Johns Hopkins Appendix B Question Development Tool: How to Complete It (Inpatient Falls Example)
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Johns Hopkins Nursing Evidence-Based Practice: Appendix B Question Development Tool
- What is the problem?
- Why is the problem important and relevant? What would happen if it were not addressed?
- What is the current practice?
- How was the problem identified? (Check all that apply: safety and risk-management concerns; quality concerns (efficiency, effectiveness, timeliness, equity, patient-centeredness); unsatisfactory patient, staff or organizational outcomes; variations in practice within the setting; variations in practice compared to community standard; current practice that has not been validated; financial concerns)
- What are the PICO components? P (patient, population or problem), I (intervention), C (comparison with other interventions, if foreground question), O (outcomes: qualitative or quantitative measures to determine the success of change)
- Initial EBP question (background or foreground)
- List possible search terms, databases to search and search strategies.
- What evidence must be gathered? (Publications such as EBSCOhost, PubMed, CINAHL, Embase; standards; guidelines; organizational data; position statements)
- Revised EBP question
- Outcome measurement plan: what will we measure (structure, process, outcome), how (metrics as rate or percent), how often, where will we obtain the data, who will collect it and to whom will we report it?
© 2017 The Johns Hopkins Hospital/Johns Hopkins University School of Nursing. The form is used to develop an answerable EBP question and to guide the team in the evidence search process.
The tool in four moves
Define the problem
Items 1–4: what, why, current practice, how it was found
Items 1–4: what, why, current practice, how it was found
Frame the question
Items 5–6: PICO and an initial background or foreground question
Items 5–6: PICO and an initial background or foreground question
Plan the search
Items 7–8: terms, databases and types of evidence
Items 7–8: terms, databases and types of evidence
Refine and measure
Items 9–10: revised question and outcome metrics
Items 9–10: revised question and outcome metrics
How to complete each item
| Item | What to write | Common mistakes |
|---|---|---|
| 1. Problem | The actual gap, quantified with local data where possible (for example, “falls with injury rose from 0.5 to 0.9 per 1,000 patient days”). | Writing a solution as the problem (“we need hourly rounding”). The tool says the problem statement must not include a solution. |
| 2. Importance | Harm to patients, staff or the organization; cost, regulatory or reputational consequences; what happens if nothing changes. | Vague statements such as “it is important for patient safety” with no evidence. |
| 3. Current practice | What staff actually do now, based on policies, observation and conversations with clinicians. | Copying the written policy without checking whether it is followed. |
| 4. How identified | Check every box that applies and briefly explain the data source (incident reports, audits, dashboards, complaints). | Checking boxes without evidence. |
| 5. PICO | P: specific population and setting. I: the intervention or best practice. C: current practice or alternative (foreground only). O: measurable outcomes. | Outcomes that cannot be measured (“better care”); a comparison listed for a background question. |
| 6. Initial question | Background if the team knows little and wants best practices; foreground if comparing specific interventions. | Yes/no questions that are too narrow to search. |
| 7. Search terms and strategy | Keywords and synonyms for each PICO element, subject headings, Boolean operators, databases and limits, documented for replication. | Listing only one keyword or one database. |
| 8. Evidence to gather | Research publications plus standards, guidelines, organizational data and position statements. | Ignoring local data and professional guidelines. |
| 9. Revised question | A refined, usually foreground PICO question after the initial evidence review. | Repeating item 6 unchanged without explanation. |
| 10. Measurement plan | Structure, process and outcome measures converted to metrics with a numerator and denominator, frequency, data source, collector and audience. | Measures without denominators (“number of falls” instead of falls per 1,000 patient days). |
Background vs. foreground questions
- Background: broad, used when the team has little knowledge or expertise; often identifies best practices. Example: “What are the best practices to prevent falls in hospitalized older adults?”
- Foreground: focused, compares two or more interventions and produces a narrower body of evidence. Example: “In hospitalized older adults, does purposeful hourly rounding compared with usual care reduce falls?”
Completed example: inpatient falls
Original example for study purposes. Unit data are placeholders; replace them with your own.
| 1. What is the problem? | Patient falls on our 32-bed adult medical-surgical unit have increased over the past year. The unit recorded [n] falls ([__] per 1,000 patient days), including [n] falls with injury ([__] per 1,000 patient days), above the hospital target of [__]. Most falls occurred when patients got up unassisted to use the bathroom, particularly at night. |
| 2. Why is the problem important? | Falls can cause fractures, head injuries, longer stays, loss of independence and death, and they increase fear and anxiety for patients and families. Falls with serious injury are considered never events, are tracked by The Joint Commission and are not reimbursed by CMS when hospital-acquired. If unaddressed, patients will continue to suffer preventable harm, and staff morale and the unit’s quality scores will decline. |
| 3. What is the current practice? | Nurses complete a fall risk score on admission and every shift and place a yellow wristband and bed alarm for high-risk patients. Rounding is inconsistent and not structured; toileting needs are addressed mainly when patients call. Observation shows bed alarms are often not activated, and call lights at night have long response times. |
| 4. How was the problem identified? | Safety and risk-management concerns (incident reports) Quality concerns (effectiveness, timeliness) Unsatisfactory patient and staff outcomes Variations in practice within the setting (rounding differs by shift) ☐ Variations in practice compared with community standard Current practice that has not been validated Financial concerns |
| 5. PICO | P: adult inpatients at high risk for falls on a medical-surgical unit. I: purposeful hourly rounding addressing pain, position, personal needs (toileting) and possessions within reach. C: usual care (unstructured rounding and call-light response). O: fall rate and falls with injury per 1,000 patient days; call-light use; patient satisfaction. |
| 6. Initial EBP question | Background: What are the best practices for preventing falls among hospitalized adults at high risk for falls? |
| 7. Search terms, databases and strategies | Terms: accidental falls, fall prevention, inpatient falls, purposeful rounding, hourly rounding, intentional rounding, toileting, bed alarm, medical-surgical, hospitalized adults. Subject headings: MeSH “Accidental Falls/prevention & control”; CINAHL “Accidental Falls,” “Nursing Rounds.” Boolean: (“accidental falls” OR “inpatient falls”) AND (“hourly rounding” OR “purposeful rounding” OR “intentional rounding”) AND (hospital* OR “medical-surgical”). Databases: CINAHL, PubMed, Cochrane Library, Joanna Briggs Institute. Limits: last 5 years, English, adults. Document the date and number of results for each search. |
| 8. What evidence must be gathered? | Publications (CINAHL, PubMed) Standards (Joint Commission sentinel event guidance) Guidelines (AHRQ Preventing Falls in Hospitals toolkit; clinical practice guidelines on fall prevention) Organizational data (incident reports, fall rates, call-light data, staff and patient input) Position statements (professional nursing organizations) |
| 9. Revised EBP question | Foreground: In adult medical-surgical inpatients at high risk for falls (P), does purposeful hourly rounding (I), compared with usual care (C), reduce falls and falls with injury per 1,000 patient days (O)? |
10. Outcome measurement plan
| What will we measure? | How (metric) | How often | Data source | Who collects | Reported to |
|---|---|---|---|---|---|
| Falls (outcome) | Number of falls ÷ patient days × 1,000 | Monthly | Incident reporting system; census data | Unit quality champion | Unit practice council; nurse manager |
| Falls with injury (outcome) | Falls with injury ÷ patient days × 1,000 | Monthly | Incident reports; NDNQI submission | Quality department | Patient safety committee |
| Rounding compliance (process) | Hourly rounding logs completed ÷ hours audited × 100 | Weekly for 3 months, then monthly | Rounding logs; direct observation | Charge nurses | Staff huddles; nurse manager |
| Call-light use (process) | Call-light activations ÷ patient days | Monthly | Nurse call system report | Unit educator | Unit practice council |
| Staffing (structure) | Nursing hours per patient day | Monthly | Staffing and scheduling system | Nurse manager | Nursing leadership |
Frequently asked questions
What is the difference between a measure and a metric?
A measure is an amount or degree of something, such as the number of falls with injury. A metric converts the measure into a rate or percentage with a numerator and denominator, such as falls with injury per 1,000 patient days, so results can be compared before and after the change.
Do I need a comparison (C) in my PICO?
Only for foreground questions. Background questions look for best practices and usually have no comparison.
Can my revised question be the same as my initial question?
Yes, if the evidence review confirms it, but explain why. Most teams move from a broad background question to a focused foreground question after the first search.
Related reading: full EBP question, search and appraisal of three primary studies (CAUTI example), ANA framework for how to read and critique a research study, 114 PICOT question examples and using data to solve a falls problem.
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