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Influence of Prior Authorization Requirements on Provider Clinical Decision-Making

The American Journal of Managed CareJuly 2023
Volume 29
Issue 7

An online survey identified that documentation requirements and communication issues with health plans are associated with providers modifying clinical decisions to avoid medication prior authorization.


Objectives: Prior authorization (PA) aims to promote the safe and effective use of medications and to control costs. However, PA-related administrative tasks can contribute to burden on health care providers. This study examines how such tasks affect treatment decisions.

Study Design: Cross-sectional, online survey.

Methods: We conducted an online survey of US medical providers in 2020 based on a convenience sample of 100,000 providers. Multivariate path analysis was used to examine associations among provider practice characteristics, step therapy and other health plan requirements, perceived burdens of PA, communication issues with insurers, and prescribing behaviors (prescribing a different medication than planned, avoiding prescribing of newer medications even if evidence-based guideline recommendations are met, and modifying a diagnosis). Weighted analyses were conducted to assess nonresponse bias.

Results: A total of 1173 respondents (1.2% response rate) provided 1147 usable surveys. Step therapy requirements had the largest effect on clinical decision-making. Other significant effects on clinical decision-making included perceived PA likelihood, communication issues, and health plan requirements (eg, clinical documentation). Weighted analyses showed that the study conclusions were unlikely to have been biased by nonresponse.

Conclusions: Respondents report that they may alter clinical decisions to avoid PA requirements and related burdens, even in cases in which use of the PA medication was clinically appropriate. Processes that reduce the administrative burden of PA through improved communication and transparency as well as standardized documentation may help ensure that PA more seamlessly achieves its goals of safe and effective use of medications.

Am J Manag Care. 2023;29(7):331-337. https://doi.org/10.37765/ajmc.2023.89394


Takeaway Points

We conducted a cross-sectional, online survey of licensed providers in the United States to evaluate attitudes toward medication prior authorization (PA) and to assess whether PA requirements are linked to clinical decisions in patient care.

  • PA aims to promote appropriate use of medications while containing cost, but it places administrative burden on providers and payers.
  • Providers report that they may prescribe different medications than originally planned, avoid prescribing newer medications, or modify a diagnosis to avoid PA burdens.
  • Reducing administrative burden of PA through improved communication, standardization, and technology may help ensure that PA more seamlessly achieves its goals of safe and effective use of medications.


Prior authorization (PA) of prescription medications is used by health insurers to manage access to costly and/or low-value medications to ensure safe, effective, and value-based use of medications.1 However, meta-analyses have found that PA and other formulary restrictions can adversely influence medication adherence, clinical outcomes, and treatment satisfaction.2,3 Moreover, recent provider surveys have affirmed that medication PA disrupts workflow and affects provider satisfaction. For instance, an American Medical Association survey4 reported that providers spend a mean of 14.4 hours on PAs per week, and a significant majority of providers rated the burden of insurance company administrative challenges as “high” or “extremely high.” Almost two-thirds stated that they wait at least a day to hear from the insurance company on a PA request and slightly under one-third reported that they wait at least 3 business days. The type of medical specialty can also significantly contribute to the PA burden, as noted in the 2019 ePA National Adoption Scorecard published by CoverMyMeds. These findings have been reinforced by more in-depth qualitative studies highlighting provider burden due to extensive paperwork and inconsistent PA requirements among health plans.5-7

There is also growing evidence that the medication PA process can delay patient care and contribute to adverse patient outcomes.8-13 When surveyed, 24% of physicians noted that PA had led to a serious adverse event in at least 1 of their patients, 91% reported that PA requirements caused care delays, and 90% reported that PA delays resulted in worse patient outcomes.4 Furthermore, PA-related cost restrictions create delays that result in patients abandoning prescriptions.14 Given these consequences and the burden of PA, providers, pharmacists, policymakers, and other stakeholders have supported efforts to limit, standardize, and/or streamline PA processes.15-18

Although there is a body of evidence on the benefits and unintended consequences of PA, there is a gap in the literature on whether medication PA alters providers’ clinical decision-making and use of work-arounds to avoid PA requirements. We identified 1 study in this area, which was a survey of psychiatrists.19 In that survey, a majority of providers reported at least occasionally using tactics such as diagnosis modification or falsification of previous medication trials to meet PA requirements. Further, two-thirds reported that they occasionally refrain from prescribing a medication they would otherwise prefer due to a known or expected PA requirement. However, this prior study did not assess factors related to PA work-arounds that can be attributed to the PA requirements and processes.

To address this concern, we conducted a nationwide survey of providers to evaluate their attitudes toward medication PA and whether PA requirements are linked to clinical decision-making in patient care. By doing so, the current study provides insight into how modifications to improve and streamline the PA process could affect provider work-arounds and help ensure that the PA process achieves the intended goal of promoting safe, high-value drug therapy.


Sampling Frame and Survey Administration

This study was part of an effort to design a digital solution for medication PA conducted under the auspices of a National Institute of Mental Health Small Business Technology Transfer grant. We developed a 58-item survey, a process informed by focus groups and stakeholder interviews. The study oversampled mental health providers because they are the current focus of the software platform. The remaining specialty types were selected based on provider likelihood of encountering PA: dermatology, gastroenterology, internal medicine, oncology, and rheumatology.20 These and other nonpsychiatry specialties often encounter PA requirements for newly emerged specialty medications or particularly expensive medications.

We administered the survey in October 2020 using the Qualtrics platform. Invitation emails with a unique hyperlink were sent out to 100,000 providers with emails drawn from a curated, nationwide list of licensed providers. All participants provided consent prior to taking the survey (University of Nebraska Institutional Review Board No. 00000672).

Survey Instrument

Exogenous measures. A single item assessed challenges associated with identifying step therapy requirements prior to prescribing (“In general, how challenging is it to identify appropriate step therapy requirements prior to prescribing a medication?”), with responses on a 5-point scale ranging from “not at all challenging” to “extremely challenging.” A single item assessed active patient load (“What is your active patient load?”), with responses on a 5-point scale ranging from “fewer than 25 patients” to “more than 200 patients.”

Nine items assessing interactions with health plans were used to create 2 subscales, one capturing health plan requirements related to PA and a second reflecting communication issues that arise between provider and health plans (see eAppendix [available at ajmc.com] for additional details). The health plan requirements subscale included 5 items, each starting with “Regarding insurance companies, how often do you find them…” and ending with the following: “requiring manual PA as opposed to electronic”; “requiring PA for generic medications”; “denying PA because of missing description of adverse effects”; and “denying PA because of dosing issues or formulation issues” (separately) (λavg = 0.64). Response options ranged from 1, “very rarely or never,” to 5, “extremely often.”

The communication subscale included a unit-weighted risk index for practice characteristics based on 4 items assessing communication with health plans (see eAppendix for details). These questions each started with “How often do you find that…” and ended with the following: “it is necessary to send additional clinical documentation,” “your software says a medication is denied by insurance and you later discover it was approved,” “you are not notified of a medication approval,” and “you are not notified of a medication denial” (λavg = 0.65). The same 5-point response format was used. These subscales were moderately correlated (r = 0.61; P < .001), suggesting that the 2 scales share some variance, yet they capture different elements of transacting with health plans.

We also created a unit-weighted risk index based on 4 items assessing practice characteristics that included, for the provider personally, the number of medication PAs that they completed in a week (6-point response scale ranging from “none” to “more than 50”); how many hours per week they spent completing PAs (5-point scale ranging from “less than 5 hours” to “more than 20 hours”); length of time they waited for PA decision from health plan (7-point scale ranging from “under 1 hour” to “more than 5 business days”); and length of time, from start to finish, they typically needed to complete PAs, including chart reviews and patient research (6-point scale ranging from “0-3 hours” to “more than 2 weeks”). Higher scores on the risk index reflect more perceived burdens of PA based on practice experiences.

Endogenous measures (outcomes). Respondents were asked in what percentage of cases they prescribe a different medication than initially planned due to PA delays (5-point scale ranging from “less than 10%” to “more than 50%”). A second item assessed how often prescribers avoided prescribing newer medications due to anticipated need for PA, even if the patients meet evidence-based guidelines, and a third item assessed how often respondents have modified a diagnosis to obtain PA (5-point response scale for both ranging from “very rarely” to “extremely often”).

Covariates. Additional information collected from respondents included gender, race, practice specialty, and practitioner type.

Statistical Analysis

We examined relations among gender, race (White vs other), practice specialty (psychiatrist vs other), and practitioner type (MD/DO vs other) using χ2 tests of independence. We used the Student t test to examine mean differences for continuous measures based on gender, practice specialty, and practitioner type. A path regression model was conducted using the Mplus statistical software program (Muthén & Muthén) with maximum likelihood estimation to examine associations between exogenous variables and PA-related changes in clinical decision-making.21 Model fit was evaluated based on the χ2 statistic (ratio of χ2:df < 5 equals good fit), comparative fit index22 and Tucker-Lewis index23,24 (numbers closer to 1.0 indicate better fit), and standardized root mean residual and the root mean error of approximation,25 both of which consider values less than 0.05 to indicate good fit.26

Adjusting for Nonresponse

To account for sampling bias, we used propensity weighting strategies27 to make the sample distributions better conform to the known characteristics of total sampled population.20 These methods are detailed in the eAppendix.


A total of 1173 respondents (1.2% response rate) generated 1147 usable surveys (at least 50% complete). The final sample was 49.6% female with a mean (SD) age of 50.5 (12.9) years (Table 1). A majority were MD/DO providers (76%; 69% MD and 7% DO), with smaller percentages of nurse practitioners (14%) and physician assistants (10%). A majority of the sample was White (67%), followed by Asian (18%). The largest proportion of survey respondents were in psychiatry, per the study design (44.9%), followed by internal medicine (18.0%), dermatology (13.1%), gastroenterology (8.0%), neurology (5.9%), oncology (5.8%), and rheumatology (4.4%).

Compared with respondents in other medical specialties, psychiatry respondents were older, had fewer providers in their practice, had smaller practice loads, encountered fewer medication PAs per week, spent fewer hours doing PAs, had lower waiting time for PA results, took less time to complete medication PAs, reported more communication issues with health plans, and perceived lower practice burden risk associated with PAs.

Regarding the 3 endogenous outcomes, 36% reported changing medications due to delays in medication more than 30% of the time, 26% reported modifying diagnoses to make the PA process easier, and 75% reported often avoiding new medications, even if evidence-based, to avoid potential difficulties with PA. By specialty, psychiatrists reported a lower percentage of cases in which they prescribed a different medication, but a higher percentage in which they avoided prescribing newer medications, even if evidence-based, and reported fewer cases in which they would modify a diagnosis.

The Figure presents the results of the path analysis. Parameter estimates and their CIs are contained in Table 2. After removing paths between exogenous and endogenous variables that were not significant, the 2 largest effects overall were identified with the paths from step therapy to “avoid prescribing newer medication” (β = 0.24; 95% CI, 0.188-0.301; P < .001) and from step therapy to “prescribe different medication” (β = 0.22; 95% CI, 0.166-0.279; P < .001). The next largest effects were from health plan insurance requirements to “avoid prescribing a newer medication” (β = 0.15; 95% CI, 0.074-0.221; P < .001) and then from the practice burden risk index to “modify a diagnosis” (β = 0.14; 95% CI, 0.086-0.204; P < .001). Overall, step therapy requirements and health plan insurance requirements were significantly related to all 3 endogenous outcomes. The remaining exogenous measures each had 2 significant paths to the endogenous outcomes. Although significant, the smallest effect overall was from patient load to “prescribe different medication” (β = 0.06; 95% CI, 0.014-0.112; P < .05).

The curved double-headed arrows on the left side of the Figure represent associations among the exogenous measures; those on the right side represent correlations among the endogenous outcome measures net of prediction. For the exogenous measures, there was a moderate association between health plan requirements and communication issues (r = 0.65; P < .001). Associations among the 3 outcome measures indicate that respondents linked “avoid prescribing newer medications” with “prescribe different medication” (r = 0.45; P < .001). The variance accounted for in the endogenous outcomes was R2 = 0.161 for “prescribe different medication,” R2 = 0.133 for “avoid newer medication,” and R2 = 0.103 for “modify diagnosis.” Additional model comparisons based on gender and provider function (MD/DO vs all others) indicated some small albeit significant differences in the model parameters. However, upon closer inspection, these differences were trivial and not clinically meaningful (results of these specific analyses are available from first author).

The comparison of psychiatrists (n = 515) with other specialty groups (n = 631) revealed a few minor differences in the magnitude of path regression coefficients. However, the absolute value of the differences in parameter estimates contrasting psychiatrists and other specialty groups was small (0.002), indicating relatively similar effects for the 2 groups. Further analyses suggest that the 2 groups can be considered randomly drawn from the same population. Detailed information on path model differences by specialty and related statistical analyses are detailed in the eAppendix.

Adjusting for Nonresponse

The results of the weighted vs unweighted analyses for the fully saturated model using the composite weight indicated some trivial differences in model parameter estimates. However, the overall mean difference (risk bias) in the regression parameters was Δβs = 0.0005 and the same for the ΔSEs was 0.004. Also worth noting, the mean difference in mean estimates for model variables between unweighted and weighted data was 0.001 (SE = 0.006). Taken together, the findings from the weighted and unweighted analyses suggest that nonresponse bias had minimal effect on the final results.


To our knowledge, this study is the first to provide clarity on factors that contribute to providers’ perceptions of medication PA burdens and the effect of these burdens on their prescribing behavior. Other nationwide surveys have reported that providers find PA burdensome, both personally and administratively, and that PA affects clinical decision-making. However, the current study drilled deeper into what these burdens involve and how these burdens affect clinical decision-making.

Overall, step therapy requirements had the largest effect on prescribing behavior, being significantly associated with all 3 prescribing behavior outcomes and most strongly with prescribing a different medication and avoiding prescribing a newer medication. This finding is not unexpected given that step therapy is an ordered protocol. In its most efficient form, information exchange for step therapy is based on available medication dispensing data and is fully automated. However, at times, step therapy can require providers to complete extensive documentation of prior treatment history, including adverse effects, intolerances, or medication failures.

Health plans vary not only in their PA policies but also in the specific documentation that they require. This adds a layer of administrative burden to providers who must keep up with the different requests from different health plans. Accordingly, we distinguished step therapy from other measures because it represents a different facet of provider-payer interaction specific to demonstrating a patient’s outcomes with prior treatment. Indeed, previous studies’ findings have shown considerable variation with respect to how providers view step therapy, specifically whether they feel it is a viable method to ensure clinically appropriate use of medication and for managing drug costs.28

Perceived practice burden and health plan communication issues were both associated with prescribing behavior and changing a diagnosis. Workplace perception of burdens was reflected in the PA volume and time spent on completing PAs or waiting from a response from health plans. The additional hours that accrue from processing PAs most likely represent what respondents view as “burdens” that have been noted in various professional surveys. Practices have tried to ease the PA burden through numerous methods, including hiring dedicated staff or outsourcing PA processes. In this study, we were able to show that these burdens are linked with changes in prescribing behavior. Interestingly, patient load, which reflects practice size, had a very small effect on prescribing a different medication and no effect on the other 2 markers of clinical decision-making. These null findings may indicate that respondents are not hampered by the size of their practice or how many patients they see on a weekly basis as much as by the insurance-specific requirements of medication PA.

Health plan requirements, such as the need to send additional information to the health plan, and communication issues, such as discrepancies between the PA decision communicated to the provider and how it is adjudicated at the pharmacy, also significantly influenced clinical decision-making. PA requirements and communication issues reduce patient care time and therefore lead respondents to seek “work-arounds” to avoid the PA process.

This study also identified modest differences between psychiatry and other specialties. Psychiatry respondents were less inclined to prescribe a different medication or avoid a newer medication in response to patient load and health plan requirements compared with other specialty groups. However, psychiatry respondents were more likely than other specialties to avoid a newer medication when faced with health plan communication issues. This may suggest that psychiatry respondents alter their medication prescribing behavior when they feel that communication with the health plan is too demanding. This occurs despite their having smaller patient loads and completing fewer medication PAs on average. Other differences between psychiatry respondents and other specialty groups may reflect the types of drugs being prescribed and the diagnoses relevant to the different specialty types.

Study findings related to PA requirements and communication issues highlight the fundamental desirability for standardization of PA criteria across health plans and continued development and adoption of digital solutions for PA processes. By reducing PA burdens, these efforts may reduce the need for respondents to seek work-arounds, which can lead to suboptimal drug treatment and poor patient outcomes. Yet in the absence of widespread solutions to the burden of PA, various stakeholders are advocating for local and federal legislation that will limit the scope of PA and step edits. In addition to numerous PA legislative initiatives by individual states, the US House of Representatives recently passed the Improving Seniors’ Timely Access to Care Act of 2021 (HR 3173), which establishes several requirements and standards relating to PA processes under Medicare Advantage plans. This trend is likely to continue should the burden of PA on providers and patients not be addressed through standardization, digital solutions, or other means.29


This study has certain limitations. Selection bias could be present because providers who have a strong opinion or complaint about the PA process might be more likely to respond. Cross-sectional data can be used to identify association but not causation. Survey response was low, and although expected given the online approach, it contributes to the risk of nonresponse bias. Despite this low response rate, the resulting sample demographic profile matched well to national numbers for all physicians. Although propensity weighting procedures were used to bring the sample data in line with population estimates, the pool of adjustment variables was limited. As a result, the final sample may contain moderate bias, which may affect generalizability of the findings.

We did not sample all medical specialties but focused on providers with high-volume medication PA, according to the CoverMyMeds 2019 ePA National Adoption Scorecard. Future studies may want to include additional specialties with different PA volume and experiences and widen the subject pool to include pharmacists, administrators, and support staff involved in the PA process. Finally, we did not qualify PA process burdens by payer type. Given Medicare Part D requirements for expedited processing of medication PAs, future studies may want to consider payer type and delve more deeply into how communication with health plans can be modified to reflect patient-centric concerns.


In this nationwide survey, respondents reported that they may alter clinical decisions to avoid PA requirements and related burdens, even in cases in which use of the PA medication was clinically appropriate. Processes that reduce the administrative burden of PA through improved communication and transparency as well as standardized documentation may help ensure that PA more seamlessly achieves its goals of safe and effective use of medications.

Author Affiliations: Department of Psychiatry (SGS, ML, HG) and Department of Pharmacy Practice and Science (CM-M), University of Nebraska Medical Center, Omaha, NE; Methodology Evaluation Research Core, Social & Behavioral Sciences Research Consortium, University of Nebraska, Lincoln (BH, PWH), Lincoln, NE; LARS Research Institute, Inc (LMS), Sun City, AZ; Prevention Strategies (LMS), Greensboro, NC.

Source of Funding: National Institute of Mental Health (NIMH) #1R41MH124600-01.

Author Disclosures: Dr Salzbrenner is the CEO of Breezmed LLC, an electronic prior authorization company (the University of Nebraska Medical Center Board of Regents owns the Breezmed intellectual property and licenses it to Breezmed); received grants funding this research from NIMH; and reports patents received and pending for “Healthcare Provider Interface for Treatment Option and Authorization.” Dr Lydiatt was paid via NIMH grant for the present study. Dr Helding was part of the team funded by an NIMH grant that conducted the research that led to this publication. The remaining authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (SS, ML, BH, LMS, PWH, CM-M); acquisition of data (ML, BH, LMS, PWH); analysis and interpretation of data (SS, BH, LMS, PWH, CM-M); drafting of the manuscript (SS, ML, BH, LMS, HG); critical revision of the manuscript for important intellectual content (SS, ML, BH, LMS, HG, CM-M); statistical analysis (BH, LMS, PWH); provision of patients or study materials (PWH); obtaining funding (SS, PWH); administrative, technical, or logistic support (BH, HG, PWH); and supervision (SS, LMS).

Address Correspondence to: Stephen G. Salzbrenner, MD, Department of Psychiatry, University of Nebraska Medical Center, 985578 Nebraska Medical Center, Omaha, NE 68198-5578. Email: Stephen.salzbrenner@unmc.edu.


1. 2018-2019 Academy of Managed Care Pharmacy Professional Practice Committee. Prior authorization and utilization management concepts in managed care pharmacy. J Manag Care Spec Pharm. 2019;25(6):641-644. doi:10.18553/jmcp.2019.19069

2. Happe LE, Clark D, Holliday E, Young T. A systematic literature review assessing the directional impact of managed care formulary restrictions on medication adherence, clinical outcomes, economic outcomes, and health care resource utilization. J Manag Care Spec Pharm. 2014;20(7):677-684. doi:10.18553/jmcp.2014.20.7.677

3. Park Y, Raza S, George A, Agrawal R, Ko J. The effect of formulary restrictions on patient and payer outcomes: a systematic literature review. J Manag Care Spec Pharm. 2017;23(8):893-901. doi:10.18553/jmcp.2017.23.8.893

4. 2019 AMA prior authorization (PA) physician survey. American Medical Association. June 2020. Accessed December 20, 2020. https://web.archive.org/web/20200625150228/https://www.ama-assn.org/system/files/2020-06/prior-authorization-survey-2019.pdf

5. Bhattacharjee S, Murcko AC, Fair MK, Warholak TL. Medication prior authorization from the providers perspective: a prospective observational study. Res Social Adm Pharm. 2019;15(9):1138-1144.

6. Jones LK, Ladd IG, Gregor C, Evans MA, Graham J, Gionfriddo MR. Understanding the medication prior-authorization process: a case study of patients and clinical staff from a large rural integrated health delivery system. Am J Health Syst Pharm. 2019;76(7):453-459. doi:10.1093/ajhp/zxy083

7. Kane L. Medscape national physician burnout & suicide report 2020: the generational divide. Medscape. January 15, 2020. Accessed March 8, 2022. https://www.medscape.com/slideshow/2020-lifestyle-

8. Abouzaid S, Jutkowitz E, Foley KA, Pizzi LT, Kim E, Bates J. Economic impact of prior authorization policies for atypical antipsychotics in the treatment of schizophrenia. Popul Health Manag. 2010;13(5):247-254. doi:10.1089/pop.2009.0063

9. Bianchini ML, Kenney RM, Lentz R, Zervos M, Malhotra M, Davis SL. Discharge delays and costs associated with outpatient parenteral antimicrobial therapy for high-priced antibiotics. Clin Infect Dis. 2020;71(7):e88-e93. doi:10.1093/cid/ciz1076

10. Desai S, Lo K, Nambudiri VE, Villa-Ruiz C, LaChance A, Vleugels RA. Challenges of securing insurance approval for oral tofacitinib for the treatment of alopecia areata: a multi-institution retrospective review.
Arch Dermatol Res. 2022;314(5):487-489. doi:10.1007/s00403-020-02158-y

11. Dudiak GJ, Popyack J, Grimm C, Tyson S, Solic J, Ishmael FT. Prior authorization delays biologic initiation and is associated with a risk of asthma exacerbations. Allergy Asthma Proc. 2021;42(1):65-71. doi:10.2500/aap.2021.42.200101

12. Wallace ZS, Harkness T, Fu X, Stone JH, Choi HK, Walensky RP. Treatment delays associated with prior authorization for infusible medications: a cohort study. Arthritis Care Res (Hoboken). 2020;72(11):1543-1549. doi:10.1002/acr.24062

13. Yu NY, Sio TT, Mohindra P, et al. The insurance approval process for proton beam therapy must change: prior authorization is crippling access to appropriate health care. Int J Radiat Oncol Biol Phys. 2019;104(4):737-739. doi:10.1016/j.ijrobp.2019.04.007

14. Mehta D, Davis M, Epstein AJ, Lee A. Impact of formulary restrictions on antiepileptic drug dispensation outcomes. Neurol Ther. 2020;9(2):505-519. doi:10.1007/s40120-020-00195-3

15. AMCP Electronic Prior Authorization Work Group. Proceedings of the AMCP Partnership Forum: NCPDP electronic prior authorization standards—building a managed care implementation plan. J Manag Care Spec Pharm. 2015;21(7):545-550. doi:10.18553/jmcp.2015.21.7.545

16. Hoffman S. Step therapy: legal and ethical implications of a cost-cutting measure. Case Western Research University School of Law. 2018. Accessed April 26, 2021. https://scholarlycommons.law.case.edu/faculty_publications/2009

17. Miller AP, Shor R, Waites T, Wilson BH. Prior authorization reform for better patient care. J Am Coll Cardiol. 2018;71(17):1937-1939. doi:10.1016/j.jacc.2018.03.465

18. Nayak RK, Pearson SD. The ethics of ‘fail first’: guidelines and practical scenarios for step therapy coverage policies. Health Aff (Millwood). 2014;33(10):1779-1785. doi:10.1377/hlthaff.2014.0516

19. Barnett BS, Bodkin JA. A survey of American psychiatrists concerning medication prior authorization requirements. J Nerv Ment Dis. 2020;208(7):566-573. doi:10.1097/NMD.0000000000001171

20. Physician specialty data report: number of people per active physician by specialty, 2019. American Association of Medical Colleges. Accessed January 14, 2022. https://www.aamc.org/what-we-do/mission-areas/health-care/workforce-studies/interactive-data/number-people-active-physician-specialty-2019

21. Muthén LK, Muthén BO. How to use a Monte Carlo study to decide on sample size and determine power. Struct Equ Modeling. 2002;9(4):599-620. doi:10.1207/S15328007SEM0904_8

22. Bentler PM. Comparative fit indexes in structural models. Psychol Bull. 1990;107(2):238-246. doi:10.1037/0033-2909.107.2.238

23. Kaplan D. Evaluating and modifying covariance structure models: a review and recommendation. Multivariate Behav Res. 1990;25(2):137-155. doi:10.1207/s15327906mbr2502_1

24. Tucker LR, Lewis C. A reliability coefficient for maximum likelihood factor analysis. Psychometrika. 1973;38(1):1-10. doi:10.1007/BF02291170

25. Steiger JH, Lind JC. Statistically based tests for the number of common factors. Paper presented at: Psychometric Society Annual Meeting; May 28, 1980; Iowa City, IA.

26. Hu Lt, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6(1):1-55. doi:10.1080/10705519909540118

27. Kalton G, Flores-Cervantes I. Weighting methods. J Off Stat. 2003;19(2):81-97.

28. Fischer MA, Kesselheim AS, Lu Z, Ross KM, Tessema FA, Avorn J. Physician perceptions of step therapy prescribing requirements. J Manag Care Spec Pharm. 2019;25(11):1210-1224. doi:10.18553/jmcp.2019.25.11.1210

29. Yang E, Yang S. Prior authorization: overwhelming burden and critical need for reform. JACC Case Rep. 2020;2(10):1466-1469. doi:10.1016/j.jaccas.2020.05.095

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