Skip to main content

Advertisement

SpringerLink
Log in

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

  • Original Research
  • Published:
Journal of General Internal Medicine Aims and scope Submit manuscript

ABSTRACT

BACKGROUND

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.

OBJECTIVE

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

DESIGN

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

PARTICIPANTS

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.

MAIN MEASURES

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

KEY RESULTS

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.

CONCLUSIONS

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

REFERENCES

  1. Gandhi TK, Weingart SN, Borus J, et al. Adverse drug events in ambulatory care. N Engl J Med. 2003;348(16):1556–64.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    PubMed  Google Scholar 

  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.

    PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  13. Canizares MJ, Penneys NS. The incidence of nonattendance at an urgent care dermatology clinic. J Am Acad Dermatol. 2002;46(3):457–9.

    Article  PubMed  Google Scholar 

  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.

    PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  17. Lash S, Harding J. “Abandoned prescriptions” a quantitative assessment of their cause. J Manag Care Pharm. 1995;1:193–9.

    Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    PubMed  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  26. Rogers WH. Regression standard errors in clustered samples. Stata Tech Bull. 1993;13:19–23.

    Google Scholar 

  27. Williams RL. A note on robust variance estimation for cluster-correlated data. Biometrics. 2000;56(2):645–6.

    Article  PubMed  CAS  Google Scholar 

  28. Wooldridge JM. Econometric Analysis of Cross Section and Panel Data. Cambridge: MIT Press; 2002.

    Google Scholar 

  29. Froot KA. Consistent covariance matrix estimation with cross-sectional dependence and heteroskedasticity in financial data. J Financ Quant Anal. 1989;24:333–55.

    Article  Google Scholar 

  30. Harrell FE. Regression Modeling Strategies: with Applications to Linear Models, Logistic Regression, and Survival Analysis. Springer Verlag; 2001.

  31. Oppenheim GL, Bergman JJ, English EC. Failed appointments: a review. J Fam Pract. 1979;8(4):789–96.

    PubMed  CAS  Google Scholar 

  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.

    Article  CAS  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  Google Scholar 

  37. Higginbottom GM. Sampling issues in qualitative research. Nurse Res. 2004;12(1):7–19.

    PubMed  Google Scholar 

  38. Marshall MN. Sampling for qualitative research. Fam Pract. 1996;13(6):522–5.

    Article  PubMed  CAS  Google Scholar 

  39. Glaser B, Strauss A. Grounded Theory: the Discovery of Grounded Theory. New York: de Gruyter; 1967.

    Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    Article  PubMed  CAS  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    PubMed  Google Scholar 

  44. McMahan R. Operationalizing MTM, through the use of health information technology. J Manag Care Pharm. 2008;14(2 Suppl):S18–21.

    PubMed  Google Scholar 

  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.

    Article  PubMed  Google Scholar 

  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.

    PubMed  Google Scholar 

  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.

    PubMed  Google Scholar 

Download references

Acknowledgements

Contributors

The authors would like to acknowledge the contributions of Yanfang Zhao, MA.

Funders

This study was funding by grant R18 HS 017906 from the Agency for Healthcare Research and Quality (AHRQ).

Prior Presentations

We presented the manuscript abstract at the Clinical and Translational Science Research Retreat at the University of Massachusetts Medical School on May 22, 2012 and the qualitative results only on May 20, 2011. We presented the results from the qualitative interviews at the HMO Research Network Conference in Boston, MA, on March 24, 2011. We presented a portion of the quantitative study findings as a poster at the AHRQ Health Information Technology conference on September 27, 2010.

Conflict of Interest

The authors declare that they do not have a conflict of interest.

Author information

Authors and Affiliations

  1. Beth Israel Deaconess Medical Center, Division of Clinical Informatics, 1330 Beacon St., Suite 400, Brookline, MA, 02446, USA

    Shira H. Fischer MD, PhD

  2. Meyers Primary Care Institute, A Joint Endeavor of University of Massachusetts Medical School, Reliant Medical Group, and Fallon Community Health Plan, 630 Plantation Street, Biotech Four, Worcester, MA, 01605, USA

    Shira H. Fischer MD, PhD, Terry S. Field DSc, Shawn J. Gagne BA, Kathleen M. Mazor EdD, Peggy Preusse RN, Daniel Peterson MS, Jerry H. Gurwitz MD & Jennifer Tjia MD, MSCE

  3. University of Massachusetts Medical School, Biotech Four, 377 Plantation Street, Suite 315, Worcester, MA, 01605, USA

    George Reed PhD

Authors
  1. Shira H. Fischer MD, PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Terry S. Field DSc
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shawn J. Gagne BA
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Kathleen M. Mazor EdD
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Peggy Preusse RN
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. George Reed PhD
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Daniel Peterson MS
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jerry H. Gurwitz MD
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jennifer Tjia MD, MSCE
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shira H. Fischer MD, PhD.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fischer, S.H., Field, T.S., Gagne, S.J. et al. Patient Completion of Laboratory Tests to Monitor Medication Therapy: A Mixed-Methods Study. J GEN INTERN MED 28, 513–521 (2013). https://doi.org/10.1007/s11606-012-2271-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11606-012-2271-6

KEY WORDS

Search

Navigation