Monitoring Adherence to Evidence-Based Practices

Summary

Background: Clinical pathways are evidence-based recommendations for treating a diagnosis. Although implementations of clinical pathways have reduced medical errors, lowered costs, and improved patient outcomes, monitoring whether a patient is following the intended pathway is problematic. Implementing a variance reporting program is impeded by the lack of a reliable source of electronic data and automatic retrieval methods.

Objectives: Our objective is to develop an automated method of measuring and reporting patient variance from a clinical pathway.

Methods: We identify a viable and ubiquitous data source for establishing the realized patient’s path- Health Level Seven (HL7) formatted message exchanges between Hospital Information Systems. This is in contrast to current practices in most hospitals where data for clinical pathway variance reporting is obtained from multiple data sources, often retrospectively. This paper develops a method to use message exchanges to automatically establish and compare a patient’s path against a clinical pathway. Our method not only considers pathway activities as is common practice, but also extracts patient outcomes from HL7 messages and reports this in addition to the variance.

Results: Using data from our partner hospital, we illustrate our clinical pathway variance analysis tool using major joint replacement patients. We validate our method by comparing audit results for a random sample of HL7 constructed pathways with data extracted from patient charts. We report several variances such as omitted laboratory tests or additional activities such as blood transfusions. Our method successfully identifies variances and reports them in a quantified way to support decisions related to quality control.

Conclusions: Our approach differs from previous studies in that a quantitative measure is established over three dimensions: (1) omissions from the pathway, (2) additions to the pathway, and (3) patient outcomes. By examining variances providers can evaluate clinical decisions, and support quality feedback and training mechanisms.

Citation: Konrad R, Tulu B, Lawley M. Monitoring adherence to evidence based practices – a method to utilize HL7 messages from hospital information systems. Appl Clin Inf 2013; 4: 126–143

http://dx.doi.org/10.4338/ACI-2012-06-RA-0026

Keywords

HL7 - inpatient care - workflow - monitoring - surveillance - clinical decision support

 

Conflicts of Interest

The authors declare that they have no conflicts of interest in the research.

• References

• 1 Kwan J. Care pathways for acute stroke care and stroke rehabilitation: From theory to evidence. Journal of Clinical neuroscience 2007; 14 (03) 189-200.
• 2 Coffey RJ, Richards JS, Remmert CS, LeRoy SS, Schoville RR, Baldwin PJ. An introduction to critical paths. Quality Management in Healthcare 2005; 14 (01) 46.
• 3 Pearson SD, Goulart-Fisher D, Lee TH. Critical pathways as a strategy for improving care: Problems and potential. Ann Intern Med 1995; 123 (12) 941.
• 4 Barbieri A, Vanhaecht K, Van Herck P, Sermeus W, Faggiano F, Marchisio S. et al. Effects of clinical pathways in the joint replacement: A meta-analysis. BMC Medicine 2009; 7 (01) 32.
• 5 Marrie TJ, Lau CY, Wheeler SL, Wong CJ, Vandervoort MK, Feagan BG. A controlled trial of a critical pathway for treatment of community-acquired pneumonia. JAMA 2000; 283: 749-755.
• 6 Macario A, Horne M, Goodman S, Vitez T, Dexter F, Heinen R. et al. The effect of a perioperative clinical pathway for knee replacement surgery on hospital costs. Anesth Analg 1998; 86: 978-984.
• 7 Vanhaecht K, Sermeus W, Tuerlinckx G, Witters I, Vandenneucker H, Bellemans J. Development of a clinical pathway for total knee arthroplasty and the effect on length of stay and in-hospital functional outcome. Acta Orthop Belg 2005; 71 (04) 439.
• 8 Pearson SD, Kleefield SF, Soukop JR, Cook EF, Lee TH. Critical pathways intervention to reduce length of hospital stay* 1. Am J Med 2001; 110 (03) 175-180.
• 9 Panella M, Marchisio S, Di Stanislao F. Reducing clinical variations with clinical pathways: Do pathways work?. Int J Qual Health Care 2003; 15 (06) 509-521.
• 10 Dykes PC, Currie LM, Cimino JJ. Adequacy of evolving national standardized terminologies for interdisciplinary coded concepts in an automated clinical pathway. J Biomed Inform 2003; 36 4-5 313-325.
• 11 van de Klundert J, Gorissen P, Zeemering S. Measuring clinical pathway adherence. J Biomed Inform. 2010
• 12 Allen D, Gillen E, Rixson L. Systematic review of the effectiveness of integrated care pathways: What works, for whom, in which circumstances?. International Journal of Evidence Based Healthcare. 2009; 7 (02) 61-74.
• 13 Cannon DS, Allen SN. A comparison of the effects of computer and manual reminders on compliance with a mental health clinical practice guideline. Journal of the American Medical Informatics Association 2000; 7 (02) 196-203.
• 14 Mehta RH, Montoye CK, Gallogly M, Baker P, Blount A, Faul J. et al. Improving quality of care for acute myocardial infarction: The guidelines applied in practice (GAP) initiative. JAMA: the journal of the American Medical Association 2002; 287 (10) 1269.
• 15 Atsushi Okita, Motohiro Yamashita, Keiko Abe, Chizuru Nagai, Akiko Matsumoto, Mika Akehi, Ryoko Yamashita, Naomi Ishida, Mikiko Seike, and Shigeko Yokota. et al. Variance analysis of a clinical pathway of video-assisted single lobectomy for lung cancer. Surgery Today 2009; 39 (02) 104-109.
• 16 Sanchez M, Espinosa G, Estrada C, Jimenez S, Tomas S, Miro O. Direct discharge from triage in emergency departments: Assessment, risks and patient satisfaction. Ann Emerg Med 2005; 46 (03) 24 -■.
• 17 Leong TY. Decision support systems in healthcare: Emerging trends and success factors. Studies in Fuzziness and Soft Computing 2003; 124: 151-179.
• 18 Lenz R, Reichert M. IT support for healthcare processes –premises, challenges, perspectives. Data Knowl Eng 2007; 61 (01) 39-58.
• 19 Wakamiya S, Yamauchi K. What are the standard functions of electronic clinical pathways?. Int J Med Inf 2009; 78 (08) 543-550.
• 20 Rajagopalan B, Isken MW. Exploiting data preparation to enhance mining and knowledge discovery. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 2002; 31 (04) 460-467.
• 21 Ceglowski R, Churilov L, Wasserthiel J. Combining data mining and discrete event simulation for a value-added view of a hospital emergency department. J Oper Res Soc 2006; 58 (02) 246-254.
• 22 Vasilakis C, Lecznarowicz D, Lee C. Developing model requirements for patient flow simulation studies using the unified modelling language (UML). Journal of Simulation 2009; 3 (03) 141-149.
• 23 Brennan PF, Stead WW. Assessing data quality from concordance, through correctness and completeness, to valid manipulatable representations. Journal of the American Medical Informatics Association 2000; 7 (01) 106-107.
• 24 Hogan WR, Wagner MM. Accuracy of data in computer-based patient records. Journal of the American Medical Informatics Association 1997; 4 (05) 342.
• 25 Blois M, Shortliffe E, Shortliffe E, Perreault L, Wiederhold G, Fagan L. Medical informatics: Computer applications in health care. Medical informatics: computer applications in health care. 2001
• 26 Tange H. The paper-based patient record: Is it really so bad?. Comput Methods Programs Biomed 1995; 48 1-2 127-131.
• 27 Stausberg J, Koch D, Ingenerf J, Betzler M. Comparing paper-based with electronic patient records: Lessons learned during a study on diagnosis and procedure codes. Journal of the American Medical Informatics Association 2003; 10 (05) 470-477.
• 28 Jones CA, Beaupre LA, Johnston D, Suarez-Almazor ME. Total joint arthroplasties: Current concepts of patient outcomes after surgery. Rheumatic Disease Clinics of North America 2007; 33 (01) 71-86.
• 29 Sokka T, Kautiainen H, Hannonen P. Stable occurrence of knee and hip total joint replacement in central finland between 1986 and 2003: An indication of improved long-term outcomes of rheumatoid arthritis. Ann Rheum Dis 2007; 66 (03) 341.
• 30 Hawker G, Wright J, Coyte P, Paul J, Dittus R, Croxford R. et al. Health-related quality of life after knee replacement: Results of the knee replacement patient outcomes research team study. Journal of bone and joint surgery. American volume 1998; 80 (02) 163-173.
• 31 Brunenberg DE, van Steyn MJ, Sluimer JC, Bekebrede LL, Bulstra SK, Joore MA. Joint recovery programme versus usual care: An economic evaluation of a clinical pathway for joint replacement surgery. Med Care 2005; 43 (10) 1018.
• 32 Dalton P, Macintosh DJ, Pearson B. Variance analysis in clinical pathways for total hip and knee joint arthroplasty. J Qual Clin Pract 2000; 20: 145-149.
• 33 Schuld J, Schäfer T, Nickel S, Jacob P, Schilling MK, Richter S. Impact of IT-supported clinical pathways on medical staff satisfaction. A prospective longitudinal cohort study. Int J Med Inf 2011; 80 (03) 151-156.