Working paper

Impact of X-waiver policies on buprenorphine prescribing for opioid use disorder in the United States, 2019-2025

This is part of a working paper on the impact of X-waiver policy changes on buprenorphine prescribing for treatment of opioid use disorder in the United States. I presented our preliminary findings at the Society for Medical Decision Making (SMDM) Annual Meeting in Oslo, Norway on 29 June 2026.

(Updates are expected)


Objectives

The primary objective evaluated the impact of X-waiver policies on the number and period prevalence of individuals with OUD who were prescribed buprenorphine from 2019 to 2025.

The secondary objective evaluated the impact of X-waiver policies on buprenorphine prescribing across social vulnerability index (SVI) quantiles from 2019 to 2025.

 

Methods

Study design

An interrupted time series analysis (ITSA) was used to evaluate the trends and level changes associated with X-waiver policies on buprenorphine prescribing for adults with opioid use disorder (OUD) in the United States (US). Two X-waiver policy interventions were evaluated with the X-waiver relaxation policy occurring in April 2021 (time = 29) and the X-waiver elimination policy occurring in January 2023 (time = 50). The period before the first X-waiver policy change is labelled “Segment 1,” the period after the first X-waiver policy and before the second X-waiver policy is labelled “Segment 2,” and the period after the second X-waiver policy is labelled “Segment 3.” We used 30-day time intervals instead of monthly time intervals to establish a consistent replicable dataset. Consequently, our study’s time horizon ended on 25 December 2025. The online supplement provides a visualization of the ITSA framework.

 

Sample and Social Vulnerability Index

Adults (>=18 years old) with a diagnosis of opioid use disorder were included for analysis. Individuals with opioid use disorder were categorized according to their social vulnerability index (SVI) quantiles. SVI index was based on data from the Centers for Disease Control and Prevention (CDC).[1,2] Epic Cosmos assigned SVI score to individuals using Federal Information Processing Standards (FIPS) codes. Individuals are considered least socially vulnerable if they are in the first quantile (SVI =1) and most socially vulnerable if they are in the fourth quantile (SVI = 4).

 

Data source

Epic Cosmos was used to gather data on the number of buprenorphine prescriptions prescribed to unique individuals with opioid use disorder between 01 January 2019 to 25 December 2025. Epic Cosmos is a collaborative network of health systems that aggregates data from institutions that use the Epic Electronic Health Record System for approximately more than 300 million unique individuals.[3,4] Opioid use disorder diagnosis was determined based on ICD10 codes.

 

Outcome variable

Buprenorphine prescriptions dispensed were determined using Epic Cosmos. Dispensed buprenorphine prescriptions are based on Epic’s Medication (ERX) master file, which is constructed using from third-party medication databases such as First DataBank and Medi-Span in the United States.

We opted to measure the number of individuals with OUD diagnosis prescribed buprenorphine and the period prevalence of individuals with OUD prescribed buprenorphine per 1000 persons due to the changing number of individuals with OUD in the US population.

The period prevalence (number of individuals with OUD prescribed buprenorphine per 1000 persons) was estimated as:

 

Statistical analysis

Descriptive analyses on the number of individuals with OUD prescribed buprenorphine and the number of individuals with a diagnosis of OUD was performed from 2019 to 2025.

In the primary analyses, two ITSAs were constructed using (1) the number of individuals with OUD who were prescribed buprenorphine and (2) period prevalence of individuals with OUD who were prescribe buprenorphine as outcomes.

For the first outcome, a linear mixed effects model with a random intercept was used to evaluate the number of buprenorphine prescriptions prescribed during the study time horizon and their associations with X-waiver policies changes.

For the second outcome, a negative binomial model was used to evaluate the period prevalence of buprenorphine prescriptions prescribed during the study time horizon and their associations with X-waiver policies changes. We used the log of the population size of adults with opioid use disorder as the offset term in the model.

For both models, the mean monthly changes (slopes) for each period segments and the level changes at each period when the X-waiver policies were implemented were estimated. For the primary analyses, differences between slopes between each segment were estimated. Robust standard errors were estimated for all measures except for differences in slopes, which were estimated using non-parametric bootstrap with 1000 replications. Stata does not allow for estimation of standard errors in terms of the period prevalence; hence, bootstrap methods were used. Results are presented as the mean change along with their corresponding 95% confidence intervals (CI).

For the secondary analyses, social vulnerability index (SVI) quantiles were used as the grouping variable to evaluate the impact of X-waiver policy changes on buprenorphine prescribing among adults with opioid use disorder. Interaction terms between the SVI quantiles (grouping variable) and the period when the X-waiver policy was relaxed (time = 29) and eliminated (time = 50) were used to compare the differences in buprenorphine prescribing between individuals in different SVI quantiles. Similar approaches were applied to the SVI quantiles analyses as in the primary analyses.

Statistical significance was defined as a two-tailed alpha < 0.05. All analyses were performed using Stata SE 18 (StataCorp, LLC, College Station, TX).

 

Results

By December 2025, 1,213,349 individuals had a diagnosis of OUD compared to 444,739 in January 2019—a 173% relative increase. The total number of individuals with OUD who received buprenorphine increased from 71,508 in January 2019 to 319,371 in December 2025—a 347% relative increase.

 

Primary analyses

In the primary analyses, there was a significant increase in the number of individuals with OUD who received buprenorphine from Segment 1 to Segment 2 and from Segment 2 to Segment 3 (Table 1, Figure 1A). Between Segment 1 to Segment 2, the number of individuals with OUD who received buprenorphine increased by 246 prescriptions (95% CI: 135, 357). Between Segment 2 to Segment 3, the number of individuals with OUD who received buprenorphine increased by 515 prescriptions (95% CI: 413, 617). Immediately after the X-waiver relaxation policy was implemented, there was a significant increase in the number of individuals with OUD who received buprenorphine (+1612; 95% CI: 133, 3091). Similarly, immediately after the X-waiver elimination policy was implemented, there was a significant increase in the number of individuals with OUD who received buprenorphine (+1454; 95% CI: 13, 2894).

Conversely, there was a significant decrease in the period prevalence of individuals with OUD who received buprenorphine from Segment 1 to Segment 2 and from Segment 2 to Segment 3 (Table 1, Figure 1B). Between Segment 1 to Segment 2, the number of individuals with OUD who received buprenorphine decreased by 1.60 prescriptions per 1000 persons (95% CI: (-1.68, -1.51). Between Segment 2 to Segment 3, the number of individuals with OUD who received buprenorphine decreased by 0.27 prescriptions per 1000 persons (95% CI: -0.34, -0.20). Immediately after the X-waiver relaxation policy was implemented, there was a significant decrease in the period prevalence of individuals with OUD who received buprenorphine (-2.88; 95% CI: -4.34, -1.42). However, immediately after the X-waiver elimination policy was implemented, there was a significant increase in the period prevalence of individuals with OUD who received buprenorphine (+1.21; 95% CI: 0.27, 2.15).

 

Secondary analyses

In the secondary analyses, the number of individuals with buprenorphine was greater among those with greater social vulnerability compared to those with lesser social vulnerability (Table 2, Figures 2A and 2B). Significant increases in the number of individuals with OUD who received buprenorphine were observed for all SVI quantiles (Figure 2A). Among the least vulnerable (SVI = 1) the number of individuals with buprenorphine increased from a rate of 307 per month (in Segment 1) to a rate of 361 per month (in Segment 2), a relative increase of 17.6% [(361 – 307) / 307]. Similarly, among the most vulnerable (SVI = 4), the number of individuals with buprenorphine increased from a rate of 486 per month (in Segment 1) to 526 per month (in Segment 2), a relative increase 8.3% [(526 – 486) / 486]. This pattern was observed when individuals transitioned from Segment 2 to Segment 3 (Table 2); there was a 7.8% relative increase [(389 - 361) / 361] among the least vulnerable (SVI = 1), and a 41.3% relative increase [(743 - 526) / 526] among the most vulnerable (SVI = 4).

 

However, when reporting on period prevalence outcomes, the trends were mostly reversed. Significant decreases in the period prevalence of individuals with OUD who received buprenorphine were observed for most SVI quantiles (Figure 2B). Among the least vulnerable (SVI = 1) the period prevalence of individuals with buprenorphine decreased from a monthly rate of 3.62 per 1000 persons (in Segment 1) to a monthly rate of 1.63 per 1000 persons (in Segment 2), a relative decrease of 55.0% [(1.63 – 3.62) / 3.62]. Similarly, among the most vulnerable (SVI = 4), the period prevalence of individuals with buprenorphine decreased from a monthly rate of 4.01 per 1000 persons (in Segment 1) to 1.44 per 1000 persons (in Segment 2), a relative decrease 64.1% [(1.47 – 4.01) / 4.01]. Between Segment 2 and Segment 3, there was a 41.7% relative decrease in the monthly rate among the least vulnerable individuals (SVI = 1) with OUD (Table 2). Individuals in SVI quantiles 2 and 3 followed similar patterns as individuals in SVI quantile 1. However, among the most vulnerable (SVI = 4), there was a 18.4% relative increase [(1.74 – 1.47) / 1.47] in the monthly rate when transitioning from Segment 2 to Segment 3.

 

Discussion

The impact of the X-waiver policies has had mixed effects on buprenorphine prescribing. When reporting the impact of these X-waiver policies on the number of individuals with OUD who were prescribed buprenorphine, the trends indicated that there was a significant increase. However, when reporting on the period prevalence of individuals with OUD who were prescribed buprenorphine, the trends were mostly negative. These apparent differences can be explained by the type of outcomes used in our analysis.

Period prevalence takes into consideration the population at risk, which in our case were those individuals with OUD; whereas simply relying on the number of individuals with OUD who were prescribed buprenorphine does not capture this changing trend in the population at risk. In our descriptive analysis, we reported that the population of individuals with OUD increased at a greater rate than individuals who were prescribed buprenorphine, which has had an impact the observed positive trends of the numerator. Although the number of individuals with OUD who were prescribed buprenorphine increased, this pattern reverses when the total population at risk was incorporated. Decision makers can use both outcomes to influence policy; however, caution should be exercised when the denominator undergoes substantial changes across time.

Additionally, the opioid crisis has resulted in a large number of new OUD diagnosis, which has overwhelmed the capacity of public health efforts to improve access to essential treatments such as buprenorphine.[5–7] These findings highlight the challenges in meeting the high demand of the OUD population for buprenorphine. As long as the OUD population continues to increase, any positive trends in the number of individuals with OUD who are prescribed buprenorphine will be attenuated. Therefore, it is essential that decision makers use an outcome measure that captures both the change in the number of individuals with OUD who were prescribed buprenorphine and the prevalence individuals with OUD who were prescribed buprenorphine for resource planning and understanding the burden of the disease.

In our analyses, we identified certain patterns in buprenorphine prescribing across SVI quantiles. When viewing the number of individuals with OUD who were prescribed buprenorphine, we observed that those in the lowest social vulnerability index (SVI = 1) has the least amount of buprenorphine compared to those in the highest social vulnerability index (SVI = 4). This was counter to our expectations that individuals in a socially vulnerable environment would have lower opportunities for access to medication treatment for opioid use disorder. Yang and colleagues reported that among older adults (>= 65 years), the number of individuals with OUD is greater in counties with high social vulnerability compared to counties with low social vulnerability.[8] Similarly, Joudrey and colleagues reported that counties with greater social vulnerability had limited access to buprenorphine and other medications for OUD treatment.[9] Lastly, other community-level factors such as high provider density and high mental health service availability interact with SVI to improve buprenorphine retention.[10]

Previous studies are mixed when it comes to the impact of the X-waiver policies on buprenorphine prescribing. Stone and colleagues reported that the X-waiver elimination was associated with increased clinicians prescribing buprenorphine but an overall decrease in patients receiving buprenorphine.[11] Similarly, Chua, Bohnert, and Nguyen reported a significant increase in the number of buprenorphine prescribers, but a limited impact on buprenorphine prescriptions.[12] Conversely, Tuan and colleagues reported that elimination of the X-waiver was associated with a 14% increase in the odds of buprenorphine initiation after a new OUD diagnosis.[13] We speculate that the differences in these findings may depend on the type of patients receiving buprenorphine and the specialties of their providers. For instance, Stone and colleagues reported that there was an overall decrease in buprenorphine prescribing by all physician groups after the X-waiver except for behavioral health physicians.[11]

 

Limitations

This study has several limitations. First, the number and cumulative prevalence of individuals with OUD who were prescribed buprenorphine were based on a single electronic health record system that may not be representative of the whole US population. Epic Cosmos only captures data on patients who engaged with healthcare systems that use its Epic Electronic Health Record System. Thus, it does not capture other patients outside this platform, and any findings may not be reflective of the general US population. Second, the data are an aggregate of individuals with OUD and do not include patient-level characteristics. Consequently, we were unable to control for patient-level characteristics in our models, which introduces potential confounding issues. Additionally, since the data are aggregated for the US, ecological fallacy could be present.[14,15] To address this, we grouped the data into SVI quantiles to observe any difference in community-level vulnerabilities as a secondary aim. However, this strategy only allows us to stratify the findings across SVI quantiles and does not adjust for potential confounding. Lastly, OUD diagnosis is challenging to diagnose and could lead to misclassification bias. Epic Cosmos used ICD10 diagnostic codes to capture OUD diagnosis, which has been reported to be insufficient in properly identifying OUD and could result in potential misclassification.[16]

 

Conclusions

Overall, the X-waiver policies appeared to have the intended effect of increasing buprenorphine prescribing in terms of raw numbers, but the change in the period prevalence of individuals with OUD who received buprenorphine was not a great in the periods after the X-waiver policies compared to before. Selection of outcomes can influence interpretations of findings; thus, it is recommended that presentation of findings include all outcomes.  

 

References

1.         Centers for Disease Control and Prevention and Agency for Toxic Substances and Disease Registry. CDC/ATSDR Social Vulnerability Index (CDC/ATSDR SVI). June 14, 2024. Accessed October 11, 2024. https://www.atsdr.cdc.gov/placeandhealth/svi/index.html

2.         Flanagan BE, Gregory EW, Hallisey EJ, Heitgerd JL, Lewis B. A social vulnerability index for disaster management. Journal of Homeland Security and Emergency Management. 2011;8(1). doi:10.2202/1547-7355.1792

3.         Tarabichi Y, Frees A, Honeywell S, et al. The Cosmos Collaborative: A Vendor-Facilitated Electronic Health Record Data Aggregation Platform. ACI open. 2021;5(1):e36-e46. doi:10.1055/s-0041-1731004

4.         Noel A, Bartelt K. Cosmos: Real-World Data Powered by the Healthcare Community. Journal of the Society for Clinical Data Management. 2023;3(S1). doi:10.47912/jscdm.246

5.         Lee YK, Gold MS, Blum K, Thanos PK, Hanna C, Fuehrlein BS. Opioid use disorder: current trends and potential treatments. Front Public Health. 2024;11:1274719. doi:10.3389/fpubh.2023.1274719

6.         Wang S, He Y, Huang Y. Global, regional, and national trends and burden of opioid use disorder in individuals aged 15 years and above: 1990 to 2021 and projections to 2040. Epidemiol Psychiatr Sci. 2025;34:e32. doi:10.1017/S2045796025100085

7.         Bergeria CL, Strain EC. Opioid Use Disorder: Pernicious and Persistent. Am J Psychiatry. 2022;179(10):708-714. doi:10.1176/appi.ajp.20220699

8.         Yang TC, Kim S, Matthews SA, Shoff C. Social vulnerability and the prevalence of opioid use disorder among older Medicare beneficiaries in US counties. J Gerontol B Psychol Sci Soc Sci. Published online October 3, 2023:gbad146. doi:10.1093/geronb/gbad146

9.         Joudrey PJ, Kolak M, Lin Q, Paykin S, Anguiano V Jr, Wang EA. Assessment of community-level vulnerability and access to medications for opioid use disorder. JAMA Network Open. 2022;5(4):e227028. doi:10.1001/jamanetworkopen.2022.7028

10.       Jaimes-Buitron PA, Zhang K, Gong Y, Guo Y, Bauer C, Vivas-Valencia C. Community-level factors influencing the duration of buprenorphine treatment in individuals with opioid use disorder: a cohort study using US longitudinal claims data. bmjph. 2025;3(2). doi:10.1136/bmjph-2025-003767

11.       Stone EM, Xie F, Miles J, Samples H, Olfson M, Crystal S. Buprenorphine Dispensation After X-Waiver Elimination by Clinician Specialty. American Journal of Preventive Medicine. 2025;69(5):108055. doi:10.1016/j.amepre.2025.108055

12.       Chua KP, Bicket MC, Bohnert ASB, Conti RM, Lagisetty P, Nguyen TD. Buprenorphine Dispensing after Elimination of the Waiver Requirement. New England Journal of Medicine. 2024;390(16):1530-1532. doi:10.1056/NEJMc2312906

13.       Tuan WJ, Park S, Altaf S, Zgierska AE. Assessing the Initial Impact of X-Waiver Elimination on Buprenorphine Prescribing for Opioid Use Disorder. Subst Use Addctn J. Published online January 30, 2026:29767342251414541. doi:10.1177/29767342251414541

14.       Piantadosi S, Byar DP, Green SB. The ecological fallacy. Am J Epidemiol. 1988;127(5):893-904. doi:10.1093/oxfordjournals.aje.a114892

15.       Robinson WS. Ecological Correlations and the Behavior of Individuals. American Sociological Review. 1950;15(3):351-357. doi:10.2307/2087176

16.       Lagisetty P, Garpestad C, Larkin A, et al. Identifying individuals with opioid use disorder: Validity of International Classification of Diseases diagnostic codes for opioid use, dependence and abuse. Drug Alcohol Depend. 2021;221:108583. doi:10.1016/j.drugalcdep.2021.108583

Is there a Principal-Agent problem between large retail-chain pharmacy corporations and community pharmacists?

Is there a Principal-Agent problem between large retail-chain pharmacy corporations and community pharmacists?

 

Date: 31 October 2025

 

(This is part of a working paper. Updates are expected.)

 

The principal-agent problem is a type of market failure involving a misalignment of incentives between the owners of a company or firm (principals) and the employees that they hire (agents).[1] The conflict arises when the agents (or employees) do not act in accordance with the interest or expectation of the principals (firm). Principals delegate the agents to represent their interest, oftentimes providing them with decision-making capabilities. However, the principals cannot control the agents, nor do they have the same level of information as the agents (asymmetric information). This conflict can lead to non-Pareto optimal conditions, which would result in inefficiency and harm to both the principals and agents.

Misalignment of incentives is a central issue between large retail-chain pharmacy corporations and community pharmacists. Using the principal-agent problem framework, large retail-chain pharmacies (principals) hire community pharmacists (agents) to represent their interest to maximize their shareholder value (e.g., increased profits). However, community pharmacists are not interested in maximizing profits for the large retail-chain pharmacy corporations. Rather, they are interested in providing high-quality care to their patients and community, which is in direct conflict with the expectations of the large retail-chain pharmacy corporations. This misalignment of interests between the large retail-chain pharmacy corporations and community pharmacists has been responsible for unsafe working conditions, increased medication errors, pharmacist burnout and retention issues, and corporate bankruptcy.

This article will attempt to establish the misalignment between the large retail-chain pharmacy corporations and community pharmacists and offer some recommendations to address this principal-agent problem.

 

Misalignment of Incentives

A substantial number of licensed pharmacists work in the community pharmacy setting, which includes independent pharmacies, small and large retail chain pharmacies, supermarkets, mass merchandizers, and health-system retail pharmacies. In a 2024 work survey of pharmacists in the United States (US), 59.1% of pharmacists were working in the community setting, and, among those, 22.4% were working in large chain pharmacies.[2]

Large-chain retail pharmacy corporate leaderships are focused on maximizing shareholder value and downplaying the professional and ethical responsibilities of the pharmacist working in the community setting. Community pharmacists, on the other hand, often are not interested in these corporate incentives (e.g., increased earnings per share) and are focused on upholding their professional and ethical responsibilities to providing the highest quality care to their patients and community. Large retail chain pharmacy corporations expect their pharmacists to not only perform their duties as community pharmacists but also expect them to meet productivity performance measures such as filling and dispensing quotas. According to the California Code, Business and Professions Code,  a “quota” is defined as a fixed number or formula related to prescriptions filled, services rendered to patients, programs offered to patients, and revenue obtained as a means to evaluate or measure the performance of the pharmacy staff.[3] This practice of using quotas to evaluate the productivity performance of community pharmacists has resulted in unsafe working conditions, increased medication errors, and increased burnout and retention issues.[4] According to a 2020 survey conducted among community pharmacist in Ohio, 82% of respondents reported either “agreed” or “strongly agreed” with the statement, “I feel pressure by my employer or supervisor to meet standards or metrics that may interfere with safe patient care.”[5]

The burden of meeting productivity performance-based measures such as prescription quotas contribute to an unsafe work environment where medication errors can occur. Compound this with an increased workload and lack of proper staffing and resources, and the conditions for a serious medication error increase the probability of a serious event. In a 2024 workload report, 91% of pharmacists working at chain pharmacies reported having “high” or “excessively high” workload compared to 55% of pharmacists working at independent pharmacies.[2] Similar findings were reported in 2019, suggesting that this practice and culture has been normalized for many years. Additionally, 54.9% of community pharmacists responded that they “disagreed” or “strongly disagreed” with the statement that “My organization is willing to extend resources to help me perform my job to the best of my ability,” and 56.3% of community pharmacists reported that they “disagreed” or “strongly disagreed” with the statement, “My organization is committed to employee health and well-being.”

Another factor that complicates the principal-agent problem is the involvement of the pharmacy benefits management (PBM) that do not provide pharmacies with enough compensation for filling prescriptions.[6] The reimbursement rate for each prescription fill is often less than the cost of supply (e.g., label and bottle), which means that the community pharmacies are losing revenue with each fill. This has dangerous consequences as the large, chain retail pharmacy corporations will try to maximize profits by imposing unreasonable prescription fill quotas and decreasing staffing levels, thereby creating an unsafe work environment and causing pharmacist burnout.[7,8] Moreover, the lack of proper reimbursements for prescription fills has led to many pharmacies closing their doors. According Guadamuz and colleagues, 29.4% of pharmacies that were opened between 2010-2020 closed their doors in 2021.[9] Further, Rite-Aid, one of the largest retail-chain pharmacies in the US filed for bankruptcy in October 2025, partly due to poor reimbursements from PBMs.[10]

 

Recommendations

Given the market failure associated with this principal-agent problem, governmental action in the form of legal policy may be needed. For instance, California passed a law prohibiting chain community pharmacies from using quotas to measure the performance of pharmacy staff to address the concerns of rising medication errors due to unsafe work environments in 2021.[3,11] Shortly afterwards in October 2022, Walgreens, one of the largest retail-chain pharmacies in the US announced that they would abandon the use of quotas for productivity performance-based evaluations of pharmacy staff.[12] However, these changes have not eliminated the use of these productivity performance-based metrics, and misaligned incentives between large retail-chain pharmacy corporations and community pharmacists have continued to result in unsafe environments, increased medication errors, and increased burnout and retention issues. 

To address unsafe working conditions due to understaffing and resulting in increased medication errors, California Governor Gavin Newsom signed into law the “Stop Dangerous Pharmacies Act” (AB 1286) in 2024.[13] This bill, the first of its kind, requires large retail-chain pharmacies to staff their pharmacies with at least one pharmacy technician or clerk to assist with pharmacy-related services and to report all medication errors. Although current California law requires that pharmacies include a pharmacy staff to assist the pharmacist, this had not been enforced. The new bill will empower pharmacists to make staffing decisions instead of the large retail-chain pharmacy corporations thereby improving working conditions and reducing medication errors.   

In October 2025, California Governor Gavin Newsom signed into law Senate Bill 41 (SB-41) that impose upon pharmacy benefits managements (PBMs) a fiduciary duty to their clients, ban spread pricing (profiting from the difference in costs between health plans and pharmacies), and providing fair reimbursement to pharmacies.[14] This bill has important implications for pharmacies by improving their reimbursement rates for each prescription filled thereby allowing for improved a profit margin. For community pharmacists, this could lead to reduced burden from the large retail-chain pharmacy corporations to increase revenue through unreasonable prescription fill quotas. However, future evaluation will need to assess the effectiveness of this policy on the principal-agent problem between large retail-chain pharmacies and community pharmacists.   

 

Conclusions

In this brief article about the principal-agent problem between large retail-chain pharmacy corporations and pharmacists, I try to establish the framework for potential misaligned incentives leading to negative consequences. Pharmacists (agents) are delegated to represent the interests of the large retail-chain pharmacy corporations (principals), but their incentive to their professional oath and service to community are in direct conflict with the rent-seeking behaviors of large retail-chain pharmacy corporations. This conflict inevitably led to unsafe working conditions, increased medication errors, and increased burnout and retention issues. Recently legislation has been developed to address the issues of staffing, medication errors, and quotas to improve patient and community safety. However, the impact of these legal interventions has yet to be determined.

References

  1. Jensen MC, Meckling WH. Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics. 1976;3(4):305-360. doi:10.1016/0304-405X(76)90026-X

  2. Mott DA, Bakken BK, Nadi S, et al. Final Report of The 2024 National Pharmacist Workforce Survey. Pharmacy Workforce Center; 2024. https://www.aacp.org/article/national-pharmacist-workforce-studies

  3. California Code, Business and Professions Code - BPC § 4113.7.; 2023. Accessed October 31, 2025. https://codes.findlaw.com/ca/business-and-professions-code/bpc-sect-4113-7/

  4. O’Donnell J, Vogenberg FR. Imperatives for Oversight Of Professional Personnel. P T. 2015;40(11):744-774.

  5. Francis SG. Pharmacists’ Perceptions About the Effect of Work Environment Factors on Patient Safety in Large-Chain Retail Pharmacies. J Pharm Technol. 2022;38(6):376-378. doi:10.1177/87551225221116000

  6. Joyce G. The cost of misaligned incentives in the pharmaceutical supply chain. Health Aff Sch. 2025;3(7):qxaf126. doi:10.1093/haschl/qxaf126

  7. Mott D, Doucette W, Schommer J, Gaither CA. Opinion: Why your chain-store pharmacist is so unhappy. CNN. December 13, 2023. Accessed October 4, 2025. https://www.cnn.com/2023/12/13/opinions/pharmacy-working-conditions-mott-doucette-schommer-gaither

  8. Coz EL. Prescription for disaster: America’s broken pharmacy system in revolt over burnout and errors. USA TODAY. Accessed October 4, 2025. https://www.usatoday.com/story/news/investigations/2023/10/26/pharmacy-chains-dangerous-conditions-medication-errors/71153960007/

  9. Guadamuz JS, Alexander GC, Kanter GP, Qato DM. More US Pharmacies Closed Than Opened In 2018–21; Independent Pharmacies, Those In Black, Latinx Communities Most At Risk. Health Affairs. 2024;43(12):1703-1711. doi:10.1377/hlthaff.2024.00192

  10. Case Summary: Rite Aid Chapter 11. Bondoro. May 10, 2025. Accessed October 31, 2025. https://bondoro.com/rite-aid/

  11. SB 362- CHAPTERED. Accessed October 31, 2025. https://leginfo.legislature.ca.gov/faces/billTextClient.xhtml?bill_id=202120220SB362

  12. Kaplan A. Walgreens will stop judging its pharmacy staff by how fast they work. NBC News. October 28, 2022. Accessed October 31, 2025. https://www.nbcnews.com/health/health-care/walgreens-will-stop-judging-pharmacy-staff-fast-work-rcna54297

  13. Stop Dangerous Pharmacies Act is signed into California Law | Official Website - Assemblymember Matt Haney Representing the 17th California Assembly District. Accessed October 31, 2025. https://a17.asmdc.org/press-releases/20231009-stop-dangerous-pharmacies-act-signed-california-law

  14. California SB41 | 2025-2026 | Regular Session. LegiScan. Accessed October 31, 2025. https://legiscan.com/CA/bill/SB41/2025