Skip to main content
Top
Published in: Journal of General Internal Medicine 10/2015

01-10-2015 | Original Research

Appointment “no-shows” are an independent predictor of subsequent quality of care and resource utilization outcomes

Authors: Andrew S. Hwang, BS, Steven J. Atlas, MD, MPH, Patrick Cronin, MA, Jeffrey M. Ashburner, MPH, Sachin J. Shah, MD, Wei He, MS, Clemens S. Hong, MD, MPH

Published in: Journal of General Internal Medicine | Issue 10/2015

Login to get access

Abstract

BACKGROUND

Identifying individuals at high risk for suboptimal outcomes is an important goal of healthcare delivery systems. Appointment no-shows may be an important risk predictor.

OBJECTIVES

To test the hypothesis that patients with a high propensity to "no-show" for appointments will have worse clinical and acute care utilization outcomes compared to patients with a lower propensity.

DESIGN

We calculated the no-show propensity factor (NSPF) for patients of a large academic primary care network using 5 years of outpatient appointment data. NSPF corrects for patients with fewer appointments to avoid over-weighting of no-show visits in such patients. We divided patients into three NSPF risk groups and evaluated the association between NSPF and clinical and acute care utilization outcomes after adjusting for baseline patient characteristics.

PARTICIPANTS

A total of 140,947 patients who visited a network practice from January 1, 2007, through December 31, 2009, and were either connected to a primary care physician or to a primary care practice, based on a previously validated algorithm.

MAIN MEASURES

Outcomes of interest were incomplete colorectal, cervical, and breast cancer screening, and above-goal hemoglobin A1c (HbA1c) and low-density lipoprotein (LDL) levels at 1-year follow-up, and hospitalizations and emergency department visits in the subsequent 3 years.

KEY RESULTS

Compared to patients in the low NSPF group, patients in the high NSPF group (n=14,081) were significantly more likely to have incomplete preventive cancer screening (aOR 2.41 [2.19–.66] for colorectal, aOR 1.85 [1.65–.08] for cervical, aOR 2.93 [2.62–3.28] for breast cancer), above-goal chronic disease control measures (aOR 2.64 [2.22–3.14] for HbA1c, aOR 1.39 [1.15–1.67] for LDL], and increased rates of acute care utilization (aRR 1.37 [1.31–1.44] for hospitalization, aRR 1.39 [1.35–1.43] for emergency department visits).

CONCLUSIONS

NSPF is an independent predictor of suboptimal primary care outcomes and acute care utilization. NSPF may play an important role in helping healthcare systems identify high-risk patients.
Appendix
Available only for authorised users
Literature
1.
go back to reference Schectman JM, Schorling JB, Voss JD. Appointment adherence and disparities in outcomes among patients with diabetes. J Gen Intern Med. 2008;23(10):1685–7.PubMedCentralCrossRefPubMed Schectman JM, Schorling JB, Voss JD. Appointment adherence and disparities in outcomes among patients with diabetes. J Gen Intern Med. 2008;23(10):1685–7.PubMedCentralCrossRefPubMed
2.
go back to reference Nuti LA, Lawley M, Turkcan A, et al. No-shows to primary care appointments: subsequent acute care utilization among diabetic patients. BMC Health Serv Res. 2012;12:304.PubMedCentralCrossRefPubMed Nuti LA, Lawley M, Turkcan A, et al. No-shows to primary care appointments: subsequent acute care utilization among diabetic patients. BMC Health Serv Res. 2012;12:304.PubMedCentralCrossRefPubMed
3.
go back to reference Walurn A, Swindells S, Fisher C, High R, Islam KM. Missed visits and decline in CD4 cell count among HIV-infected patients: a mixed method study. Int J Infect Dis. 2012;16:e779–85.CrossRef Walurn A, Swindells S, Fisher C, High R, Islam KM. Missed visits and decline in CD4 cell count among HIV-infected patients: a mixed method study. Int J Infect Dis. 2012;16:e779–85.CrossRef
4.
go back to reference Mugavero MJ, Lin HY, Willig JH, et al. Missed visits and mortality among patients establishing initial outpatient HIV treatment. Clin Infect Dis. 2009;48:248–56.PubMedCentralCrossRefPubMed Mugavero MJ, Lin HY, Willig JH, et al. Missed visits and mortality among patients establishing initial outpatient HIV treatment. Clin Infect Dis. 2009;48:248–56.PubMedCentralCrossRefPubMed
5.
go back to reference Colubi MM, Pérez-Elías MJ, Pumares M, et al. Missing scheduled visits in the outpatient clinic as a marker of short-term admissions and death. HIV Clin Trials. 2012;13(5):289–95.CrossRefPubMed Colubi MM, Pérez-Elías MJ, Pumares M, et al. Missing scheduled visits in the outpatient clinic as a marker of short-term admissions and death. HIV Clin Trials. 2012;13(5):289–95.CrossRefPubMed
6.
go back to reference Berg MB, Safren SA, Mimiaga MJ, Grasso C, Boswell S, Mayer KH. Nonadherence to medical appointments is associated with increased plasma HIV RNA and decreased CD4 cell counts in a community-based HIV primary care clinic. AIDS Care. 2005;17(7):902–7.CrossRefPubMed Berg MB, Safren SA, Mimiaga MJ, Grasso C, Boswell S, Mayer KH. Nonadherence to medical appointments is associated with increased plasma HIV RNA and decreased CD4 cell counts in a community-based HIV primary care clinic. AIDS Care. 2005;17(7):902–7.CrossRefPubMed
7.
8.
go back to reference Atlas SJ, Chang Y, Lasko TA, et al. Is this “my” patient? Development and validation of a predictive model to link patients to primary care providers. J Gen Intern Med. 2006;21(9):973–8.PubMedCentralCrossRefPubMed Atlas SJ, Chang Y, Lasko TA, et al. Is this “my” patient? Development and validation of a predictive model to link patients to primary care providers. J Gen Intern Med. 2006;21(9):973–8.PubMedCentralCrossRefPubMed
9.
go back to reference Cronin PR, Kimball AB. Success of automated algorithmic scheduling in an outpatient setting. Am J Manag Care. 2014;20(7):570–6.PubMed Cronin PR, Kimball AB. Success of automated algorithmic scheduling in an outpatient setting. Am J Manag Care. 2014;20(7):570–6.PubMed
10.
go back to reference Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45:613–9.CrossRefPubMed Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45:613–9.CrossRefPubMed
11.
go back to reference 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;40:373–83.CrossRefPubMed 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;40:373–83.CrossRefPubMed
12.
go back to reference Greenland S. Introduction to regression modeling. In: Rothman KJ, Greenland S, Lash TL, eds. Modern epidemiology. 3rd ed. Philadelphia: Lippincott, Williams & Wilkins; 2008:418–58. Greenland S. Introduction to regression modeling. In: Rothman KJ, Greenland S, Lash TL, eds. Modern epidemiology. 3rd ed. Philadelphia: Lippincott, Williams & Wilkins; 2008:418–58.
13.
go back to reference Percac-Lima S, Grant RW, Green AR, et al. A culturally tailored navigator program for colorectal cancer screening in a community health center: a randomized, controlled trial. J Gen Intern Med. 2009;24(2):211–7.PubMedCentralCrossRefPubMed Percac-Lima S, Grant RW, Green AR, et al. A culturally tailored navigator program for colorectal cancer screening in a community health center: a randomized, controlled trial. J Gen Intern Med. 2009;24(2):211–7.PubMedCentralCrossRefPubMed
15.
go back to reference Smith LL, Lake NH, Simmons LA, Perlman A, Wroth S, Wolever RQ. Integrative health coach training: a model for shifting the paradigm toward patient-centricity and meeting new national prevention goals. Glob Adv Health Med. 2013;2(3):66–74.PubMedCentralCrossRefPubMed Smith LL, Lake NH, Simmons LA, Perlman A, Wroth S, Wolever RQ. Integrative health coach training: a model for shifting the paradigm toward patient-centricity and meeting new national prevention goals. Glob Adv Health Med. 2013;2(3):66–74.PubMedCentralCrossRefPubMed
16.
go back to reference Thom DH, Ghorob A, Hessler D, De Vore D, Chen E, Bodenheimer TA. Impact of peer health coaching on glycemic control in low-income patients with diabetes: a randomized controlled trial. Ann Fam Med. 2013;11(2):137–44.PubMedCentralCrossRefPubMed Thom DH, Ghorob A, Hessler D, De Vore D, Chen E, Bodenheimer TA. Impact of peer health coaching on glycemic control in low-income patients with diabetes: a randomized controlled trial. Ann Fam Med. 2013;11(2):137–44.PubMedCentralCrossRefPubMed
17.
go back to reference Bodenheimer T, Berry-Millet R. Care management of patients with complex health care needs. Princeton: Robert Wood Johnson Foundation; 2009. December 2009. Research synthesis report 19. Bodenheimer T, Berry-Millet R. Care management of patients with complex health care needs. Princeton: Robert Wood Johnson Foundation; 2009. December 2009. Research synthesis report 19.
18.
go back to reference Freund T, Mahler C, Erler A, et al. Identification of patients likely to benefit from care management programs. Am J Manag Care. 2011;17(5):345–52.PubMed Freund T, Mahler C, Erler A, et al. Identification of patients likely to benefit from care management programs. Am J Manag Care. 2011;17(5):345–52.PubMed
19.
go back to reference James J. Health Policy Brief: Pay-for-Performance. Health Affairs. October 11, 2012. James J. Health Policy Brief: Pay-for-Performance. Health Affairs. October 11, 2012.
21.
go back to reference Zezza MA. The Final Rule for the Medicare Shared Savings Program. The Commonwealth Fund. December 2011. Zezza MA. The Final Rule for the Medicare Shared Savings Program. The Commonwealth Fund. December 2011.
23.
go back to reference Hughes JS, Averill RF, Eisenhandler J, et al. Clinical Risk Groups (CRGs): a classification system for risk-adjusted capitation-based payment and health care management. Med Care. 2004;42(1):81–90.CrossRefPubMed Hughes JS, Averill RF, Eisenhandler J, et al. Clinical Risk Groups (CRGs): a classification system for risk-adjusted capitation-based payment and health care management. Med Care. 2004;42(1):81–90.CrossRefPubMed
25.
go back to reference Rosen AK, Reid R, Broemeling AM, Rakovski CC. Applying a risk-adjustment framework to primary care: can We improve on existing measures? Ann Fam Med. 2003;1(1):44–51.PubMedCentralCrossRefPubMed Rosen AK, Reid R, Broemeling AM, Rakovski CC. Applying a risk-adjustment framework to primary care: can We improve on existing measures? Ann Fam Med. 2003;1(1):44–51.PubMedCentralCrossRefPubMed
26.
go back to reference Grant RW, Ashburner JM, Hong CS, Chang Y, Barry MJ, Atlas SJ. Defining patient complexity from the primary care physician’s perspective: a cohort study. Ann Intern Med. 2011;155(12):797–804.CrossRefPubMed Grant RW, Ashburner JM, Hong CS, Chang Y, Barry MJ, Atlas SJ. Defining patient complexity from the primary care physician’s perspective: a cohort study. Ann Intern Med. 2011;155(12):797–804.CrossRefPubMed
27.
go back to reference Grant RW, Pirraglia PA, Meigs JB, Singer DE. Trends in complexity of diabetes care in the United States from 1991 to 2000. Arch Intern Med. 2004;164(10):1134–9.CrossRefPubMed Grant RW, Pirraglia PA, Meigs JB, Singer DE. Trends in complexity of diabetes care in the United States from 1991 to 2000. Arch Intern Med. 2004;164(10):1134–9.CrossRefPubMed
28.
go back to reference Safford MM, Allison JJ, Kiefe CI. Patient complexity: more than comorbidity. The Vector Model of Complexity. J Gen Intern Med. 2007;22(Suppl 3):382–90.PubMedCentralCrossRefPubMed Safford MM, Allison JJ, Kiefe CI. Patient complexity: more than comorbidity. The Vector Model of Complexity. J Gen Intern Med. 2007;22(Suppl 3):382–90.PubMedCentralCrossRefPubMed
29.
go back to reference Nardi R, Scanelli G, Corrao S, Iori I, Mathieu G, Cataldi Amatrian R. Co-morbidity does not reflect complexity in internal medicine patients. Eur J Intern Med. 2007;18(5):359–68.CrossRefPubMed Nardi R, Scanelli G, Corrao S, Iori I, Mathieu G, Cataldi Amatrian R. Co-morbidity does not reflect complexity in internal medicine patients. Eur J Intern Med. 2007;18(5):359–68.CrossRefPubMed
Metadata
Title
Appointment “no-shows” are an independent predictor of subsequent quality of care and resource utilization outcomes
Authors
Andrew S. Hwang, BS
Steven J. Atlas, MD, MPH
Patrick Cronin, MA
Jeffrey M. Ashburner, MPH
Sachin J. Shah, MD
Wei He, MS
Clemens S. Hong, MD, MPH
Publication date
01-10-2015
Publisher
Springer US
Published in
Journal of General Internal Medicine / Issue 10/2015
Print ISSN: 0884-8734
Electronic ISSN: 1525-1497
DOI
https://doi.org/10.1007/s11606-015-3252-3

Other articles of this Issue 10/2015

Journal of General Internal Medicine 10/2015 Go to the issue
Live Webinar | 27-06-2024 | 18:00 (CEST)

Keynote webinar | Spotlight on medication adherence

Live: Thursday 27th June 2024, 18:00-19:30 (CEST)

WHO estimates that half of all patients worldwide are non-adherent to their prescribed medication. The consequences of poor adherence can be catastrophic, on both the individual and population level.

Join our expert panel to discover why you need to understand the drivers of non-adherence in your patients, and how you can optimize medication adherence in your clinics to drastically improve patient outcomes.

Prof. Kevin Dolgin
Prof. Florian Limbourg
Prof. Anoop Chauhan
Developed by: Springer Medicine
Obesity Clinical Trial Summary

At a glance: The STEP trials

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine

Highlights from the ACC 2024 Congress

Year in Review: Pediatric cardiology

Watch Dr. Anne Marie Valente present the last year's highlights in pediatric and congenital heart disease in the official ACC.24 Year in Review session.

Year in Review: Pulmonary vascular disease

The last year's highlights in pulmonary vascular disease are presented by Dr. Jane Leopold in this official video from ACC.24.

Year in Review: Valvular heart disease

Watch Prof. William Zoghbi present the last year's highlights in valvular heart disease from the official ACC.24 Year in Review session.

Year in Review: Heart failure and cardiomyopathies

Watch this official video from ACC.24. Dr. Biykem Bozkurt discusses last year's major advances in heart failure and cardiomyopathies.