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Domain harvesting

by with No Comment Data Science

Domain harvesting is an endeavor to harvest as much material as is technologically possible with a minimum of human intervention. The legal environment of a nation is considerably significant to global businesses. However, dissimilarities in the legal climate greatly hinder the appeal of a government as a market or asset site. It is the country’s responsibility and law to regulate commercial practices and define policies of a business, rights, and commitments involved in business transactions. As a result of no sole constant commercial law governing international trade transactions, many counties and participants have involved themselves in global business. Moreover, it has created room for great investors to engage deeply in the business sector, which helps to accelerate good interpersonal relationships. Most industries tackled their communication through domain and DNS servers. So, this is a kind of Hot zone to spam or hack into their system and earn a good amount of information for the hacker’s benefit.

A Domain harvesting attack (DHA) is a fashion used by spammers in an attempt to find valid/ existent dispatch addresses at a sphere by using brute force. The occasion is generally carried out through a standard dictionary attack, where valid dispatch addresses are planted by brute force, guessing valid dispatch addresses at a sphere using different permutations of common usernames. These attacks are more effective for chancing dispatch addresses of companies since they’re likely to have a standard format for sanctioned dispatch aliases (i.e.,jdoe@example.domain, johnd@example.domain, or johndoe@example.domain).

There are two main ways for generating the addresses that a DHA targets. In the first, the spammer creates a list of all possible combinations of letters and figures up to a maximum length and also appends the sphere name. This would be described as a standard brute force attack. This fashion would be impracticable for usernames longer than 5-7 characters. For illustration, one would have to try 368 ( nearly 3 trillion) dispatch addresses to exhaust all 8- character sequences (WIKI 2, 2022).

The other, more targeted fashion is to produce a list that combines common first names, surnames, and initials (as in the illustration over). This would be considered a standard dictionary attack when guessing usernames for dispatch addresses. The success of a directory crop attack relies on the philanthropist dispatch garçon rejecting emails transferred to invalid philanthropist dispatch addresses during the Simple Correspondence Transport Protocol (SMTP) session. Any addresses to which dispatch is accepted are considered valid and are added to the spammer’s list (which is generally vented between spammers). Although the attack could also calculate Delivery Status Announcements (DSNs) to be transferred to the sender address to notify of delivery failures, directory crop attacks probably do not use a valid dispatch address.

 

 

Reference:

WIKI 2 (Accessed on March 25-2022). Retrieved from URL: https://wiki2.org/en/Directory_Harvest_Attack

Wrightson, T. (2015). Advanced persistent threat hacking is the art and science of hacking any organization, Tyler Wrightson. eCampus.com. Retrieved from https://www.ecampus.com/advanced-persistent-threat-hacking-art/bk/9780071828369

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Artificial Intelligence in Improving Healthcare

by with No Comment Data SciencePublic Health

Artificial intelligence (AI) application in the medical field is a sensitive issue for human beings. When AI is mentioned, many people relate it with the recent science-fiction movie where super machines have taken over and enslaved people. This fear has no basis, experts in the medical field forecast that by 2060, artificial intelligence will be able to do all tasks humans do. Although AI usage has attracted condemnation in the medical field, it is making great strides in facilitating and improving life. Artificial intelligence is one of the industries that are growing very fast. Its growth is bringing positive impact in the society, especially in the medical field. Not only will AI contribute to saving lives by providing effective medical machinery but will impact hugely on the growth of the economy in the medical world, and it will also provide safer options for patients.


Agus, D., (2018, December 28). AI is transforming medicine. Aging Reversed [Video file). Retrieved from https://www.youtube.com/watch?v=v09ixmM3clYThis blogpost mainly focused on various articles that outline applications of AI technology in improving healthcare.

     Dr. Agus is a researcher on artificial intelligence at the University of Southern California. According to Agus video file, artificial intelligence computers have been designed to make decisions with minimal human intervention. The speaker notes that AI is fundamentally playing a critical role in the medical field. Doctors and hospitals are now able to access a lot of data which contains life-saving information. According to Agus, physicians are now able to access information on treatment methods, survival rates, outcomes, and healthcare speed, which have been gathered from millions of patients. Artificial intelligence provides computing power to analyze and detect large trends from data and make predictions to identify potential health outcomes. Agus gives an example of how the accuracy of tools such as a microscope has improved to 95.5%, especially precision in the identification of cancer cells. This video is relevant to the subject because it describes the contribution of AI in improving the medical field.

Blease, C., Kaptchuk, T.J., Bernstein, M.H., Mandi, K.D., Halamka, J.D. & DesRoches, C.M. (2019). Artificial intelligence and the future of primary care: Exploratory qualitative study of U.K. general practitioners’ views. Journal of Medical Internet Research, 21(3).  Retrieved from http://preprints.jmir.org/preprint/12802

                 Blease et al.’s (2019) article explain how artificial intelligence is disrupting the medical profession, especially in the biomedical informatics field. The article explores the opinions of general practitioners perceptions of the artificial application in the medical field. The article surveys the opinions of 720 general practitioners on the likelihood of artificial intelligence replacing human intelligence. The research results indicate that general practitioners are in support of the application of the use of technology in healthcare to improve efficiencies, especially the reduction of administrative costs on medical professionals. The article gives timely information on the views of physicians on the AI scope in primary health care. The article is relevant to the subject matter because it samples opinions of general practitioners on the artificial intelligence application in healthcare and how AI is playing a vital role in the improvement of healthcare.

 

Daven, H.T., Hongsermeier, T.M., & Cord, K.A.M. (2018). Using AI to improve electronic health records. Harvard Business Review. Retrieved from https://hbr.org/2018/12/using-ai-to-improve-electronic-health-records

                 The article examines how electronic medical records at large have played a critical role in improving decision making in the medical field. According to Daven, Hongsermeier, and Cord, EHR (electronic health records) are assisting clinicians in providing patient-centered quality healthcare. Moreover, the artificial intelligence application in the medical field has helped in making EHR systems more intelligent and flexible. Artificial intelligence capabilities for electronic health records have helped in quick medical data extraction whereas AI is helping in extracting index data from medical notes. AI has also helped in improving the predictive and diagnostic algorithms which are integrated with the EHRs as support in decision making. Finally, AI has played a critical role in enhancing data entry and clinical documentation by providing support tools that integrate clinical note composition and data collection.

                 The article’s information is vital to the topic of the application of AI in the medical field. The information contained in the article is up-to-date because the article was published in December last year, and it is contained in the Harvard Business Review, which is renowned for publishing relevant quality articles and quality. The authors have the authority to talk about the topic; Daven is a Distinguished Professor in Management and Information, Hongsermeier is a Chief Medical Information Officer and Cord is a Ph.D. candidate in Biostatistics and Epidemiology.

Donovan, F., (2018). Healthcare artificial intelligence, making sense of data flood. HIT Infrastructure. Retrieved from https://hitinfrastructure.com/news/healthcare-artificial-intelligence-making-sense-of-data-flood

                 Donovan gives statistics on the application of AI data in managing and cleaning of data in healthcare. AI and machine learning in healthcare are allowing medical practitioners to make sense of medical data. The author notes that many challenges, such as inefficiencies that encumbered digital transition, are being eliminated. By 2026, the author notes robot-assisted surgery will be more than $40 billion, virtual nursing assistance will be $20 billion, fraud detection will be $14 billion, clinical trial participant identifier will be $13 billion, and automated image diagnosis will be $3 billion. The author notes that AI algorithms have become complicated, for instance, image recognition. The article does not give many details about AI application and does not have authority about artificial intelligence application in the medical field, but the article gives prospects of artificial intelligence in the future. Moreover, the article provides specific estimates of how AI will reduce the cost of healthcare in the United States. The article is up-to-date because it was published in 2018 and contains relevant information about artificial intelligence applications in the medical field.

Harvard Medical School. (2019, April 9). MD vs. Machine: Artificial intelligence in health care [Video file]. Retrieved from https://www.youtube.com/watch?v=xSDfma4VEx8

                 The video explains various ways in which artificial intelligence is impacting the healthcare sector. From cancer and chronic diseases to radiology and also an assessment of risk, AI had created endless opportunities that healthcare can leverage. According to the speakers in the video file, AI offers advantages over traditional clinical decision-making techniques and analytics. Through AI integration in the medical field, algorithms learning have become accurate and precise where humans have gained unprecedented knowledge about care processes, diagnostics, patient outcomes, and treatment variability. One importance of artificial intelligence that is emphasized in the video file is how AI plays a critical role in eliminating biases in healthcare methods. The video file article presents concise and facts about artificial intelligence application and is relevant to the subject matter because it provides up-to-date information about AI application in healthcare.

Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S.,…Wang, Y. (2017). Artificial intelligence in healthcare: past, present, and future. Strove and Vascular Neurology, 2(4). DOI: http://dx.doi.org/10.1136/svn-2017-00010

                 The article highlights how AI in healthcare mimics and perfects human intelligence. Increasing healthcare data availability and improving medical analytical techniques. The article highlights popular medical fields where healthcare applies artificial intelligence like in neurology and cardiology. The article examines four major areas that AI is applied in a disease treatment process, and these areas are; detection, treatment, prediction of outcome, and evaluation of prognosis. The article predicts how artificial intelligence will be relied on by medical professionals by 2030. The article is a scholarly article that has an elaborate abstract and many references to support the healthcare use of artificial intelligence. The authors of the article are specialists in the medical field in different universities in China. The article contains relevant information on how AI technologies are used in the research of cancer.

Panch, T., Szolovits, P. & Atun, R. (2018). Artificial intelligence, machine learning, and health systems. Journal of Global Health, 8(2). DOI: https://dx.doi.org/10.7189%2Fjogh.08.020303

                 The authors explain how health sector systems globally are facing challenges such as increased disability, illness, and morbidity, which is caused by epidemiological transition and aging population, increased healthcare expenditures, and higher societal expectations. The authors note the need for the transformation of healthcare systems to overcome these challenges. The ingredient of the transformation of health systems is the introduction of AI in the healthcare systems. The article explores how artificial intelligence application in healthcare will help the world achieve universal healthcare (UHC) through improving effectiveness, responsiveness, efficiency, and equity of healthcare services provision and health of the public. The article is relevant and accurate on the topic because it discusses the impact of artificial intelligence in healthcare systems. The authors give balanced information about artificial intelligence, and they are an authority in the subject matter.  The lead author Dr. Trishan Panch is Chief Medical Officer and Co-founder of Wellframe, who leads in AI strategy and initiatives in the medical field. The article gives recent information about the utilization of AI in the health systems.

Pearl, R. (2018). Artificial intelligence in healthcare: Separating reality from hype. Forbes. Retrieved from https://www.forbes.com/sites/robertpearl/2018/03/13/artificial-intelligence-in-healthcare/#6afd85aa1d75

                 Pearl notes that the application of artificial intelligence in the medical field has been around since 1956. The author explains that most commonly used artificial intelligence application in healthcare is algorithmic that uses evidence-based approaches by clinicians and medical researchers. The second application that Pearl identifies is the use of visual tools for recognition in healthcare. According to Pearl (2018), the human eye fails even the best medical experts, and therefore, tools that use AI are necessary to replace human eye diagnosis. The author notes that the accuracy gap between the human eye and the digital eye is wide, especially machines used in diagnostic fields such as MRI, CT, and mammography information interpretation. The article compares artificial intelligence application in the medical field with traditional methods. The article contains up-to-date insights about information technology application in healthcare, and the author can provide accurate statistics on how AI has revolutionized the medical field. Although the article is not scholarly, it gives credible and balanced information about the utilization of artificial intelligence in the medical field. Robert Pearl is an authority in the medical field, who was the CEO of The Permanente Medical Group, the largest medical group in the United States. His article provides candid tidbits about how artificial intelligence application is impacting different medical fields in the United States.

Reddy, S., (2018). Use of Artificial intelligence in healthcare delivery. Research Gate. DOI: 10.5772/intechopen.74714

                 Reddy notes that adoption of artificial intelligence in healthcare has rapidly improved healthcare delivery. He adds that AI is being applied in numerous healthcare sectors, including clinical laboratories, hospitals, and research facilities to improve the healthcare delivery systems. The author continues to explain how the application of AI in healthcare has been fundamental in creating myriad opportunities for medical professionals and healthcare organizations. The article gives recent information about artificial intelligence. The author has expertise in the medical field, and therefore, he has the authority to speak about artificial intelligence in the medical field. The article gives detailed factual information about the application of artificial intelligence in the medical field. The article is a balanced source because it gives critical information about artificial intelligence and healthcare delivery. The Author is an associate professor at Deakin University in the School of Medicine. He has done a lot of research in hospital management, health evaluation, and artificial intelligence. The article gives recent details about the application of artificial intelligence in healthcare.

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Health Information Systems (HIM)

by with No Comment Data SciencePublic Health

Health Information Systems (HIM) is a technology used to manage healthcare data in various healthcare institutions. The systems carry out different functions such as collection, storage, and data management. The integration of Information technology in healthcare is growing fast as the industry turns to technology to solve problems that have existed for years. He is one of the approaches used in healthcare to streamline operations and protect patient data. The following text will discuss the concept of HIM as it applies to healthcare and ideas that relate to the integration of Information Technology in the healthcare industry.

With major innovations in technology, the current Health care Information (IT) environment has grown exponentially. IT is a part of the healthcare system, and it’s almost impossible to operate without IT in the modern healthcare environment. Currently, the atmosphere is filled with many opportunities, potential, and issues that affect the integration of IT in healthcare. Some of the problems in the current healthcare information technology are related to the security of patient data (Ehrenfeld, 2017). Electronic Health Records can easily be compromised if the necessary steps to protect them are not taken. Moreover, the current IT environment has also played a vital role in improving healthcare through the advancement of various components. These components may include accessibility, operations management, and accountability. With innovations in the industry still taking place, IT will entirely revolutionize the industry and become an essential part of the industry,

Current Health Care Information Environment How Vila Health Hospital’s HIM Systems Fit into the External Health Care Technology Environment

            External environments refer to Vila healthcare’s environment and affect its performance and the utilization of HIM systems. Vila hospital handles the building, installation, maintenance, and troubleshooting of in-house designs and maintenance and upgrading. However, third-party applications are installed by vendors who also monitor and maintain them. Various factors make up the external environment and may include; error reporting, oversight processes, medical liability, and care delivery. Vila Health hospital’s HIM systems fit this external environment based on how they integrate to improve the quality of care provided by the facility.

            To begin with, error reporting is highly affected by the external environment and majorly has a positive impact on the systems. Error reporting allows the HIMS to connect with the external environment by offering information that can lead to peer review of errors and improvement of systems. Although HIMS is highly advanced, it may experience many mistakes that may affect the quality of service. As such, enhancing this process and connecting to the external technology environment allows for solutions to be formed and used to prevent any future errors from occurring, hence, protecting the organization and its patients. Apart from HIM errors, diagnostic error reporting increases disclosure of errors while improving reporting, analyzing the mistakes, and learning from such mistakes (Kruse, Frederick, Jacobson & Monticone, 2017). It’s a critical part of reducing medical liability and ensuring that the organization is competent enough to avoid costly lawsuits. Thirdly, HIMS ensures that the organization complies with oversight processes such as regulatory requirements (Hoolhorst, 2020). By connecting with the external technological environment, HIMS protects Vila Health’s stakeholders and patients by promoting competency and reporting any issues affecting care delivery.

The interrelatedness of HIT throughout the Organization

            Vila healthcare uses an electronic medical record system that allows patients to access their history and any information they may need. All hospitals and clinics are part of the system to ensure that providers can access information and details about medical history. Furthermore, it allows the providers to access information and efficiently coordinate care. There is an interrelatedness in a very complex healthcare system to achieve this. Interrelatedness refers to the ability of systems to influence each other, with complexity increasing as the number of components in a system increases. For instance, once a patient is entered into the system, data can be added, changed, or edited at different levels to ensure efficiency. For example, a patient registers in the system on their first visit, diagnostic results are added under the original file by a physician, treatment and approaches are added once treated, and information about medications is sent to the pharmacy where they can collect their drugs. As stated by Vila healthcare, the system can be accessed by all providers, including nurses, physicians, and pharmacists. Once a patient is registered, they are awarded a unique number and file under which all their information and transactions with Vila healthcare are stored (MHA Vila Health, 2021). Therefore, a nurse can access the same file as the physician and pharmacists. The same information is provided to the patient, who can access their entire history in the healthcare system and analyze all their data as they require.

Unique HIM needs of Each Hospital

            Every hospital has its own unique needs that the HIM system has to solve. These needs may include; ease of access to patient data, cost-effectiveness, stopping leakages in revenue, improving the quality of care, increased data security, accountability, and operational effectiveness. Every organization can identify what it needs and then develop a system to achieve these goals. The most crucial aspect is understanding how he can be used to achieve these goals. The HIM system achieves this by collecting, storing, and managing data. For instance, the HIM system collects all data available and makes it accessible to authorized parties. For example, in the case of accountability, access to information is done through secure log-ins (Hoolhorst, 2020). Therefore, the hospital can determine who accessed what or entered data into the system, bringing about accountability. In another example, security can be increased with data stored in servers or clouds and can only be accessed through logins. As such, he can be used to solve a variety of problems and needs for hospitals. Therefore, before installing the systems, it is essential to conduct research and determine the hospital’s needs to have the best plan possible.

Regulations and Guidelines that Must Be Followed for Particular HIM

            Due to the sensitive nature of patient data, many regulations and guidelines must be followed for particular HIM. This also includes policies that are put in place to protect patient data and enhance the safety of organizational systems. Regulations are set by the Health Insurance Portability and Accountability Act of 1996 (HIPAA), where specific rules are provided to protect patient data. The rules focus on; ensuring confidentiality, availability, integrity, identification, and protection against anticipated threats, protection against impermissible use or disclosure, and ensuring compliance by the workforce (Moore & Frye, 2019). The rules protect data from being misused, accessed illegally, shared illegally, or accessed unlawfully by third parties. Individual facilities are responsible for protecting their data, which could attract fines from relevant authorities (Moore & Frye, 2019). For instance, in 2017, CardioNet paid $2.5 million for failing to follow HIPAA Privacy and Security Rules. The organization lost data after a laptop was stolen from an employee, to which it was determined that the company’s security approaches were flawed and put patient data at risk.

Standard Procedures and Best Practices for Securing Sensitive Health Information

            There are various approaches that professionals use to secure patient data. The most common procedure is following HIPAA Privacy and Security Rules which offer the basis for data protection. Another best practice is protecting data from outside threats. Cyber-attacks are increasing with time as individuals attempt to access data that they use maliciously. Therefore, it is essential to protect against these external threats by purchasing the latest security systems, updating passwords from time to time, and installing anti-malware software to protect against malware, ransomware, and phishing attacks. By doing this, organizations also need to understand that employees from within the organization can initiate attacks. Therefore, updating security measures can prevent the likelihood of this happening. Furthermore, influencing laws and standards can also aid in promoting patient data security. Every HIM professional is responsible for protecting the entire industry by contributing to identifying the best rules and practices that can impact data security.

Integration Decisions and Recommendations

            Health IT has improved healthcare processes over the year as it enables providers to manage patient care through secure sharing of information. With the help of Health IT, organizations can have; accurate information about patient health, the ability to coordinate, the ability to share information securely, the availability of information to diagnose patients, and the ability to reduce the occurrences of medical errors within an organization. It is highly recommended for professionals to address specific areas for successful integration. The recommended areas include; patient focus, standardized delivery of care, performance management, creation of organizational culture and leadership, integration of physicians into the system, development of a governance structure, and sound financial management. Inpatient focus, IT helps improve accountability. Medical records can show the type of healthcare a patient received and whether it met set standards. With such a process, it is possible to ensure that the integration of IT in healthcare is successful and improves patient outcomes.

            Ultimately, IT is now a significant part of the healthcare system and has had significant advantages since it was integrated. However, further improvements can be made to the plans to improve efficiency and security. Hospitals are highly recommended to adopt IT within their facilities to reap the many benefits technology can add to their practices.

References

Davis, J (2017). CardioNet was slammed with a $2.5 million fine for failed risk management and analysis. Privacy & Security. Retrieved from https://www.healthcareitnews.com/news/cardionet-slammed-25-million-fine-failed-risk-management-and-analysis

Ehrenfeld, J. M. (2017). Wannacry, cybersecurity, and health information technology: A time to act. Journal of medical systems, 41(7), 104.

Hoolhorst, T. (2020). The influence of HIM implementations on hospital organizations and how this affected hospital healthcare performances.

Kruse, C. S., Frederick, B., Jacobson, T., & Monticone, D. K. (2017). Cybersecurity in healthcare: A systematic review of modern threats and trends. Technology and Health Care, 25(1), 1-10.

MHA Vila Health (2021). Health Information System Characteristics and Needs. Transcript.

Moore, W., & Frye, S. (2019). Review of HIPAA, part 1: history, protected health information, and privacy and security rules. Journal of nuclear medicine technology, 47(4), 269-272.

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Data Analytics in Healthcare

by with No Comment Data Science

he healthcare industry historically has generated large amounts of data, driven by record keeping, compliance & regulatory requirements, and patient care. While most data is stored in hard copy form, the current trend is toward rapid digitization of these large amounts of data. Driven by mandatory requirements and the potential to improve the quality of healthcare delivery meanwhile reducing the costs, these massive quantities of data (known as ‘big data’) hold the promise of supporting a wide range of medical and healthcare functions, including among others clinical decision support, disease surveillance, and population health management [2-5]. Reports say data from the U.S. healthcare system alone reached, in 2011, 150 exabytes. At this rate of growth, big data for U.S. healthcare will soon reach the zettabyte (1021 gigabytes) scale and, not long after, the yottabyte (1024 gigabytes). Kaiser Permanente, the California-based health network, which has more than 9 million members, is believed to have between 26.5 and 44 petabytes of potentially rich data from EHRs, including images and annotations.

By definition, big data in healthcare refers to electronic health data sets so large and complex that they are difficult (or impossible) to manage with traditional software and/ or hardware; nor can they be easily managed with traditional or common data management tools and methods . Big data in healthcare is overwhelming not only because of its volume but also because of the diversity of data types and the speed at which it must be managed .The totality of data related to patient healthcare and wellbeing make up “big data” in the healthcare industry. It includes clinical data from CPOE and clinical decision support systems (physician’s written notes and prescriptions, medical imaging, laboratory, pharmacy, insurance, and other administrative data); patient data in electronic patient records (EPRs); machine generated/sensor data, such as from monitoring vital signs; social media posts, including Twitter feeds (so-called tweets) , blogs ,

Big Data Analytics in Healthcare

Health data volume is expected to grow dramatically in the years ahead. In addition, healthcare reimbursement models are changing; meaningful use and pay for performance are emerging as critical new factors in today’s healthcare environment. Although profit is not and should not be a primary motivator, it is vitally important for healthcare organizations to acquire the available tools, infrastructure, and techniques to leverage big data effectively or else risk losing potentially millions of dollars in revenue and profits. What exactly is big data? A report delivered to the U.S. Congress in August 2012 defines big data as “large volumes of high velocity, complex, and   variable data that require advanced techniques and technologies to enable the capture, storage, distribution, management and analysis of the information”. Big data encompasses such characteristics as variety, velocity and, with respect specifically to healthcare, veracity. Existing analytical techniques can be applied to the vast amount of existing (but currently unanalyzed) patient-related health and medical data to reach a deeper understanding of outcomes, which then can be applied at the point of care. Ideally, individual and population data would inform each physician and her patient during the decision-making process and help determine the most appropriate treatment option for that particular patient.

Advantages to Healthcare

By digitizing, combining and effectively using big data, healthcare organizations ranging from single-physician offices and multi-provider groups to large hospital networks and accountable care organizations stand to realize significant benefits. Potential benefits include detecting diseases at earlier stages when they can be treated more easily and effectively; managing specific individual and population health and detecting health care fraud more quickly and efficiently. Numerous questions can be addressed with big data analytics. Certain developments or outcomes may be predicted and/or estimated based on vast amounts of historical data, such as length of stay (LOS); patients who will choose elective surgery; patients who likely will not benefit from surgery; complications; patients at risk for medical complications; patients at risk for sepsis, MRSA, C. difficile, or other hospital-acquired illness; illness/disease progression; patients at risk for advancement in disease states; causal factors of illness/disease progression; and possible comorbid conditions (EMC Consulting). McKinsey estimates that big data analytics can enable more than $300 billion in savings per year in U.S. healthcare, two thirds of that through reductions of approximately 8% in national healthcare expenditures. Clinical operations and R & D are two of the largest areas for potential savings with $165 billion and $108 billion in waste respectively

Architectural Framework

The conceptual framework for a big data analytics project in healthcare is similar to that of a traditional health informatics or analytics project. The key difference lies in how processing is executed. In a regular health analytics project, the analysis can be performed with a business intelligence tool installed on a stand-alone system, such as a desktop or laptop. Because big data is by definition large, processing is broken down and executed across multiple nodes. The concept of distributed processing has existed for decades. What is relatively new is its use in analyzing very large data sets as healthcare providers start to tap into their large data repositories to gain insight for making better-informed health-related decisions. Furthermore, open source platforms such as Hadoop/MapReduce, available on the cloud, have encouraged the application of big data analytics in healthcare. While the algorithms and models are similar, the user interfaces of traditional analytics tools and those used for big data are entirely different; traditional health analytics tools have become very user friendly and transparent. Big data analytics tools, on the other hand, are extremely complex, programming intensive, and require the application of a variety of skills.

For the purpose of big data analytics, this data has to be pooled. In the second component the data is in a ‘raw’ state and needs to be processed or transformed, at which point several options are available. A service oriented architectural approach combined with web services (middleware) is one possibility [27]. The data stays raw and services are used to call, retrieve and process the data. Another approach is data warehousing wherein data from various sources is aggregated and made ready for processing, although the data is not available in real-time.

While several different methodologies are being developed in this rapidly emerging discipline, here we outline one that is practical and hands-on. There are main stages of the methodology. In Step 1, the interdisciplinary big data analytics in healthcare team develops a ‘concept statement’. This is a first cut at establishing the need for such a project. The concept statement is followed by a description of the project’s significance. The healthcare organization will note that there are trade-offs in terms of alternative options, cost, scalability, etc. Once the concept statement is approved, the team can proceed to Step 2, the proposal development stage. Here, more details are filled in. Based on the concept statement, several questions are addressed: What problem is being addressed? Why is it important and interesting to the healthcare provider? What is the case for a ‘big data’ analytics approach? (Because the complexity and cost of big data analytics are significantly higher compared to traditional analytics approaches, it is important to justify their use). The project team also should provide background information on the problem domain as well as prior projects and research done in this domain. Next, in Step 3, the steps in the methodology are fleshed out and implemented. The concept statement is broken down into a series of propositions. (Note these are not rigorous as they would be in the case of statistical approaches. Rather, they are developed to help guide the big data analytics process). Simultaneously, the independent and dependent variables or indicators are identified. The data sources, as outlined in Figure 1, are also identified; the data is collected, described, and transformed in preparation for analytics. A very important step at this point is platform/tool evaluation and selection. There are several options available, as indicated previously, including AWS Hadoop, Cloudera, and IBM BigInsights. The next step is to apply the various big data analytics techniques to the data. This process differs from routine analytics only in that the techniques are scaled up to large data sets. Through a series of iterations and what-if analyses, insight is gained from the big data analytics. From the insight, informed decisions can be made. In Step 4, the models and their findings are tested and validated and presented to stakeholders for action. Implementation is a staged approach with feedback loops built in at each stage to minimize risk of failure.Methodology

The next section describes several reported big data analytics applications in healthcare. We draw on publicly available material from numerous sources, including vendor sites. In this emerging discipline, there is little independent research to cite. These examples are from secondary sources. Nevertheless, they are illustrative of the potential of big data analytics in healthcare.

Examples

Premier, the U.S. healthcare alliance network, has more than 2,700 members, hospitals and health systems, 90,000 non-acute facilities and 400,000 physicians and is reported to have data on approximately one in four patients discharged from hospitals. Naturally, the network has assembled a large database of clinical, financial, patient, and supply chain data, with which the network has generated comprehensive, and comparable clinical outcome measures, resource utilization reports and transaction level cost data. These outputs have informed decision-making and improved the healthcare processes at approximately 330 hospitals, saving an estimated 29,000 lives and reducing healthcare spending by nearly $7 billion . North York General Hospital, a 450-bed community teaching hospital in Toronto, Canada, reports using real-time analytics to improve patient outcomes and gain greater insight into the operations of healthcare delivery. North York is reported to have implemented a scalable real-time analytics application to provide multiple perspectives, including clinical, administrative, and financial . Another example, reported by IBM, is that of the large, unnamed healthcare provider that is analyzing data in the electronic medical record (EMR) system with the goal of reducing costs and improving patient care. (Data in the EMR include the unstructured data from physician notes, pathology reports and other sources). Big data analytics is used to develop care protocols and case pathways and to assist caregivers in performing customized queries

Challenges

At minimum, a big data analytics platform in healthcare must support the key functions necessary for processing the data. The criteria for platform evaluation may include availability, continuity, ease of use, scalability, ability to manipulate at different levels of granularity, privacy and security enablement, and quality assurance In addition, while most platforms currently available are open source, the typical advantages and limitations of open source platforms apply. To succeed, big data analytics in healthcare needs to be packaged so it is menu driven, user-friendly and transparent. Real-time big data analytics is a key requirement in healthcare. The lag between data collection and processing has to be addressed. The dynamic availability of numerous analytics algorithms, models and methods in a pull-down type of menu is also necessary for large-scale adoption. The important managerial issues of ownership, governance and standards have to be considered. And woven through these issues are those of continuous data acquisition and data cleansing. Health care data is rarely standardized, often fragmented, or generated in legacy IT systems with incompatible formats. This great challenge needs to be addressed as well.

Conclusions

Big data analytics has the potential to transform the way healthcare providers use sophisticated technologies to gain insight from their clinical and other data repositories and make informed decisions. In the future we’ll see the rapid, widespread implementation and use of big data analytics across the healthcare organization and the healthcare industry. To that end, the several challenges highlighted above, must be addressed. As big data analytics becomes more mainstream, issues such as guaranteeing privacy, safeguarding security, establishing standards and governance, and continually improving the tools and technologies will garner attention. Big data analytics and applications in healthcare are at a nascent stage of development, but rapid advances in platforms and tools can accelerate their maturing process.

Work Cited

Raghupathi W: Data Mining in Health Care. In Healthcare Informatics: Improving Efficiency and Productivity. Edited by Kudyba S. Taylor & Francis; 2010:211–223.

Burghard C: Big Data and Analytics Key to Accountable Care Success. ID Health Insights; 2012.

Bian J, Topaloglu U, Yu F, Yu F: Towards Large-scale Twitter Mining for Drugrelated Adverse Events. Maui, Hawaii: SHB; 2012.

Raghupathi W, Raghupathi V: An Overview of Health Analytics. Working paper; 2013.

Zikopoulos PC, DeRoos D, Parasuraman K, Deutsch T, Corrigan D, Giles J: Harness the Power of Big Data. McGraw-Hill: The IBM Big Data Platform; 2013.

 Zikopoulos PC, Eaton C, DeRoos D, Deutsch T, Lapis G: Understanding Big Data – Analytics for Enterprise Class Hadoop and Streaming Data.

McGraw-Hill: Aspen Institute; 2012. 32. Bollier D: The Promise and Peril of Big Data. Washington, DC: The Aspen Institute; 2010.

cloud-computing

TOOLS EVALUATION – Cloud security at the age of 2022

by with No Comment Data SciencePublic Health

Several tools and services have been developed to tackle different cloud security issues. I have chosen the top 5 tools and services that are being used by many companies and companies relying on cloud services. These tools include Bitglass, Netskope, Skyhigh Networks, Okta, and CipherCloud. Unfortunately, all these tools are commercial and should be purchased for a business to use them. Besides, they have one-month trial periods. Businesses can make use of this grace period before deciding to implement them in their cloud services such as devices and applications. I selected specifically these 5 tools since they tackle and offer a solution to the security of data posed by cloud computing. Skyhigh Networks, CipherCloud and Netskope tackle ITshadow problem. Bitglass service offers encryption of data and also assists in monitoring data of business regardless of geographical area. For multifactor authentication and automated user management, Okta is the best tool (Kausik, 2015).


Describe each tool

Bitglass
A tool used to offer transparent data protection for every business. It’s applicable both in mobile and computer applications, it maintains visibility oof data as well as reducing the loss of data in mobile devices and also in the cloud.

 Skyhigh Networks
When it is time to discover, analyze and secure cloud apps usage, Skyhigh Networks is the correct tool. It capitalizes logs from the business implemented firewalls, gateways, and proxies to find out employee’s activities within the premise.

 Netskope
This is a service used in discovering and monitoring network-related services such as shadow IT and applications of a cloud. It provided detailed information based on the analysis carried out from downloaded content, user sessions and details of shared content.

CipherCloud
It is a secure cloud-based tool that encrypts or tokenize data directly to the gateway of the business. CipherCloud tool’s main objective is ensuring data security stored within well-defined cloud platforms (JIN, H. M. (2012).

Okta
Okta is a tool based on ensuring all the cloud services that include mobile and on-premises apps are implemented with secure Single Sign-On. The tool has been pre-integrated with applications that are common to many businesses, the applications are salesforce, google, and others (Latif, 2009).

Collect all features

  • Single Sign-On (SSO)
  • Integration of LDAP and Active Directory
  • Multifactor authentication
  • Detection of cloud apps usage.
  • Adaptive access control
  • Data encryption
  • Threat protection

Justify why these are sufficient

Cloud computing has grown tremendously, and it has gain popularity very quickly resulting in many businesses migrating their services to the cloud. Computers and mobile applications in the cloud have raised security issues. Privacy of data, the security of devices and residency are among security issues facing cloud. Businesses consider data security as the first option before migrating their services to the cloud. The features mentioned above are sufficient since they cut across almost all security issues facing the cloud. These features can ensure data encryption, web filtering, secure login, encryption of cloud, multifactor authentication among others Krutz, (2010). Businesses can choose tools that meet their business security demands. On implementing these features, a business can significantly secure their applications and devices stored and connected to cloud up to 95%.

Measure the importance of the feature

Features Weight Justification
Single Sign ON 5% Enables users to use the same credentials for multiple related services. Credentials falling on the wrong hands may lead to security issues.
Integration of LDAP and Active Directory 5% Low cost since businesses can utilize available resources without incurring extra resources.
multifactor authentication 10% Users are granted permission upon offering two data pieces.
detection of cloud apps usage. 15% Businesses can detect and discover the usage of cloud services. Unauthorized usage can be detected
Data encryption 30% Data encryption is the most important cloud security issue. Encryption ensures information has been secured and can’t be used anyhow.
Adaptive access control 15% The feature detects and forbids unauthorizes access to cloud services and applications
Threat protection 20% This feature ensures threats are detected, prevented and investigated hence offering protection integration.

Support

Feature Bitglass Skyhigh Networks Netskope Okta CipherCloud
Single Sign-On(C1) 1 3 0 0 0
Integration of LDAP and Active Directory(C2) 1 0 0 0 0
detection of cloud apps usage. (C3) 2 1 1 1 1
Adaptive access control(C4) 2 1 1 2 1
Threat protection(C5) 2 1 1 1 1
multifactor authentication (C6) 1 2 2 1 2
Data encryption(C7) 1 1 1 1 1

Evaluation

Bitglass service

Criteria Weight Bitglass
Support Weighted Support
C1 0.05 1 1*0.05=0.05
C2 .05 1 1*.05= .05
C3 0.15 2 2*0.15=0.3
C4 0.15 2 2*0.15=0.3
C5 0.2 2 2*0.2=0.4
C6 0.1 1 1*0.1-0.1
C7 0.3 1 1*0.3=0.3

Sky-high Networks service

Criteria Weight Skyhigh Networks
Support Weighted Support
C1 0.05 0 0*0.05=0
C2 .05 0 0*.05= 0
C3 0.15 1 1*0.15=0.15
C4 0.15 1 1*0.15=0.15
C5 0.2 1 1*0.2=0.2
C6 0.1 2 2*0.1=0.2
C7 0.3 1 1*0.3=0.3

Netskope

Criteria Weight Netskope
Support Weighted Support
C1 0.05 0 0*0.05=0
C2 .05 0 0*.05= 0
C3 0.15 1 1*0.15=0.15
C4 0.15 1 1*0.15=0.15
C5 0.2 1 1*0.2=0.2
C6 0.1 2 2*0.1=0.2
C7 0.3 1 1*0.3=0.3

Okta tool

Criteria Weight Okta
Support Weighted Support
C1 0.05 0 0*0.05=0
C2 .05 0 0*.05= 0
C3 0.15 1 1*0.15=0.15
C4 0.15 2 2*0.15=0.3
C5 0.2 1 1*0.2=0.2
C6 0.1 1 1*0.1=0.1
C7 0.3 1 1*0.3=0.3

Cipher Cloud tool

Criteria Weight CipherCloud
Support Weighted Support
C1 0.05 0 0*0.05=0
C2 .05 0 0*.05= 0
C3 0.15 1 1*0.15=0.15
C4 0.15 1 1*0.15=0.15
C5 0.2 1 1*0.2=0.2
C6 0.1 2 2*0.1=0.2
C7 0.3 1 1*0.3=0.3

Discussion

Upon evaluation of features and support for each cloud security application, Netscope scores the highest. It supports large cloud applications. It also carries out analytic of threats among other security solutions. Services offered by Netscope override the cost of implementing the tool. I would advise businesses to choose this tool as the first option.

Work cited

  1. Kahol, A., Bhattacharjya, A. K., & Kausik, B. N. (2015). U.S. Patent No. 9,047,480. Washington, DC: U.S. Patent and Trademark Office.
  2. Vines, R. L. K. R. D., & Krutz, R. L. (2010). Cloud security: A comprehensive guide to secure cloud computing (pp. 35-41). Wiley Publishing, Inc.
  3. Mather, T., Kumaraswamy, S., & Latif, S. (2009). Cloud security and privacy: an enterprise perspective on risks and compliance. ” O’Reilly Media, Inc.”.
  4. WANG, L. F., SHEN, J., & JIN, H. M. (2012). Study on Application of Commercial Cipher Cloud Storage System [J]. Information Security and Communications Privacy, 11.
iStock-1189286664_1868x1080

Conflict Resolution- An approach that can solve ?

by with No Comment Data SciencePublic Health

Conflict is a confrontation between individuals that arises from a difference in attitudes, understanding, interests, thoughts, and perceptions. Conflict resolution is how two or more parties find a peaceful way of solving a financial, personal, emotional, or political disagreement among themselves.

The ways of solving conflicts include the following:

  1. Confrontation.

In this stage, the parties disagreeing to come together and discuss the problem at hand. They focus on finding the solution to the conflict by getting the best course of action for the team members. Every member of the team participates hence bringing a win-win outcome. An example is when the team members need to solve a problem with time management.

  1. Compromising.

In this stage, the team thinks of a middle path whereby they decide to give up on something and identify a temporary resolution. The decision taken should last for a short period, bringing a lose-lose outcome to the members. An example is when the team members need to decide on the type of resources used in the organization.

iii. Avoiding

This happens when one of the parties decides to retract from the discussion and decides to take others’ opinions.  They decide to silence to avoid conflicts completely. An example is when one of the team members is emotional or gets angry. The individual decides to shut to cool up themselves.

  1. Forcing.

In this stage, the leader or person in authority forces their opinion and gives the resolution without involving anyone in the team. This may end up as a win-lose outcome since one party may be a loser and the other winner. An example is when the leaders may decide to input some regulations without involving the team.

  1. Smoothing.

This technique provides one with the authority to bring things together by emphasizing the agreements and avoiding disagreements. This happens when distrust is noticed in the organization, and things have to be brought together again. An example is when there is no trust among the team members, and one of the parties brings about a feeling of trust among them.

Reference:

  1. Csilla, K. M. (2019). Conflict Management-Resolution Based on Trust?. Ekonomicko-manazerske spektrum, 13(1), 72-82.
  2. Filippidou, A., & O’Brien, T. (2020). Trust and distrust in the resolution of protracted social conflicts: the case of Colombia. Behavioral Sciences of Terrorism and Political Aggression, 1-21.
  3. Getha-Taylor, H., Grayer, M. J., Kempf, R. J., & O’Leary, R. (2019). Collaborating in the absence of trust? What collaborative governance theory and practice can learn from the literatures of conflict resolution, psychology, and law. The American Review of Public Administration, 49(1), 51-64.