Appl Clin Inform 2012; 03(03): 337-348
DOI: 10.4338/ACI-2012-04-RA-0012
Case Report
Schattauer GmbH

Initial implementation and evaluation of a Hepatitis C treatment clinical decision support system (CDSS)

A nurse practitioner-driven quality improvement initiative
L. Fathauer
1   Southeastern Indiana Gastroenterology
,
J. Meek
2   Indiana University Purdue University Indianapolis
› Author Affiliations
Further Information

Publication History

received: 05 April 2012

accepted: 01 September 2012

Publication Date:
16 December 2017 (online)

Summary

Background: Clinician compliance with clinical guidelines in the treatment of patients with Hepatitis C (HCV) has been reported to be as low as 18.5%. Treatment is complex and patient compliance is often inconsistent thus, active clinician surveillance and support is essential to successful outcomes. A clinical decision support system (CDSS) embedded within an electronic health record can provide reminders, summarize key data, and facilitate coordination of care. To date, the literature is bereft of information describing the implementation and evaluation of a CDSS to support HCV treatment.

Objective: The purpose of this case report is to describe the design, implementation, and initial evaluation of an HCV-specific CDSS while piloting data collection metrics and methods to be used in a larger study across multiple practices.

Methods: The case report describes the design and implementation processes with preliminary reporting on impact of the CDSS on quality indicator completion by comparing the pre-CDSS group to the post-CDSS group.

Results: The CDSS was successfully designed and implemented using an iterative, collaborative process. Pilot testing of the clinical outcomes of the CDSS revealed high rates of quality indicator completion in both the pre- and post-CDSS; although the post-CDSS group received a higher frequency of reminders (4.25 per patient) than the pre-CDSS group (.25 per patient).

Conclusions: This case report documents the processes used to successfully design and implement an HCV CDSS. While the small sample size precludes generalizability of findings, results did positively demonstrate the feasibility of comparing quality indicator completion rates pre-CDSS and post-CDSS. It is recommended that future studies include a larger sample size across multiple providers with expanded outcomes measures related to patient outcomes, staff satisfaction with the CDSS, and time studies to evaluate efficiency and cost effectiveness of the CDSS.

 
  • References

  • 1 Ghany MG, Strader DB, Thomas DL, Seef LB. AASLD practice guidelines: diagnosis, management, and treatment of hepatitis C an update. Hepatology 2009; 49 (04) 1335-1374.
  • 2 Centers for Disease Control.. Hepatitis C information for health professionals [Internet]. Atlanta (GA): Centers for Disease Control and Prevention; 2011 [updated 2008 July 28; cited 2012 Jan 12]. Available from: http://www.cdc.gov/hepatitis/hcv/hcvfaq.htm.
  • 3 Barnes E. Initiatives for vaccine research [Internet]. United Kingdom: World Health Organization; 2010 [updated 2010 Feb 8; cited 2012 Jan 12]. Available from: http://www.who.int/vaccine_research/disease viral_cancers/en/index2.html.
  • 4 Shiratori Y, Ito Y, Yokosuka O, Imazeki F, Nakata R, Tanaka N, Arakawa Y, Hashimoto E, Hirota K, Yoshida H, Ohashi Y, Omata M. Antiviral therapy for cirrhotic hepatitis C: association with reduced hepatocellular carcinoma development and improved survival. Ann Intern Med 2005; 142 (Suppl. 02) 105-114.
  • 5 Yoshida H, Arakawa Y, Sata M, Nishiguchi S, Yano M, Fujiyama S, Yamada G, Yokosuka O, Shiratori Y, Omata M. Interferon therapy prolonged life expectancy among chronic hepatitis C patients. Gastroenterology 2002; 123 (02) 483-491.
  • 6 Ghany MG, Nelson DR, Strader DB, Thomas DL, Seef LB. An update on treatment of genotype 1 chronic hepatitis C virus infection: 2011 practice guideline by the American association for the study of liver diseases. Hepatology 2011; 54 (04) 1433-1444.
  • 7 Basseri B, Yamini D, Chee G, Enayati P, Tran T, Poordad F. Comorbidities associated with the increasing burden of hepatitis C infection. Liver International 2010; 30 (07) 1012-1018.
  • 8 Dorr D. Primary care managers supported by information technology systems improve outcomes, reduce costs for patients with complex conditions [Internet]. Rockville (MD): AHRQ Health Care Innovations Exchange; 2008 [updated 2011 Jul 20; cited 2012 Jan 15]. Available from: http://www.innovations.ahrq. gov/content.aspx?id=264.
  • 9 Kanwal F, Schnitzler MS, Bacon BR, Hoang T, Buchanan PM, Asch SM. Quality of care in patients with chronic hepatitis C virus infection: a cohort study. Ann Intern Med 2010; 153 (04) 231-239.
  • 10 Hernandez B, Hasson NK, Cheung R. Hepatitis C performance measure on hepatitis A and B vaccination: missed opportunities?. Am J Gastroenterol 2009; 104 (08) 1961-1967.
  • 11 Hachem CY, Kramer JR, Kanwal F, El-Serag HB. Hepatitis vaccination in patients with hepatitis C: practice and validation of codes at a large veterans administration medical center. Aliment Pharm Therap 2008; 28 (09) 1078-1087.
  • 12 Shim M, Khaykis I, Park J, Bini EJ. Susceptibility to hepatitis A in patients with chronic liver disease due to hepatitis C virus infection: missed opportunities for vaccination. Hepatology 2005; 42 (03) 688-695.
  • 13 Chandra T, Reyes M, Nguyen H, Borum M. Frequency of alcohol and smoking cessation counseling in hepatitis C patients among internists and gastroenterologists. World J Gastroentero 2009; 15 (47) 6010-6011.
  • 14 Rongey CA, Kanwal F, Hoang T, Gifford AL, Asch SM. Viral RNA testing in hepatitis C antibody-positive veterans. Am J Prev Med 2009; 36 (03) 235-238.
  • 15 Wong V, Wreghitt TG, Alexander GJ. Prospective study of hepatitis B vaccination in patients with chronic hepatitis C. Brit Med J 1996; 312 7042 1336-1337.
  • 16 Institute of Medicine.. Crossing the quality chasm: a new health system for the 21st Century. Washington (DC): National Academy Press; 2001
  • 17 Wagner EH, Austin BT, Davis C, Hindmarsh M, Schaefer J, Bonomi A. Improving chronic illness care: translating evidence into action. Health Affair 2001; 20 (06) 64-78.
  • 18 Pestotnik SL. Expert clinical decision support systems to enhance antimicrobial stewardship programs: insights from the Society of Infectious Disease Pharmacists. Pharmacotherapy 2005; 25 (08) 1116-1125.
  • 19 Smith SA, Murphy ME, Huschka TR, Dinneen SF, Gorman CA, Zimmerman BR, Rizza RA, Naessens JM. Impact of a diabetes electronic management system on the care of patients seen in a subspecialty diabetes clinic. Diabetes Care 1998; 21 (06) 972-976.
  • 20 Fiks AG, Grundmeier RW, Biggs LM, Localio AR, Alessandrini EA. Impact of clinical alerts within an electronic health record on routine childhood immunization in an urban pediatric population. Pediatrics 2007; 120 (04) 707-714.
  • 21 Montgomery AA, Fahey T, Peters TJ, MacIntosh C, Sharp DJ. Evaluation of computer based clinical decision support system and risk chart for management of hypertension in primary care: randomised controlled trial. Brit Med J 2000; 320 7236 686-690.
  • 22 Goud R, de Keizer NF, ter Reit G, Wyatt JC, Hasman A, Hellemans IM, Peek N. Effect of guideline based computerised decision support on decision making of multidisciplinary teams: cluster randomised trial in cardiac rehabilitation. Brit Med J. 2009: 338: b1880. Available from: http://www.bmj.com/content/338/bmj.b1880
  • 23 Kitahata MM, Dillingham PW, Chaiyakunapruk N, Buskin SE, Jones JL, Harrington RD, Hooton TM, Holmes KK. Electronic human immunodeficiency virus (HIV) clinical reminder system improves adherence to practice guidelines among the University of Washington HIV study cohort. Clin Infect Dis 2003; 36 (06) 803-811.
  • 24 Bell LM, Grundmeier R, Localio R, Zorc J, Fiks AG, Zhang X, Stephens TB, Swietlik M, Guevara JP. Electronic health record based decision support to improve asthma care: a cluster-randomized trial. Pediatrics 2010; 125 (04) 770-777.
  • 25 Reynolds J, Roble D. The financial implications of ACOs for providers. Healthcare Financial Management 2011; 65 (10) 76-82.
  • 26 CMS.gov [Internet].. Baltimore (MD): 2010 PQRI measures list; 2009 Nov 13 [cited 2012 Jan 16]. Available from: https://www.cms.gov/PQRS/Downloads/2010_PQRI_MeasuresList_111309.pdf
  • 27 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. Brit Med J. 2005 2. 330 Available from: 10.1136/bmj.38398.500764.8F.
  • 28 McDonald CJ, Overhage JM, Barnes M, Schadow G, Blevins L, Dexter PR, Mamlin B. The Indiana network for patient care: A working local health information infrastructure?. Health Affairs 2005; 24: 1214-1220.
  • 29 Feldstein AC, Glasgow RE. A practical, robust implementation and sustainability model (PRISM) for integrating research findings into practice. Joint Comm J Qual Im 2008; 34 (04) 228-243.
  • 30 Denekamp Y. Clinical decision support systems for addressing information needs of physicians. Israel Med Assoc J 2007; 9 (11) 771-776.