The idea of the systems theory was introduced in the 1940s. It emphasizes that the environment influences actual systems they interact with and continuously acquire other properties leading to evolution. The application of the systems theory is diverse, i.e., healthcare management, ecology, computing, and engineering (Bhavnani et al., 2017). Through well-developed sophisticated system analysis tools, system theory facilitates decision making by controlling and optimizing a specific system using different resources and constraints. Systems theory is closely related to system dynamics, which simulates how changes in variables affect a particular network. Therefore, this study will investigate the application of systems theory in the healthcare system, using the GIGO concept.
GIGO, also referred to as garbage in garbage out, is an example of the system theory concept. It converts well-structured input data to useful results through the use of scrupulous quality control (Williams, 2010). The garbage in garbage out concept is extensively applied in healthcare as a means of correcting flawed processes resulting from the use of lousy data through machine learning technologies. A critical step in the use of GIGO is the labeling and sorting of numerous types of information through controlled 'ontologies'/vocabularies that employ comprehensive representations of biological facts. A lot is still to be adopted in the application of controlled ontologies to make effective use of the technologies used in the incorporation of large quantities of information into the healthcare practice.
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It is without a doubt that GIGO is the crucial ingredient to reliable information in the healthcare sector. William (2010) argues that it provides medical interoperability unrivaled by any other system, its chief characteristic being the ability to exchange real-time data between approved third-party systems. It also facilitates brokering between patients and approved healthcare and insurance providers and enable the provision of secure medications. The GIGO concept proved to be very useful in the creation of a healthcare system that allows collaboration of information between approved institutions and restriction of others that lack sufficient access approval. Thus, GIGO continues to play a crucial role in the creation of trusted healthcare systems.
A brief study of the lifecycle of data in healthcare systems reveals how multiple errors flow in and out of the system leading to highly unsymmetrical results. It is an illustration of the GIGO concept at its best. While the right type of data might be collected, i.e., genetic, laboratory, or diagnostic information, the manner of gathering that information might lead to severe shortcomings. Ideally, three main factors are known to influence health care data. Sukumar (2015) identifies these three factors as 1) Inaccuracies and errors in the data; 2) the pedigree of information; and 3) how the purpose of collecting data influences analytic processing. He proceeds to argue that it is possible to measure the appropriateness and potential limitations of analysis just by examining where it came from.
Sukumar classifies sources of healthcare data using the following five main categories: entity resolution, data staging errors, diversity and evolving standards, data entry errors, and relevance and context. In a bid to address the issue of inaccurate data sources, Sukumar (2015) proposed that several initiatives be adopted. The initiatives include the use of enterprise-wide management, modernization of legacy systems, and the application of good data governance. Therefore, by following the recommendations provided by Sukumar, it is possible to produce high-quality output through the use of quality data derived from reliable sources.
The use of technology in the health care industry has led to mixed reactions, particularly among patients. Some individuals argue that integrating technology into clinical practice produces negative effects such as issues with the protection of data, cross-compatibility problems, and information overload. However, Grace et al. (2013) was able to illustrate that about 55% of users are confident and support the integration of technology with healthcare provision. Besides, 76% of these users can comfortably use and navigate the current systems in use. Grace et al. (2013) refers to the systems in the application as clinical decision support systems (CDSS). One significant advantage of CDSS is increased clinician adherence to the standard guidelines; thus, eliminating the complexities associated with the system (Alotaibi & Federico, 2017). Besides, instead of completely automating decision-making, CDSS adds value by informing. Therefore, it is without a doubt that the integration of technology complements healthcare rather than hinder its provision.
Advancements in technology have prompted the introduction of new procedures/systems of managing patient information. The systems have grown tremendously and played a significant role in healthcare, even rivaling the traditional human approach to healthcare. Differences between the modern and traditional approach to healthcare have pitted the human-centered process against the human-technology interface. Numerous authors have tried to measure the difference in performance between the two systems. Persson (2015) lists the benefits and disadvantages of a human-technology interface. For instance, he argued the use of the human-technology interface enables the creation of effective simulation tools that improve the training of clinicians. However, he proceeds to say that it fails to accurately capture the needs of end-users, which makes virtual reality tools unacceptable training grounds. One can effectively claim that the human-technology interface is only complementary to the traditional human-centered form of instruction and training.
Numerous developments and policy frameworks are being implemented in the current healthcare system. The policies are aimed at improving access to patient data. Some of the organizations that are making an active contribution in the implementation of policy frameworks are the Office of the National Coordinator for Health (ONC) and the Centers for Medicare & Medicaid Services (CMS). One of the initiatives is aimed at making healthcare information tools (HITs) easily available to users (Escobar-Rodríguez et al., 2012). An excellent example of a HIT tool is the use of mobile applications and devices. The use of mobile applications has considerably improved the self-awareness of patients by facilitating self-monitoring. Besides, it allows care providers to collect information on the daily activities of their patients easily. Such information has resulted in a new type of health data that is referred to as Observations of Daily Living (ODLs). The use of ODLs is not only restricted to use by medical personnel, but it also informs patients about their health status for them to take the necessary precautionary actions (Koliner & Brennan, 2013). An application referred to as the Project HealthDesign application allows the tracking of pain and energy levels of young individuals diagnosed with Crohn disease. Hence, HITs play a critical role by improving an individual's quality of life and improving the manner with which healthcare providers manage their patients.
Perhaps, the most significant technological development in healthcare is the use of electronic health records (EHRs). Currently, numerous healthcare providers have already adopted the use of electronic health records. The EHRs effectively capture patient data and enable easy sharing and tracking of information between different departments (Koliner & Brennan, 2013). Both the patients and healthcare institutions benefit from EHR tools. Currently, researchers investigating how EHR tools can facilitate 2-way dialogue and improve patient engagement, primarily through Observations of Daily Living (ODLs). Healthcare practitioners are trying hard to improve the ability of EHR tools to prioritize high-risk and complicated healthcare conditions. Therefore, EHR tools play a critical role in the incorporation of patient data.
In conclusion, effective Healthcare systems are characterized by high-quality input patient data. The concept of garbage in garbage out (GIGO) illustrates that a machine/computer can't produce correct answers/output when it is fed with the wrong input data. The concept is extensively applied in the healthcare setting, particularly in the management of patients' data. Application of the Systems Theory into healthcare management using the GIGO concept shows that it is essential to collect/gather the correct information. Various steps/procedures are followed when designing effective health systems. One of the steps involves identifying the major factors or sources of errors that compromise the quality of patient data. It is imperative to recognize new systems/procedures introduced by technology to healthcare management. They include healthcare information technologies (HITs) and Observations of Daily Living (ODLs). While it is impossible to state which is the best between the systems versus the human-centered approach, it is apparent that the two methods complement one another.
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