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Record W4403071801 · doi:10.1093/clinchem/hvae106.182

A-184 Evidence-based Test Utilization: Test Ordering Patterns in Functional Medicine and Community Clinics, and Implications for Laboratory Stewardship

2024· article· en· W4403071801 on OpenAlexaffabout
S. Ezra, C W Lewis, S.M. Hossein Sadrzadeh

Bibliographic record

VenueClinical Chemistry · 2024
Typearticle
Languageen
FieldMedicine
TopicComplementary and Alternative Medicine Studies
Canadian institutionsCalgary Laboratory ServicesUniversity of SaskatchewanUniversity of Calgary
Fundersnot available
KeywordsStewardship (theology)Test (biology)MedicineFamily medicineIntensive care medicineMedical physicsPolitical scienceBiologyEcology

Abstract

fetched live from OpenAlex

Abstract Background Functional Medicine Physicians (FMPs) use a holistic approach to identify and address “the root cause of a disease”. These physicians routinely order laboratory tests that are not supported by clinical suspicion and practice guidelines (i.e., Centers for Disease Control and Prevention Laboratory Medicine Best Practices, and local diagnostic algorithms) and are unnecessarily duplicative, with inappropriate testing patterns. Ordering unnecessary tests does not improve diagnosis; indeed, 5% of disease-free patients falsely test positive for a given diagnostic test. Test overutilization increases the rate of false positives, leading to unnecessary medical follow-ups, compromised patient care, and financial constraints on the healthcare system. The objective of this study is to: 1) Compare test ordering patterns between FMPs and General Practitioners (GPs) to identify disparities and opportunities for laboratory stewardship improvements. 2) Analyze the potential factors impacting test utilization in areas served by FMPs, 3) investigate the proper approach to improve test utilization. Methods Using our laboratory’s information system, we conducted a retrospective study spanning from January 2022 to December 2022 to assess testing patterns by 15 FMPs in Southern Alberta, comparing them to those of randomly selected community GPs within the same geographical region. A query was executed to retrieve data on the top 50 chemistry tests ordered by functional medicine clinics and community clinics. These tests were further categorized into six groups: general chemistry, endocrinology, immunology, trace elements, toxic metals, and vitamins. Data analysis included determining the geographical locations of functional medicine clinics and assessing the socioeconomic status of their patient populations. Additionally, the percentage of abnormal test results were calculated for both functional medicine clinic and community clinics. Furthermore, the average cost of tests per patient, per physician, and per clinic was computed to evaluate the financial implications of testing practices. Results Our preliminary results showed significant variability in test ordering practices among FMPs, with potential implications for patient care and healthcare costs. Most tests ordered by FMPs were normal (94%), suggesting a need for further scrutiny regarding the necessity and appropriateness of these tests. A notable trend observed was the disproportionate preference of FMPs for expensive tests, including hormones, vitamins, and toxic metals. Our analysis reveals that the cost per reportable test from FMPs was four times higher compared to the tests ordered by GPs. Furthermore, on average, functional medicine clinics in urban areas ordered around 60% more tests annually compared to their rural counterparts. Conclusions Our preliminary findings clearly show a need to monitor the testing behavior more carefully by the functional medicine clinics. The data from this study will be shared with the physicians practicing functional medicine in Alberta. Strategies to improve the test utilization and patient care include educating the functional medicine doctors and provincial healthcare authorities about the appropriate test utilization and the impact of inappropriate use of lab on patient care. In addition, using algorithms for test ordering will be used if the pattern of inappropriate testing continues.

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.

How this classification was reachedexpand

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)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.405
GPT teacher head0.487
Teacher spread0.082 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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