PM Compliance Collapse at a Three-Site Community Hospital
A 340-bed community hospital with a two-building off-campus footprint runs about 4,100 devices on a 15-year-old CMMS. PM completion has fallen from 94% to 78% over three quarters, and the last two survey cycles produced findings on missed PMs for anesthesia machines and defibrillators. The department has 6.5 FTE biomeds against a benchmark of 8.2. Roughly 38% of pump work orders are logged as 'no problem found,' concentrated on two nursing units. Leadership has approved one additional FTE for next fiscal year only and wants a 90-day plan. The CMMS vendor quotes $85,000 to migrate to a scheduling module; IT will export flat files but will not build or support a custom interface. The CNO has offered nursing staff time for any data collection you need.
1. Sequence the first 90 days. What happens in weeks 1-2, weeks 3-6, and weeks 7-12, and what do you deliberately defer? Justify the ordering.
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Weeks 1-2 - contain the accreditation exposure. Do not start with software. Pull the life-support population (anesthesia, ventilators, defibrillators, critical-care pumps) and hand-verify PM status against current manufacturer intervals. Anything overdue is completed or the device comes out of service with a documented notice. This is the only item with a hard external deadline and a patient-safety consequence.
Weeks 3-6 - fix the data before the process. Build a clean inventory from the flat-file export plus a physical sweep of the off-campus buildings. Missed PMs at satellite sites usually trace to inventory inaccuracy, not technician diligence. Score every device on a simple risk matrix (life support, utilization, prior finding, redundancy) and start a weekly 60-day lookahead huddle with named owners.
Weeks 7-12 - harden the process and attack the pump cluster. Write interval-change control and escalation rules (30/60/90 days overdue), stand up a one-page monthly scorecard for the safety committee, and run the pump investigation.
Defer: the CMMS migration decision, non-critical device inventory, and a staffing benchmark study. Rationale: the finding is live risk; the CMMS is a contributing cause, not the cause, and buys no compliance in the next two quarters.
2. The CFO offers $85,000: migrate the CMMS or buy third-party PM coverage for imaging. How do you frame the decision, and what would change your answer?
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Frame it as risk reduced per dollar and time to effect, not as a software-versus-service preference.
- The migration has a 6-9 month lead time, depends on an interface IT will not support, and would leave the scheduling module partly unpopulated on day one. It moves the 78% number slowly and indirectly.
- Third-party imaging PM buys out technician hours and competency you may genuinely lack, but imaging PMs are not what the survey cited. It does not touch the anesthesia and defibrillator gap.
So the honest answer is a third option: spend a small amount (roughly $10-15k of internal and consultant time) on inventory cleanup, interval control, and a flat-file-driven lookahead report, and defer the $85,000 until an interface is supported. Consider third-party coverage only for modalities where in-house competency is genuinely absent and the vendor is the only realistic source of calibration.
What would change my answer: if the flat-file export cannot produce a reliable due-date report, or if the 78% figure is driven mainly by imaging PMs, the migration or the service contract respectively becomes the higher-yield purchase. State the test explicitly: which option moves compliance in the next two quarters, and at what cost per point of compliance.
3. Nursing wants the 'no problem found' pump cluster fixed. What do you investigate, and how do you avoid a blame framing?
Read the worked answer
Treat the 38% as a signal to be decomposed, not a behavior problem.
Pull data first: serial numbers and lot codes of every pump generating an NPF order, the unit and shift, the error or alarm code captured before the ticket was opened, battery age and last battery test, drop and impact logs if the fleet supports them, and whether the drug library was current on those units.
Then set a discriminating threshold. If a small set of serials accounts for most NPFs, it is equipment - likely end-of-life batteries causing undervoltage or occlusion faults, worn door or air-in-line sensors, or a firmware defect - and the fix is bench testing, battery replacement, or removal from service. If NPFs are spread evenly across the fleet but clustered on one shift or one unit, it is a workflow or training issue, and it routes to nursing education.
Avoid blame framing by bringing data to a short multidisciplinary review (nursing, pharmacy, biomed) rather than a memo about user error, and by stating up front that the purpose is to find the failure mode. Report back in writing with the counts and the disposition. Nurses who fear the answer will be 'you broke it' will stop reporting, and the NPF rate will look better while the risk stays exactly where it is.