NUR 7069 Module 4 Structural Competency Paper: Implicit Bias, Structural Racism and a Clinic No-Show Policy Example

Three Strikes and Out: What a No-Show Policy Reveals About Bias and Structure at an Asbury Park Clinic

Student Name

M.S. in Nursing, Family and Psychiatric Mental Health Nurse Practitioner, Thomas Edison State University

NUR-7069: Professional Identity Development for Nurse Practitioners

Mentor Name

Month Day, Year

Three Strikes and Out: What a No-Show Policy Reveals About Bias and Structure at an Asbury Park Clinic

The Pattern

The clinic and its patients are invented for this paper. The clinic in Asbury Park serves about 9,000 patients, roughly half of them Black or Hispanic, many on Medicaid. Its policy, adopted years ago to reduce wasted appointments, discharges any patient who misses three scheduled visits in twelve months without calling to cancel. As a nurse practitioner there, I noticed that two of my patients with poorly controlled diabetes had been discharged under the rule. Curious, I asked the practice manager for a year of data. Of 214 patients discharged, 71 percent were Black or Hispanic, compared with about half of the clinic's patients overall. Most had Medicaid, and many lived in neighborhoods without direct bus service to the clinic.

My first reaction was defensive. I do not think of myself or my colleagues as biased. This paper examines whether bias, structure or both explain the pattern.

Implicit Bias

Hall et al. (2015) pulled together studies that measured implicit racial and ethnic bias in health professionals. Across 15 studies, most found low to moderate levels of implicit bias against people of color, similar to the general population. Implicit bias was significantly related to the quality of patient and provider interactions in several studies and to treatment decisions, adherence and outcomes in some, though not all, of the studies that examined them. The authors called for more research on how bias affects care and for training and systems that reduce its effects.

I cannot rule out bias in how staff responded to missed visits. Some patients called the day before were given new appointments; others were marked as no-shows without a call back, and the decision depended on who answered the phone. But bias alone does not explain why patients without bus service, with shift work and with prepaid phones that ran out of minutes missed visits more often.

Structural Racism

Bailey et al. (2017) wrote that structural racism is the full range of ways a society produces racial discrimination, through systems (housing, schooling, jobs, pay, benefits, credit, media, health care, policing and courts) that prop each other up. In their account, those systems keep discriminatory beliefs and an unequal share of resources in place, and health suffers as a result. They pointed to residential segregation as a key mechanism and called for interventions that address structures, not only individual attitudes.

The clinic's discharge pattern fits that description. Neighborhoods with less bus service in Asbury Park and surrounding towns are those shaped by decades of housing policy. Patients in hourly jobs without paid leave cannot easily leave work for a morning appointment. A policy that treats every missed visit the same way turns those structural differences into a racial disparity in who loses care.

Structural Competency

Metzl and Hansen (2014) proposed that clinicians develop structural competency, the trained ability to see how social, economic and political structures shape symptoms, diagnoses and health. They described five skills: recognizing the structures that shape clinical interactions; developing a language of structure beyond the clinic; rearticulating cultural presentations in structural terms; observing and imagining structural interventions; and developing structural humility.

My Own Part

Structural competency does not excuse individual bias, and I looked at my own charting. In three of the discharged patients' records, I had written "noncompliant" without recording why visits were missed. Those words follow a patient from clinic to clinic and shape how the next clinician sees them. I now record the barrier a patient describes, such as no ride or a changed work shift, and I avoid labels that place the whole problem in the patient. The change is modest, yet entirely within my control, and it applies the second skill Metzl and Hansen describe, finding language that names structures rather than blaming people.

Asking Patients

I called eight discharged patients, with the practice manager's approval, to ask why they had missed visits. Six named transport or work. One had moved and not received reminder letters. One said she stopped coming because she felt judged about her weight. Their answers matched the structural explanation, and one pointed to the kind of bias Hall et al. described.

The Revised Policy

The clinic leadership accepted a revised policy proposal. Discharge for missed visits ends. Instead, after two missed visits, a community health worker calls to ask about barriers and offers solutions: Medicaid transport booking, telehealth for visits that do not need an examination and appointments early in the morning or late in the afternoon. Reminders go by text and voice in the patient's language, two days and two hours before. Each week, the clinic holds same-day slots so that patients who miss a visit can come when they can. All staff complete a short training on bias that uses the clinic's own data, and the rule for rescheduling callers becomes the same for everyone.

Measures

The clinic will track missed visit rates by race, ethnicity, payer and neighborhood, the number of patients lost to follow-up, diabetes and blood pressure control by group and patient experience scores, reported quarterly.

Limits

Data on race and ethnicity at registration are incomplete. Eight phone calls are not a study. Same-day slots and community health worker time cost money, which the clinic will need to protect in its budget. Bias training has limited evidence of lasting effect, so the plan relies mainly on structural changes.

Conclusion

A no-show policy that looked neutral produced unequal results. Evidence on implicit bias explained part of the pattern, and the concept of structural racism explained more. Structural competency gave me a way to turn discomfort into a different rule, one that keeps patients in care instead of discharging them.

References

Bailey, Z. D., Krieger, N., Agénor, M., Graves, J., Linos, N., & Bassett, M. T. (2017). Structural racism and health inequities in the USA: Evidence and interventions. The Lancet, 389(10077), 1453-1463. https://doi.org/10.1016/S0140-6736(17)30569-X

Hall, W. J., Chapman, M. V., Lee, K. M., Merino, Y. M., Thomas, T. W., Payne, B. K., Eng, E., Day, S. H., & Coyne-Beasley, T. (2015). Implicit racial/ethnic bias among health care professionals and its influence on health care outcomes: A systematic review. American Journal of Public Health, 105(12), e60-e76. https://doi.org/10.2105/AJPH.2015.302903

Metzl, J. M., & Hansen, H. (2014). Structural competency: Theorizing a new medical engagement with stigma and inequality. Social Science & Medicine, 103, 126-133. https://doi.org/10.1016/j.socscimed.2013.06.032