ArticleApplied clinical informatics2023
Identifying High-Need Primary Care Patients Using Nursing Knowledge and Machine Learning Methods.
Article in Applied clinical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review.BMC primary care · 2025Pooled it
- A Novel Digital Phenotype for Burn Sepsis: Leveraging Electronic Health Record Data and Natural Language Processing to Improve Case Definition.Applied clinical informatics · 2026Article
- Balancing fidelity and flexibility: a case study presentation of an augmented dynamic adaptation process for socio-technical innovations in healthcare.Frontiers in health services · 2026Article
- Evaluating Treatment Burden in Patients with Complex Needs Receiving a Transition of Care Intervention: A Rapid Qualitative Analysis.Clinical nursing research · 2026Article
- Characteristics and related factors of high-need high-cost children in Shanghai, China: a retrospective cohort study in inpatient setting.BMC pediatrics · 2025Article
- Leveraging Artificial Intelligence to Inform Care Coordination by Identifying and Intervening in Patients' Unmet Social Needs: A Scoping Review.Journal of advanced nursing · 2025Article
- Modeling Patients' Progression through Health-Related Social Needs.Applied clinical informatics · 2025Article
- External Validation of an Electronic Phenotyping Algorithm Detecting Attention to High Body Mass Index in Pediatric Primary Care.Applied clinical informatics · 2024Article
- Medical-informed machine learning: integrating prior knowledge into medical decision systems.BMC medical informatics and decision making · 2024Review
- A Systematic Review of the Application of Artificial Intelligence in Nursing Care: Where are We, and What's Next?Journal of multidisciplinary healthcare · 2024Review
- A clinical classification framework for identifying persons with high social and medical needs: The COMPLEXedex-SDH.Nursing outlookArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundPatient cohorts generated by machine learning can be enhanced with clinical knowledge to increase translational value and provide a practical approach to patient segmentation based on a mix of medical, behavioral, and social factors.
objectivesThis study aimed to generate a pragmatic example of how machine learning could be used to quickly and meaningfully cohort patients using unsupervised classification methods. Additionally, to demonstrate increased translational value of machine learning models through the integration of nursing knowledge.
methodsA primary care practice dataset (
resultsFour distinct clusters interpreted and mapped to psychosocial need profiles, allowing for immediate translation to clinical practice through the creation of actionable social and medical care plans. (1) A large cluster of racially diverse female, non-English speakers with low medical complexity, and history of childhood illness; (2) a large cluster of English speakers with significant comorbidities (obesity and respiratory disease); (3) a small cluster of males with substance use disorder and significant comorbidities (mental health, liver and cardiovascular disease) who frequently visit the hospital; and (4) a moderate cluster of older, racially diverse patients with renal failure.
conclusionThis manuscript provides a practical method for analysis of primary care practice data using machine learning in tandem with expert clinical knowledge.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.