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Record W1957319348

The burden of paperwork.

2006· letter· fr· W1957319348 on OpenAlexaboutno aff
Shelagh McRae, Robert C. Hamilton

Bibliographic record

VenuePubMed · 2006
Typeletter
Languagefr
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsnot available
Fundersnot available
KeywordsMedical prescriptionWorkloadMedicineComputer scienceFamily medicineNursing
DOInot available

Abstract

fetched live from OpenAlex

Time required for paperwork has been increasing to the detriment of other aspects of physicians’ work.1 Physicians’ satisfaction is inversely related to this burden.2 In this era of electronic medical records (EMRs), paperwork is being supplanted by electronic “document handling.” We have been unable to find articles in the literature that quantify this aspect of Canadian family doctors’ workload and, therefore, we analyzed document handling in our practice. We began implementing an office EMR system for our rural family practice in 2002. All correspondence and laboratory, imaging, and consultant reports are entered into patients’ electronic records. Prescriptions are generated electronically. Electronic documents handled by physicians in our office during a 3-year period (2003-2005, inclusive) were identified and counted. During this time clinic progress notes were still handwritten; we estimated 1 for each office visit. There were 30 213 visits in the EMR appointment book over the 3 years. We electronically signed 28 304 pages of received correspondence and 21 774 pages of laboratory results (each with an average of 11 test results). The physicians wrote 17 874 prescriptions, with an average of 2 medications per prescription. The practice generated and sent 6109 pages of correspondence. One handwritten note per visit adds 30 213 progress note entries. Every week, on average, each physician saw 97 patients and handled 335 pages of documents (including 91 pages of received correspondence, 70 pages of laboratory results, 57 prescriptions, 20 pages of outgoing correspondence, and 97 progress notes). These numbers underestimate our overall document handling workload and paperwork burden. We have not included estimates for handwritten requisitions (laboratory and imaging), notes to patients, immunization cards, additional progress note entries related to patient phone calls, or follow-up of results. Much of the document workload generated at the local nursing home is not included. We have also not included a count of the many third-party or government forms (eg, drug plan limited-use forms, travel grant applications) that are not entered into the EMR. Paperwork related to billing, office administration, practice audit, quality assurance, continuing education, teaching, research, or coroner duties are not included in this analysis. We believe that our experience of each handling well over 17 000 pages yearly will be similar for other family doctors who provide a range of clinical services. Our numbers for laboratory results are similar to those reported for American primary care physicians by Poon and colleagues in 2003.3 The document burden, however, could be much greater in practices with higher rates of patient referral to specialists or private health insurance coverage. While an EMR might not reduce physician time needed for document handling, it does allow for quantification of this component of physicians’ work. Canadian physicians were already spending 5.4 hours weekly on “indirect patient care” in 2002.4 Increasing time needed for this will exacerbate physician shortages and contribute to longer wait times. Modern clinical practice demands high-quality documentation. Electronic medical records are powerful tools to improve quality of care; however, increased need for documentation will require increased physician manpower. Medical software vendors must strive to find ways to streamline EMR interfaces. Administrators, government agencies, and third parties must be encouraged to prioritize, simplify, and reduce documentation requests from physicians. Efforts to reduce the burdens of paperwork and document handling must become a priority to help reduce physician burnout and frustration and to contribute to solving the problems of doctor shortages and long waiting lists.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.183
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.253
Threshold uncertainty score0.846

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0350.183
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0060.007
Science and technology studies0.0030.003
Scholarly communication0.0150.005
Open science0.0040.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.2530.308

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 head0.017
GPT teacher head0.195
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations6
Published2006
Admission routes1
Has abstractyes

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