---
title: "Real-World AI in Health Systems: Early Insights from AIM-HI’s Five Funded Projects"
id: "pubmed-42745618"
canonical_url: "https://medichelpline.com/clinical-feed/pubmed-42745618"
content_type: "clinical_feed_article"
specialty: "Critical Care"
source_name: "PubMed / NCBI"
source_url: "https://pubmed.ncbi.nlm.nih.gov/42745618/"
doi: "10.7812/TPP/26.065"
published_at: "2026-09-16T00:00:00.000Z"
evidence_level: "Journal Article"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Real-World AI in Health Systems: Early Insights from AIM-HI’s Five Funded Projects
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/pubmed-42745618
- **Specialty:** [Critical Care](https://medichelpline.com/clinical-feed/critical-care.md)
- **Primary Source:** PubMed / NCBI
- **Source URL:** [Original Journal Publication](https://pubmed.ncbi.nlm.nih.gov/42745618/)
- **DOI:** [10.7812/TPP/26.065](https://doi.org/10.7812%2FTPP%2F26.065)
- **Published At:** 2026-09-16T00:00:00.000Z
- **Evidence Rating:** Journal Article
## Executive GIST (TL;DR)
- The Augmented Intelligence in Medicine and Healthcare Initiative (**AIM-HI**) funded five projects to evaluate integration of artificial intelligence in routine clinical care. - Projects were selected through a national, multistage review using a structured scoring rubric and addressed diverse clinical problems: **sepsis management**, venous thromboembolism risk assessment, diabetic retinopathy screening, cardiac amyloidosis detection, and pediatric asthma risk prediction. - AIM-HI focused on real-world deployment rather than controlled model development, aiming to build evidence for implementation across varied health care settings. - Common implementation challenges across projects included **electronic health record (EHR) integration**, data complexity, regulatory requirements, and variability in clinical workflows. - Cross-project lessons emphasized the importance of stakeholder engagement, local adaptation of tools and workflows, robust quality assurance, and ongoing performance monitoring. - Authors reported that real-world deployment was feasible but required thoughtful integration into clinical environments and strong partnerships between implementers and clinical teams. - Sustained evaluation and monitoring were cited as necessary for assessing performance and ensuring safe, scalable AI adoption. - The initiative’s early findings support the view that programs like AIM-HI are essential for generating **real-world evidence** about AI implementation in health care. - Keywords highlighted by the source include **Artificial intelligence**, Clinical decision making, Electronic health records, Implementation science, and Real-world evidence.
## Clinical Analysis & Structured Key Points
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Online ahead of print. # Can Artificial Intelligence Deliver in Real-World Health Systems? Early Insights From Augmented Intelligence in Medicine and Healthcare Initiative's 5 Funded Projects [Isaac J Ergas](https://pubmed.ncbi.nlm.nih.gov/?term=Ergas+IJ&cauthor_id=42745618)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42745618/#full-view-affiliation-1 "Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA."), [Heather Clancy](https://pubmed.ncbi.nlm.nih.gov/?term=Clancy+H&cauthor_id=42745618)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42745618/#full-view-affiliation-1 "Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA."), [Thomas Wang](https://pubmed.ncbi.nlm.nih.gov/?term=Wang+T&cauthor_id=42745618)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42745618/#full-view-affiliation-2 "Gordon and Betty Moore Foundation, Palo Alto, CA, USA."), [Vincent Liu](https://pubmed.ncbi.nlm.nih.gov/?term=Liu+V&cauthor_id=42745618)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42745618/#full-view-affiliation-1 "Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA.") Affiliations Expand ### Affiliations * 1 Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA. * 2 Gordon and Betty Moore Foundation, Palo Alto, CA, USA. * PMID: **42745618** * DOI: [ 10.7812/TPP/26.065 ](https://doi.org/10.7812/tpp/26.065) Item in Clipboard # Can Artificial Intelligence Deliver in Real-World Health Systems? Early Insights From Augmented Intelligence in Medicine and Healthcare Initiative's 5 Funded Projects Isaac J Ergas et al. Perm J. 2026. Show details Display options Display options Format Abstract PubMed PMID Perm J Actions * [ Search in PubMed ](https://pubmed.ncbi.nlm.nih.gov/?term=%22Perm+J%22%5Bjour%5D&sort=date&sort_order=desc) * [ Search in NLM Catalog ](https://www.ncbi.nlm.nih.gov/nlmcatalog?term=%22Perm+J%22%5BTitle+Abbreviation%5D) * [ Add to Search ](https://pubmed.ncbi.nlm.nih.gov/42745618/) . 2026 Sep 16:1-10. doi: 10.7812/TPP/26.065. Online ahead of print. ### Authors [Isaac J Ergas](https://pubmed.ncbi.nlm.nih.gov/?term=Ergas+IJ&cauthor_id=42745618)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42745618/#short-view-affiliation-1 "Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA."), [Heather Clancy](https://pubmed.ncbi.nlm.nih.gov/?term=Clancy+H&cauthor_id=42745618)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42745618/#short-view-affiliation-1 "Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA."), [Thomas Wang](https://pubmed.ncbi.nlm.nih.gov/?term=Wang+T&cauthor_id=42745618)[ 2 ](https://pubmed.ncbi.nlm.nih.gov/42745618/#short-view-affiliation-2 "Gordon and Betty Moore Foundation, Palo Alto, CA, USA."), [Vincent Liu](https://pubmed.ncbi.nlm.nih.gov/?term=Liu+V&cauthor_id=42745618)[ 1 ](https://pubmed.ncbi.nlm.nih.gov/42745618/#short-view-affiliation-1 "Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA.") ### Affiliations * 1 Division of Research, Kaiser Permanente Northern California, Pleasanton, CA, USA. * 2 Gordon and Betty Moore Foundation, Palo Alto, CA, USA. * PMID: **42745618** * DOI: [ 10.7812/TPP/26.065 ](https://doi.org/10.7812/tpp/26.065) Item in Clipboard Cite Display options Display options Format Abstract PubMed PMID ## Abstract **Introduction:** Artificial intelligence (AI) holds tremendous promise to improve clinical decision-making across diagnosis, risk assessment, and patient care. However, most prior work has focused on model development in controlled settings with limited evidence on real-world implementation. The Augmented Intelligence in Medicine and Healthcare Initiative (AIM-HI), led by Kaiser Permanente and funded by the Gordon and Betty Moore Foundation, was established to evaluate and support integration of AI tools into routine clinical practice. This article summarizes early insights from AIM-HI-funded projects to inform real-world AI implementation. **Methods:** AIM-HI funded 5 projects through a national, multistage review process using a structured scoring rubric. Projects addressed sepsis management, venous thromboembolism risk assessment, diabetic retinopathy screening, cardiac amyloidosis detection, and pediatric asthma risk prediction across diverse health care settings. The authors synthesized cross-project findings related to implementation processes, challenges, and lessons learned. **Results:** Real-world AI deployment was feasible across varied clinical environments. Common challenges included electronic health record integration, data complexity, regulatory requirements, and variation in clinical workflows. Other common themes included stakeholder engagement, local adaptation, quality assurance, and performance monitoring. **Discussion:** Findings highlight that implementation success depends on thoughtful integration into clinical environments, strong partnerships with stakeholders, as well as sustained evaluation and monitoring. **Conclusion:** Effective AI adoption in health care requires careful integration, stakeholder alignment, and ongoing evaluation. Initiatives such as AIM-HI are essential for building the evidence base for scalable, real-world AI implementation. **Keywords:** Artificial intelligence; Clinical decision making; Electronic health records; Health care delivery; Implementation science; Real-world evidence. 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