The importance of achieving a 360-degree view of patient identity: A survey of US healthcare providers
Bibliographic record
Abstract
Objective: To describe the perceived importance among healthcare leaders of accurate patient identity in meeting organizational needs and objectives for improved clinical, operational and financial performance.Methods: Survey of 100 US healthcare executives evaluated priorities and needs of care organizations as impacted by the imperative to ensure accurate patient identity in care delivery, operations, and meeting strategic objectives.Results: Healthcare executives (72%) reported concern that inaccurate patient identity data reduces care quality/safety and healthcare organization financial performance. Only 14% were highly or extremely satisfied with the accuracy level of their existing patient identity management solutions. Inability to know “who is who” is perceived as increasing risk of patient harm and inferior care outcomes, low patient satisfaction, impeded operational efficiency and financial performance, and a key challenge to achieving strategic initiatives such as digital transformation and effective population health management. Accuracy in patient identity was linked to nearly all strategic priorities, with 60% considering it vital to every aspect of organizational performance, and 64% stating it can improve operational efficiency. Eighty-eight percent regarded accurate patient identity as essential to improving patient experience, care management (75%), and establishing an effective digital front door (73%). Majorities recognized the importance of accurate patient identity to organizational growth initiatives and digital transformation.Conclusions: Although patient identity impacts most aspects of healthcare operations, leadership of most healthcare organizations surveyed understood the criticality of accurate patient identity in optimizing organizational performance, but lacked confidence in their ability to achieve a complete an accurate 360-degree view of patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".