Paul Wrights Healthcare Information and Big Data Mind Map

Paul Wrights Mind map of his Semester one Journey in Bag Data and Health care

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Paul Wrights Healthcare Information and Big Data Mind Map by Mind Map: Paul Wrights Healthcare Information and Big Data Mind Map

1. Weeks 1 and 2 Basics of Big Data and Data Science in Healthcare

1.1. Always Remember what Marlies said " Don't be afraid to fail safely"

1.2. Week One Anxiety, Back to school after 14 years of fulltime nursing, Sense of self pride.

1.3. Innovation is a frequently used buzzword in healthcare. What does it mean ...

1.3.1. "Innovation is not a magical fog-laden concept." 16(p205) Instead, innovation is palpable and living. Innovation includes many things and excludes many things

1.3.2. The first and most important distinction to be made about innovation is that it is not invention.1 Invention may be a precursor to innovation but it is not innovation itself.

1.3.2.1. Innovation is also not necessarily a product in the traditional sense, although that is what our society has grown to associate innovation with.18 This means that innovation does not have to be something physical like a television.

1.3.2.1.1. newness does not necessarily mean innovation, but all innovations are new. This can be accomplished by recombining old ideas in a new way, creating a new process or product, using a process from another industry in one that has not used that process, or reordering an organization in a new and different way.

1.3.3. Porter-O'Grady and Malloch provided the best description of innovation leadership, "the role of the quantum leader is to create an infrastructure that integrates innovation into the overall work of the organization."16(p206

1.3.3.1. THE NEW DEFINITION OF INNOVATION IN HEALTHCARE Innovation is something new, or perceived new by the population experiencing the innovation, that has the potential to drive change and redefine healthcare's economic and/or social potential.

1.4. Based on their commitment to improving patient outcomes and their extensive bedside experience, nurses often are the best of creative problemsolvers for patients and their families.

1.4.1. “Nurses have an unbelievably detailed expertise in the day-to-day operations of clinical delivery. They have extensive clinical knowledge and their bedside experience gives them an intricate understanding of diseases and their diagnoses. Providers will always respect a leader who can bring clinical proficiency to their position, and nurses have no shortage of that knowledge” Michael Dowling (2018),

1.4.1.1. Nurse Executive Call to Action Based on their commitment to improving patient outcomes and their extensive bedside experience, nurses often are the best of creative problem-solvers for patients and their families.

1.4.1.2. The nurse executive has a distinctive role in developing and supporting a culture that brings such moments to the forefront in search of innovative ways to create solutions in care provision.

1.5. What Is Big Data in Healthcare? “Big data in healthcare” refers to the abundant health data amassed from numerous sources including electronic health records (EHRs), medical imaging, genomic sequencing, payor records, pharmaceutical research, wearables, and medical devices, to name a few.

1.5.1. But adoption of big data analysis in healthcare has lagged behind other industries due to challenges such as privacy of health information, security, siloed data, and budget constraints.

1.5.2. Applications for Big Data in Healthcare Diagnostics, Preventative medicine, precision medicine, Medical research, reduction of adverse events, cost reduction, population health. -

1.5.2.1. Challenges for Implementing Big Data in Healthcare Healthcare organizations face challenges with healthcare data that fall into several major categories including data aggregation, policy and process, and management. Let’s explore these further

1.5.3. “data lake” is often used to describe a collection of raw big data, several events are underway that promise to build what might be called “data oceans” brimming with research and analysis opportunities.

1.6. Definition of Big Data According to SAS: “Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.”

1.6.1. Big data relies on three Vs: Volume Velocity Variety

1.6.2. How Healthcare Uses Big Data:Product Development,Patient Outcomes, Operational Efficiency, Driving innovation

1.6.2.1. In light of the increased use of automation, artificial intelligence and big data in healthcare, the specialty must also reconceptualize the roles of both nurses and informaticians to ensure that the nursing profession is ready to operate within future digitalized healthcare ecosystems.

1.6.2.1.1. nursing will need to readdress its value proposition to health(care) if it wishes to retain some level of control in this evolution. Unlike 10–15 years ago where nursing informaticians were voicing concerns related to nurses being replaced by other businesses and health providers if they continued resisting technology (Feeg 2004; Simpson 1998),

2. Weeks 3 and 4 The IoT ( Internet of Things) and Healthcare Applications

2.1. A number of technologies can reduce overall costs for the prevention or management of chronic illnesses. These include devices that constantly monitor health indicators, devices that auto-administer therapies, or devices that track real-time health data when a patient self-administers a therapy.

2.2. The Internet of Things (IoT) is a network of physical devices and other items, embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data [1].

2.2.1. 40% of IoT-related technology will be health-related, more than any other category, making up a $117 billion market

2.2.1.1. It sounds pretty basic, but the adoption of Electronic Health Records (EHRs) is a game changer. In less than a decade, an ink-and-paper system of managing records that goes back thousands of years will be digitized and replaced

2.2.1.1.1. AHS capacity to use connect care to its fullest could be a game changer if optimized.

2.2.2. Visual of IT and user interface Medical Internet of Things and Big Data in Healthcare

2.2.2.1. three V's"—Volume (vast amounts of data), Variety (significant heterogeneity in the type of data available in the set), and Velocity (speed at which a data scientist or user can access and analyze the data) ( Link to week 1 and 2)

2.2.3. Precision medicine, as it's called, is a term that will be frequently heard in coming years [4]. It begins with genomics and goes through the rest of the omics platforms, providing multiscale data for analysis and interpretation (Link to week 9)

2.2.4. Devices and Mobile Apps for Healthcare We are heading into the age of information, where knowledge and data will be key. We are also entering the age of the customer, in which more than ever the customer is going to determine what they want. myTomorrows is one example of the changing look of business models, in this case, directly connecting customers and pharma

2.2.4.1. Challenges for Big Data in Healthcare The challenges fall into two main categories: fiscal/policy and technology.

2.2.4.1.1. Fiscal and policy issues: In a fee-for-service environment, the only way that healthcare practitioners get paid is to have face-to-face encounters with patients. This creates heavy bias against promoting technologies that streamline non-face-to-face interactions.

2.2.4.1.2. Technology issues: The biggest technical barrier to achieving this vision is the state of health data. Created by legacy EHR systems, health data is largely fragmented into institution-centered silos. Sometimes those silos are large, but they are still silos.

2.2.4.2. Health Selfie ( love this idea and concept to connect with the younger generations ) Awesome idea to look at improving health literacy and self management )

2.3. In the healthcare industry, various sources for big data include hospital records, medical records of patients, results of medical examinations, and devices that are a part of internet of things.

2.3.1. Data Science’. Data science deals with various aspects including data management and analysis, to extract deeper insights for improving the functionality or services of a system (for example, healthcare and transport system).

2.3.2. IBM Watson This is one of the unique ideas of the tech-giant IBM that targets big data analytics in almost every professional sector. This platform utilizes ML and AI based algorithms extensively to extract the maximum information from minimal input.

2.3.2.1. Big data in healthcare: management, analysis and future prospects

2.4. Internet of Things (IoT) is a new paradigm that has changed the traditional way of living into a high tech life style. Smart city, smart homes, pollution control, energy saving, smart transportation, smart industries are such transformations due to IoT.

2.4.1. lot of challenges and issues that need to be addressed to achieve the full potential of IoT. These challenges and issues must be considered from various aspects of IoT such as applications, challenges, enabling technologies, social and environmental impacts etc.

2.4.2. Internet of Things is a revolutionary approach for future technology enhancement: a review

2.4.3. Smart Home Systems (SHS) and appliances that consist of internet based devices, automation system for homes and reliable energy management system [3]. Besides, another important achievement of IoT is Smart Health Sensing system (SHSS).

2.4.3.1. Apple Watch, Telemtry, Garmin

2.4.4. Therefore, the development of a secure path for collaboration between social networks and privacy concerns is a hot topic in IoT and IoT developers are working hard for this.

2.4.4.1. Internet of Things is a revolutionary approach for future technology enhancement: a review

2.4.4.2. Security and privacy issues One of the most important and challenging issues in the IoT is the security and privacy due to several threats, cyber attacks, risks and vulnerabilities [41]. The issues that give rise to device level privacy are insufficient authorization and authentication, insecure software, firmware, web interface and poor transport layer encryption [42]. Security and privacy issues are very important parameters to develop confidence in IoT Systems with respect to various aspects [43].

2.4.4.2.1. Security mechanisms must be embedded at every layer of IoT architecture to prevent security threats and attacks [23]. Several protocols are developed and efficiently deployed on every layer of communication channel to ensure the security and privacy in IoT based systems [44, 45]. Secure Socket Layer (SSL) and Datagram Transport Layer Security (DTLS) are one of the cryptographic protocols that are implemented between transport and application layer to provide security solutions in various IoT systems [44].

3. Weeks 5 and 6 Data Security, cybersecurity and Access

3.1. Due to the increasing dependency on digitalization and Internet-of-Things (IoT) [1], various security incidents such as unauthorized access [2], malware attack [3], zero-day attack [4], data breach [5], denial of service (DoS) [2], social engineering or phishing [6] etc. have grown at an exponential rate in recent years. ( Link to week 3/4)

3.1.1. t’s estimated that, a data breach costs 8.19 million USD for the United States and 3.9 million USD on an average [8], and the annual cost to the global economy from cybercrime is 400 billion USD [9].

3.1.1.1. https://link.springer.com/article/10.1186/s40537-020-00318-5/figures/1

3.2. Cybersecurity is a set of technologies and processes designed to protect computers, networks, programs and data from attack, damage, or unauthorized access [12].

3.2.1. Types of attacks :“cybersecurity as a set of tools, practices, and guidelines that can be used to protect computer networks, software programs, and data from attack, damage, or unauthorized access”

3.2.1.1. Confidentiality is a property used to prevent the access and disclosure of information to unauthorized individuals, entities or systems.

3.2.1.1.1. Integrity is a property used to prevent any modification or destruction of information in an unauthorized manner.

3.2.1.1.2. Availability is a property used to ensure timely and reliable access of information assets and systems to an authorized entity.

3.2.1.2. Malware known as malicious software, is any program or software that intentionally designed to cause damage to a computer, client, server, or computer network, e.g., botnets. Examples of different types of malware including computer viruses, worms, Trojan horses, adware, ransomware, spyware, malicious bots, etc. [3, 26]; Ransom malware, or ransomware, is an emerging form of malware that prevents users from accessing their systems or personal files, or the devices, then demands an anonymous online payment in order to restore access.

3.2.1.2.1. https://link.springer.com/article/10.1186/s40537-020-00318-5/tables/1 (detection )

3.2.1.3. -Denial-of-Services (DoS), Privilege Escalation Man in the Middle (MITM) or Eavesdropping- Cryptographic Attack, Structured Query Language Injections Exploit Malicious Software

3.2.1.3.1. Denial-of-Service is an attack meant to shut down a machine or network, making it inaccessible to its intended users by flooding the target with traffic that triggers a crash. The Denial-of-Service (DoS) attack typically uses one computer with an Internet connection, while distributed denial-of-service (DDoS) attack uses multiple computers and Internet connections to flood the targeted resource [2];

3.2.2. “data science” to describe the interdisciplinary field of data collection, preprocessing, inferring, or making decisions by analyzing the data. To understand and analyze the actual phenomena with data, various scientific methods, machine learning techniques, processes, and systems are used, which is commonly known as data science.

3.2.2.1. Exaples in industry: https://link.springer.com/article/10.1186/s40537-020-00318-5/tables/2

3.3. Terms and definitions : https://link.springer.com/article/10.1186/s40537-020-00318-5/tables/3

3.4. Moving Forward for cyber safe care:

3.4.1. Principle #1: Healthcare cybersecurity is a patient safety issue.Principle #2: Healthcare organizations are prime targets Principle #3: No one organization or sector can ‘go it alone’ Principle #4: Healthcare needs the leadership and capacity to respond to threats Principle #5: Cyber resilience and response is an enterprise- wide responsibility Principle #6: Training is essential and simplicity is key Principle #7: Cybersecurity expertise is in short supply Principle #8: Cybersecurity standards are important, but have been challenging to deploy in healthcare Principle #9: Medical device cybersecurity needs to be managed as a matter of policy

3.4.1.1. 1. Champion: Championing cybersafety in Canada’s health sector; 2. Inform: Ensuring that our leaders, staff, and partners are informed about the scope of the challenge and opportunities to mitigate risk; 3. Contribute: Contributing to shared action plans that build resilience to cyberattacks; 4. Advance: Progressing cybersafety in ways consistent with our mandates, considering opportunities for prevention, mitigation, preparedness, response, and recovery; 5. Share: Sharing information, best practices, and tools with others within and beyond the health sector to build collective capacity and resilience; and 6. Transparency: Publishing by Cybersecurity Awareness Month in October 2018 how we will apply these commitments in our unique context and/or with our community.

3.5. Types of Cyber Attacks :

3.6. Social network analysis (SNA) aims at understanding the underlying social structure in a social network by description, visualization, and statistical modeling. Social network data consist of various elements which can help to explore and visualize patterns found within collections of linked entities that also include people.

3.6.1. SNA mainly involves eight steps which include the following: determining the type of analysis, defining the relationships in the network using a theoretically relevant measure, collecting the network data, measuring the relations, determining whether to include actor attribute information, analyzing the network data, creating descriptive indices, and presenting the network data.

3.6.1.1. ocial networks have emerged over time as a great platform for sharing medical opinions, and it has been noted that online communities can develop quasi-professional knowledge about the health status of the communities in general and about the individuals in particular.

3.6.1.1.1. Privacy concernshttps://journals.sagepub.com/na101/home/literatum/publisher/sage/journals/content/jhib/2019/jhib_25_2/1460458217706184/20190506/images/large/10.1177_1460458217706184-fig3.jpeg

3.6.1.1.2. https://journals.sagepub.com/na101/home/literatum/publisher/sage/journals/content/jhib/2019/jhib_25_2/1460458217706184/20190506/images/large/10.1177_1460458217706184-table2.jpeg

3.6.1.2. Socal media Group Patients 4 Change is a great example of local

4. Weeks 7 and 8 Data Literacy and Patient Perspectives

4.1. The rapid integration of artificial intelligence (AI) into healthcare delivery has not only provided a glimpse into an enhanced digital future but also raised significant concerns about the social and ethical implications of this evolution. Nursing leaders have a critical role to play in advocating for the just and effective use of AI health solutions. To fulfill this responsibility, nurses need information on the widespread reach of AI and, perhaps more importantly, how the development, deployment and evaluation of these technologies can be influenced.

4.1.1. Nursing Leadership can step up in a big way and move this forward.

4.1.1.1. HOW CAN I STEP UP IN A BIG WAY TO LEAD ?

4.2. The increasing presence of technology in health care has created new opportunities for patient engagement and with this, an intensified exploration of patient empowerment within the digital health context.

4.2.1. Virtual health future, Impacts of COVID-19 on thrusting engagements forward in new ways .

4.2.2. This research produced a view of patient empowerment within the digital health context summarized in two overarching categories: (1) Being Heard and (2) Moving Forward.

4.2.2.1. patient empowerment, which in turn is considered to facilitate patient independence, self-management, and self-efficacy.

4.2.2.1.1. definitional consensus of empowerment remains elusive, impeding efforts to translate the conceptual ideals of empowerment into a measurable entity associated with changes in health care behavior or outcomes.

4.3. his shift from empowerment to companionship is advocated by showing the conceptual, ethical, and methodological issues challenging the narrative of empowerment, and by arguing that such challenges, as well as the risk of medical paternalism, can be overcome by focusing on the potential for mHealth tools to mediate the relationship between recipients of clinical advice and givers of clinical advice, in ways that allow for contextual flexibility in the balance between patiency and agency.

4.3.1. In the context of healthcare, the popularity of the empowerment narrative has led to a proliferation of research on the ways in which mHealth (Rich and Miah 2014) can be used to put individuals in control of their health (Lupton 2014), with researchers, technology providers and policy makers claiming that empowered patients will be more independent, have a better quality of life, and even adapt better to existing health conditions (Bravo et al. 2015).

4.3.1.1. oversimplified exaggerations about the benefits of digital health that fail to account for the variations in individual health needs, concerns, circumstances, behaviours, and digital literacy levels (Vezyridis and Timmons 2015; Smith and Vonthethoff 2017), and for how these varying individuals are affected by being subjected to normative constructs of patienthood, prevention, and illness.

4.3.1.2. Core Concepts of Patient- and Family-Centered Care Dignity and Respect. Health care practitioners listen to and honor patient and family perspectives and choices. Patient and family knowledge, values, beliefs and cultural backgrounds are incorporated into the planning and delivery of care. Information Sharing. Health care practitioners communicate and share complete and unbiased information with patients and families in ways that are affirming and useful. Patients and families receive timely, complete and accurate information in order to effectively participate in care and decision-making. Participation. Patients and families are encouraged and supported in participating in care and decision-making at the level they choose. Collaboration. Patients, families, health care practitioners, and health care leaders collaborate in policy and program development, implementation and evaluation; in research; in facility design; and in professional education, as well as in the delivery of care.

4.3.1.2.1. Patient Empowerment or patient Powered

4.3.1.2.2. PEOPLE POWERED HEALTH: HEALTH FOR PEOPLE, BY PEOPLE AND WITH PEOPLE https://www.innovationunit.org/wp-content/uploads/2017/04/7-PPH-system-change-paper.pdf

5. Weeks 9 and 10 Personalized Healthcare, Precision Health

5.1. four key ironies of sensor driven self-quantification: (1) know more, know better versus no more, no better; (2) greater self-control versus greater social control; (3) well-being versus never being well enough; (4) more choice versus erosion of choice.

5.1.1. IRONIES OF AUTOMATION IRONIES OF AUTOMATION

5.2. Outcome-based prescribing for management of patients with comorbidities and comedications

5.2.1. The case study is based on 5 years of data for 19,223 diabetic patients. Our results indicate that certain patient characteristics (e.g., clinical and demographic features) influence optimal treatment decisions. Among patients examined, monotherapy with metformin was the most common optimal medication decision. The results are consistent with the relevant clinical guidelines and reports in the medical literature. The proposed algorithm obviates the need for knowledge of the whole Bayesian network model, which can be very complex in big data problems.

5.2.1.1. How can we use Alogryhtms in personalized care to help support positivept outcomes?

5.3. The rise of genomic technologies has catalyzed shifts in the health care landscape through the commercialization of genome sequencing and testing services in the genomics marketplace. The development of consumer genomics into a growing array of information technologies aimed at collecting, curating, and broadly sharing personal data and biological materials reconstitutes the meaning of health and reframes patients into biocitizens.

5.3.1. DNA Genetic Testing & Analysis - 23andMe Canada

5.3.2. AncestryDNA® | DNA Tests for Ethnicity & Genealogy DNA Test

5.3.3. Genomics and Responsibility: “Welcome to You”Genetic information has come to be seen as a portal for improved health and as a gold mine for personal knowledge and understanding. Building on the popular concept of empowerment, the personal genetic testing industry has marketed its products as tools of discovery and health management.

5.3.3.1. Owning personal health/ health literacy/health promotion.

5.4. technology to support precision health must be centered on the user and designed to be desirable, feasible, and viable.

5.4.1. A Roadmap for Technology Use in Precision Health

5.4.1.1. Step 1, contextual inquiry, focuses on the relationships among humans, and the tools and equipment used in day‐to‐day life.

5.4.1.2. Step 2, value specification, end‐user values are translated into end‐user requirements.

5.4.1.3. The purpose of Step 3, design, is to verify that the technology or device can be created (i.e., resources are available, costs are aligned with budget) and to develop the prototype(s) using technology design concepts that conform to user values and the exact technical specifications.

5.4.1.3.1. Alberta’s innovation networks are too fragmented, with too much competition and not enough collaboration. There is also a lack of supports and commercialization channels; entrepreneurs don’t know where to turn or whom to talk to.

5.4.1.4. tep 4, operationalization, the intervention is used in a real‐world setting.

5.4.1.5. Step 5, summative evaluation, collection of viability metrics, including the analysis of process data, is a major priority.

5.4.2. Precision health involves approaches that everyone can do on their own to protect their health as well as steps that public health can take (sometimes called “precision public health”)

5.4.2.1. COPD/ Heart Faiure work in AHS is a wonderful example of precision health

6. Weeks 11 and 12 Equity, Access, Inclusion and Participation in the Digital healthcare World and PRESENTATIONS!!

6.1. Big Data remains a fuzzy idea, emerging across social, scientific, and business contexts sometimes seemingly related only by the gigantic size of the datasets being considered.

6.1.1. Using aspects of software development practice, an analytic framework was conceived as part of an interdisciplinary research process allowing nurses to integrate their disciplinary expertise in user-centred digital design. The framework allows nurses to parse collected data into a robust set of functional and non-functional requirements for software developers while still engaging in a fulsome interpretive analysis.

6.1.1.1. There is a need for nursing to occupy a more significant role in the advancement of technology innovation in healthcare. However, a lack of familiarity with design-thinking and associated practical experience impedes nursing voices in this area. Tools and processes are introduced to enhance an existing nursing methodology as a means to extend our disciplinary design capacity.

6.1.1.1.1. Nursing has the potential to step up in a bog way. Must look to the future and not the past on doing. Rn vs LPN scope creep.

6.1.2. n order to bridge such a gap, this article systematically and comprehensively analyses academic literature concerning the ethical implications of Big Data, providing a watershed for future ethical investigations and regulations.

6.1.2.1. Five key areas of concern are identified: (1) informed consent, (2) privacy (including anonymisation and data protection), (3) ownership, (4) epistemology and objectivity, and (5) 'Big Data Divides' created between those who have or lack the necessary resources to analyse increasingly large datasets.

6.1.2.2. It is argued that they will require much closer scrutiny in the immediate future: (6) the dangers of ignoring group-level ethical harms; (7) the importance of epistemology in assessing the ethics of Big Data; (8) the changing nature of fiduciary relationships that become increasingly data saturated; (9) the need to distinguish between 'academic' and 'commercial' Big Data practices in terms of potential harm to data subjects; (10) future problems with ownership of intellectual property generated from analysis of aggregated datasets; and (11) the difficulty of providing meaningful access rights to individual data subjects that lack necessary resources.

6.2. Measuring patient-perceived quality of care in US hospitals using Twitter

6.2.1. How will this correlate to HCAHPS data sets.

6.2.1.1. Home

6.2.2. Tweets describing patient experiences in hospitals cover a wide range of patient care aspects and can be identified using automated approaches. These tweets represent a potentially untapped indicator of quality and may be valuable to patients, researchers, policy makers and hospital administrators.

6.3. Diversity and inclusion and the support of vulnerable populations.

6.3.1. Ethical and Legal Considerations for the Inclusion of Underserved and Underrepresented Immigrant Populations in Precision Health and Genomic Research in the United States

6.3.1.1. I conclude that it is not a question of whether the law is a help or a threat but, rather, whether we collectively will prioritize authentic diversity and inclusion policies and also insist on compliance with the laws intended to ensure the human right of every individual – regardless of immigration status or national origin – to share in the advancement of science.

7. .

8. Pauls Parking Lot of Thought (New concepts and ideas for my nursing professional practice)

8.1. What can I do to help data use with eadership in my organization. How can I be a good steward of data.

8.2. Health Selfie ( unpack the concept of health literacy and mange own care)

8.3. Cyber security tools to review and explore on my own time : https://www.healthcarecan.ca/wp-content/themes/camyno/assets/document/Reports/2018/HCC/EN/CyberReport_finalweb.pdf

8.4. Share Article : What nursing leaders need to know about AI with my leadership teams.

8.5. Ask my Team : How is patient empowerment (and/or engagement or activation) influenced by accessing personal health information through a tethered patient portal?

8.6. Look at focusing COPD/HF as precision health and not so much a package of outcomes. Engage with Dr Eastwood.

8.7. Measuring patient-perceived quality of care in US hospitals using Twitter. Look to use Social media in AHS studies round engagemetns and outcomes.

8.8. Look to use Robots in Virtual music program for Neuro Rehab at Foothills medical centre. Huge potential for pt impacts and outcomes with minimization of staff time and resources.

9. Unintended Consequences for Healthcare with big data

9.1. In healthcare, innovation results in new processes or ways of work, new policies, and new ways of organizing, all of which lead to changes in outcomes. It is also important to note that innovation consequences can have both positive and negative effects.

9.2. HYPE or Potential does not equal outcomes

9.2.1. A Dimensional Insight study found that 56% of hospitals and medical practices do not have appropriate big data governance or long-term analytics plans.

9.2.2. The Healthcare Cost Institute Database reported that 17% of patients are responsible for nearly 75% of all health care expenditures.

9.3. One of the major challenges to implementing the IoT has to do with communication; although many devices now have sensors to collect data, they often talk with the server in their own language. Manufacturers each have their own proprietary protocols, which means sensors by different makers can't necessarily speak with each other. This fragmented software environment, coupled with privacy concerns and the bureaucratic tendency to hoard all collected information, frequently maroons valuable info on data islands, undermining the whole idea of the IoT.

9.4. The technological advances have helped us in generating more and more data, even to a level where it has become unmanageable with currently available technologies.

9.5. Nursing will be left in the dust if we don't expand out knowledge, traing and education to include big data, technology and user design.

9.5.1. Can AI take over the nursing roles?

10. Common Themes to unpack over the semester/ certificate

10.1. AI Potential is limitless and can be very scary

10.2. Patient Centred Design is key

10.3. Data Security is a huge factor and not always a achieved.

10.4. Data Breaches or Privacy impacts are huge on a financial scale and a personal scale.

10.5. Nursing can make a huge impact but we need to stop being silent on these issues. A common voice needs to emerge.

11. Money Ball Paper :

11.1. Data in isolation

11.1.1. Patient voice/ Lived experience

11.1.1.1. How can you use pt stories and lived experience

11.1.1.1.1. Reflections on Storytelling in Healthcare with Dr. Verna Yiu

11.1.2. One point of view

11.1.3. Let the data speak for itself

11.1.3.1. What matters to you

11.1.3.1.1. BC/Europe/AHS

11.1.4. Is the data strong enough

11.2. Distruptive innovation

11.2.1. Blockbuster/ Netflix

11.2.1.1. Uber/Cabs

11.2.2. How will it impact care?

11.2.3. Can it exist in a safety focused system in health?

11.2.4. Would patients be okay to try new things when outcomes could be great or not?