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

2018 ◽  
Vol 27 (11) ◽  
pp. 937-946 ◽  
Author(s):  
Maria T Britto ◽  
Sandra C Fuller ◽  
Heather C Kaplan ◽  
Uma Kotagal ◽  
Carole Lannon ◽  
...  
Keyword(s):  
Healthcare System ◽  
Point Of Care ◽  
Data Entry ◽  
Regulatory Compliance ◽  
Clinical Encounter ◽  
Learning System ◽  
National Academy Of Sciences ◽  
Statistical Process ◽  
Learning Healthcare System ◽  
Communication Policies

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.

scholarly journals 517. Subcutaneous Sarilumab for the Treatment of Hospitalized patients with Moderate to Severe COVID19 Disease: A Pragmatic, Embedded, Multi-Center Randomized Clinical Trial

2021 ◽  
Vol 8 (Supplement_1) ◽  
pp. S360-S360
Author(s):  
Westyn Branch-Elliman ◽  
Ryan Ferguson ◽  
Gheorghe Doros ◽  
Patricia Woods ◽  
Sarah Leatherman ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Healthcare System ◽  
Standard Care ◽  
Point Of Care ◽  
Data Monitoring Committee ◽  
Panel Member ◽  
Standard Of Care ◽  
Hospitalized Patients ◽  
Open Label ◽  
Learning Healthcare System

Abstract Background The aim of this pragmatic, embedded adaptive trial was to measure the effectiveness of subcutaneous sarilumab in addition to an evolving standard of care for clinical management of inpatients with moderate to severe COVID-19 disease (NCT04359901). The study is also a real-world demonstration of the realization of a prospective learning healthcare system. Methods Two-arm, randomized, open-label controlled 5-center trial comparing standard care alone to standard care (SOC), which evolved over time, with addition of subcutaneous sarilumab (200 mg or 400 mg anti-IL6R) among hospitalized patients with moderate to severe COVID-19 not requiring mechanical ventilation. The primary outcome was 14-day incidence of intubation or death. The trial used a randomized play-the-winner design and was fully embedded within the EHR system, including the adaptive randomization process. Results Among 417 patients screened, 162 were eligible based on chart review, 53 consented, and 50 were evaluated for the primary endpoint of intubation or death ( >30% of eligible patients enrolled) (Figure 1). After the second interim review, the unblinded Data Monitoring Committee recommended that the study be stopped due to concern for safety: a high probability that rates of intubation or death were higher with addition of sarilumab to SOC (92.6%), and a very low probability (3.4%) that sarilumab would be found to be superior. Figure 1. Key Study Milestones, Outcomes, and Adaptations Conclusion This randomized trial of patients hospitalized with COVID-19 and requiring supplemental oxygen but not mechanical ventilation found no evidence of benefit from subcutaneous sarilumab in addition to an evolving standard-of-care. The numbers of patients and events were too low to allow independent conclusions to be drawn, but this study contributes valuable information about the role of subcutaneous IL-6 inhibition in the treatment of patients hospitalized with COVID-19. The major innovation of this trial was the advancement of embedded, point-of-care clinical trials for FDA-approved drugs; this represents a realization of the learning healthcare system. Methods developed and piloted during the conduct of this trial can be used in future investigations to speed the advancement of clinical science. Disclosures Nishant Shah, MD, General Electric (Shareholder)Pfizer, Inc. (Research Grant or Support) Karen Visnaw, RN, Liquidia (Shareholder) Paul Monach, MD,PhD, Celgene (Consultant)ChemoCentryx (Consultant)Kiniksa (Advisor or Review Panel member)

scholarly journals Implementation of a learning healthcare system for sickle cell disease

JAMIA Open ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 349-359
Author(s):  
Robin Miller ◽  
Erin Coyne ◽  
Erin L Crowgey ◽  
Dan Eckrich ◽  
Jeffrey C Myers ◽  
...  
Keyword(s):  
Sickle Cell Disease ◽  
Healthcare System ◽  
Sickle Cell ◽  
Clinical Data ◽  
Data Entry ◽  
Data Dictionary ◽  
Cell Disease ◽  
Health Maintenance ◽  
Learning Healthcare System ◽  
Data Elements

ABSTRCT Objective Using sickle cell disease (SCD) as a model, the objective of this study was to create a comprehensive learning healthcare system to support disease management and research. A multidisciplinary team developed a SCD clinical data dictionary to standardize bedside data entry and inform a scalable environment capable of converting complex electronic healthcare records (EHRs) into knowledge accessible in real time. Materials and Methods Clinicians expert in SCD care developed a data dictionary to describe important SCD-associated health maintenance and adverse events. The SCD data dictionary was deployed in the EHR using EPIC SmartForms, an efficient bedside data entry tool. Additional data elements were extracted from the EHR database (Clarity) using Pentaho Data Integration and stored in a data analytics database (SQL). A custom application, the Sickle Cell Knowledgebase, was developed to improve data analysis and visualization. Utilization, accuracy, and completeness of data entry were assessed. Results The SCD Knowledgebase facilitates generation of patient-level and aggregate data visualization, driving the translation of data into knowledge that can impact care. A single patient can be selected to monitor health maintenance, comorbidities, adverse event frequency and severity, and medication dosing/adherence. Conclusions Disease-specific data dictionaries used at the bedside will ultimately increase the meaningful use of EHR datasets to drive consistent clinical data entry, improve data accuracy, and support analytics that will facilitate quality improvement and research.

Establishing a learning healthcare system to improve health outcomes for people with epilepsy

2021 ◽  
Vol 117 ◽  
pp. 107805
Author(s):  
Maria A. Donahue ◽  
Susan T. Herman ◽  
Deepika Dass ◽  
Kathleen Farrell ◽  
Alison Kukla ◽  
...  
Keyword(s):  
Health Outcomes ◽  
Healthcare System ◽  
Learning Healthcare System ◽  
Improve Health

scholarly journals Wired and Wireless Distributed e-Home Healthcare System

2016 ◽  
pp. 207-250
Author(s):  
Booma Devi Sekar ◽  
JiaLi Ma ◽  
MingChui Dong
Keyword(s):  
Healthcare System ◽  
Point Of Care ◽  
Access Point ◽  
Data Access ◽  
Medical Personnel ◽  
Home Healthcare ◽  
Information Communication ◽  
Healthcare Data ◽  
Remote Healthcare ◽  
Healthcare Monitoring

The proactive development in electronic health (e-health) has introduced seemingly endless number of applications such as telemedicine, electronic records, healthcare score cards, healthcare monitoring etc. Yet, these applications confront the key challenges of network dependence and medical personnel necessity, which hinders the development of universality of e-health services. To mitigate such key challenges, this chapter presents a versatile wired and wireless distributed e-home healthcare system. By exploiting the benefit of body sensor network and information communication technology, the dedicated system model methodically integrates some of the comprehensive functions such as pervasive health monitoring, remote healthcare data access, point-of-care signal interpretation and diagnosis, disease-driven uplink update and synchronization (UUS) scheme and emergency management to design a complete and independent e-home healthcare system.

scholarly journals Opportunities and Challenges for Interpreting Rare Variation in Clinically Important Genes

2020 ◽  
Author(s):  
Gregory McInnes ◽  
Andrew G. Sharo ◽  
Megan L. Koleske ◽  
Julie E. H. Brown ◽  
Matthew Norstad ◽  
...  
Keyword(s):  
Healthcare System ◽  
Protein Function ◽  
Rare Variants ◽  
Clinical Importance ◽  
Computational Prediction ◽  
Ethical Challenges ◽  
Inborn Errors ◽  
Rare Variation ◽  
Learning Healthcare System ◽  
The Impact

Genome sequencing is enabling precision medicine—tailoring treatment to the unique constellation of variants in an individual’s genome. The impact of recurrent pathogenic variants is often understood, leaving a long tail of rare genetic variants that are uncharacterized. The problem of uncharacterized rare variation is especially acute when it occurs in genes of known clinical importance with functionally consequent frequent variants and associated mechanisms. Variants of unknown significance (VUS) in these genes are discovered at a rate that outpaces current ability to classify them using databases of previous cases, experimental evaluation, and computational predictors. Clinicians are thus left without guidance about the significance of variants that may have actionable consequences. Computational prediction of the impact of rare genetic variation is increasingly becoming an important capability. In this paper, we review the technical and ethical challenges of interpreting the function of rare variants in two settings: inborn errors of metabolism in newborns, and pharmacogenomics. We propose a framework for a genomic learning healthcare system with an initial focus on early-onset treatable disease in newborns and actionable pharmacogenomics. We argue that (1) a genomic learning healthcare system must allow for continuous collection and assessment of rare variants, (2) emerging machine learning methods will enable algorithms to predict the clinical impact of rare variants on protein function, and (3) ethical considerations must inform the construction and deployment of all rare-variation triage strategies, particularly with respect to health disparities arising from unbalanced ancestry representation.

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