Discuss about the Health IT Vendors and Adaptive SDLC.
The existence of mobile equipment and their applications have introduced an ideal change in medical settings. The introduction of new participants and new technologies in the market are presenting better ways to acquire knowledge from different sectors for delivering exceptional care experience. Technologies such as cloud computing, big data and Internet of Things are promoting collaboration and better communication across the whole care continuum and are improving care delivery. Besides, medical industry is adjusting towards a business approach of high value, developing systematic competency to drive clinical and operational developments, and advance care quality (Mostashari, 2014). From intelligent algorithms, worn to smart sensors and creative practices, the entrance of novel technology is making big moves in the massive data world to contribute productivity and efficiency in different operations and enhance management of chronic diseases. Other big elements leading such changes are digital medical records and drug dose calculators, electronic health records, preparing way for improved care delivery with comprehensive, real-time and better view of the health of a patient.
As more medical companies invest in system capabilities and these technologies, they are experiencing proven outcome. Therefore, the current market is well-stocked with large group of technology solution vendors to help organizations boost productivity and performance (Barnard, 2015). The context will discuss three vendors that offer medical systems, advantages and disadvantages of cloud-based solutions, and advantages and disadvantages of predictive SDLC and adaptive SDLC.
Technology is needed in healthcare sector to assist clinicians carry out their work more effectively and health systems boost their services, but essentially, technology is needed to provide patients with the best possible care. Absence of technology in healthcare does not mean that technology is missing everywhere else. There are many organizations who have created smart ways to enhance patient care and clinician workflows. The issue is that healthcare utilizes legal systems that are incompatible with the current technology, and reconstructing the whole industry to be more advanced in technology is challenging.
Healthcare technology change is in its mid, as such people are determining how to put into effect more improved ways of taking care of individuals in an environment that is not structured to receive new tools easily. More time is needed to determine the full healthcare technology potential. Currently, it’s the responsibility of healthcare society to increase knowledge of the excellent solutions out there and to support change. In the end, the more people learn about the tools that are at their disposal, the more they will advocate for their use. The following are the three healthcare technology firms who are describing the healthcare future.
Siemens Healthcare is among the largest providers to the medical industry and a leader in laboratory diagnostics, medical imaging and health IT. It plays a special role of supporting medical professionals to carry out their work by offering healthcare technologies that assist in delivering a better healthcare quality and allow constant improvement degrees of personal care through improved diagnostics, healthcare IT solutions, imaging and therapy. It offers solutions and products for the whole range of patient care from one source, from early detection and prevention to diagnosis to treatment and to aftercare. Besides, by improving medical workflows for the most known diseases, Siemens makes healthcare better, faster and cheaper.
Orion Health is a leading vendor of medical data integration solutions and clinical workflows. It has over 20 years of experience in developing quality standards within its medical software portfolio. Acknowledged as a market leader across the world, the product portfolio of Orion Health includes applications of clinical workflow such as delivering excellent user experience, EHR (Electronic Health Record), integration technology and clinical portals, a web-based interface of the user and rapid clinical adoption for a reduced ownership cost for medical companies across the world. Besides, Orion Health provide detailed audit capabilities and security that adhere to appropriate security and privacy regulations to safeguard the integrity of sensitive information of a patient.
GE Healthcare offers life-changing healthcare services and technologies assisting in delivering patient care to individuals around the globe. The organization offers medical diagnostics, drug discovery, performance solutions services, information technologies and medical imaging, patient monitoring systems, and biopharmaceutical manufacturing technologies.
There are substantial benefits to putting into effect cloud technologies in health care. Generally, cloud computing is a computer design that permits access to users on-demand to an application and its information via a third-party provider utilizing the internet (Wang, 2015). There are many types of models. The most interesting is the software as a service, which is a technique of offering the EHR (electronic health record) or EMR (electronic medical record) as a service. The pros of the model are:
In general, these advantages free up resources for user support and implementation and result in considerable cost savings (Calabrese & Cannataro, 2015). According to the findings of a study carried out by CDW Healthcare, 88% of health care companies that have employed cloud technologies have saved approximately 20% of their costs of health care.
These advantages should result in significant cost savings, freeing up resources for implementation and user support. According to the results of a survey conducted by CDW Healthcare, 88 percent of health care organizations that have implemented cloud technologies have saved, on average, 20 percent of their health care costs (Wang & Alexander, 2013).
Besides, there are considerable risks that every health care company must encounter when shifting to cloud-based solutions. Security, data control, transfer and availability to a third party suggests that an organization lacks control over where its information lives (YorkWilliams, 2017). Trust in an organization cloud provider takes on an entirely different meaning. Privacy and security, major issues in the market of health care, have to be secure.
Other vital issues comprise of error limitations, rapid response times, data availability and disaster backup (Esposito, De Santis, Tortora, Chang & Choo, 2018). Most providers will have extensive capabilities than a single user. Unapproved revelation of data results in unpleasant consequences to the company and considerable expenses in restoring and recovering data and alerting affected persons.
In consideration of the stability of a host provider, uptime or availability guarantees and stability of pricing structure are vital. A vendor and a company must have adequate bandwidth to contain ones needs (Casola, Castiglione, Choo & Esposito, 2016). Nothing is worse than to rely on a slow software, it becomes useless. The size of the provider is unsuitable sole measurement of growth. For instance, Microsoft and Google have invested largely in their private health care databases and modules, but Google has declared to scrap off its whole venture.
When shifting to a cloud-based provider, you hand over control of all information and IT functions. If a company experiences problems with its data and software and the vendor is uncooperative, its whole network can be severely affected (Masrom & Rahimli, 2015). The only leverage the company has is its contract that makes the provider assist in its issues. As such, contracts are essential and a company should not agree to a standard contract offered by the provider, which will probably be unfair to his or her favor. Since the vendor has control of the application and data, the contract should outline that the vendor would bear the expenses of solving a security or data breach, including alerting all affected individuals.
Adaptive and Predictive SDLC are two different methodologies of software development. The predictive methodology can importantly be defined as a linear software design model. Predictive SDLC applies a continuous design process. Development runs continuously from the beginning to the final point, with various stages: understanding of the idea, introduction, examination and determination, designing, constructing, testing, implementing and maintaining. An example of predictive SDLC is the waterfall method. Adaptive SDLC, on the other hand, suggests an iterative and incremental approach to software design (Kim, 2016). It was importantly created as a result of the limitations of predictive SDLC, as way of offering more freedom to designers. The process of design is divided into single models that designers work on. The plan or course of action in the methodology is predetermined. Instead, designers have the freedom to respond to requirement changes as they emerge and make adjustments as the project continues. Adaptive SDLC is an emerging player to the game of development. Nevertheless, it has made significant gains in popularity and use in the past years. An example of adaptive SDLC is the Agile method.
Adaptive methodology provides an amazingly adjustable design model, supporting evolutionary development and adaptive planning. Developers of software operate on small parts at a time. Feedback of the customer and software testing happens concurrently with development. Several advantages are obtained from that, particularly in projects environments where developments need to have the ability to respond to requirements changes effectively and rapidly (Shankarmani, Pawar, S. Mantha & Babu, 2012). Moreover, adaptive methodology is beneficial particularly in situations where the project’s end-goals is unclearly defined. The requirements of a client will probably be clear as the project continues and development can be adjusted to meet the emerging requirements. Besides, adaptive SDLC is the best option for experimental software design. In addition, the method promotes communication and interaction.
The emphasis of predictive SDLC is the plan of the project and thus before starting any development, an organization should first set a clear vision and clear plan. Since the predictive method requires direct and comprehensive planning, the initiation of a project can be quick. Besides, it is possible to estimate budgets and timetables more accurately, which appear to please customers (Karim, Albuolayan, Saba & Rehman, 2016). Moreover, predictive development processes appear to be more safe since they are plan aligned. For instance, if a designer leaves a project uncompleted, it will not cause a huge issue as the methodology requires comprehensive documentation and planning. A new designer can be hired in place of the old designer, following the plan of development without a problem.
Adaptive methodology lacks a structure similar to that of predictive SDLC and thus introduces some drawbacks. Adaptive projects appear to be challenging to predict, from budgets to timelines. Without an actual plan, everything is nebulous and unclear. Besides, intense collaboration and active user engagement are needed throughout the adaptive process. It can cause many problems. First, the development method can take a lot of time, as such, the designers need to be dedicated for the project duration (Altameem, 2015). Secondly, if a designer chooses to leave in the middle of project development, it can be more problematic as no plan is set for replacing the designer.
The predictive methodology is inflexible and rigid. Changing the design of the project an any project stage can cause problems and once a stage is complete, it is difficult to make adjustments to it (Sharma & Singh, 2015). Therefore, when planning to use predictive methodology, all requirements should be gathered upfront. Besides, testing and feedback are put off till a later time. As such, if a problem emerges, it is challenging to address it, requiring a significant amount of effort, time and money.
Adaptive SDLC is better than predictive SDLC since it allows changes to made during the process of design. Besides, the method promotes communication and interaction. As such, when few problem emerges, they are addressed instantly, thus saving a company future effort, time and money.
Conclusion
Cloud-based solutions free up resources for user support and implementation and result in considerable cost savings. However, security, data control, transfer and availability to a third party suggests that an organization lacks control over where its information lives. Trust in an organization cloud provider takes on an entirely different meaning. Unapproved revelation of data results in unpleasant consequences to the company and considerable expenses in restoring and recovering data and alerting affected persons. As such, since the vendor has control of the application and data, the contract between a company and the provider should outline that the vendor would bear the expenses of solving a security or data breach, including alerting all affected individuals.
References
Altameem, E. (2015). Impact of Agile Methodology on Software Development. Computer And Information Science, 8(2). doi: 10.5539/cis.v8n2p9
Barnard, A. (2015). Radical nursing and the emergence of technique as healthcare technology. Nursing Philosophy, 17(1), 8-18. doi: 10.1111/nup.12103
Calabrese, B., & Cannataro, M. (2015). Cloud Computing in Healthcare and Biomedicine. Scalable Computing: Practice And Experience, 16(1). doi: 10.12694/scpe.v16i1.1057
Casola, V., Castiglione, A., Choo, K., & Esposito, C. (2016). Healthcare-Related Data in the Cloud: Challenges and Opportunities. IEEE Cloud Computing, 3(6), 10-14. doi: 10.1109/mcc.2016.139
Esposito, C., De Santis, A., Tortora, G., Chang, H., & Choo, K. (2018). Blockchain: A Panacea for Healthcare Cloud-Based Data Security and Privacy?. IEEE Cloud Computing, 5(1), 31-37. doi: 10.1109/mcc.2018.011791712
Karim, N., Albuolayan, A., Saba, T., & Rehman, A. (2016). The practice of secure software development in SDLC: an investigation through existing model and a case study. Security And Communication Networks, 9(18), 5333-5345. doi: 10.1002/sec.1700
Kim, t. (2016). Software development project management using Agile methodology. The Journal Of The Institute Of Internet Broadcasting And Communication, 16(1), 155-162. doi: 10.7236/jiibc.2016.16.1.155
Masrom, M., & Rahimli, A. (2015). Cloud Computing Adoption in the Healthcare Sector: A SWOT Analysis. Asian Social Science, 11(10). doi: 10.5539/ass.v11n10p12
Mostashari, F. (2014). Health information technology and Healthcare. Healthcare, 2(1), 1-2. doi: 10.1016/j.hjdsi.2013.12.008
Shankarmani, R., Pawar, R., S. Mantha, S., & Babu, V. (2012). Agile Methodology Adoption: Benefits and Constraints. International Journal Of Computer Applications, 58(15), 31-37. doi: 10.5120/9361-3698
Sharma, P., & Singh, D. (2015). Comparative Study of Various SDLC Models on Different Parameters. International Journal Of Engineering Research, 4(4), 188-191. doi: 10.17950/ijer/v4s4/405
Wang, D. (2015). What will cloud computing bring to healthcare? – Overview of cloud applications in healthcare. International Journal Of Healthcare Technology And Management, 15(1), 49. doi: 10.1504/ijhtm.2015.070540
Wang, L., & Alexander, C. (2013). Medical Applications and Healthcare Based on Cloud Computing. International Journal Of Cloud Computing And Services Science (IJ-CLOSER), 2(4). doi: 10.11591/closer.v2i4.3452
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