MHA 599 Week 4 Implementation and Execution Plan Example

From One Clinic to Four: An Implementation Plan With Real Test Cycles, Clear Roles and Run Charts for the Home Blood Pressure Pilot

[Student Name]

University of Phoenix

MHA/599: Capstone: Leading the Organization Through Change

Week 4 Assignment

[Instructor Name]

[Date]

The health system, its clinics, timeline, roles and pilot data are composites written for a model paper; research findings come from the sources cited.

When the director of population health presented her change plan to the executive team of the composite Montana health system, the chief operating officer asked a practical question: what happens on Monday morning, and who does it? This paper answers with an implementation plan that begins small, tests changes in real cycles and assigns every task an owner.

Organizing the Work

The implementation framework used for the readiness assessment includes a process domain covering planning, engaging the right people, executing and reflecting and evaluating (Damschroder et al., 2009). The plan uses these as its four phases, repeated as the program grows.

Phase One: Plan

Over eight weeks, the team finalizes protocols, chooses devices, builds the monitoring platform's connection to the electronic record, sets up billing workflows, trains pharmacists and selects the first clinic, one with an engaged physician champion, a stable staff and good cellular coverage.

Phase Two: Engage

Over the same period, coalition members brief the first clinic's staff, the physician champion presents the protocols and community health workers are trained in device setup.

Phase Three: Execute Through Test Cycles

Execution begins with 25 patients at one clinic, testing each part of the process in weekly plan-do-study-act cycles before adding more patients.

Why Real Cycles Matter

Many projects describe themselves as using plan-do-study-act cycles without following the method. A systematic review of 73 articles found that fewer than 20% fully documented a sequence of iterative cycles, that many ignored the principle of small-scale change and that only 15% of those analyzed used quantitative data at monthly or more frequent intervals to guide the next cycle (Taylor et al., 2014). A cycle that is never repeated is a pilot, not a test of change.

Cycle One: Enrollment

Prediction: the clinic can enroll ten eligible patients in a week using a list from the record. Result: seven enrolled; three could not be reached by phone. Learning: add enrollment at scheduled visits. Next cycle adopted both methods.

Cycle Two: Device Setup

Prediction: community health workers can set up devices by phone in 15 minutes. Result: most setups took 25 minutes, and older patients preferred in-person help. Learning: offer brief in-person setup at the clinic or during home visits.

Cycle Three: Transmission

Prediction: 80% of patients will transmit readings at least 16 days a month. Result: 68%, with gaps in two areas of weak coverage. Learning: switch to cuffs that hold readings until a signal is available, and call patients after three silent days.

Reading the Data Over Time

Weekly data are plotted on run charts for control rates, transmission days, time from high reading to pharmacist call and pharmacist time per patient. Charts built on statistical process control let a team read its own data week by week and separate a genuine shift from normal ups and downs, rather than waiting months for totals that arrive too late to act on (Benneyan, 2003). The team acts on patterns across several weeks, not on single weeks.

Phase Four: Reflect

Every four weeks, the coalition reviews the charts and cycle notes, decides what to keep and what to change and updates the protocols and guides.

The Responsibility Matrix

Each task has one responsible owner. The project director owns the timeline and reports. The pharmacy director owns pharmacist staffing and protocols. The clinic manager owns enrollment. The community health worker supervisor owns device setup. The information technology lead owns the platform and record connection. The revenue cycle manager owns billing. The chief medical officer approves protocol changes, and the coalition is consulted on major decisions.

Keeping the Clinic Running

The first clinic still had a full schedule of patients. To avoid disrupting it, the pilot's work happens outside clinic visits wherever possible, and the clinic manager reviews the pilot's effect on visit times each week. In the first month, visit times did not lengthen, which reassured the other clinics waiting their turn.

Communication During Execution

Each week, a one-page update goes to the first clinic's staff and the coalition: enrollment, transmission, medication changes, safety events and what the next cycle will test. Staff said the updates made them feel part of the work rather than subjects of it.

Timeline

Months one and two: plan and engage. Months three to five: 25 patients at the first clinic, growing to 100 as cycles succeed. Month six: decision point. Months seven to twelve: expansion to three more clinics, one at a time, reaching 800 patients.

Decision Rules

Expansion to the second clinic requires at least 75% of patients transmitting on 16 or more days a month, pharmacist response to high readings within one business day in 90% of cases and control improving on the run chart without safety events. If these are not met, the team adapts and continues testing; if serious safety problems occur, the pilot pauses.

Patient Safety During the Pilot

Home monitoring can reveal dangerously high or low readings between visits. The plan sets thresholds: readings above a crisis level trigger an immediate call and, if symptoms are present, direction to emergency care; very low readings prompt a same-day pharmacist call. Pharmacists document every contact, and the chief medical officer reviews any safety concern within one business day.

Training Before Launch

Pharmacists complete a two-day training on the protocols, the monitoring platform, motivational interviewing and escalation. Community health workers practice device setup with volunteers, including older adults, before meeting patients. Clinicians receive a short session on reading the summary reports and how to contact the pharmacist.

Integration With the Record

Pharmacist notes and medication changes appear in the electronic record, and clinicians receive a monthly summary for each enrolled patient rather than every reading. The information technology lead tested the connection with dummy patients before launch to prevent lost or duplicated data.

Barriers From the Readiness Assessment

The plan addresses barriers identified earlier: weak coverage through store-and-forward devices, clinician concern through protocols and summary reports and workload through dedicated setup outside clinic visits.

Resources

Resources include four pharmacist positions phased in with enrollment, two community health workers, devices, platform fees, a part-time data analyst and project management time.

Report From the First Three Cycles

After six weeks, the first clinic had enrolled 31 patients, 71% were transmitting on at least 16 days, pharmacists had made 22 medication changes and no safety events had occurred. Control had not yet changed, as expected so early, and the team resisted the temptation to declare success or failure on six weeks of data.

Conclusion

The plan answers the Monday morning question: one clinic, a few patients, weekly test cycles with written predictions, run charts, clear owners and decision rules for growth. Evidence that many projects claim but do not practice iterative testing, and that time-series data support faster, better decisions, shaped a plan designed to learn before it scales.

References

Benneyan, J. C. (2003). Statistical process control as a tool for research and healthcare improvement. Quality and Safety in Health Care, 12(6), 458-464. https://doi.org/10.1136/qhc.12.6.458

Damschroder, L. J., Aron, D. C., Keith, R. E., Kirsh, S. R., Alexander, J. A., & Lowery, J. C. (2009). Fostering implementation of health services research findings into practice: A consolidated framework for advancing implementation science. Implementation Science, 4(1), 50. https://doi.org/10.1186/1748-5908-4-50

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