The Universal & Atomic Elements of Organization
A falsifiable scientific theory proposing that the same irreducible structures organize both thought and reality — from the quantum to the social scale.
O-Theory proposes that four irreducible structures — Distinctions, Systems, Relationships and Perspectives (DSRP) — form the universal grammar of organization. Rather than being merely useful ways of thinking, these structures are hypothesized to be the atomic elements from which every organized phenomenon emerges, whether in cognition, biology, physics, mathematics, society, or the cosmos.
| D | S | R | P |
| Distinctions | Systems | Relationships | Perspectives |
| Identity ↔ Other | Part ↔ Whole | Action ↔ Reaction | Point ↔ View |
| D := (i ↔ o) | S := (p ↔ w) | R := (a ↔ r) | P := (ṗ ↔ v) |
This is a living scientific evidence compendium: an open, continually evolving collection of independent empirical research, formal theory, mathematical proofs, cross-disciplinary analyses, applications, critiques, and proposed falsifications. Every entry is included because it supports, refines, challenges, or attempts to falsify the theory.
Scientific theories are strengthened not only by evidence that confirms their predictions, but also by surviving attempts to falsify them. This compendium brings both together: independent evidence from researchers who were not testing DSRP and proposed counterexamples evaluated against the formal theory.
One counterexample is enough to falsify O-Theory. Until then, the question remains: do the same four structures organize everything from quantum systems to human thought?
Independent convergence is one of the strongest forms of scientific evidence because researchers arrive at the same conclusion while investigating different questions for different reasons.
The number is not the point. Researchers in different fields, studying different questions with different methods, repeatedly arrived at the same structural predictions—almost always without testing DSRP or using its language.
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This compendium began as the peer-reviewed literature review “A Literature Review of the Universal and Atomic Elements of Complex Cognition,” published in the Journal of Systems Thinking with 109 studies. That paper is the peer-reviewed foundation. What you see here is its living, continuously updated version . New studies are checked before they are added, and the collection now holds and keeps growing. Open any card to see what the researchers found, why it bears on DSRP, and where the original review discusses it, the fuller account.
The collection is updated continuously, so the citation carries the date you consulted it rather than a study count — the count changes weekly, and putting it in the reference would make the same collection look like a different work to everyone who cites it. To cite a single claim or study, use its own address: every one has a permanent link.
This is the adversarial half of the compendium. Where the evidence track asks what converges on DSRP, this one asks what would end it: a single organized phenomenon whose structure needs a fifth pattern, a ninth element, or a fifth structural dynamic. It holds written up from candidates across territories, and resolutions — the general answers those cases settle against. Every case is published whether it held or failed, including the ones still open.
Cite this rather than the evidence collection when the point is what survived attack. The two are separate works with separate addresses: one asks what converges on the theory, the other asks what would end it, and a reference to the first does not support a claim about the second. Case and resolution numbers change when the record is revised, so cite a case by its own permanent link rather than by number.
This collection is meant to keep growing. Send us a study, paper, book or critique that bears on DSRP, whether it supports the theory or cuts against it, and we will read it and decide whether it belongs.
Authors Feldman, Todd
Year 2026
Publisher Medika Life
Kind of work article
Discipline Public Health
Secondary disciplines Business & Management, Medicine
Kind of engagement public
Applied false
To argue that healthcare's AI investment rests on data produced by organizations that are not designed to learn, and that the Learning Health System, as defined by the Institute of Medicine in 2007, is the organizational prerequisite that AI deployment is skipping. Feldman states the piece is not primarily an argument about technology, and that its aim is to make the problem more visible, more precisely named and more honestly held rather than to prescribe a solution.
A long-form argumentative white paper in nine sections, published on a healthcare media site. Feldman frames health systems as complex adaptive systems and uses the Cabreras' VMCL (Vision, Mission, Capacity, Learning) as a diagnostic lens, then uses DSRP as the mapping toolkit he recommends to leaders. Sections V and VI carry the analytic weight: EHR burden, payer prior authorization and physician burnout are presented as a single causal chain and mapped onto VMCL functions, and each of the four DSRP patterns is then applied to that chain. Section VII offers a 2020 statewide convening as a model for building a cross-disciplinary ecosystem. He cites Flock Not Clock, the Cabrera Research Lab's VMCL overview, two Cabrera glossary entries and the Cabreras' "Why You Should Map" white paper, alongside the 2007 IOM workshop summary, Rittel and Webber (1973) and recent health-systems literature.
Feldman concludes that the three conditions are one failure rather than three. EHR infrastructure is capacity built for the wrong mission; payer prior authorization overrides internal clinical purpose at the mission level and corrupts the learning loop at its source; and burnout is the human output of capacity designed for the wrong mission, not an independent variable sitting alongside the other two. He holds that the four VMCL functions are not sequential steps but simultaneous and mutually dependent, and that AI trained on data from such a system will reproduce its dysfunction at scale. His recommendation is to map before intervening — the system must be mapped, not fixed and not optimized — using DSRP, and to build an ecosystem of interdependent actors that includes patients as active contributors. He states that the paper has not proposed a solution but has drawn a map, and closes that the conversation is open and the map is incomplete.
Feldman uses VMCL to diagnose why the Learning Health System has stalled, mapping EHR burden, prior authorization and physician burnout onto Capacity, Mission and Learning as one causal chain rather than three separate problems. DSRP supplies the mapping discipline he argues health system leaders should apply before reaching for an AI solution.
The paper's argument runs from data quality back to organizational design. AI in healthcare learns from records produced by institutions under conditions the models never see — shift fatigue, copy-forward documentation, reimbursement-driven coding — so the output inherits whatever the organization was actually doing. Feldman's claim is that the fix is not better models but organizations built to learn, and that the Learning Health System has been the stated goal since 2007 without the organizational conditions for it being built. VMCL is how he diagnoses the gap. Section IV sets it out as a thinking-design lens rather than a checklist: vision as destination, mission as the small set of simple rules autonomous agents enact so that coordinated movement emerges, capacity as the infrastructure that has to be aligned to that mission, and learning as the loop of gathering data, analyzing it and changing practice. Section V then maps three named conditions onto those functions. The electronic health record is capacity built for billing rather than for care, which produces both clinician friction and compromised data. Prior authorization is an external rule set overriding internal clinical purpose, which breaks the learning loop where it starts. Burnout is the endpoint, the human output of capacity misdesign, and it forecloses the reflection and participation that learning needs. Section VI is where DSRP does the work. Rather than prescribe, Feldman offers diagnostic questions organized by pattern. Distinctions separate a learning system from the things that resemble one — a teaching hospital, a quality dashboard, an EHR installation. Systems holds a phenomenon as both part and whole: burnout is part of a capacity failure and is itself a whole composed of EHR load, prior authorization and isolation. Relationships are the causal links among the three, and the reason intervening on any one alone gives only partial relief. Perspectives he treats as an epistemic requirement, insisting on the burned-out physician's view and the patient's view as conditions of seeing the system at all. The section's central instruction is that the system must be mapped before it is fixed, and the paper describes itself as a partial map.
Feldman uses both models for what they are built to do. VMCL is an organizational design model and he applies it as one: capacity is infrastructure built for a particular mission, mission is the set of simple rules agents enact, learning is the feedback loop that lets an organization change its own behavior. Reading EHR burden as capacity built for the wrong mission, rather than as a technology problem, is the move the model exists for, and the chain he draws from there to prior authorization to burnout is a relationship analysis rather than a list of complaints. The DSRP application in Section VI is accurate at the level it operates. The distinction between a learning system and the institutions that resemble one is the right first move, because the resemblance is what lets a quality dashboard stand in for a learning loop. Holding burnout as both part and whole is a correct use of the part-whole pattern rather than a figure of speech. And treating perspectives as an epistemic requirement rather than a courtesy reflects what the pattern claims: a point of view is where a distinction comes from, not an addition to one already made.
Patterns it shows D, S, R, P
Added 2026-10-28
How to cite this Feldman, Todd (2026). Garbage In, Garbage Out: The Organizational Crisis Beneath Healthcare's AI Gold Rush. Medika Life.