NURS FPX 6424 Assessment 1: Interprofessional Collaboration in Nursing Practice

Introduction

Data analytics is changing the way nurses work by turning normal clinical data into useful information. This assessment looks at literal unit data to find patterns of missed nursing care, such as missed rounds, delayed drug administration, and deficient attestation. It also suggests an analytics-driven intervention (a targeted dashboard and workflow changes) and lays out a plan for evaluation and sustainability. The design shows how nanny leaders can use descriptive and prophetic analytics to make nanny-sensitive issues and patient safety more.

Background & Problem Statement

Not getting the right nursing care leads to worse-case issues and less happy staff. A 28-bed medical-surgical unit keeps track of “missed hourly rounding” and late drug passes that occur frequently when the night turns into day. The birth review from the last three months shows that 68% of cases are following the hourly rounding rules. It also shows a small but steady rise in the number of times cases ring the call bell and minor cascade. Raise hourly rounding compliance from 68 to 90 in four months and cut down on call-bell use by 25 per case day in the same time frame.

Methods & Analytic Approach

Data sources

• EHR flowsheets (check boxes for rounding, time prints for specifics)
• System for reporting incidents (cascade, call-bell events)
• System for staffing (rates of nurses to cases, skill blend)
• The demographics and inflexibility of the cases (case blend indicator or deputy variables)

Data preparation

• Get 6 months of literal data, remove patient identifiers, and combine datasets using hassle IDs.
• Clean the timestamp, get relief from the duplicate, make secondary variables (e.g., nanosecond intervals and with passage of passage), and total at the shift position.

Descriptive analytics

• Find out how numerous the shifts are, day and nanny, or platoon.
• Use the driving chart to see the pattern and find the variations that have a specific reason.
• Cross-tabulate rounding the match with Call Academy prices and falling events to find some link.

Predictive analytics (lightweight / interpretable)

• produce an introductory logistic region or decision-tree model that predicts the possibility of a case passing further than three call-ball events daily, with a view to rounding matching, staffing conditions, day, and patient sharpness.
• Use perceptivity, particularity, and the field under the ROC wind (AUC) to measure performance. Be apprehensive of clarifying effects so that the nursing leaders can understand what drives them.

Visualization & intervention design

• Make a unit dashboard that reflects the current match with rounding (after shift), the top 5 cases with the loftiest call-bail threat, and a staffing image.
• Make targeted changes, similar to micro-heads on high-threat cranes, rosters to prefer with-passed, and fast job aids to help with attestation.

NURS FPX 6424 Assessment 1: Implementation Plan & Evaluation

PDSA cycles

• PDSA Cycles P (Plan): Micro-huddles for an airman dashboard and a nursing platoon for two weeks.
• D (DO): Use a dashboard every day and make small micro rows at the end of each shift.
• S (study) Keep an eye on how the nurses follow the rules, how frequently they have a discussion, and what they say.
• A (Act): Change the timing of huddles and the triggers on the dashboard.

Metrics

• The number of call-bell events per 1,000 case-hours and the number of cascades per 1,000 case-days.
• Process The chance of high-threat cases that get a micro-huddle and the chance of hourly rounding compliance by shift.
• Balancing the number of twinkles nurses say they spend rounding each shift and the number of overtime hours.

Timeline & stakeholders

• Weeks 0–2 getting data, making a dashboard prototype, and getting input from stakeholders (the nanny director, frontline nurses, the informaticist, and the QI critic).
• Weeks 3 and 4 test and ameliorate.
• Months 2–4: rollout to all units and ongoing monitoring.

Results 

After two PDSA cycles, the airman platoon’s rounding compliance went from 70 to 92, the unit call-bell frequency for airman shifts went down by 30, and staff said that micro-huddles added 5 twinkles to each shift but made it easier to prioritize work. The logistic model showed that missed rounding and staffing blend were the stylish signs of high call-bell days (AUC = 0.78).

Discussion & Leadership Reflection

The platoon used data-driven tools to figure out when and why care was missed, and they supported simple, frontline-led interventions. Nanny leaders need to promote data knowledge, make sure the data is accurate, and stop the blame culture by using dashboards to train and ameliorate instead of discipline. My particular development plan includes learning introductory analytics (Excel → Tableau/Power BI) and being involved in governance for data delineations.

Conclusion

Indeed, introductory analytics (simple, easy-to-understand descriptive and prophetic models) can help plan targeted interventions that cut down on missed care and make the case’s experience better. Frontline power, clear KPI delineations, and regular monitoring linked to unit huddles and performance reviews are each important for sustainability.Discover key insights and a polished structure in our NURS FPX 6424 Assessment 1: Data Mining in Healthcare Practice sample paper.