Clinical Informatics Flashcards
What is Clinical Informatics?
HIMSS defines Clinical Informatics as:
* “activities that promote the understanding, integration and application of
information technology in healthcare settings.”
AMIA differentiates clinical informatics by explicitly noting that it is the use of
information by clinicians
ANIA defines as:
* “explicitly addressing improvements for the health of populations, communities,
families and individuals by optimizing information management and
communication.”
Clinical Informatics Role
Need to understand the basic underpinnings of computers and how they work to manage healthcare information
Computer Technology Basics
Fundamental building blocks
* Bits
* Bytes
* Hardware
* Software
* Connectivity considerations
* Clinical informatics professionals need to understand these and often fill a
role to manage these components for the clinical teams alongside IT
colleagues
Hardware Specifications
Processing speeds
* Memory requirements
* Interface requirements
* Operating systems
Software Considerations
System software
* Used to start and run the computer
* Related to what the software does within the computer system to support the use of the
computer
* e.g. device-driver software operates and manages all devices attached to the computer
* Application software
* Generally has a function or purpose specific to its use
* e.g. accounting/financial applications
* Programming tools
* Used to compile programs and link, or translate, computer program source code and
libraries that belong to either the system software of the application
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Connectivity Considerations and Terminology
- Short messaging services (SMS)
- Virtual private network (VPN)
- Local area network (LAN)
- Mobile technologies
- Cloud computing
Workflow Redesign
- Used as a quality improvement technique
- Identify how the work is currently being done, the current or “as is” state
- Often uses visual representations which allow role and responsibility mapping as
well as determining when actions and decisions are made within the workflow - Can be useful in identifying the “root cause” of an adverse event and then
mitigate the error from happening again, often through a redesign producing a
“future” workflow
Data Visualization
- Clinical Informaticists should understand best practices in developing and
presenting data - Common methods include through graphs, charts and tables (examples of
these on following slides) - Graphical display of data can covey complex ideas with clarity, precision
and efficiency
Data Visualization Best Practices - Clear display of the data
- Induce the viewer to think about substance rather than methods
- Avoid distortion
- Present numbers in small space
- Make a large data set coherent
- Encourage the eye to compare different pieces of data
- Review the data at multiple levels
- Serve a clear purpose to describe, explore, tabulate or enhance a report
- Be closely integrated with verbal or descriptive information in a report
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Clinical Decision Support
Office of the National Coordinator for Health Information Technology (ONC)
defines clinical decision support (CDS) as:
* “a component that “provides clinicians, staff, patients, or other individuals with
knowledge and person-specific information, intelligently filtered or presented at
appropriate times, to enhance health and healthcare.”
* CDS tools may include;
* Computerized alerts and reminders to care providers and patients
* Clinical guidelines
* Condition-specific order sets
* Focused patient data reports and summaries
* Documentation templates;
* Diagnostic support
* Contextually relevant reference information
Who are Clinical Informaticists?
Translators in the interprofessional team, as a professional that speaks both
informatics and healthcare
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Basic Technology Vocabulary and Terms
AM Morning
BP Blood pressure
AC Before meals
BS Blood sugar
AD Right ear
CC Chief complaint
Ad lib Freely
Cap Capsule
Amp Ampule
CM Centimeter
Ante Before
CXR Chest x-ray
AS Left ear
DC Discontinue
ASA Aspirin
Disp Dispense
AU Both ears
ER/EC/ED Emergency Room
BID Twice a day
G Gram
BMI Body mass index
Gr Grain
National Standards for Health Information
Technology
- Standards support certification of electronic health records and the ability to
capture and report data with consistency - Have evolved over time and continue to evolve
- Critical to capturing and transmitting data effectively across institutions,
states, nationally and internationally - Developed and maintained by a number of global organizations
Technology Standards for Healthcare
Messaging Standards Used for
HL7® Clinical data
FHIR® Clinical and administrative data
X12N Financial data, HIPAA-mandated transactions, transport of data
DICOM Images
NCPDP Standards for pharmacy business functions, HIPAA-mandated transactions
IEEE Bedside instruments, medical information bus
Terminology Standards Used for
Lab LOINC
Drugs NLM/FDA/VA collaboration on RxNorm, NDF-RT
Billing CPT, ICD-10-CM
Clinical UMLS, SNOMED, and others
Computers in Healthcare
- Not a new phenomenon
- Largely impacted in the U.S. by the American Recovery and Reinvestment
Act (ARRA) passed in 2009 - Health Information for Economic and Clinical Health Act (HITECH)
- Created to motivate the implementation of electronic health records (EHRs) and supporting
technology - Created an economic stimulus effect
- New jobs/roles created to support the expansion of EHRs and the support of the clinicians who use the
systems
Computer Technology Basics – Internal Components
Motherboard The backbone of the computer Connects all of the parts of the computer together
Central processing unit (CPU) Often thought of as “the brains” of the computer Responsible for interpreting and executing most of the commands from the computer’s hardware and software
Random-access memory (RAM) The working memory of the computer Allows a computer to work with more information at the same time in active memory processing
Power supply A converter that supplies the power to the machine Used to convert the power provided from the outlet into usable power for the many parts inside the computer case
Video card Graphics adapter or expansion card Allows the computer to send graphical information to a video display device such as a monitor, TV, or projector
Hard disc drive (HDD) Data storage device and an electromechanical magnetic disk drive The HDD is the main, and usually largest, data storage hardware device in a computer where the operating system, software, and most files are stored
Solid-state drive (SSD) Data storage device; no moving (mechanical) components Storage device that is typically more resistant to physical shock, runs silently, and has lower access time and less latency, but more expensive than HDD
Optical drive (e.g., Bluray/DVD/CD drive) Optical storage devices Optical drives retrieve and/or store data on optical discs like CDs, DVDs, and Blu-ray discs (BDs)
Optimizing Clinical Effectiveness of Health
Information Technology
- Increased ability to capture data through the use of clinical systems, but can
also lead to - Increased
- Burden of documentation
- Interoperability challenges
- Health IT safety consideration
- Clinician stress
- Clinical Informaticists may use quality improvement techniques as a tool to
optimize technology
Workflow Redesign
- Used as a quality improvement technique
- Identify how the work is currently being done, the current or “as is” state
- Often uses visual representations which allow role and responsibility mapping as
well as determining when actions and decisions are made within the workflow - Can be useful in identifying the “root cause” of an adverse event and then
mitigate the error from happening again, often through a redesign producing a
“future” workflow
Common Clinical Metrics Used in Healthcare
- Defined across three categories
- Process
- Reflective of a clinical guideline and key interventions that impact a clinical outcome
- e.g. door to balloon time for a myocardial infarction (heart attack)
- Outcome
- Outcome measures are often “risk-adjusted” and take into account things like comorbidities
and other factors that may have influenced the metric - e.g. mortality rates, 30-day readmission rate, surgical site infection rate
- Balancing
- Address potential unintended consequences of quality improvement interventions used to
improve process or outcomes - e.g. decreased patient satisfaction due to the reluctance of a provider to prescribe pain medications
for a patient, because of the opioid crisis
Five Rights of CDS
- Combine all the tools and types to strategically use CDS within an
organization following a “five rights” framework - Who, what, when, where and how
- Emphasize the clear goals and objectives of all five components
Regulatory and Compliance Terms
Health Insurance Portability and Accountability Act (HIPAA): A U.S. law designed to provide privacy standards to protect patients’ medical records and other health information provided to health plans, doctors, hospitals, and other healthcare providers2.
Affordable Care Act (ACA): A comprehensive healthcare reform law enacted in March 2010, aimed at expanding health insurance coverage, controlling healthcare costs, and improving healthcare delivery systems2.
Accountable Care Organization (ACO): A group of healthcare providers who come together voluntarily to give coordinated high-quality care to their Medicare patients
Standards and Interoperability
Health Level Seven (HL7): A set of international standards for the exchange, integration, sharing, and retrieval of electronic health information2.
Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT): A systematically organized computer processable collection of medical terms providing codes, terms, synonyms, and definitions used in clinical documentation and reporting2.
International Classification of Diseases (ICD): A globally used diagnostic tool for epidemiology, health management, and clinical purposes
Health Information Exchange (HIE)
The electronic movement of health-related information among organizations according to nationally recognized standards
Data and Analytics
Big Data: Large and complex data sets that traditional data-processing software cannot manage. In healthcare, big data can be used to improve patient outcomes, predict epidemics, gain valuable insights, and reduce healthcare costs1.
Predictive Analytics: The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data1.
Data Warehouse: A central repository of integrated data from one or more disparate sources, used for reporting and data analysis
Needs Analysis and Requirements Gathering
Identify Clinical Needs: Understand the specific clinical needs and workflows that the system must support. This includes gathering input from clinicians, nurses, and other healthcare professionals.
Define Requirements: Clearly define the functional and non-functional requirements of the system. This includes clinical decision support, patient management, data integration, and reporting capabilities.
System Design and Architecture
Interoperability: Ensure that the system can integrate seamlessly with existing healthcare information systems, such as EHRs, laboratory systems, and radiology systems. Use standards like HL7 and FHIR for data exchange.
Scalability: Design the system to be scalable to accommodate future growth and additional functionalities.
Security and Privacy: Implement robust security measures to protect patient data and comply with regulations like HIPAA.
Implementation Planning
Project Management: Develop a detailed project plan that includes timelines, milestones, and resource allocation. Use project management methodologies like Agile or Waterfall as appropriate.
Stakeholder Engagement: Engage stakeholders throughout the implementation process to ensure their needs are met and to gain their buy-in.
System Development and Customization
Clinical Decision Support: Implement clinical decision support tools that provide real-time alerts and recommendations to clinicians based on patient data.
Workflow Automation: Automate routine clinical workflows to reduce manual tasks and improve efficiency. This includes order entry, medication administration, and patient discharge processes.
User Interface Design: Design an intuitive and user-friendly interface that minimizes the learning curve for healthcare professionals.
Testing and Validation
Unit Testing: Test individual components of the system to ensure they function correctly.
Integration Testing: Test the integration of different system components to ensure they work together seamlessly.
User Acceptance Testing (UAT): Conduct UAT with end-users to validate that the system meets their needs and performs as expected.
Training and Change Management
Training Programs: Develop comprehensive training programs for all users, including clinicians, nurses, and administrative staff. Use a combination of classroom training, e-learning, and hands-on practice.
Change Management: Implement change management strategies to address resistance and ensure smooth adoption of the new system.
Go-Live and Post-Implementation Support
Go-Live Planning: Plan the go-live carefully, including data migration, system configuration, and user support.
Post-Implementation Support: Provide ongoing support to users, including a helpdesk, troubleshooting, and system updates.
Continuous Improvement
Monitor Performance: Continuously monitor system performance and user feedback to identify areas for improvement.
Iterative Enhancements: Implement iterative enhancements based on feedback and changing clinical needs.
Clinical Outcomes
Clinical outcomes are specific changes in health or health quality that result from healthcare interventions. These outcomes are measurable and can be evaluated through various metrics such as hospital readmission rates, infection rates, and patient satisfaction scores. Data analytics tools help in tracking and analyzing these metrics to identify trends, measure the effectiveness of interventions, and improve patient care.
Operational Outcomes
Operational outcomes focus on the efficiency and effectiveness of healthcare processes. These outcomes include metrics such as average length of stay, bed occupancy rates, and staff productivity. By analyzing these metrics, healthcare organizations can identify bottlenecks, optimize resource allocation, and improve overall operational efficiency.
Data Analytics Tools
Several data analytics tools are commonly used in healthcare to interpret clinical and operational outcomes:
Reports: Standard and custom reports provide detailed insights into various aspects of healthcare operations and clinical outcomes. These reports can be generated periodically to monitor performance and identify areas for improvement.
Tables: Tables organize data in a structured format, making it easier to compare and analyze different metrics. They are useful for presenting numerical data and summarizing key findings.
Graphs and Charts: Visual representations such as bar charts, line graphs, and pie charts help in understanding trends and patterns in data. They make complex data more accessible and easier to interpret.
Predictive Models: Predictive analytics uses historical data to forecast future outcomes. These models can predict patient readmissions, disease outbreaks, and other critical events, allowing healthcare organizations to take proactive measures.
Implementing Data Analytics
To effectively implement data analytics in healthcare, organizations should follow these steps:
Define Objectives: Clearly define the clinical and operational outcomes you want to measure and improve.
Collect Data: Gather relevant data from various sources, including electronic health records (EHRs), patient surveys, and operational systems.
Analyze Data: Use data analytics tools to analyze the collected data. Look for trends, correlations, and anomalies that can provide insights into clinical and operational performance.
Interpret Results: Interpret the results of the data analysis to understand the underlying factors affecting clinical and operational outcomes.
Take Action: Based on the insights gained, implement changes to improve patient care and operational efficiency. Monitor the impact of these changes and adjust as needed.
By leveraging data analytics tools, healthcare organizations can gain valuable insights into their clinical and operational performance, leading to better decision-making and improved outcome
