NR 707A Week 4 Midpoint Data Summary Example

Midpoint Data Summary: Six Weeks of the Same-Day Behavioral Health Introduction, Counts Beside Every Rate and the Outcome Still to Come

Student Name

College of Nursing, Chamberlain University

NR 707A: DNP Project & Practicum III

Instructor Name

Month Day, Year

Midpoint Data Summary: Six Weeks of the Same-Day Behavioral Health Introduction, Counts Beside Every Rate and the Outcome Still to Come

Coverage

Every figure below covers the same dates and population, so that each can be compared with the others. Visits from Monday 1 March to Friday 9 April 2027, six weeks. Adult primary care department at the one site. All weekday sessions and the Saturday morning session. File pulled by the quality analyst on 12 April 2027, using the weekly template version 2.0.

Is It Happening at All

The first table answers the midpoint's main question. | Process measure | Count | Of | Rate |

The process figures show the change is being used, from the first week onward: almost all eligible visits in behavioral health hours generate a message, and more than half of those patients meet the consultant before leaving.

By Week

The Outcome So Far

The outcome measure lags the change by a month by design, since a patient seen on day one has 30 days to complete a contact. Completed behavioral health contact within 30 days can be counted only for visits at least 30 days before the pull date, which means visits from 1 to 12 March. Of those 46 eligible patients, 26 completed a contact within 30 days: 57%. The baseline over 26 weeks was 189 of 580, 33%.

That is a difference of 24 percentage points in the first two weeks. It is not yet an effect. Two weeks are two points on a run chart, which cannot show a shift (Perla et al., 2011), and the first weeks of any change carry extra attention.

Balancing Measures

The balancing measures ask whether the change is costing something elsewhere. Median visit length for eligible visits: 55 minutes, against 52 in the baseline. Missed behavioral health follow-up visits in March: 18% of booked follow-ups, against 15% in the baseline months. Both moved in the direction the design warned about, by small amounts that could be chance, and both are within the stop rule's limits: visit length has not risen by 15 minutes, and missed follow-ups have not exceeded 25%.

By Preferred Language

Language was split out because the design defense promised it, and because one study gave a reason to look. Same-day contacts during behavioral health hours: English-preferring patients 34 of 59, 58%; Spanish-preferring patients 27 of 45, 60%. Declines: 9 of 59 and 5 of 45. The numbers are small, but they do not show the pattern one published study found, in which warm handoffs did worse for English-speaking Latino patients (Horevitz et al., 2015). The site cannot separate Latino patients who prefer English from other English-speaking patients in the aggregate data, which limits how far the comparison can go. Ethnicity is recorded, but splitting by both ethnicity and language would produce cells under five, which the data arrangement does not allow.

Reading the Weekly Pattern

Same-day contacts dipped in week 2, when the consultant was on leave for two days and the psychologist covered one of the two open slots, then settled at about half of eligible visits in behavioral health hours. Calls by noon the next day stayed between 73% and 86% each week, with the low weeks matching the Fridays when the assistant was out. Neither pattern suggests the change is fading; both suggest it depends on who is present. Implementation research would describe this as fidelity varying with staffing, a separate question from whether the change works (Proctor et al., 2011).

What the Midpoint Can and Cannot Support

It can support saying that, on most days, staff are doing what the design asks, that patients mostly accept it and that the balancing measures have moved slightly but within limits. It cannot support saying that the change improves completed behavioral health contact, since only two weeks of outcomes exist and those weeks were the first, when attention was highest.

How the Figures Were Checked

The analyst pulled the file on 12 April; the student checked the eligible count against the message group log and the same-day contacts against the consultant's schedule for two weeks chosen at random, weeks 3 and 5. Both matched. The language split was checked against the registration field for ten visits. The run chart was updated with the six weekly process points and the two weekly outcome points, and the student initialed the check sheet so that the verification is recorded.

What the Site Has Said About the Figures

The director of behavioral health read the summary on 13 April and said the same-day share matched what the consultant felt on the floor. The site medical director asked whether the three-minute rise in visit length was concentrated on particular clinicians or particular days; the data cannot show that without identifying clinicians, which the summary does not do. Both comments are recorded for the evaluation.

Questions the Midpoint Raises

The midpoint is useful mainly for the questions it lets the project ask while there is still time to watch for the answers. Whether the same-day share will hold when the novelty wears off. Whether the dip in week 2 shows that the change depends too heavily on one consultant. Whether missed follow-ups will keep rising as the consultant's booked slots are displaced. Each will be checked against the remaining weeks, and the evaluation will report the answer to each whether or not it favors the change.

What Is Still to Come

Thirty-day outcomes for weeks 3 to 12. The staff survey in May. The comparison of the full test period with the baseline on the run chart. Any judgment of whether the change works waits for those, and the summary makes no such judgment.

References

Horevitz, E., Organista, K. C., & Arean, P. A. (2015). Depression treatment uptake in integrated primary care: How a "warm handoff" and other factors affect decision making by Latinos. Psychiatric Services, 66(8), 824-830. https://doi.org/10.1176/appi.ps.201400085

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

Proctor, E., Silmere, H., Raghavan, R., Hovmand, P., Aarons, G., Bunger, A., Griffey, R., & Hensley, M. (2011). Outcomes for implementation research: Conceptual distinctions, measurement challenges, and research agenda. Administration and Policy in Mental Health and Mental Health Services Research, 38(2), 65-76. https://doi.org/10.1007/s10488-010-0319-7