How does big data and technology advance health care?
Mind map on how data affects technology and advances health care
1. Cybersecurity: set of technologies and process designed to protect computers, networks, programs and data from attack
1.1. The ultimate goal is data driven intelligent decision making from security data for smart cybersecurity solutions
1.1.1. Defense strategies that preserve several properties:
1.1.1.1. 1. Confidentiality- property used to prevent access and disclosure of information to unauthorized individuals or systems
1.1.1.2. 2. Integrity- property used to prevent any modification or destruction of information in unauthorized manner
1.1.1.3. 3. Availability- property used to ensure timely and reliable access of information assets and systems to an authorized entity
1.2. Cybersecurity data science- refers to collect a large amount of security event data from different sources and analyze it using machine learning technology
1.2.1. Applies in a variety of context and can be divided into several common categories:
1.2.1.1. 1. Network security- focuses on securing computers from cyber attacks or intruders
1.2.1.2. 2. Application security- keeps the software and devices free of threats
1.2.1.3. 3. Information security- mainly considers security and privacy of relevant data
1.2.1.4. 4. Operational security- includes the proesses of handling and protecting data sets
1.3. It is a research or working area existing at the intersection of cybersecurity, data science, machine learning and AI
1.3.1. Machine learning is a branch of AI related to computational statistics, data mining and analytics, data science, parituclarly focusing on computers learning from data
2. Health Information Technology for Economic and Clinical Health Act (HITECH)
2.1. Enacted to increase the number of healthcare organizations adopting health information technology (HIT)
2.1.1. Moving forward to cubersafe health care includes the following principles:
2.1.1.1. Principle 1: Healthcare cybersecurity is a patient safety issue
2.1.1.2. Principle 2: Healthare organizations are prime targets of cybersecurity threats
2.1.1.3. Principle 3: No one organization or sector can do it alone
2.1.1.4. Principle 4: Healthcare needs lthe leadership and capacity to respond to threats
2.1.1.5. Principle 5: Cyber resilience and response is an enterprise wide responsibility. Back ups must be frequent
2.1.1.6. Principle 6: Training is essential and simplicity is key
2.1.1.7. Principle 7: Cybesecurity expertise ins in short supply
2.1.1.8. Principle 8: Cybersecurity standards are important but have been challengig to deploy in healthcare
2.1.1.9. Principle 9: Medical device cybersecurity needs to be managed as a matter of policy
3. Fun fact: An EHR system is worth 10-100x more than credit card information in the black market. EHR contains sensitive data such as patient information, credit card accounts, diagnostic results, SIN numbers and more!
3.1. Healthcare data is vital, personal, profitable and information dense!
4. Precision health: provides individuals personalised health strategies through innovations from personal data such as social determinants of health and family medical history
4.1. Upstream problem solving to decrease medical interventions
4.2. Requires interdisciplinary collaboration because it spans a number of sectors including IoT, information and communication technologies, legal development, social work, etc.
4.2.1. National Institute of Nursing Research (NINR) developed a roadmap for technologyuse in precision health that is broken down in 5 steps
4.2.1.1. Step 1: Contextual inquiry- focuses on relationship among humans, tools and equipment used everyday.
4.2.1.1.1. Must assemble interdisciplinary team of collaborators including nurses, engineers, and scientists
4.2.1.1.2. Goal is to acquire deep understanding of the people's technology readiness
4.2.1.2. Step 2: Value Specification: End user values are translated into end user requirements which begins with focus groups to collect purpose driven data
4.2.1.2.1. Goal is to understand how technology should be personalized
4.2.1.3. Step 3: Design- verify that technology or device can be created
4.2.1.3.1. The design should be intuitive and think the same way as the user
4.2.1.4. Step 4: Operationalization- end users are taught to use the technology, plan for adoption, and workflow
4.2.1.5. Step 5: Summative evaluation- collection of viability metrics including the analysis of data
4.2.1.5.1. Pilot study is conducted to establish preliminary outcomes on clinical feasibility and efficacy as well as clinical validity
5. Internet of Things: devices connected to the internet that spport the daily function and health of society
5.1. Examples include: smart home devices, healthcare and medicine devices and structures such as traffic lights and modern vehicles
6. Self Quantification: is the practice of tracking and measuring various health aspects using technology. This can include devices that measure heart rate, steps, sleep quality and more
6.1. Four key ironies include:
6.1.1. 1. Know moew, know better VS no more, no better: knowing more does not necessarily knowing better. Certain situations require adequate data but no more than necessary
6.1.2. 2. Greater self control VS greater social control: Self control for wellness rather than social control of insurance companies and employers
6.1.3. 3. Well being VS never being well enough: Knowing when internal well being is met rather than meeting well being standards by an external force
6.1.4. 4. More choice vs erosion of choice
6.2. Self sensor data (SSD): wearable devices used to collect data from individuals.
6.2.1. Data are then submitted to companies, employers and/or insurance corporations to direct next step in health marketing
6.3. Medicalization: describes the process in which the expansion of medical authority goes beyond a legitimate boundary and problematizes wellness
7. Collection of big data involves technology development
7.1. Challenges with health are big data:
7.1.1. 1. Storage- advantage of control over security and access
7.1.2. 2. Cleansing- data needs to be scrubbed to ensure accuracy
7.1.3. 3. Unified format 4. Accuracy
7.1.4. 5. Image pre-processing- various physical factors can lead to altered data quality and misinterpretations
7.1.5. 6. Security- HIPAA security rules help guide organizations with storing, transmitting, authentiation and control over data
8. Big data is a the abudnant health data collected from numerous sources including electronic health records, medical imaging and research data
8.1. Applied in: diagnostics, medicine, research, cost reduction and population health
8.2. Big data relies on 3 Vs:
8.2.1. 1. Volume- amount of data
8.2.2. 2. Velocity- how fast data is generated
8.2.3. 3. Variety- being able to trasnlate data into specific categories
8.3. Big data usage in healthcare includes product development, patient outcomes, operational efficiency, and driving innovation
9. Moving beyond the pill program
9.1. Medicine alone are often not enough to achieve optimal clinical outcomes for patients
9.1.1. Patient empowerment is a key element to patient-centred practice
9.1.2. Empowerment is an essential public health strategy that includes better access to technological advancements in healthcare
9.2. Beyond the Pill businesses can be valuable sources of revenues
10. Key capabilities of health platforms in health care:
10.1. Simple Connectivity- easy to cnnect devides and perform device management functions
10.2. Easy device management- enables improved asset availability, increased throughput, minimize unplanned outages and reduce maintenance cost
10.3. Information ingestion- intelligently transform and store data
10.4. Informative analytics- gain insight from huge volumes of data to make better decisions and optimize operations
10.5. Reduce risk- act on notifications and isolate incidents generated anywhere in the company environment from a single console
11. Artificial intelligence is making its way through each area of nursing.
11.1. If you think you are not using AI, then AI is using you!
11.2. Nurses should play a bigger role in the development of AI technology for healthcare.
12. Technologies may incorporate innovative tools and data science that customize disease prevention, detection and management.
12.1. Active systems for physiological monitoring such as wearable devices
12.2. Passive systems that capture symptoms data via online patient portals
12.3. Interactive systems that support the exchange of information between the user and healthcare provider
13. Human Genome Project: genomic sequencing used to advance technology, language of health, human progress and justice
13.1. 23andMe is an example of genomic consumerism
13.1.1. Genetic testing is an 11.9 billion dollar industry in the US
13.1.2. Genetic ancestry information testing includes nutrigenetics, pharmacogentics, paternity testing, and newborn screening
13.1.3. Allows consumers to access their personal raw data
13.1.4. This company was initially created by two women from San Francisco