Een mensgericht, gezondheidsgegevens-gedreven ecosysteem

2021 ◽  
Author(s):  
G. STEVENS ◽  
L. HANSTON ◽  
P. VERDONCK
Keyword(s):  
Health Data ◽  
Data Driven ◽  
National Guidelines ◽  
Life Phase ◽  
Norms And Values ◽  
Societal Changes ◽  
Future Value ◽  
And Shifts ◽  
Human Actors ◽  
New Generation

A human-centered, health data-driven ecosystem Value-based, connected and integrated healthcare are gaining momentum in the healthcare landscape. Industry 4.0 is transforming healthcare into a data-driven sector. Data and innovation are the foundations of future value-driven healthcare ecosystems. But how will the human aspect remain to play a lasting role? Healthcare continuums are being rolled out as healthcare goes beyond traditional diagnosis and treatment towards prevention and early detection. Health institutions are facing a new generation of ‘health-conscious’ consumers and ‘technology-minded and -adapted’ healthcare professionals. In order to accelerate innovation within healthcare institutions, it must be powered by the personal health-data of an individual, independent of location, life phase and health status. This data will be continuously generated by the daily use of different technologies. Based upon these concepts and shifts, this paper describes a human-centered, health data-driven ecosystem that is built upon the interaction and balance of human actors in every life phase, different environments and societal changes and with different technologies. International and national guidelines and regulations, and ethical norms and values need to be taken into account. Implementation of this health data model will create future value at any time, place and location within this ecosystem. This ecosystem will create value through the realized integrated care by connecting diverse human actors, different environments and various technologies, while still maintaining the empathic relations between the caretaker and the patient/client.

scholarly journals Health Data Driven on Continuous Blood Pressure Prediction Based on Gradient Boosting Decision Tree Algorithm

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 32423-32433 ◽  
Author(s):  
Bing Zhang ◽  
Jiadong Ren ◽  
Yongqiang Cheng ◽  
Bing Wang ◽  
Zhiyao Wei
Keyword(s):  
Blood Pressure ◽  
Decision Tree ◽  
Health Data ◽  
Data Driven ◽  
Gradient Boosting ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Continuous Blood Pressure

Introducing NASN’s New Data Initiative: National School Health Data Set: Every Student Counts! Make This YOUR Year of Data

2018 ◽  
Vol 33 (5) ◽  
pp. 291-294 ◽  
Author(s):  
Erin D. Maughan ◽  
Kathleen H. Johnson ◽  
Martha Dewey Bergren
Keyword(s):  
School Health ◽  
School Nurses ◽  
Health Data ◽  
National Association ◽  
Data Driven ◽  
School Nursing ◽  
Health Culture ◽  
Data Set ◽  
School Year ◽  
National School

The National Association of School Nurses (NASN) is launching a new data initiative: National School Health Data Set: Every Student Counts! This article describes the vision of the initiative, as well as what school nurses can do to advance a data-driven school health culture. This is the first article in a data and school nursing series for the 2018-2019 school year. For more information on NASN’s initiative and to learn how school nurses can join the data revolution, go to http://nasn.org/everystudentcounts

scholarly journals Development of a health data-driven model for a thermal comfort study

2020 ◽  
Vol 177 ◽  
pp. 106874
Author(s):  
Yi Jiang ◽  
Zhe Wang ◽  
Borong Lin ◽  
Dejan Mumovic
Keyword(s):  
Thermal Comfort ◽  
Health Data ◽  
Data Driven

The Management of Dental Patients Taking New Generation Oral Anticoagulants

2014 ◽  
Vol 3 (4) ◽  
pp. 54-58 ◽  
Author(s):  
Alun Scott ◽  
John Gibson ◽  
Alexander Crighton
Keyword(s):  
Health Planning ◽  
Oral Anticoagulants ◽  
Invasive Procedure ◽  
Medical Practitioner ◽  
Therapeutic Agents ◽  
National Guidelines ◽  
Dental Patients ◽  
Dental Practitioners ◽  
The Individual ◽  
New Generation

Recently, new oral anticoagulants have been introduced as alternatives to warfarin. While national guidelines for treatment of dental patients taking warfarin as an anticoagulant are well-established, no such information is available for these novel therapeutic agents. At present, the local guidance available is contradictory between different health boards/health planning units, and liaison with the medical practitioner managing the individual patient's anticoagulation is imperative if any invasive procedure is proposed. This paper examines the available evidence regarding these drugs and sets out proposals for clinical guidance of dental practitioners treating these patients in primary dental care.

scholarly journals Opportunities and Challenges in Data-Driven Healthcare Research

2018 ◽  
Author(s):  
You Chen
Keyword(s):  
Good Practice ◽  
High Volume ◽  
Health Data ◽  
Healthcare Research ◽  
Care Quality ◽  
Data Driven ◽  
Unplanned Readmission ◽  
Patient Centered ◽  
Smart Healthcare ◽  
Centered Care

Health information technology has been widely used in healthcare, which has contributed a huge amount of data. Health data has four characteristics: high volume; high velocity; high variety and high value. Thus, they can be leveraged to i) discover associations between genes, diseases and drugs to implement precision medicine; ii) predict diseases and identify their corresponding causal factors to prevent or control the diseases at an earlier time; iii) learn risk factors related to clinical outcomes (e.g., patients’ unplanned readmission), to improve care quality and reduce healthcare expenditure; and iv) discover care coordination patterns representing good practice in the implementation of collaborative patient-centered care. At the same time, there are major challenges existing in data-driven healthcare research, which include: i) inefficient health data exchanges across different sources; ii) learned knowledge is biased to specific institution; iii) inefficient strategies to evaluate plausibility of the learned patterns and v) incorrect interpretation and translation of the learned patterns. In this paper, we review various types of health data, discuss opportunities and challenges existing in the data-driven healthcare research, provide solutions to solve the challenges, and state the important role of the data-driven healthcare research in the establishment of smart healthcare system.

scholarly journals Leaders’ perspectives on learning health systems: a qualitative study

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Joanne Enticott ◽  
Sandra Braaf ◽  
Alison Johnson ◽  
Angela Jones ◽  
Helena J. Teede
Keyword(s):  
Qualitative Study ◽  
Health Data ◽  
Health Science ◽  
Data Driven ◽  
Quality Data ◽  
Continuous Learning ◽  
Academic Health ◽  
Science Centre ◽  
Academic Health Science Centre ◽  
Skilled Workforce

Abstract Background Integrated utilisation of digital health data has the power to transform healthcare to deliver more efficient and effective services, and the learning health system (LHS) is emerging as a model to achieve this. The LHS uses routine data from service delivery and patient care to generate knowledge to continuously improve healthcare. The aim of this project was to explore key features of a successful and sustainable LHS to inform implementation in an Academic Health Science Centre context. Methods We purposively identified and conducted semi-structured qualitative interviews with leaders, experienced in supporting or developing data driven innovations in healthcare. A thematic analysis using NVivo was undertaken. Results Analysis of 26 interviews revealed five themes thought to be integral in an effective, sustainable LHS: (1) Systematic approaches and iterative, continuous learning with implementation into healthcare contributing to new best-practice care; (2) Broad stakeholder, clinician and academic engagement, with collective vision, leadership, governance and a culture of trust, transparency and co-design; (3) Skilled workforce, capability and capacity building; (4) Resources with sustained investment over time and; (5) Data access, systems and processes being integral to a sustainable LHS. Conclusions This qualitative study provides insights into the elements of a sustainable LHS across a range of leaders in data-driven healthcare improvement. Fundamentally, an LHS requires continuous learning with implementation of new evidence back into frontline care to improve outcomes. Structure, governance, trust, culture, vision and leadership were all seen as important along with a skilled workforce and sustained investment. Processes and systems to optimise access to quality data were also seen as vital in an effective, sustainable LHS. These findings will inform a co-designed framework for implementing a sustainable LHS within the Australian healthcare and Academic Health Science Centre context. It is anticipated that application of these findings will assist to embed and accelerate the use of routine health data to continuously generate new knowledge and ongoing improvement in healthcare delivery and health outcomes.

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