MEASUREMENT TOOLS

Patrick Pizarro

YesterdayJun 7 at 6:26am

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Practice Experience – Week 2

For this week’s post, this writer conducted a brief informal interview with the Anson Correctional Institution’s Nursing Supervisor and the Lead Nurse (also serving as this writer’s preceptor for the duration of this course). The topic discussed was access to care: specifically, the typical wait times for offenders at said institution requesting basic medical attention, or as it is termed a “sick call”.

The basic yardstick for measuring how the organization is doing in regard to timely access to care consists of the offender herself initiating a request by completing, dating, and submitting a sick call request form and placing it in her respective housing unit’s mail box. Medical staff, once daily, conduct a “mail pick up” collecting the forms and bringing them back to the medical records department where they are scanned and scheduled.

The time it takes between the initial form submission and the day in which the offender is seen for their sick call appointment varies widely and is subject to several variables, but it would seem a key indicator of timely access to care in the facility. Surprisingly, this metric is not measured. Instead, the facility focuses on the backlog as a whole – how many sick calls have not been completed at any given time. For example, the Lead Nurse shared with me that a backlog number of 50 to 75 sick calls is optimal (the facility has seen backlogs of 200 or more in the recent past).

This writer would seek to achieve this abovementioned measurement – days from submission to sick call appointment as a reliable descriptor of timely access to care. Although this number is not actively measured, it can be easily obtained by this writer as the EHR system employed by the facility records a scanned copy of the sick call request and then the scheduled appointment date.

Thankfully, a review of the research literature reveals a wealth of articles on the topic designed to improve wait times for appointments and as a result, timely access to care.

Ansell, D., Crispo, J. A. G., Simard, B., & Bjerre, L. M. (2017). Interventions to reduce wait times for primary care appointments: a systematic review.  BMC health services research,  17(1), 295.  https://doi.org/10.1186/s12913-017-2219-yLinks to an external site.

Ansell, D., Crispo, J.A.G., Simard, B.  et al. Interventions to reduce wait times for primary care appointments: a systematic review.  BMC Health Serv Res  17, 295 (2017).  https://doi.org/10.1186/s12913-017-2219-yLinks to an external site.

Grot, M., Kugai, S., Degen, L., Wiemer, I., Werners, B., & Weltermann, B. M. (2023). Small Changes in Patient Arrival and Consultation Times Have Large Effects on Patients' Waiting Times: Simulation Analyses for Primary Care.  International journal of environmental research and public health,  20(3), 1767.  https://doi.org/10.3390/ijerph20031767Links to an external site.

Griffiths, P., Saville, C., Ball, J., Jones, J., Pattison, N., Monks, T., & Safer Nursing Care Study Group (2020). Nursing workload, nurse staffing methodologies and tools: A systematic scoping review and discussion.  International journal of nursing studies,  103, 103487.  https://doi.org/10.1016/j.ijnurstu.2019.103487Links to an external site.

Chiara Dall'Ora, Christina Saville, Bruna Rubbo, Lesley Turner, Jeremy Jones, Peter Griffiths, (2022) Nurse staffing levels and patient outcomes: A systematic review of longitudinal studies. International Journal of Nursing Studies, Volume 134,2022, 104311. https://doi.org/10.1016/j.ijnurstu.2022.104311.