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Record W1966598618 · doi:10.2345/i0899-8205-40-6-418.1

Who Drank My Wine?

2006· letter· en· W1966598618 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBiomedical Instrumentation & Technology · 2006
Typeletter
Languageen
FieldHealth Professions
TopicQuality and Safety in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsBenchmarkingSurpriseBenchmark (surveying)Action (physics)Work (physics)MarketingPsychologyBusinessMedical educationPublic relationsMedicineEngineeringPolitical scienceSocial psychology

Abstract

fetched live from OpenAlex

I was, at the same time, happy and sad reading the two Final Word editorials in the July/August issue of BI&T. I was happy because it has been almost a decade since the subject of benchmarking has been discussed among clinical engineering (CE) professionals. I was sad, however, to see that suspicions and skepticisms, as well as misunderstandings, are still so prevalent among our colleagues.In spite of agreeing with the positive aspects that Ken Maddock pointed out, I am afraid that I have to disagree with his choice of title (“Glass is Half Full”). While we have been arguing with each other over benchmarking minutia (what is the right definition of inventory, how to count work orders, which expenses should be included or excluded, etc.), hospital administrators and chief financial officers have long since gone ahead to benchmark CE using whatever measures they—not us—believe are correct. Today, almost every hospital participates in one of the benchmarking projects offered by a number of “performance solutions” consulting companies and each one of these companies has created some type of benchmark for CE services. In other words, our “wine” has already been consumed by others while we are busy fighting each other!For example, I recently reported at the 29th Canadian Medical and Biological Engineering Conference held in Vancouver, BC, a preliminary analysis of CE “measures” collected by Solucient LLC, one of the leading performance improvement companies. To my surprise, over 170 hospitals (among the over 850 subscribers to Solucient's Action O-I® database) have been reporting CE- and technology-related data for numerous years. More surprising even is the fact that in spite of all the well-known and discussed concerns and disagreements among CE professionals on how to measure each indicator, a fairly clear picture of what is happening with medical technology adoption and management has emerged.For instance, most hospitals have about 13 pieces of capital equipment per staffed (not licensed) bed and invest about $3,000 for each patient discharge, regardless of hospital size or teaching nature. Typically, a hospital spends per year about 4% of its total capital equipment investment to maintain it. The annual total CE budget (including labor, parts, service contracts, etc.) is generally less than 1% of the hospital's annual total operating budget (thus our “invisibility” to the C-suite). Furthermore, the amount of CE FTEs hovers around 2.6 per 100 staffed beds or each CE FTE covers about 520 pieces of capital equipment. Obviously, these are “averages” and not benchmark goals for others to aspire to, but they seem to provide useful insights and “rules of thumb” for healthcare administrators and CFOs.Perhaps more alien yet to the CE community is the direction healthcare benchmarking has taken. Instead of using “capacity” metrics (e.g., beds or pieces of equipment), hospitals are adopting “operational” and “output” metrics such as patient days and discharges as the denominators. This trend reflects the realization that hospitals are production sites that should be measured by their operations and output rather than capacity, as well as the fact that most reimbursements nowadays are for cases (diagnostic related group—DRG) or amount of patient covered (“capitation”) rather than equipment used or procedures performed.While I am not sure suspicion and skepticism are the fundamental reasons for our lack of consensus (e.g., while scientists are required to have similar professional traits, they seem to agree on most fundamental laws of nature), I think Boyd Hutchins is right in pointing out that CE professionals are likely to remain for sometime in the dungeons fighting themselves and invisible dragons before realizing that the sun is shining above and there are numerous other challenges and opportunities waiting for us above ground (e.g., licensing the profession, assuming broader roles, or simply going fishing).

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0080.008
Insufficient payload (model declined to judge)0.0030.002

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 head0.066
GPT teacher head0.427
Teacher spread0.360 · 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