Edna Shenvi, Aziz Boxwala, Sarah Shaw, Andre Berro, Saugat Karki, Matthew Pooser, Jane E. Yang, Gema Dumitru, Jeanne Ocampo, Amrita Tailor, Sanjat Kanjilal, Carlos Paredes, Ritche Hao, Nitu Kashyap, David Liebovitz, Alejandro Pérez
Objective
Neisseria gonorrhoeae (or gonococcus, GC) infections have increased in incidence and antibiotic resistance, prompting updates to management guidelines. Additionally, patients with recent GC infections have been identified as a target population for human immunodeficiency virus (HIV) screening and prevention. With the rapidly changing nature of such biomedical knowledge, there is an imperative to adopt public health guidelines speedily to deliver knowledge to clinicians at the point of care. Therefore, the Public Health Informatics Institute and the United States Centers for Disease Control and Prevention sought to create a scalable clinical decision support (CDS) solution that could be easily integrated using interoperability standards with widely used electronic health record (EHR) systems in the United States.
Methods
A project team including informaticians and clinical partners developed a cloud-hosted CDS system, with rule logic written in the Health Level Seven (HL7) Clinical Quality Language and using the HL7 CDS Hooks protocol. The system was designed to present alerts to clinicians who, based on available data, appeared to be treating uncomplicated GC with doses or agents that were not recommended based on the patient's weight, allergies, and other characteristics. Actionable suggestions were offered for the correct regimen, and HIV testing, if applicable.
Results
After extensive testing, the system was successfully integrated with the EHR system at two academic medical institutions. Subsequently, a three-month pilot was conducted in emergency and urgent care settings.
Conclusions
There were limitations related to functionality, workflow issues, and data quality. This successful implementation, however, demonstrates the feasibility of this approach for select use cases of CDS, and yields further understanding of the requirements for developing scalable solutions for disseminating public health guidance.
{"title":"A Standards-Based, Cloud-Hosted CDS System for Gonorrhea Treatment and HIV Screening","authors":"Edna Shenvi, Aziz Boxwala, Sarah Shaw, Andre Berro, Saugat Karki, Matthew Pooser, Jane E. Yang, Gema Dumitru, Jeanne Ocampo, Amrita Tailor, Sanjat Kanjilal, Carlos Paredes, Ritche Hao, Nitu Kashyap, David Liebovitz, Alejandro Pérez","doi":"10.1002/lrh2.70104","DOIUrl":"https://doi.org/10.1002/lrh2.70104","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p><i>Neisseria gonorrhoeae</i> (or gonococcus, GC) infections have increased in incidence and antibiotic resistance, prompting updates to management guidelines. Additionally, patients with recent GC infections have been identified as a target population for human immunodeficiency virus (HIV) screening and prevention. With the rapidly changing nature of such biomedical knowledge, there is an imperative to adopt public health guidelines speedily to deliver knowledge to clinicians at the point of care. Therefore, the Public Health Informatics Institute and the United States Centers for Disease Control and Prevention sought to create a scalable clinical decision support (CDS) solution that could be easily integrated using interoperability standards with widely used electronic health record (EHR) systems in the United States.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A project team including informaticians and clinical partners developed a cloud-hosted CDS system, with rule logic written in the Health Level Seven (HL7) Clinical Quality Language and using the HL7 CDS Hooks protocol. The system was designed to present alerts to clinicians who, based on available data, appeared to be treating uncomplicated GC with doses or agents that were not recommended based on the patient's weight, allergies, and other characteristics. Actionable suggestions were offered for the correct regimen, and HIV testing, if applicable.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>After extensive testing, the system was successfully integrated with the EHR system at two academic medical institutions. Subsequently, a three-month pilot was conducted in emergency and urgent care settings.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>There were limitations related to functionality, workflow issues, and data quality. This successful implementation, however, demonstrates the feasibility of this approach for select use cases of CDS, and yields further understanding of the requirements for developing scalable solutions for disseminating public health guidance.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>Multiple chronic conditions (MCC) affect approximately 130 million American adults, representing a critical health care challenge that standard, fragmented disease management has failed to adequately resolve. Evidence increasingly supports person-centered care planning (PCCP)—defined as a longitudinal care approach that places patient goals, values, and preferences at the center of treatment—as a necessary standard of practice. To address the gaps in current approaches and care models for management of MCC, this special collection presents four manuscripts emerging from a multi-faceted initiative to advance PCCP for those with or at risk for MCC, supported collectively by funding from the Agency for Healthcare Research and Quality (AHRQ), the John A. Hartford Foundation, and the Patient-Centered Outcomes Research Institute (PCORI). Together, these papers evaluate the current evidence base, operational challenges, on-the-ground clinician and patient experiences, and the evidence-based integration of symptom management into primary care.</p><p>The collection opens with foundational work by Totten, Dorr, Bierman, and colleagues, who conducted an environmental scan that informed the broader AHRQ initiative. Through a rigorous multi-component review of published and gray literature, the authors identified 40 PCCP models currently in use across diverse US health care settings. These models share a common commitment to whole-person care but differ substantially in how they achieve it—through making changes in the way care is provided. The authors characterized this change in care as focused on team roles and composition, technology, payment structures, clinical functions, and care focus. Approximately two-thirds of evaluated models demonstrated better or equivalent outcomes compared with usual care. The scan was complemented by qualitative interviews with 18 key informants who were able to provide rich contextual details on what works, how approaches can be improved, and what is impeding broadscale change. Barriers to adoption were consistent across settings: insufficient time, misaligned payment structures, and the cultural challenge of reorienting care delivery from disease management to patient-defined goals. Facilitators included flexible team structures, alignment with organizational mission, and leadership support. The environmental scan makes clear that a strong evidence base exists—what is lacking is the infrastructure, incentives, and implementation support to bring these models to scale.</p><p>The summary article by Davis, Coury, Nagykaldi, and colleagues presents findings from the PCCP for Persons with MCC Summit, a culminating event convened at AHRQ headquarters in Rockville, Maryland in March 2025. Drawing on 14 months of prior work, the Summit brought together 115 participants across sectors, including researchers, payers, health system leaders, community organizations, patients, and policymakers. Three broad takeaways emerged as essent
{"title":"Introduction to the Special Collection: Person-Centered Care Planning for Persons With Multiple Chronic Conditions","authors":"David A. Dorr, Ana R. Quiñones","doi":"10.1002/lrh2.70112","DOIUrl":"https://doi.org/10.1002/lrh2.70112","url":null,"abstract":"<p>Multiple chronic conditions (MCC) affect approximately 130 million American adults, representing a critical health care challenge that standard, fragmented disease management has failed to adequately resolve. Evidence increasingly supports person-centered care planning (PCCP)—defined as a longitudinal care approach that places patient goals, values, and preferences at the center of treatment—as a necessary standard of practice. To address the gaps in current approaches and care models for management of MCC, this special collection presents four manuscripts emerging from a multi-faceted initiative to advance PCCP for those with or at risk for MCC, supported collectively by funding from the Agency for Healthcare Research and Quality (AHRQ), the John A. Hartford Foundation, and the Patient-Centered Outcomes Research Institute (PCORI). Together, these papers evaluate the current evidence base, operational challenges, on-the-ground clinician and patient experiences, and the evidence-based integration of symptom management into primary care.</p><p>The collection opens with foundational work by Totten, Dorr, Bierman, and colleagues, who conducted an environmental scan that informed the broader AHRQ initiative. Through a rigorous multi-component review of published and gray literature, the authors identified 40 PCCP models currently in use across diverse US health care settings. These models share a common commitment to whole-person care but differ substantially in how they achieve it—through making changes in the way care is provided. The authors characterized this change in care as focused on team roles and composition, technology, payment structures, clinical functions, and care focus. Approximately two-thirds of evaluated models demonstrated better or equivalent outcomes compared with usual care. The scan was complemented by qualitative interviews with 18 key informants who were able to provide rich contextual details on what works, how approaches can be improved, and what is impeding broadscale change. Barriers to adoption were consistent across settings: insufficient time, misaligned payment structures, and the cultural challenge of reorienting care delivery from disease management to patient-defined goals. Facilitators included flexible team structures, alignment with organizational mission, and leadership support. The environmental scan makes clear that a strong evidence base exists—what is lacking is the infrastructure, incentives, and implementation support to bring these models to scale.</p><p>The summary article by Davis, Coury, Nagykaldi, and colleagues presents findings from the PCCP for Persons with MCC Summit, a culminating event convened at AHRQ headquarters in Rockville, Maryland in March 2025. Drawing on 14 months of prior work, the Summit brought together 115 participants across sectors, including researchers, payers, health system leaders, community organizations, patients, and policymakers. Three broad takeaways emerged as essent","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 4","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70112","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kim Kuebler, Julian Gallegos, Eduardo Bruera, Matthew Sorenson
Background
Multiple chronic conditions (MCC) drive high symptom burden and utilization of healthcare resources. Person-centered palliative symptom management approaches are underused in primary care.
Approach
Narrative synthesis of major policy recommendations and practice literature to propose a primary-care-implementable framework (screening, triage, and management using Edmonton Symptom Assessment System).
Key Points
Early, protocolized symptom screening and management in MCC is associated with fewer exacerbations, hospitalizations, improved physical functioning, and quality of life; feasible to deliver by primary care teams with targeted training.
Implications
Shift palliative skills upstream in primary care; adopt standardized tools; and measure quality outcomes.
{"title":"Person-Centered Care Planning: Palliative Symptom Management in Multiple Chronic Conditions","authors":"Kim Kuebler, Julian Gallegos, Eduardo Bruera, Matthew Sorenson","doi":"10.1002/lrh2.70105","DOIUrl":"10.1002/lrh2.70105","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Multiple chronic conditions (MCC) drive high symptom burden and utilization of healthcare resources. Person-centered palliative symptom management approaches are underused in primary care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Approach</h3>\u0000 \u0000 <p>Narrative synthesis of major policy recommendations and practice literature to propose a primary-care-implementable framework (screening, triage, and management using Edmonton Symptom Assessment System).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Key Points</h3>\u0000 \u0000 <p>Early, protocolized symptom screening and management in MCC is associated with fewer exacerbations, hospitalizations, improved physical functioning, and quality of life; feasible to deliver by primary care teams with targeted training.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Implications</h3>\u0000 \u0000 <p>Shift palliative skills upstream in primary care; adopt standardized tools; and measure quality outcomes.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13402855/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148608524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anita Walden, Allen Yiu, Gregory Welk, Yawen Guo, Shlomit Radom-Aizik, Margaret Schneider, Allyson Reeds, AbdulMalik Shakir, Dan M. Cooper
Introduction
Unique challenges of data quality, standardization, and interoperability face non-traditional sources of real world data (RWD), those outside of the common RWD source, the electronic health record (EHR). The experience of our clinical, research, and community group in addressing the endangered school-based physical fitness testing (SB-PFT) data set may be useful as a paradigm for a variety of nontraditional instances of health-related RWD. Until recently, SB-PFT was mandated annually within public schools in ~60% of U.S. children and adolescents representing one of the most robust and inclusive data sets available in youth. When implemented rigorously, SB-PFT produced data that improved pediatric health and student learning. Driven by perceived and real gaps in data quality, inconsistent and insensitive school-site implementation, and uneven evidence for health-relevance, calls for revamping (and, in some cases, ending) school testing are growing.
Methods
To preserve and enhance this unique resource, SB-PFT must incorporate emerging science and technology. We describe here the rationale, structure, and process involved in our building an HL7-associated Domain Analysis Model for SB-PFT. Our team consisted of clinicians, exercise scientists, school personnel, publish health officials, parents and students. The structure of the DAM was facilitated by data standard experts and skilled Unified Modeling Language professionals. Administrative support was rendered by our institution's NIH-funded Clinical Translational Science Award.
Results
The DAM will serve as a necessary first step in integrating Fast Healthcare Interoperability Resources (FHIR) and Learning Health Systems (LHS) models essential to realize the full potential of SB-PFT data in advancing child health.
Conclusion
The experience presented here can serve as a roadmap for addressing critical issues of data standardization and RWE generation in nontraditional instances of RWD. The DAM can be deployed in SB-PFT data exchange in real world settings.
{"title":"Pathways to Real World Evidence From the Real World of School-Based Physical Fitness Testing (SB-PFT)","authors":"Anita Walden, Allen Yiu, Gregory Welk, Yawen Guo, Shlomit Radom-Aizik, Margaret Schneider, Allyson Reeds, AbdulMalik Shakir, Dan M. Cooper","doi":"10.1002/lrh2.70108","DOIUrl":"10.1002/lrh2.70108","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Unique challenges of data quality, standardization, and interoperability face non-traditional sources of real world data (RWD), those outside of the common RWD source, the electronic health record (EHR). The experience of our clinical, research, and community group in addressing the endangered school-based physical fitness testing (SB-PFT) data set may be useful as a paradigm for a variety of nontraditional instances of health-related RWD. Until recently, SB-PFT was mandated annually within public schools in ~60% of U.S. children and adolescents representing one of the most robust and inclusive data sets available in youth. When implemented rigorously, SB-PFT produced data that improved pediatric health and student learning. Driven by perceived and real gaps in data quality, inconsistent and insensitive school-site implementation, and uneven evidence for health-relevance, calls for revamping (and, in some cases, ending) school testing are growing.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To preserve and enhance this unique resource, SB-PFT must incorporate emerging science and technology. We describe here the rationale, structure, and process involved in our building an HL7-associated Domain Analysis Model for SB-PFT. Our team consisted of clinicians, exercise scientists, school personnel, publish health officials, parents and students. The structure of the DAM was facilitated by data standard experts and skilled Unified Modeling Language professionals. Administrative support was rendered by our institution's NIH-funded Clinical Translational Science Award.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The DAM will serve as a necessary first step in integrating Fast Healthcare Interoperability Resources (FHIR) and Learning Health Systems (LHS) models essential to realize the full potential of SB-PFT data in advancing child health.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The experience presented here can serve as a roadmap for addressing critical issues of data standardization and RWE generation in nontraditional instances of RWD. The DAM can be deployed in SB-PFT data exchange in real world settings.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13402859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148608587","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<div> <section> <h3> Introduction</h3> <p>The allocation and timing for kidney matching and transplantation among incompatible donor-recipient pairs in the Paired Kidney Exchange face significant operational challenges of managing an uncertain waitlist. The time for dialysis of patients waiting for biologically compatible kidneys follows the concept of a stochastic queueing system. This study embodies the Learning Health System approach by integrating real-world data and mathematical modeling to generate actionable insights that guide decision-making.</p> </section> <section> <h3> Methods</h3> <p>This work employs an <span></span><math> <semantics> <mrow> <mi>M</mi> <mo>/</mo> <mi>M</mi> <mo>/</mo> <mn>1</mn> </mrow> <annotation>$$ M/M/1 $$</annotation> </semantics></math> model with Poisson arrivals and exponentially distributed service times for incompatible donor–recipient pairs in a single transplant facility. Inter-arrival and service times are used to derive queue length and waiting-time measures to characterize system congestion and instability, with performance measures evaluated as indicative approximations analyzing the corresponding metrics.</p> </section> <section> <h3> Results</h3> <p>The system is persistently overloaded, with arrival rates exceeding service rates (<span></span><math> <semantics> <mrow> <mi>λ</mi> </mrow> <annotation>$$ lambda $$</annotation> </semantics></math> <span></span><math> <semantics> <mrow> <mo>></mo> </mrow> <annotation>$$ > $$</annotation> </semantics></math> <span></span><math> <semantics> <mrow> <mi>μ</mi> </mrow> <annotation>$$ mu $$</annotation> </semantics></math>; <span></span><math> <semantics> <mrow> <mi>ρ</mi> <mo>></mo> </mrow> <annotation>$$ rho > $$</annotation> </semantics></math> 1), causing prolonged waiting times. Analytical and Hutson-B bootstrap confide
在配对肾脏交换中，在不相容的供体-受体对之间进行肾脏匹配和移植的分配和时机面临着管理不确定候补名单的重大操作挑战。等待生物相容肾脏的患者透析时间遵循随机排队系统的概念。本研究通过整合真实世界的数据和数学模型来产生指导决策的可操作的见解，体现了学习健康系统方法。方法：本工作采用M / M / 1模型，泊松到达和指数分布的服务时间在单个移植机构中不相容的供体-受体对。到达间隔时间和服务时间用于导出队列长度和等待时间度量，以表征系统拥塞和不稳定性，性能度量被评估为分析相应度量的指示性近似值。结果：系统持续过载，到达率超过服务率（λ > μ; ρ > 1），导致等待时间延长。分析和Hutson-B自举置信区间分析表明，拥塞在可能的变化中持续存在，敏感性分析表明适度的扰动不会减轻过载。回归和相关分析表明，延迟与到达压力和生物施加的限制有关，支持兼容性约束匹配作为主要的操作挑战。患者经历不确定的、延长的等待期，反映了生物约束排队系统的随机不稳定性。结论：移植服务管理需要系统性改革，因为临床匹配和分配延迟阻碍了及时护理。建立国家移植登记、综合实时队列监测、分散规划（扩大移植中心）、改善中心间的配对交换协调并增加交换周期长度，对于确保有效的服务提供和知情的医疗保健政策至关重要。这些干预措施创建了一个持续的反馈循环，其中操作数据为排队理论分析提供信息，为政策、系统重新设计提供信息，并最终改善患者访问和结果，展示了数据驱动的学习如何优化复杂的医疗保健服务。
{"title":"Learning From Queues: Operational Analysis and Performance Evaluation of Dialysis Duration in a Paired Kidney Exchange With an M/M/1 System","authors":"Samrajya Raj Acharya, Sushil Ghimire, Ram Prasad Ghimire","doi":"10.1002/lrh2.70107","DOIUrl":"10.1002/lrh2.70107","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The allocation and timing for kidney matching and transplantation among incompatible donor-recipient pairs in the Paired Kidney Exchange face significant operational challenges of managing an uncertain waitlist. The time for dialysis of patients waiting for biologically compatible kidneys follows the concept of a stochastic queueing system. This study embodies the Learning Health System approach by integrating real-world data and mathematical modeling to generate actionable insights that guide decision-making.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This work employs an <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>M</mi>\u0000 <mo>/</mo>\u0000 <mi>M</mi>\u0000 <mo>/</mo>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 <annotation>$$ M/M/1 $$</annotation>\u0000 </semantics></math> model with Poisson arrivals and exponentially distributed service times for incompatible donor–recipient pairs in a single transplant facility. Inter-arrival and service times are used to derive queue length and waiting-time measures to characterize system congestion and instability, with performance measures evaluated as indicative approximations analyzing the corresponding metrics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The system is persistently overloaded, with arrival rates exceeding service rates (<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>λ</mi>\u0000 </mrow>\u0000 <annotation>$$ lambda $$</annotation>\u0000 </semantics></math> <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>></mo>\u0000 </mrow>\u0000 <annotation>$$ > $$</annotation>\u0000 </semantics></math> <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>μ</mi>\u0000 </mrow>\u0000 <annotation>$$ mu $$</annotation>\u0000 </semantics></math>; <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>ρ</mi>\u0000 <mo>></mo>\u0000 </mrow>\u0000 <annotation>$$ rho > $$</annotation>\u0000 </semantics></math> 1), causing prolonged waiting times. Analytical and Hutson-B bootstrap confide","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13381077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148521098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Louise Shaw, Meg Perrier, Kayley Lyons, Mikayla Wolfe, Rahul Barmanray, Daniel Capurro, Wendy Chapman, Sathana Dushyanthen
Objective
To analyze the barriers, enablers, and strategies affecting the implementation of learning health systems (LHS) projects across individual (micro), organizational (meso), and system (macro) levels within Australian healthcare settings
Methods
Semistructured interviews were conducted with 26 clinicians undertaking LHS projects in their organizations, as part of a LHS Academy fellowship program. Interviews were completed at two discrete time points: early in project implementation and again at project completion. They explored fellows' experiences with implementing LHS projects in real-world organizational contexts. Data were analyzed using a hybrid deductive-inductive approach guided by a multilevel ecological codebook developed by Shaw et al.
Results
Participants' early interviews highlighted limited confidence, emerging leadership skills, and uncertainty in navigating implementation challenges, while later interviews described greater confidence, stronger leadership capability, and more refined adaptive strategies at the micro level. Organizational influences were described at both time points as slow and uneven, though later interviews included examples of small cultural shifts and early collaborative efforts alongside persistent constraints such as limited resources, siloed cultures, and inadequate digital infrastructure. At the system level, barriers including regulatory complexity, policy misalignment, and limited funding were reported consistently across both interviews and were perceived as largely unchanged.
Conclusions
Building individual capability was necessary but insufficient for successful LHS project implementation. Alignment across micro-, meso-, and macrolevels is critical, requiring parallel investment in organizational readiness, digital infrastructure, and policy reform. Fellowship programs may support individual change and foster proof-of-concept projects, but scaling impact demands coordinated, multilevel strategies.
{"title":"Exploring Barriers, Enablers, and Strategies for Implementing Learning Health System Projects in Healthcare Organizations: A Multilevel Analysis in an Australian Setting","authors":"Louise Shaw, Meg Perrier, Kayley Lyons, Mikayla Wolfe, Rahul Barmanray, Daniel Capurro, Wendy Chapman, Sathana Dushyanthen","doi":"10.1002/lrh2.70103","DOIUrl":"10.1002/lrh2.70103","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>To analyze the barriers, enablers, and strategies affecting the implementation of learning health systems (LHS) projects across individual (micro), organizational (meso), and system (macro) levels within Australian healthcare settings</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Semistructured interviews were conducted with 26 clinicians undertaking LHS projects in their organizations, as part of a LHS Academy fellowship program. Interviews were completed at two discrete time points: early in project implementation and again at project completion. They explored fellows' experiences with implementing LHS projects in real-world organizational contexts. Data were analyzed using a hybrid deductive-inductive approach guided by a multilevel ecological codebook developed by Shaw et al.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Participants' early interviews highlighted limited confidence, emerging leadership skills, and uncertainty in navigating implementation challenges, while later interviews described greater confidence, stronger leadership capability, and more refined adaptive strategies at the micro level. Organizational influences were described at both time points as slow and uneven, though later interviews included examples of small cultural shifts and early collaborative efforts alongside persistent constraints such as limited resources, siloed cultures, and inadequate digital infrastructure. At the system level, barriers including regulatory complexity, policy misalignment, and limited funding were reported consistently across both interviews and were perceived as largely unchanged.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Building individual capability was necessary but insufficient for successful LHS project implementation. Alignment across micro-, meso-, and macrolevels is critical, requiring parallel investment in organizational readiness, digital infrastructure, and policy reform. Fellowship programs may support individual change and foster proof-of-concept projects, but scaling impact demands coordinated, multilevel strategies.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13364508/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nardeen M. Shafik, John P. Donnelly, Jessica Johnson, Linda M. Kawentel, Nicholas W. Bowersox
Introduction
Learning Health Systems (LHSs) depend on the ability to effectively evaluate programs and translate data into practice. Within the Veterans Affairs (VA) health system, formal mandates require programmatic decision-making to be data-driven; however, opportunities for staff to develop applied program evaluation skills that support LHS learning cycles remain limited.
Methods
To address this need, the synchronous virtual Evaluation Bootcamp Training (EBcT) was developed to equip learners with practical, applied evaluation skills. This manuscript describes the design rationale, structure, and core concepts of the training, emphasizing design decisions intended to support evaluation capacity building within a large and complex health system.
Results
EBcT was delivered over 4 days across 2 training cohorts (8 learners in 4 project teams from Cohort 1; 12 learners in 5 project teams from Cohort 2). Project teams addressed a range of clinical and operational initiatives within VA. Learners represented diverse professional roles, including lead evaluators, clinical subject matter experts, project managers, operational leaders, and analysts. Learners reported high satisfaction throughout the training, with full attendance at application sessions and strong participation in didactic components. A majority reported meeting predefined evaluation learning targets at the end of the training.
Conclusion
Findings suggest that structured, team-based training models such as EBcT can strengthen evaluation capacity and support LHS functioning by equipping project teams to engage in repeated cycles of measurement, learning, and performance improvement.
{"title":"Developing a Program Evaluation Training Within a National Federal Healthcare Setting: Veterans Affairs Evaluation Bootcamp Training","authors":"Nardeen M. Shafik, John P. Donnelly, Jessica Johnson, Linda M. Kawentel, Nicholas W. Bowersox","doi":"10.1002/lrh2.70102","DOIUrl":"10.1002/lrh2.70102","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Learning Health Systems (LHSs) depend on the ability to effectively evaluate programs and translate data into practice. Within the Veterans Affairs (VA) health system, formal mandates require programmatic decision-making to be data-driven; however, opportunities for staff to develop applied program evaluation skills that support LHS learning cycles remain limited.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>To address this need, the synchronous virtual Evaluation Bootcamp Training (EBcT) was developed to equip learners with practical, applied evaluation skills. This manuscript describes the design rationale, structure, and core concepts of the training, emphasizing design decisions intended to support evaluation capacity building within a large and complex health system.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>EBcT was delivered over 4 days across 2 training cohorts (8 learners in 4 project teams from Cohort 1; 12 learners in 5 project teams from Cohort 2). Project teams addressed a range of clinical and operational initiatives within VA. Learners represented diverse professional roles, including lead evaluators, clinical subject matter experts, project managers, operational leaders, and analysts. Learners reported high satisfaction throughout the training, with full attendance at application sessions and strong participation in didactic components. A majority reported meeting predefined evaluation learning targets at the end of the training.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Findings suggest that structured, team-based training models such as EBcT can strengthen evaluation capacity and support LHS functioning by equipping project teams to engage in repeated cycles of measurement, learning, and performance improvement.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13364505/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Annette M. Totten, David A. Dorr, Arlene S. Bierman, Kisha Coa, Chandler Atchison, Cynthia Davis-O'Reilly, Katherine D. Peak, Shannon Sweeney, Deborah J. Cohen, Ana R. Quiñones
<div> <section> <h3> Introduction</h3> <p>Person-centered care planning (PCCP) involves active collaboration between people seeking care, clinical teams, and others to co-create longitudinal treatment plans. It is a crucial part of care for people with multiple chronic conditions (MCCs) and other complex needs. Despite the widespread acceptance of the concept, the use of PCCP in the US is variable. Many effective models of PCCP exist but uptake has been limited.</p> </section> <section> <h3> Objective</h3> <p>We sought to identify and assess current models and approaches for PCCP by performing a multi-component environmental scan.</p> </section> <section> <h3> Methods</h3> <p>We conducted targeted literature reviews based on streamlined systematic review methods and qualitative interviews with key informants, identified for their relevant expertise and frontline knowledge of PCCP models.</p> </section> <section> <h3> Results</h3> <p>In all, 966 abstracts and 187 full articles met review criteria. We identified 40 models with elements fitting into 7 categories of how they differed from usual care (adding People/Roles, innovative Payment/Incentives, novel Technology, decision support Tools, Services, Functions, and Changing Focus). Most were multi-component models with evidence of effectiveness in outcomes that address the quintuple aim. Barriers to adoption and integration across sectors were primarily lack of time and appropriate payment mechanisms. A small set of measures for PCCP was identified that focused primarily on patient experience and goal setting. Similarly, a small number of PCCP models connected with social services organizations to address health-related social needs and highlighted challenges with data sharing and payment as well as growing collaborations in community care hubs to address social needs. KI interviews echoed and deepened our understanding of these findings by contextualizing experiences with barriers and factors facilitating the delivery of PCCP.</p> </section> <section> <h3> Discussion</h3> <p>PCCP is a key aspect of high-quality care for people with MCCs; uptake is limited with barriers related to time and resources. Facilitators include alignment with healthcare system objectives and practices, and ready-to-deploy resources that enable coordinated PCCP care across providers. To promote greater adoption of PCCP concerted efforts are needed to share implementation details, success stories, and build the case for PCCP
{"title":"Person-Centered Care Planning for People With Multiple Chronic Conditions: An Environmental Scan of Models and Approaches","authors":"Annette M. Totten, David A. Dorr, Arlene S. Bierman, Kisha Coa, Chandler Atchison, Cynthia Davis-O'Reilly, Katherine D. Peak, Shannon Sweeney, Deborah J. Cohen, Ana R. Quiñones","doi":"10.1002/lrh2.70101","DOIUrl":"https://doi.org/10.1002/lrh2.70101","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Person-centered care planning (PCCP) involves active collaboration between people seeking care, clinical teams, and others to co-create longitudinal treatment plans. It is a crucial part of care for people with multiple chronic conditions (MCCs) and other complex needs. Despite the widespread acceptance of the concept, the use of PCCP in the US is variable. Many effective models of PCCP exist but uptake has been limited.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>We sought to identify and assess current models and approaches for PCCP by performing a multi-component environmental scan.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted targeted literature reviews based on streamlined systematic review methods and qualitative interviews with key informants, identified for their relevant expertise and frontline knowledge of PCCP models.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In all, 966 abstracts and 187 full articles met review criteria. We identified 40 models with elements fitting into 7 categories of how they differed from usual care (adding People/Roles, innovative Payment/Incentives, novel Technology, decision support Tools, Services, Functions, and Changing Focus). Most were multi-component models with evidence of effectiveness in outcomes that address the quintuple aim. Barriers to adoption and integration across sectors were primarily lack of time and appropriate payment mechanisms. A small set of measures for PCCP was identified that focused primarily on patient experience and goal setting. Similarly, a small number of PCCP models connected with social services organizations to address health-related social needs and highlighted challenges with data sharing and payment as well as growing collaborations in community care hubs to address social needs. KI interviews echoed and deepened our understanding of these findings by contextualizing experiences with barriers and factors facilitating the delivery of PCCP.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Discussion</h3>\u0000 \u0000 <p>PCCP is a key aspect of high-quality care for people with MCCs; uptake is limited with barriers related to time and resources. Facilitators include alignment with healthcare system objectives and practices, and ready-to-deploy resources that enable coordinated PCCP care across providers. To promote greater adoption of PCCP concerted efforts are needed to share implementation details, success stories, and build the case for PCCP ","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.3,"publicationDate":"2026-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.70101","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adrian H. Zai, Mohammad Adibuzzaman, David D. McManus, Allan Walkey
Introduction
Artificial intelligence is increasingly embedded in healthcare delivery, yet existing Learning Health System (LHS) models do not fully account for the lifecycle management and continuous assurance requirements of AI systems. This gap limits health systems' ability to safely and sustainably integrate AI as a learning component of care.
Methods
We conducted a conceptual system modeling investigation grounded in LHS theory and contemporary AI governance frameworks. Through structured theoretical integration, we aligned the classical LHS learning cycle with an action-oriented AI lifecycle and five continuous assurance dimensions, developing a unified framework to support operational implementation within health systems.
Results
The resulting Health AI Learning and Oversight (HALO) model specifies how AI functions as a dynamic knowledge artifact within an LHS. Application of the model illustrates how integrating lifecycle stages and continuous assurance instantiates iterative learning loops, enables adaptive governance, and supports operational lifecycle management, including ongoing monitoring of performance, safety, equity, transparency, and security across clinical environments.
Conclusions
By extending LHS theory to incorporate AI lifecycle and assurance requirements explicitly, the HALO framework operationalizes continuous learning and oversight for AI-enabled health systems. This model provides a foundation for designing, governing, and sustaining responsible and adaptive AI deployment as healthcare environments evolve.
{"title":"The HALO Model: A Learning Health System Framework for Artificial Intelligence","authors":"Adrian H. Zai, Mohammad Adibuzzaman, David D. McManus, Allan Walkey","doi":"10.1002/lrh2.70100","DOIUrl":"10.1002/lrh2.70100","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Artificial intelligence is increasingly embedded in healthcare delivery, yet existing Learning Health System (LHS) models do not fully account for the lifecycle management and continuous assurance requirements of AI systems. This gap limits health systems' ability to safely and sustainably integrate AI as a learning component of care.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We conducted a conceptual system modeling investigation grounded in LHS theory and contemporary AI governance frameworks. Through structured theoretical integration, we aligned the classical LHS learning cycle with an action-oriented AI lifecycle and five continuous assurance dimensions, developing a unified framework to support operational implementation within health systems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The resulting Health AI Learning and Oversight (HALO) model specifies how AI functions as a dynamic knowledge artifact within an LHS. Application of the model illustrates how integrating lifecycle stages and continuous assurance instantiates iterative learning loops, enables adaptive governance, and supports operational lifecycle management, including ongoing monitoring of performance, safety, equity, transparency, and security across clinical environments.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>By extending LHS theory to incorporate AI lifecycle and assurance requirements explicitly, the HALO framework operationalizes continuous learning and oversight for AI-enabled health systems. This model provides a foundation for designing, governing, and sustaining responsible and adaptive AI deployment as healthcare environments evolve.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13247301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148220053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Harris Butler, John D. Rice, Nichole E. Carlson, Elaine H. Morrato
Introduction
Active post-marketing surveillance of prescribing behavior of high-risk drugs may provide early warning of unforeseen issues in a population, yet analysis approaches for surveillance using real-world data are underdeveloped. This paper evaluates a modified statistical process control (SPC) method for surveillance of risk minimization measures derived from administrative claims data. The approach detects changes in population-level prescribing behaviors and informs investigators of the timing and nature of any detected changes.
Methods
We investigated prescription drug claims for tapentadol extended release (ER) from the Colorado All-Payers Claims Database (2012–2019). The cohort was 2702 unique patients receiving their first prescriptions of tapentadol ER (an opiate with an FDA Risk Evaluation and Mitigation Strategy). The risk minimization measures were the prescribing rate and the proportion of prescriptions with appropriate dosing. A statistical model was fitted to data from January–December 2012 to establish a stable baseline and then updated biweekly through June 2019. We conducted surveillance with our modified SPC method and a classical SPC method, controlling the false alarm rate to 0.005 for each, and compared how detections aligned with external policy actions.
Results
Our method detected two periods of unusual prescribing behavior beginning in March 2015 (p < 0.005) and April 2016 (p < 0.005). The classical method detected a single period in October 2016 (p < 0.005). Our detections aligned with risk minimization activities in Colorado; the classical method aligned with only the second activity, 6 months later.
Conclusion
Our modified SPC method, which monitors model misspecification rather than raw prescribing data, detects more periods of changing behavior that better temporally align with risk minimization actions. This tool may be useful for regulatory agencies to independently monitor for emerging risks and prescribing trends to complement REMS assessments.
引言：对高风险药物的处方行为进行积极的上市后监测可以为人群中不可预见的问题提供早期预警，但使用真实世界数据进行监测的分析方法尚不发达。本文评估了一种改进的统计过程控制（SPC）方法，用于监控源自行政索赔数据的风险最小化措施。该方法检测人群水平处方行为的变化，并告知调查人员任何检测到的变化的时间和性质。方法：我们从科罗拉多州所有付款人索赔数据库（2012-2019）中调查他他多缓释片（ER）的处方药索赔。该队列是2702名首次接受他他多ER（一种FDA风险评估和缓解策略的阿片类药物）处方的独特患者。风险最小化的措施是处方率和适当剂量处方的比例。对2012年1月至12月的数据进行统计模型拟合，建立稳定的基线，然后每两周更新一次，直到2019年6月。我们使用改进的SPC方法和经典的SPC方法进行监测，将每种方法的误报率控制在0.005，并比较检测结果与外部政策行动的一致性。结果：我们的方法检测到2015年3月开始的两个异常处方行为时期（p p p）。结论：我们改进的SPC方法，监测模型错误而不是原始处方数据，检测到更多的行为变化时期，更好地与风险最小化行动保持一致。该工具可能有助于监管机构独立监测新出现的风险和处方趋势，以补充REMS评估。
{"title":"Surveillance of Pharmaceutical Risk-Mitigation Behavior: Applying and Comparing Statistical Process Control Methods Using Real World Data","authors":"Harris Butler, John D. Rice, Nichole E. Carlson, Elaine H. Morrato","doi":"10.1002/lrh2.70098","DOIUrl":"10.1002/lrh2.70098","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Active post-marketing surveillance of prescribing behavior of high-risk drugs may provide early warning of unforeseen issues in a population, yet analysis approaches for surveillance using real-world data are underdeveloped. This paper evaluates a modified statistical process control (SPC) method for surveillance of risk minimization measures derived from administrative claims data. The approach detects changes in population-level prescribing behaviors and informs investigators of the timing and nature of any detected changes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We investigated prescription drug claims for tapentadol extended release (ER) from the Colorado All-Payers Claims Database (2012–2019). The cohort was 2702 unique patients receiving their first prescriptions of tapentadol ER (an opiate with an FDA Risk Evaluation and Mitigation Strategy). The risk minimization measures were the prescribing rate and the proportion of prescriptions with appropriate dosing. A statistical model was fitted to data from January–December 2012 to establish a stable baseline and then updated biweekly through June 2019. We conducted surveillance with our modified SPC method and a classical SPC method, controlling the false alarm rate to 0.005 for each, and compared how detections aligned with external policy actions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our method detected two periods of unusual prescribing behavior beginning in March 2015 (<i>p</i> < 0.005) and April 2016 (<i>p</i> < 0.005). The classical method detected a single period in October 2016 (<i>p</i> < 0.005). Our detections aligned with risk minimization activities in Colorado; the classical method aligned with only the second activity, 6 months later.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our modified SPC method, which monitors model misspecification rather than raw prescribing data, detects more periods of changing behavior that better temporally align with risk minimization actions. This tool may be useful for regulatory agencies to independently monitor for emerging risks and prescribing trends to complement REMS assessments.</p>\u0000 </section>\u0000 </div>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"10 3","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13242977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148212247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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