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Generative artificial intelligence, especially large language models (LLMs), has the potential to affect every level of pediatric education and training. Demonstrating speed and adaptability, LLMs can aid educators, trainees, and practicing pediatricians with tasks such as enhancing curriculum design through the creation of cases, videos, and assessments; creating individualized study plans and providing real-time feedback for trainees; and supporting pediatricians by enhancing information searches, clinic efficiency, and bedside teaching. LLMs can refine patient education materials to address patients' specific needs. The current versions of LLMs sometimes provide "hallucinations" or incorrect information but are likely to improve. There are ethical concerns related to bias in the output of LLMs, the potential for plagiarism, and the possibility of the overuse of an online tool at the expense of in-person learning. The potential benefits of LLMs in pediatric education can outweigh the potential risks if employed judiciously by content experts who conscientiously review the output. All stakeholders must firmly establish rules and policies to provide rigorous guidance and assure the safe and proper use of this transformative tool in the care of the child. In this article, we outline the history, current uses, and challenges with generative artificial intelligence in pediatrics education. We provide examples of LLM output, including performance on a pediatrics examination guide and the creation of patient care instructions. Future directions to establish a safe and appropriate path for the use of LLMs will be discussed.
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Inteligencia Artificial , Pediatría , Pediatría/educación , Humanos , Inteligencia Artificial/tendencias , Curriculum , PredicciónRESUMEN
BACKGROUND: High flow nasal cannula (HFNC) has been increasingly adopted in the past 2 decades as a mode of respiratory support for children hospitalized with bronchiolitis. The growing use of HFNC despite a paucity of high-quality data regarding the therapy's efficacy has led to concerns about overutilization. We developed an electronic health record (EHR) embedded, quality improvement (QI) oriented clinical trial to determine whether standardized management of HFNC weaning guided by clinical decision support (CDS) results in a reduction in the duration of HFNC compared to usual care for children with bronchiolitis. METHODS: The design and summary of the statistical analysis plan for the REspiratory SupporT for Efficient and cost-Effective Care (REST EEC; "rest easy") trial are presented. The investigators hypothesize that CDS-coupled, standardized HFNC weaning will reduce the duration of HFNC, the trial's primary endpoint, for children with bronchiolitis compared to usual care. Data supporting trial design and eventual analyses are collected from the EHR and other real world data sources using existing informatics infrastructure and QI data sources. The trial workflow, including randomization and deployment of the intervention, is embedded within the EHR of a large children's hospital using existing vendor features. Trial simulations indicate that by assuming a true hazard ratio effect size of 1.27, equivalent to a 6-h reduction in the median duration of HFNC, and enrolling a maximum of 350 children, there will be a > 0.75 probability of declaring superiority (interim analysis posterior probability of intervention effect > 0.99 or final analysis posterior probability of intervention effect > 0.9) and a > 0.85 probability of declaring superiority or the CDS intervention showing promise (final analysis posterior probability of intervention effect > 0.8). Iterative plan-do-study-act cycles are used to monitor the trial and provide targeted education to the workforce. DISCUSSION: Through incorporation of the trial into usual care workflows, relying on QI tools and resources to support trial conduct, and relying on Bayesian inference to determine whether the intervention is superior to usual care, REST EEC is a learning health system intervention that blends health system operations with active evidence generation to optimize the use of HFNC and associated patient outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT05909566. Registered on June 18, 2023.
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Teorema de Bayes , Bronquiolitis , Cánula , Sistemas de Apoyo a Decisiones Clínicas , Registros Electrónicos de Salud , Terapia por Inhalación de Oxígeno , Humanos , Bronquiolitis/terapia , Terapia por Inhalación de Oxígeno/métodos , Lactante , Resultado del Tratamiento , Ensayos Clínicos Pragmáticos como Asunto , Interpretación Estadística de Datos , Mejoramiento de la Calidad , Factores de Tiempo , Análisis Costo-BeneficioRESUMEN
This article examines the integration of OpenAI's Chat Generative Pre-trained Transformer (ChatGPT) into Objective Structured Clinical Examinations (OSCEs) for medical education. OSCEs, essential in evaluating medical trainees, are time and resource-intensive for educators and medical colleges. ChatGPT emerges as a solution, aiding educators in efficient OSCE preparation, including case development, standardized patient training, assessment methods, and grading rubrics. We explore ChatGPT's role in reducing trainee stress through simulated interactions of realistic practice scenarios and real-time trainee feedback. We also discuss the importance of validating ChatGPT outputs for medical accuracy and address compliance concerns. While highlighting ChatGPT's potential in reducing time and cost burdens for educators, we underscore the need for careful and informed application of Artificial Intelligence in medical education. Through examples, we outline ChatGPT's promising future in augmenting medical training and assessment, balancing technological innovation with educational integrity.
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Importance: The profile of gastrointestinal (GI) outcomes that may affect children in post-acute and chronic phases of COVID-19 remains unclear. Objective: To investigate the risks of GI symptoms and disorders during the post-acute phase (28 days to 179 days after SARS-CoV-2 infection) and the chronic phase (180 days to 729 days after SARS-CoV-2 infection) in the pediatric population. Design: We used a retrospective cohort design from March 2020 to Sept 2023. Setting: twenty-nine healthcare institutions. Participants: A total of 413,455 patients aged not above 18 with SARS-CoV-2 infection and 1,163,478 patients without SARS-CoV-2 infection. Exposures: Documented SARS-CoV-2 infection, including positive polymerase chain reaction (PCR), serology, or antigen tests for SARS-CoV-2, or diagnoses of COVID-19 and COVID-related conditions. Main Outcomes and Measures: Prespecified GI symptoms and disorders during two intervals: post-acute phase and chronic phase following the documented SARS-CoV-2 infection. The adjusted risk ratio (aRR) was determined using a stratified Poisson regression model, with strata computed based on the propensity score. Results: Our cohort comprised 1,576,933 patients, with females representing 48.0% of the sample. The analysis revealed that children with SARS-CoV-2 infection had an increased risk of developing at least one GI symptom or disorder in both the post-acute (8.64% vs. 6.85%; aRR 1.25, 95% CI 1.24-1.27) and chronic phases (12.60% vs. 9.47%; aRR 1.28, 95% CI 1.26-1.30) compared to uninfected peers. Specifically, the risk of abdominal pain was higher in COVID-19 positive patients during the post-acute phase (2.54% vs. 2.06%; aRR 1.14, 95% CI 1.11-1.17) and chronic phase (4.57% vs. 3.40%; aRR 1.24, 95% CI 1.22-1.27). Conclusions and Relevance: In the post-acute phase or chronic phase of COVID-19, the risk of GI symptoms and disorders was increased for COVID-positive patients in the pediatric population.
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OBJECTIVES: This study aims to examine the association between primary care practice characteristics (enhanced access services) and practice-level rates of nonurgent emergency department (ED) visits using ED and practice-level data. Survey data suggest that enhanced access services within a child's primary care practice may be associated with reduced nonurgent ED visits. METHODS: We performed a cross-sectional analysis of nonurgent ED visits to a tertiary pediatric hospital in Western Pennsylvania with nearly 85,000 annual ED visits. We obtained patient encounter data of all nonurgent pediatric ED (PED) visits between January 2018 and December 2019. We identified the primary care provider at the time of the study period. For each of the 42 included offices, we determined the number of unique children in the office with a nonurgent PED visit, allowing us to determine the percentage of children in the practice with such a visit during the study period. We then stratified the 42 offices into low, intermediate, and high tertiles of nonurgent PED use. Using Kruskal-Wallis tests, logistic regression, and Pearson χ 2 tests, we compared practice characteristics, enhanced access services, practice location Child Opportunity Index 2.0, and PED visit diagnoses across tertiles. RESULTS: We examined 52,459 nonurgent PED encounters by 33,209 unique patients across 42 outpatient offices. Primary care practices in the lowest ED visit tertile were more likely to have 4 or more evenings with office hours (36% vs 14%, P = 0.04), 4 or more evenings of weekday extended hours (43% vs 14%, P = 0.05), and at least 1 day of any weekend hours (86% vs 29%, P = 0.01), compared with practices in other tertiles. High PED use tertile offices were also associated with lower Child Opportunity Index scores. CONCLUSIONS: Primary care offices with higher nonurgent PED utilization had fewer enhanced access services and were located in neighborhood with fewer child-focused resources.
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Servicio de Urgencia en Hospital , Accesibilidad a los Servicios de Salud , Atención Primaria de Salud , Humanos , Servicio de Urgencia en Hospital/estadística & datos numéricos , Estudios Transversales , Atención Primaria de Salud/estadística & datos numéricos , Accesibilidad a los Servicios de Salud/estadística & datos numéricos , Niño , Masculino , Femenino , Preescolar , Pennsylvania , Lactante , Adolescente , Hospitales Pediátricos/estadística & datos numéricosRESUMEN
BACKGROUND: Hospitalization rates for childhood pneumonia vary widely. Risk-based clinical decision support (CDS) interventions may reduce unwarranted variation. METHODS: We conducted a pragmatic randomized trial in two US pediatric emergency departments (EDs) comparing electronic health record (EHR)-integrated prognostic CDS versus usual care for promoting appropriate ED disposition in children (<18 years) with pneumonia. Encounters were randomized 1:1 to usual care versus custom CDS featuring a validated pneumonia severity score predicting risk for severe in-hospital outcomes. Clinicians retained full decision-making authority. The primary outcome was inappropriate ED disposition, defined as early transition to lower- or higher-level care. Safety and implementation outcomes were also evaluated. RESULTS: The study enrolled 536 encounters (269 usual care and 267 CDS). Baseline characteristics were similar across arms. Inappropriate disposition occurred in 3% of usual care encounters and 2% of CDS encounters (adjusted odds ratio: 0.99, 95% confidence interval: [0.32, 2.95]). Length of stay was also similar and adverse safety outcomes were uncommon in both arms. The tool's custom user interface and content were viewed as strengths by surveyed clinicians (>70% satisfied). Implementation barriers include intrinsic (e.g., reaching the right person at the right time) and extrinsic factors (i.e., global pandemic). CONCLUSIONS: EHR-based prognostic CDS did not improve ED disposition decisions for children with pneumonia. Although the intervention's content was favorably received, low subject accrual and workflow integration problems likely limited effectiveness. Clinical Trials Registration: NCT06033079.
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Sistemas de Apoyo a Decisiones Clínicas , Servicio de Urgencia en Hospital , Neumonía , Humanos , Masculino , Femenino , Neumonía/diagnóstico , Niño , Preescolar , Pronóstico , Registros Electrónicos de Salud , Lactante , Hospitalización , Índice de Severidad de la Enfermedad , Adolescente , Tiempo de InternaciónRESUMEN
OBJECTIVES: To support a pragmatic, electronic health record (EHR)-based randomized controlled trial, we applied user-centered design (UCD) principles, evidence-based risk communication strategies, and interoperable software architecture to design, test, and deploy a prognostic tool for children in emergency departments (EDs) with pneumonia. METHODS: Risk for severe in-hospital outcomes was estimated using a validated ordinal logistic regression model to classify pneumonia severity. To render the results usable for ED clinicians, we created an integrated SMART on Fast Healthcare Interoperability Resources (FHIR) web application built for interoperable use in two pediatric EDs using different EHR vendors: Epic and Cerner. We followed a UCD framework, including problem analysis and user research, conceptual design and early prototyping, user interface development, formative evaluation, and postdeployment summative evaluation. RESULTS: Problem analysis and user research from 39 clinicians and nurses revealed user preferences for risk aversion, accessibility, and timing of risk communication. Early prototyping and iterative design incorporated evidence-based design principles, including numeracy, risk framing, and best-practice visualization techniques. After rigorous unit and end-to-end testing, the application was successfully deployed in both EDs, which facilitated enrollment, randomization, model visualization, data capture, and reporting for trial purposes. CONCLUSION: The successful implementation of a custom application for pneumonia prognosis and clinical trial support in two health systems on different EHRs demonstrates the importance of UCD, adherence to modern clinical data standards, and rigorous testing. Key lessons included the need for understanding users' real-world needs, regular knowledge management, application maintenance, and the recognition that FHIR applications require careful configuration for interoperability.
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Registros Electrónicos de Salud , Neumonía , Humanos , Pronóstico , Neumonía/terapia , Niño , Interfaz Usuario-Computador , Programas Informáticos , Interoperabilidad de la Información en SaludRESUMEN
Artificial intelligence (AI) broadly describes a branch of computer science focused on developing machines capable of performing tasks typically associated with human intelligence. Those who connect AI with the world of science fiction may meet its growing rise with hesitancy or outright skepticism. However, AI is becoming increasingly pervasive in our society, from algorithms helping to sift through airline fares to substituting words in emails and SMS text messages based on user choices. Data collection is ongoing and is being leveraged by software platforms to analyze patterns and make predictions across multiple industries. Health care is gradually becoming part of this technological transformation, as advancements in computational power and storage converge with the rapid expansion of digitized medical information. Given the growing and inevitable integration of AI into health care systems, it is our viewpoint that pediatricians urgently require training and orientation to the uses, promises, and pitfalls of AI in medicine. AI is unlikely to solve the full array of complex challenges confronting pediatricians today; however, if used responsibly, it holds great potential to improve many aspects of care for providers, children, and families. Our aim in this viewpoint is to provide clinicians with a targeted introduction to the field of AI in pediatrics, including key promises, pitfalls, and clinical applications, so they can play a more active role in shaping the future impact of AI in medicine.
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Inteligencia Artificial , Medicina , Humanos , Niño , Algoritmos , Programas Informáticos , InteligenciaRESUMEN
BACKGROUND: Improving child health using health information technology (IT) requires a unique set of functionalities that are built into the electronic health record (EHR) and are used to support patient care. In this article, we review and discuss the milestones preceding the development of a new child health EHR standard and describe the salient features of this contemporary standard. METHODS AND RESULTS: The Health Level Seven Pediatric Care Health IT Functional Profile (HL7 PCHIT FP) is an informative standard that encompasses the EHR functions required to care for patients less than 21 years of age in any clinical setting, developed to address the pediatric-specific functionality gaps in the EHR. It includes criteria that support communication between providers and all caregivers, inclusion of pediatric-specific vital signs and diagnosis, support for transition to adult care, and support for reporting and documentation of child abuse or neglect including communication with involved authorities. EHR functionalities for documentation and reporting of newborn screening tests with follow-up for abnormal results and functionality for children with special health care needs with support for identifying patients who may benefit from care coordination are also incorporated, in addition to school-based linkages enabling communication between the health care system and their school environment. CONCLUSION: The HL7 PCHIT FP is envisioned to be another vital step towards improving pediatric health by enhancing EHRs to address the unique health IT needs of children and their health providers. While the HL7 PCHIT FP is the most recently published standard on pediatric EHR systems, standards development is an iterative process, and recommendations for continuous refinement and additional functionalities for the next standards release are encouraged.
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Registros Electrónicos de Salud , Informática Médica , Recién Nacido , Humanos , Niño , Salud Infantil , Atención a la Salud , Programas InformáticosRESUMEN
Severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) may impair immune modulating host microRNAs, causing severe disease. Our objectives were to determine the salivary miRNA profile in children with SARS-CoV-2 infection at presentation and compare the expression in those with and without severe outcomes. Children <18 years with SARS-CoV-2 infection evaluated at two hospitals between March 2021 and February 2022 were prospectively enrolled. Severe outcomes included respiratory failure, shock or death. Saliva microRNAs were quantified with RNA sequencing. Data on 197 infected children (severe = 45) were analyzed. Of the known human miRNAs, 1606 (60%) were measured and compared across saliva samples. There were 43 miRNAs with ≥2-fold difference between severe and non-severe cases (adjusted p-value < 0.05). The majority (31/43) were downregulated in severe cases. The largest between-group differences involved miR-4495, miR-296-5p, miR-548ao-3p and miR-1273c. These microRNAs displayed enrichment for 32 gene ontology pathways including viral processing and transforming growth factor beta and Fc-gamma receptor signaling. In conclusion, salivary miRNA levels are perturbed in children with severe COVID-19, with the majority of miRNAs being down regulated. Further studies are required to validate and determine the utility of salivary miRNAs as biomarkers of severe COVID-19.
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COVID-19 , MicroARNs , Humanos , Niño , Saliva/metabolismo , COVID-19/genética , COVID-19/metabolismo , SARS-CoV-2/metabolismo , MicroARNs/genética , MicroARNs/metabolismo , Transducción de SeñalRESUMEN
BACKGROUND: Electronic health record-based clinical decision support (CDS) is a promising antibiotic stewardship strategy. Few studies have evaluated the effectiveness of antibiotic CDS in the pediatric emergency department (ED). OBJECTIVE: To compare the effectiveness of antibiotic CDS vs. usual care for promoting guideline-concordant antibiotic prescribing for pneumonia in the pediatric ED. DESIGN: Pragmatic randomized clinical trial. SETTING AND PARTICIPANTS: Encounters for children (6 months-18 years) with pneumonia presenting to two tertiary care children s hospital EDs in the United States. INTERVENTION: CDS or usual care was randomly assigned during 4-week periods within each site. The CDS intervention provided antibiotic recommendations tailored to each encounter and in accordance with national guidelines. MAIN OUTCOME AND MEASURES: The primary outcome was exclusive guideline-concordant antibiotic prescribing within the first 24 h of care. Safety outcomes included time to first antibiotic order, encounter length of stay, delayed intensive care, and 3- and 7-day revisits. RESULTS: 1027 encounters were included, encompassing 478 randomized to usual care and 549 to CDS. Exclusive guideline-concordant prescribing did not differ at 24 h (CDS, 51.7% vs. usual care, 53.3%; odds ratio [OR] 0.94 [95% confidence interval [CI]: 0.73, 1.20]). In pre-specified stratified analyses, CDS was associated with guideline-concordant prescribing among encounters discharged from the ED (74.9% vs. 66.0%; OR 1.53 [95% CI: 1.01, 2.33]), but not among hospitalized encounters. Mean time to first antibiotic was shorter in the CDS group (3.0 vs 3.4 h; p = .024). There were no differences in safety outcomes. CONCLUSIONS: Effectiveness of ED-based antibiotic CDS was greatest among those discharged from the ED. Longitudinal interventions designed to target both ED and inpatient clinicians and to address common implementation challenges may enhance the effectiveness of CDS as a stewardship tool.
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Programas de Optimización del Uso de los Antimicrobianos , Sistemas de Apoyo a Decisiones Clínicas , Neumonía , Niño , Humanos , Estados Unidos , Antibacterianos/uso terapéutico , Neumonía/diagnóstico , Neumonía/tratamiento farmacológico , Servicio de Urgencia en HospitalRESUMEN
Objective: Chest radiographs are frequently used to diagnose community-acquired pneumonia (CAP) for children in the acute care setting. Natural language processing (NLP)-based tools may be incorporated into the electronic health record and combined with other clinical data to develop meaningful clinical decision support tools for this common pediatric infection. We sought to develop and internally validate NLP algorithms to identify pediatric chest radiograph (CXR) reports with pneumonia. Materials and methods: We performed a retrospective study of encounters for patients from six pediatric hospitals over a 3-year period. We utilized six NLP techniques: word embedding, support vector machines, extreme gradient boosting (XGBoost), light gradient boosting machines Naïve Bayes and logistic regression. We evaluated their performance of each model from a validation sample of 1,350 chest radiographs developed as a stratified random sample of 35% admitted and 65% discharged patients when both using expert consensus and diagnosis codes. Results: Of 172,662 encounters in the derivation sample, 15.6% had a discharge diagnosis of pneumonia in a primary or secondary position. The median patient age in the derivation sample was 3.7 years (interquartile range, 1.4-9.5 years). In the validation sample, 185/1350 (13.8%) and 205/1350 (15.3%) were classified as pneumonia by content experts and by diagnosis codes, respectively. Compared to content experts, Naïve Bayes had the highest sensitivity (93.5%) and XGBoost had the highest F1 score (72.4). Compared to a diagnosis code of pneumonia, the highest sensitivity was again with the Naïve Bayes (80.1%), and the highest F1 score was with the support vector machine (53.0%). Conclusion: NLP algorithms can accurately identify pediatric pneumonia from radiography reports. Following external validation and implementation into the electronic health record, these algorithms can facilitate clinical decision support and inform large database research.
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Area deprivation index (ADI) is associated with the risk of severe COVID-19 in adults. However, this association has not been established in children. Information on ADI, demographics, clinical features, disease severity, and outcomes was analyzed for 3434 children with COVID-19. A multivariate logistic regression revealed that non-Hispanic Asians, extremes of weight, and higher ADI were associated with severe disease.
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COVID-19 , Adulto , Humanos , Niño , Gravedad del Paciente , Características de la Residencia , Modelos Logísticos , Estudios RetrospectivosRESUMEN
OBJECTIVES: Healthcare utilization after bronchiolitis hospitalization is incompletely understood. We aimed to characterize readmissions and outpatient visits within 1 year after hospital discharge. METHODS: Retrospective multicenter observational cohort study of children under 24-months old admitted with bronchiolitis between January 1, 2010 and December 12, 2019 to the Pediatric Health Information Systems database. A single-center nested subset using linked electronic health records allowed analysis of outpatient visits. RESULTS: There were 308 306 admissions for bronchiolitis among 271 115 patients across 47 hospitals between 2010-2019. The percent of patients readmitted within 30 days after discharge was 6.0% (16 167 of 271 115), and 17.8% (48 332 of 271 115) of patients were readmitted within 1 year. 22.9% (16 919 of 74 001) of patients admitted to an ICU and 26.8% (7865 of 29 378) of patients undergoing mechanical ventilation were readmitted within 1 year. There were 1438 patients with outpatient healthcare data available. There were a median (interquartile range) of 9 (6-13) outpatient visits per patient within 1 year after discharge. Outpatient healthcare use increased for 4 months following bronchiolitis hospitalization compared with previously reported age-matched controls. Higher income, white race, commercial insurance, complex chronic conditions, ICU admission, and mechanical ventilation were associated with higher outpatient utilization. Higher quartiles of outpatient use were associated with readmission for bronchiolitis and all-cause readmissions. CONCLUSIONS: Readmissions in the year after bronchiolitis hospitalization are common, and outpatient healthcare use is increased for 4 months following discharge. Prospective study is needed to track long-term outcomes of infants with bronchiolitis.
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Bronquiolitis , Readmisión del Paciente , Lactante , Humanos , Niño , Preescolar , Estudios Retrospectivos , Hospitalización , Bronquiolitis/epidemiología , Bronquiolitis/terapia , Atención a la SaludRESUMEN
OBJECTIVES: To evaluate the impact of layering routine child abuse screening on top of a preexisting electronic health record-embedded Child Abuse Clinical Decision Support System (CA-CDSS) in a pediatric emergency department. STUDY DESIGN: The Pittsburgh Child Abuse Screening Tool (P-CAST) was performed in all children aged <13 years and in nonverbal children aged ≥13 years who presented to a pediatric tertiary care center over a 6-month period. The P-CAST was layered on top of a preexisting CA-CDSS that included passive triggers, alerts, and abuse-specific order sets. RESULTS: Of the 28 797 screens performed, 1.8% were positive in children aged <13 years and 1.6% were positive in nonverbal children aged ≥13 years. One-half of the children with a positive P-CAST also triggered the CA-CDSS; the other one-half triggered only because of the P-CAST. Nineteen percent of the patients with a positive P-CAST were reported to Child Protective Services (CPS). There was no relationship between race and the odds of a positive P-CAST or between race and the likelihood of a report being made to CPS. CONCLUSIONS: Active routine child abuse screening improves identification of suspected child maltreatment in a children's hospital above and beyond what is identified with a CA-CDSS, which depends on passive triggers. The lack of a relationship between race and a positive P-CAST or a report to CPS suggest that systematic child abuse screening may mitigate well-recognized racial disparities in identifying and reporting suspected child maltreatment.
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Maltrato a los Niños , Hospitales Pediátricos , Niño , Maltrato a los Niños/diagnóstico , Maltrato a los Niños/prevención & control , Servicios de Protección Infantil , Humanos , Notificación Obligatoria , Atención Terciaria de SaludRESUMEN
OBJECTIVE: The purpose of this report is to provide insight from pediatric stakeholders with a shared desire to facilitate a revision of the current United States regulatory pathways for the development of pediatric healthcare devices. METHODS: On August 5, 2020, a group of innovators, engineers, professors and clinicians met to discuss challenges and opportunities for the development of new medical devices for pediatric health and the importance of creating a regulatory environment that encourages and accelerates the research and development of such devices. On January 6, 2021, this group joined regulatory experts at a follow-up meeting. RESULTS: One of the primary issues identified was the need to present decision-makers with opportunities that change the return-on-investment balance between adult and pediatric devices to promote investment in pediatric devices. DISCUSSION/CONCLUSION: Several proposed strategies were discussed, and these strategies can be divided into two broad categories: 1. Removal of real and perceived barriers to pediatric device innovation; 2. Increasing incentives for pediatric device innovation.
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Atención a la Salud , Niño , Humanos , Estados UnidosRESUMEN
Materials are traditionally tested either by imposing controlled displacements and measuring the corresponding forces, or by imposing controlled forces. The first of these approaches is more common because it is straightforward to control the displacements of a stiff apparatus and, if the material suddenly fails, little energy is released. However, when testing gecko-inspired adhesives, an applied force paradigm is closer to how the adhesives are loaded in practice. Moreover, we demonstrate that the controlled displacement paradigm can lead to artefacts in the assumed behaviour unless the imposed loading trajectory precisely matches the deflections that would occur in applications. We present the design of a controlled-force system and protocol for testing directional gecko-inspired adhesives and show that results obtained with it are in some cases substantially different from those with controlled-displacement testing. An advantage of the controlled-force testing approach is that it allows accurate generation of adhesive limit curves without prior knowledge of the expected behaviour of the material or the loading details associated with practical applications.
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Adhesivos , Lagartos , Adhesividad , AnimalesRESUMEN
Clinical documentation has dramatically changed since the implementation and use of electronic health records and electronic provider documentation. The purpose of this report is to review these changes and promote the development of standards and best practices for electronic documentation for pediatric patients. In this report, we evaluate the unique aspects of clinical documentation for pediatric care, including specialized information needs and stakeholders specific to the care of children. Additionally, we explore new models of documentation, such as shared documentation, in which patients may be both authors and consumers, and among care teams while still maintaining the ability to clearly define care and services provided to patients in a given day or encounter. Finally, we describe alternative documentation techniques and newer technologies that could improve provider efficiency and the reuse of clinical data.
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Registros Electrónicos de Salud , Pediatría , Niño , Registros Electrónicos de Salud/organización & administración , Registros Electrónicos de Salud/normas , HumanosRESUMEN
Clinical documentation is a fundamental component of the practice of medicine. It has significantly evolved over the past decade, largely because of the growth of health information technology and electronic health records. Although government agencies and other professional organizations have published position statements on the structure and use of electronic documentation, few have specifically addressed the documentation needs for the care of children. A policy statement on electronic documentation of clinical care by general pediatric and subspecialist providers by the American Academy of Pediatrics is needed. This statement provides insight on the unmet needs of key stakeholders to direct future research and development of the electronic media necessary to enhance the wellness of children and improve health care delivery. It also addresses the challenges and opportunities for efficient and effective clinical documentation in pediatrics.