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Opportunity Costs of Ambulatory Medical Care in the United States
Kristin N. Ray, MD, MS; Amalavoyal V. Chari, PhD; John Engberg, PhD; Marnie Bertolet, PhD; and Ateev Mehrotra, MD, MPH
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Opportunity Costs of Ambulatory Medical Care in the United States

Kristin N. Ray, MD, MS; Amalavoyal V. Chari, PhD; John Engberg, PhD; Marnie Bertolet, PhD; and Ateev Mehrotra, MD, MPH
An analysis of the opportunity cost associated with ambulatory medical care in the United States demonstrates substantial time costs for individuals and society.
Over the study period (2003-2010), there were no significant changes in total time, clinic time, travel time, or face-to-face time. Travel time was similar for respondents in metropolitan statistical areas (37 minutes) and nonmetropolitan statistical areas (38 minutes).

Opportunity Costs and Direct Medical Costs

Across all visits for all ATUS respondents (including the unemployed), mean total opportunity cost per visit was $43 (95% CI, $42-45). Opportunity costs for care for themselves, other adults, or children were $42 (95% CI, $40-$44), $47 (95% CI, $43-$52), and $44 (95% CI, $39-$49), respectively (Table 3).

Across all visits among employed ATUS respondents (49%), the mean total opportunity cost per visit was $41 (95% CI, $39-$44). The average opportunity costs for care for themselves, other adults, or children were $39 (95% CI, $36-$41), $46 (95% CI, $39-$53), and $47 (95% CI, $39-$54), respectively (Table 3). Sensitivity analysis, limited to self-reported wages, resulted in no substantive change in estimated opportunity costs among employed adults (see eAppendix Table, available at

In comparison, the per ambulatory physician visit mean patient out-of-pocket cost was $32 and total provider reimbursement was $279.

In total, there were 1.034 billion visits (870 million and 164 million adult and child visits, respectively) to physicians in 2010. Of these, we estimate that 599 million visits involved employed individuals seeking care for themselves or others. This represents 1.1 billion hours in time spent and $25 billion in opportunity costs among employed adults in total. Among the entire population (including the unemployed), we estimate 2.4 billion hours in time spent and $52 billion in opportunity costs annually.

In the first national estimate of opportunity costs associated with ambulatory medical care, we found $43 in opportunity costs per visit among the entire adult population. The time per visit underlying our opportunity cost estimates (just over 2 hours) is similar to a prior study by Russell et al,6 which used earlier years of the ATUS. Our analysis furthers this important prior analysis by including more recent years of ATUS data, by including consideration of adults attending pediatric visits, and most notably, by translating this time burden into opportunity costs. These opportunity costs may be more readily interpreted by payers, policy makers, and employers, and also allows for comparison to direct medical spending. The opportunity costs per visit exceeded average out-of-pocket costs per visit. Opportunity costs added 15 additional cents in indirect costs to every dollar spent in physician visit reimbursement.

While the fact that individuals incur significant opportunity costs when seeking care may not be surprising, quantifying opportunity costs illuminates a hidden piece of healthcare spending, which we estimate to be $52 billion annually for the adult US population. Our estimates, specifically among the employed, demonstrate the potential financial impact on worker productivity, which may have particular importance from the employer perspective. The indirect healthcare cost just for employed adults is 1.1 billion hours of time (equivalent to the total annual hours worked by 563,000 full-time employees, which is approximately the employed adult population of Dallas, Texas22) and $25 billion in opportunity costs.

Much of these opportunity costs are due to time spent in activities other than actually receiving care. Comparing ATUS total time estimates with NAMCS face-to-face time suggests that more than 80% of time associated with visits was in activities other than face-to-face care with a physician. While some of this time may be spent receiving care or counseling from other members of the care team, the remainder is spent traveling, waiting at the clinic, or in ancillary tasks such as paying bills. This high time burden, primarily due to activities other than direct patient care, translates into high opportunity costs and reflects an ambulatory health system that has room to improve in terms of patient centeredness and efficiency. As discussed below, how much nondirect patient care time can be eliminated via improved efficiency or use of alternative methods of delivering care remains unclear, but opportunity costs provide a metric to monitor and evaluate improvement efforts.

There are several possible mechanisms to decrease patient opportunity costs. One approach is reducing inefficiencies in physician clinical settings. Although some amount of patient wait time is unavoidable in a clinic setting,23 prior work has demonstrated that it is possible to significantly decrease patient wait time through appropriate scheduling.24 Another approach is to promote alternative means of providing care. Work-site, retail, and school-based health clinics have the potential to reduce opportunity costs associated with physician visits by reducing travel and/or wait times.25-27 Telemedicine, including care via telephone, e-mail, Internet, and videoconference, has the potential to reduce or eliminate travel and wait times even more radically.28-31 What fraction of physician office visits could be replaced by telemedicine remains unclear. Estimates of the potential for telemedicine to replace face-to-face care range from 7% of internist visits5 to 47% of nursing home visits.4 Including patient opportunity costs may be important to fairly assess the comparative effectiveness of these alternative methods of care delivery.

While reducing opportunity costs associated with visits may be valued by patients, we recognize it could also result in increased ambulatory care utilization. As co-payments aim to reduce excess healthcare utilization by addressing “moral hazard,” opportunity costs may also decrease outpatient utilization.32 Small changes in co-payment amounts can drive significant change in care-seeking behavior; for example, elderly patients exposed to an increase in patient co-payment of less than $10 decreased outpatient utilization by 20 fewer outpatient visits per 100 people.33 Given that the average opportunity cost ($43) substantially exceeds average co-payment ($32), opportunity costs may be a significant disincentive to ambulatory care. A decrease in opportunity costs may render healthcare more accessible, resulting in increased demand for care and increased ambulatory healthcare spending. While this may lead to improved outcomes in populations that have previously foregone or delayed needed care due to opportunity costs, decreasing opportunity costs may also generate unnecessary visits and spending.

To our knowledge, ours is the first nationally representative study of opportunity costs associated with ambulatory medical visits. It utilizes the ATUS, which is a unique data source that has the best current national estimates of how US citizens use their time, and has key socioeconomic variables such as individual wages. Supplementing the ATUS with data from the 2 additional surveys allows us to contextualize how opportunity costs relate to face-to-face physician time and direct medical spending. However, there are also several critical limitations to the ATUS data. ATUS does not include many health-related variables such as health status, health conditions, type of provider, or nature of visit. As we note above in our methods, one concern is that the ATUS coding does not adequately differentiate between visits to physicians and nonphysicians, which might bias our results. If the time associated with nonphysician visits is less than the time associated with physician visits, we may be underestimating the opportunity costs of a physician visit.

ATUS data are also unable to distinguish between face-to-face provider time and time spent on clerical matters or waiting. For this reason, we used NAMCS to estimate face-to-face provider time to allow readers to compare this face-to-face time with the total clinic time reported in ATUS. Time reported in both ATUS and NAMCS relies on respondent reporting, raising the potential for recall and selection bias. As an example, direct observation studies have found that NAMCS may overestimate face-to-face provider time by 30% to 40%,34,35 suggesting that face-to-face time may be still lower than the values we present. Additionally, while NAMCS and MEPS allow us to differentiate between visits for children and adults, they do not allow us to distinguish between visits where adults are alone or accompanied, requiring us to use estimates for adult visits in general as the estimates for accompanied adults as well. This may underestimate face-to-face time and direct medical costs for accompanied adults, as these visits might be more complex. Finally, we recognize that there are controversies on how to value time, particularly for the unemployed. We used accepted labor economic approaches to value the time of employed and unemployed individuals.19,21 Additionally, we included estimates focusing specifically on employed individuals because these estimates may be of particular interest to employers and employer-based health plans.

In the United States, opportunity costs of seeking care are substantial for the average individual. For every dollar of direct medical expenditures for ambulatory physician visits, 15 additional cents were spent on the indirect costs of patient time. Time spent per year by employed adults seeking medical care exceeded the number of annual hours worked by more than half a million full-time employees and the societal opportunity costs are greater than $50 billion a year. Accounting for patient opportunity costs is important for examining US healthcare system efficiency and evaluating methods to improve the efficient delivery of care.

Author Affiliations: University of Pittsburgh School of Medicine (KNR), PA; Children’s Hospital of Pittsburgh (KNR), PA; RAND Corporation (AVC, JE), Pittsburgh, PA; University of Pittsburgh Graduate School of Public Health (MB), PA; Harvard Medical School (AM), Boston, MA; RAND Corporation (AM), Boston, MA.

Source of Funding: This study was supported in part by grants from the California HealthCare Foundation, the Health Resources and Services Administration National Research Service Award for Primary Medical Care (T32HP22240, Dr Ray), the Agency for Healthcare Research and Quality Patient-Centered Outcomes Research Career Development Award (K12HS022989, Dr Ray), and the National Institutes of Health (UL1TR000005, Dr Bertolet). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article. Dr Chari’s current affiliation is University of Sussex, Brighton, UK. This work was presented in part at the Pediatric Academic Societies meeting on May 5, 2013, and at the AcademyHealth meeting on June 24, 2013.

Authorship Information: Concept and design (KNR, AVC, JE, AM); acquisition of data (AVC, KNR, AM); analysis and interpretation of data (KNR, AVC, JE, AM, MB); drafting of the manuscript (KNR, MB); critical revision of the manuscript for important intellectual content (KNR, AVC, JE, MB, AM); statistical analysis (KNR, AVC, MB); provision of study materials or patients (AVC); obtaining funding (AVC, AM); and supervision (JE, AM).

Address correspondence to: Ateev Mehrotra, MD, MPH, Harvard Medical School, Department of Health Care Policy, 180 Longwood Ave, Boston, MA 02115. E-mail:
1. Committee on Quality of Health Care in America; Institute of Medicine. Crossing the Quality Chasm: A New Health System for the 21st Century. Washington, DC: National Academies Press; 2001.

2. Weinstein MC, Siegel JE, Gold MR, Kamlet MS, Russell LB. Recommendations of the Panel on Cost-effectiveness in Health and Medicine. JAMA. 1996;276(15):1253-1258. Review.

3. Russell LB. Completing costs: patients’ time. Med Care. 2009;47(7, suppl 1):S89-S93.

4. Shah MN, McDermott R, Gillespie SM, Philbrick EB, Nelson D. Potential of telemedicine to provide acute medical care for adults in senior living communities. Acad Emerg Med. 2013;20(2):162-168.

5. Chen MA, Hollenberg JP, Michelen W, Peterson JC, Casalino LP. Patient care outside of office visits: a primary care physician time study. J Gen Intern Med. 2011;26(1):58-63.

6. Russell LB, Ibuka Y, Carr D. How much time do patients spend on outpatient visits? the American Time Use Survey. Patient. 2008;1(3):211-222.

7. Probst JC, Laditka SB, Wang JY, Johnson AO. Effects of residence and race on burden of travel for care: cross sectional analysis of the 2001 US National Household Travel Survey. BMC Health Serv Res. 2007;7:40.

8. Mechanic D, McAlpine DD, Rosenthal M. Are patients’ office visits with physicians getting shorter? N Engl J Med. 2001;344(3):198-204.

9. Federico CA, Hsu PC, Krajden M, et al. Patient time costs and out-of-pocket costs in hepatitis C. Liver Int. 2012;32(5):815-825.

10. Yabroff KR, Davis WW, Lamont EB, et al. Patient time costs associated with cancer care. J Natl Cancer Inst. 2007;99(1):14-23.

11. Yabroff KR, Warren JL, Knopf K, Davis WW, Brown ML. Estimating patient time costs associated with colorectal cancer care. Med Care. 2005;43(7):640-648.

12. Jonas DE, Russell LB, Sandler RS, Chou J, Pignone M. Value of patient time invested in the colonoscopy screening process: time requirements for colonoscopy study. Med Decis Making. 2008;28(1):56-65.

13. Hurd MD, Martorell P, Delavande A, Mullen KJ, Langa KM. Monetary costs of dementia in the United States. N Engl J Med. 2013;368(14):1326-1334.

14. American Time Use Survey. 2003-2010. Bureau of Labor Statistics website. Accessed August 13, 2012.

15. American Time Use Survey user’s guide: understanding ATUS 2003 to 2011. Bureau of Labor Statistics website. Published 2012. Accessed August 13, 2012.

16. Ambulatory health care data: NAMCS public use data files. 2003-2010. CDC website. Accessed February 26, 2013.

17. Medical Expenditure Panel Survey: download data files, documentation, and codebooks, 2010. Accessed January 16, 2013.

18. Chari AV, Engberg J, Ray KN, Mehrotra A. The opportunity costs of informal elder-care in the United States: new estimates from the American Time Use Survey. Health Serv Res. 2015;50(3):871-872.

19. Becker GS. A theory of the allocation of time. The Economic Journal. 1965;75(299):493-517.

20. Consumer Price Index. Bureau of Labor Statistics website. Accessed December 13, 2012.

21. Heckman JJ. Sample selection bias as a specification error. Econometrica. 1979;47(1):153-161.

22. American Community Survey 5-year estimates, 2007-2011. US Census Bureau website. Accessed April 24, 2012.

23. Savin S. Managing patient appointments in primary care. In: Hall RW, ed. Patient Flow: Reducing Delay in Healthcare Delivery. New York, NY: Springer Science+Business Media; 2006:173-196.

24. Cayirli T, Veral E. Outpatient scheduling in health care: a review of the literature. Prod Oper Manag. 2003;12(4):519-549.

25. Hunter LP, Weber CE, Morreale AP, Wall JH. Patient satisfaction with retail health clinic care. J Am Acad Nurse Pract. 2009;21(10):565-570.

26. Rudavsky R, Pollack CE, Mehrotra A. The geographic distribution, ownership, prices, and scope of practice at retail clinics. Ann Intern Med. 2009;151(5):315-320.

27. Sherman BW, Fabius RJ. Quantifying the value of worksite clinic nonoccupational health care services: a critical analysis and review of the literature. J Occup Environ Med. 2012;54(4):394-403.

28. Wootton R, Bahaadinbeigy K, Hailey D. Estimating travel reduction associated with the use of telemedicine by patients and healthcare professionals: proposal for quantitative synthesis in a systematic review. BMC Health Serv Res. 2011;11:185.

29. Lustig TA; Board on Health Care Services; Institute of Medicine. The Role of Telehealth in an Evolving Health Care Environment: Workshop Summary. Washington, DC: The National Academies Press; 2012.

30. McConnochie KM, Wood NE, Herendeen NE, ten Hoopen CB, Roghmann KJ. Telemedicine in urban and suburban childcare and elementary schools lightens family burdens. Telemed J E Health. 2010;16(5):533-542.

31. Young TL, Ireson C. Effectiveness of school-based telehealth care in urban and rural elementary schools. Pediatrics. 2003;112(5):1088-1094.

32. Grumbach K, Keane D, Bindman A. Primary care and public emergency department overcrowding. Am J Public Health. 1993;83(3):372-378.

33. Trivedi AN, Moloo H, Mor V. Increased ambulatory care copayments and hospitalizations among the elderly. N Engl J Med. 2010;362(4):320-328.

34. Gottschalk A, Flocke SA. Time spent in face-to-face patient care and work outside the examination room. Ann Fam Med. 2005;3(6):488-493.

35. Gilchrist VJ, Stange KC, Flocke SA, McCord G, Bourguet CC. A comparison of the National Ambulatory Medical Care Survey (NAMCS) measurement approach with direct observation of outpatient visits. Med Care. 2004;42(3):276-280. 
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