The mismeasurement of complexity: provider narratives of patients with complex needs in primary care settings
Bibliographic record
Abstract
PURPOSE: Chronic disease is a global concern. While ample research has aimed to identify the epidemiology of multimorbidity and patient complexity using administrative data, little attention has been paid to the processes of care that treating complex patients entail. Consequently, the concept of patient complexity itself does not directly speak to how challenging it may be to care for a given patient. The purpose of this study was to investigate how primary care providers define, encounter, and manage complex patients, especially those with chronic pain. To our knowledge, this is the first study to move beyond general narrative descriptions of complexity towards an interrogation that is grounded in the work practices of caring for these patients. METHODS: We undertook an institutional ethnography (IE) in Ontario, Canada. IE uses people's everyday work problems as the starting point for an exploration of the often-invisible social relations that orient experiences. Grounded in the everyday experience of primary care providers, we draw here on 51 interviews that were collected as part of our larger IE study, to interrogate the utility of definitions of patient complexity as medical multimorbidity. FINDINGS: Care providers consider patients challenging due to their socio-economic status more so than their medical problems alone. Our data shows that patients' issues are often bound up with poverty, trauma, and mental health concerns, and are challenging for health care providers in part because the interventions needed exceed the scope of their medical expertise, while social issues render the treatment of potentially straightforward medical problems complicated. This was especially so for patients with chronic pain. CONCLUSION: Defining patient complexity as morbidity alone is inadequate; such models neglect syndromes and conditions that are not included in formal disease classifications. Chronic pain should be included among the chronic conditions that are considered to constitute multimorbidity. In order to provide effective patient-centered care, discussions of patient complexity must also attend to the complex social and economic circumstances in which many patients live and include broader issues of inequity and social justice. This approach would enable policies to better support primary care providers who struggle to manage their patients with complex needs across domains of physiological health, mental health, and the quality of their living conditions, and in so doing improve the care that patients receive.
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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 | 0.001 | 0.000 |
| 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 | 0.000 |
| 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".