The Case for the Use of Nurse Practitioners in the Care of Children with Medical Complexity

Samuels, Cheryl; Harris, Tomika S.; Gonzales, Traci; Mosquera, Ricardo A.

doi:10.3390/children4040024

Cited by 5 publications

(3 citation statements)

References 24 publications

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“…History of prior hospital admissions, medical complexity, and non-acute post-discharge were found important features in predicting readmission, which is consistent with the previous investigations [22,24,28,49]. Although these factors are not easily modifiable for most conditions, comprehensive intervention strategies including better discharge planning (e.g., telephone call) and care coordination can mitigate the risk of pediatric readmission [13,31,50,51]. The findings of prior hospital visits in all three readmission models suggest that there might exist an unresolved system issue associated with the quality and clarity of discharge education and access to pediatric care for a certain patient population [36].…”

Section: Discussionsupporting

confidence: 85%

Identifying Children at Readmission Risk: At-Admission versus Traditional At-Discharge Readmission Prediction Model

Symum

Zayas‐Castro

2021

Healthcare

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The timing of 30-day pediatric readmissions is skewed with approximately 40% of the incidents occurring within the first week of hospital discharges. The skewed readmission time distribution coupled with delay in health information exchange among healthcare providers might offer a limited time to devise a comprehensive intervention plan. However, pediatric readmission studies are thus far limited to the development of the prediction model after hospital discharges. In this study, we proposed a novel pediatric readmission prediction model at the time of hospital admission which can improve the high-risk patient selection process. We also compared proposed models with the standard at-discharge readmission prediction model. Using the Hospital Cost and Utilization Project database, this prognostic study included pediatric hospital discharges in Florida from January 2016 through September 2017. Four machine learning algorithms—logistic regression with backward stepwise selection, decision tree, Support Vector machines (SVM) with the polynomial kernel, and Gradient Boosting—were developed for at-admission and at-discharge models using a recursive feature elimination technique with a repeated cross-validation process. The performance of the at-admission and at-discharge model was measured by the area under the curve. The performance of the at-admission model was comparable with the at-discharge model for all four algorithms. SVM with Polynomial Kernel algorithms outperformed all other algorithms for at-admission and at-discharge models. Important features associated with increased readmission risk varied widely across the type of prediction model and were mostly related to patients’ demographics, social determinates, clinical factors, and hospital characteristics. Proposed at-admission readmission risk decision support model could help hospitals and providers with additional time for intervention planning, particularly for those targeting social determinants of children’s overall health.

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Section: Discussionsupporting

confidence: 85%

Identifying Children at Readmission Risk: At-Admission versus Traditional At-Discharge Readmission Prediction Model

Symum

Zayas‐Castro

2021

Healthcare

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“…22 Ademais, os profissionais de enfermagem ajudam a superar as dificuldades cotidianas, fornecendo cuidados abrangentes e econômicos no domicílio, clínicas especializadas e em ambientes hospitalares, com melhores resultados de saúde e maior satisfação das famílias. 23 Nessa diretiva, a participação da equipe multiprofissional na atenção primária, tendo o enfermeiro como o coordenador dos cuidados às CRIANES, favorece um cuidado humanizado e integral a essas crianças e suas famílias. Contudo, a literatura aponta que a rede de atenção à saúde para essas crianças é considerada frágil e desarticulada, e que os serviços de urgência e emergência e os de nível terciário são acessados preferencialmente em detrimento dos serviços de nível primário.…”

Section: Discussionunclassified

Crianças com necessidades de saúde especiais em um serviço de pronto atendimento pediátrico: estudo transversal

Bastos¹,

Santos²,

Ledo³

et al. 2022

Rev Enferm UFSM

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Objetivos: rastrear crianças com necessidades de saúde especiais em um serviço de pronto atendimento pediátrico e analisar suas demandas de cuidado. Método: estudo transversal, desenvolvido de março a junho de 2019, em serviço hospitalar de emergência infantil, no município de Rio das Ostras, Rio de Janeiro, Brasil. Aplicaram-se a versão brasileira do Children with Special Health Care Needs Screener (CSHCN Screener®) e o instrumento caracterizador de demandas com familiares cuidadores de crianças. Utilizou-se a estatística descritiva. Resultados: crianças com necessidades de saúde especiais corresponderam a 5,1%, dentre 235 famílias rastreadas. Os principais domínios de necessidades de saúde foram maior utilização de serviços e dependência de medicamentos. Dentre as demandas de cuidados, destacaram-se os cuidados habituais modificados. Conclusão: as seis demandas de cuidados foram identificadas entre as crianças rastreadas. Recomenda-se a adoção do CSHCN Screener® para ampliar a visibilidade e indicar estratégias assistenciais a esse grupo populacional.

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“…In areas where there is limited availability of specialists and subspecialists, other health care providers may also be required to step in to fulfill care tasks. 89 • Expanded care coordination roles requires dedicated time, such as a set number of hours per week for care coordination for an existing staff member, such as nurses, or even the creation of new, dedicated part-or full-time positions. 89 It will also require health care providers to change how they organize and provide care, such as requiring additional time for multidisciplinary meetings to share information and improve care coordination.…”

Section: Evolving Practice For Health Care Professionalsmentioning

confidence: 99%

2024 Watch List: Top 10 Technologies and Issues Related to Caring for Children and Youth With Medical Complexity

CADTH

2024

cjht

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What Is the 2024 Watch List? The Watch List annual Horizon Scan report presents emerging technologies and issues that have the potential to shape the future of health care in Canada. CADTH’s 2024 Watch List focuses on care for children and youth with medical complexity. This top 10 has been divided in 2 parts — the top 5 technologies and top 5 issues related to children and youth with medical complexity that have the potential to make a significant and meaningful impact in transforming health systems in Canada over the next 5 years. These technologies and issues could shape the future of health care for not only children and youth with medical complexity, but also for others with chronic health conditions. Why Is This an Issue? Children and youth (people aged 24 years and younger) with medical complexity are a diverse group with a range of needs (e.g., single or multiple conditions, rare diseases). Although there is no single definition, common characteristics include significant functional limitations often causing the child or youth to be reliant on technology; high health care utilization, often requiring specialized care and services from different providers in multiple settings; and high health care service needs, such as care provision in the home and care coordination, which can have significant social and financial impacts on caregivers and the family. Children and youth with medical complexity account for less than 1% of all children and youth in Canada, but they account for 37% of hospital stays and 17% of emergency department visits. Due in part to the high number of health care interactions, this group experiences the effects of challenges within the health care system more acutely than their less medically complex peers. What Is the Potential Impact? The Watch List highlights areas for innovation, systems change, and investment. Advances in medical care have resulted in more children living with conditions that previously would not have been survivable in infancy and childhood. Now there are more children and youth with medical complexity, and they are living longer. Our current health systems were not designed to meet the complex needs of this group of people and their caregivers, including that many of them face challenges in accessing needed care. This year’s Watch List spotlights new and emerging technologies and key issues that could have a major impact on how patient care is provided to children and youth with medical complexity. What Else Do We Need to Know? In the 2024 Watch List, we identify and describe the top 5 new and emerging technologies that could shape the future of health care for children and youth with medical complexity in Canada, including new models of care and technologies and systems to improve communication. We also explore some considerations for health care decision-makers about the potential impact of these technologies on care pathways, health care human resources, health care infrastructure, and health equity. The 2024 Watch List also identifies the top 5 issues that limit health systems from providing optimal quality of care for children and youth with medical complexity. Key issues such as the need for increased interoperability between health systems and the need for strategies to improve sustainability warrant more attention and will influence the wider adoption, diffusion, and implementation of new and emerging technologies for children and youth with medical complexity. Monitoring ongoing developments and evidence related to the top technologies and issues highlighted in the 2024 Watch List can help guide health system planning in Canada and improve access to high-quality care.

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The Case for the Use of Nurse Practitioners in the Care of Children with Medical Complexity

Cited by 5 publications

References 24 publications

Identifying Children at Readmission Risk: At-Admission versus Traditional At-Discharge Readmission Prediction Model

Identifying Children at Readmission Risk: At-Admission versus Traditional At-Discharge Readmission Prediction Model

Crianças com necessidades de saúde especiais em um serviço de pronto atendimento pediátrico: estudo transversal

2024 Watch List: Top 10 Technologies and Issues Related to Caring for Children and Youth With Medical Complexity

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