in emergency department patients with mild traumatic brain injury. J Am Med Inform Assoc. 2014;21:e347-e351. 9. Garg AX, Adhikari NK, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA. 2005;293: 1223-1238. 10. Dean NC, Jones BE, Jones JP, et al. Impact of an electronic clinical decision support tool for emergency department patients with pneumonia. Ann Emerg Med. 2015. http://dx.doi.org/10.1016/ j.annemergmed.2015.02.003. 11. Bates DW, Kuperman GJ, Wang S, et al. Ten commandments for effective clinical decision support: making the practice of evidencebased medicine a reality. J Am Med Inform Assoc. 2003;10: 523-530. 12. Kett DH, Cano E, Quartin AA, et al. Implementation of guidelines for management of possible multidrug-resistant pneumonia in intensive care: an observational, multicentre cohort study. Lancet Infect Dis. 2011;11:181-189. 13. Buntin MB, Burke MF, Hoaglin MC, et al. The benefits of health information technology: a review of the recent literature shows predominantly positive results. Health Aff (Millwood). 2011;30: 464-471. 14. Han YY, Carcillo JA, Venkataraman ST, et al. Unexpected increased mortality after implementation of a commercially sold computerized physician order entry system. Pediatrics. 2005;116: 1506-1512. 15. Koppel R, Metlay JP, Cohen A, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005;293:1197-1203. 16. Strom BL, Schinnar R, Aberra F, et al. Unintended effects of a computerized physician order entry nearly hard-stop alert to prevent a drug interaction: a randomized controlled trial. Arch Intern Med. 2010;170:1578-1583.
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