Primary Care Informatics: Vitalizing the Bedrock of Health Care
Bibliographic record
Abstract
Primary care informatics (PCI) professionals address workflow and technology solutions in a wide spectrum of health, ranging from optimizing the experience of the individual patient in the clinic room to supporting the health of populations and augmenting the work of frontline primary care clinical teams. PCI overlaps uniquely with 2 disciplines with an impact on societal health-primary care and health informatics. Primary care is a gateway to health care access and aims to synthesize and coordinate numerous, complex elements of patients' health and medical care in a holistic manner. However, over the past 25 years, primary care has become a specialty in crisis: in a post-COVID-19 world, workforce shortages, clinician burnout, and continuing challenges in health care access all contribute to difficulties in sustaining primary care. Informatics professionals are poised to change this trajectory. In this viewpoint, we aim to inform readers of the discipline of PCI and its importance in the design, support, and maintenance of essential primary care services. Although this work focuses on primary care in the United States, which includes general internal medicine, family medicine, and pediatrics (and depending on definition, includes specialties such as obstetrics and gynecology), many of the principles outlined can also be applied to comparable health care services and settings in other countries. We highlight (1) common global challenges in primary care, (2) recent trends in the evolution of PCI (personalized medicine, population health, social drivers of health, and team-based care), and (3) opportunities to move forward PCI with current and emerging technologies using the 4Cs of primary care framework. In summary, PCI offers important contributions to health care and the informatics field, and there are many opportunities for informatics professionals to enhance the primary care experience for patients, families, and their care teams.
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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 | 0.029 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.033 |
| Scholarly communication | 0.027 | 0.029 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.011 | 0.019 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".