Quality Improvement for Well Child Care

Presented to the Faculty of University of Alaska Anchorage in Partial Fulfillment of Requirements for the Degree of MASTER OF SCIENCEThe American Academy of Pediatrics (AAP) Bright Futures (BF) guidelines for well child care were designed to provide quality pediatric care. Adherence to AAP-BF guidelines improves: screenings, identification of developmental delay, immunization rates, and early identification of children with special healthcare needs. The current guideline set is comprehensive and includes thirty one well child exams, thirty three universal screening exams and one hundred seventeen selective screening exams. Many providers have difficulty meeting all guideline requirements and are at risk of committing Medicaid fraud if a well exam is coded and requirements are not met. The goal of this quality improvement project was to design open source and adaptable templates for each pediatric age group to improve provider adherence to the BF guidelines. A Plan-Do-Study-Act (PDSA) quality improvement model was used to implement the project. Templates were created for ages twelve months to eighteen years and disseminated to a pilot clinic in Anchorage, Alaska. The providers were given pre-implementation and postimplementation surveys to determine the efficacy and usefulness of the templates. Templates were determined to be useful and efficient means in providing Bright Futures focused well child care. The templates are in the process of being disseminated on a large scale to assist other providers in meeting BF guideline requirements.Title Page / Table of Contents / List of Tables / List of Appendices / Abstract / Introduction / Background / Clinical Significance / Current Clinical Practice / Research Question / Literature Review / Framework: Evidence Based Practice Model/ Ethical Considerations and Institutional Review Board / Methods / Implementation Barriers / Findings / Discussion / Disseminatio

Structured data entry for narrative data in a broad specialty: patient history and physical examination in pediatrics.

BACKGROUND: Whereas an electronic medical record (EMR) system can partly address the limitations, of paper-based documentation, such as fragmentation of patient data, physical paper records missing and poor legibility, structured data entry (SDE, i.e. data entry based on selection of predefined medical concepts) is essential for uniformity of data, easier reporting, decision support, quality assessment, and patient-oriented clinical research. The aim of this project was to explore whether a previously developed generic (i.e. content independent) SDE application to support the structured documentation of narrative data (called OpenSDE) can be used to model data obtained at history taking and physical examination of a broad specialty. METHODS: OpenSDE was customized for the broad domain of general pediatrics: medical concepts and its descriptors from history taking and physical examination were modeled into a tree structure. RESULTS: An EMR system allowing structured recording (OpenSDE) of pediatric narrative data was developed. Patient history is described by 20 main concepts and physical examination by 11. In total, the thesaurus consists of about 1800 items, used in 8648 nodes in the tree with a maximum depth of 9 levels. Patient history contained 6312 nodes, and physical examination 2336. User-defined entry forms can be composed according to individual needs, without affecting the underlying data representation. The content of the tree can be adjusted easily and sharing records among different disciplines is possible. Data that are relevant in more than one context can be accessed from multiple branches of the tree without duplication or ambiguity of data entry via "shortcuts". CONCLUSION: An expandable EMR system with structured data entry (OpenSDE) for pediatrics was developed, allowing structured documentation of patient history and physical examination. For further evaluation in other environments, the tree structure for general pediatrics is available at the Erasmus MC Web site (in Dutch, translation into English in progress) 1. The generic OpenSDE application is available at the OpenSDE Web site 2

Pediatric Private Practice on the National Health Information Network: The PedOne® System

The National Health Information Network (NHIN) is a federal mandate of the US Government. It involves setting the standards for interoperability and effective information technology in health care for hospitals, urgent care, private practices, insurance carriers, and other health care participants. Much attention has been paid to mixed NHIN funding outcomes of Regional Health Information Organizations (RHIOs) but here we take the different perspective of the private practice. This paper examines a Pediatric implementation, PedOne®, that is designed to deliver an intuitive and friendly environment to provide clinical data management and decision support. PedOne® interfaces to public health and internal medical knowledge bases. In an analogy to the Internet Engineering Task Force (IETF) role in building the Internet, we focus on the running code and the physician design involvement to provide lessons learned with respect to Pediatric Electronic Health Records system evolution. The design principles we uncover can be extrapolated to other medical specialties

Design and Implementation of a Collaborative Clinical Practice and Research Documentation System Using SNOMED-CT and HL7-CDA in the Context of a Pediatric Neurodevelopmental Unit

This paper introduces a prototype for clinical research documentation using the structured information model HL7 CDA and clinical terminology (SNOMED CT). The proposed solution was integrated with the current electronic health record system (EHR-S) and aimed to implement interoperability and structure information, and to create a collaborative platform between clinical and research teams. The framework also aims to overcome the limitations imposed by classical documentation strategies in real-time healthcare encounters that may require fast access to complex information. The solution was developed in the pediatric hospital (HP) of the University Hospital Center of Coimbra (CHUC), a national reference for neurodevelopmental disorders, particularly for autism spectrum disorder (ASD), which is very demanding in terms of longitudinal and cross-sectional data throughput. The platform uses a three-layer approach to reduce components’ dependencies and facilitate maintenance, scalability, and security. The system was validated in a real-life context of the neurodevelopmental and autism unit (UNDA) in the HP and assessed based on the functionalities model of EHR-S (EHR-S FM) regarding their successful implementation and comparison with state-of-the-art alternative platforms. A global approach to the clinical history of neurodevelopmental disorders was worked out, providing transparent healthcare data coding and structuring while preserving information quality. Thus, the platform enabled the development of user-defined structured templates and the creation of structured documents with standardized clinical terminology that can be used in many healthcare contexts. Moreover, storing structured data associated with healthcare encounters supports a longitudinal view of the patient’s healthcare data and health status over time, which is critical in routine and pediatric research contexts. Additionally, it enables queries on population statistics that are key to supporting the definition of local and global policies, whose importance was recently emphasized by the COVID pandemic.info:eu-repo/semantics/publishedVersio

Integration of modeling and simulation into hospital-based decision support systems guiding pediatric pharmacotherapy

<p>Abstract</p> <p>Background</p> <p>Decision analysis in hospital-based settings is becoming more common place. The application of modeling and simulation approaches has likewise become more prevalent in order to support decision analytics. With respect to clinical decision making at the level of the patient, modeling and simulation approaches have been used to study and forecast treatment options, examine and rate caregiver performance and assign resources (staffing, beds, patient throughput). There us a great need to facilitate pharmacotherapeutic decision making in pediatrics given the often limited data available to guide dosing and manage patient response. We have employed nonlinear mixed effect models and Bayesian forecasting algorithms coupled with data summary and visualization tools to create drug-specific decision support systems that utilize individualized patient data from our electronic medical records systems.</p> <p>Methods</p> <p>Pharmacokinetic and pharmacodynamic nonlinear mixed-effect models of specific drugs are generated based on historical data in relevant pediatric populations or from adults when no pediatric data is available. These models are re-executed with individual patient data allowing for patient-specific guidance via a Bayesian forecasting approach. The models are called and executed in an interactive manner through our web-based dashboard environment which interfaces to the hospital's electronic medical records system.</p> <p>Results</p> <p>The methotrexate dashboard utilizes a two-compartment, population-based, PK mixed-effect model to project patient response to specific dosing events. Projected plasma concentrations are viewable against protocol-specific nomograms to provide dosing guidance for potential rescue therapy with leucovorin. These data are also viewable against common biomarkers used to assess patient safety (e.g., vital signs and plasma creatinine levels). As additional data become available via therapeutic drug monitoring, the model is re-executed and projections are revised.</p> <p>Conclusion</p> <p>The management of pediatric pharmacotherapy can be greatly enhanced via the immediate feedback provided by decision analytics which incorporate the current, best-available knowledge pertaining to dose-exposure and exposure-response relationships, especially for narrow therapeutic agents that are difficult to manage.</p