Source: Enterprise Imaging Operations - Incident and Service Request Data (2022-2024)
Analyze trends regionally and at the hospital level to gain insights into IT service management performance for imaging systems.
- Date Reported: When the incident or request was created.
- Category: Incident (unplanned disruption) vs. Service Request (standard service request).
- System Affected: PACS (Picture Archiving and Communication Systems), RIS (Radiology Information Systems), VNA (Vendor Neutral Archives), DICOM (Digital Imaging and Communications in Medicine), Radiology Workstations, etc.
- Hospital Location & Region
- Resolution Time: Total time taken to resolve the issue.
- Data Acquisition: Obtain the dataset from Enterprise Imaging Operations.
- Data Cleaning and Preparation: Use Python (Pandas) to clean and standardize the data.
- Data Analysis: Identify trends in IT incidents and service requests.
- Data Visualization: Use Tableau to develop reports and dashboards.
- Reporting: Compile insights and recommendations.
- Handle missing values and inconsistencies.
- Remove duplicate records.
- Standardize date and time formats.
- Normalize categorical variables.
- Priority: Remove confidential information (employee/patient names).
- Regional Incident Trends: Identify the most frequent imaging system issues by region and hospital.
- Service Performance: Analyze average resolution times across different locations.
- Hospital Comparisons: Identify hospitals and regions with the highest incident volume.
- Recurring Issues: Determine if certain failures are repeated.
- Total Tickets: 75,991
- Service Requests: 47,073
- Incidents: 28,918
- Service requests outnumber incidents, indicating routine maintenance/support is more frequent, though critical failures still occur.
- 2022: 27,547 tickets (highest volume)
- Possible causes: new system launches, equipment malfunctions, protocol changes, staffing impacts.
- March: 2,773 tickets (peak)
- Potential causes: seasonal demand spikes or widespread failures.
- Orange: 4,470 tickets
- East Orange: 3,986 tickets
- Mid-Amelia: 3,899 tickets
- Dragonwood: 3,362 tickets
- North Orange: 3,198 tickets
Possible causes: high patient volumes, system concentration, or inadequate proactive maintenance.
- Shawnee Regional Hospital: 2,727 tickets
- Orange Hospital: 2,601 tickets
- Wondergroove Women's Hospital: 1,908 tickets
- VAGA Victoria Medical Park: 1,892 tickets
- Timberland Medical: 1,702 tickets
Indicates either high usage/complexity or need for staff training in technical issue management.
| Priority | Closed Tickets | Cancelled Tickets | Rejected Tickets |
|---|---|---|---|
| Low | 45,194 | 430 | 14 |
| Medium | 835 | 12 | - |
| High | 578 | 9 | - |
| Critical | - | 1 | - |
Most tickets are low priority, showing routine issues are common, but higher priority tickets underscore the need for strong IT systems.
- Orange: 8,018 tickets
- East Orange: 3,042 tickets
- Overlong: 2,613 tickets
- Algrove: 1,936 tickets
- Gardner Woods: 1,521 tickets
Orange region stands out for systemic issues or heavy system usage.
- Orange Hospital
- Celebration Hospital
- Solutions Center Hospital
- East Orange Hospital
- Algrove General Hospital
Orange Hospital has especially high incident rates (notably for PACS), suggesting frequent failures or monitoring gaps.
| Priority | Closed Tickets | Cancelled Tickets |
|---|---|---|
| Low | 12,530 | 1,210 |
| Medium | 13,960 | 549 |
| High | 646 | 22 |
| Critical | 1 | - |
- Orange Region and Orange Hospital should be prioritized for more IT support, training, and proactive maintenance.
- Consider increased staffing and automated monitoring tools.
- Regions like AIT Enterprise Imaging Apopka and FHPG Brandon with slow resolution times need targeted reviews and possible upgrades.
- High incident volumes (especially in Orange) require preventive strategies: regular maintenance, health checks, predictive analytics.
- High-volume hospitals (e.g., AH Orlando, AH Altamonte) may benefit from additional technical training to reduce ticket volumes.
- Drill down into data for high-incident regions to identify root causes and enable targeted, efficient resource allocation.
