An Institutional Perspective on Accountable Care Organizations
Bibliographic record
Abstract
We employ aspects of institutional theory to explore how Accountable Care Organizations (ACOs) can effectively manage the multiplicity of ideas and pressures within which they are embedded and consequently better serve patients and their communities. More specifically, we draw on the concept of institutional logics to highlight the importance of understanding the conflicting principles upon which ACOs were founded. Based on previous research conducted both inside and outside health care settings, we argue that ACOs can combine attention to these principles (or institutional logics) in different ways; the options fall on a continuum from (a) segregating the effects of multiple logics from each other by compartmentalizing responses to multiple logics to (b) fully hybridizing the different logics. We suggest that the most productive path for ACOs is to situate their approach between the two extremes of "segregating" and "fully hybridizing." This strategic approach allows ACOs to develop effective responses that combine logics without fully integrating them. We identify three ways that ACOs can embrace institutional complexity short of fully hybridizing disparate logics: (1) reinterpreting practices to make them compatible with other logics; (2) engaging in strategies that take advantage of existing synergy between conflicting logics; (3) creating opportunities for people at frontline to develop innovative ways of working that combine multiple logics.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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 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".