Assessment of Readiness for Clinical Decision Support to Aid Laboratory Monitoring of Immunosuppressive Care at U.S. Liver Transplant Centers
10 August 2014
accepted: 26 November 2014
19 December 2017 (online)
Background: Following liver transplantation, patients require lifelong immunosuppressive care and monitoring. Computerized clinical decision support (CDS) has been shown to improve post-transplant immunosuppressive care processes and outcomes. The readiness of transplant information systems to implement computerized CDS to support post-transplant care is unknown.
Objectives: a) Describe the current clinical information system functionality and manual and automated processes for laboratory monitoring of immunosuppressive care, b) describe the use of guidelines that may be used to produce computable logic and the use of computerized alerts to support guideline adherence, and c) explore barriers to implementation of CDS in U.S. liver transplant centers.
Methods: We developed a web-based survey using cognitive interviewing techniques. We surveyed 119 U.S. transplant programs that performed at least five liver transplantations per year during 2010–2012. Responses were summarized using descriptive analyses; barriers were identified using qualitative methods.
Results: Respondents from 80 programs (67% response rate) completed the survey. While 98% of programs reported having an electronic health record (EHR), all programs used paper-based manual processes to receive or track immunosuppressive laboratory results. Most programs (85%) reported that 30% or more of their patients used external laboratories for routine testing. Few programs (19%) received most external laboratory results as discrete data via electronic interfaces while most (80%) manually entered laboratory results into the EHR; less than half (42%) could integrate internal and external laboratory results. Nearly all programs had guidelines regarding pre-specified target ranges (92%) or testing schedules (97%) for managing immunosuppressive care. Few programs used computerized alerting to notify transplant coordinators of out-of-range (27%) or overdue laboratory results (20%).
Conclusions: Use of EHRs is common, yet all liver transplant programs were largely dependent on manual paper-based processes to monitor immunosuppression for post-liver transplant patients. Similar immunosuppression guidelines provide opportunities for sharing CDS once integrated laboratory data are available.
Citation: Jacobs J, Weir C, Evans RS, Staes C. Assessment of readiness for clinical decision support to aid laboratory monitoring of immunosuppressive care at U.S. liver transplant centers. Appl Clin Inf 2014; 5: 988–1004
- 1 U. S. Department of Health & Human Services. Organ Procurement and Transplantation Network (OPTN) and Scientific Registry of Transplant Recipients (SRTR). 2014 http://optn.transplant.hrsa.gov/members/regions.asp (accessed 31 Jan 2014).
- 2 U. S. Department of Health & Human Services. OPTN/SRTR 2012 Annual Data Report. 2012 http://srtr.transplant.hrsa.gov/ADR.aspx
- 3 Marshall B, Swearingen JP. Complexities in transplant revenue management. Prog Transplant 2007; 17: 94-98.
- 4 Medicare Program; Hospital Conditions of Participation: Requirements for Approval and Re-Approval of Transplant Centers To Perform Organ Transplants. United States: U. S. Department of Health and Human Services. 2007 http://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/GuidanceforLawsAndRegulations/Transplant-Laws-and-Regulations.html
- 5 Taylor AL, Watson CJE, Bradley JA. Immunosuppressive agents in solid organ transplantation: Mechanisms of action and therapeutic efficacy. Crit Rev Oncol Hematol 2005; 56: 23-46.
- 6 Staes CJ, Evans RS, Rocha BHSC, Sorensen JB, Huff SM, Arata J, Narus SP. Computerized alerts improve outpatient laboratory monitoring of transplant patients. J Am Med Inform Assoc 2008; 15: 324-332.
- 7 Park ES, Peccoud MR, Wicks KA, Halldorson JB, Carithers RL, Reyes JD, Perkins JD. Use of an automated clinical management system improves outpatient immunosuppressive care following liver transplantation. J Am Med Inform Assoc 2010; 17: 396-402.
- 8 Blumenthal D, Tavenner M. The “Meaningful Use” Regulation for Electronic Health Records. N Engl J Med 2010; 363: 501-504.
- 9 Centers for Medicare & Medicaid Services. EHR Incentive Programs. 2013 http://www.cms.gov/ehrincentiveprograms/ (accessed 28 Feb 2014).
- 10 Hsiao C, Hing E. Use and Characteristics of Electronic Health Record Systems Among Office-based Physician Practices: United States. 2001–2013. NCHS Data Brief 2014
- 11 Office of the National Coordinator for Health IT, Health IT Adoption and Use Dashboard. http://dash-board.healthit.gov/HITAdoption (accessed 1 Aug 2013).
- 12 Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. A roadmap for national action on clinical decision support. J Am Med Inform Assoc 2007; 14: 141-145.
- 13 Kawamoto K, Lobach DF, Willard HF, Ginsburg GS. A national clinical decision support infrastructure to enable the widespread and consistent practice of genomic and personalized medicine. BMC Med Inform Decis Mak 2009; 9: 17.
- 14 O’Connor PJ, Sperl-Hillen JM, Rush WA, Johnson PE, Amundson GH, Asche SE, Ekstrom HL, Gilmer TP. Impact of electronic health record clinical decision support on diabetes care: a randomized trial. Ann Fam Med 2011; 9: 12-21.
- 15 Soper J, Chan GTC, Skinner JR, Spinetto HD, Gentles TL. Management of oral anticoagulation in a population of children with cardiac disease using a computerised system to support decision-making. Cardiol Young 2006; 16: 256-260.
- 16 Tapuria A, Austin T, Sun S, Lea N, Iliffe S, Kalra D, Ingram D, Patterson D. Clinical advantages of decision support tool for anticoagulation control. In: 2013 IEEE Point-of-Care Healthcare Technologies (PHT). IEEE 2013. 331-334.
- 17 Tawadrous D, Shariff SZ, Haynes RB, Iansavichus AV, Jain AK, Garg AX. Use of clinical decision support systems for kidney-related drug prescribing: a systematic review. Am J Kidney Dis 2011; 58: 903-914.
- 18 Tiwari R, Tsapepas DS, Powell JT, Martin ST. Enhancements in healthcare information technology systems: customizing vendor-supplied clinical decision support for a high-risk patient population. J Am Med Inform Assoc 2013; 20: 377-380.
- 19 Staes CJ, Evans RS, Narus SP, Huff SM, Sorensen JB. System analysis and improvement in the process of transplant patient care. Stud Health Technol Inform 2007; 129: 915-919.
- 20 Staes CJ, Bennett ST, Evans RS, Narus SP, Huff SM, Sorensen JB. A case for manual entry of structured, coded laboratory data from multiple sources into an ambulatory electronic health record. J Am Med Inform Assoc 2006; 13: 12-15.
- 21 Willis GB. Cognitive Interviewing: A “How To” Guide. Reducing Surv. Error through Res. Cogn. Decis. Process. Surv. 1999 http://www.hkr.se/pagefiles/35002/gordonwillis.pdf
- 22 Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009; 42: 377-381.
- 23 R Core Team. R: A Language and Environment for Statistical Computing. 2013 http://www.r-project.org
- 24 Coxon APM. Sorting Data: Collection and Analysis. Thousand Oaks, CA: Sage Publications; 1999
- 25 Fincher S, Tenenberg J. Making sense of card sorting data. Expert Syst 2005; 22: 89-93.
- 26 Health Level 7 International. Implementation Guide: Orders and Observations; Interoperable Laboratory Result Reporting to EHR (US Realm), Release 1. 2007
- 27 Logical Observation Identifiers Names and Codes (LOINC®). http://loinc.org (accessed 1 Aug 2014).
- 28 Kawamoto K, Del Fiol G, Orton C, Lobach DF. System-agnostic clinical decision support services: benefits and challenges for scalable decision support. Open Med Inform J 2010; 4: 245-254.
- 29 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. BMJ 2005; 330: 765.
- 30 Berner ES. editor. Clinical Decision Support Systems: Theory and Practice. Second Edi. Springer; 2006
- 31 National Research Council.. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press; 2009