Improvement of workflow and processes to ease and enrich meaningful use of health information technology
Bibliographic record
Abstract
The introduction of health information technology (HIT) can have unexpected and unintended patient safety and/or quality consequences. This highly desirable but complex intervention requires workflow changes in order to be effective. Workflow is often cited by providers as the number one 'pain point'. Its redesign needs to be tailored to the organizational context, current workflow, HIT system being introduced, and the resources available. Primary care practices lack the required expertise and need external assistance. Unfortunately, the current methods of using esoteric charts or software are alien to health care workers and are, therefore, perceived to be barriers. Most importantly and ironically, these do not readily educate or enable staff to inculcate a common vision, ownership, and empowerment among all stakeholders. These attributes are necessary for creating highly reliable organizations. We present a tool that addresses US Accreditation Council for Graduate Medical (ACGME) competency requirements. Of the six competencies called for by the ACGME, the two that this tool particularly addresses are 'system-based practice' and 'practice-based learning and continuing improvement'. This toolkit is founded on a systems engineering approach. It includes a motivational and orientation presentation, 128 magnetic pictorial and write-erase icons of 40 designs, dry-erase magnetic board, and five visual aids for reducing cognitive and emotive biases in staff. Pilot tests were carried out in practices in Western New York and Colorado, USA. In addition, the toolkit was presented at the 2011 North American Primary Care Research Group (NAPCRG) meeting and an Agency for Health Research and Quality (AHRQ) meeting in 2013 to solicit responses from attendees. It was also presented to the officers of the Office of the National Coordinator (ONC) for HIT. All qualitative feedback was extremely positive and enthusiastic. The respondents recommended that the toolkit be disseminated widely to improve staff education and training, leading to practice improvements.
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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 | 0.002 | 0.026 |
| 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 | 0.001 |
| 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".