How a Regional Healthcare Network Reduced Downtime by 67% Using Active and Passive Monitoring

Executive Summary

Organization: MidState Healthcare Network, a regional healthcare provider operating 12 facilities across three states with 2,400 employees and critical patient care systems running 24/7.

The Challenge: Frequent network outages affecting electronic health records (EHR), patient monitoring systems, and administrative applications. The IT team relied solely on passive monitoring, which only alerted them after users reported problems. Average monthly downtime: 18 hours across all facilities.

The Solution: Implemented a hybrid monitoring strategy combining active monitoring for predictive alerts with passive monitoring for root cause analysis and real user experience tracking.

Key Results:
• 67% reduction in monthly downtime (from 18 hours to 6 hours)
• 85% faster mean time to resolution (MTTR dropped from 47 minutes to 7 minutes)
• 92% of issues detected before affecting end-users
• $340,000 annual savings from reduced downtime and improved productivity
• Zero critical outages during business hours in the six months following implementation

The Challenge: When Reactive Monitoring Isn’t Enough

MidState Healthcare Network faced a problem familiar to many network engineers: they didn’t know about network issues until users were already experiencing downtime.

“We’d get a call from a nurse saying the EHR system was down,” explained David Chen, Network Operations Manager at MidState. “By the time we got the alert, patients were waiting, doctors couldn’t access records, and we were already 10 minutes into an outage. We were always playing catch-up.”

The specific problems:

Reactive-only approach: Their existing monitoring solution used passive network monitoring exclusively. It captured real network traffic and analyzed performance metrics, but only alerted the team when problems were already affecting real users. There was no predictive capability.

Blind spots in critical systems: The passive monitoring showed them what was happening on the network, but didn’t proactively test critical application workflows. A database server could be responding slowly, but they wouldn’t know until users complained about performance issues.

Long troubleshooting cycles: When outages occurred, the team had historical data from passive monitoring but struggled to identify root causes quickly. “We could see that traffic was flowing, but we couldn’t pinpoint exactly where the bottleneck was or what caused the initial failure,” Chen noted.

Business impact: Each hour of EHR downtime cost approximately $18,000 in lost productivity, delayed patient care, and staff overtime. With 18 hours of monthly downtime, the organization was losing over $320,000 annually—not counting the impact on patient satisfaction and regulatory compliance.

The IT leadership team knew they needed a better approach. They needed to catch problems before users noticed them, while still maintaining visibility into real user experience.

The Solution: Building a Hybrid Monitoring Strategy

After evaluating their options, MidState decided to implement a comprehensive monitoring solution that combined both active and passive monitoring approaches. They chose network monitoring tools that could handle both monitoring types in a single platform.

Phase 1: Active Monitoring Implementation (Weeks 1-3)

The team started by deploying active monitoring for their most critical systems:

End-to-end application testing: They configured synthetic tests that simulated complete user workflows—logging into the EHR system, retrieving patient records, updating charts, and logging out. These tests ran every 2 minutes from multiple locations across their network.

Infrastructure availability checks: Active monitoring continuously tested routers, switches, firewalls, and servers with ICMP pings and SNMP queries. Any device that failed to respond triggered immediate alerts.

Database performance validation: Synthetic SQL queries ran every 5 minutes against their critical databases, measuring response time and verifying data accessibility. This caught database performance degradation before it affected applications.

External service monitoring: They set up active checks for their cloud-based backup service, telehealth platform, and third-party lab integration systems—services where they couldn’t deploy passive monitoring sensors.

“The active monitoring gave us eyes on everything critical,” Chen explained. “We went from finding out about problems when users called to getting alerts 5-10 minutes before users would even notice an issue.”

Phase 2: Passive Monitoring Enhancement (Weeks 4-6)

Rather than replacing their existing passive monitoring, they enhanced it to work alongside the new active monitoring:

Real user experience tracking: Passive monitoring continued capturing actual network traffic, showing them how real users experienced applications under various load conditions. This validated what the active monitoring tests predicted.

Bandwidth and capacity analysis: Flow-based passive monitoring revealed which applications consumed network resources, helping them optimize QoS policies and plan capacity upgrades.

Security and anomaly detection: Passive traffic analysis identified unauthorized applications, unusual traffic patterns, and potential security breaches that active monitoring wouldn’t catch.

Root cause analysis capability: When active monitoring detected a problem, the team used passive monitoring data to understand exactly what was happening with real traffic—which packets were being dropped, where latency increased, which users were affected.

Phase 3: Integration and Correlation (Weeks 7-8)

The final phase focused on making both monitoring types work together:

Correlated alerting: They configured alerts that required both active and passive monitoring to confirm issues before paging the on-call engineer. This reduced false positives by 78%.

Unified dashboards: A single dashboard showed both synthetic test results and real user experience metrics, giving the team complete visibility at a glance.

Automated troubleshooting workflows: When active monitoring detected potential issues, it automatically triggered deeper passive analysis of affected network segments, accelerating root cause identification.

The Results: Measurable Improvements Across the Board

Six months after implementing the hybrid monitoring strategy, MidState Healthcare Network saw dramatic improvements in network reliability and operational efficiency.

Downtime reduction: 67% improvement

Monthly downtime dropped from 18 hours to just 6 hours—a 67% reduction. More importantly, 92% of potential issues were now detected and resolved before affecting end-users.

“We went from reactive firefighting to proactive prevention,” Chen said. “The active monitoring catches problems early, and the passive monitoring confirms whether our fixes actually improved the real user experience.”

Faster problem resolution: 85% improvement

Mean time to resolution (MTTR) dropped from 47 minutes to just 7 minutes. The combination of predictive alerts from active monitoring and detailed root cause data from passive monitoring accelerated troubleshooting dramatically.

Example: When active monitoring detected increased database response time at 6:15 AM, the team investigated immediately. Passive monitoring data showed a backup job consuming database resources. They rescheduled the backup, and by 6:22 AM—before the 7:00 AM shift change—the issue was resolved. Total impact: zero users affected.

Cost savings: $340,000 annually

With downtime reduced by 12 hours per month, MidState saved approximately $216,000 annually in direct downtime costs. Additional savings came from reduced troubleshooting time (freeing IT staff for other projects) and improved user productivity, bringing total annual savings to $340,000.

Zero critical outages during business hours

Perhaps most impressively, MidState experienced zero critical outages during business hours in the six months following implementation. All potential issues were caught and resolved during off-peak hours or before they could escalate.

Unexpected benefits:

The team discovered several unexpected advantages of the hybrid approach:

Capacity planning: Passive monitoring data revealed that their video conferencing system consumed 40% more bandwidth than expected during peak hours. They proactively upgraded network capacity before it became a bottleneck.

Security improvements: Passive traffic analysis identified an unauthorized file-sharing application running on 23 workstations, which they quickly removed.

SLA validation: Active monitoring provided objective evidence of uptime and performance for their SLA reports to hospital administration, demonstrating 99.7% availability for critical systems.

Key Takeaways: Lessons Learned

MidState’s experience offers valuable insights for other organizations considering a hybrid monitoring approach.

What worked well:

“Starting with active monitoring for our most critical systems was the right call,” Chen reflected. “We got immediate value—catching problems before users noticed them—which built momentum for the full implementation.”

The team also found that using a single platform for both monitoring types simplified management significantly. “We didn’t want to juggle multiple tools,” Chen noted. “Having both active and passive monitoring in one comprehensive solution made correlation and troubleshooting much easier.”

What they’d do differently:

“We should have implemented correlated alerting from day one,” Chen admitted. “In the first two weeks, we got too many alerts because active and passive monitoring were both alerting independently. Once we configured correlation—requiring both to confirm issues—false positives dropped dramatically.”

The team also wished they’d documented their synthetic test scenarios more thoroughly. “We created tests based on what we thought were critical workflows, but we missed a few edge cases that users actually relied on. Involving end-users in defining test scenarios would have helped.”

Advice for others:

Chen’s recommendations for network engineers considering a similar approach:

Start with your most critical systems: Don’t try to monitor everything at once. Identify your top 5-10 critical applications and infrastructure components, implement active monitoring for those, then expand.

Use both monitoring types together: “Don’t choose between active and passive monitoring—use both,” Chen emphasized. “Active monitoring predicts problems, passive monitoring validates solutions. You need both perspectives.”

Invest time in alert tuning: Expect to spend 2-3 weeks fine-tuning alert thresholds and correlation rules. “The initial alert volume can be overwhelming, but proper tuning makes the system incredibly valuable.”

Measure and communicate results: Track metrics like MTTR, downtime hours, and cost savings. “We presented monthly reports to hospital leadership showing the ROI. That secured budget for expanding the monitoring program to all facilities.”

How You Can Apply This Approach

MidState’s success demonstrates that combining active and passive monitoring delivers measurable results—but implementation requires planning and commitment.

Actionable steps to get started:

Step 1: Audit your current monitoring (Week 1)
Document what you’re monitoring today, identify gaps, and list your most critical systems. Determine whether you’re relying too heavily on reactive passive monitoring or missing real user experience data.

Step 2: Define critical workflows (Week 2)
Work with end-users to identify the most important application workflows and infrastructure components. These become your first active monitoring targets.

Step 3: Implement active monitoring for top priorities (Weeks 3-4)
Deploy synthetic tests for your critical systems. Start with simple availability checks, then add more complex workflow tests. Configure alerts with appropriate thresholds.

Step 4: Enhance passive monitoring (Weeks 5-6)
Ensure your passive monitoring captures real user experience data and provides the detail needed for root cause analysis. Consider implementing protocol monitoring tools for deeper traffic visibility.

Step 5: Integrate and correlate (Weeks 7-8)
Build dashboards that show both active and passive data. Configure correlated alerting to reduce false positives. Create troubleshooting workflows that leverage both monitoring types.

Resources needed:
• Monitoring platform supporting both active and passive monitoring
• 2-3 weeks of dedicated implementation time
• Involvement from application owners and end-users
• Budget for monitoring infrastructure (sensors, storage, licensing)

Expected timeline:
Most organizations see initial results within 2-3 weeks of implementing active monitoring. Full ROI typically materializes within 3-6 months as downtime decreases and troubleshooting efficiency improves.

Ready to build a comprehensive monitoring strategy like MidState Healthcare Network? Explore PRTG Network Monitor for a unified platform that supports both active and passive monitoring, giving you the predictive power and real-world visibility you need to prevent downtime and accelerate troubleshooting.