
Connected Medication Systems Assessment for a 12-Hospital Group
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Overview
What we built
A 12-hospital health system had three generations of medication dispensing cabinets and infusion pumps scattered across its sites and no record of what was actually installed, or what it was paying to keep it running. We built the inventory, then the roadmap to fix it.
In plain terms: years of mergers had left the health system running three generations of connected medication dispensing cabinets and infusion pumps, with nobody holding a single list of what sat in which hospital. Service contracts for overlapping platforms kept renewing automatically, nobody could say how old the firmware on any given device was, and at some hospitals the pumps fed readings straight into the electronic health record while at others nurses had to copy the same infusion numbers in by hand.
We ran a structured assessment across all 12 hospitals to find out what was really there and what to do about it. Network discovery and physical walkthroughs built a device-level inventory, each platform was scored against the others, and every contract was checked against actual usage. The result was a board-approved roadmap naming one target platform and a phased plan to retire the rest, cutting annual device support and licensing spend by 19% in the first budget cycle alone.
The Problem
Uninventoried medication device sprawl
Every hospital that joined the system through a merger brought its own medication dispensing cabinets and infusion pumps with it, and over time that left three distinct generations of equipment running side by side across all 12 hospitals. Nobody held a single inventory of what was actually installed where, so basic questions, how many devices, which platform, which hospital, took weeks to answer instead of minutes.
Because there was no consolidated view, service contracts for the overlapping platforms simply kept renewing on their own schedules, with different teams managing different vendor relationships and little visibility into whether any given contract still matched what was in use. Firmware ages were unknown across the fleet, so nobody could say with confidence which devices were still receiving security updates and which had quietly fallen behind.
The gap showed up hardest at the bedside. Pump data reached the electronic health record automatically at some hospitals but not others, so nurses at the unintegrated sites had to read infusion numbers off the pump and key them into the chart by hand, doubling the documentation work on every shift and adding a point where transcription errors could creep in.
Merger-driven sprawl
Three generations of dispensing cabinets and infusion pumps accumulated through mergers, spread across 12 hospitals with no single record of what was installed where.
Contracts on autopilot
Service contracts for overlapping platforms renewed automatically, managed by different teams with no shared check against what devices were actually still in use.
Unknown firmware ages
Nobody could say with confidence how old the firmware on any given device was, leaving support status and security exposure impossible to assess.
Manual double documentation
Pump data reached the electronic health record automatically at some hospitals but not others, forcing nurses at the rest to copy infusion numbers in by hand.
What it was costing them
Every month the sprawl went unaddressed meant paying for overlapping service contracts on platforms doing the same job, running devices whose firmware and security posture nobody could vouch for, and asking nurses at some hospitals to double-document every infusion by hand instead of trusting a system connection that already worked elsewhere in the network.
The Solution
Assessment-led consolidation roadmap
We treated the assessment as a single structured programme run across all 12 hospitals rather than site-by-site audits. Network discovery tools mapped every connected device automatically, and physical walkthroughs at each hospital confirmed what the network scans found and caught anything sitting off the network entirely, building a device-level inventory the health system had never had.
With the inventory in hand, we scored each dispensing and infusion platform on integration depth, security posture and support status, giving leadership a like-for-like comparison instead of competing vendor pitches. Every existing contract was mapped against actual device usage, exposing which agreements covered equipment that was barely deployed and which had quietly kept overlapping platforms alive past their usefulness.
The output was a single board-approved roadmap: one target platform named, and a sequenced three-year plan for migrating hospitals onto it and retiring the rest. Sequencing gave priority to the hospitals with the worst pump-to-EHR gaps, so the sites forcing nurses into manual double documentation were among the first scheduled to move.
Key decisions
Inventory before decisions
Network discovery and physical walkthroughs built a full device-level inventory across all 12 hospitals before any platform comparison began.
Score platforms like-for-like
Each dispensing and infusion platform was scored on integration depth, security posture and support status, replacing vendor pitches with one shared comparison.
Map contracts to usage
Every service contract was checked against actual device usage, surfacing overlapping agreements that had been renewing on autopilot for platforms nobody still relied on.
One named target platform
Leadership backed a single target platform rather than the fleet of three generations already running, giving the whole system one architecture to migrate toward.
Sequence by EHR gap
The three-year migration plan prioritised hospitals with the worst pump-to-EHR integration gaps first, so manual double documentation ended soonest where it hurt most.
Measurable Impact
What changed after launch
The assessment turned a sprawling, undocumented device fleet into a single number the health system could act on: 1,380 connected devices inventoried across the 12 hospitals, with 27% found running past end-of-support firmware. That visibility alone reframed the consolidation conversation from guesswork to a scored, board-approved decision.
The roadmap it produced is already paying for itself. Retiring overlapping contracts cut annual device support and licensing spend by 19% in the first budget cycle, and the pump-to-EHR integration gap was closed at 4 hospitals within 90 days, ending manual infusion double-charting at those sites.
Device inventory
No record of what was deployed where
1,380 devices inventoried across 12 hospitals
Platform strategy
Three overlapping generations of equipment
One board-approved target platform for the system
EHR integration
Pump data missing at some hospitals
Gaps closed at 4 hospitals within 90 days
Support spend
Overlapping service contracts renewing on autopilot
Annual spend cut 19% in first budget cycle
Headline results
Annual device support and licensing spend cut by 19% in the first budget cycle by retiring overlapping contracts
Three overlapping dispensing and infusion platforms consolidated to one board-approved target architecture
Pump-to-EHR integration gaps closed at 4 hospitals within 90 days, ending manual infusion double-charting there
1,380 connected devices inventoried across 12 hospitals, surfacing 27% running past end-of-support firmware
Tech & Tools Used
What powered the build
Every tool below earned its place in this engagement. Here is the part each one played.
Lansweeper
Ran the network discovery scans that surfaced every connected dispensing cabinet and infusion pump across the hospital group, forming the backbone of the device-level inventory.
Nmap
Swept hospital networks during the walkthroughs to catch devices sitting outside normal discovery, confirming nothing in the fleet was missed from the inventory.
ServiceNow CMDB
Became the system of record for the consolidated device inventory, linking each cabinet and pump to its platform, hospital and support status.
Mirth Connect
Handled the interface engineering behind pump-to-EHR connections, closing the integration gaps that had been forcing nurses into manual double documentation.
PostgreSQL
Stored the structured inventory and scoring data behind the assessment, keeping device records, platform scores and contract mappings queryable through the programme.
Python (pandas)
Reconciled contract records against the device inventory, matching what was being paid for against what was actually deployed and in use.
Power BI
Turned the inventory and scoring data into the dashboards leadership used to compare platforms and track firmware age across the fleet.
Jira
Tracked the phased three-year migration and retirement plan, carrying tasks and owners for each hospital as it moved to the target platform.
Confluence
Held the assessment findings, platform scoring rationale and the board-approved roadmap in one place the whole programme referenced.
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