IHP 604 Module 8 Milestone Three Example

Milestone Three: What the Pilot Clinics Learned and How to Spread It

[Student Name]

Southern New Hampshire University

IHP 604: Healthcare Quality and Improvement

Module Eight Milestone Three

[Instructor Name]

[Date]

The organization, setting and figures below are a composite written as a model document. No real employer, client, colleague or patient is described.

Milestone Three: What the Pilot Clinics Learned and How to Spread It

Milestone Two selected four interventions for poor diabetes control at Crestline Medical Group. This milestone reports how they were tested at two pilot clinics, Eastside and Riverside, through Plan-Do-Study-Act cycles, what the results show and how the successful changes will be spread across the remaining 16 clinics.

Using PDSA Properly

Reed and Card (2016) argued that PDSA cycles look simple but are often applied in ways that strip away their power. They stressed that cycles should be iterative, each building on the last; that teams should state a prediction before testing so they can learn from surprises; that tests should start small and grow in scale as confidence rises; and that data should be collected at a frequency that lets teams see the effect of each change. They also cautioned that PDSA works best as part of a broader improvement approach rather than as a stand-alone form to complete. Crestline's teams used a one-page cycle form requiring a prediction, a data plan and a decision to adapt, adopt or abandon.

The Cycle Log

Six linked cycles were run at Eastside between months one and seven, and the successful versions were then tested at Riverside. Each started with a handful of patients or a single clinician before growing.

Table 1. PDSA Cycle Log, Eastside Clinic

Note. Composite results; cycles 1-6 ran over seven months.

What the Cycles Taught

The first cycle's failure was the most useful. The team predicted that most patients would answer a nurse's call, but wrong phone numbers defeated the plan, a problem no one had anticipated. Verifying numbers at check-in and adding a text message fixed it. The titration protocol worked quickly with one physician, but it surfaced a barrier the team had underestimated: patients could not afford some newer drugs. Adding a pharmacist to check coverage before a change turned a promising test into a reliable process. The Spanish classes showed that content was welcome but daytime scheduling was not.

Results at the Pilot Clinics

As shown in Module Seven, Eastside's poor-control rate shifted from a stable 36% to 29% by month nine, a special-cause signal on its p chart. Riverside, which started three months later, moved from 41% to 37% over six months, a smaller change that had not yet produced a signal. At both clinics, the share of patients reached within two weeks of a high reading climbed past 80%, and medication intensification within 90 days rose from 38% to 74% at Eastside and 61% at Riverside. Hypoglycemia visits showed no increase.

Table 2. Pilot Clinic Results

Note. Composite data from registry and chart review.

Why Riverside Moved More Slowly

Kaplan et al. (2010) systematically reviewed studies of how context affects quality improvement success and found that factors such as leadership from top management, a culture supportive of improvement, the leadership of the improvement team itself, data infrastructure, prior experience with improvement and physician involvement were associated with better results, though the evidence was still developing. Riverside differed from Eastside on several of these. Its medical director was on leave for two months, only two of its five physicians attended huddles and its care manager position was vacant for six weeks. The interventions were the same; the context was not.

Deciding What to Spread

Three changes are ready: text-plus-call outreach within 14 days, the nurse and pharmacist titration protocol and standing A1c orders. The Spanish classes need another cycle at evening times before spreading. Redesigned monthly feedback began at both pilots in month four and will spread with the other changes.

How Innovations Spread

Greenhalgh et al. (2004) pulled together hundreds of studies on how new practices travel through hospitals, clinics and other service organizations. They found that innovations spread more readily when potential adopters see a clear advantage, when the change is compatible with their values and ways of working, when it is simple, when staff can trial it on a small scale first and when its results are visible. They also emphasized that adopters often adapt innovations to local conditions and that organizational readiness, leadership support and dedicated resources shape whether adoption becomes routine.

The Spread Plan

The remaining 16 clinics will adopt the changes in four waves of four clinics, each wave starting three months after the last. Each wave will begin with a visit to Eastside so staff can see the process working, a feature Greenhalgh and colleagues associate with observability. Clinics may adapt details, such as who sends texts, but must keep the core elements: contact within 14 days, a protocol for intensification and standing orders. Clinics will be grouped so each wave includes one with strong context, following Kaplan and colleagues, to act as a peer model.

Resources for Spread

Spread requires two additional nurse care managers, a quarter of a pharmacist's time and the quality manager's coaching. The expanded health plan bonus, if earned, would cover these costs. Each wave's clinic lead will receive a half-day training, weekly coaching for the first six weeks and access to a shared dashboard showing their clinic's p chart.

Measuring the Spread

Each clinic will track contact within 14 days and intensification within 90 days weekly for the first three months, then monthly. The group-level poor-control rate will be charted monthly, with a target of 24% at eighteen months. Hypoglycemia visits will remain the balancing measure.

Conclusion

Six disciplined PDSA cycles turned four interventions into reliable processes at Eastside, with a real shift in outcomes and no signal of harm. Riverside's slower progress shows that context matters as much as design. Spreading in waves, with visible models, room for local adaptation and attention to leadership and staffing, gives the rest of the group the best chance of repeating Eastside's results.

References

Greenhalgh, T., Robert, G., Macfarlane, F., Bate, P., & Kyriakidou, O. (2004). Diffusion of innovations in service organizations: Systematic review and recommendations. The Milbank Quarterly, 82(4), 581-629. https://doi.org/10.1111/j.0887-378X.2004.00325.x

Kaplan, H. C., Brady, P. W., Dritz, M. C., Hooper, D. K., Linam, W. M., Froehle, C. M., & Margolis, P. (2010). The influence of context on quality improvement success in health care: A systematic review of the literature. The Milbank Quarterly, 88(4), 500-559. https://doi.org/10.1111/j.1468-0009.2010.00611.x

Reed, J. E., & Card, A. J. (2016). The problem with Plan-Do-Study-Act cycles. BMJ Quality & Safety, 25(3), 147-152. https://doi.org/10.1136/bmjqs-2015-005076