NURS 732 Week 7 Quality Improvement Plan Part II Example

Quality Improvement Plan Part II: Process Map, Root Causes, Driver Diagram, Measures and Planned PDSA Cycles for Blood Pressure Control

Introduction

Part I established that 58% of the 1,412 adults with hypertension at Riverbend Community Health Center are at goal, that Spanish-speaking and uninsured patients trail the rest, and set an aim of 70% control and a narrower equity gap within twelve months. This section analyzes why control is low, builds a theory of change and plans the first tests. Following the Model for Improvement (Langley et al., 2009), it defines how the team will know whether a change is an improvement and which changes it will try first.

Current Process Map

The team mapped a typical hypertension visit by observing twelve visits across the four care teams and interviewing the staff involved. The steps and the problems found at each are listed below.

• Scheduling: follow-up visits are booked at the end of a visit only if the patient goes to the front desk; about one patient in five leaves without a follow-up appointment.
• Rooming: a medical assistant has seven scheduled minutes, takes one reading with the cuff in the room, often without a rest period, and with the patient talking or having just walked from the lobby.
• Provider visit: the provider reviews one reading; when it is high, the provider may intensify treatment, repeat the reading, or attribute it to anxiety, with wide variation among providers.
• Medication: new prescriptions go to the pharmacy, but no one checks whether they are filled or tolerated.
• Between visits: no process exists for patients with high readings to be contacted before their next visit, which may be three to six months later.
• Home readings: patients who measure at home share readings only if they bring them, and they are rarely entered in the record.

The map showed that the problem was not one step but a chain, with weak links at measurement, follow-up and adherence. It also showed how the steps interact. A rushed reading inflates the number the provider sees, which can prompt a medication change the patient does not need; the patient then feels dizzy, stops the medication and is lost to follow-up because no one checks between visits. A fix at any single step would help, but the largest gains are likely to come from strengthening several links at once, which is why the driver diagram that follows has four drivers rather than one.

Root Cause Analysis

Fishbone Diagram

The team spent forty minutes on an Ishikawa diagram whose head read "blood pressure not controlled at last visit." The causes are summarized in the table.

Five Whys

The team applied five whys to one cause rated high in impact: "patients with high readings are not followed up between visits." Why? Because no one is assigned to follow up. Why? Because high readings are handled during the visit, and the visit is assumed to be enough. Why? Because the center's workflows were designed around visits, not populations. Why? Because until recently, the center had no registry to see patients between visits. Why? Because population management was not part of anyone's role. The root cause, a care model built around visits rather than populations, pointed the team toward assigning between-visit follow-up to the registered nurses and community health workers. The five whys led the team from a missing phone call to a missing idea: that the clinic is responsible for patients between visits, not only during them.

Driver Diagram

The driver diagram summarizes the team's theory of change, linking the aim to four primary drivers.

The adherence driver was strengthened after the team read Song et al.'s secondary analysis of trial data, in which a guideline-based decision support system raised treatment intensification from 11.6% to 47.3% of visits with uncontrolled readings but did not improve overall control rates, prompting the authors to call for stronger implementation strategies (Song et al., 2026). Intensifying treatment is not enough if patients do not fill, tolerate or take the medications, so the plan pairs every intensification with a pharmacist follow-up.

Selecting Changes From the Analysis and the Evidence

Each change idea in the driver diagram traces to a cause in the fishbone or the process map and is supported by evidence or by the experience of comparable programs. The measurement changes respond directly to the equipment and methods causes, and the 2025 guideline's emphasis on proper office technique supports them (Jones et al., 2025). The between-visit changes respond to the root cause found through the five whys. Khatib et al. (2026) describe a clinic package for self-monitoring in a low-income neighborhood that paired no-cost monitors with teaching, written logs, reminder messages, social needs support and charting steps, and the team's cuff library and text check-ins borrow from that design.

The team also chose changes with implementation in mind. Kharbanda et al. (2026) showed in 40 rural clinics that an identical alert prompted far more repeat readings where staff were trained face to face and coached each month than where they only completed an online module. For that reason the plan introduces the alert through the daily huddle and the dashboard rather than through an email. Sollecito and Johnson (2013) caution that changes chosen without regard to the people who must carry them out rarely last, and every change idea in the diagram has a named owner on the team.

Input From Staff and the Patient Advisor

Before finalizing the change ideas, the team shared the draft driver diagram at a staff meeting and asked each care team to rate the ideas for likely impact and ease. Medical assistants rated the cuff and rest-period changes as easy and likely to help; providers were less certain about the shared treatment protocol, which the team therefore placed later in the sequence of tests. The patient advisor reviewed the text message wording and the Spanish materials and asked that the pharmacist follow-up be offered by phone in the evening, since many patients cannot take calls at work. These changes were incorporated before any testing began.

Measures

Measures were defined so that anyone could collect them the same way.

The repeat-reading measure reflects evidence that repeating high readings both improves accuracy and can be raised quickly through feedback and data infrastructure (Murphy et al., 2026). The balancing measures address the two most likely harms: slower clinic flow and symptomatic low blood pressure from more aggressive treatment.

Planned PDSA Cycles

Following the evidence that many teams skip small-scale testing and iterative cycles (Taylor et al., 2014), each change begins small, with a prediction.

Each cycle will be recorded on a one-page PDSA form, reviewed at the weekly team huddle, and adapted, adopted or abandoned before the next. The cycles are sequenced deliberately. Measurement changes come first because every later measure depends on accurate readings; follow-up and adherence changes come next because they address the root cause; and the home monitoring changes run alongside them because they take longest to show effects. Successful changes will expand from one person to one care team and then to all four teams, with a new cycle at each step.

Data Analysis Plan

The team will chart each monthly outcome and weekly process value in time order around its median. Perla et al. (2011) explain how a handful of probability-based rules applied to such charts, covering runs, shifts and trends, let a team separate signals of change from noise, and they note that the charts suit the small numbers and frequent data of clinic improvement. Changes to data definitions, such as registry cleanup, will be annotated on the charts so they are not mistaken for improvement. The equity gap will be charted separately.

Ethical Considerations and Christian Worldview

Intensifying treatment carries risk, especially for older patients prone to falls, so the side-effect balancing measure and pharmacist follow-up protect patients from harm in pursuit of a number. Text messages will contain no clinical details beyond a request for readings, and patients may opt out at any time. The equity measure keeps the team accountable to the patients with the lowest control. The Preacher urged that whatever work the hand finds to do be done with all one's strength (English Standard Version Bible, 2001/2016, Ecclesiastes 9:10). Wholehearted attention to a five-minute rest, a correctly sized cuff and a follow-up phone call is how a clinic honors the patients it serves.

Conclusion

Part II traced low blood pressure control to weak links in measurement, follow-up and adherence, with a root cause in a visit-based model of care. A driver diagram organizes four drivers and their change ideas, seven precisely defined measures will track outcomes, processes and potential harms, and six small PDSA cycles will begin testing changes. Part III will describe how successful changes will be implemented and spread, how results will be evaluated against the aim and how the improvements will be sustained and governed.

References

English Standard Version Bible. (2016). Crossway. (Original work published 2001)

Jones, D. W., Ferdinand, K. C., Taler, S. J., Johnson, H. M., Shimbo, D., Abdalla, M., Altieri, M. M., Bansal, N., Bello, N. A., Bress, A. P., Carter, J., Cohen, J. B., Collins, K. J., Commodore-Mensah, Y., Davis, L. L., Egan, B., Khan, S. S., Lloyd-Jones, D. M., Melnyk, B. M., . . . Williamson, J. D. (2025). 2025 AHA/ACC/AANP/AAPA/ABC/ACCP/ACPM/AGS/AMA/ASPC/NMA/PCNA/SGIM guideline for the prevention, detection, evaluation and management of high blood pressure in adults: A report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Hypertension, 82(10). https://doi.org/10.1161/HYP.0000000000000249

Kharbanda, E. O., Asche, S. E., Essien, I., Allen, C. I., Freitag, L. A., Ekstrom, H. L., Kromrey, K. A., Muthineni, A., Saman, D. M., Thirumalai, V., O'Connor, P. J., & Benziger, C. P. (2026). Implementation of a clinical decision support tool for hypertension in a rural health system. Journal of the American Medical Informatics Association, 33(5), 1000-1008. https://doi.org/10.1093/jamia/ocag031

Khatib, R., Shields, M., Ozoani, O., Guzman, I., Glowacki, N., Ross, K. M., Mora, N., Lauffenburger, J. C., & Gordon, M. (2026). Feasibility of implementing a clinical integration package to support self-measured blood pressure monitoring in primary care: A qualitative study. Circulation: Population Health and Outcomes, 19(5), Article e012252. https://doi.org/10.1161/CIRCOUTCOMES.125.012252

Langley, G. J., Moen, R. D., Nolan, K. M., Nolan, T. W., Norman, C. L., & Provost, L. P. (2009). The improvement guide: A practical approach to enhancing organizational performance (2nd ed.). Jossey-Bass.

Murphy, K., Hood, C. J., Dirisala, K., Voit, J., Vigen, R., Bowen, M. E., & Bajaj, P. (2026). Impact of a centralized quality improvement initiative on repeat blood pressure measurement at an academic medical center. The Joint Commission Journal on Quality and Patient Safety. Advance online publication. https://doi.org/10.1016/j.jcjq.2026.08.008

Perla, R. J., Provost, L. P., & Murray, S. K. (2011). The run chart: A simple analytical tool for learning from variation in healthcare processes. BMJ Quality & Safety, 20(1), 46-51. https://doi.org/10.1136/bmjqs.2009.037895

Sollecito, W. A., & Johnson, J. K. (Eds.). (2013). McLaughlin and Kaluzny's continuous quality improvement in health care (4th ed.). Jones & Bartlett Learning.

Song, J., Liu, Y., Shang, Q., Liu, J., Zhang, H., Zhou, Z., Wang, W., Lu, J., & Zheng, X. (2026). Clinical decision support system, antihypertensive treatment intensification, and blood pressure control: A post hoc secondary analysis of a cluster randomized trial. JAMA Network Open, 9(5), Article e2614858. https://doi.org/10.1001/jamanetworkopen.2026.14858

Taylor, M. J., McNicholas, C., Nicolay, C., Darzi, A., Bell, D., & Reed, J. E. (2014). Systematic review of the application of the plan-do-study-act method to improve quality in healthcare. BMJ Quality & Safety, 23(4), 290-298. https://doi.org/10.1136/bmjqs-2013-001862