HAI Surveillance Dashboard Template

Purpose and how to use this template

This starter dashboard helps IPC teams detect trends and outbreaks quickly, focus prevention where it matters, and turn surveillance into timely action. Use it as a reproducible template you can copy, map to your data sources, and adapt to facility type (acute care, long-term care, outpatient, home health).

Primary hungers this dashboard serves

• Detect rising infection rates and clusters early
• Prioritize prevention resources by location, device, and pathogen
• Provide repeatable data for weekly surveillance huddles and action tracking

High-level layout

• Top row: current key indicators and alerts (SIRs, device-associated rates, percent change)
• Middle: trend charts (by week/month), pathogen breakdown, location heatmap
• Bottom: table of active investigations, recent culture-positive patients, and data-quality flags
• Side panel / filter: date range, facility/unit, device type, pathogen, admitting service, risk group

Key metrics and definitions (implement exactly)

1. Observed infections (numerator) — count of surveillance-defined HAI events during the period. Be explicit about surveillance definitions (e.g., NHSN criteria) and include the event date used for counting (onset date vs. report date).
2. Denominator — device-days, patient-days, or procedures performed depending on HAI type. Define calculation window (e.g., device-days measured daily and summed for the reporting period).
3. Rate — (Numerator / Denominator) × standard multiplier (e.g., per 1,000 device-days or per 100 procedures).
4. Standardized Infection Ratio (SIR) — observed infections divided by predicted infections using your chosen baseline and risk adjustment model. Document baseline period and model used.
5. Percent change vs baseline — short-term change (week-over-week) and longer-term change (month-over-month).
6. Cluster count — number of units/locations with signals above threshold in period.

Suggested visualizations

• Small multiples line charts: weekly rates for each HAI type (CLABSI, CAUTI, SSI, VAE, CDI)
• Heatmap by unit and week: quickly surfaces where rates spike
• Stacked bar or pie: pathogen distribution for device-associated infections
• Table with sparklines: recent trend per unit with signal flag
• Map or floor-plan overlay (if available): visualize clusters spatially

Filters and drill-downs

• Date range (rolling 4 weeks, 12 weeks, 12 months)
• Facility / campus / unit / ward
• Device type (central line, urinary catheter, ventilator)
• Procedure type (for SSI dashboards)
• Organism / pathogen
• Onset vs. admission timing (community onset vs. hospital onset)

Alert thresholds (examples and rationale)

Thresholds must be adapted to local baseline rates and statistical stability. Use a combination of rule types:

1. Absolute threshold: e.g., ≥2 device-associated infections in a single unit within 7 days triggers review for potential cluster.
2. Relative increase: e.g., >50% week-over-week increase in rate (with minimum denominator) to avoid noise on small volumes.
3. Statistical control rule: Shewhart or CUSUM rules applied to weekly rates to detect non-random variation.
4. SIR exceedance: SIR >1.5 or statistically significant SIR >1 depending on risk tolerance.

Sample query pseudocode (map to your schema)

Data-quality checks

• Missing onset dates or admission dates — flag and exclude until corrected.
• Zero or implausible denominators — require minimum device-day threshold before computing rates.
• Duplicate event IDs — identify by patient, event type, and onset date.
• Sudden denominator drops — compare daily device-days to prior week to detect recording issues.
• Late-reporting adjustments — mark events entered after the surveillance period to avoid double-counting in running dashboards.

Weekly surveillance huddle agenda (30–45 minutes)

1. Opening (2 min): facilitator states objective (rapid detection + action).
2. Top-line metrics (5 min): current rates, SIRs, and any active alerts.
3. Drill-down (10–15 min): review units with signals, review individual cases for common source or process gaps.
4. Action review (8–10 min): status update on previously assigned actions (audits, staffing, supplies, isolation).
5. Assign actions (5 min): clear owners, due dates, required evidence (audit, staff re-education, engineering fixes).
6. Data quality and next steps (5 min): note data issues, confirm next meeting time, and who will run deep-dive analyses if needed.

Investigation workflow (quick checklist for triggered alerts)

• Confirm case definitions and dates
• Check device utilization and insertion practices during exposure window
• Review microbiology and antibiogram patterns
• Audit compliance with relevant bundles (central line bundle, catheter care, SSI checklists)
• Consider unit-level environmental or personnel links

Adaptations for different settings

• Smaller hospitals/LTC: prefer absolute counts and simple rolling averages (statistical rules may be unstable with small denominators).
• Outpatient surgical centers: focus on SSI per procedure and 30-day follow-up capture.
• Home health: focus on wound and device-related infection reporting, with patient-visit denominators.

Implementation notes and governance

• Document your numerator/denominator logic and baseline period inside dashboard metadata so viewers understand comparability.
• Maintain a short glossary panel (definitions, baseline, multipliers) visible to users.
• Set up role-based views: IPC specialists get case lists and microbiology, unit leaders see unit-level trends and actions.
• Log all alert acknowledgements and investigation outcomes in the dashboard to create organizational memory for learning.

Exportable mockups and handoffs

Create PNG/PDF exports of the top-line view for leadership and a detailed export (CSV) for deeper case review. Include an export template that populates fields needed for external reporting and accreditation.

Next steps and tailoring checklist

1. Map data sources to the required fields (patient, encounter, onset_date, device flags, unit, organism, procedure).
2. Implement the numerator/denominator queries and validate against historical reports.
3. Configure alert rules and test with backfilled historical data to tune thresholds.
4. Run the dashboard in parallel with current surveillance for 4–8 weeks before switching to it as the primary view.

Note: This dashboard supports timely detection and coordinated response but does not replace clinical judgment, infection prevention policy, or required reporting pathways. Tailor thresholds and definitions to your local standards and accreditation rules.