NEJM Catalyst

NEJM Catalyst Practical innovations in health care delivery:

Ideas, solutions, and case studies to improve patient care and drive value in health organizations.

Health care delivery is undergoing a major transformation around quality, cost, and access. NEJM Catalyst brings health care executives, clinician leaders, and clinicians together to share innovative ideas and practical applications for enhancing the value of health care delivery. NEJM Catalyst brings insightful articles and real-life examples from a network of top thought leaders, experts and advisors to provide:

Practical innovations in health care delivery;

Impeccable quality and impact;

Active contributions from renowned authorities, thought-leaders, and advisors;

Independent and impartial curation; and

An exchange of ideas among executives and clinicians. NEJM Catalyst is produced by NEJM Group, a division of the Massachusetts Medical Society, located in Waltham, Massachusetts.

Despite widespread adoption of electronic health records (EHRs), health systems remain heavily dependent on faxed docume...
02/23/2026

Despite widespread adoption of electronic health records (EHRs), health systems remain heavily dependent on faxed documents for critical patient information. At NYU Langone Health, this represents nearly 20 million document-pages per year, each requiring manual review and indexing. These workflows are time consuming, involve multiple staff touchpoints, can be prone to error, and may create delays for patients awaiting follow-up care.

To provide the highest quality of care and augment staff experience, NYU Langone developed an Intelligent Document Processing (IDP) solution leveraging existing enterprise technologies for document management, robotic process automation, data classification and extraction, and EHR-integrated indexing. This solution identifies electronically faxed documents, extracts patient and provider information, matches the EHR record, sorts the documents into clinical or administrative queues, and assigns a document type for indexing.

The IDP solution was deployed and monitored at one high-volume multispecialty practice August-October 2025. The system processed about 20,000 document-pages, representing 13,700 faxes or scans. Of these, 8500 (62%) were successfully classified to one of the predefined in-scope clinical and administrative document types the system was trained to recognize; they were then routed to the appropriate work queue for indexing. The remaining 38% required manual review.

Implementation required not only technical integration, but also operational redesign. Change management was paramount, as individual practices had developed varied and entrenched fax workflows that required reengineering and preproduction dress rehearsals prior to go-live.

This experience demonstrates the potential for an AI–enabled IDP solution to meaningfully reduce administrative burden, improve timeliness and accuracy of document indexing, and unlock structured data from scanned pages.

Although challenges remain in scaling across diverse workflows, this case illustrates how health systems can pragmatically deploy AI using existing infrastructure to improve efficiency, reduce staff burden, and support better care delivery: https://nej.md/3ZEpmb9

Su***de rates in the United States have increased steadily over the past 20 years, a trend coinciding with rising use of...
02/20/2026

Su***de rates in the United States have increased steadily over the past 20 years, a trend coinciding with rising use of mental health services across the country. To help patients before a su***de attempt, health systems must be able to screen for su***de risk and take action at a large scale.

Recently, powerful machine learning (ML) models have emerged that can accurately predict su***de attempts by using historical electronic health record data, and yet there exists no standardized framework for implementing these models in care delivery.

In this article, the authors present a case study describing the deployment of a su***de risk prediction model within a large virtual mental health care program at Kaiser Permanente Northern California that handled more than 5000 intake visits per month. Their approach used data science to evaluate model validity for their novel use case (intake visits). They integrated patient and clinician voices to design a model-augmented su***de assessment workflow, which they tested iteratively with continuous input from clinician managers.

Understanding the opportunities and pitfalls of model-augmented su***de risk assessment from the perspective of patients and clinicians provided an intuitive framework for mapping clinical actions to potential prediction scenarios.

This playbook can be applied to ongoing codevelopment of ML uses as part of health systems’ continuous learning initiatives, integrating ML to serve today’s public health needs: https://nej.md/4tYZLHV

For patients with complex health needs, the periods between clinical encounters are times of significant vulnerability, ...
02/20/2026

For patients with complex health needs, the periods between clinical encounters are times of significant vulnerability, during which unobserved risks can escalate into acute events.

The Waymark community-based care management program was designed to reduce this vulnerability for the patients it serves who are enrolled in Medicaid programs, but the organization faced the challenge of processing thousands of unstructured daily encounter notes from its field-based teams.

To meet this challenge in a scalable way, the authors developed and implemented an artificial intelligence (AI) oracle, a system that continuously analyzes these notes. The oracle has three functions: (1) to identify urgent safety red flags; (2) to surface opportunities for preventive care; and (3) to generate monthly, strengths-based feedback to foster a learning health system.

Following a phased implementation that began with a silent validation period, the system now delivers real-time, actionable alerts directly into the care teams’ messaging system. Iterative refinement of the AI prompts, driven by clinical leadership review, improved the actionable true-positive rate of safety alerts from 68% of 147 to over 95% of 203 over 12 weeks.

The system’s value lies in its ability to synthesize disparate data streams — such as community health worker observations; pharmacy data; rising risk scores; and hospital admission, discharge, and transfer feeds — to reveal complex risks that could be missed by manual, siloed review.

The AI oracle has become an integrated, “over-the-shoulder” digital coach, transforming a reactive care model into a proactive, learning health system and offering a scalable solution to improve outpatient safety for vulnerable populations: https://nej.md/4alhyj9

📣**Special call for papers: Assessing Implementation and Impact of the 4Ms Framework to Advance Age-Friendly Health Syst...
02/19/2026

📣**Special call for papers: Assessing Implementation and Impact of the 4Ms Framework to Advance Age-Friendly Health Systems**📝

NEJM Catalyst Innovations in Care Delivery invites manuscripts to be considered for inclusion in an upcoming theme issue on implementation, scaling, and impact of the Age-Friendly Health Systems (AFHS) 4Ms Framework. Many health care provider organizations across a range of settings — including hospitals, physician practices, retail pharmacy clinics, nursing homes, home care providers, and others — are working to improve the health outcomes of older adults, avoid harm, and lower the costs of care. The 4Ms Framework consists of evidence-based practices to meet the unique needs of older adults around What Matters, Medication, Mentation, and Mobility.

This call for papers seeks submissions that assess real-world implementation of the 4Ms within and across care settings. Papers should demonstrate measurable improvement; show how teams operationalized the reliable practice of the 4Ms; address scale and sustainability, with a particular interest in understanding the business case for the 4Ms; and include clear descriptions of implementation challenges, trade-offs, and lessons learned that others can apply. Priority will be given to submissions that address all four Ms as a set.

In keeping with our emphasis on practical innovations in care delivery, we seek papers that demonstrate impact for patients and family caregivers, populations, and communities.

This special issue is sponsored by The John A. Hartford Foundation.

Submissions deadline: June 1, 2026. Issue date: November 2026.

Learn more and submit your work: https://nej.md/4kNTpXw

Language discordance in surgical care is a structural driver of inequity that affects patient safety, trust, and outcome...
02/19/2026

Language discordance in surgical care is a structural driver of inequity that affects patient safety, trust, and outcomes. Emerging interpreter technologies, including artificial intelligence (AI) and remote video interpretation (RVI), are rapidly entering clinical settings. However, implementation decisions are often made without understanding how patients themselves perceive these modalities or whether they view them as replacements or complementary tools within their care.

To explore Spanish-speaking surgical patients’ perceptions of AI- and RVI-based interpreter technologies, and to understand how clinical context influences modality preferences, the author team conducted a descriptive concurrent mixed-methods study within a U.S. academic health system, enrolling 23 adult patients with Spanish language preference across the surgical continuum.

The patients did not choose a single preferred modality; instead, they expressed context-dependent needs. AI was viewed as advantageous for its speed, privacy, and literal translation in straightforward or time-sensitive scenarios. RVI was favored for emotionally complex conversations and cultural nuance. Across narratives, patient agency emerged as a dominant theme.

These findings support the development of a multifaceted language access infrastructure in which AI and remote human interpreters are deployed synergistically based on clinical sensitivity, urgency, and patient preference: https://nej.md/46XDy2K

An artificial intelligence boom is underway. But what is the real impact of AI on health care delivery? In November 2025...
02/19/2026

An artificial intelligence boom is underway. But what is the real impact of AI on health care delivery? In November 2025, NEJM Catalyst surveyed its Insights Council about AI implementation at their organizations. NEJM Catalyst Insights Council members are excited about artificial intelligence in health care delivery for a range of tasks, and they say it already aids patients and clinicians. But the greatest impacts lie in the future. Read the full report: https://nej.md/4qMXQDp

The March 2026 special theme issue of NEJM Catalyst Innovations in Care Delivery, guest edited by Sara Murray, MD, MAS, ...
02/18/2026

The March 2026 special theme issue of NEJM Catalyst Innovations in Care Delivery, guest edited by Sara Murray, MD, MAS, includes articles, case studies, and research reports on the impacts of AI on clinical workflows, quality and operations, patient-centered care, and health care policy. Read the letter from Dr. Murray: https://nej.md/4tM9cKE

The March 2026 issue of NEJM Catalyst Innovations in Care Delivery is a special theme issue on the hard work of implemen...
02/18/2026

The March 2026 issue of NEJM Catalyst Innovations in Care Delivery is a special theme issue on the hard work of implementing artificial intelligence in real-world health care, exploring the impacts of AI on clinical workflows, quality and operations, patient-centered care, and health care policy.

📖 View the issue - https://nej.md/40e5VG6

🤖 From our guest editor, Sarah Murray (UCSF Health) - From Promise to Practice: The Next Era of AI in Health Care https://nej.md/4tM9cKE

📝 Insights Report - AI in Care Delivery: Enormous Potential but Little True Transformation https://nej.md/4qMXQDp

💬 In Depth - Artificial Intelligence for Language Access in Surgical Care: Patient Preferences and an Implementation Framework https://nej.md/46XDy2K

🌎 Case Study - An Artificial Intelligence Oracle for Proactive Population Health Quality Improvement https://nej.md/4alhyj9

🧠 Case Study - Predicting and Preventing Su***de at Entry to Mental Health Care: A Community-Engaged, Machine Learning Model Implementation https://nej.md/4tYZLHV

📠 Case Study - An Affordable Artificial Intelligence Solution for Intelligent Document Processing of Faxed Documents https://nej.md/3ZEpmb9

🏥 Article - AI-Driven Interventions for Imminent Hospital Admissions in Patients with End-Stage Kidney Disease: A Medicare and EMR-Based Analysis https://nej.md/4aEGToC

📈 Article - Closing the Loop: A Custom Artificial Intelligence Agent to Improve Detection of Radiologist Follow-Up Recommendations https://nej.md/46d3eIw

👩‍⚕️ Article - Artificial Intelligence in the Clinic: Don’t Pay for the Tool, Pay for the Care https://nej.md/3ZEpqYr

Scaling Value-Based Care across the United States: Progress from Risant Health https://nej.md/4pHGDKO
02/12/2026

Scaling Value-Based Care across the United States: Progress from Risant Health https://nej.md/4pHGDKO

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Innovations in Health Care Delivery

Health care delivery is undergoing a major transformation around quality, cost, and access. NEJM Catalyst brings health care executives, clinician leaders, and clinicians together to share innovative ideas and practical applications for enhancing the value of health care delivery.

Since December 2015, NEJM Catalyst has brought together a network of health care leaders to share insightful ideas and real-life examples of innovations in health care delivery, in the form of articles, case studies, quarterly events, and monthly surveys of our Insights Council.

NEJM Catalyst is produced by NEJM Group, a division of the Massachusetts Medical Society, located in Waltham, Massachusetts.