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Changing Patient Expectations Decreases Length of Stay in an Enhanced Recovery Program for THA

2018· article· en· W2790871335 on OpenAlexaff
Dylan Tanzer, Karen Smith CRA, Michael Tänzer

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2018
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineSports medicineOrthopedic surgeryPhysical therapyPhysical medicine and rehabilitationSurgery

Abstract

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BACKGROUND: The implementation of care pathways in hip arthroplasty programs has been shown to result in a decreased length of stay (LOS), but often multiple elements of a care pathway are implemented at the same time. As a result, it is difficult to understand the impact each of the individual modifications has made to the patient's prepathway care. In particular, it is unknown what the role of patient expectations pertaining to anticipated LOS alone is on the LOS after primary THA. QUESTIONS/PURPOSES: (1) Does changing the patient's expectations regarding his or her anticipated LOS, without intentionally changing the rest of the care pathway, result in a change in the patient's LOS after primary THA? (2) Is the resultant LOS associated with the patient's age, gender, or day of the week the surgery was performed? METHODS: We retrospectively compared the LOS in 100 consecutive patients undergoing THA immediately after the implementation of a 4-day care pathway (4-day Group) with 100 consecutive patients, 3 months later, who were also in the same pathway but were told by their surgeon preoperatively and in the hospital to expect a LOS of 2 days (2-day Group). Aside from reeducation by the surgeon, there was no difference in the surgery or intentional changes to the intraoperative or postoperative management of the two groups. Only the patient and the surgeon were made aware of the accelerated discharge plan. We compared the LOS between the two groups and the number of patients who met their discharge goal. As well, the ability to meet the discharge goal for each group was further determined based on age, gender, and day of the week the surgery was performed. RESULTS: Overall, patients in the 2-day Group had a shorter LOS than those in the 4-day Group (2.9 ± 0.88 days versus 3.9 ± 1.71 days; mean difference 1 day; 95% confidence interval [CI], 0.60-1.36; p = 0.001). In the 2-day Group, the LOS was 2 days in 32% compared with 8% in the 4-day Group (odds ratio, 4.0; 95% CI, 1.76-9.11; p < 0.001). Men in the 4-day Group had a shorter LOS than women (3.4 ± 1.22 days versus 4.2 ± 1.89 days; mean difference 0.8 days; 95% CI, 0.17-1.78; p = 0.019), but there was no difference in LOS by gender in the 2-day Group (2.8 ± 0.81 days versus 3.1 ± 0.93 days; mean difference 0.3 days; 95% CI, -0.14 to 0.61; p = 0.219). For all patients > 40 years and < 90 years of age, a greater percentage of patients in the 2-day Group went home by postoperative day 2 than those in the 4-day Group (32% compared with 7%; odds ratio, 4.6; p < 0.001). In both groups, there was no difference in the LOS if the surgery was on Friday compared with an earlier day of the week (4-day Group: 3.4 ± 0.67 days versus 4.0 ± 1.80 days; p = 0.477 and 2-day Group: 2.8 ± 0.62 days versus 3.0 ± 0.93 days; p = 0.547). CONCLUSIONS: We found that a surgeon who sets a clear expectation in terms of LOS could achieve a reduction in this parameter. Although it is impossible to be certain in the context of a retrospective study whether other caregivers adjusted the pathway in response to the surgeon's preferences, and we suspect this probably did occur, this still points to an opportunity on the topic of expectations setting that future studies should explore. This study highlights the influence patient education and expectations has on the effectiveness of care pathways in THA as well as the importance of continuous reinforcement of discharge planning both preoperatively and in the hospital. LEVEL OF EVIDENCE: Level III, therapeutic study.

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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)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.461
Teacher spread0.377 · 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 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".

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Citations37
Published2018
Admission routes1
Has abstractyes

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