Leveraging Deep Learning for Designing Healthcare Analytics Heuristic for Diagnostics.

Healthcare Informatics is a phenomenon being talked about from the early 21st century in the era in which we are living. With evolution of new computing technologies huge amount of data in healthcare is produced opening several research areas. Managing the massiveness of this data is required while extracting knowledge for decision making is the main concern of today. For this task researchers are doing explorations in big data analytics, deep learning (advanced form of machine learning known as deep neural nets), predictive analytics and various other algorithms to bring innovation in healthcare. Through all these innovations happening it is not wrong to establish that disease prediction with anticipation of its cure is no longer unrealistic. First, Dengue Fever (DF) and then Covid-19 likewise are new outbreak in infectious lethal diseases and diagnosing at all stages is crucial to decrease mortality rate. In case of Diabetes, clinicians and experts are finding challenging the timely diagnosis and analyzing the chances of developing underlying diseases. In this paper, Louvain Mani-Hierarchical Fold Learning healthcare analytics, a hybrid deep learning technique is proposed for medical diagnostics and is tested and validated using real-time dataset of 104 instances of patients with dengue fever made available by Holy Family Hospital, Pakistan and 810 instances found for infectious diseases including prognosis of; Covid-19, SARS, ARDS, Pneumocystis, Streptococcus, Chlamydophila, Klebsiella, Legionella, Lipoid, etc. on GitHub. Louvain Mani-Hierarchical Fold Learning healthcare analytics showed maximum 0.952 correlations between two clusters with Spearman when applied on 240 instances extracted from comorbidities diagnostic data model derived from 15696 endocrine records of multiple visits of 100 patients identified by a unique ID. Accuracy for induced rules is evaluated by Laplace (Fig. 8) as 0.727, 0.701 and 0.203 for 41, 18 and 24 rules, respectively. Endocrine diagnostic data is made available by Shifa International Hospital, Islamabad, Pakistan. Our results show that in future this algorithm may be tested for diagnostics on healthcare big data.

The University of Alabama at Birmingham COVID-19 Collaborative Outcomes Research Enterprise: Developing an institutional learning health system in response to the global pandemic.

Introduction: As a local response to the COVID-19 global pandemic, the University of Alabama at Birmingham (UAB) established the UAB COVID-19 Collaborative Outcomes Research Enterprise (CORE), an institutional learning health system (LHS) to achieve an integrated health services outcomes and research response. Methods: We developed a network of expertise and capabilities to rapidly develop and deploy an institutional-level interdisciplinary LHS. Based upon a scoping review of the literature and the Knowledge to Action Framework, we adopted a LHS framework identifying contributors and components necessary to developing a system within and between the university academic and medical centers. We used social network analysis to examine the emergence of informal work patterns and diversified network capabilities based on the LHS framework. Results: This experience report details three principal characteristics of the UAB COVID-19 CORE LHS development: (a) identifying network contributors and components; (b) building the institutional network; and (c) diversifying network capabilities. Contributors and committees were identified from seven components of LHS: (a) collaborative and executive leadership committee, (b) research coordinating committee, (c) oversight and ethics committee, (d) thematic scientific working groups, (e) programmatic working groups, (f) informatics capabilities, and (g) patient advisory groups. Evolving from the topical interests of the initial CORE participants, scientific working groups emerged to support the learning system network. Programmatic working groups were charged with developing a comprehensive and mutually accessible COVID-19 database. Discussion: Our LHS framework allowed for effective integration of multiple academic and medical centers into a cohesive institutional-level learning system. Network analysis indicated diversity of institutional disciplines, professional rank, and topical focus pertaining to COVID-19, with each center leveraging existing institutional responsibilities to minimize gaps in network capabilities. Conclusion: Incorporating an adapted LHS framework designed for academic medical centers served as a foundational resource supporting further institutional-level efforts to develop agile and responsive learning networks.

Adapting coordinated specialty care in the post-COVID-19 era: study protocol for an integrative mixed-methods study.

BACKGROUND: Coordinated Specialty Care (CSC) programs provide evidence-based services for young people with a recent onset of a psychotic disorder. OnTrackNY is a nationally recognized model of CSC treatment in New York state. In 2019, OnTrackNY was awarded a hub within the Early Psychosis Intervention Network (EPINET) to advance its learning health care system (LHS). The OnTrackNY network is comprised of 23 CSC teams across New York state. OnTrack Central, an intermediary organization, provides training and implementation support to OnTrackNY teams. OnTrack Central coordinates a centralized data collection protocol for quality improvement and evaluation of program fidelity and a mechanism to support practice based-research. OnTrackNY sites' breadth coupled with OnTrack Central oversight provides an opportunity to examine the impacts of the COVID-19 crisis in New York State, and supplementary funding was awarded to the OnTrackNY EPINET hub in 2021 for that purpose. METHODS: This project will examine the implications of modifications to service delivery within the OnTrackNY LHS during and after the COVID-19 crisis. We will use the Framework for Reporting Adaptations and Modification-Enhanced (FRAME) to classify systematically, code, and analyze modifications to CSC services and ascertain their impact. We will utilize integrative mixed methods. Qualitative interviews with multi-level stakeholders (program participants, families, providers, team leaders, agency leaders, trainers (OnTrack Central), and decision-makers at the state and local levels) will be used to understand the process of making decisions, information about modifications to CSC services, and their impact. Analysis of OnTrackNY program data will facilitate examining trends in team staffing and functioning, and participant service utilization and outcomes. Study findings will be summarized in a CSC Model Adaptation Guide, which will identify modifications as fidelity consistent or not, and their impact on service utilization and care outcomes. DISCUSSION: A CSC Model Adaptation Guide will inform CSC programs, and the state and local mental health authorities to which they are accountable, regarding modifications to CSC services and the impact of these changes on care process, and participant service utilization and outcomes. The guide will also inform the development of tailored technical assistance that CSC programs may need within OnTrackNY, the EPINET network, and CSC programs nationally. TRIAL REGISTRATION: NCT04021719 , July 16th, 2019.