The evolution of quality improvement in healthcare: Patient-centered care and health information technology applications
Bibliographic record
Abstract
Objective: Quality improvement in the healthcare industry has evolved over the past few decades. In recent years, an increased focus on coordination of care efforts and the introduction of health information technology has been of high importance in improving the quality of patient care.Methods: In this review, we present a history of quality improvement efforts, discuss quality improvement in the healthcare industry, and examine quality improvement strategies with a focus on patient-centered care and information technology applications via patient registries.Results: Evidence shows that the key to quality improvement efforts in the healthcare industry is the coordination of patient care efforts through better data evaluation processes. By utilizing patient registries that can be linked to electronic health records (EHRs) and the Patient-Centered Medical Home (PCMH) framework, the quality of care provided to patients can be improved.Conclusions: While many healthcare organizations have quality improvement departments or teams in place that may be able to handle these types of efforts, it is important for organizations to be familiar with processes and frameworks that employees at different levels of the organization can be involved in. In order to ensure successful outcomes from quality improvement initiatives, managers and clinicians should work together in identifying problems and developing solutions.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".