The Clinical Informatics Data Architect will design clinical data models and standards to enhance City of Hope's ability to use data to achieve strategic goals. This position will work with clinicians to understand their data needs and ensure the clinical information systems can achieve clinical objectives. As the head of clinical data architecture, responsibilities include designing data structures, clinical decision support tools, and reports/dashboards to analyze clinical data and meet requirements.
An overview of the MIS database, showing the importance of medical congress data and how this can be harnessed and searched to answer business critical questions.
An overview of the MIS database, showing the importance of medical congress data and how this can be harnessed and searched to answer business critical questions.
Report out: SMART Emergency Medical TeamsUS-Ignite
SMART Emergency Medical Teams will help inter-disciplinary
teams improve quality of transition-of-care, promote
situational awareness, and the efficacy of simulation
debriefing.
Pipeline is a cloud-based system for managing drug discovery projects. More than simply a dashboard, Pipeline lets you track all of the artifacts from your drug discovery projects including target information, tasks, links to ELN and LIMS data, team information, meeting notes, decisions, lessons learned and more. This presentation walks you through some of the major features of Pipeline.
A template for a basic data management plan. Handout to accompany the presentations Introduction to Research Data Management and Preparing Your Research Data for the Future.
What it takes to build a model for detecting patients that defaults from medi...Olga Zinkevych
Topic of presentation: What it takes to build a model for detecting patients that defaults from medication
The main points of the presentation:
Why data exploration is important?
Clean data is a half of success
why subject knowledge experts are crucial in healthcare project
feature engineerings as a way to make you model more accurate
We will talk about how using clinical data tyr to predict if patients will or will not defect from their medication.
http://dataconf.com.ua/speaker-page/jaya-plmanabhan.php
https://www.youtube.com/watch?v=vjvwzhyLOX4&list=PL5_LBM8-5sLjbRFUtXaUpg84gtJtyc4Pu&t=0s&index=7
http://dataconf.com.ua/speaker-page/khrystyna-kosenko.php
Quahog Life Sciences is building an AI based Healthcare Decision System (Health DS) that promises to take the accuracy of health care decisions to a new level using machine learning and advanced analytics.
Monitor and Engage Clinical Trial Participants for better outcomeQuahog Life Sciences
Quahog platform provides a comprehensive solution for Clinical Trial administrators to collect and integrate data from devices, so that participant health parameters can be monitored daily and get rich insights on trial effectiveness. Monitoring in real-time can also allow them to handle adverse events more effectively
You almost need to be a super sleuth to decode the acronyms in clinical metadata. Our confidential (not really) dossier of some of the important acronyms in clinical data standards will debrief you on the case.
Report out: SMART Emergency Medical TeamsUS-Ignite
SMART Emergency Medical Teams will help inter-disciplinary
teams improve quality of transition-of-care, promote
situational awareness, and the efficacy of simulation
debriefing.
Pipeline is a cloud-based system for managing drug discovery projects. More than simply a dashboard, Pipeline lets you track all of the artifacts from your drug discovery projects including target information, tasks, links to ELN and LIMS data, team information, meeting notes, decisions, lessons learned and more. This presentation walks you through some of the major features of Pipeline.
A template for a basic data management plan. Handout to accompany the presentations Introduction to Research Data Management and Preparing Your Research Data for the Future.
What it takes to build a model for detecting patients that defaults from medi...Olga Zinkevych
Topic of presentation: What it takes to build a model for detecting patients that defaults from medication
The main points of the presentation:
Why data exploration is important?
Clean data is a half of success
why subject knowledge experts are crucial in healthcare project
feature engineerings as a way to make you model more accurate
We will talk about how using clinical data tyr to predict if patients will or will not defect from their medication.
http://dataconf.com.ua/speaker-page/jaya-plmanabhan.php
https://www.youtube.com/watch?v=vjvwzhyLOX4&list=PL5_LBM8-5sLjbRFUtXaUpg84gtJtyc4Pu&t=0s&index=7
http://dataconf.com.ua/speaker-page/khrystyna-kosenko.php
Quahog Life Sciences is building an AI based Healthcare Decision System (Health DS) that promises to take the accuracy of health care decisions to a new level using machine learning and advanced analytics.
Monitor and Engage Clinical Trial Participants for better outcomeQuahog Life Sciences
Quahog platform provides a comprehensive solution for Clinical Trial administrators to collect and integrate data from devices, so that participant health parameters can be monitored daily and get rich insights on trial effectiveness. Monitoring in real-time can also allow them to handle adverse events more effectively
You almost need to be a super sleuth to decode the acronyms in clinical metadata. Our confidential (not really) dossier of some of the important acronyms in clinical data standards will debrief you on the case.
Excel in healthcare analytics with our top SAS Clinical training in Hyderabad. Gain hands-on expertise from clinical data analysis to SAS certification prep. Tailored for all, from fresh grads to pros. Enroll now!
Chapter 4The Enterprise SolutionA Modern Model of HIM PractWilheminaRossi174
Chapter 4
The Enterprise
Solution
:
A Modern Model of HIM Practice
EIM Team Questions
How is the management of digital data different from the management of paper records?
What are differences and similarities?
What is traditional HIM practice?
What type of practices are needed to manage information in a digital era?
Traditional him practice
Traditional HIM Practice
Departmental focus
Synergy among people, processes, and documents
Management of physical records (objects)
Concerned with tracking, filing, and retrieving records, not information
Contemporary Model of Enterprise Health Information Management (EHIM) Practice
Focus on enterprise management
Synergy among people, processes, content, and technology
Data management functions across many domains
Ehim domains
Data Life Cycle Management
Managing data from beginning to end points
Establishes:
What data are collected
Standards for data capture
Standards for data storage and retention
Processes for data access and distribution
Standards for data archival and disposal
Data Architecture Management
Integrated specification artifacts
Establishes:
Standards, policies, procedures for data collection, storage, and integration
Standards for information storage (IS) design
Identifying and documenting requirements
Developing and maintaining data models
Metadata Management
Structured information that describes, explains, locates, or helps retrieve, use, or manage an information resource
Manage data dictionaries
Establish enterprise metadata strategy
Develop policies and procedures for metadata identification, management and use
Establish standards for metadata schemas
Establish and implement metadata metrics
Monitor policy implementation
Master Data Management
Management of key business entity data
Identifying reference data sources (databases, files)
Maintaining authoritative value lists and metadata
Establishing organization data sets
Defining and maintaining match rules
Reconciling system of record
Master Data
Patients
Vendors
Employees
Providers
Products
Location
Reference Data
Business Units
Content and Record Management
Management of unstructured data
Developing and implementing policies and procedures for the organization and categorization of unstructured data (content) in electronic, paper, image, and audio files for its delivery, use, reuse, and preservation
Developing and adopting taxonomic systems
Developing and maintaining an information architecture and metadata schema that identify links and relationships among documents and defines the content within a document
Data Security Management
Protection measures and safeguards for data
Data security planning and organization
Developing, implementing and enforcing data security policies and procedures
Risk management
Business continuity
Audit trails
Information Intelligence and Big Data
Management of applications and technologies for gathering, storing, analyzing, and providing data for d ...
SAS Clinical training program in Hyderabadyeswitha3zen
Excel in healthcare analytics with our top SAS Clinical training in Hyderabad. Gain hands-on expertise from clinical data analysis to SAS certification prep. Tailored for all, from fresh grads to pros. Enroll now!
Avoid PRM failures by avoiding ensuring it's not simply a repository for documenting simple tasks. PRM failures occur when the IT solutions only serves to document activities instead of serving to streamline the physician experience.
Challenges in Clinical Research: Aridhia's Disruptive Technology Approach to ...Aridhia Informatics Ltd
This webinar with our partner Pivotal aired in July 2016.
The increasing sophistication of modern medicine, a seemingly endless supply of data, and the ability to perform large-scale computation is transforming clinical research. However, utilising data to generate new treatments and therapies has continued to prove complicated. The silo-based information systems built over the last 30 years are simply unable to scale to support today’s use cases.
Aridhia, creators of AnalytiXagility, the ground-breaking research and healthcare data analysis platform, is now enabling its customers to rapidly analyse massive amounts of data in meaningful ways to change how diseases are understood, managed and treated. Powered by Pivotal Greenplum, AnalytiXagility is at the forefront of Advanced Clinical Research Information Systems (ACRIS), one of Gartner’s 10 “Transformational Digital Disruptors in Healthcare by 2025”.
Learn how big data and data science are being applied to clinical research and:
• Why research-oriented healthcare delivery organizations and academic medical centers need an ACRIS
• How improving collaboration and productivity accelerates the discovery of insights and increases competiveness
• Why robust data security is critical to modernizing engagement between academia, industry and healthcare
• How to reduce research costs while improving commercialization opportunities
• Why enabling transparent analysis and reproducibility of research are key to scientific progress
• Best practices to get started on your digital transformation and Big Data journey
Transform your career with our Data Science course in Hyderabad. Master machine learning, Python, big data analysis, and data visualization. Our training and expert mentors prepare you for high-demand roles, making you a sought-after data scientist in Hyderabad's tech scene.
Similar to Clinical informatics data architect v2 0 (20)
1. Clinical Informatics Data Architect
Position Summary
The Clinical Informatics Data Architect will understand City of Hope’s strategic goals and
clinical objectives and will enhance City of Hope’s ability to use data to achieve these goals.
This position will work closely with clinicians and business leaders across the medical center to
understand their data needs. This position will ensure that the design of clinical information
systems (from a data perspective) represents these needs and is optimal in order to achieve
clinical goals and outcomes. This position will be responsible for the design of the clinical data
component of clinical information systems through designing data models, standard
terminologies, metadata and clinical decision support elements. This role will use clinical
analytic/predictive analytic and intelligence tools to help clinicians analyze clinical data.
Through an understanding of this data, this position will design reports and dashboards to meet
clinical requirements and to represent data in a manner that is useful to clinicians.
Essential Functions
Define data models and standards.
Design metadata component of clinical data elements
Develop a plan for use of data models and standards
Stay up to date on data architecture standards, methodologies and tools.
Contribute to data quality assurance objectives
Develop and implement a model for clinical data management, standardization,
governance and stewardship including training and developing others skills.
Establish a plan for monitoring data quality.
Collaborate with other departments to define data interface and messaging standards used
at COH.
Collaborate with other departments to ensure data architecture conforms to regulatory
requirements
Work with vendors and service providers during selection or implementation phases of
clinical analytic and data tools to support City of Hope objectives.
Develop and maintain the overall clinical data architecture model
Mine and analyze clinical data to identify patterns and correlations among the various
data elements that may indicate clinically significant events.
Develop and manage a Clinical Decision Support (CDS) program using clinician
requirements and system capabilities to drive outcomes.
Through working with clinicians, develop an assessment program to monitor the
effectiveness of individual CDS interventions
Participates in research in clinical informatics
Serve as an expert resource and consultant for all areas of the medical center with regard
to data collection, standards, management, governance, retrieval and use as well as design
and use of CDS elements and tools.
2. Minimum Education:
Bachelor's Degree in Computer Science, Information Systems or a related field.
Minimum Experience:
3 or more years of data or information architecture experience in a health care
environment
5 years of experience with data aspects of information system design or implementation.
Experience with data mining and analysis
Implementation of decision support elements in clinical information systems including
rules and alerts.
Experience in enterprise data management processes
Strong understanding of relational, dimensional and object-oriented data structures,
theories, principles, and practices.
Strong familiarity with master data and metadata management and associated processes.
Hands-on knowledge of enterprise repository tools, data modeling tools, data mapping
tools, data profiling tools, and data and information system life cycle methodologies.
Experience with Microsoft SQL Server Integration Services (SSIS).
Understanding of Healthcare Data Interoperability standards (HL7, RIM)
Understanding of basic terminology modeling
Understanding of metadata standards (Dublin Core, SKOS) preferred
Excellent communication skills including ability to understand user requirements and to
facilitate user understanding of clinical data.
A working understanding of data privacy standards and regulations,