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Record W2037828899 · doi:10.2345/0899-8205-47.1.39

Proper Battery Maintenance Can Lead to Savings

2013· article· en· W2037828899 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBiomedical Instrumentation & Technology · 2013
Typearticle
Languageen
FieldHealth Professions
TopicQuality and Safety in Healthcare
Canadian institutionsAlpha Technologies (Canada)
Fundersnot available
KeywordsWarrantyTroubleshootingOriginal equipment manufacturerOperations managementBattery (electricity)WorkstationComputer scienceBusinessMedical emergencyEngineeringOperating systemMedicine

Abstract

fetched live from OpenAlex

A healthcare facility in Milwaukee, WI was in the final stages of preparation to launch an electronic medical record (EMR) initiative involving mobile computing workstations. Much earlier in the project, a software issue forced the mobile workstations to sit idly in storage for almost two years. When the time came to launch the project, none of the 42 workstations started. After troubleshooting, the healthcare technology management (HTM) staff determined that the batteries within the carts were “faulty.” Further investigation revealed that the batteries were not properly maintained during the two years they were stored. The clinical engineers went back to the original equipment manufacturer (OEM), but were told the batteries were out of warranty and would need to be replaced. The replacement cost totaled almost $28,000. Already under a tight budget, they were not going to be able to afford the huge cost. They were left with a hard decision—figure out where else to cut costs in order to come up with the additional $28,000 or delay the project even further until the budget was available. If the staff had identified that the batteries were nickel metal hydride (NiMH), they would have had been aware of the high rate of self-discharge and known that they required maintenance during storage. This knowledge could have saved the facility time and money. As demonstrated in this case, HTM departments cannot afford unexpected costs and delays associated with product failures due to improper maintenance and storage. Every year, departments are asked to do more with a smaller budget and fewer resources, making it imperative to find ways to save money and time. A commonly overlooked source of savings is proper battery maintenance. The benefits of proper maintenance include prolonged battery life, which can extend the replacement interval, and overall higher peak performance for longer periods of time. To maximize battery efficiency and performance, proper battery identification is paramount. Identifying battery chemistry, application, and proper maintenance will ensure a long and productive lifecycle. Lastly, coupling this knowledge with the proper battery charger or analyzer will help any department turn concept into tangible savings. About the Author

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.609
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.061
GPT teacher head0.406
Teacher spread0.345 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it