Journal of General Internal Medicine

, Volume 28, Issue 4, pp 513–521

Patient Completion of Laboratory Tests to Monitor Medication Therapy: A Mixed-Methods Study

  • Shira H. Fischer
  • Terry S. Field
  • Shawn J. Gagne
  • Kathleen M. Mazor
  • Peggy Preusse
  • George Reed
  • Daniel Peterson
  • Jerry H. Gurwitz
  • Jennifer Tjia
Original Research



Little is known about the contribution of patient behavior to incomplete laboratory monitoring, and the reasons for patient non-completion of ordered laboratory tests remain unclear.


To describe factors, including patient-reported reasons, associated with non-completion of ordered laboratory tests.


Mixed-Methods study including a quantitative assessment of the frequency of patient completion of ordered monitoring tests combined with qualitative, semi-structured, patient interviews.


Quantitative assessment included patients 18 years or older from a large multispecialty group practice, who were prescribed a medication requiring monitoring. Qualitative interviews included a subset of show and no-show patients prescribed a cardiovascular, anticonvulsant, or thyroid replacement medication.


Proportion of recommended monitoring tests for each medication not completed, factors associated with patient non-completion, and patient-reported reasons for non-completion.


Of 27,802 patients who were prescribed one of 34 medications, patient non-completion of ordered tests varied (range: 0–24 %, by drug-test pair). Factors associated with higher odds of test non-completion included: younger patient age (< 40 years vs. ≥ 80 years, adjusted odds ratio [AOR] 1.52, 95 % confidence interval [95 % CI] 1.27–1.83); lower medication burden (one medication vs. more than one drug, AOR for non-completion 1.26, 95 % CI 1.15–1.37), and lower visit frequency (0–5 visits/year vs. ≥ 19 visits/year, AOR 1.41, 95 % CI 1.25 to 1.59). Drug-test pairs with black box warning status were associated with greater odds of non-completion, compared to drugs without a black box warning or other guideline for testing (AOR 1.91, 95 % CI 1.66–2.19). Qualitative interviews, with 16 no-show and seven show patients, identified forgetting as the main cause of non-completion of ordered tests.


Patient non-completion contributed to missed opportunities to monitor medications, and was associated with younger patient age, lower medication burden and black box warning status. Interventions to improve laboratory monitoring should target patients as well as physicians.


laboratory monitoring patient completion drug research 


  1. 1.
    Gandhi TK, Weingart SN, Borus J, et al. Adverse drug events in ambulatory care. N Engl J Med. 2003;348(16):1556–64.PubMedCrossRefGoogle Scholar
  2. 2.
    Gurwitz JH, Field TS, Harrold LR, et al. Incidence and preventability of adverse drug events among older persons in the ambulatory setting. JAMA. 2003;289(9):1107–16.PubMedCrossRefGoogle Scholar
  3. 3.
    Raebel MA, Lyons EE, Andrade SE, et al. Laboratory monitoring of drugs at initiation of therapy in ambulatory care. J Gen Intern Med. 2005;20(12):1120–6.PubMedCrossRefGoogle Scholar
  4. 4.
    Simon SR, Andrade SE, Ellis JL, et al. Baseline laboratory monitoring of cardiovascular medications in elderly health maintenance organization enrollees. J Am Geriatr Soc. 2005;53(12):2165–9.PubMedCrossRefGoogle Scholar
  5. 5.
    Lo HG, Matheny ME, Seger DL, Bates DW, Gandhi TK. Impact of non-interruptive medication laboratory monitoring alerts in ambulatory care. J Am Med Inform Assoc. 2009;16(1):66–71.PubMedCrossRefGoogle Scholar
  6. 6.
    Steele AW, Eisert S, Witter J, et al. The effect of automated alerts on provider ordering behavior in an outpatient setting. PLoS Med. 2005;2(9):e255.PubMedCrossRefGoogle Scholar
  7. 7.
    Feldstein AC, Smith DH, Perrin N, et al. Improved therapeutic monitoring with several interventions: a randomized trial. Arch Intern Med. 2006;166(17):1848–54.PubMedCrossRefGoogle Scholar
  8. 8.
    Hoch I, Heymann AD, Kurman I, Valinsky LJ, Chodick G, Shalev V. Countrywide computer alerts to community physicians improve potassium testing in patients receiving diuretics. J Am Med Inform Assoc. 2003;10(6):541–6.PubMedCrossRefGoogle Scholar
  9. 9.
    Matheny ME, Sequist TD, Seger AC, et al. A randomized trial of electronic clinical reminders to improve medication laboratory monitoring. J Am Med Inform Assoc. 2008;15(4):424–9.PubMedCrossRefGoogle Scholar
  10. 10.
    Palen TE, Raebel M, Lyons E, Magid DM. Evaluation of laboratory monitoring alerts within a computerized physician order entry system for medication orders. Am J Manag Care. 2006;12(7):389–95.PubMedGoogle Scholar
  11. 11.
    Raebel MA, Carroll NM, Andrade SE, et al. Monitoring of drugs with a narrow therapeutic range in ambulatory care. Am J Manag Care. 2006;12(5):268–74.PubMedGoogle Scholar
  12. 12.
    Tang EO, Lai CS, Lee KK, Wong RS, Cheng G, Chan TY. Relationship between patients’ warfarin knowledge and anticoagulation control. Ann Pharmacother. 2003;37(1):34–9.PubMedCrossRefGoogle Scholar
  13. 13.
    Canizares MJ, Penneys NS. The incidence of nonattendance at an urgent care dermatology clinic. J Am Acad Dermatol. 2002;46(3):457–9.PubMedCrossRefGoogle Scholar
  14. 14.
    Pal B, Taberner DA, Readman LP, Jones P. Why do outpatients fail to keep their clinic appointments? Results from a survey and recommended remedial actions. Int J Clin Pract. 1998;52(6):436–7.PubMedGoogle Scholar
  15. 15.
    Martin C, Perfect T, Mantle G. Non-attendance in primary care: the views of patients and practices on its causes, impact and solutions. Fam Pract. 2005;22(6):638–43.PubMedCrossRefGoogle Scholar
  16. 16.
    Lacy NL, Paulman A, Reuter MD, Lovejoy B. Why we don’t come: patient perceptions on no-shows. Ann Fam Med. 2004;2(6):541–5.PubMedCrossRefGoogle Scholar
  17. 17.
    Lash S, Harding J. “Abandoned prescriptions” a quantitative assessment of their cause. J Manag Care Pharm. 1995;1:193–9.Google Scholar
  18. 18.
    Briesacher BA, Andrade SE, Fouayzi H, Chan KA. Comparison of drug adherence rates among patients with seven different medical conditions. Pharmacotherapy. 2008;28(4):437–43.PubMedCrossRefGoogle Scholar
  19. 19.
    George A, Rubin G. Non-attendance in general practice: a systematic review and its implications for access to primary health care. Fam Pract. 2003;20(2):178–84.PubMedCrossRefGoogle Scholar
  20. 20.
    Tjia J, Field TS, Garber LD, et al. Development and pilot testing of guidelines to monitor high-risk medications in the ambulatory setting. Am J Manag Care. 2010;16(7):489–96.PubMedGoogle Scholar
  21. 21.
    Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol. 1993;46(10):1075–9. discussion 1081–1090.PubMedCrossRefGoogle Scholar
  22. 22.
    Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613–9.PubMedCrossRefGoogle Scholar
  23. 23.
    Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373–83.PubMedCrossRefGoogle Scholar
  24. 24.
    Chu YT, Ng YY, Wu SC. Comparison of different comorbidity measures for use with administrative data in predicting short- and long-term mortality. BMC Health Serv Res. 2010;10:140.PubMedCrossRefGoogle Scholar
  25. 25.
    Wang LM, Wong M, Lightwood JM, Cheng CM. Black box warning contraindicated comedications: concordance among three major drug interaction screening programs. Ann Pharmacother. 2010;44(1):28–34.PubMedCrossRefGoogle Scholar
  26. 26.
    Rogers WH. Regression standard errors in clustered samples. Stata Tech Bull. 1993;13:19–23.Google Scholar
  27. 27.
    Williams RL. A note on robust variance estimation for cluster-correlated data. Biometrics. 2000;56(2):645–6.PubMedCrossRefGoogle Scholar
  28. 28.
    Wooldridge JM. Econometric Analysis of Cross Section and Panel Data. Cambridge: MIT Press; 2002.Google Scholar
  29. 29.
    Froot KA. Consistent covariance matrix estimation with cross-sectional dependence and heteroskedasticity in financial data. J Financ Quant Anal. 1989;24:333–55.CrossRefGoogle Scholar
  30. 30.
    Harrell FE. Regression Modeling Strategies: with Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer Verlag; 2001.Google Scholar
  31. 31.
    Oppenheim GL, Bergman JJ, English EC. Failed appointments: a review. J Fam Pract. 1979;8(4):789–96.PubMedGoogle Scholar
  32. 32.
    Rodriguez Pacheco R, Negro Alvarez JM, Campuzano Lopez FJ, et al. Non-compliance with appointments amongst patients attending an Allergology Clinic, after implementation of an improvement plan. Allergol Immunopathol (Madr). 2007;35(4):136–44.CrossRefGoogle Scholar
  33. 33.
    Zailinawati AH, Ng CJ, Nik-Sherina H. Why do patients with chronic illnesses fail to keep their appointments? A telephone interview. Asia Pac J Public Health. 2006;18(1):10–5.PubMedCrossRefGoogle Scholar
  34. 34.
    Simmons AV, Atkinson K, Atkinson P, Crosse B. Failure of patients to attend a medical outpatient clinic. J R Coll Physicians Lond. 1997;31(1):70–3.PubMedGoogle Scholar
  35. 35.
    Collins J, Santamaria N, Clayton L. Why outpatients fail to attend their scheduled appointments: a prospective comparison of differences between attenders and non-attenders. Aust Health Rev. 2003;26(1):52–63.PubMedCrossRefGoogle Scholar
  36. 36.
    Guest G, Bunce A, Johnson L. How many interviews are enough?: An experiment with data saturation and variability. Field Methods. 2006;18(1):59–82.CrossRefGoogle Scholar
  37. 37.
    Higginbottom GM. Sampling issues in qualitative research. Nurse Res. 2004;12(1):7–19.PubMedGoogle Scholar
  38. 38.
    Marshall MN. Sampling for qualitative research. Fam Pract. 1996;13(6):522–5.PubMedCrossRefGoogle Scholar
  39. 39.
    Glaser B, Strauss A. Grounded Theory: the Discovery of Grounded Theory. New York: de Gruyter; 1967.Google Scholar
  40. 40.
    Raebel MA, Lyons EE, Chester EA, et al. Improving laboratory monitoring at initiation of drug therapy in ambulatory care: a randomized trial. Arch Intern Med. 2005;165(20):2395–401.PubMedCrossRefGoogle Scholar
  41. 41.
    Tang PC, Ralston M, Arrigotti MF, Qureshi L, Graham J. Comparison of methodologies for calculating quality measures based on administrative data versus clinical data from an electronic health record system: implications for performance measures. J Am Med Inform Assoc. 2007;14(1):10–5.PubMedCrossRefGoogle Scholar
  42. 42.
    Fischer SH, Tjia J, Field TS. Impact of health information technology interventions to improve medication laboratory monitoring for ambulatory patients: a systematic review. J Am Med Inform Assoc. 2010;17(6):631–6.PubMedCrossRefGoogle Scholar
  43. 43.
    Chaudhry B, Wang J, Wu S, et al. Systematic review: impact of health information technology on quality, efficiency, and costs of medical care. Ann Intern Med. 2006;144(10):742–52.PubMedGoogle Scholar
  44. 44.
    McMahan R. Operationalizing MTM, through the use of health information technology. J Manag Care Pharm. 2008;14(2 Suppl):S18–21.PubMedGoogle Scholar
  45. 45.
    Black AD, Car J, Pagliari C, et al. The impact of eHealth on the quality and safety of health care: a systematic overview. PLoS Med. 2011;8(1):e1000387.PubMedCrossRefGoogle Scholar
  46. 46.
    Tjia J, Field TS, Fischer SH, et al. Quality measurement of medication monitoring in the “meaningful use” era. Am J Manag Care. 2011;17(9):633–7.PubMedGoogle Scholar
  47. 47.
    Raebel MA, Carroll NM, Simon SR, et al. Liver and thyroid monitoring in ambulatory patients prescribed amiodarone in 10 HMOs. J Manag Care Pharm. 2006;12(8):656–64.PubMedGoogle Scholar

Copyright information

© Society of General Internal Medicine 2012

Authors and Affiliations

  • Shira H. Fischer
    • 1
    • 2
  • Terry S. Field
    • 2
  • Shawn J. Gagne
    • 2
  • Kathleen M. Mazor
    • 2
  • Peggy Preusse
    • 2
  • George Reed
    • 3
  • Daniel Peterson
    • 2
  • Jerry H. Gurwitz
    • 2
  • Jennifer Tjia
    • 2
  1. 1.Beth Israel Deaconess Medical CenterDivision of Clinical InformaticsBrooklineUSA
  2. 2.Meyers Primary Care Institute, A Joint Endeavor of University of Massachusetts Medical School, Reliant Medical Group, and Fallon Community Health PlanWorcesterUSA
  3. 3.University of Massachusetts Medical SchoolWorcesterUSA

Personalised recommendations