Anticoagulation clinics in an integrated healthcare system differed widely in their organization and management, but these differences were not consistently related to their performance.
To describe variations in the structure of anticoagulation clinic (ACC) care within the Veterans Health Administration (VA) and to identify structures of care that are associated with better site-level anticoagulation control.
Questionnaire correlated with automated clinical data.
We characterized 90 VA ACCs using a questionnaire administered by the VA Central Office. Site descriptors included staffing levels, provider training, visit modalities, quality improvement programs, documentation, and care coordination. Patient outcomes were measured by site mean risk-adjusted percentage time in therapeutic range, a measure of anticoagulation control over time. Our study was powered to detect a 3% difference in risk-adjusted percentage time in therapeutic range, a small-to-moderate effect size, between sites with and without a certain characteristic.
We observed considerable variation in the structure of ACC care. For example, 48 sites had fewer than 400 patients per provider, 25 sites had 400 to 599 patients per provider, and 17 sites had 600 patients or more per provider. However, none of the site characteristics measured were significantly related to anticoagulation control.
We found substantial variation in guideline-targeted organizational and management features of ACC care within the VA. However, no single feature was associated with better anticoagulation control. Current guidelines for organizing an ACC may have limited relevance for improving patient outcomes.
(Am J Manag Care. 2011;17(4):284-289)
Dedicated anticoagulation clinics (ACCs) improve anticoagulation control and patient outcomes.
Millions of patients receive oral anticoagulation therapy to treat or prevent thromboembolic disease.1 While effective management of warfarin sodium therapy is not easy, better anticoagulation control can improve outcomes and reduce adverse events.2 Indeed, a growing body of literature has shown that patients whose care is managed in dedicated anticoagulation clinics (ACCs) have better outcomes than those managed in usual care.3-6 Clinical guidelines1,7 recommend that all ACCs should have certain features, including quality improvement programs, adequate documentation of care, sufficient training for providers, and a ratio of patients to providers of less than 400:1. However, there is little evidence as to which, if any, of these features contribute to the improved outcomes seen with ACC care.
Although oral anticoagulation therapy has been used for more than 5 decades, quality measurement has had little penetration into this field.2 Our group recently undertook the first systematic effort to profile riskadjusted anticoagulation control in an integrated health system. Within the Veterans Health Administration (VA), a system in which essentially all anticoagulation therapy is managed in an ACC, the mean percentage time in therapeutic range (TTR) varied widely among sites,8 with some sites performing more than 10% better or worse than would be expected based on the risk-adjustment model. Variations of this magnitude are associated with important differences in rates of stroke, venous thromboembolism, and major hemorrhage.9-13 To help all sites in the VA improve their performance, it is necessary to understand the site-level correlates of better or worse anticoagulation control.
Therefore, this study had the following 2 objectives: first, to describe differences in the organization and management of ACCs within the VA, and second, to assess whether these differences help explain sitelevel differences in anticoagulation control. We measured site characteristics using a survey of VA ACCs that examined structural features that seem likely to affect anticoagulation control, including staffing ratios, provider training protocols, and the existence of quality improvement programs.1,7 We hypothesized that these variables would be associated with anticoagulation control. In this study, either a positive finding or a negative finding would be useful because it can help determine whether enforcing conformity to any specific feature is likely to improve anticoagulation care and control in the VA.
The data for this study have been described elsewhere. The Veterans Affairs Study to Improve Anticoagulation8,14 included all patients who received oral anticoagulation therapy from the VA between October 1, 2006, and September 30, 2008. The study was approved by the institutional review board of the Bedford VA Medical Center, Bedford, Massachusetts.
We included international normalized ratio (INR) values when patients were “on warfarin” (ie, when a patient was in possession of warfarin or was having INR tests performed at least every 42 days). We defined the period of warfarin possession as the duration of the most recent VA prescription for warfarin, plus 30 days. We calculated percentage TTR using the method by Rosendaal et al,15 which assigns an INR value to each day by linear interpolation between successively observed INR values. Gaps of 56 days or more between INR values are not interpolated. The patient’s TTR equals the percentage of days for which the interpolated INR values lie between 2.0 and 3.0 (from 0%-100%).
Independent Variables of Site-Level Characteristics
There are 128 sites of care within the VA, each of which includes a hospital, an outpatient care center, and several outlying community-based clinics. Each site has a specialized ACC, which is usually run by clinical pharmacists under the supervision of a medical director.16 Therefore, essentially all anticoagulation care within the VA is delivered within specialized ACCs. In October 2006, VA Pharmacy Benefits Management surveyed all 128 VA sites of care for the organization and management of their ACCs. Topics included visit modalities (face to face, telephone, and mail), quality improvement programs, clinic staffing, provider training, documentation, and care coordination (the verbatim text of the questionnaire is given in the [available at www.ajmc.com]). We abstracted our independent variables from the responses to the questionnaire.
Dependent Variable of Risk-Adjusted Anticoagulation Control
Site mean TTR was adjusted for case mix using a model that incorporates patient demographics, comorbid conditions that have an adverse effect on TTR, and general measures of comorbidity, including the number of medications and the number of hospitalizations. The derivation and validation of this model, which achieved an R2 of 13.3%, has been described previously.8,14 The model was used to calculate the expected mean TTR for each site (“E”) based on patient characteristics. The expected mean TTR was compared with the observed mean TTR for each site (“O”). Therefore, each site’s performance was characterized by an O minus E (“O − E”) score, our measure of risk-adjusted TTR (RA-TTR).
Statistical Analysis and Power
We began by first calculating the mean observed TTR and the mean expected TTR for each site of care and then computing an O − E score for each site. We compared O − E scores between sites with and without various organizational characteristics using unpaired t test and compared O − E scores for multilevel variables using analysis of variance. We did not adjust for multiple comparisons. With regard to statistical power, for a characteristic present at half of the sites, t test would have 80% power to detect a 3% difference in the O − E score (a small-to-moderate effect size). Analyses were performed using commercially available statistical software (SAS version 9.1; SAS Institute, Cary, North Carolina).
Of 128 sites in the VA, 28 were excluded because their data were insufficiently complete to fully assess TTR. Of the remaining 100 sites, 5 did not respond to the questionnaire. Five other sites included more than 1 ACC, and the responses we received indicated that practices at the multiple ACCs of these 5 sites were not uniform. However, the dependent variable (RA-TTR) was assessed at the level of the overall site. Because we were unable to match our data for structure and outcomes, we excluded these 5 sites as well. This left 90 sites with complete data on structure and outcomes of care. The mean (SD) number of patients managed at each site was 1244 (799). The site mean TTR ranged from 38% to 69% (median, 58%). Site O − E scores ranged from −17% (ie, 17% below the expected value) to 12%.
Structure of Care and Relationship With Performance
We observed considerable variation in structure of care (). About half of the sites (n = 50) conducted most of their visits face to face; at other sites, most care was provided by telephone or mail. Most sites (n = 59) had some sort of quality improvement program, although only 7 sites used formal plan-do-check-act cycles. Most sites (n = 77) had some sort of support; 43 sites had clerical support. About half of the sites (n = 41) had a formal protocol for training new clinic providers. Many sites (n = 48) adhered to the recommended staffing ratio of less than 400 patients per provider (), although 17 sites had 600 or more patients per provider. Sites differed markedly for the perceived likelihood of being informed about a new drug-drug interaction.
Despite these differences in structure of care, we found no statistically significant predictors of site-level performance. A finding of marginal statistical significance was that 8 sites that did not allow telephone follow-up visits had almost 3% worse TTR (P = .10). There was also a hint, although not statistically significant, that sites with fewer than 500 patients under management might have worse control (performance difference, −1.6%).
We performed sensitivity analyses by altering the format of the dependent variable. To ensure that our risk-adjustment model was not obscuring relationships between structure and outcomes, we also examined the same independent variables as predictors of the mean unadjusted TTR by site, with similar results (data not shown). We also tried categorizing site O − E scores into the top quintile, bottom quintile, and all others and repeated our analyses; the results were unchanged (data not shown).
We also performed sensitivity analyses by altering the format of the independent variables. For example, we varied the cutoffs for what constituted a small site of care or for what constituted adequate staffing levels; the results were unchanged. Finally, we created a combination quality score by assigning a point for each putative quality indicator that a site fulfilled, to examine whether these factors predicted performance better in the aggregate than individually. For example, a site might receive 1 point for having a dedicated program to train providers, 1 point for having fewer than 400 patients per provider, and 1 point for using a computerized system for documentation and patient tracking. We examined several different versions of this combination quality score, including various measures and assigning them variable relative weights, but none of these combination scores predicted site RA-TTR.
We examined site-level organizational factors (structure of care) as potential predictors of outpatient oral anticoagulation control (outcomes of care).17 Although our hypotheses were supported by consensus guidelines,1,7 the site-level characteristics that we studied were not associated with anticoagulation control. Many studies relating structure and processes of care have been performed within the VA among sample sizes similar to that of this study. One such study18 found that organizational culture and commitment to continuous quality improvement were not associated with better processes of care for depression. However, most other studies have found associations between various aspects of organizational culture and processes of care, including rates of cancer screening19-22 and recommended processes of care for heart failure,23 chronic lung disease,24 and diabetes mellitus.25 In the diabetes study, the authors also found associations between organizational culture and intermediate
outcomes of diabetes care,25 a relationship more directly comparable to our examination of structure and intermediate outcomes. However, unlike these previous studies, we directly measured structural aspects of care within the ACC itself, which we expected to more directly affect anticoagulation control. Therefore, our null findings are all the more surprising; furthermore, they suggest the need to rethink quality improvement guidelines in anticoagulation care. For example, while some VA sites seem to emphasize face-to-face visits, this may not always be an efficient use of resources for the patient or for the provider, as telephone-based or mail-based care seemed to achieve similar results in this and other studies.26,27
Our study results do not suggest, nor do we believe, that efforts to improve the quality of oral anticoagulation therapy are futile. In fact, our group plans to implement a program to improve anticoagulation control in the VA, as we believe that this is feasible and important. Rather, our study findings suggest that we must look beyond the limited measures examined herein to find the true determinants of high-quality oral anticoagulation care. Which aspects of structure of care did this study not measure and what might be fitting targets for future studies? We did not directly investigate how warfarin dosages are managed or which dosing protocols or computerized dosing aids are used. A rich literature shows that computer-aided dosing of warfarin28-33 or simply judicious dosing in the absence of computerized support34 can improve anticoagulation control. Our study was adequately powered to detect a 3% difference in RA-TTR, which is modest. By comparison, using a computerized dosing algorithm improves TTR by approximately 10% over usual care,30 and more judicious dosing without computer assistance could improve TTR by approximately 6%.34 It seems likely that aspects of structure or process of care that relate to actual warfarin dosage management would help explain the wide variations in ACC performance within the VA. Also, more detailed assessments of ACCs through site visits and staff interviews might identify differences between high-performing and low-performing clinics.35
This study has considerable strengths. We used measures of structure of care that are supported by prominent guidelines1,7 and evaluated structure within the ACC itself rather than in the organizational culture of the entire medical center. In addition, our outcome measure (RA-TTR) has been carefully developed8,14 and represents the state of the art in measuring intermediate outcomes of anticoagulation care.2 This study also has limitations. Responses to the survey were by self-report, and it is possible that some responses (eg, the number of providers) were not fully accurate. Also, as already discussed, this survey instrument may not have collected enough detailed information to adequately characterize some aspects of structure, such as the nature rather than the mere existence of a quality improvement program. Finally, while our risk-adjustment model for TTR controlled for multiple measures of comorbidity and achieved a high R2, it did not include data on adherence. However, part of the influence of good management may be expressed through improved adherence; adjusting for adherence could adjust away such an effect. Therefore, adherence arguably does not belong in the risk-adjustment model.
In summary, our data suggest that high-quality anticoagulation care can be provided across a wide array of structures of care. A dedicated ACC is a necessary first step for improving the quality of oral anticoagulation care.3-6 However, within the structure of an ACC, further quality improvement efforts should focus on aspects of care that have been shown to affect anticoagulation control, such as the judicious dosing of warfarin.34 A program of quality improvement based solely on the measures studied herein may not have the desired effect on outcomes.
The authors thank Fran Cunningham, PharmD, of VA Pharmacy Benefit Management for providing the data from the 2007 survey of VA Anticoagulation Clinics.
Author Affiliations: From the Center for Health Quality, Outcomes, and Economic Research (AJR, AO, JIR, DRB), Bedford VA Medical Center, Bedford, MA; Department of Medicine (AJR, EMH, ASA, DRB), Section of General Internal Medicine, Boston University School of Medicine, Boston, MA; Pharmacy Benefits Management (FC), VA Central Office, Washington, DC; Biostatistics Core (AO), Clinical Research Program, Children’s Hospital Boston, Boston, MA; Department of Quantitative Health Sciences (ASA), Division of Biostatistics and Health Services Research, University of Massachusetts School of Medicine, Worcester, MA; Manchester VA Medical Center (PC), Manchester, NH; Providence VA Medical Center (MG), Providence, RI; and Department of Health Policy and Management (DRB), Boston University School of Public Health, Boston, MA.
Funding Source: This project was supported by a Career Development Award from the Veterans Affairs Health Services Research and Development Service. The opinions expressed herein do not necessarily represent the official views of the Department of Veterans Affairs.
Author Disclosures: Dr Hylek reports receiving honoraria from Bayer and Bristol-Myers Squibb and serving on advisory boards for Boehringer-Ingelheim, Bristol-Myers Squibb, Merck and Co, and sanofi aventis. The other authors (AJR, EMH, AO, ASA, JIR, PPC, MMG, DRB) report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.
Authorship Information: Concept and design (AJR, ASA, PPC, MMG, DRB); acquisition of data (AJR); analysis and interpretation of data (AJR, EMH, AO, ASA, JIR, PPC, MMG, DRB); drafting of the manuscript (AJR, AO, MMG, DRB); critical revision of the manuscript for important intellectual content (AJR, EMH, AO, ASA, JIR, PPC, DRB); statistical analysis (AJR, AO, ASA, JIR); obtaining funding (AJR); administrative, technical, or logistic support (AJR); and supervision (AJR, EMH).
Address correspondence to: Adam J. Rose, MD, MSc, Center for Health Quality, Outcomes, and Economic Research, Bedford VA Medical Center, 200 Springs Rd, Bldg 70, Bedford, MA 01730. E-mail: firstname.lastname@example.org.
1. Ansell J, Hirsh J, Hylek E, Jacobson A, Crowther M, Palareti G; American College of Chest Physicians. Pharmacology and management of the vitamin K antagonists: American College of Chest Physicians Evidence-Based Clinical Practice Guidelines (8th ed). Chest. 2008;133 (6)(suppl):160S-198S.
2. Rose AJ, Berlowitz DR, Frayne SM, Hylek EM. Measuring quality of oral anticoagulation care: extending quality measurement to a new field. Jt Comm J Qual Patient Saf. 2009;35(3):146-155.
3. Chiquette E, Amato MG, Bussey HI. Comparison of an anticoagulation clinic with usual medical care: anticoagulation control, patient outcomes, and health care costs. Arch Intern Med. 1998;158(15):1641-1647.
4. Cortelazzo S, Finazzi G, Viero P, et al. Thrombotic and hemorrhagic complications in patients with mechanical heart valve prosthesis attending an anticoagulation clinic. Thromb Haemost. 1993;69(4): 316-320.
5. Wilson SJ, Wells PS, Kovacs MJ, et al. Comparing the quality of oral anticoagulant management by anticoagulation clinics and by family physicians: a randomized controlled trial [published correction appears in CMAJ. 2004;170(4):451]. CMAJ. 2003;169(4):293-298.
6. Witt DM, Sadler MA, Shanahan RL, Mazzoli G, Tillman DJ. Effect of a centralized clinical pharmacy anticoagulation service on the outcomes of anticoagulation therapy. Chest. 2005;127(5):1515-1522.
7. Ansell JE, Oertel LB, Wittkowsky AK. Managing Oral Anticoagulation Therapy: Clinical and Operational Guidelines. 3rd ed. Conshohocken, PA: Wolters Kluwer Health; 2009.
8. Rose AJ, Hylek EM, Ozonoff A, Ash AS, Reisman JI, Berlowitz DR. Risk-adjusted percent time in therapeutic range as a quality indicator for outpatient oral anticoagulation: results of the Veterans Affairs Study to Improve Anticoagulation (VARIA). Circ Cardiovasc Qual Outcomes. 2011;4(1):22-29.
9. Connolly SJ, Pogue J, Eikelboom J, et al; ACTIVE W Investigators. Benefit of oral anticoagulant over antiplatelet therapy in atrial fibrillation depends on the quality of international normalized ratio control achieved by centers and countries as measured by time in therapeutic range. Circulation. 2008;118(20):2029-2037.
10. van Leeuwen Y, Rosendaal FR, Cannegieter SC. Prediction of hemorrhagic and thrombotic events in patients with mechanical heart valve prostheses treated with oral anticoagulants. J Thromb Haemost. 2008;6(3):451-456.
11. van Walraven C, Oake N, Wells PS, Forster AJ. Burden of potentially avoidable anticoagulant-associated hemorrhagic and thromboembolic events in the elderly. Chest. 2007;131(5):1508-1515.
12. Veeger NJ, Piersma-Wichers M, Tijssen JG, Hillege HL, van der Meer J. Individual time within target range in patients treated with vitamin K antagonists: main determinant of quality of anticoagulation and predictor of clinical outcome: a retrospective study of 2300 consecutive patients with venous thromboembolism. Br J Haematol. 2005;128(4):513-519.
13. White HD, Gruber M, Feyzi J, et al. Comparison of outcomes among patients randomized to warfarin therapy according to anticoagulant control: results from SPORTIF III and V. Arch Intern Med. 2007;167(3):239-245.
14. Rose AJ, Hylek EM, Ozonoff A, Ash AS, Reisman JI, Berlowitz DR. Patient characteristics associated with oral anticoagulation control: results of the Veterans Affairs Study to Improve Anticoagulation (VARIA). J Thromb Haemost. 2010;8(10):2182-2191.
15. Rosendaal FR, Cannegieter SC, van der Meer FJ, Briët E. A method to determine the optimal intensity of oral anticoagulant therapy. Thromb Haemost. 1993;69(3):236-239.
16. Department of Veterans Affairs. Anticoagulation Therapy Management. Washington, DC: Veterans Health Administration; February 2, 2009. VHA directive 2009-003.
17. Donabedian A. Explorations in Quality Assessment and Monitoring. Ann Arbor, MI: Health Administration Press; 1980.
18. Charbonneau A, Parker V, Meterko M, et al. The relationship of system-level quality improvement with quality of depression care. Am J Manag Care. 2004;10(11, pt 2):846-851.
19. Goldzweig CL, Parkerton PH, Washington DL, Lanto AB, Yano EM. Primary care practice and facility quality orientation: influence on breast and cervical cancer screening rates. Am J Manag Care. 2004; 10(4):265-272.
20. Hudson SV, Ohman-Strickland P, Cunningham R, Ferrante JM, Hahn K, Crabtree BF. The effects of teamwork and system support on colorectal cancer screening in primary care practices. Cancer Detect Prev.2007;31(5):417-423.
21. Vaughn TE, McCoy KD, BootsMiller BJ, et al. Organizational predictors of adherence to ambulatory care screening guidelines. Med Care. 2002;40(12):1172-1185.
22. Yano EM, Soban LM, Parkerton PH, Etzioni DA. Primary care practice organization influences colorectal cancer screening performance. Health Serv Res. 2007;42(3, pt 1):1130-1149.
23. Subramanian U, Sutherland J, McCoy KD, Welke KF, Vaughn TE, Doebbeling BN. Facility-level factors influencing chronic heart failure care process performance in a national integrated health delivery system. Med Care. 2007;45(1):28-45.
24. Ward MM, Yankey JW, Vaughn TE, et al. Provider adherence to COPD guidelines: relationship to organizational factors. J Eval Clin Pract. 2005;11(4):379-387.
25. Ward MM, Yankey JW, Vaughn TE, et al. Physician process and patient outcome measures for diabetes care: relationships to organizational characteristics. Med Care. 2004;42(9):840-850.
26. Staresinic AG, Sorkness CA, Goodman BM, Pigarelli DW. Comparison of outcomes using 2 delivery models of anticoagulation care. Arch Intern Med. 2006;166(9):997-1002.
27. Wittkowsky AK, Nutescu EA, Blackburn J, et al. Outcomes of oral anticoagulant therapy managed by telephone vs in-office visits in an anticoagulation clinic setting. Chest. 2006;130(5):1385-1389.
28. Ageno W, Johnson J, Nowacki B, Turpie AG. A computer generated induction system for hospitalized patients starting on oral anticoagulant therapy. Thromb Haemost. 2000;83(6):849-852.
29. Manotti C, Moia M, Palareti G, Pengo V, Ria L, Dettori AG. Effect of computer-aided management on the quality of treatment in anticoagulated patients: a prospective, randomized, multicenter trial of APROAT (Automated Program for Oral Anticoagulant Treatment). Haematologica. 2001;86(10):1060-1070.
30. Poller L, Shiach CR, MacCallum PK, et al. Multicentre randomised study of computerised anticoagulant dosage: European Concerted Action on Anticoagulation. Lancet. 1998;352(9139):1505-1509.
31. Poller L, Wright D, Rowlands M. Prospective comparative study of computer programs used for management of warfarin. J Clin Pathol. 1993;46(4):299-303.
32. Ryan PJ, Gilbert M, Rose PE. Computer control of anticoagulant dose for therapeutic management. BMJ. 1989;299(6709):1207-1209.
33. Wilson R, James AH. Computer assisted management of warfarin treatment. Br Med J (Clin Res Ed). 1984;289(6442):422-424.
34. Rose AJ, Ozonoff A, Berlowitz DR, Henault LE, Hylek EM. Warfarin dose management affects INR control. J Thromb Haemost. 2009;7(1): 94-101.
35. Utarini A, Winkvist A, Pelto GH. Appraising studies in health using rapid assessment procedures (RAP): eleven critical criteria. Hum Organ. 2001;60(4):390-400.