Appl Clin Inform 2015; 06(03): 521-535
DOI: 10.4338/ACI-2015-02-RA-0019
Research Article
Schattauer GmbH

Development, Evaluation and Implementation of Chief Complaint Groupings to Activate Data Collection

A Multi-Center Study of Clinical Decision Support for Children with Head Trauma
S. J. Deakyne
1   Children’s Hospital Colorado, Department of Research Informatics, Aurora, Colorado, United States
,
L. Bajaj
2   University of Colorado, Department of Pediatrics, Section of Emergency Medicine, Aurora, Colorado, United States
,
J. Hoffman
3   Nationwide Children’s Hospital, Columbus, Ohio, United States
,
E. Alessandrini
4   Children’s Hospital Medical Center, Cincinnati, Ohio, United States
,
D. W. Ballard
5   Kaiser Permanente, San Rafael Medical Center, San Rafael, California, United States
,
R. Norris
6   Kaiser Permanente, Sacramento Medical Center, Sacramento, California, United States
,
L. Tzimenatos
7   University of California Davis School of Medicine, Departments of Emergency Medicine and Pediatrics, Sacramento, California, United States
,
M. Swietlik
8   Children’s Hospital Colorado, Department of Clinical Application Services, Aurora, Colorado, United States
,
E. Tham
2   University of Colorado, Department of Pediatrics, Section of Emergency Medicine, Aurora, Colorado, United States
,
R. W. Grundmeier
9   Children’s Hospital of Philadelphia and Perelman School of Medicine, Philadelphia, Pennsylvania, United States
,
N. Kuppermann
7   University of California Davis School of Medicine, Departments of Emergency Medicine and Pediatrics, Sacramento, California, United States
,
P. S. Dayan
10   Columbia University College of Physicians and Surgeons, Department of Pediatrics, Division of Emergency Medicine, New York, New York, United States
,
the Pediatric Emergency Care Applied Research Network (PECARN) › Institutsangaben
Weitere Informationen

Publikationsverlauf

received: 15. Februar 2015

accepted in revised form: 07. Juli 2015

Publikationsdatum:
19. Dezember 2017 (online)

Summary

Background: Overuse of cranial computed tomography scans in children with blunt head trauma unnecessarily exposes them to radiation. The Pediatric Emergency Care Applied Research Network (PECARN) blunt head trauma prediction rules identify children who do not require a computed tomography scan. Electronic health record (EHR) based clinical decision support (CDS) may effectively implement these rules but must only be provided for appropriate patients in order to minimize excessive alerts.

Objective: To develop, implement and evaluate site-specific groupings of chief complaints (CC) that accurately identify children with head trauma, in order to activate data collection in an EHR.

Methods: As part of a 13 site clinical trial comparing cranial computed tomography use before and after implementation of CDS, four PECARN sites centrally developed and locally implemented CC groupings to trigger a clinical trial alert (CTA) to facilitate the completion of an emergency department head trauma data collection template. We tested and chose CC groupings to attain high sensitivity while maintaining at least moderate specificity.

Results: Due to variability in CCs available, identical groupings across sites were not possible. We noted substantial variability in the sensitivity and specificity of seemingly similar CC groupings between sites. The implemented CC groupings had sensitivities greater than 90% with specificities between 75–89%. During the trial, formal testing and provider feedback led to tailoring of the CC groupings at some sites.

Conclusion: CC groupings can be successfully developed and implemented across multiple sites to accurately identify patients who should have a CTA triggered to facilitate EHR data collection. However, CC groupings will necessarily vary in order to attain high sensitivity and moderate-to-high specificity. In future trials, the balance between sensitivity and specificity should be considered based on the nature of the clinical condition, including prevalence and morbidity, in addition to the goals of the intervention being considered.

Citation: Deakyne SJ, Bajaj L, Hoffmann J, Alessandrini E, Ballard DW, Norris R, Tzimenatos L, Swietlik M, Tham E, Grundmeier RW, Kuppermann N, Dayan PS. Development, Evaluation and Implementation of Chief Complaint Groupings to Activate Data Collection in a Multi-Center Study of Clinical Decision Support for Children with Head Trauma. Appl Clin Inform 2015; 6: 521–535

http://dx.doi.org/10.4338/ACI-2015-02-RA-0019

 
  • References

  • 1 National Center for Injury Prevention and Control (NCIPC).. Traumatic brain injury in the United States: assessing outcomes in children. Available at: http://www.cdc.gov/ncipc/tbi/tbireport/index.htm. Accessed May 7, 2010.
  • 2 Kuppermann N, Holmes JF, Dayan PS, Hoyle Jr JD, Atabaki JR, Holubkov R, Nadel FM, Monroe D, Stanley RM, Borgialli DA, Badawy MK, Schunk JE, Quayle KS, Mahajan P, Lichenstein R, Lillis KA, Tunik MG, Jacobs ES, Callahan JM, Gorelick MH, Glass TF, Lee LK, Bachman MC, Cooper A, Powell EC, Gerardi MJ, Melville KA, Muizelaar JP, Wisner DH, Zuspan SJ, Dean JM, Wootton-Gorges SL. for the Pediatric Emergency Care Applied Research Network (PECARN). Identification of children at very low risk of clinically-important brain injuries after head trauma: a prospective cohort study. Lancet 2009; 374: 1160-1170.
  • 3 Dayan PS. Traumatic Brain Injury –Knowledge Translation (TBI-KT). In: ClinicalTrials.gov [Internet]. Bethesda (MD): National Library of Medicine (US) 2000 [cited 2014 Oct 20]. Available from: https://clinicaltrials.gov/ct2/show/NCT01453621 NLM Identifier: NCT01453621.
  • 4 Sittig DF, Wright A, Osheroff JA, Middleton B, Teich JM, Ash JS, Campbell E, Bates DW. Grand challenges in clinical decision support. J Biomed Inform 2008; 41 (02) 387-392.
  • 5 Embi PJ, Leonard AC. Evaluating alert fatigue over time to EHR-based clinical trial alerts: findings from a randomized controlled study. J Am Med Informatics Assoc 2012; 19 e1 e145-e148.
  • 6 Karsh BT. Clinical practice improvement and redesign: how change in workflow can be supported by clinical decision support. AHRQ Publication. Rockville, Maryland: Agency for Healthcare Research and Quality; 2009
  • 7 Isaac T, Weissman JS, Davis RB. Overrides of medication alerts in ambulatory care. Arch Intern Med 2009; 169: 305-311.
  • 8 Weingart SN, Toth M, Sands DZ, Aronson MD, Davis RB, Phillips RS. Physicians’ decisions to override computerized drug alerts in primary care. Arch Intern Med 2003; 163: 2625-2631.
  • 9 Glassman PA, Belperio P, Simon B, Lanto A, Lee M. Exposure to automated drug alerts over time: effects on clinicians’ knowledge and perceptions. Med Care 2006; 44: 250-256.
  • 10 Payne TH, Nichol WP, Hoey P, Savario J. Characteristics and override rates of order checks in a practitioner order entry system. Proc AMIA Symp 2002; 602-606.
  • 11 Ahearn MD, Kerr SJ. General practitioners’ perceptions of the pharmaceutical decision-support tools in their prescribing software. Med J Aust 2003; 179 (01) 34-37.
  • 12 Abookire SA, Teich JM, Sandige H, Paterno MD, Martin MT, Kuperman GJ, Bates DW. Improving allergy alerting in a computerized physician order entry system. Proc AMIA Symp 2000; 2-6.
  • 13 Shah NR, Seger AC, Seger DL, Fiskio JM, Kuperan GJ, Blumenfel B, Recklet EG, Bates DW, Gandhi TK. Improving acceptance of computerized prescribing alerts in ambulatory care. J Am Med Inform Assoc 2006; 13 (01) 5-11.
  • 14 Paterno MD, Maviglia SM, Gorman PN, Seger DL, Yoshida E, Seger AC, Bates DW, Gandhi TK. Tiering drug-drug interaction alerts by severity increases compliance rates. J Am Med Inform Assoc 2009; 16: 40-46.
  • 15 Osheroff JA. [editor]. Improving medication use and outcomes with clinical decision support: a step-by-step guide. Chicago, IL: Health Information and Management Systems Society; 2009
  • 16 Tamblyn R, Huang A, Taylor L, Kawasumi Y, Bartlett G, Grad R, Jacques A, Dawes M, Abrahamowicz M, Perreault R, Winslade N, Poissant L, Pinsonneault A. A randomized trial of the effectiveness of on-demand versus computer-triggered drug decision support in primary care. J Am Med Inform Assoc 2008; 15: 430-438.
  • 17 Embi PJ, Jain A, Clark J, Bizjack S, Hornung R, Harris CM. Effect of a clinical trial alert system on physician participation in trial recruitment. Arch Intern Med. 2005; 165 (19) 2272-2277.
  • 18 Heinemann S, Thuring S, Wedeken S, Schafer T, Scheidt-Nave C, Ketterer M, Himmel W. A clinical trial alert tool to recruit large patient samples and assess selection bias in general practice research. BMC Med Res Methodol 2011; 11: 16.
  • 19 Koplan KE, Brush AD, Packer MS, Zhang F, Senese MD, Simon SR. “Stealth” alerts to improve warfarin monitoring when initiating interacting medications. J Gen Intern Med 2012; 27 (12) 1666-1673.
  • 20 Ledwich LJ, Harrington TM, Ayoub WT, Sartorius JA, Newman ED. Improved influenza and pneumococcal vaccination in rheumatology patients taking immunosuppressants using an electronic health record best practice alert. Arthritis Rheum 2009; 61 (11) 1505-1510.
  • 21 Tang JW, Kushner RF, Cameron KA, Hicks B, Cooper AJ, Baker DW. Electronic tools to assist with identification and counseling for overweight patients: a randomized controlled trial. J Gen Intern Med 2012; 27 (08) 933-939.
  • 22 Haerian K, McKeeby J, Dipatrizio G, Cimino JJ. Use of clinical alerting to improve the collection of clinical research data. AMIA Annu Symp Proc 2009; 218-222.
  • 23 Mathias JS, Didwania AK, Baker DW. Impact of an electronic alert and order set on smoking cessation medication prescription. Nicotine Tob Res 2012; 14 (06) 674-681.
  • 24 Jain A, McCarthy K, Xu M, Stoller JK. Impact of a clinical decision support system in an electronic health record to enhance detection of [one.inferior]-antitrypsin deficiency. Chest 2011; 140 (01) 198-204.
  • 25 Grundmeier RW, Swietlik M, Bell LM. Research subject enrollment by primary care pediatricians using an electronic health record. Amia Annu Symp Proc 2007: 289-293.
  • 26 Carspecken CW, Sharek PJ, Longhurst C, Pageler NM. A clinical case of electronic health record drug alert fatigue: consequences for patient outcome. Pediatrics 2013; 131 (06) e1970-e1973.
  • 27 Coleman JJ, van der Sijs H, Haefeli WE, Slight SP, McDowell SE, Seidling HM, Eiermann B, Aarts J, Ammenwerth E, Slee A, Ferner RE. On the alert: future priorities for alerts in clinical decision support for computerized physician order entry identified from a European workshop. BMC Med Inform Decis Mak 2013; 13 (01) 111.
  • 28 Alessandrini EA, Alpern ER, Chamberlain JM, Shea JA, Holubkov R, Gorelick MH. Pediatric Emergency Care Applied Research Network. Developing a diagnosis-base severity classification system for use in emergency medical services for children. Acad Emerg Med 2012; 19 (01) 70-78.
  • 29 Iyer S, Reeves S, Varadarajan K, Alessandrini E. The Acute Care Model: A New Framework for Quality Care in Emergency Medicine. Clinical Pediatric Emergency Medicine 2011; 12 (02) 91-101.