DPH 805 Module 5 Quality Improvement Proposal Example

Improving Attendance in a Diabetes Prevention Program: A Quality Improvement Proposal

Student Name

Doctor of Public Health Program, Aspen University

DPH 805: Organizational and Systems Leadership in Public Health

Instructor Name

Month Day, Year

Improving Attendance in a Diabetes Prevention Program: A Quality Improvement Proposal

Quality improvement brings structured, data-driven methods to making programs work better. Doctoral leaders must be able to design improvement projects and create conditions for them to succeed. This paper proposes a quality improvement project for a composite county health department's diabetes prevention lifestyle program, where attendance falls well below national benchmarks.

The Problem

The program enrolls about 240 adults with prediabetes each year in 26 sessions over twelve months. Only 38% attend at least 14 sessions, the level linked to meaningful weight loss in national program data. Participants who drop out most often cite schedules, transportation and feeling that sessions repeated information.

The Model for Improvement

This approach poses three questions: what the effort should achieve, what evidence would show a change has helped and which changes deserve a test. It then uses Plan-Do-Study-Act cycles to test changes on a small scale, learn from data and adapt before spreading.

Aim

Within nine months, increase the proportion of participants attending at least 14 of 26 sessions from 38% to 55% across the program's four sites.

Measures

For the outcome, the team tracks the proportion of participants who complete 14 sessions. Process measures include weekly attendance by cohort, reminder calls completed and make-up sessions offered. A balancing measure, coach workload hours, ensures that changes do not overwhelm staff. Data will be pulled weekly from the attendance database and reviewed at a short team huddle.

Driver Diagram

A driver diagram links the aim to primary drivers and change ideas.

Planned Cycles

Cycle one will test reminder texts the day before sessions with one cohort for four weeks, tracking attendance weekly. If attendance rises, cycle two will add a call after any missed session. Cycle three will test a make-up session option. Cycles at other sites will test an evening cohort and a buddy system. Each cycle predicts results, runs briefly and uses data to decide whether to adopt, adapt or abandon. Each cycle's prediction is written down in advance so the team can learn from surprises.

Learning From the Evidence on PDSA

When Taylor and colleagues reviewed 73 published uses of PDSA, under 20% reported linked, repeated cycles, and just 15% of those examined relied on data gathered at least monthly to steer each cycle (Taylor et al., 2014). The proposal addresses these weaknesses by planning linked cycles, keeping tests small and reviewing weekly data on a run chart.

Team and Roles

The improvement team includes the program manager, two lifestyle coaches, a data analyst, a former participant and the division director as sponsor. The sponsor removes barriers and secures resources; the team runs cycles; the participant ensures changes reflect participants' experience.

Sustaining a Quality Culture

Improvement efforts often fade. In local health departments, committed leaders, protected staff time and a structured framework kept quality improvement going, whereas turnover and competing emergencies wore it down (Verma & Moran, 2014). The proposal protects two hours a week of team time and links the project to the department's performance management system.

Leadership's Role

Leaders set the tone by asking about data, celebrating learning from failed tests and protecting improvement time during busy periods. Relational leadership that values staff input supports the engagement improvement requires (Cummings et al., 2010).

Spread

Changes that improve attendance at the first site will be tested at the other sites, adapted to local conditions and documented in a change package for other county programs, such as smoking cessation classes, that face similar attendance problems.

Run Charts

Each week's attendance goes onto a run chart that shows the median line. Rules for detecting non-random patterns, such as a run of six points above the median, help the team judge whether a change produced real improvement rather than normal variation.

Participant Voice

Participants will be interviewed briefly after cycles to explain what helped or hindered attendance. Their input can generate new change ideas, such as childcare during sessions, that staff might not think of.

Equity in Improvement

Attendance will be tracked by site, age, language and income, so that changes do not improve averages while leaving some groups behind. If one group lags, the team will test changes tailored to that group.

Timeline

Months one and two: form the team, confirm measures and baseline. Months three to six: run cycles at the first site. Months seven to nine: test at other sites and adopt successful changes. Month nine: report results and plan spread.

Linking to Performance Management

The project's outcome measure will appear on the department's performance dashboard, and results will be reviewed at monthly leadership meetings. Linking improvement to performance management keeps attention on results after the project team disbands.

Anticipated Challenges

Challenges include small numbers at each site, which make data noisy; competing demands on coaches; and changes that work at one site but not another. The team will aggregate data across cohorts, protect coach time and adapt changes locally.

Documentation and Learning

Each cycle gets recorded on a single-page template recording the prediction, what was done, results and decisions. This record supports learning, allows others to see what was tried and addresses a common weakness in published improvement reports.

Resources

The project needs about four hours a week of team time, modest funds for text messaging and make-up sessions and analyst support for weekly data. The division director has approved these as part of the department's quality improvement plan.

Conclusion

The proposal applies the Model for Improvement to a clear attendance gap, with a specific aim, outcome, process and balancing measures, a driver diagram and linked PDSA cycles informed by weekly data. Learning from evidence on common PDSA weaknesses and building a sustaining culture through leadership support increase the chance that improvements last and spread.

References

Cummings, G. G., MacGregor, T., Davey, M., Lee, H., Wong, C. A., Lo, E., Muise, M., & Stafford, E. (2010). Leadership styles and outcome patterns for the nursing workforce and work environment: A systematic review. International Journal of Nursing Studies, 47(3), 363-385. https://doi.org/10.1016/j.ijnurstu.2009.08.006

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

Verma, P., & Moran, J. W. (2014). Sustaining a quality improvement culture in local health departments applying for accreditation. Journal of Public Health Management and Practice, 20(1), 43-48. https://doi.org/10.1097/PHH.0b013e3182a5a4a0