Improving Orthopedic Care Delivery Through Digital Engagement
Bibliographic record
Abstract
Background Patient activation has been hypothesized to improve medical and surgical outcomes by increasing patient involvement in the care plan. We tested this hypothesis by utilizing a patient activation tool in a population of adults having total hip or total knee replacement. We hypothesized that patient activation would be associated with increased discharge to home as opposed to a skilled nursing facility, reduced hospital length of stay, decreased inpatient readmissions, and decreased emergency department (ED) visits. Objective Using an email patient activation tool, we sought to increase patients’ involvement in their care before and after total joint replacement. Outcomes examined included day of surgery cancellation, length of hospital stay, discharge to home vs discharge to a skilled nursing facility, any ED visit within 30 days of discharge, and any inpatient readmission within 30 days of discharge Methods This was a quasi-experimental design comparing Jan-Jun 2017 to Jan-Jun 2018. We instituted an email patient activation tool for all patients with total knee or total hip replacement surgery beginning in January 2018. This tool was integrated with the electronic medical record system during the six month study period and patients could opt out at anytime if they desired. The tool was designed to prepare patients both educationally and emotionally for their operation with multiple easy-to-read emails starting from the time they were scheduled for surgery through six months postop. Percent of emails opened and clicked were used as measures of engagement for the intervention participants. Results Of the 2,027 TJR patients included, 720 were hip patients and 1,307 were knee patients. Pre- and postintervention groups were similar in gender and age. For hip replacement patients, length of stay was nearly 1/4 day lower in the postintervention group (β=-0.23; P=.001) after adjusting for gender, age and insurance; ED visits were lower among the postintervention group (OR=0.45; P=.05) after adjusting for gender, age and insurance; and postintervention patients were less likely to have day of surgery cancellation, any revisit (ED or readmission), and were more likely to be discharged home. However, these associations did not reach statistical significance. Conclusions Among patients who received the intervention, higher engagement was significantly associated with positive changes in almost all outcomes. Use of the digital patient activation tool demonstrated significant savings in length of stay and reduced ED visits among hip replacement patients. Although just under 50% of patients in the intervention group were enrolled to use the tool, these findings were still significant even when non-participants were included in the postintervention group.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".