DDHA 9902 Week 6 Findings Critique Example

Ruling on Each of a Classmate's Five Findings About Imaging No-Shows: Supported, Overstated, Understated or Not a Finding at All

Student Name

Walden University

DDHA 9902: Practice-Based Problem Analysis, Findings, and Recommendations

Instructor Name

Month Day, Year

Ruling on Each of a Classmate's Five Findings About Imaging No-Shows: Supported, Overstated, Understated or Not a Finding at All

The Brief I Worked To

My classmate Teodora sent her numbered findings on missed outpatient imaging appointments at a composite regional hospital and asked for critique. I kept her numbering and worked through each finding in order, quoting it, naming the analytic step it cites and issuing one of four verdicts: supported as written, overstated relative to the step, understated where the analysis showed more, or not a finding because it contains a judgment or a proposal. Where a finding is overstated, I propose a corrected wording.

Finding 1

Quoted: "1. The no-show rate for outpatient MRI was 14% in the first half of 2026. (Step A1)"

Step cited: A1, a count of no-show statuses over all scheduled MRI appointments from January through June.

Verdict: supported as written. The claim states what was observed, the group and the period, and matches the step. One small note: her plan defined no-shows to exclude same-day cancellations, which the finding should say, since Dantas et al. (2018) found definitions of no-shows varied widely across studies.

Finding 2

Quoted: "2. Text message reminders cut no-shows by almost half. (Step A2)"

Step cited: A2, which compared no-show rates for appointments scheduled before and after the reminder system began in March.

Verdict: overstated. The step compared two periods, not patients who did and did not receive reminders, so it cannot show that the reminders caused the change. The word "cut" asserts a cause. Anything else that changed between winter and spring, a new scheduler or milder weather, would show up in the same comparison.

Corrected wording: "2. The MRI no-show rate was 18% for appointments in January and February and 10% for appointments from March through June, after text reminders began. (Step A2)"

Finding 3

Quoted: "3. Patients without a mobile number on file had a no-show rate of 22%. (Step A3)"

Step cited: A3, a comparison of no-show rates by whether a mobile number was recorded.

Verdict: understated. Her own step output shows the comparison group too: patients with a mobile number had a rate of 11%. The finding reports only one side, which hides the comparison that makes it useful. It also leaves out that patients without a mobile number made up 19% of appointments, which tells a reader how much the difference matters.

Corrected wording: "3. Patients without a mobile number on file had a no-show rate of 22%, compared with 11% for patients with one; patients without a mobile number accounted for 19% of scheduled appointments. (Step A3)"

Finding 4

Quoted: "4. Evening appointments are a serious problem that the department needs to address. (Step A4)"

Step cited: A4, a comparison of no-show rates by appointment time.

Verdict: not a finding. "Serious problem" is a judgment and "needs to address" is a proposal. The step reported that appointments after 17:00 had a no-show rate of 16% against 13% earlier in the day, and her plan set a 5-point threshold for reporting a difference. The difference was 3 points, so under her own rule the step produced an absence.

Corrected wording: "4. Appointments after 17:00 had a no-show rate of 16% and earlier appointments 13%, a difference below the 5-point threshold set in the plan. (Step A4)"

Finding 5

Quoted: "5. Medicaid patients missed appointments more often than commercially insured patients (21% vs 12%). (Step A5)"

Step cited: A5, a comparison by payer.

Verdict: supported as written, with one caution. The claim is accurate and properly limited to what was observed. Because payer is linked to many other characteristics, including whether a mobile number is on file, readers may be tempted to read a cause into it. Kahn et al. (2016) stress that data should be interpreted within the definitions that produced them, and I would add a note in the limitations that payer here is the billing category recorded at scheduling, not necessarily the patient's coverage on the day.

Overall

Two findings stand as written, one needs its comparison restored, one claims a cause the step cannot support and one is a judgment that her own decision rule converts into an absence. Nosek et al. (2018) argue that committing to rules before analysis protects against reading results selectively, and Teodora's plan already contained the rule that corrects Finding 4. Applying it consistently would make her list shorter in tone and stronger in substance.

References

Dantas, L. F., Fleck, J. L., Cyrino Oliveira, F. L., & Hamacher, S. (2018). No-shows in appointment scheduling: A systematic literature review. Health Policy, 122(4), 412-421. https://doi.org/10.1016/j.healthpol.2018.02.002

Kahn, M. G., Callahan, T. J., Barnard, J., Bauck, A. E., Brown, J., Davidson, B. N., Estiri, H., Goerg, C., Holve, E., Johnson, S. G., Liaw, S.-T., Hamilton-Lopez, M., Meeker, D., Ong, T. C., Ryan, P., Shang, N., Weiskopf, N. G., Weng, C., Zozus, M. N., . . . Schilling, L. (2016). A harmonized data quality assessment terminology and framework for the secondary use of electronic health record data. eGEMs, 4(1), Article 18. https://doi.org/10.13063/2327-9214.1244

Nosek, B. A., Ebersole, C. R., DeHaven, A. C., & Mellor, D. T. (2018). The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), 2600-2606. https://doi.org/10.1073/pnas.1708274114