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The modern healthcare sector is characterized by the wide use of innovations with the primary aim to improve the quality of provided care, minimize efforts required to work with patients under different conditions, and ensure that all existing requirements will be met. Moreover, the implementation of technologies provides multiple benefits such as the reduced time devoted to processing data, convenient access to databases containing relevant information, enhanced data exchange between hospitals.
Additionally, the high level of patients’ demands to the quality of provided services preconditions the extensive utilization of innovative approaches to satisfy their expectations. The use of Electronic Information Management (EIM) systems is one of these methods. It guarantees more efficient functioning of the sphere; however, there are also some data quality issues associated with their introduction that should be discussed.
EIM system can be described as a completely automated software or application created to assist health workers with the creation, management, collection, use, storage, and disposal of all information and records critical to the treatment process and patients’ recovery. Nowadays, the given systems are implemented in the majority of healthcare facilities in the USA to create an environment characterized by the improved use of data and its exchange (Craswell, Moxham, & Broadbent, 2014).
Additionally, EIM systems are used in numerous processes not directly related to the provision of care such as approvals, workflows, staff management. In other words, they become an integral part of the modern healthcare sector and are devoted much attention and efforts to create the basis for EIM’s further development and growth. However, there are still some problems regarding the use of these systems that should be solved.
One of the most important quality issues associated with the use of EIM is the accuracy of data. One of the main causes for the implementation of these systems is the reduction in the number of mistakes or false information that can result in unacceptable consequences. However, researchers warn that there are still factors impacting data stored and provided with the help of EIM, for instance, the lack of universal standards regarding the format of notes and their content might result in misunderstandings and disregard of some critical facts (Chen, Hailey, Wang, & Yu, 2014). At the same time, limited system functionality and the inconsistent use of terminology can also be considered factors affecting the accuracy of data presented by EIM systems (Chen et al., 2014). In such a way, it becomes an important concern that should be given attention.
Another significant aspect is the data collection. The fact is that the efficient functioning of electronic systems depends on the relevance of information it processes (Craswell et al., 2014). For this reason, an effective and convenient procedure of information gathering is the key to positive outcomes and obtaining the benefits (Craswell et al., 2014). Unfortunately, there are still errors that occur at this very stage. They impact the credibility of information provided to specialists and treatment outcomes.
The lack of automated software to monitor patients’ states and record their showings is one of the central reasons for the emergence of such unacceptable results (Craswell et al., 2014). That is why specialists warn about the necessity to improve existing data collection procedures to ensure that all patients will be provided with the required care.
Finally, another quality concern is associated with data storage and exchange. The fact is that the efficiency of the modern healthcare sector to a greater degree depends on the ability to analyze every case using background information and to look for possible solutions in various sources (Zozus et al., 2015). Complicated cases that imply patients’ multiple consultations with specialists in different health units should be considered by a cross-functional team which possesses relevant and complete information about the history of treatment (Zozus et al., 2015). It can be achieved only if the EIM system provides access to facts demanded for the successful treatment.
Under these conditions, data storing and exchange acquire the top priority. Unfortunately, there are still numerous problems in this sector because of the absence of the universal healthcare network and database containing information about all cases.
Nevertheless, having reviewed the central concerns related to EIM and their discussion in specific literature the following list can be created:
- accuracy of data
- data collection
- data storage and exchange
These three issues acquire the top priority regarding the modern healthcare system and the attempts to align the efficient functioning of EIM systems.
Consideration of these quality concerns is crucial for the future of EIM and its increased efficiency. Thus, the term includes three domains that are content management (CM), records management (RM), and knowledge management (KM). All they are critical for the healthcare sector (Chen et al., 2014). First of all, CM is directly connected with the problem of the accuracy of data provided by systems utilized by health facilities. The improved content management will help to avoid mistakes in data and reduce the number of negative outcomes preconditioned by the false or incomplete information (Zozus et al., 2015). Additionally, the given domain becomes critical for EIM characterized by numerous problems with data accuracy and relevance.
Another domain that can be considered a remedy for the problems mentioned above is records management (RM). It encompasses all activities related to the collection of patients’ records and information about their states. In other words, there is a direct correlation between the problem of data gathering and RM domain. The modern approach presupposes the use of electronic tools to avoid a pernicious impact of the human factor and minimize the probability of a mistake (Craswell et al., 2014).
For this reason, the creation of a hospital’s network with an improved data exchange and recording is one of the possible solutions to the problem as it will eliminate all external factors affecting the credibility of data and will help to attain the desired result.
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Finally, knowledge management (KM) domain should also be addressed. As it was mentioned above, the efficient data exchange and provision of information about all cases similar to discussed ones. It will help to improve outcomes and provide specialists with the list of details they should consider to attain improved results. In such a way, the use of effective KM tools like specific libraries or unified databases is critical for this domain and the EIM in terms of health facilities’ functioning and their further rise.
Altogether, today we can observe numerous attempts to implement innovative technologies into the functioning of healthcare facilities across the state. It has a positive impact on the sphere and contributes to the improved results. However, there are still several problems with the quality of data provided by EIM systems to specialists. These are the lack of accuracy, complicated information collection, and difficulties with storage and exchange. For this reason, much attention should be devoted to content management (CM), records management (RM), and knowledge management domains as one of the possible options to improve outcomes.
Chen, H., Hailey, D., Wang, N., & Yu, P. (2014). A review of data quality assessment methods for public health information systems. International Journal of Environmental Research and Public Health, 11(5), 5170-5207. Web.
Craswell, A., Moxham, L., & Broadbent, M. (2014). Does use of computer technology for perinatal data collection influence data quality? Health Informatics Journal, 22(2), 293-303. Web.
Zozus, M., Pieper, C., Johnson, C., Johnson, T., Franklin, A., Smith, J., & Zhang, J. (2015). Factors affecting accuracy of data abstracted from medical records. PLoS One, 10(10). Web.