Electronic health records identify timely trends in childhood mental health conditions.

BACKGROUND: Electronic health records (EHRs) data provide an opportunity to collect patient information rapidly, efficiently and at scale. National collaborative research networks, such as PEDSnet, aggregate EHRs data across institutions, enabling rapid identification of pediatric disease cohorts and generating new knowledge for medical conditions. To date, aggregation of EHR data has had limited applications in advancing our understanding of mental health (MH) conditions, in part due to the limited research in clinical informatics, necessary for the translation of EHR data to child mental health research. METHODS: In this cohort study, a comprehensive EHR-based typology was developed by an interdisciplinary team, with expertise in informatics and child and adolescent psychiatry, to query aggregated, standardized EHR data for the full spectrum of MH conditions (disorders/symptoms and exposure to adverse childhood experiences (ACEs), across 13 years (2010-2023), from 9 PEDSnet centers. Patients with and without MH disorders/symptoms (without ACEs), were compared by age, gender, race/ethnicity, insurance, and chronic physical conditions. Patients with ACEs alone were compared with those that also had MH disorders/symptoms. Prevalence estimates for patients with 1+ disorder/symptoms and for specific disorders/symptoms and exposure to ACEs were calculated, as well as risk for developing MH disorder/symptoms. RESULTS: The EHR study data set included 7,852,081 patients < 21 years of age, of which 52.1% were male. Of this group, 1,552,726 (19.8%), without exposure to ACEs, had a lifetime MH disorders/symptoms, 56.5% being male. Annual prevalence estimates of MH disorders/symptoms (without exposure to ACEs) rose from 10.6% to 2010 to 15.1% in 2023, a 44% relative increase, peaking to 15.4% in 2019, prior to the Covid-19 pandemic. MH categories with the largest increases between 2010 and 2023 were exposure to ACEs (1.7, 95% CI 1.6-1.8), anxiety disorders (2.8, 95% CI 2.8-2.9), eating/feeding disorders (2.1, 95% CI 2.1-2.2), gender dysphoria/sexual dysfunction (43.6, 95% CI 35.8-53.0), and intentional self-harm/suicidality (3.3, 95% CI 3.2-3.5). White youths had the highest rates in most categories, except for disruptive behavior disorders, elimination disorders, psychotic disorders, and standalone symptoms which Black youths had higher rates. Median age of detection was 8.1 years (IQR 3.5-13.5) with all standalone symptoms recorded earlier than the corresponding MH disorder categories. CONCLUSIONS: These results support EHRs' capability in capturing the full spectrum of MH disorders/symptoms and exposure to ACEs, identifying the proportion of patients and groups at risk, and detecting trends throughout a 13-year period that included the Covid-19 pandemic. Standardized EHR data, which capture MH conditions is critical for health systems to examine past and current trends for future surveillance. Our publicly available EHR-mental health typology codes can be used in other studies to further advance research in this area.

Comparative Effectiveness of Anti-TNF in Combination With Low-Dose Methotrexate vs Anti-TNF Monotherapy in Pediatric Crohn's Disease: A Pragmatic Randomized Trial.

BACKGROUND & AIMS: Tumor necrosis factor inhibitors, including infliximab and adalimumab, are a mainstay of pediatric Crohn's disease therapy; however, nonresponse and loss of response are common. As combination therapy with methotrexate may improve response, we performed a multicenter, randomized, double-blind, placebo-controlled pragmatic trial to compare tumor necrosis factor inhibitors with oral methotrexate to tumor necrosis factor inhibitor monotherapy. METHODS: Patients with pediatric Crohn's disease initiating infliximab or adalimumab were randomized in 1:1 allocation to methotrexate or placebo and followed for 12-36 months. The primary outcome was a composite indicator of treatment failure. Secondary outcomes included anti-drug antibodies and patient-reported outcomes of pain interference and fatigue. Adverse events (AEs) and serious AEs (SAEs) were collected. RESULTS: Of 297 participants (mean age, 13.9 years, 35% were female), 156 were assigned to methotrexate (110 infliximab initiators and 46 adalimumab initiators) and 141 to placebo (102 infliximab initiators and 39 adalimumab initiators). In the overall population, time to treatment failure did not differ by study arm (hazard ratio, 0.69; 95% CI, 0.45-1.05). Among infliximab initiators, there were no differences between combination and monotherapy (hazard ratio, 0.93; 95% CI, 0.55-1.56). Among adalimumab initiators, combination therapy was associated with longer time to treatment failure (hazard ratio, 0.40; 95% CI, 0.19-0.81). A trend toward lower anti-drug antibody development in the combination therapy arm was not significant (infliximab: odds ratio, 0.72; 95% CI, 0.49-1.07; adalimumab: odds ratio, 0.71; 95% CI, 0.24-2.07). No differences in patient-reported outcomes were observed. Combination therapy resulted in more AEs but fewer SAEs. CONCLUSIONS: Among adalimumab but not infliximab initiators, patients with pediatric Crohn's disease treated with methotrexate combination therapy experienced a 2-fold reduction in treatment failure with a tolerable safety profile. CLINICALTRIALS: gov, Number: NCT02772965.

Getting what is needed, when it's needed: Sharing information, knowledge, and know-how in a Collaborative Learning Health System.

BACKGROUND: Collaborative Learning Health Systems (CLHS) improve outcomes in part by facilitating collaboration among all stakeholders. One way to facilitate collaboration is by creating conditions for the production and sharing of medical and non-medical resources (information, knowledge, and knowhow [IKK]) so anybody can get "what is needed, when it's needed" (WINWIN) to act in ways that improve health and healthcare. Matching resources to needs can facilitate accurate diagnosis, appropriate prescribing, answered questions, provision of emotional and social support, and uptake of innovations. OBJECTIVES: We describe efforts in ImproveCareNow, a CLHS improving outcomes in pediatric inflammatory bowel disease (IBD), to increase the number of patients and families creating and accessing IKK, and the challenges faced in that process. METHODS: We applied tactics such as outreach through trusted messengers, community organizing, and digital outreach such as sharing resources on our website, via social media, and email to increase the number of people producing, able to access, and accessing IKK. We applied an existing measurement system to track our progress and supplemented this with community feedback. RESULTS: In August of 2017 we identified and began measuring specific actions to track community growth. The number of patients and families producing IKK has increased by a factor of 2.7, using resources has increased by a factor of 4.1 and aware of resources as increased by a factor of 4.0. We identified challenges to measurement and scaling. CONCLUSIONS: It is possible to intentionally increase the number of patients and caregivers engaged with a CHLS to produce and share resources to improve their health.

A science of collaborative learning health systems.

INTRODUCTION: Improving the U.S. healthcare system and health outcomes is one of the most pressing public health challenges of our time. Previously described Collaborative Learning Health Systems (CLHSs) are a promising approach to outcomes improvement. In order to fully realize this promise, a deeper understanding of this phenomenon is necessary. METHODS: We drew on our experience over the past decade with CLHSs as well as qualitative literature review to answer three questions: What kind of phenomena are CLHSs? and what is an appropriate scientific approach? How might we frame CLHSs conceptually? What are potential mechanisms of action? RESULTS: CLHSs are complex adaptive systems in which all stakeholders are able to collaborate, at scale, to create and share resources to satisfy a variety of needs. This is accomplished by providing infrastructure and services that enable stakeholders to act on their inherent motivations. This framing has implications for both research and practice. CONCLUSION: Articulating this framework and potential mechanisms of action should facilitate research to test and refine hypotheses as well as guide practice to develop and optimize this promising approach to improving healthcare systems.

Measuring engagement in a collaborative learning health system: The case of ImproveCareNow.

BACKGROUND: Collaborative learning health systems have demonstrated improved outcomes for a range of different chronic conditions. Patient and healthcare provider engagement in these systems is thought to be associated with improved outcomes. We have adapted an observational framework to measure, and track over time, engagement in ImproveCareNow, a collaborative learning health system for children with inflammatory bowel disease. INTRODUCTION: We developed a categorical classification scheme for engagement in ImproveCareNow. Each tier is defined in terms of observable individual behaviors. When an individual completes one or more qualifying behavior, s/he is classified as engaged at that tier. Individuals are entered into a database, which is accessible to care centers throughout the ImproveCareNow network. Database records include fields for individual name, behavior type, time, place, and level of engagement. RESULTS: The resulting system is employed at 79 ImproveCareNow care centers in the United States. The system recognizes four levels of engagement. Behaviors are recorded in a managed vocabulary and recorded in an online database. The database is queried weekly for individual engagement behaviors, which are tracked longitudinally. Center- and network-level statistics are generated and disseminated to stakeholders. CONCLUSION: It is possible to monitor longitudinal engagement in a collaborative learning health system, thereby charting progress toward engagement goals and enabling quantitative evaluation of interventions aimed at increasing engagement.

A maturity grid assessment tool for learning networks.

BACKGROUND: The vision of learning healthcare systems (LHSs) is attractive as a more effective model for health care services, but achieving the vision is complex. There is limited literature describing the processes needed to construct such multicomponent systems or to assess development. METHODS: We used the concept of a capability maturity matrix to describe the maturation of necessary infrastructure and processes to create learning networks (LNs), multisite collaborative LHSs that use an actor-oriented network organizational architecture. We developed a network maturity grid (NMG) assessment tool by incorporating information from literature review, content theory from existing networks, and expert opinion to establish domains and components. We refined the maturity grid in response to feedback from network leadership teams. We followed NMG scores over time for nine LNs and plotted scores for each domain component with respect to SD for one participating network. We sought subjective feedback on the experience of applying the NMG to individual networks. RESULTS: LN leaders evaluated the scope, depth, and applicability of the NMG to their networks. Qualitative feedback from network leaders indicated that changes in NMG scores over time aligned with leaders' reports about growth in specific domains; changes in scores were consistent with network efforts to improve in various areas. Scores over time showed differences in maturation in the individual domains of each network. Scoring patterns, and SD for domain component scores, indicated consistency among LN leaders in some but not all aspects of network maturity. A case example from a participating network highlighted the value of the NMG in prompting strategic discussions about network development and demonstrated that the process of using the tool was itself valuable. CONCLUSIONS: The capability maturity grid proposed here provides a framework to help those interested in creating Learning Health Networks plan and develop them over time.

Technology-Enabled Health Care Collaboration in Pediatric Chronic Illness: Pre-Post Interventional Study for Feasibility, Acceptability, and Clinical Impact of an Electronic Health Record-Linked Platform for Patient-Clinician Partnership.

BACKGROUND: Mobile health (mHealth) technology has the potential to support the Chronic Care Model's vision of closed feedback loops and patient-clinician partnerships. OBJECTIVE: This study aims to evaluate the feasibility, acceptability, and short-term impact of an electronic health record-linked mHealth platform (Orchestra) supporting patient and clinician collaboration through real-time, bidirectional data sharing. METHODS: We conducted a 6-month prospective, pre-post, proof-of-concept study of Orchestra among patients and parents in the Cincinnati Children's Hospital inflammatory bowel disease (IBD) and cystic fibrosis (CF) clinics. Participants and clinicians used Orchestra during and between visits to complete and view patient-reported outcome (PRO) measures and previsit plans. Surveys completed at baseline and at 3- and 6-month follow-up visits plus data from the platform were used to assess outcomes including PRO completion rates, weekly platform use, disease self-efficacy, and impact on care. Analyses included descriptive statistics; pre-post comparisons; Pearson correlations; and, if applicable, effect sizes. RESULTS: We enrolled 92 participants (CF: n=52 and IBD: n=40), and 73% (67/92) completed the study. Average PRO completion was 61%, and average weekly platform use was 80%. Participants reported improvement in self-efficacy from baseline to 6 months (7.90 to 8.44; P=.006). At 6 months, most participants reported that the platform was useful (36/40, 90%) and had a positive impact on their care, including improved visit quality (33/40, 83%), visit collaboration (35/40, 88%), and visit preparation (31/40, 78%). PRO completion was positively associated with multiple indicators of care impact at 3 and 6 months. CONCLUSIONS: Use of an mHealth tool to support closed feedback loops through real-time data sharing and patient-clinician collaboration is feasible and shows indications of acceptability and promise as a strategy for improving pediatric chronic illness management.

Networked health care: Rethinking value creation in learning health care systems.

Creating better value in health care service today is very challenging. The social pressure to do so is real for every health care system and its leadership. Real benefit has been achieved in manufacturing sector work by the use of "value-chain" thinking, which assumes that the work is a series of linked processes necessary to make a product. For those activities in health care systems that are similar, this model may be very helpful. Attempts to "install" the value chain widely in health care systems have, however, been frustrating. As a result, well-meaning leaders seeking better value have resorted to programs of cost reduction, rather than service redesign. Professionals have not been very happy or willing participants. The work of health care service invites an expanded model of value creation, one that better matches the work. This paper proposes a networked architecture that can mobilize and integrate the resources of health care professionals, interested patients, family, and other community members in the delivery and improvement of health care systems. It also suggests how this value-creation architecture might contribute to research and the development of new knowledge. Two cases illustrate the proposed architecture and its implications for system design and practice, technology development, and roles and responsibilities of all actors involved in health care systems. We believe that this model better fits the need of making and improving health care services. This expanded understanding of how value is created invites attention by senior leaders, by those attempting to facilitate the improvement of current systems, by patients and clinicians involved in the daily work of health care service coproduction, by those charged with the preparation and formation of future professionals, by those who measure and conduct research in health care services, and by those leading policy, payment, and reimbursement systems.

Organizing for collaboration: An actor-oriented architecture in ImproveCareNow.

BACKGROUND: Collaborative learning health systems (CLHSs) enable patients, clinicians, researchers, and others to collaborate at scale to improve outcomes and generate new knowledge. An organizational framework to facilitate this collaboration is the actor-oriented architecture, composed of (a) actors (people, organizations, and databases) with the values and abilities to self-organize; (b) a commons where they create and share resources; and (c) structures, protocols, and processes that facilitate multiactor collaboration. CLHSs may implement a variety of changes to strengthen the actor-oriented architecture and enable more actors to create and share resources. OBJECTIVE: To describe and measure implementation of elements of the actor-oriented architecture in an existing Collaborative Learning Health System. METHODS: We used the case of ImproveCareNow, a CLHS improving outcomes in pediatric inflammatory bowel disease, founded in 2006. We traced several network-level indicators of actor-oriented architecture between 2010 and 2016. RESULTS: We identified measures of actors, the commons, and ways that have made it easier for network member sites to participate. These indicators show ImproveCareNow has made changes in the three elements of the actor-oriented architecture over time. CONCLUSION: It is possible to measure the implementation of an actor-oriented architecture in a CLHS. The elements of the actor-oriented architecture may provide a conceptual framework for their development and optimization. Metrics such as those described here may be actionable indicators of the "health of the system."

Co-Designing a Collaborative Chronic Care Network (C3N) for Inflammatory Bowel Disease: Development of Methods.

BACKGROUND: Our health care system fails to deliver necessary results, and incremental system improvements will not deliver needed change. Learning health systems (LHSs) are seen as a means to accelerate outcomes, improve care delivery, and further clinical research; yet, few such systems exist. We describe the process of codesigning, with all relevant stakeholders, an approach for creating a collaborative chronic care network (C3N), a peer-produced networked LHS. OBJECTIVE: The objective of this study was to report the methods used, with a diverse group of stakeholders, to translate the idea of a C3N to a set of actionable next steps. METHODS: The setting was ImproveCareNow, an improvement network for pediatric inflammatory bowel disease. In collaboration with patients and families, clinicians, researchers, social scientists, technologists, and designers, C3N leaders used a modified idealized design process to develop a design for a C3N. RESULTS: Over 100 people participated in the design process that resulted in (1) an overall concept design for the ImproveCareNow C3N, (2) a logic model for bringing about this system, and (3) 13 potential innovations likely to increase awareness and agency, make it easier to collect and share information, and to enhance collaboration that could be tested collectively to bring about the C3N. CONCLUSIONS: We demonstrate methods that resulted in a design that has the potential to transform the chronic care system into an LHS.

Using a network organisational architecture to support the development of Learning Healthcare Systems.

The US National Academy of Sciences has called for the development of a Learning Healthcare System in which patients and clinicians work together to choose care, based on best evidence, and to drive discovery as a natural outgrowth of every clinical encounter to ensure innovation, quality and value at the point of care. However, the vision of a Learning Healthcare System has remained largely aspirational. Over the last 13 years, researchers, clinicians and families, with support from our paediatric medical centre, have designed, developed and implemented a network organisational model to achieve the Learning Healthcare System vision. The network framework aligns participants around a common goal of improving health outcomes, transparency of outcome measures and a flexible and adaptive collaborative learning system. Team collaboration is promoted by using standardised processes, protocols and policies, including communication policies, data sharing, privacy protection and regulatory compliance. Learning methods include collaborative quality improvement using a modified Breakthrough Series approach and statistical process control methods. Participants observe their own results and learn from the experience of others. A common repository (a 'commons') is used to share resources that are created by participants. Standardised technology approaches reduce the burden of data entry, facilitate care and result in data useful for research and learning. We describe how this organisational framework has been replicated in four conditions, resulting in substantial improvements in outcomes, at scale across a variety of conditions.

Quality Items Required for Running a Paediatric Inflammatory Bowel Disease Centre: An ECCO Paper.

BACKGROUND: The importance of a holistic approach with a comprehensive multidisciplinary team, including nutritional and psychosocial support, is becoming well recognised as a key contributor to optimal care in paediatric inflammatory bowel disease [IBD]. The Paediatric committee of ECCO [P-ECCO] aimed to determine important components that would contribute to quality of care in a paediatric IBD centre [henceforth 'quality items']. METHODS: First, a list of items has been generated by a Delphi group of 111 international paediatric IBD experts. Through an iterative process, the group graded and ranked the items according to their perceived relative contribution to quality care. We then surveyed 101 paediatric IBD centres affiliated with the Porto and Interest groups of ESPGHAN in Europe and with the ImproveCareNow registry in North America, exploring the availability of the retained items in their centres. RESULTS: A total of 68 items were generated and reduced to a list of 60 ranked order items, grouped in six domains: Facility, Personnel, Management, Supportive Services, Patient Support and Accessibility, and Academia and Communications. Of the retained items, 52 [88%] were present in most of the 101 high-performing paediatric IBD centres, and there was a trend for increased availability with increased patient volume at the centres. CONCLUSION: In this P-ECCO study, we attempted to tabulate, for the first time in paediatrics, 60 quality items that paediatric IBD referral centres may wish to include.

Coughing, sneezing, and aching online: Twitter and the volume of influenza-like illness in a pediatric hospital.

This study investigates the relation of the incidence of georeferenced tweets related to respiratory illness to the incidence of influenza-like illness (ILI) in the emergency department (ED) and urgent care clinics (UCCs) of a large pediatric hospital. We collected (1) tweets in English originating in our hospital's primary service area between 11/1/2014 and 5/1/2015 and containing one or more specific terms related to respiratory illness and (2) the daily number of patients presenting to our hospital's EDs and UCCs with ILI, as captured by ICD-9 codes. A Support Vector Machine classifier was applied to the set of tweets to remove those unlikely to be related to ILI. Time series of the pooled set of remaining tweets involving any term, of tweets involving individual terms, and of the ICD-9 data were constructed, and temporal cross-correlation between the social media and clinical data was computed. A statistically significant correlation (Spearman ρ = 0.23) between tweets involving the term flu and ED and UCC volume related to ILI 11 days in the future was observed. Tweets involving the terms coughing (Spearman ρ = 0.24) and headache (Spearman ρ = 0.19) individually were also significantly correlated to ILI-related clinical volume four and two days in the future, respectively. In the 2014-2015 cold and flu season, the incidence of local tweets containing the terms flu, coughing, and headache were early indicators of the incidence of ILI-related cases presenting to EDs and UCCs at our children's hospital.

Protocol of a Pilot Study of Technology-Enabled Coproduction in Pediatric Chronic Illness Care.

BACKGROUND: Pediatric chronic illness care models are traditionally organized around acute episodes of care and may not meet the needs of patients and their families. Interventions that extend the patient-clinician interaction beyond the health care visit, allow for asynchronous and bidirectional feedback loops that span visits and daily life, and facilitate seamless sharing of information are needed to support a care delivery system that is more collaborative, continuous, and data-driven. Orchestra is a mobile health technology platform and intervention designed to transform the management of chronic diseases by optimizing patient-clinician coproduction of care. OBJECTIVE: The aim of this study is to assess the feasibility, acceptability, and preliminary impact of the Orchestra technology and intervention in the context of pediatric chronic illness care. METHODS: This study will be conducted in the cystic fibrosis and inflammatory bowel disease clinics at Cincinnati Children's Hospital Medical Center. We will enroll interested patients and their caregivers to work with clinicians to use the Orchestra technology platform and care model over a 6-month period. In parallel, we will use quality improvement methods to improve processes for integrating Orchestra into clinic workflows and patient/family lifestyles. We will use surveys, interviews, technology use data, and measures of clinical outcomes to assess the feasibility, acceptability, and preliminary impact of Orchestra. Outcome measures will include assessments of: (1) enrollment and dropout rates; (2) duration of engagement/sustained use; (3) symptom and patient-reported outcome tracker completion rates; (4) perceived impact on treatment plan, communication with the clinical team, visit preparation, and overall care; (5) changes in disease self-efficacy and engagement in care; and (6) clinical outcomes and health care utilization. RESULTS: Participant recruitment began in mid-2015, with results expected in 2017. CONCLUSIONS: Chronic disease management needs a dramatic transformation to support more collaborative, effective, and patient-centered care. This study is unique in that it is testing not only the impact of technology, but also the necessary processes that facilitate patient and clinician collaboration. This pilot study is designed to examine how technology-enabled coproduction can be implemented in real-life clinical contexts. Once the Orchestra technology and intervention are optimized to ensure feasibility and acceptability, future studies can test the effectiveness of this approach to improve patient outcomes and health care value.

Appreciating the Nuance of Daily Symptom Variation to Individualize Patient Care.

INTRODUCTION: Improving symptoms for patients with chronic illness is difficult due to poor recall and imprecise assessments of therapeutic response to inform treatment decisions. Daily variation in symptoms may obscure subtle improvement or lead to erroneous associations between symptom changes and alteration in medication or dietary regimens. This may lead to mistaken impressions of treatment efficacy (or inefficacy). Mobile health technologies that collect daily patient reported outcome (PRO) data have the potential to improve care by providing more detailed information for clinical decision-making in practice and may facilitate conducting single subject (n-of-1) trials. METHODS: Interrupted time series to prototype mobile health enabled data collection for three patients. We recruited pediatric patients with established inflammatory bowel disease who had persistent symptoms. Based on their self-identified most troubling symptoms, patients were sent customized, daily-automated text messages to assess the extent of their symptoms. Standardized, PRO Measurement Information System (PROMIS) surveys were deployed weekly. Individual statistical process control charts were used to assess variation. Patients met with physicians regularly to interpret their data jointly. RESULTS: We report the experience of 3 patients with inflammatory bowel disease, each with different symptoms. Daily symptom monitoring uncovered important patterns, some of which even patients were unaware before reviewing their symptom data. Important associations were found between symptom variation and changes in medications and diet. PROMIS survey results assessed longitudinally accurately reflected changes in patient symptoms. CONCLUSIONS: We demonstrated how PROs can be implemented in practice. Monitoring and analyzing daily symptom data, using both customized and standard PROs, has the potential to detect meaningful variation in symptom patterns, which can inform clinical decision-making or can facilitate conducting formal n-of-1 trials to further improve outcomes.

A Digital Architecture for a Network-Based Learning Health System: Integrating Chronic Care Management, Quality Improvement, and Research.

INTRODUCTION: We collaborated with the ImproveCareNow Network to create a proof-of-concept architecture for a network-based Learning Health System. This collaboration involved transitioning an existing registry to one that is linked to the electronic health record (EHR), enabling a "data in once" strategy. We sought to automate a series of reports that support care improvement while also demonstrating the use of observational registry data for comparative effectiveness research. DESCRIPTION OF ARCHITECTURE: We worked with three leading EHR vendors to create EHR-based data collection forms. We automated many of ImproveCareNow's analytic reports and developed an application for storing protected health information and tracking patient consent. Finally, we deployed a cohort identification tool to support feasibility studies and hypothesis generation. There is ongoing uptake of the system. To date, 31 centers have adopted the EHR-based forms and 21 centers are uploading data to the registry. Usage of the automated reports remains high and investigators have used the cohort identification tools to respond to several clinical trial requests. SUGGESTIONS FOR FUTURE USE: The current process for creating EHR-based data collection forms requires groups to work individually with each vendor. A vendor-agnostic model would allow for more rapid uptake. We believe that interfacing network-based registries with the EHR would allow them to serve as a source of decision support. Additional standards are needed in order for this vision to be achieved, however. CONCLUSIONS: We have successfully implemented a proof-of-concept Learning Health System while providing a foundation on which others can build. We have also highlighted opportunities where sponsors could help accelerate progress.

Effectiveness of anti-TNFα for Crohn disease: research in a pediatric learning health system.

OBJECTIVES: ImproveCareNow (ICN) is the largest pediatric learning health system in the nation and started as a quality improvement collaborative. To test the feasibility and validity of using ICN data for clinical research, we evaluated the effectiveness of anti-tumor necrosis factor-α (anti-TNFα) agents in the management of pediatric Crohn disease (CD). METHODS: Data were collected in 35 pediatric gastroenterology practices (April 2007 to March 2012) and analyzed as a sequence of nonrandomized trials. Patients who had moderate to severe CD were classified as initiators or non-initiators of anti-TNFα therapy. Among 4130 patients who had pediatric CD, 603 were new users and 1211 were receiving anti-TNFα therapy on entry into ICN. RESULTS: During a 26-week follow-up period, rate ratios obtained from Cox proportional hazards models, adjusting for patient and disease characteristics and concurrent medications, were 1.53 (95% confidence interval [CI], 1.20-1.96) for clinical remission and 1.74 (95% CI, 1.33-2.29) for corticosteroid-free remission. The rate ratio for corticosteroid-free remission was comparable to the estimate produced by the adult SONIC study, which was a randomized controlled trial on the efficacy of anti-TNFα therapy. The number needed to treat was 5.2 (95% CI, 3.4-11.1) for clinical remission and 5.0 (95% CI, 3.4-10.0) for corticosteroid-free remission. CONCLUSIONS: In routine pediatric gastroenterology practice settings, anti-TNFα therapy was effective at achieving clinical and corticosteroid-free remission for patients who had Crohn disease. Using data from the ICN learning health system for the purpose of observational research is feasible and produces valuable new knowledge.

PEDSnet: a National Pediatric Learning Health System.

A learning health system (LHS) integrates research done in routine care settings, structured data capture during every encounter, and quality improvement processes to rapidly implement advances in new knowledge, all with active and meaningful patient participation. While disease-specific pediatric LHSs have shown tremendous impact on improved clinical outcomes, a national digital architecture to rapidly implement LHSs across multiple pediatric conditions does not exist. PEDSnet is a clinical data research network that provides the infrastructure to support a national pediatric LHS. A consortium consisting of PEDSnet, which includes eight academic medical centers, two existing disease-specific pediatric networks, and two national data partners form the initial partners in the National Pediatric Learning Health System (NPLHS). PEDSnet is implementing a flexible dual data architecture that incorporates two widely used data models and national terminology standards to support multi-institutional data integration, cohort discovery, and advanced analytics that enable rapid learning.