| Course | DNP 825 Health Information Management and Informatics |
|---|---|
| Module | Module 7 |
| Paper type | Informatics data plan |
| Length | About 1,016 words, 6 pages |
| Format | APA 7 student paper |
| School | Aspen University |
| Program | DNP |
| Updated | September 2026 |
Free sample paper for DNP 825 Module 7
From Risk Score to Bedside Poster: The Data Plan for an Electronic Fall Prevention Tool Kit
Student Name
Doctor of Nursing Practice Program, Aspen University
DNP 825: Health Information Management and Informatics
Instructor Name
Month Day, Year
From Risk Score to Bedside Poster: The Data Plan for an Electronic Fall Prevention Tool Kit
Informatics-supported quality programs depend on data at two levels: data that drive the intervention itself, and data that show whether the intervention works. A program that uses the electronic health record to generate individualized fall prevention plans needs both. This paper sets out the data plan for implementing an electronic fall prevention tool kit on six medical-surgical units of a composite 280-bed hospital, describing the evidence for the tool kit, its data inputs and outputs, the outcome and process measures, data sources and owners, and quality checks.
The Evidence
The tool kit approach was tested in a cluster randomized trial on 8 units in four hospitals. Nurses completed a fall risk assessment in the record, and software generated a tailored plan, including a bed poster, a patient education handout and a care plan, based on each patient's specific risk factors. Falls were significantly lower on intervention units, 3.15 compared with 4.18 per 1,000 patient-days, with the largest effect among patients aged 65 and older, although there was no significant effect on fall-related injuries (Dykes et al., 2010). A later evaluation of a revised, patient-centered version that engaged patients and families in the plan found a 15% reduction in falls and a 34% reduction in injurious falls after implementation across 14 units in three health systems (Dykes et al., 2020).
Data That Drive the Intervention
The tool kit's inputs come from nursing documentation already in the record. The foundation is the Morse Fall Scale, a validated tool developed from a prospective study of hospitalized patients with six items: prior falls, a secondary diagnosis, walking aids, an intravenous line or lock, gait, and the patient's awareness of their own limits (Morse et al., 1989). The software also draws on the medication list to flag drugs associated with falls, such as sedatives and diuretics, on elimination needs documented by nurses, and on the mobility assessment. From these inputs, the software produces three outputs: a bedside poster with plain-language icons showing the patient's specific risks and interventions, a handout reviewed with the patient and family, and nursing care plan entries.
Because the plan is only as good as its inputs, the data plan specifies when inputs must be updated: at admission, every shift, after any fall, after transfer, and when a sedating or diuretic medication is started. A rule in the record prompts reassessment when any of these events occurs.
Outcome and Process Measures
The primary outcome is the fall rate per 1,000 patient-days, with falls defined as unplanned descents to the floor, with or without injury, including assisted falls. The secondary outcome is the injurious fall rate, falls resulting in any injury, graded by severity. Both are reported monthly by unit. Process measures include the proportion of patients with a completed fall risk assessment within four hours of admission, the share of at-risk patients whose printed bedside poster is current verified at a weekly audit, and the proportion of patients who can state their fall risk and one prevention step when asked during leadership rounds. A balancing measure, the use of bed alarms and restraints, checks that the program does not increase restrictive practices.
| Measure | Definition | Source | Owner |
|---|---|---|---|
| Falls per 1,000 patient-days | All falls divided by patient-days, multiplied by 1,000 | Safety event reports; census | Quality analyst |
| Injurious falls per 1,000 patient-days | Falls with any injury | Safety event reports verified by chart review | Quality analyst |
| Timely risk assessment, % | Morse score within 4 hours of admission | Record flowsheet | Nurse informaticist |
| Current bedside poster, % | Poster present and matches current plan | Weekly unit audit | Unit manager |
| Patient engagement, % | Patient states risk and one step | Leadership rounding tool | Nurse manager |
| Bed alarm and restraint use | Patient-days with alarm or restraint | Record flowsheet | Nurse informaticist |
Privacy and Access
Most of the program's data are used within routine care and quality improvement, but some require care. Bedside posters display a patient's fall risks in a room where visitors may see them, so the posters use icons and general phrases, such as needs help to walk, rather than diagnoses or medication names. Unit-level fall reports shared with staff omit patient names, and the chart reviews that verify injuries are performed only by quality staff with a need to know. Data extracts for the quality analyst are stored on the hospital's secure network, and any use of the data for publication will go through the organization's review process for quality improvement or research, as appropriate.
Data Quality Checks
Falls reported through the safety event system are known to be undercounted, so each month the quality analyst compares event reports with a search of nursing notes for words indicating a fall and reviews discrepancies. Injury severity is confirmed by chart review rather than relying on the initial report. The nurse informaticist tests the software's logic monthly by reviewing ten plans against the patients' documented risk factors. Patient-days come from the census system and are reconciled with the finance department's figures quarterly.
Analysis and Reporting
Monthly fall and injurious fall rates will be plotted on control charts appropriate for rates, with 12 months of baseline data, and results will be shared with each unit's staff monthly and with the nursing quality council quarterly. Because falls are relatively rare on a single unit, the program will also report the combined rate for all six units and will not draw conclusions from a single unit's month-to-month changes.
Process measures will be reviewed weekly during the first three months, when problems with inputs and printing are most likely to appear.
Conclusion
An electronic fall prevention tool kit uses nursing data to generate patient-specific plans, and evidence shows it can reduce falls and, in its patient-centered version, injurious falls. The data plan specifies the inputs that drive the plans and when they are updated, defines outcome, process and balancing measures with sources and owners, builds in quality checks for undercounted events and faulty logic, and sets an approach to analysis suited to rare events. With that foundation, the program can be judged on reliable data.
References
Dykes, P. C., Burns, Z., Adelman, J., Benneyan, J., Bogaisky, M., Carter, E., Ergai, A., Lindros, M. E., Lipsitz, S. R., Scanlan, M., Shaykevich, S., & Bates, D. W. (2020). Evaluation of a patient-centered fall-prevention tool kit to reduce falls and injuries: A nonrandomized controlled trial. JAMA Network Open, 3(11), Article e2025889. https://doi.org/10.1001/jamanetworkopen.2020.25889
Dykes, P. C., Carroll, D. L., Hurley, A., Lipsitz, S., Benoit, A., Chang, F., Meltzer, S., Tsurikova, R., Zuyov, L., & Middleton, B. (2010). Fall prevention in acute care hospitals: A randomized trial. JAMA, 304(17), 1912-1918. https://doi.org/10.1001/jama.2010.1567
Morse, J. M., Black, C., Oberle, K., & Donahue, P. (1989). A prospective study to identify the fall-prone patient. Social Science & Medicine, 28(1), 81-86. https://doi.org/10.1016/0277-9536(89)90309-2
DNP 825 Module 7 instructions, in plain terms
The Module 7 prompt is shared with Aspen students in the classroom only, so this example is matched to the catalog wording on tools and techniques for planning and implementing data-supported quality improvement programs. A data plan paper usually asks you to specify which data an intervention uses, which measures will evaluate it, where the data come from, and how quality and privacy are protected. Check whether your prompt requires a measures table, a specific analysis method or a data dictionary. Some instructors want the plan tied to your project from earlier modules. Ask whether an appendix is permitted, and check the page range and source minimum.
Inside the DNP 825 Module 7 example
At roughly 1,015 words, the example is built in seven sections. The evidence section summarizes two trials and notes the difference in their findings on injuries. Data that drive the intervention explains the inputs, including the Morse score, mobility and medications, and the bedside outputs they generate. Outcome and process measures appear in a table with definitions, sources, frequencies and owners. Privacy and access sets who can see which data. Data quality checks include auditing that scores are entered and that the software logic matches its specification. Analysis and reporting explains why falls are charted as rates on control charts and injuries tracked over longer periods. The conclusion restates the plan's two levels of data.
Reading the DNP 825 Module 7 grading rubric
The rubric for a data plan will give the most weight to completeness and precision. This example earns those points with a measures table that defines each measure and names its owner, and the margin notes explain why ownership makes a plan operational. Checking the software's logic is a detail that shows informatics depth, which graders at the doctoral level look for. The analysis section addresses the statistical criterion by choosing methods suited to rare events. Privacy and access cover the ethical criterion. Organization moves from evidence to inputs to measures to quality to analysis. Formatting credit here depends on the measures table following APA rules and on citing both trials without error.
DNP 825 Module 7 help from the desk
A frequent mistake is listing measures without definitions, which makes the plan impossible to carry out consistently. Define numerators, denominators and sources. Students also forget that the intervention itself runs on data, so errors in inputs, such as a missing risk score, undermine it before any outcome is measured. Include checks on inputs. Another problem is analyzing rare events, such as injurious falls, month by month, which produces noise. Use longer periods or appropriate charts. Papers also skip privacy, even for data that seem harmless. Finally, match your measures to the evidence. If the trials showed effects on injuries, your plan should track injuries, not only total falls.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Aspen University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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DNP 825 Module 7 questions, answered
What does DNP 825 Module 7 usually ask for?
Aspen's DNP 825 description includes planning informatics-supported quality programs, so a data plan for such a program is a typical assignment. Check your classroom for the prompt.
How should fall rates be measured?
Commonly as falls per 1,000 patient-days, with a clear definition of a fall and separate reporting of injurious falls, using event reports checked against other sources.
Why check the software's logic?
Because a tool that generates care plans from record data can produce wrong plans if inputs are missing or rules are faulty, so regular testing against patients' actual risk factors is needed.
Where can I find a free DNP 825 Module 7 sample paper?
The entire data plan for an electronic fall prevention tool kit is posted on this page, including the measures table, and each section carries a note on why it is there; reading it costs nothing. If you want a data plan for a different intervention, send your prompt through the form.
How should fall rates be measured in DNP 825 Module 7?
Report falls per 1,000 patient-days so units of different sizes can be compared, and track injurious falls separately because they are rarer and matter more. This example charts both and assigns each a data source and an owner.