Practice Redesign Isn’t Going To Erase The Primary Care Shortage
Bibliographic record
Abstract
Most experts agree that primary care needs to be re-invented. There are a lot of promising ingredients of practice redesign: better scheduling, electronic medical records with patient portals, redesigned clinician workflow, and work sharing. Linda Green’s intriguing article in the January Health Affairs simulates a strategic combination of these changes and argues if they all happened at once, we would have no primary care physician shortage. Even if we make much more effective use of clinical time and energy, however, Green’s formula isn’t going to get us far enough fast enough. The baby boom generation of physicians is fast nearing its “sell by” date. In 2010, one quarter of the 242,000 primary care physicians in the US were 56 or older. One in six general internists left their practices in mid-career. Many more hardworking clinicians delayed retirement due to the 2008 financial collapse. Few manpower specialists have noted the cohort effect likely to manifest itself shortly. A continued economic recovery and, more importantly, a recovery in retirement plan and medical real estate asset values will lead as many as 100,000 physicians of all stripes to leave practice in the next few years. We will be replacing a generation of workaholic, 70-hour-a-week baby boom physicians with Gen Y physicians with a revealed preference for 35-hour work weeks. During this same period, we’ll be adding 3 million new Medicare beneficiaries a year and enfranchising perhaps 25 million newly insured folks through health reform. “Train wreck” is the right descriptor of the emerging primary care supply situation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.008 | 0.018 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.013 | 0.021 |
| Insufficient payload (model declined to judge) | 0.019 | 0.010 |
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".