Community Ment Health J (2015) 51:782–789 DOI 10.1007/s10597-015-9897-x
ORIGINAL PAPER
How Long Do Adolescents Wait for Psychiatry Appointments? Kenneth J. Steinman1
•
Abigail B. Shoben2 • Allard E. Dembe3 • Kelly J. Kelleher4
Received: 11 November 2013 / Accepted: 10 June 2015 / Published online: 25 June 2015 Ó Springer Science+Business Media New York 2015
Abstract Appointment wait times are a neglected dimension of children’s access to psychiatry. We systematically examined how long an adolescent waits for a new patient appointment with a psychiatrist for routine medication management. From state directories, we identified 578 providers of adolescent psychiatric care in Ohio. Researchers posing as parents telephoned randomly selected offices, seeking care for a hypothetical 14-year-old patient under different scenarios. Overall, we measured 498 wait times at 140 unique offices. The median wait time was 50 days (interquartile range = 29–81 days). In adjusted models, adolescents with Medicaid waited longer than those with private insurance, especially during the spring (geometric mean = 50.9 vs. 41.9 days; p = 0.02). Wait times also varied markedly by region, with geometric means ranging from 22.4 to 75.1 days (p \ 0.01). This study demonstrates that adolescents often experience lengthy wait times for routine care. This methodology represents a useful approach to real-time monitoring of psychiatric services. & Kenneth J. Steinman
[email protected] 1
Division of Health Behavior and Health Promotion, The Ohio State University College of Public Health, 359-A Cunz Hall, 1841 Neil Avenue, Columbus, OH 43210, USA
2
Division of Biostatistics, The Ohio State University College of Public Health, 249 Cunz Hall, 1841 Neil Avenue, Columbus, OH 43210, USA
3
Division of Health Services Management and Policy, The Ohio State University College of Public Health, 238 Cunz Hall, 1841 Neil Avenue, Columbus, OH 43210, USA
4
Center for Innovation in Pediatric Practice, The Research Institute at Nationwide Children’s Hospital, 700 Children’s Drive, Columbus, OH 43205, USA
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Keywords Adolescent Appointments and schedules Health insurance Medicaid Mental health services Patient simulation
Introduction The severe shortage of child and adolescent psychiatrists in the United States is well documented (Thomas and Holzer 2006; Kim 2003; Pomerantz et al. 2008) especially in rural locations and areas with significant levels of poverty. (Thomas and Holzer 1999; Smalley et al. 2010) Even with the support of primary care providers (PCPs), parents often have great difficulty obtaining prompt psychiatric medication management for their children. About two-thirds of PCPs, for example, report they cannot get outpatient mental health services for patients—a rate at least twice as high as that for other services (Cunningham 2009). Understandably, health systems prioritize serving patients with emergency or emergent conditions. Yet as a result, patients with routine mental health conditions must often wait a lengthy period of time to obtain care. According to the Commonwealth Fund, wait times of 4 to 6 weeks for initial psychiatric appointments are common in Massachusetts, and community mental health centers in that state report three-month waits (Holt 2010). Similar wait times have been recorded elsewhere (Abrahams and Udwin 2002; Canadian Psychiatric Association 2006), and one study found that wait times often exceed 1 year (Pfefferlem 2007). This is unfortunate, because longer wait times increase the likelihood that patients will not keep their appointments (Folkins 1980; Gallucci et al. 2005; Foreman and Hanna 2000). Delays in mental health services also create an elevated risk of poor outcomes and potentially dangerous
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exacerbation of the patient’s problem (Nath and Marcus 2006). For individuals, long waits for appointments can prolong physical and emotional distress (Kowalewski et al. 2011). Patients at Veterans Administration facilities with wait times of more than 31 days have an elevated risk of mortality compared to veterans with wait times of less than 30 days (Prentice and Pizer 2007). Despite these concerns, wait times for child and adolescent psychiatry are not always lengthy in all places and at all times. Some patients are able to access appointments relatively quickly. Few studies have sought to systematically describe variation in appointment wait times for psychiatry. Studies of wait times either have done so incidentally or have published results that have not been peer-reviewed. The present study aims to assess patient wait times for adolescents with a focus on understanding the influence of health insurance, region and season. Why Might Appointment Wait Times Vary? Health insurance is an important indicator of access to care. Because over 90 % of US children have health insurance (US Department of Health and Human Services 2011), many studies have focused on how different types of insurance affect access to care. Compared to adolescents with private insurance, for example, those with Medicaid may have a more difficult time obtaining specialty care (Bisgaier and Rhodes 2011). Yet for specialties with a shortage of providers—such as child and adolescent psychiatry—adequate insurance may amount to little more than a ‘‘hunting license,’’ enabling patients to pay for care only if they can find an available provider. Appointment wait times for psychiatry may also vary by region. Numerous studies, for example, document statelevel variations in health care utilization among children, independent of differences in need and population characteristics (Sturm et al. 2003). Such state-level differences may also reflect differences in the scope of services covered by different Medicaid plans and State Children’s Health Insurance Programs (SCHIP). Yet even within a single state, regions with fewer providers per capita (e.g., rural areas) may have limited access, and thus, presumably, longer wait times. One study, for instance, found that wait times for different medical specialties varied considerably across 15 metropolitan areas (Merritt Hawkins and Associates 2010). Alternatively, some regions may differentially invest in non-physician psychotherapy services. Finally, the time of year may also influence how long adolescents have to wait for a psychiatry appointment, because of the timing of school vacations, examinations, and other factors. Greater situational stress during the academic year may increase the demand for such appointments, while the availability of school-based
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providers may facilitate referral to specialists. Measuring differences by season may also offer insights into the stability of measures of appointment wait times. The more that wait times vary by season, the less reliable are episodic efforts to measure them. Despite these explanations, little empirical research has described how long youth must wait for an appointment with a psychiatrist. The present study aims to assess patient wait times for adolescents with a focus on understanding the influence of health insurance, region and season.
Methods In February 2012, we selected psychiatry offices in Ohio from all available online medical provider directories for Medicaid and for a large private health insurance company in Ohio. Because some psychiatrists not trained in child and adolescent psychiatry may still see adolescents, we included all psychiatrists in the sampling frame. The primary sampling unit was a psychiatry office at a unique address. Many psychiatrists practice in more than one office, and many offices have more than one psychiatrist on site, yet these variables change frequently and there exists no up-to-date, credible source of such information. We were therefore unable to construct sampling weights to account for differences in office size. We stratified the sample across nine regions of Ohio, including three core metropolitan counties (Cuyahoga, Franklin, Hamilton) corresponding to the state’s three largest cities (respectively, Cleveland, Columbus and Cincinnati). We also included three multi-county regions that are adjacent to the core metropolitan counties. We refer to these regions respectively as ‘‘core’’ (e.g., ‘‘Columbus core’’) and adjacent (e.g., ‘‘Columbus adjacent’’). Finally, we included three multi-county ‘‘rural’’ non-metropolitan regions, for an overall total of nine regions. For each region, we randomly sorted the list of office addresses from the provider directories for each of four scenarios of patients seeking an appointment: (1) an adolescent with depression, covered by Medicaid; (2) an adolescent with an anxiety disorder, covered by Medicaid; (3) an adolescent with depression, covered through the private insurer; and (4) an adolescent with an anxiety disorder, covered through the private insurer. In the Cleveland core county, for example, we randomly sorted the list of offices listed in the Medicaid directory to call under scenario 1 and then randomly sorted the same list of offices to call under scenario 2. Next, we randomly sorted the list of offices listed in the private insurer directory to call under scenario 3 and randomly resorted the list to call under scenario 4. This procedure insured that each office in a directory had an equal chance of being called under the relevant scenarios. For
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offices with more than one phone number, we randomly selected one number to begin calling. For each region, we purposefully assigned different scenarios to each team member, so that no one researcher was exclusively responsible for completing all calls in any one region, condition, insurance type or season. (Random assignment of callers was impractical, as it could result in the same caller phoning the same office on the same day under different scenarios—thereby raising the suspicions of participants.) The three research team members posing as parents included a 44 year old man, and two women, aged 24 and 26 years. This approach enabled us to maintain our deception. During debriefing calls with participating offices, not one acknowledged any suspicions that a study had been conducted. Calls were made during spring (March 15 through May 25, 2012) and summer (June 21–August 14, 2012) to address seasonal variation because the timeline established for this project by the funder required completion of the study within 6 months. Calling Procedure During a call, a member of the research team posed as a parent seeking a new patient appointment for routine medication management for his/her 14-year-old daughter under 1 of the 4 scenarios. The study focused on fictitious new patients because of legal and ethical issues involved in using real patient names and insurance numbers. In calling an office, we attempted to obtain the earliest possible appointment time under the given scenario at the listed address. If the office indicated that there was a psychiatrist who would be able to see a patient under the given scenario, we then asked when the soonest available appointment was with any psychiatrist in that office who met those criteria. To minimize the administrative burden to psychiatry offices, the caller never actually made an appointment. Overall, the research team made 1945 calls to 561 listed offices. Recording Wait Times Often, offices indicated that an intake assessment was required before patients could schedule an appointment with a psychiatrist. In such cases, we recorded the wait time as the sum of the days until the intake assessment plus the days until the soonest available psychiatrist appointment. When an office required multiple appointments with a counselor prior to seeing a psychiatrist, the wait time consisted of the cumulative wait until the psychiatry appointment. A few offices had a psychiatrist on staff as part of a larger multi-specialty practice and would only
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accept new patient appointments from one of the practice’s own in-house primary care providers. In such a case, we assessed the wait time for a new patient appointment with a primary care provider and added that to the subsequent wait time for a referral to the psychiatrist at that address. When a receptionist could not provide a precise estimate of the next available appointment, we recorded the estimated range and employed a standardized method for converting this range to a specific number of days. For a report of ‘‘about one month,’’ for example, we would code as a range of 27–33 days with a point estimate of 30 days for analyses. Statistical Analyses Three different summary measures for wait times are provided: the geometric mean, median, and range. To compare across factors, we used geometric means (log transformed wait times). The geometric mean is a useful summary measure for wait times, because although it is less sensitive to large outlying values than the arithmetic mean, it is not completely uninfluenced by outlying values. The planned experiment was a factorial design with four factors: season (2 levels), region (9), condition (2), and insurance (2). Within each cell, we planned to make eight calls, yet some cells in this experiment had less than the planned number of observations because fewer than expected offices in certain areas could make a new patient appointment for outpatient medication management. Due to this imbalance in the final sampling scheme, we performed adjusted linear regression models to account for possible confounding by region, condition, season, and insurance. Results from the regression models are presented as estimated geometric means assuming this complete balance. We used generalized estimating equations with the robust Sandwich variance estimator to account for correlation of wait times from the same office. All analyses were conducted using Stata 12.0 and R 2.14. Statistical significance was p \ 0.05 and results are unadjusted for multiple comparisons. This study was approved by the institutional review board of the Ohio State University and the authors have no known conflicts of interest. Although the study was conducted in Ohio, the issue is relevant to other states across the nation. A more complete description of the study methodology appears elsewhere (Steinman et al. 2012).
Results We were able to find an appointment with a psychiatrist at 18 % of the 431 offices listed in the Medicaid provider directories, and at 25 % of the 406 offices listed in the
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private insurer directories (Table 1). Because some offices appeared in both directories, the final data set included 498 wait times from 140 unique office addresses. The most common reasons for failure to obtain an appointment were that the office did not see children (26 %), did not offer psychiatry services (24 %), or that researchers were unable to contact the office, despite repeated attempts (23 %). Additionally, some offices were not taking new patients (9 % of total) or were no longer accepting the listed insurance type (6 %). The median wait time for a new patient appointment with a psychiatrist for routine medication management was 50 days (interquartile range = 29–81 days). Wait times ranged widely, from as little as 1 day up to 345 days. Table 2 presents the results by region, insurance and problem. Wait times in northwest rural Ohio, the Columbus core and Columbus adjacent counties were noticeably longer compared to those in the Cleveland core and Cleveland adjacent counties. For each of metropolitan areas, adjacent counties reported similar wait times as the core county. In contrast, there were no consistent differences in wait times in core counties compared to adjacent regions or to rural regions. Condition (depression vs. anxiety) was not statistically associated with wait times. Nevertheless, considerable variation was observed within each region. In each of the metropolitan core and adjacent counties, the minimum wait times were less than 7 days, whereas in the rural areas they were closer to 2 weeks. Even considering patients with Medicaid insurance only, it was possible to get an appointment within
2 weeks in eight out of the nine regions (data not shown). Only in northwest rural counties was the minimum Medicaid wait time greater, at 28 days. At the other end of the distribution, one in four appointments involved a wait of at least 81 days, although the upper bound of the interquartile range was much greater in certain regions. In northwest rural counties, for example, one in four appointments involved a wait of at least 105 days, while in Columbus core the median wait was 150 days or nearly 5 months. In the adjusted model (Table 3), there were statistically significant differences in geometric mean wait times observed by insurance (p = 0.04) and region (p \ 0.01) but not for season (p = 0.33) or condition (p = 0.18). There were, however, significant interactions between season and insurance (p = 0.02) and season and region (p = 0.02). In the spring, teens with Medicaid waited longer than those covered by the private insurer (50.9 vs. 41.9 days). During the summer, however, geometric mean wait times were much more similar among insurance types, with the estimate for Medicaid slightly less than for the private insurer (41.9 vs. 43.8 days).
Discussion Studies of adolescents’ limited access to psychiatry have focused on the dearth of trained child and adolescent psychiatrists (Koppelman 2004), insufficient insurance coverage (Ellis et al. 2012), and other barriers (Owens et al. 2002). Our findings highlight an understudied dimension of
Table 1 Regional variations in the number and proportion of offices in which it was possible to schedule a child psychiatry appointment Medicaid # of listed offices called
Private insurer # of offices able to schedule
% of offices able to schedule
# of listed offices called
# of offices able to schedule
% of offices able to schedule
Northwest rural
19
4
21
19
5
26
North central rural
38
9
24
34
10
29
Appalachia
55
16
29
43
18
42
Cincinnati adjacent
52
8
15
35
14
40
Columbus adjacent
29
9
31
26
11
42
Cleveland adjacent
43
9
21
45
13
29 21
Cincinnati core
57
5
9
39
8
Columbus core
45
8
18
58
9
16
Cleveland core
93
11
12
107
15
14
431
79
18
406
103
25
Total
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786 Table 2 Psychiatry appointment wait times (in days) for adolescents by condition, insurance, region and season
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n
Geometric mean (SD)
Median (IQR)
Range (min, max)
Overall
498
46.2 (2.4)
50.0 (29,81)
(1, 345)
Anxiety
246
47.8 (2.3)
52.0 (30,81)
(2, 345)
Depression
252
44.7 (2.5)
47.0 (28,78)
(1, 305)
Medicaid
234
50.3 (2.4)
55.5 (31,84)
(2, 305)
Private
264
42.8 (2.4)
47.3 (26,78)
(1, 345)
Northwest rural
32
64.2 (1.9)
73.3 (37,105)
(11, 149)
North central rural
60
45.1 (1.7)
48.0 (30,64)
(12, 166)
Appalachia
64
46.2 (1.8)
46.5 (33,70)
(14, 177)
Cincinnati adjacent
64
50.1 (2.2)
55.3 (33,91)
(4, 229)
Columbus adjacent
62
60.9 (2.2)
60.5 (37,93)
(7, 345)
Cleveland adjacent
61
30.3 (2.5)
37.0 (15,61)
(2, 113)
Cincinnati core
43
47.4 (2.4)
66.5 (39,81)
(1, 137)
Columbus core
56
69.6 (3.1)
84.5 (26,150)
(2, 305)
Cleveland core Spring Summer
56
27.1 (2.6)
28.0 (14,55)
(2, 210)
251 247
47.8 (2.5) 44.6 (2.3)
51.0 (28,89) 50.0 (30,76)
(1, 345) (2, 270)
Not adjusted for clustering by office or imbalance in calls made IQR interquartile range
Table 3 Adjusted geometric mean (and 95 % CI) psychiatry appointment wait times (in days) for adolescents in Ohio
Overall Overall
p
Spring
Summer
pa
44.5 (39.1, 50.6)
–
46.2 (40.1, 53.2)
42.8 (37.3, 49.2)
0.33
Medicaid
46.2 (39.5, 54.1)
0.04
50.9 (42.3, 61.2)
41.9 (35.2, 50.0)
0.02
Private
42.8 (37.3, 49.1)
41.9 (35.8, 48.9)
43.8 (37.9, 50.5)
47.4 (41.1, 54.7)
44.0 (38.0, 50.9)
45.0 (38.6, 52.3)
41.7 (36.2, 48.1)
75.6 (48.1, 118.9)
74.6 (53.0, 105.0) 41.9 (31.0, 56.6)
Insurance
Condition Anxiety
45.7 (40.0, 52.2)
Depression
43.3 (37.8, 49.6)
0.18
0.70
Region
a
75.1 (51.3, 110.0)
North central rural
44.7 (33.8, 59.1)
47.7 (35.8, 63.5)
Appalachia
49.7 (39.7, 62.2)
47.9 (37.4, 61.2)
51.6 (40.7, 65.5)
Cincinnati adjacent
47.5 (33.9, 66.4)
47.9 (31.2, 73.4)
47.0 (34.0, 65.1)
Columbus adjacent Cleveland adjacent
51.0 (35.0, 74.2) 31.1 (20.9, 46.2)
59.2 (40.3, 86.8) 26.2 (17.0, 40.4)
43.9 (28.2, 68.4) 36.9 (25.0, 54.5)
Cincinnati core
42.9 (26.3, 70.0)
42.5 (24.3, 74.5)
43.3 (26.6, 70.5)
Columbus core
56.5 (34.7, 92.1)
72.6 (43.8, 120.5)
44.0 (24.2, 79.9)
Cleveland core
22.4 (15.1, 33.2)
24.1 (15.9, 36.6)
20.7 (13.0, 33.0)
0.02
p value of the model coefficient for the season by factor interaction
access to care: appointment wait times. This study confirms what clinicians have long suspected; that adolescents often need to wait a very long time to obtain an appointment with a psychiatrist. Long appointment wait times for specialty care are generally associated with lower compliance and worse medical outcomes (Nath and Marcus 2006; Kowalewski et al. 2011; Prentice and Pizer 2007). But when seeking a
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\0.01
Northwest rural
new patient appointment for routine medication management, exactly how long is too long? Foreman and Hanna (2000) observed that children with psychiatry appointment wait times over 30 days are significantly less likely to make follow up visits. By this metric, three-quarters of the wait times we measured were too long and may have contributed to the common problem of patients missing scheduled appointments. In terms of outcomes, it is unclear
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what happens to adolescents and their families who simply forgo care when faced with an excessively long wait for an appointment. Some patients are likely to improve spontaneously whereas others’ conditions may deteriorate, thus making treatment more difficult and expensive. Further research will be necessary to assess these effects. Nonetheless, our findings are not uniformly bleak. Some regions performed markedly better than others, suggesting that timely access to psychiatric care may be achievable. Acknowledging that there will never be sufficient child psychiatrists to serve all of the children with mental disorders in the current model (Kazdin 2011), several ideas for improving availability have been promulgated recently. These include electronic therapies that allow children and families to receive interventions online (e.g. Recovery4Teens, MoodGym and Triple P Online), digital tools for facilitating primary care management of some psychiatric disorders (e.g. MyADHD.com) and peer advocacy programs to facilitate self-management by children and families. While none of these directly replace child psychiatrists in medication management, all of them would off load considerable burden from the child mental health system. It is also important to consider the role of insurance, as we found that Medicaid patients waited somewhat longer than those with private insurance for an appointment. Other mystery shopper studies that examined differences in appointment wait times have yielded mixed results. For a variety of pediatric specialties in Cook County, Illinois, Bisgaier and Rhodes (2011) found that average wait time for Medicaid–CHIP enrollees was 22 days longer than for privately insured children. In contrast, a recent, large study of adults in 10 states found that insurance type had no effect on wait times for an appointment with a primary care provider (Rhodes et al. 2014). Implementation of Medicaid expansion and the Affordable Care Act will likely alter how insurance type affects adolescents’ access to psychiatric care. Further research can help document such changes. Of the three previous mystery shopper studies of appointment wait times (e.g., Bisgaier and Rhodes 2011; Merritt Hawkins and Associates 2010; Rhodes et al. 2014) none were conducted during the summer and none reported seasonal trends. So it was noteworthy to find an interaction of season with region and with insurance type (Table 3). In adjusted models, wait times from spring to summer decreased only for Medicaid and for the Columbus core and Columbus adjacent regions. (There was no significant changes for private insurance or in 6 of the 7 other regions.) One possible explanation for this pattern is that young people experience more stress during the school year than during the summer. As a result, greater demand for mental health care may have resulted in longer wait times during high demand periods (e.g., spring) in provider networks with limited capacity (e.g., Medicaid; Columbus
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core), whereas networks with more capacity could accommodate greater demand without significantly longer wait times. A complimentary explanation is that the opening of individual practices may have had a ripple effect on wait times throughout the region. Between the spring and summer data collection periods, a new adolescent psychiatry practice opened in Columbus—near the only two regions with a decrease in wait times. Similarly in our pilot study, the one region that had shorter wait times for simulated patients with ADHD also happened to have just opened a grant-funded clinic devoted to that condition (Steinman et al. 2012). Such events may be merely coincidental, yet at the very least, such findings suggest that appointment wait times can change quickly and mystery shopper studies may be well-suited to real-time monitoring of access to psychiatric services. Limitations Despite the growing popularity of mystery shopper studies of health care delivery, many researchers are unfamiliar with the distinct methodological challenges of the approach (Steinman 2014). In this study, one concern is that the researchers posing as parents never actually made an appointment. Rather, they only asked about the availability of obtaining an appointment with a psychiatrist and the process for obtaining the services. It is unclear how well this would have accorded with the real wait time had a caller actually made an appointment. In practice, psychiatrists must often delay and reschedule patients. Also, sometimes new slots open up earlier and patients can be seen sooner than initially planned. Nonetheless, avoiding actual appointments was necessary to minimize the administrative burden to participating offices and to avoid the considerable legal and ethical issues involved in using real patient names and insurance numbers. Debriefing discussions with participating offices suggested that it was possible to collect useful data without placing a noticeable burden on providers and without compromising the perceived authenticity of the callers. Our experience with an earlier pilot study (Steinman et al. 2012) reached a similar conclusion, and studies in other healthcare settings have found that the mystery shopper technique generates useful results that accord with actual medical practice (Lazarus 2009; Rhodes 2011). Another limitation relates to our inability to weight the data to account for variation in the size of each practice location. Some offices house many psychiatrists (e.g., an academic medical center), whereas others have only one. Weighing data to account for office size could best describe the typical wait time for an appointment, yet doing so was impractical. Many psychiatrists practice in more than one
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office, and other offices have more than one psychiatrist on site. Because these variables change frequently and there exists no up-to-date, credible source of such information, we were unable to use each individual psychiatrist as the unit of analysis or to weight our analyses to account for differences in practice size. Even with these limitations, it is difficult to envision how such methodological factors would explain the differences we observed by region, insurance type and season. Future studies could help to assess the validity of results generated by mystery shopper studies. One approach might involve comparing such findings to medical records that note when an appointment was first made and when it was actually held. Medical claims data omit such information, but it is likely available at certain large, multi-site health systems having a strong administrative infrastructure. The primary focus of our study was on access to care rather than quality of care, yet these two dimensions are strongly related. It is likely that some health care providers were concerned about long wait times compromising the quality of their care and so refused to see new patients, thus excluding them from the sample. Elsewhere, short waiting times may have indicated a less intensive or suboptimal approach to care. More importantly, speed of access to non-psychiatrists was beyond the scope of the present study. Psychotherapy and non-medication services are important aspects of children mental health services, and in many cases, an office required visits with such professionals before an appointment with a psychiatrist. Future studies should assess access to these other types of mental health care and examine how appointment wait times relate to the quality of care for adolescents.
Conclusion Long appointment wait times are an important, yet understudied aspect of children’s access to psychiatric care. The wait times observed in this study could indicate potential problems in timely access to care, especially for Medicaid populations during the spring. Yet the results are not uniformly bleak, as some regions had much shorter wait times than others. Future studies of wait times (and the audience that reads their results) should pay particular attention to the region and season under study, for even within a single state, appointment wait times may vary markedly and change quickly. As Medicaid expansion and the Affordable Care Act continue to reshape health care delivery in the United States, efforts to measure access to care should consider monitoring appointment wait times as one key metric. Mystery shopper studies offer an efficient and effective approach for doing so.
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Community Ment Health J (2015) 51:782–789 Acknowledgments This study was made possible by the support of the Ohio Office of Medical Assistance’s MEDTAPP Program, the Ohio Department of Mental Health, and the Ohio Colleges of Medicine Government Resource Center. The opinions expressed here do not represent those of any State official or agency and are solely those of the authors.
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