Concept paper on Cloud Adoption in Healthcare
Claude Louis-Charles, PhD
Abstract
The rate with which technology is infiltrating everyday life has led to previously effective medical data processing methods to become outdated. Realizing that there are more efficient methods to collect, store and access medical records has resulted in a new branch of information technology, known as Health IT. With the advancements being made within this emerging industry it is clear that the next step in the technological evolution of healthcare, is to develop a cloud-based record-keeping system that will decrease the intensity of manual medical record keeping and provide a more effective means to access patient records and medical information. This concept paper presents a research proposal for the study of cloud computing and its impact upon the healthcare community. The proposed research intends to study the interoperability of healthcare systems, cloud IT solutions and the potential security risks; Furthermore, this research will examine the evident reluctance in making use of cloud computing technology and the competitive pressure versus the concerns and doubts faced by those considering this move.
Statement of the Problem
This study was prompted by the challenges faced by the researcher when being involved in a motorcycle accident. Specifically, it was difficult to access medical records from the physicians that had attended to the researcher in prior medical matters and the billing system was not easy to understand. Technology in the healthcare industry is evolving, enhancing the creation of better healthcare systems all over the world (Darwish, Hassanien, Elhoseny, Sangaiah, & Muhammad, 2017). However, due to the large number of legacy systems in place and the amount of personal and confidential information in hospitals, it is hard for patients to access their medical records. Notably, this challenge can be overcome by the adoption of cloud computing technology.
Evidently, the challenges faced in the healthcare system, specifically when patients need to access their medical records can be solved by cloud computing. Specifically, by enabling healthcare practitioners improve and reduce costs (Rallapalli, Gondkar, & Ketavarapu, 2016). Moreover, the adoption of cloud computing may reduce the amount of time used in selecting software, enhance data mining, and the analysis of health care data. Therefore, the findings of the study can assist healthcare managers to make decisions regarding how medical records can be easily accessible to patients.
Despite the advantages of cloud computing, the healthcare industry lags in adopting the technology. Besides, the inherent management and technical issues involved in adopting cloud computing lead to failure of recognizing its benefits (Lo'ai, Bakhader, Mehmood, & Song, 2016). In fact, if my prior physicians had cloud computing in their healthcare facilities, it would have been easier to access my records. Owing to this challenges, there is a need to recommend solutions for adopting cloud computing by healthcare organizations by investigating how the technology impacts the healthcare industry by ensuring medical records are easily accessible to patients whenever they need them.
Purpose of the Study
With increasing need for healthcare organizations to avail better methods of information access for patients, this qualitative research will examine the potential impacts of cloud computing technology on healthcare. Despite the underlying benefits if cloud computing especially in data management, past studies show that the health sector has been reluctant to implement the technology (Darwish et al., 2017). According to Takabi et al. (2010), privacy and security concerns are critical to implementing the model. Gao et al. (2018) also show that unclear HIPAA regulations have limited the ability of healthcare organizations to use cloud computing technology. Finally, interoperability problems between legacy systems and cloud IT tools still pose as major challenges (Nigam & Bhatia, 2016). Therefore, this study examines the possibility of overcoming the above challenges and seeks to offer recommendations for healthcare organizations in their deployment of cloud computing technology.
Research Questions
After observing the reluctance in the health sector at implementing cloud computing technologies despite the distinct advantages of such a technology, it is the goal of this study to investigate how cloud information technology impacts the healthcare field. It is in the general wonderment of the research, herein, whether the cloud technology is trailed with efficiency or deficiency in relation to the field of healthcare. Further, the study purposes to determine the ways in which healthcare providers use cloud information technology to provide solutions to their problems. And lastly, the study will explore the issues that affect the utilization of cloud information technology in the field of healthcare. These potentially influential factors can be categorized into a variety of questions which are to be answered by the individuals chosen to complete the survey group. These range from questions about perceptions to those about technology offerings and even some that deal with vendor guarantees.
The first question to be considered is whether the cloud storage provider with which the healthcare establishment has contracted appears to be doing an adequate job. In this context, adequacy can be measured in terms of ease of storing and retrieving data and, of course, overall availability of the cloud service, recognizing that data centers do indeed crash from time to time. The second question to be considered is whether the provider is aware of the specifically applicable dictates of HIPAA and HITECH and possesses the technical wherewithal to address them. The third question is whether the healthcare establishment is aware of any particular security breaches that have occurred and that thereby place the establishment in jeopardy vis-à-vis government imprimatur for continued operation. Along quite similar lines, the fourth question is whether and how the cloud provider relies upon cryptography and other means to safeguard data in transit and, in particular, how well the healthcare firm understands these mechanisms and is empowered maximally to avail itself of them (“Cloud Breach,” 2019).
To meet the aims of the study; recommending possible solutions to problems and issues facing the health care organizations in deploying cloud computing technology, the above specific objectives of this current study can be summarized into threefold study questions as bellow;
I. How does cloud information technology affect the healthcare industry?
II. How are the providers of healthcare utilizing cloud information technology to provide solutions to their problems?
III. What are the barriers that prevent the deployment of cloud information technology in the healthcare field?
Hypotheses
Based on the aforementioned study questions, three researcher’s hypotheses that encompass TOE can be formulated as bellow;
I. When the interoperability of the healthcare systems, the complexity of cloud IT solutions and the unique privacy and security factors are factored in when considering outsourcing health cloud solutions, the end economic benefits of involving the cloud computing technology in the health sector far outweighs the initial cost of deploying the technology.
II. The reluctance in deploying cloud computing technology is hugely due to lack of, or minimum support from the management and also the healthcare system governance structure plays a vast role in the delay of integrating IT into the healthcare.
III. The competitive pressure is a major factor in considering cloud technology for the healthcare sector, however, the concern over the sensitivity and the privacy of health data and the doubts of competency of cloud providers to meet contractual agreements waters down this interest.
Definition of Key Terms
In examining the problem and the proposed research it becomes apparent that there are certain key terms that should be comprehensively defined in order to fully grasp the presented information. In this segment of the concept paper, it is intended that these terms will be adequately addressed and defined through the support of relevant resources.
Cloud Computing is the first term to be defined and according to Microsoft Azure (2019), cloud computing is the provision of computing services such as diverse servers, data storage, digital databases networking platforms and software, via the internet (Microsoft Azure, 2019). This service can provide great flexibility in ensuring data is readily available to those authorized to access it, especially in a setting such as the healthcare industry.
IT in Healthcare, Health IT or information technology when applied to the healthcare industry can mean anything from the provision of digital data caches to tools used by medical professionals in their day to day practice. USF Health has determined that Health IT can provide ease of access to patient information regardless of patient location, hours of operation or attending medical professional (USF Health, 2019). The tools required to make this seamless integration of digital data and physical health possible are extensive, physically maintaining such a database of information could be near-impossible for larger medical facilities, at least without the convenience of cloud computing. Other terminology may be discovered during the course of conducting this research, in which case this section should be expanded to deliver clarity on any confusing or generic terms not yet addressed.
Theoretical Framework
The theoretical framework of this paper will be based around change management. When healthcare providers adopt cloud based data storage system, there can be resistance to moving from a privately owned database in house. Not only are there concerns about finance, security and training of staff to operate the new system, but the change itself can present some challenges.
Jacobs et al (2012) point out that change management has to consider external factors as well as internal and that resistance from one or both sectors can be severe. Hayes (2018) points out the need for management to filter down to staff the requirements for change and Gill (2012) concurs. Hence with the example of moving to a cloud based healthcare system, management might explain to staff that the efficacy of such a system will make their job easier, to reassure them that security threats will be minimized and to emphasize that full training will be given. In this way, management can pre-empt and address many concerns that may have been important to staff. Offering the chance to give feedback and ask questions is another important part of this communicative process.
Walton et al explore the theory that change can be implemented by management in one of three ways, with either the escape, force or foster strategy. Management can escape resistance to change by removing staff who resist the new strategy force a change through or can foster relations with staff in order to integrate structural changes via negotiation. When investigating the implementation of a cloud based system within a healthcare operation, management may have to negotiate with finance officers around cost, data protection officers around security and with line managers around training.
Weiner (2009) describes the importance of engendering a state of readiness before implementing change and again, this is an area that management can assist with. By embracing the change, and by passing down that enthusiasm to direct reports, managers can have a positive effect on the acceptance of change within an organisation. The author also mentions that this acceptance depends upon how much value employee place on the change and how much it will impact their working day. Weiner also concludes that readiness is equitable to acceptance and ease of implementation, hence ensuring that a company and its workforce are prepared for change is essential for smooth transition.
There are numerous theories of the change process, but Van de Ven and Poole (1995) narrowed them down into four categories: Teleological; Dialectical; Life Cycle and Evolutionary. The selection of one of these theories to implement change depends on the situation and of course on the outcome. For example, if a healthcare executive were to implement a life cycle theory to implement introduction of the cloud within a hospital, each part of the change process would organically lead to the next until completion. Within a teleological process of change there is more of a focus on learning, which can lead to modification of processes as the change unfolds.
When considering any major change within an organization, Arthur (1994) identified four factors which can inhibit or contribute to successful implementation. The first of these is cost. If the set up cost is high, it is important to see the change with a long-term view in order to recoup the size of the initial investment. With a cloud based system, initial cost is high, yet over time that money should be recouped thanks to minimal maintenance costs in comparison with regular upgrades of in house technology because it becomes quickly outdated. Secondly, Arthur points to learning. Whilst any new system presents a challenge to users because it is unfamiliar and new, hence can induce fear, prolonged use of the same system will inspire confidence, especially if results are better than the system which was previously in place. Hence with the cloud based technology in a healthcare setting, the ease of accessing patient notes and diagnostic tools may inspire staff to promote the use of the new setup as it makes their job easier and more efficient. Thirdly, Arthur believes co-ordination of the change process offers a higher chance of success. For instance, if other healthcare facilities are using the same cloud based systems, interoperability will be enhanced and information can be transmitted more smoothly. Lastly Arthur encourages betting on the right horse. This philosophy suggests that by adopting what has been successfully implemented by peers will have aa greater chance of acceptance and success. Certainly, if neighbouring health care providers have moved to a cloud based system and are effusive in their praise of the ease of use and efficacy of the system, others are likely to follow suit.
Brief Review of the Literature
Technological innovations continue to drive improvements in healthcare, and for the last decade, there has been a considerable push to digitize healthcare by implementing electronic records (Gao et al., 2018; Nigam & Bhatia, 2016; Rallapalli et al., 2016; Rueckel & Koch, 2017). Digitization of healthcare continues to evolve, and today, providers are finding new methods to implement information technology (IT) to achieve improvement measures in regard to patient safety, quality of care, customer satisfaction, accessibility and affordability, costs savings and accuracy and efficiency (Sujan et al., 2018; Rueckel & Koch, 2017; Khan et al., 2018). The following review provides an overview of the extant literature on IT in healthcare, specifically focusing on the uses of IT in healthcare and how IT is being used to improve accuracy and efficiency, to make healthcare more affordable and accessible, and to improve patient safety, quality of care and customer satisfaction.
Themes/Subtopics Apparent in Literature Review
Accuracy and Efficiency
Improving efficiency in healthcare is accomplished by implementing measures that reduce clinical errors, eliminate fraud and security breaches, enhance coordination and support research and innovation (Rallapalli et al., 2016; Darwish et al., 2017; Kasthurirathne et al., 2015). As such, efficiency is concerned with increasing value—value for all stakeholders, including patients, providers, medical facilities and other related parties/enterprises (Khan et al., 2018; CITE). Research shows that the use of IT, particularly software applications supported by Cloud computing, have revolutionized electronic recordkeeping systems (Gao et al., 2018; Tawalbeh et al., 2018; Nigam & Bhatia, 2016; Rallapalli et al., 2016). Cloud computing, which gives providers and patients on-demand access to electronic records, allows for a shared pool of knowledge and information that helps enhance coordination and accuracy in healthcare (Gao et al., 2018). Rallapalli et al. (2016) points out that large sets of health care data, which is available in the form of Electronic Medical Records (EMR), Electronic Health Records (EHR) and Patient Medical Records (PMR), have been increasing over time, and the Cloud provides the architecture needed to support and compute such complex and growing data sets. The value of digitization, software applications and the Cloud lies in the collective ability to store and analyze data in the attempt to resolve the increasing problems that the healthcare field is facing, such as quality of care, reducing costs and times associated with receiving quality care, increasing accuracy and removing the burden of human-caused error, and reducing redundancy and finding meaningful patterns (Rallapalli et al., 2016; Darwish et al., 2017; Khan et al., 2018).
While the Cloud and Big Data have profound implications for improving efficiency and accuracy in healthcare recordkeeping and delivery, there are still some challenges and inefficiencies in the methods used for gathering, sharing and using the vast amounts of data available (Rueckel & Koch, 2017). Rueckel and Koch (2017) point out that there is a need for appropriate methods, tools and techniques to make use of the growing amount of data available so that the data can be used effectively. For example, making effective use of healthcare data can turn the field of healthcare into a more proactive practice instead of a reactive one, which is the purpose of applying predictive analytics on large data sets (Rueckel & Koch, 2017). Research has been carried out to determine effective data analysis models and strategies so that healthcare providers can make better operational and patient care decisions (Rallapalli et al., 2016; Brooks et al., 2015).
Affordability and Accessibility
Some of the major problems in healthcare practice and delivery are concerned with affordability and accessibility (Khan et al., 2018; Rallapalli et al., 2016; Gao et al., 2018; Tawalbeh et al., 2016). As innovations in healthcare and technologies have improved quality of life across the globe, people live longer, which in turn increases the demand for healthcare services (Khan et al., 2018). Khan et al. (2018) emphasize how IT is necessary to expand healthcare accessibility in terms of making healthcare more affordable and widely available. Specifically, Khan et al. (2018) researched microfluidic technology, which is the science of controlling and manipulating fluids, to assist in data management in terms of its potential diverse applications. For example, microfluidic applications can be used to make lab diagnostic tasks more accessible and affordable, such as biochemical assays, drug screenings, genetic analysis and electrochromatography (Khan et al., 2018). Additionally, improving access and affordability of diagnostic testing and screening can have considerable implications in terms of the effectiveness of treatments and to reduce problems of misdiagnosis and unnecessary medical treatments, which also reduces expenses and wasted time for both the patient and the provider (Brooks et al., 2015; Materla et al., 2019).
Other data-driven models that help improve access and affordability of healthcare involve the use of Big Data, the Cloud and software applications that help pool resources and knowledge so that patients and providers can reduce redundancy and inefficiencies (Gao et al., 2018; Rallapalli et al., 2016). Gao et al.’s (2018) systematic literature review found that Cloud computing services in healthcare organizations can reduce costs, errors and inefficiencies and waste, but they must be scaled and implemented based on the specific specifications of the healthcare setting. Otherwise, data-driven efforts become inefficient and irrelevant to the needs of the stakeholders (Gao et al., 2018). Similarly, research from Brooks et al. (2015) on business intelligence (BI), which is a term used to describe how technology, data and business processes work together to inform decisions, discusses how BI strategies are successful as far as they are aligned with the people, processes and infrastructure of the environment (e.g., medical facility). In other words, if IT strategies and models are implemented without considering these factors, then it is unlikely that measurable improvements in care, affordability and/or accessibility will occur (Brooks et al., 2015).
Patient Safety and Quality of Care
Data use in healthcare is particularly complex considering the various security, safety, privacy and other issues that accompany the collection and deployment of such data (Brooks et al., 2015). Integrated data from different sources, therefore, is not as simple as simply collecting and using data because there are necessary protections and standards in place to protect patients to ensure confidentiality (Gao et al., 2018). However, systems that allow coordination between providers have been shown to reduce inefficiencies and improve quality of care due to the ability for providers to have access to real-time information about their patients’ healthcare and medical history, care from other providers, hospital stays, diagnoses, prescriptions, etc., and Cloud services provide such coordination without the high cost (Gao et al., 2018). Brooks et al. (2015) point out that in the drive for patient safety and transparency in healthcare, there has been an increased focus on error reduction and increased efficiency.
As such, research has found that errors made in the healthcare setting can be reduced using IT, such as automation to reduce human errors and to increase accuracy and using data analytics for predictive purposes and reducing redundancy and unnecessary appointments and treatments (Khan et al., 2018; Sujan et al., 2018; Rallapalli et al., 2016). Therefore, it is argued that clinical outcomes can be greatly improved (reduce errors, improve accuracy, reduce wait times, costs, etc.) by implementing appropriate IT measures (Rueckel & Koch, 2017). For example, Sujan et al.’s (2018) research on safety in healthcare investigated human reliability and points out that the healthcare industry must learn from other sectors that have implemented safety management measures using IT. Specifically, Sujan et al. (2018) explored human reliability in regard to the fact that there are simply no systems available that can be completely error-proof and applied this model to safety in healthcare. As a result, Sujan et al. (2018) suggest that poor levels of reliability in healthcare processes and a lack of proactive risk management can be addressed using predictive analytics, which in turn reducing human error and improving quality of patient care. Further, data management also plays an integral role in diagnostics, early detection, management and control of disease and increasing survivability among patients, as shown by Khan et al.’s (2018) research into microfluidic technology.
Customer Satisfaction
Customer satisfaction constantly evolves alongside customer needs, demographics and factors such as affordability and technological innovations (Materla et al., 2019). In healthcare, the focus on customer satisfaction has not been particularly important, which is evidenced by the industry’s reluctance to implement customer service models into their service delivery systems (Materla et al., 2019). Research from Materla et al. (2019) on customer needs in healthcare investigated patient perceptions over time of regarding quality of care, care that meets specific needs, and how technological innovations have fundamentally changed patients’ perceptions toward healthcare. For the most part, in the healthcare setting, patients are treated not as customers, but as ‘objects’ that rely on their providers for their expertise and care (Materla et al., 2019). However, patients’ needs in healthcare are constantly changing due to factors of costs, reduced quality, long wait times, inaccessibility, among others, all of which add to the complexity of the situation (Materla et al., 2019). Brooks et al.’s (2015) also points out that these changes have shaped the delivery of healthcare, and as such, customer satisfaction is about improving the quality of care. Therefore, the use of IT in healthcare can improve customer satisfaction by making care more efficient, affordable and accessible (Materla et al., 2019; Brooks et al., 2015; Gao, 2018).
Summary of Literature Review
This literature review examined current studies on the use of IT in healthcare, focusing on how the industry is currently utilizing IT to improve efficiency and patient care in the effort to satisfy customers. Technologies such as Cloud computing, Big Data analytics, predictive models, innovative diagnostic measures (microfluidic technology, etc.), human reliability analytical models, and customer service measures, have been examined and discussed in regard to their contributions and implications to the healthcare industry. As such, the literature review has found that IT in healthcare provides significant opportunities to make healthcare more affordable and accessible, and to increase the quality of care that patients receive due to greater accuracy, reduced errors, greater transparency and coordination between providers, and reduced wait times and unnecessary and redundant appointments and treatments.
Research Method
Cloud computing has become ubiquitous around the world. Corporate and private customers in all business sectors and walks of life have become enamored of the notion that huge tracts of remote storage can be purchased or rented cheaply and efficiently. At the same time, cloud providers are marketing furiously to ensure that prospective customers dismiss their security concerns from consideration, such concerns ranging from protection of intellectual property to privacy from competitors to keeping private information out of the hands of cognizant governments.
It is recognized that the actual technical issues associated with cloud computing are quite intricate. Consequently, it is difficult fully to understand the gamut of issues and fairly evaluate whether the cloud computing paradigm is fully consonant with the requirements of the healthcare community. These requirements particularly include conformance with government-mandated HIPAA (“Are You,” 2019) and HITECH (“HITECH,” 2019) initiatives that guarantee safety of confidential or sensitive patient data from unwarranted disclosure to or modification by unauthorized individuals. Since the actual, technical details of the techniques by which cloud storage providers succeed or fail at providing the requisite protections is out of scope, what is more important is to consider the perceptions of those healthcare officials who are directly responsible for contracting with these providers. To this extent, the chief mechanism by which this research will be conducted is in the form of survey questionnaires to be completed by both the administrative and technical staff of hospitals, medical centers, and private physicians’ offices who choose to procure cloud services as part of a generally applicable information management solution. At the same time, healthcare establishments that do not procure cloud services but, rather, choose fully to address their own information storage and retrieval solutions will be interviewed to the extent that they constitute a control group against which comparison is both worthwhile and justifiable.
The qualitative case study for this research will include selected health facilities in North America. While conducting the research, emphasis will be made on assessing the methods of data management within healthcare organizations, ability of patients to access their medical records, and ease of understanding the stored data. Methods of data collection that will be used in this research include interviews with experts, observations, and literature reviews. The study population in this research includes all stakeholders in the healthcare industry.
Operational Definition of Variables
Operational variables that serve specifically to address the foregoing hypotheses are as follows. The first set deals with the specific goals that healthcare firms do or do not expect their cloud providers to fulfill. These will be presented in yes/no fashion so that survey respondents can indicate their respective choices. The second set deals with whether and how the cloud provider responds to each of the healthcare organization’s business requirements. In each case, the possible requirements are associated with indications on a five-point Likert scale of the extent to which each one is or is not satisfied (Allen, 2007). The third set deals with frequency and recentness of known breaches. This presupposes, of course, that the cloud provider is actually honest with its customers regarding breaches rather than requiring them to get their information about breaches from the evening news or, even worse, the grapevine. These variables are likely numeric, dealing with such issues as dates and counts.
In each case, yes/no responses will be transduced to binary indicators, which are, one or zero, which can subsequently be algorithmically analyzed. By the same token, response variables on the Likert scale will be translated to a facile numerical equivalent, likely ranging from -2 for very unsatisfied, through 0 for neutral, to +2 for highly satisfied.
Measurement
The first hypothesis will be addressed by reconciling the fourth set of variables as dependent parameters with the first set of variables as independent parameters. Linear regression will be relied upon to derive metrics of level of understanding of procured cloud services (Davidson & MacKinnon, 2004). The second hypothesis will be evaluated by comparing the collected performance metrics and comparing those as dependent variables against the first set of variables. Similar regression techniques will be availed of to either aver or refute the hypothesis. The third hypothesis will be tested by comparing the third set of variables against the first and performing the indicated linear regression. Similarly, the fourth and final hypothesis will be evaluated by comparing the fourth set of variables against the first set.
Summary
This study, while inspired by the researcher’s personal experience, has shown evidence that Health IT has much greater potential than what is currently being experienced within the healthcare industry. The introduction of cloud computing in particular has the opportunity to streamline medical data processing and create efficient and convenient access to patients’ medical information for authorized medical personnel. This cross-facility sharing of information being coordinated via cloud computing has endless possibilities in terms of database size, areas of accessibility and number of medical professionals who can access the database. The proposed study will investigate the interoperability of healthcare systems, cloud IT solutions and the potential security risks; as well as exploring the reluctance to utilize cloud computing demonstrated in some cases, and finally the competitive pressure versus the concerns and doubts expressed by those considering this change to cloud based record-keeping. This comprehensive study will make use of qualitative research methodology in the form of a questionnaire to collect data that will support or disprove the hypotheses addressed herein.
By completing this research, proper recommendations will be made to healthcare organizations grappling with various challenges when deploying cloud computing technology. The researcher also hopes to bridge the existing information gap regarding applicability of cloud IT in healthcare. In so doing, information gathered in this research can be transferred or used as a background for future studies. Overall, this research will constructively engage healthcare stakeholders to provide rational, timely, and measurable recommendations to healthcare organizations.
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Appendix
Annotated Bibliography
Brooks, P., El-Gayar, O., & Sarnikar, S. (2015). A Framework for Developing A Domain Specific Business Intelligence Maturity Model: Application to Healthcare. International Journal of Information Management, 35(3), 337-345.
According to the authors, the implementation of health records has become a necessity, as a consequence of the accumulation of big data. To substantiate their arguments, the researchers carried out a case study within a health organization. From the research, the authors found out that health organizations have an opportunity to apply Business Intelligence (BI) models to enhance efficiency as well as leverage data. In their study, the authors placed much emphasis on a framework for the development of BI maturity models, which has the potency to enhance clinical efficiency. However, it became evident that the study found that some of the BI maturity models could not meet industry requirement, precisely because they were generic. Consequently, additional research was required to offer more insights into such models. An in-depth analysis of the study revealed that without more research, none of the BI maturity models would be acceptable within the healthcare industry by experts.
Darwish, A., Hassanien, A. E., Elhoseny, M., Sangaiah, A. K., & Muhammad, K. (2017). The Impact of The Hybrid Platform of Internet of Things and Cloud Computing on Healthcare Systems: Opportunities, Challenges, And Open Problems. Journal of Ambient Intelligence and Humanized Computing, 1-16.
The authors of this article are professors at leading universities and have written extensively on the link between health and information technology. In the current article, they discuss the several opportunities existing within medical IT as a consequence of the adoption of the Internet of Things (IoT). They argue that the 21st century has witnessed a revolution within the ICT sector, to the extent that a paradigm shift has been envisaged within the healthcare industry. The article is quite educative, as it defines critical concepts, and can hence be used as an introductory paper for those who are not well versed in the field. For instance, the link between cloud computing and IoT can create a health application referred to as a cloudioT-Health paradigm. Primarily, the article is highly objective, considering that it also discusses the existing challenges associated with integrating IT into the healthcare field. Under the challenges, the authors propose additional research for future direction.
Gao, F., Thiebes, S., & Sunyaev, A. (2018). Rethinking the Meaning of Cloud Computing for Health Care: A Taxonomic Perspective and Future Research Directions. Journal of medical Internet research, 20(7), e10041.
Gao is a professor at the department of information systems at the university of cologne in Germany. Thiebes teaches at Karlsruhe institute of technology in the department of economics and management. In the article, they explore how cloud computing has provided an innovative paradigm that has enhanced the delivery of service. In particular, cloud computing services have been beneficial to the healthcare system. The objective of the empirical research was to generate insights into the concept of cloud computing as it is applied within health IT research. A two-stage approach was used as a method of developing a taxonomy of cloud computing services within a health organization. The results illustrated that cloud computing is relevant to health care organizations. The study offers an excellent analysis of the impact of cloud computing on healthcare. However, the authors failed to get a valid conclusion, considering that they indicate that the work only offers a conceptual basis that can inform more research.
Kasthurirathne, S. N., Mamlin, B., Kumara, H., Grieve, G., & Biondich, P. (2015). Enabling Better Interoperability for Healthcare: Lessons in Developing A Standards Based Application Programing Interface for Electronic Medical Record Systems. Journal of medical systems, 39(11), 182.
Application Programming Interface (API) forms the basis of this study, with the authors affirming its significance in Electronic Medical Record (EMR) systems. All the authors are highly qualified in systems development. In the perspective of the authors, EMR, when used in conjunction with Fast Healthcare Interoperability Resources (FHIR), can form a basis for service delivery. The authors believe that such resources have that capability to influence clinical procedures. This study is quite intensive, with the authors being more passionate about the use of third-party medical apps as well as other smart technologies. Of great significance are the Substitutable Medical Apps and Reusable Technology (SMART), which the authors believe can be well integrated into open MRS through FHIR. As a highly intensive medical research, the jargons used in the study can only be understood by health care experts who have a strong leaning towards technological matters. The fact that the authors were confident that their research was conclusive is envisaged in the manner in which they avoid proposing additional research. Even then, none of the authors have any qualification in any health competency. This provides a basis through which their application needs to be tested by health practitioners to illustrate its efficiency.
Khan, S. M., Gumus, A., Nassar, J. M., & Hussain, M. M. (2018). CMOS Enabled Microfluidic Systems for Healthcare Based Applications. Advanced Materials, 30(16), 1705759.
The authors make an emphasis on the need to expand the accessibility of affordable healthcare to all citizens. This cannot happen without technology. The study considers a seamless integration of microfluidic technology, which can assist in data management. According to the researchers, Complementary Metal Oxide Semiconductor (CMOS) has the potency to guarantee the accessibility of affordable personal healthcare. The study goes deeper and explores the critical components of the CMOS technology such as packaging and fabrication. The authors consider that the emergence of the Internet of Things (IoT) signifies that CMOS can ensure ease of access to affordable healthcare to many citizens. Basing the study on CMOS, a more in-depth analysis of the content of the review indicates that it has the potency to be embraced by governments, due to the need to expand the accessibility of healthcare. This study is timely, considering that governments are battling with the challenge of effectively managing healthcare services.
Lo'ai, A. T., Bakhader, W., Mehmood, R., & Song, H. (2016, December). Cloudlet-Based Mobile Cloud Computing for Healthcare Applications. In 2016 IEEE Global Communications Conference (GLOBECOM) (pp. 1-6). IEEE.
In this article, the authors consider how the distribution of files can be enhanced through the use of mobile computing technology. The researchers appreciate how the modern cloudlet concept has evolved from smartphone applications so that it can be used within the healthcare sector. They argue that the use of 3G or LTE technologies associates with an assortment of challenges, including the issue of costs, limited bandwidth as well as latency. In their opinion, however, the cloudlet concept offers immense opportunities for healthcare practitioners. This is because it helps users to directly connect to cloud resources through cheaper technologies such as WI-FI. This article excellently explores how the cloudlet model can assist healthcare professionals to analyze patients’ medical records. As a new model, there exists no evidence that it has been tested. However, the authors remain optimistic that cloudlet is more reliable and efficient when compared to other cloud computing models.
Materla, T., Cudney, E. A., & Antony, J. (2019). The Application of Kano Model in The Healthcare Industry: A Systematic Literature Review. Total Quality Management & Business Excellence, 30(5-6), 660-681.
Materla and Cudney believe that the success of any industry is determined by the perception created by customers on the offered products. Therefore, like any other industry, the healthcare sector needs to focus more on customer satisfaction through the improvement of quality. In this regard, the authors affirm the need to apply the kano model in the health sector. They regret that despite other industries aggressively embracing the model, it is still within the infancy stage in the health sector. The study dwells on a review of literature, intending to find whether the kano model has been employed with other methodologies to enhance healthcare quality. It remains clear that the study was deficient. The lack of empirical research and reliance on literature review justifies further research. On the converse, the study provides an excellent analysis of the kano model, specifically regarding the issue of improving customer perception about healthcare delivery.
Nigam, V. K., & Bhatia, S. (2016). Impact of Cloud Computing on Health Care. Int Res J Eng Technol, 3(5), 2804-2810.
This article offers a descriptive analysis of the impact of cloud computing in healthcare. The authors’ objective is to provide a practical reference to assist enterprise information technology and business decision-makers within the healthcare industry. The reference aids in analyzing and consideration of the implication of cloud computing within their firms. Based on the content of the paper, it remains evident that it has been written for decision-makers within the healthcare industry so that they can embrace innovations existing within the IT sector. In this regard, the authors are highly informative, considering that they discuss IT concepts such as Commercial Cloud Service Providers (CSPs) and their enhancement of practice within the hospital setting. On the converse, the authors admit the risks associated with the use of cloud computing. Hence the reason as to why they believe that the work only offers the foundation upon which more research need be carried out.
Rallapalli, S., Gondkar, R., & Ketavarapu, U. P. K. (2016). Impact of Processing and Analyzing Healthcare Big Data on Cloud Computing Environment by Implementing Hadoop Cluster. Procedia Computer Science, 85, 16-22.
Rallapalli is a research scholar within the field of research and development(R&D) at Bharathiyar university in India. Gondkar is a professor at Bangalore University in India and specializes within the area of AIT. Therefore, their combined competencies in technology research qualify them to offer compelling content in IT matters. The authors believe that the most significant challenge that healthcare organizations often face is analyzing big data. To this extent, they suggest that more efficient tools have to be used to guarantee better decision making. They propose the application of cloud computing since it can store, process as well as analyze data much effectively. In particular, they believe that Hadoop can be used to process extensive data. As a new concept of cloud computing, the authors have a difficult time trying to explain to the audience the effectiveness of Hadoop. Even then, their discussion is quite convincing, specifically considering how they emphasize the need to have real-time information that can guarantee quality healthcare delivery.
Rueckel, D., & Koch, S. (2017). Application Areas of Predictive Analytics in Healthcare.
The authors, both fellows at Johannes Kepler University, place much emphasis on the use of predictive analysis to manage vast databases in the healthcare sector. They believe that clinical outcome can be enhanced through the adaptation of IT procedures. In their empirical study, the authors carried out a qualitative analysis of how predictive analytics can be used to manage bowel cancer. The researchers conducted an ABC analysis and then evaluated the results using appropriate criteria. The conclusions of the study revealed that there exist various opportunities that demand the application of predictive analytics within the healthcare industry. Although the authors’ review was not comprehensive, it offered a new area of application of predictive analysis. With cancer becoming a global killer, the use of predictive analysis to predict cancer occurrence is a section worth studying intensively. This study can be hailed as forming the basis through which more discourse can be undertaken.
Sujan, M. A., Embrey, D., & Huang, H. (2018). On the Application of Human Reliability Analysis in Healthcare: Opportunities and Challenges. Reliability Engineering & System Safety.
The current study offers a unique perspective, which other authors have not so far explored. Safety in the healthcare industry has been a grappling problem for ages. Per the authors, the healthcare industry needs to learn from other sectors by using safety management techniques. Therefore, the Human Reliability Analysis (HRA)has been proposed, based on its ability to reduce risks when performing medical procedures. Despite being passionate about the essence of safety, the authors fail to offer a conclusive argument, considering regulatory and cultural challenges existing. However, HRA can be applied to guarantee security if the ownership challenges bedeviling the framework can be managed. Considering that it is difficult to quantify the cost-effective attributes of HRA, further studies are needed. Thus, the area of focus should be on how to overcome existing barriers that can hinder the technology from being actively used by patients. Moreover, more significant research is required to understand its reliability.
