NU804 Unit 6 Interim Data Report Example

Process Rising, Outcome Too Early to Read: A Unit 6 Interim Data Report on an Insulin Discharge Bundle After Three Months

[Student Name]

Purdue University Global

NU804: DNP Project II

Unit 6 Interim Data Report

[Instructor Name]

[Date]

The hospital, units, patients and data are composite and illustrative; the methods sources cited are real.

The Data So Far

Data cover project months one to three. Month one covers only the two medical-surgical units, because the cardiac and surgical units began in week six, partway through month two. Counts are small: five patients in month one, 12 in month two and 16 in month three. Every figure below is a count over a total, not only a percentage, so readers can see how little each point rests on.

Process Measures

All five parts delivered. Three of five patients in month one, 7 of 12 in month two and 12 of 16 in month three, or 60, 58 and 75 percent. The rise in month three followed the changes logged after the fidelity audit: weekend take-home pens, the daily list for prior authorization and plans generated from the final reconciliation.

Pen by the first insulin day. Three of five, 7 of 12, then 13 of 16. This is the component that moved most, from about 60 percent to 81 percent, and it is the one the adaptations targeted.

Supervised injection on the pen going home. Four of five, 10 of 12, then 15 of 16.

Contact by day three. Five of five, 11 of 12, then 15 of 16. Day-one text replies came from 27 of the 33 patients, 82 percent, close to the rate reached in the improvement cycles before the project.

Class seat accepted. Two of five, 3 of 12 and 6 of 16, about a third overall. Attendance is not yet complete for month three.

The measures the project changed moved first; the measure the project exists for has barely begun to report.

Reading the Run Charts

Perla et al. (2011) describe four signals a run chart can give, a sustained shift away from the median, a steady trend, too many or too few crossings of the median and one wildly out-of-place value, each with a minimum count of points before it can be trusted. Three monthly points cannot show any of those signals. Weekly data give more points but very few patients per point, as few as one in some weeks, which makes each weekly percentage swing between zero and 100. Benneyan et al. (2003) explain that control charts for proportions use limits that widen when the sample is small, and on these numbers the limits would span almost the whole chart.

The report therefore reads the charts for direction only. The all-five measure and the pen measure both rose after specific changes, at the time those changes began. That timing is consistent with the changes working, but three months on four units cannot rule out chance or other events, such as a quieter month for admissions on 3 West.

Outcome Measure

Thirty days of follow-up are complete only for patients discharged in months one and two, 17 in all. One returned with a glucose problem: a cardiac step-down patient seen in the emergency department on day 11 with a glucose of 412 after a missed basal dose during a family emergency. One of 17 is about 6 percent, below the 14 percent baseline for these units, but a single return more or less would change the figure by six points. No conclusion about returns is possible yet, and none is drawn.

Balancing Measures

Discharge order to departure. The median time was 2 hours 35 minutes, against 2 hours 40 minutes in the baseline year for the same units, so the bundle has not delayed discharge. The shift of the supervised dose to the evening before, agreed with the hospitalists, likely helps.

Nurse time on follow-up. The follow-up queue took about 1.4 hours a week across the four units, well under the three-hour review trigger.

Hypoglycemia returns. None. The stopping rule has not been triggered.

Equity Check

Stratified counts are reported as counts only, since no group yet has 20 patients. Of nine Medicaid or uninsured patients, five received all five parts, against 17 of 24 other patients. The gap sits almost entirely in the pen measure, and three of the four Medicaid misses occurred before the daily prior authorization list began. Four patients were 80 or older; all received a check-in, two by phone through a family member.

What the Data Suggest

Three things. First, the changes made after the fidelity audit appear to be working, especially for the pen, and should carry into the next wave unchanged. Second, the class remains the weakest component, and the virtual class planned in the quality improvement course should start before oncology goes live. Third, the outcome measure will not say anything useful until each unit has at least six months of follow-up, so the committee's reports should continue to lead with process measures until then.

Display for the Committee

The committee will receive four small multiples, one per unit, each showing the all-five measure by month with go-live and change dates marked, and a fifth chart combining the four units with the baseline median drawn across it. Each point will carry its count. Provost and Murray (2011) recommend annotating charts with the changes tested so that viewers can relate movement to action, and the charts follow that advice.

Conclusion

After three months, process measures are rising where the project made changes, and balancing measures show no harm. The outcome measure rests on 17 patients and one return, which is too few to judge. The report recommends keeping the adaptations, starting the virtual class and continuing to report process first until each unit has six months of outcome data.

References

Benneyan, J. C., Lloyd, R. C., & Plsek, P. E. (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

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

Provost, L. P., & Murray, S. K. (2011). The health care data guide: Learning from data for improvement. Jossey-Bass.