Use Of Big Data Analytics

Published Date: 02 Nov 2017

Use of Big Data Analytics to Improve detection of Nosocomial Infection at the Sick Kids Hospital, Canada

1 Introduction

1.1 Big Data…..what is it all about?

Information Technology is the most rapidly changing facet of our society today. The change is continuing at an ever-increasing pace without any sign of abating, and has resulted in new ways of computing and information sharing especially over the last 10 years.
This acceleration has been fueled by the increased number of internet and computer users, the plummeting cost of electronic and storage devices and invention of new technology which didn’t exist before.

One outcome of this is the proliferation and growth of data. More data is being collected, processed and transferred than ever before. Data is collected by billions of connected devices, people and sensors that record trillions of transactions and behaviors each day ( download.cfm ). Global Pulse has developed a relatively loose taxonomy of types of new, digital data sources:

Data Exhaust – Made up of passively collected transactional data from people’s use of mobile phones, purchases, web searches, etc., data collected by governments, agencies, Organizations, educational and research institutions, etc. These are described as data from sensors of human behavior.

Online Information – Comprises web content like news media, RSS, social networks interactions, news articles, electronic commerce, job postings and other text and multimedia information. These are data from sensors of human intent, sentiments, perceptions, and want.

Physical Sensors – RFID, satellite/infrared imagery of changing landscapes, traffic patterns, light emissions, urban development and topographic changes, monitors CCTVs etc; These are remote sensors of changes in human activity

Citizen Reporting or Crowd-sourced Data – Information actively produced or submitted by citizens through phone and online surveys, hotlines, user- generated maps, etc; This information is valuable for verification and feedback

This large amount of data is generated in multiple ways such as from individuals through traditional data collection means, as a by-product of transactions on the internet and other data smart systems such as phones, credit card purchases, sensors, satellites, security cameras, etc. Also the use of machine-to-machine transactions on a daily basis generates significant amounts of data further contributing to this enormous growth.

According to the McKinsey Report 2011, there were 5 billion mobile phones in use, about 30 billion pieces of content shared on facebook every month, in the US alone 235 terabytes of data was collected by the Library of Congress by April 2011 whiles at the same time 15 out of 17 sectors in the US have more data stored per company than the library of congress all this is coupled with a projected 40% growth in global data generated per year. This aggregation of large amounts of data led to the coinage of the term Big Data.

1.2 What is Big Data

A phenomenon resulting from the vast amount of raw information generated across society, and collected by commercial and government organizations. This phenomenon represents both a challenge in harnessing this volume of data, and an opportunity for government agencies who seek to enhance their effectiveness. (…)

"Big data" refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. (…)

Big data refers to the massive amounts of digital information companies and governments collect about us and our surroundings. (…)

A number of different definitions have been proposed for big data including as stated above. A common theme which runs through all these definitions is the huge amount of data available in the world now. However big data is not only defined on the basis of size but also in terms of its relationship to technological advancement. This means that as technology advances datasets that are considered big based on size will also change. Also datasets which can be termed as big data differ, depending on the sector of interest, the kinds of software tools available in that sector and the amount of data generated in that field. With this in mind, big data can be considered to range from a few terabytes to petabytes.

Some schools of thought consider the definition of big data based on size to be erroneous. Proponents of this argue that size is just an attribute of big data and not necessarily its determinant. Thus big data can be very small and not all large datasets are big (mike 2.0). According to them, the definition of big is in terms of the complexity of combinations of the data or in terms of the number of useful permutations of sources thereby making useful querying difficult. Thus a 3 GB data generated by an aircraft on a 1 hour flight is big data by virtue of its complexity.

1.2.1 Characteristics of Big Data

The big data phenomenon characterized by four dimensions namely volume, velocity, variety and veracity

Volume refers to the either amount of data generated or the intensity of data which must be mined to identify relevant patterns and make sound decisions. Currently data generation continues to grow as the world is in the process of generating more. Driven by new technology and increasing data sources, about 2.5 quintillion bytes of data is created every day with about 90% of the data in the world today having been created in the last two years alone. This trend shows no signs of waning. In 2011, 1.8 zetabytes of information were created globally, and that amount is expected to double every year reaching a projected amount of 35 zettabytes in 2020 (IDC digital univ study).

Velocity is the speed with which the data is being produced and changed as well as the speed at which it must be received, understood and processed for use. The velocity of data is described by how accessible it is for use in terms of location, time and format, how relevant or valuable for use in application to real time issues and how the data can contribute to business improvement in a timely manner. Factors contributing to the velocity of data include improved technology and analytical tools as well as better connectivity.

Variety refers to the different sources of information. These different sources can be from consumers, government, an organization or a totally unrelated individual or event. Variety of data is usually made up of 15% structured data from relational databases, structured spreadsheets, transactions, etc and 85% unstructured data from emails, presentations, reports, letters, etc. The variety of data is generated form user devices, social media, videos, RFIDs etc.

Veracity is the quality of data as determined by its origin. Data can come from a variety of sources most of which are not verified giving it a high likelihood of being inaccurate. Thus the quality of big data may be bad, good or a partial combination of the two. It may also suffer from data issues such as inconsistency, incompleteness and untimeliness. Some may even be deliberate perpetration of misinformation to deceive people.

1.2.2 Uses of Big Data

The potential benefits of big data if properly managed are innumerable. Such benefits spans all sectors of society. "The amount of data in our world has been exploding, and analyzing large data sets—so-called big data—will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus." (McKinsey Global Institute, "Big Data: The next frontier for innovation, competition, and productivity", May 2011.)

These benefits have been classified into the following four areas.

Personalization of Services - Big data analytics can reveal distinct details about individual customers or customer groups. This is achieved this through big data’s characteristic granularity (its ability to combine data from disparate multiple sources to generate new insights and knowledge). This granularity may assist in determining the possibility of providing personalized services tailored to the individual. This can be seen largely in the e-retail services where customers are presented with possible items they may want to buy when they log on. Businesses especially use customization of services to target individuals for product advertisements and offers. It also helps them maintain customers who are likely to defect.

Societal Development - Big data has great potential for changing our society. Analysis of big data can be used to improve all facets of society such as agriculture, health education and commerce. For example the US has boosted milk production by using big data to determine the best bull to father milk making cows (…). In the field of health, big data can be mined to determine trends in health outcomes and populations within which certain interventions work for improved healthcare. In education, big data analysis can be used to determine learning and absorption patterns so as to tailor teaching and learning activities to suit them.

Problem solving and predictive analytics - Analysis of data from different sources using advanced analytic software technologies can go a long way to improve predictive analysis and problem solving. Predictive analysis will help in areas such as fraud detection and crime fighting by predicting possible trouble spots and activities. It can help detect fraudulent insurance claims and improper payments in the insurance industry. Another area of predictive analysis will be in predicting weather patterns to determine storms and tsunamis and implement early warning systems. Droughts could also be predicted to avoid starvation of communities. Cyber security is another area which could benefit from predictive analysis. With governments and businesses now constantly at risk, knowing where and when an attack is likely to happen can go a long way to mitigate these risks

Productivity and efficiency - Analysis of big data can be used to improve productivity by weeding out repetitive and non-productive and focusing on productive ones. This can save cost, time and resources as well as increase efficiency, thereby improving productivity to a large extent. This can be especially beneficial in areas such as the transportation industry, tax collection and social services.

1.3 Policy Issues

Notwithstanding the numerous benefits to be gained from big data, there are still a number of policy issues which need to be addressed in this area. The issues are broadly categorized under data issues and analysis issues.

1.3.3 Challenges relating to Data

Privacy

Privacy is defined by the International Telecommunications Union as the "right of individuals to control or influence what information related to them may be disclosed."(devt) Privacy is perhaps the most import policy issue arising out of big data. With today’s technology and monitoring devices most primary producers are not explicitly aware that they are generating data (voluntarily or involuntarily) and thus will not be aware of what it is being used for. For example it is common to receive advertisements from companies with which an individual has had no interaction whatsoever. The question it raises is how they were able to contact you. This means that information released for one purpose has been released to a third party for another purpose which is a breach of privacy. To cover themselves institutions place obscure disclaimers on data collection systems. People routinely consent to the collection and use of web-generated data by simply ticking a box without fully realizing how their data might be used or misused. (47) Individuals are voluntarily uploading personal information on social network sites like twitter and facebook, but it is unclear whether bloggers and twitter users, for instance, actually consent to their data being analysed. (48) In addition, recent research showing that it was possible to ‘de-anonymise’ previously anonymized datasets raises concerns. (49) In addition to this governments, businesses and other organizations are constantly collecting information about citizens in one form or the other. Traffic cameras, CCTVs in schools, supermarkets, etc. are churning out never ending bits of information about people, use of mobiles phones and now digitized cards for all activities such as buying, paying for transport, even as keys. As if this is not enough, Tim Berners Lee is calling for more data from individuals, governments and institutions. It is actually scary what power an organization will have if it manages to pull all these together. Privacy is a fundamental human right. (devt) Without privacy, safety, diversity, pluralism and innovation our basic freedoms are at risk. The risk of big data compromising privacy is therefore a very import concern which Policies must thoroughly address. Privacy by Design which has been widely adopted around the world is key to ensuring privacy is proactively embedded into the technology itself (WEF). Privacy by design has to be extended to the use of big data.

Protection and Security

Closely related to the issue of privacy is that of data security. Security refers to how to protect other data that should be kept private. With the increased reliance on computers and networks for storage of data vis-a-vis new technologies, the risk of exposure of data is very high. Recent occurrences have proved that it is possible for data to be breached and this can expose both personal, corporate and government information bordering on issues of national security and world peace. An example of this happened in 2008 when Heartland Payment Systems, a payment processor in Princeton, New Jersey, became a major cyber-attack victim. A spying software was installed on the company’s network and stole about 100 million credit and debit card numbers. (big data big security) With serious breaches such as these on the increase, addressing data security through technological and policy tools is essential. Governments can serve as a mediator to provide further guidance and foster cooperation between industry and relevant stakeholders on applying existing privacy and data protection practices and guidelines to current technology and cultural realities as well as fashion out new ones to cover current grey areas.

Access and Sharing

Accessibility and sharing is another grey area of consideration in big data. Although a lot of data is available publicly both on the internet and in organizations, a larger amount is still closely guarded by organizations and not accessible for use. The interesting fact is that such closely guarded data is usually more accurate and holds a greater value than the public ones. Reasons for organizations to refuse to share data vary widely and includes but not limited to legal or reputational considerations, a need to protect their competitiveness, a culture of secrecy, and, more broadly, the absence of the right incentive and information structures. (devt) Other access and sharing difficulties are a result of institutional and technical challenges such as poor data storage locations making it difficult to access data and storage in formats which make it difficult to retrieve.

Issues of access and sharing is not only in relation to large datasets but also in relation to personal data. Over the years personal data has been governed by strict restrictions as against the relatively lax non personal information. It was also well defined in terms of content. However, what is considered personal data is increasingly becoming contextual and increasingly determined by personal preferences, new applications, context of uses, and changes in cultural and social norms. Also, technological advances and the ability to associate data across multiple sources is blurring the difference between personal data and non-personal data. Access policies should therefore be modified to include properly defined parameters for personal data as traditional restrictions are now fast eroding.

Another topical issue in terms of access and sharing is intellectual property rights. Intellectual Property is a legal concept which refers to creations of the mind for which exclusive rights are recognized. Under intellectual property law, owners are granted certain exclusive rights to a variety of intangible assets, such as musical, literary, and artistic works; discoveries and inventions; words and phrases, symbols, and designs. (Wikipedia) Advancements in technology now ensures that the creation of most of these works are electronic in nature, access and sharing should therefore be restricted to ensure that these laws are not breached. Stakeholder engagement to determine clear pathways and procedures to access and share both public and public data, the institution of legal frameworks and accountability measures and signing formal access and licensing agreements for accessing data are key if access is to be gained to this big data.

1.4.4 Analysis Related issues

Human Capacity

Human Capacity is one of the biggest policy challenges relating to big data analysis. The question raised here is the accuracy of big data analytics. Big data analytics has the potential of obtaining different results from the analysis of data from disparate data sources. The result of analysed data is however not guaranteed in terms of accuracy. The concept of big data, its technology and analysis are relatively new therefore not many people have all the requisite knowhow to effectively extract its benefits. The interconnected nature of big data requires skills in various fields such science, research, statistics, analytics and interpretive skills as well as an understanding of business processes, policy and legal issues. These varied skill require people from multiple disciplines to produce the best results. Many observers have noted that there is currently a major skills gap for data scientists with experience in big data analytics. According to Gartner, by 2015, big data demand will reach 4.4 million jobs globally, with two thirds of these positions remaining unfilled. (Big dat stra paper). This makes it imperative that policies be made to develop the existing talent pool by setting up formal recognized career pathways and training programmes which will have a unique blend of business and IT to provide big data related training and certification. Improved cooperation between industry, academic institutions and professional groups can also be established to communicate and maintain competency and professional standards in the big data field.

Technology and Analytical Systems

Big data as the name connotes also requires big resources. The ability to undertake complex analysis of very large data sets cannot be easily achieved with existing technology. Using current ICT systems and will impose a huge burden which will literally cripple the organizations information systems. This may be a disincentive for organizations to adopt and use this resource. On the other hand, new technologies that enable big data analysis are huge and may be difficult for individual organizations to set up on their own. This means that governments may be required to put in the infrastructural backbone to serve multiple organizations. Such infrastructure will have to be managed well and will require policies guiding its use. Government agencies will need to manage such infrastructure efficiently in order to ensure its fair use and deliver the expected outcomes.

Content

Working big data brings about a number of analytical challenges. The question "what is the data really telling us?" has great significance especially on the analysis being conducted and decisions that the data might eventually inform. The main questions which arises is "what data are we looking at?" A large amount of data are generated from individuals in various ways including social networking sites like facebook and twitter. Such data is added by the user and not verified. It is common knowledge that users sometimes upload fictitious profiles and other information about themselves. Analyzing such data for certain uses can therefore lead to wrong conclusions. This problem is compounded by inadequate knowledge of how pervasive such practices are. Individuals speaking under their actual identity (citizen reporters, bloggers, even journalists) may publish or fabricate or falsify facts. External actors or factors might also interfere in ways that could make data paint a misleading picture of reality. (devt)

Even in the face of accurate data other issues arise. These include summarizing the data appropriately; interpreting, or making sense of the data through inferences; defining and detecting anomalies, etc may be subject to human error which will affect the quality of outputs.

An in-depth analysis of the policy challenges facing big data use are outside the scope of this document. Therefore a number of issues are still not covered. The effect of all these issues can however be greatly minimized through proper stakeholder analysis and engagement. The rest of the document presents a case study of the Artemis project at the SickKids clinic and undertakes a stakeholder analysis on the implementation of a big data project.

2. THE ARTEMIS PROJECT

2.1 Overview

The Artemis project is a big data project implemented at the sickkids hospital. The hospital had a huge amount of big data which it was not making use of. Data is continuously collected from patients connected to medical equipment that monitors vital signs on a continual basis. In most cases, signs of problems begin to appear long before a critical point is reached but even a well-trained nurse or physician might not identify and interpret these signs in time to prevent the resultant complications. The policy to implement a big data project was therefore to enable the analysis of data from the monitors to detect early signs of nosocomial infections.

"Overview

The rapid advance of medical monitoring technology has done wonders to improve patient outcomes. Today, patients are routinely connected to equipment that continuously monitors vital signs such as blood pressure, heart rate and temperature.

Business need: 
To better detect subtle warning signs of complications, clinicians need to gain greater insight into the moment-by-moment condition of patients.

Solution:
A first-of-its-kind, stream-computing platform was developed to capture and analyze real-time data from medical monitors, alerting hospital staff to potential health problems before patients manifest clinical signs of infection or other issues.

Results: 
Early warning gives caregivers the ability to proactively deal with potential complications—such as detecting infections in premature infants up to 24 hours before they exhibit symptoms.

Benefits: 
-Holds the potential to give clinicians an unprecedented ability to interpret vast amounts of heterogeneous data in real time, enabling them to spot subtle trends -Combines physician and nurse knowledge and experience with technology capabilities to yield more robust results than can be provided by monitoring devices alone -Provides a flexible platform that can adapt to a wide variety of medical monitoring needs "

(IBM, 2009)

2.2 Stakeholder Analysis

Stakeholders can greatly influence the expected results and outcome of a program or project. (sta) Their involvement can take place during any stage of the project but it is usually best to involve them at the initial stages of the project to foster a sense of belonging and ownership of the project.

Stakeholders can help make a project successful in a number of ways. They provide useful information concerning the needs, required resources, realistic goals and objectives, and practical considerations for a project. They further unearth hidden pitfalls that might not be obvious in the initial planning of the project and brings out possible points of opposition thereby preventing problems which may appear later during implementation. They also encourage a sense of ownership in the project and involvement during the implementation stage.

2.2.1 Stakeholders in the Artemis Project

Patients

Patients constitute a major stakeholder group in the project. Patients in this case does not refer to the babies who are actually sick since they are too young to make any meaningful contribution but rather to the parents and guardians taking care of them. The implementation the new policy derives its driving force from patient care. According to the physicians at the SickKids hospital, many patients die of nosocomial infectious ( l page agric) Though the monitors in the hospital churn out a continuous stream of data, the quantity and its complexity is too much for the human brain to process. Using the big data solution is therefore the way to quickly process all the data and spot meaningful outcomes. It is clear from this that without the patent (ones to be saved) there will be no change in policy. Patients are also stakeholders because they are the main source of data for the new system which makes them very important for its success.

Though the primary beneficiaries’ of the new policy are the patients, it does not presuppose that all of them are in favour of it. Varying patent perspective on the policy has some of them being pro-policy whiles others are anti-policy. For the pro-policy group the new system is seen as an innovation in technology which will save lives. It will help in getting the needed attention at the appropriate time. In addition to this they see the policy as an offer of assurance of quality, error free service. Though physicians are good and do the best they can, they are human and are prone to errors and mistakes. Some of these mistakes may not be intentional but the environment and other limitations may lead to arch mistakes. Even in the absence of errors, the physicians are presently not able to process the data anyway. A well implemented system will to a large extent not be subject to errors but will give accurate measurements which can save lives.

Patients against the policy also have their perspective on the issue. The first view is that of privacy and confidentiality. The new policy requires the implementation of a system which collects all kinds of data about the patient and stores it in the cloud. This makes the information prone to all kinds of attacks. Examples provided earlier in the introduction of big data shows that it is possible for even governments to be breached. Therefore considering the amount and detail of information going out, which includes personal and medical it is only right that the people worry about it. Furthermore, the information will be leaving the premises of the hospital. Most people are comfortable with the traditional hospital setting and operations but will feel jittery when their sensitive information is going out even if the aim is to provide better health care. Another issue of concern is the possible impact on the life of the baby. The immediate expectation of this policy is to be the early detection of early signs of infection for timely medial help. This is however expected to expand to the use of wireless devices on patents outside the hospital. This means that there is going to be the use of new technologies which has the possibility of being detrimental to the health of the patient. The use of electronic medical equipment has in the past been known to cause problems due to radiation and exposure to some forms of microwaves. The concern of health impact in future is therefore a concern for them. Finally, the new policy has not been implemented before. There is thus the inherent suspicion that this first implementation, especially as it is a pilot is to say "making guinea pigs of the patients"

The concerns of patients must be taken very seriously as they are very important stakeholders. In analyzing the role of this stakeholder, using the ..... it was identified that patients have a high level of power and high level of interest but are very unpredictable. Their power stems from the fact that If they decide that the policy will be detrimental to their interests and are therefore not going to patronize the facility then the hospital is going to collapse. This makes them very influential and powerful as the existence of the hospital itself depends on them. Most organizations now invest in customer management programs because they recognize their power ( ). The patients also have high interest in the new policy. This is because it affects them directly with implications for both the long and short term, medically and in terms of privacy. With privacy and health issues concerned they are bound to be interested. The way they will use their influence is however unpredictable as they cannot be expected to be rational or follow a set of rules. Every one of them has got their own mindset. This unpredictability is compounded when one considers the fact that emotions may play a major role in how they react since possible outcomes of life and death of loved ones are involved. A powerful stakeholder with high unpredictability can be the greatest danger or opportunity especially if they have high interest (..). With patients displaying all these characteristics it is very important to seek their views at all stages of policy development and implementation to ensure its success.

In looking at all their concerns, patients as stakeholders want a policy which assures them of their privacy and confidentiality as well as safety.

Sick kids Hospital

The hospital where the policy is to be implemented is made up of different groups with different perspective and positions on the policy.

Employees

Employees are stakeholders because they are the ones who will be implementing the policy. The policy will also be implemented in their work environment and will receive input from them. The health workers serve as a critical success factor because of their influence on patients (stakeholders). Health workers interact with the patients and in may assure them of the benefits of the new policy or influence them to kick against it. This means that an alliance may be forged with a powerful stakeholder which will make them a force to reckon with. For the policy to work therefore, their involvement is paramount. The perspective of employees on this policy is based on the class into which they fall, either as core health professionals (Physicians and nurses) or Health Information Managers.

The position of the core health professionals toward the policy is generally supportive. This is mainly because the policy is aimed at making their work easier and more efficient. Previously nurses had to take hourly aggregates of data on recording charts which were then reviewed by the physician. This was prone to errors and the possibility of no recording due to external conditions such as excessive workload. This is thus an opportunity to improve on their performance. Health worker performance is critical especially in health setting where a minor mistake can make the difference between life and death or a life of perpetual disability. Any system with the potential of reducing the errors they make and improving their output is therefore welcome especially in this age where legal suits for negligence are common. For physicians, the new policy will result in the creation of a new diagnostic tool saving them the additional work of diagnosing patients using the data generated. A minor concern will be with the accuracy of the diagnosis made since a lot of diseases present similar signs and symptoms, which may lead to a wrong diagnosis.

The position of Health Information Managers is that of partial support bordering on going against the policy. Information officers have traditionally been handling the collection and analysis of data in the hospital. The new policy will be seen as a possible intrusion into their domain. The new policy will not only introduce new systems but also includes bringing in experts from the university of Ontario (. ). This can be very intimidating as it may raise fears of loss of authority and trigger intrinsic defense mechanisms against the policy. Again it will entail opening up their department to relative public scrutiny thereby exposing things which they may want hidden. Appropriate inclusion of this class of employees at the initial stage of the policy can go a long way to allay such fears.

In terms of influence "employees in the medical profession" exercise high power. All professions in the medial field have associations such as the Medical Association, Nurses and Midwives Association, etc. These associations are very active in fighting for their members and would not hesitate to call industrial actions and invoke other labour laws should they feel the rights of their members are threatened. Being the implementers of a policy which has the probability of increasing their workload as well as eroding their authority places them in a position of high interest. An advantage here however is that they are predictable. Employee concerns usually relates to job security, workload and motivation (recognition and remuneration). Eliminating these predictable obstacles and keeping them satisfied by addressing other possible concerns and issues which they may have e.g. making information managers feel involved in the policy from the beginning and coming to a common understanding regarding their concerns is a good way of gaining their support.

Hospital Management

The management of the hospital has the responsibility of ensuring the profitable running of the hospital. This means that the ultimate responsibility of the failure or success of the hospital lies with them. They therefore hold a strong stake in this policy change. In terms of the implementation of the new policy, they are responsible for the primary site of implementation, they are the primary data owners and they are going to spend a large amount of money on the new system. They are also responsible for managing the resultant changes from the new policy.

Hospital management is generally supportive of the policy. This is because they are the main driving force behind its introduction. For the management of the hospital the policy is a possible way of increasing the profit margin of the institution. A successful Implementation will mean that the status of the hospital in terms of saving lives will improve. More clients will therefore patronize the place which will mean more profits. In addition to the financial benefits, non-financial benefits such as organizational efficiency and ultimately the saving of lives will mean that the hospital will achieve its targets.

However, despite management’s view of the potential benefits there are potential pitfalls which will make them cautious in going all out to embrace the policy. The concerns border on the reputation of the hospital which may be dented by a number of issues. Concerns include the security of the data, the cost of implementing the policy, medico-legal and ethical issues.

Hospital management is a very powerful stakeholder. The have the authority to determine whether the policy will be implemented or not and can decide modifications to be made to it. They are also the managers of the purse (budget) of the hospital and therefore derive a lot of power in terms of financial management. Their interest in the policy is very high by virtue of their role. As managers in charge of the facility they are highly interested in anything which goes on in the hospital whether good or bad. Implementing such a policy which has wide reaching implications and cost will thus certainly be of interest to them. They are however predictable as they operate within the policy framework of the hospital and cannot do anything of their own will.

Hospital Ethics committee

This committee is responsible for giving approval for programmes with ethical considerations to be undertaken in the hospital. Their job is to ensure that all research and other interventions carried out are within acceptable ethical limits. This makes them a stakeholder in the Artemis project. In terms of power, this committee wields a considerable amount. A recommendation of unethicalness can lead to the suspension of the policy since their authority in terms of this even transcends that of the hospital management. The interest level of the committee in the policy medium since it is part of their routine job. The level of interest should not be misconstrued as it can shift from medium interest to high interest once there is the suspicion of a practice which breaches the hospitals ethical requirements. Stakeholder positions have in a number of cases been known to shift radically from one end of a spectrum to another (...) within a short time, moving them from a being a powerful ally to an insurmountable obstacle. They should therefore be kept satisfied which can easily be done as they are predictable and follow clearly laid down regulations.

University of Ontario

The Artemis project is being conducted in conjunction with the University of Ontario. The two are therefore joint owners of the project. This University for its part committed resources, both human material and material to the project. The university involvement extents to the point where they are the main body analysing the big data. This is done through the provision of the research facility and the statistical experts for the project. This makes them primarily responsible for the success of the most important part of the project which places their reputation at risk in the event of a failure.

In view of this concern, the university is in a position of support with a high level of interest. Their position stems from the fact that as an academic institution one of the yardsticks for ranking is their research outcomes. Success in such an endeavour will therefore provide a large boost to their ranking. This will indirectly result in a greater number of students choosing the institution thereby increasing profit margins. Another perspective of the university is the knowledge to be gained from this policy and its implications for the future of medical science. Successful implementation means that this can be the first in a series of advances where big data can be used to help cure illnesses, making the potential for other breakthroughs endless. This view is similar to that of the hospital management which is another stakeholder, and forms the basis for the partnership between the two institutions.

Dr. Carolyn McGregor – Dr. McGregor is the expert from the university who is heading the implementation of the policy. Though part of the university team, she deserves special mention because of other portfolios she has. The main one is that she is the Chair of the Canadian Research Association. This position places her in a position where the success of the policy is not just the interest of the university but also of the wider scientific community which she heads. Her motivation for ensuring the success of the policy is therefore greater than even that of the university she works for.

The partnership between the hospital and the university provides several benefits. One benefit is that it gives the university as a stakeholder more power. On its own the university, though with a high interest has very little power. It is not the primary controller of the data and cannot force the management of the hospital to agree to any changes in the policy. This seeming lack of power is compensated for by the alliance forged with the university. This is in line with the idea that slake holder positions are not static. A seemingly powerless stakeholder can on forming alliances with others gain a huge influence it previously did not hold. Dr. McGregor by her position also has considerable power as chair of the larger scientific community further boosting the University’s position. Management of the university as a stakeholder is easy. Their support and position makes them very predictable and more amenable to success and therefore easier to control than other stakeholders

Health Regulatory Bodies

Health regulatory bodies are responsible for regulating the profession and practice of health systems and professionals by promulgating standards. This safety of the general public is the prime concern of this body. The policy in question has implications for the safety of people, in this case the pre-term babies in the neonatal and intensive care unit. They are going to be exposed to new systems to collect data which though geared at saving their lives may end up being detrimental to their health. This makes the regulatory body a stakeholder as they will be ensuring adherence to all appropriate practices.

The perspective of the regulatory body will determine their stance on the policy. In one frame this may be seen as an innovation and a revolution in medicine. It may therefore be viewed as harmless and helpful. Another frame will consider same work as a possible experimentation on the patients. With big data being relatively young it is not totally proven and still subject to analytical errors. Any work in this field will therefore have an element of experimentation attached to it. The policy they will want is one which assures them of the complete safety of the patients and proof of assurance that they are not being exploited in any way.

Regulatory bodies are very powerful stakeholders. Their influence can either result in a good ally or the end of the project. This is because the general public usually have confidence in them and usually follow the recommendations they make. Coupled with this they have the full backing of the law and are even able to go against the governments wishes in undertaking their duty. They can shut down the project totally or reducing the amount of support funding coming in from the government. Their interest level is however passive as they will only be watching to ensure that all regulatory requirements are followed. The existence of the requirements makes them a predictable stakeholder who can easily be satisfied. One way of satisfying them is by running a pilot programme on a small group and gradually extend the scope as early wins e demonstrated.

Other Paediatric Hospitals

The field of paediatrics is not the preserve of the Sickkids hospital alone. There are a number of different hospitals specializing in paediatric care. Ordinary hospitals also have intensive care units for neonates and can therefore serve as competitors or allies to the sickkids hospital. These stakeholders have however been left out of the policy.

The perspective of these hospitals are mainly that of competition. This is became the success of the sickkids hospital will most likely give them a clear competitive advantage. With this frame of mind competitors can institute similar programmes aimed at obtaining results within a shorter time to beat the Sickkids hospital. In extreme circumstances, these competitors may engage in public relationship stunts aimed at discrediting the work being done by the sickkids hospital.

In terms of, influence, competitors have very little power. They do not exercise any control over the decisions influencing the project in any way and it may seem good to even exclude them as a means of gaining advantage. Their interest level is however high as the existence of their business and their profit margins may be negatively impacted by this new policy. Coupled with this high interest is their high unpredictability. There is no way of knowing if this stakeholder will sit passively or proactively take negative steps against the policy. Their influence should therefore not be underestimated as they may even seek partnerships with powerful stakeholders such as a Regulatory body just to derail the smooth implementation of the policy. In this case, keeping a close watch over these stakeholders and monitoring their activities is a prudent thing to do.