Reports From the Field

A Quality Improvement Initiative to Improve Emergency Department Care for Pediatric Patients with Sickle Cell Disease

Journal of Clinical Outcomes Management. 2014 February;21(2)

References

1. Yusuf HR, Atrash HK, Grosse SD, et al. Emergency department visits made by patients with sickle cell disease: a descriptive study, 1999-2007. Am J Preventive Med 2010;38 (4 Suppl):S536–41.

2. Benjamin L, Dampier C, Jacox A, et al. Guideline for the management of acute and chronic pain in sickle cell disease. American Pain Society; 1999.

3. Rees DC, Olujohungbe AD, Parker NE, et al. Guidelines for the management of the acute painful crisis in sickle cell disease. Br J Haematology 2003;120:744–52.

4. Solomon LR. Pain management in adults with sickle cell disease in a medical center emergency department. J Nat Med Assoc 2010;102:1025–32.

5. Lazio MP, Costello HH, Courtney DM, et al. A comparison of analgesic management for emergency department patients with sickle cell disease and renal colic. Clin J Pain 2010;26:199–205.

6. Shenoi R, Ma L, Syblik D, Yusuf S. Emergency department crowding and analgesic delay in pediatric sickle cell pain crises. Ped Emerg Care 2011;27:911–7.

7. Tanabe P, Artz N, Mark Courtney D, et al. Adult emergency department patients with sickle cell pain crisis: a learning collaborative model to improve analgesic management. Acad Emerg Med 2010;17:399–407.

8. Zempsky WT. Evaluation and treatment of sickle cell pain in the emergency department: paths to a better future. Clin Ped Emerg Med 2010;11:265–73.

9. Haywood C Jr, Tanabe P, Naik R, et al. The impact of race and disease on sickle cell patient wait times in the emergency department. Am J Emerg Med 2013;31:651–6.

10. Solomon LR. Treatment and prevention of pain due to vaso-occlusive crises in adults with sickle cell disease: an educational void. Blood 2008;111:997–1003.

11. Ballas SK. New era dawns on sickle cell pain. Blood 2010;116:311–2.

12. Haywood C Jr, Lanzkron S, Ratanawongsa N, et al. The association of provider communication with trust among adults with sickle cell disease. J Gen Intern Med 2010;25:543–8.

13. Zempsky WT. Treatment of sickle cell pain: fostering trust and justice. JAMA 2009;302:2479–80.

14. Elander J, Lusher J, Bevan D, Telfer P. Pain management and symptoms of substance dependence among patients with sickle cell disease. Soc Sci Med 2003;57:1683–96.

15. Elander J, Lusher J, Bevan D, et al. Understanding the causes of problematic pain management in sickle cell disease: evidence that pseudoaddiction plays a more important role than genuine analgesic dependence. J Pain Sympt Manag 2004;27:156–69.

16. Smith WR, Penberthy LT, Bovbjerg VE, et al. Daily assessment of pain in adults with sickle cell disease. Ann Intern Med 2008;148:94–101.

17. Harris A, Parker N, Baker C. Adults with sickle cell. Psychol Health Med 1998;3:171–9.

18. Jenerette CM, Brewer C. Health-related stigma in young adults with sickle cell disease. J Nat Med Assoc 2010;102:1050–5.

19. Maxwell K, Streetly A, Bevan D. Experiences of hospital care and treatment seeking for pain from sickle cell disease: qualitative study. BMJ 1999;318:1585–90.

20. Oyeku SO, Wang CJ, Scoville R, et al. Hemoglobinopathy Learning Collaborative: using quality improvement (QI) to achieve equity in health care quality, coordination, and outcomes for sickle cell disease. J Health Care Poor Underserved 2012;23(3 Suppl):34–48.

21. Wang CJ, Kavanagh PL, Little AA, et al. Quality-of-care indicators for children with sickle cell disease. Pediatrics 2011;128:484–93.

22. Mansouri M, Lockyer J. A meta-analysis of continuing medical education effectiveness. J Contin Ed Health Prof 2007;27:6–15.

23. The breakthrough series: IHI’s collaborative model for achieving breakthrough improvement. Boston: Institute for Healthcare Improvement; 2003.

24. Berwick DM. Improvement, trust, and the healthcare workforce. Qual Safety Health Care 2003;12:448–52.

25. Hovlid E, Bukve O, Haug K, et al. Sustainability of healthcare improvement: what can we learn from learning theory? BMC Health Serv Res 2012;12:235.

26. Tanabe P, Hafner JW, Martinovich Z, Artz N. Adult emergency department patients with sickle cell pain crisis: results from a quality improvement learning collaborative model to improve analgesic management. Acad Emerg Med 2012;19:430–8.

27. Brousseau DC, Owens PL, Mosso AL, et al. Acute care utilization and rehospitalizations for sickle cell disease. JAMA 2010;303:1288–94.

28. Frei-Jones MJ, Field JJ, DeBaun MR. Multi-modal intervention and prospective implementation of standardized sickle cell pain admission orders reduces 30-day readmission rate. Pediatr Blood Cancer 2009;53:401–5.

). Prior to implementation of the order set, over 90% of ED staff (nurses, residents and attendings), hematology attendings, and fellows participated in in-service training on the new protocol, its rationale, and expectations for the intervention. Nursing staff were empowered to initiate the protocol upon triage and asked to alert a physician immediately to the presence of a patient requiring management on the protocol. Physicians were asked to make the immediate pain relief of patients with VOE a top priority. Staff were notified that charts would be audited at regular intervals. Completed order sets were reviewed daily during the week, and whenever there was a deviation from the order set or another question, the ED nurse and/or MD champions contacted staff involved as quickly as possible to discuss what had occurred and to refresh staff on the appropriate implementation of the protocol if needed. The multidisciplinary QI team had regular email contact and monthly meetings to review progress and concerns.

Measures

We selected performance measures from the bank developed by the WISCH team to track improvement and evaluate progress. These performance measures included (1) mean time from triage to first analgesic dose, (2) percentage of patients that received their first dose of analgesic within 30 minutes of triage, (3) percentage of patients who had a pain assessment performed within 30 minutes of triage, and (4) percentage of patients re-assessed within 30 minutes after the first dose of analgesic had been administered. Our aims were to have 80% of patients assessed and given pain medications within 30 minutes of triage, and to have 80% of patients re-assessed within 30 minutes after having received their first dose of an analgesic, within 12 months of implementing our intervention.

Data Collection and Analysis

The WISCH project coordinator reviewed records of visits to the ED for a baseline period of 6 months and post-order set implementaton. Demographic data (age, gender), clinical data (hemoglobin type), pain scores, utilization data (number of ED visits during the study period), and data pertaining to the metrics chosen from the WISCH measurement bank were extracted from each eligible patient’s ED chart after the visit was completed. If patients were admitted, their length of hospitalization was extracted from their inpatient medical record.

All biostatistical analyses were conducted using Stata 9.2 (StataCorp, College Station, TX). Descriptive statistics computed at 2 time-points (pre and post order set implementation) were utilized to examine means, standard deviations and percentages. The 2 time-points were initially compared at the visit level of measurement, using Student’s t tests corrected for unequal variances where necessary for continuous variables and chi-square analyses for categorical variables, to evaluate if there was an improvement in timely triage, assessment, and treatment of acute VOE pain for all ED visits pre and post order set implementation. To account for trends and possible correlations across the months post order set implementation, we ran a mixed linear model with repeated measures over time to compare visits during all months post order set implementation with the baseline months, for metric 1, time from triage to first pain medication. If significant differences were found, we used Dunnett’s method of multiple comparisons to determine which months differed from baseline. For metrics 2 through 4, we ran linear models with a binary outcome, a logit link function and using general estimating equations to determine trends and to account for correlations over time.