NR 558 Week 3 Dashboard Report Interpretation Example

Report Interpretation Brief: A Dashboard Showing 98.6% Fall Risk Reassessment Compliance, Read From Its Footer First, One Counted Shift Walked Through, and Each Exclusion Named

Student Name

College of Nursing, Chamberlain University

NR 558: Informatics for Professional Nursing Practice

Instructor Name

Month Day, Year

Report Interpretation Brief: A Dashboard Showing 98.6% Fall Risk Reassessment Compliance, Read From Its Footer First, One Counted Shift Walked Through, and Each Exclusion Named

The Headline

Our nursing quality dashboard reports "Fall Risk Reassessment Compliance" for each unit. For our 30-bed medical-surgical unit last month, it showed 98.6%, above the hospital's 95% target and shaded green. The figure is presented at monthly unit meetings and in the chief nursing officer's quality report as evidence that fall risk is being reassessed every shift. This brief asks what the number actually counts before accepting what it appears to say.

The Footer Before the Headline

The footer of the dashboard, in small type below the chart, reads: "Source: EHR flowsheet data warehouse. Run date: 3rd of the month, 02:00. Refresh: monthly. Window: previous calendar month. Denominator: eligible patient-shifts. Numerator: patient-shifts with Morse Fall Scale section filed."

Three things follow before anything else is read. The data are a month behind, so the dashboard cannot show the effect of anything done this month. The window is a calendar month, so a patient admitted on the 30th and discharged on the 3rd is split across two reports. And the numerator is a filed section, not a completed assessment, which is the most important phrase on the page.

One Counted Shift, Walked Through

To see what the number means, I followed one patient-shift from the unit to the report. A 74-year-old patient was admitted to our unit on a Tuesday afternoon with a urinary tract infection. On Wednesday night, the patient became confused and was started on intravenous fluids. On Thursday day shift, the nurse opened the shift assessment. The Morse section arrived pre-filled with Wednesday day shift's answers, including "mental status: oriented to own ability" and "IV or heparin lock: no." The nurse, busy with an admission, filed the section without changing it.

For that Thursday day shift to count in the numerator, five things had to be true: the patient was on an inpatient unit included in the report; the patient was 18 or older; the patient was on the unit for at least four hours of the shift; a Morse section was filed during the shift; and the filed section contained a total score. All five were true. The shift counted as compliant.

What the report cannot see is that the filed section described the patient as oriented with no intravenous line, when the patient was confused and had an intravenous line running. The actual Morse score was 35 points higher than the one filed, enough to move the patient from low to high risk. The report counted that shift as a success.

Exclusions Named One at a Time

The denominator, "eligible patient-shifts," excludes several groups. Each is named here, with its effect.

Patients under 18. None on our unit; no effect.

Patients on the unit for less than four hours of a shift. Excludes shifts with admissions late in the shift and discharges early in it. Those are the shifts when reassessment is hardest to complete, so excluding them raises the compliance rate.

Patients on comfort care. A reasonable exclusion, since fall prevention goals change, but the flag is set manually and sometimes missed.

Patients in observation status. Excluded because they are not formally inpatients, even though they occupy the same beds and face the same risks. On our unit, observation patients make up about 15% of patient-shifts, so a meaningful share of care is invisible to the report.

Shifts with no nurse assignment recorded. A technical exclusion that removes shifts with documentation gaps, the very shifts most likely to lack a real assessment.

What the Number Actually Says

Read with its footer, walkthrough and exclusions, 98.6% means that in almost every eligible patient-shift a Morse section was filed. It does not mean that fall risk was reassessed. Because the section arrives pre-filled with the previous shift's answers, filing it requires no new assessment. Copy-forward functions can make documentation appear complete while carrying forward outdated information (Tsou et al., 2017). The dashboard is measuring whether a button was clicked, and the default makes clicking it nearly automatic. Defaults strongly shape what people do, which is why a measure built on a defaulted action tells us more about the default than about the behavior (Halpern et al., 2007).

Who Uses the Number and How

The compliance figure reaches three audiences. Unit managers use it to judge whether their staff are completing reassessments; a green result closes the discussion. The chief nursing officer uses it in a quality report to the board as evidence that the hospital's fall prevention policy is followed. And surveyors may see it as evidence of a working process. Each audience reads the figure as describing assessment, and none sees the footer, the walkthrough or the exclusions. A number that is technically accurate about what it counts can still mislead every reader about what it means.

A related measure, falls per 1,000 patient-days, is reported on the same dashboard and has not fallen in three years. A reader who saw both figures side by side might wonder how near-perfect reassessment compliance coexists with an unchanged fall rate. The answer this brief suggests is that the reassessments being counted are, in many shifts, not reassessments at all.

What Would Make the Number Meaningful

Three changes would help. First, count as compliant only those shifts in which at least one Morse item was actively entered or confirmed, which the system can detect through its audit trail. Second, add a companion measure: the proportion of patient-shifts in which the Morse score changed from the previous shift, since a unit where scores almost never change is probably not reassessing. Third, include observation patients. A validity check would compare a sample of filed scores with a bedside reassessment by a second nurse; if they disagree often, the dashboard is measuring documentation, not assessment. Sociotechnical models of health information technology emphasize that measures, workflows and technology interact, and that a measure can look healthy while the practice beneath it has drifted (Sittig & Singh, 2010).

Until the measure changes, managers should treat a green result as evidence that sections were filed, and nothing more.

The quality department has agreed to discuss these changes at its next measures review.

A draft of the revised measure is attached to this brief for comment.

References

Halpern, S. D., Ubel, P. A., & Asch, D. A. (2007). Harnessing the power of default options to improve health care. New England Journal of Medicine, 357(13), 1340-1344. https://doi.org/10.1056/NEJMsb071595

Sittig, D. F., & Singh, H. (2010). A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Quality and Safety in Health Care, 19(Suppl. 3), i68-i74. https://doi.org/10.1136/qshc.2010.042085

Tsou, A. Y., Lehmann, C. U., Michel, J., Solomon, R., Possanza, L., & Gandhi, T. (2017). Safe practices for copy and paste in the EHR: Systematic review, recommendations, and novel model for health IT collaboration. Applied Clinical Informatics, 8(1), 12-34. https://doi.org/10.4338/ACI-2016-09-R-0150