Avoiding The Maintenance Data Dump: Instrument Management With Style
If you’re a bench scientist, you’re familiar with the phenomenon of lab disorganization and the tactic of using information technology to remedy that disorganization–at least in theory. In many lab networks, there is a digital or physical folder dealing with “ELISA machine maintenance logs.” Lab personnel often dread diving into these folder for a number of reasons, with the largest being that shuffling through incomplete and unstandardized maintenance logs feels like punishment.
Most research labs don’t have or follow the latest manufacturer approved standard operating procedures (SOPs) for operations like basic equipment maintenance, and yet lab personnel are tasked with keeping the machines running however they are able. Keeping machines running means tracking data about their performance, of course–and aggregating that data in one single spot.1 Labs with superior quality information technology software and high usage of that software have an easier time with their maintenance data aggregation, but there’s more to the story than installing a program.
Science Is Passed Down By Word of Mouth
Unfortunately, most labs pass down maintenance procedures by spoken word and demonstrated actions, and the same goes for the metrics tracked during maintenance. In fact, a common practice is for one person to train another in the “correct” maintenance procedure and equipment data tracking, resulting in the blind leading the blind and a total mess being made of equipment data. Especially in academic or unorthodox laboratory environments where equipment ownership is important and adherence to SOP may be nonexistent, data aggregation at the point of lab equipment is a serious problem.2
If historically a laboratory has tracked the performance of an instrument by taking a certain measurement, that’s the measurement that will get recorded, and often promptly lost or ignored by another user if they think the machine can be pushed a bit further before maintenance or repair is really necessary. It doesn’t matter if by the manufacturer’s standard the lab personnel are measuring the wrong metric to assess equipment functionality–if that was ever known, it was forgotten long ago, likely lost with the equipment’s manual. These problems grow greater and greater the older the equipment that’s being used, though having “core” facilities are a strong mitigating factor.3
No Maintenance Data Means No FDA Compliance
Keeping equipment maintenance and operation data records up to date and accurate is important because these documents are often necessary components of a warranty claim or repair ticket for the machine’s manufacturer to justify sending a technician of their own to repair a machine.4 Furthermore, in laboratories that practice GMP or GLP, failing to aggregate maintenance and use data–and failing to have it prepared to hand over upon command–can constitute regulatory violations which are costly to dispute and even costlier to lose.
Part of smarter laboratory equipment maintenance and smarter data aggregation in the lab is careful selection of which metrics are important to note while running maintenance, as is developing robust maintenance SOPs that personnel will actually follow. The smartest labs will have maintenance SOPs that work, and are followed by responsible people, with regularity and data organization enforced by laboratory software packages. Doing things in this way keeps data aggregated, equipment happy, and avoids the dreaded “can I have a minute of your time” phenomenon.
Weak Maintenance SOPs Lead To the Most Experienced Personnel Being Interrupted the Most
The “can I have a minute of your time” phenomenon in laboratories that don’t practice effective equipment maintenance and maintenance data aggregation practices should be familiar to all bench scientists, but in the event that it isn’t, here’s a quick outline:
- A researcher is running an experiment using a piece of laboratory equipment, which seems to be malfunctioning. Alternatively, a researcher is performing maintenance on a piece of equipment that they’re unfamiliar with.
- The maintenance records of the equipment are unclear regarding what constitutes a normal range of the chosen maintenance metrics to measure. This causes the researcher to attempt to conclude maintenance or repair on their own by searching for an SOP or equipment manual, which have been lost.
- The researcher knows that there is another researcher that will be able to solve the problem of the machine–likely a senior researcher, and most likely one of the researchers who taught them how to use the piece of equipment and perform maintenance on it.
- The researcher leaves the machine’s station, and begins to look for the more experienced researcher. Eventually, a more experienced researcher who has more knowledge of the equipment is found, and asked “can I have a minute of your time?”
- Graciously, the senior researcher helps the junior researcher complete the maintenance or repair process, likely disrupting their ongoing experiments as a result.
- The record of this incident doesn’t make it to the logs associated with the machine’s maintenance, nor is a new SOP written based off of what has occurred.
- The problem occurs again at a point in the indeterminate future.
As comical as this may sound, it’s a real problem. Senior researchers face consistent and often debilitating interruptions for their knowledge which could easily be codified into an SOP, and equipment maintenance data is rarely monitored correctly as a result.5 6 This entire set of problems could be avoided by synching SOPs and maintenance records into one single aggregated data platform which could also help with the scheduling and what-if-it’s-doing-this FAQs via an effective software package for sharing lab information.
Luckily, labs now have access to the lab software that they need to avoid wasting time. Science Cloud is the software package which the labs of the future will use to cut down on human to human interruption where human to computer interfacing could solve a problem by providing information. Using Science Cloud, your team will save everyone a bit of time by having a repository of maintenance data and a list of flowcharts for what to do when things aren’t working out. Contact us today to find out how we can help you run a smoother lab.
- “Integrated Planning Principles That Reduce Deferred Maintenance & Ops Costs.” 2014, https://www.osti.gov/scitech/servlets/purl/1315257. ↩
- “Parasite labs: Laboratory protocols of do-it-yourself biology.” 2015, https://search.proquest.com/openview/5c5246f3f130d657313179c094cb2426/1?pq-origsite=gscholar&cbl=18750&diss=y ↩
- “Metrics for Success: Strategies for Enabling Core Facility Performance and Assessing Outcomes.” 2016, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4736753/ ↩
- “Equipment and Analytical Companies Meeting Continuous Challenges.” 2014, http://onlinelibrary.wiley.com/doi/10.1002/jps.24282/full. ↩
- “How do interruptions affect clinician performance in healthcare? Negotiating fidelity, control, and potential generalizability in the search for answers.” 2015, http://www.sciencedirect.com/science/article/pii/S1071581914001591. ↩
- “Recovering from an interruption: Investigating speed−accuracy trade-offs in task resumption behavior.” 2013, http://psycnet.apa.org/record/2013-21648-001. ↩