In healthcare, information technology (IT) is used with increasing frequency. The primary reason for this development is that IT has multiple positive features when compared to the more traditional tools of nursing and healthcare (Zhang, Qiu, Tsai, Hassan, & Alamri,2017,p88). Additionally, nursing depends on data to a noticeable extent, which highlights the potential benefits of appropriate IT management for the field (Sensmeier,2015,p22). However, IT remains a relatively new addition to the context, which is why it is still not optimized in many respects (Alkraiji, Jackson, & Murray,2011,pp345-346). As a result, even though data standards are of great importance due to their ability to make IT use more efficient, their development and adoption in healthcare and nursing are not finished yet.
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The present paper will focus on recent research on the topic and offer some conclusions about the need for data standards in healthcare and nursing. Also, the challenges to their development will be considered to explain their current absence and make recommendations for the future. It can be suggested that data standards promise important benefits for health information, but to ensure their development, more combined governmental, academic, and practitioner efforts should be directed at resolving the issue.
The Importance of Data Standards in Nursing
Data standards are significant for a variety of reasons in any field, but in healthcare and nursing, they may be particularly important. The first evidence of professionals attempting to develop healthcare standards dates back to the London Bills of Mortality, which were established in 1528 and could be described as an early classification of diseases (Richesson & Chute,2015,p492). Since then, more advanced classifications were offered, and in the previous century, specific nursing terminologies were developed to distinguish and standardize nursing knowledge as well (Westra et al.,2015a,p601). No universal nomenclature in the field exists for the time being, but the need for it becomes increasingly apparent. As more modern tools are employed in healthcare, they also still require standardization (Zhang et al.,2017,p88), and this fact is especially true for health IT.
IT is extremely important for modern healthcare; it is widely employed and has multiple positive outcomes (Zhang et al.,2017,p88), which is why nursing is interested in using it to its full potential. Data standards are among the tools that can improve the use of IT in healthcare. Indeed, the primary aim of data standards consists of ensuring the consistency of data in one system and the improved exchange of data between different systems (Boris et al.,2017,p3). It is immediately apparent why data standards can be beneficial, but some additional details can further explicate their value.
An important feature that data standards enable is interoperability: it refers to the ability of different systems to understand each other’s texts, which depends on the syntax (structure) and semantics (meaning) that they use (Alkraiji et al.,2011,p348; Westra et al.,2015b,p307). Semantics refers to the terminology and codes employed in healthcare, but both the syntax and semantics of texts need to be standardized for two (or more) systems to be able to communicate effectively. Alkraiji et al. (2011) also point out that there can be other related standards, which might specify, for instance, the appropriate response to different messages and related decision-making (p348). However, the point of data standardization is generally concerned with the specific rules that govern the structure and content of data for its improved communication.
Thus, it is apparent how standardization is beneficial for nursing education, practice, and research: it directly facilitates information exchange in all its forms. As a result, the standardized data that is communicated within or between systems is more likely to be accurate and consistent and have high quality, which should facilitate the process of working with it (Alkraiji et al.,2011,p353; Sensmeier,2015,p25). This outcome can be especially significant for clinical research, which would benefit from standardization greatly (Kush & Goldman,2014,p2163; Richesson & Nadkarni,2011,p341; Westra et al.,2015a,p605). In addition to that, knowledge exchange is significant for quality standards and its management: the collection of standardized data on performance is simpler and more convenient when different systems use the same language (Boris et al.,2017,p3). Overall, for improved data quality and use, data standards are essential.
Other outcomes that are not directly related to the quality of data can also be mentioned. Additional benefits of data standards include the facilitation of staff training, system replacements, and their customization when acquired from a vendor; also, from vendors’ perspective, standardized systems are easier to support (Alkraiji et al.,2011,p353). Furthermore, standards can promote the use of IT, making it particularly meaningful and simple (Boris et al.,2017,p3). In summary, data standards are supposed to have multiple positive outcomes for healthcare.
Standards are also very important for health data because their lack tends to have negative consequences. For instance, the coordination of data is crucial for nursing because the mistakes in the field may endanger the quality of care and the health of patients (Alkraiji et al.,2011,p353). Additionally, the lack of standards can lead to inaccurate reporting of data to various supervising bodies, which can have negative effects on official statistics, research, quality management, and quality improvement (McCormick et al.,2015,par15). Furthermore, researchers report that the lack of data standardization in clinical research makes the meta-analysis of data extremely difficult and even impossible in certain cases (Kush & Goldman,2014,p2163). Finally, there are concerns about the quality and usefulness of unstandardized data, and the lack of its consistent integration into datasets can result in new attempts at gathering the same information which can lead to additional expenses (Mathys & Boulos,2011,p2). Overall, the absence of data standards can become a hindrance to high-quality care and health research, which highlights the need for them in the field of nursing.
The Current State of Data Standardisation in Nursing
Despite the crucial importance of data standards for healthcare, modern research indicates that there is no actual standardization in nursing nowadays. The problem seems to be present in different countries; for instance, Alkraiji et al. (2011) report the issue for Saudi Arabia (p346), and McCormick et al. (2015) describe it for the US (par15). In particular, McCormick et al. (2015) state that in the US, even the documentation that is maintained within one hospital can have inconsistencies; consequently, comparability and consistency are a major challenge for larger systems as well (par15). The outcomes of this issue are similar to the ones mentioned above: there are difficulties in transferring information from one system to another, which can lead to mistakes; also, the quality of the data can be questionable, and its use for quality management is problematic. Additionally, modern clinical research is held back by the difficulty of working with the results that lack standardization and, consequently, may be incompatible and incomparable (Kush & Goldman,2014,p2163). Overall, the absence of data standardization in nursing is a problem, which is why the solutions to it should be considered.
Challenges and Solutions
The obvious solution to the issue of the lack of standardization is the introduction of general standards, and this approach has been implemented in a variety of efforts. For instance, the Logical Observation Identifiers Names and Codes (LOINC) is a set of standards that have been developed to facilitate the information exchange between computer systems in healthcare (Richesson & Chute,2015,p492). It is a promising method that can be used for a wide variety of purposes, especially in healthcare research (Vreeman, Chiaravalloti, Hook, & McDonald,2012,p667). There have been attempts at translating LOINC to several languages and using it in different countries as shown by Vreeman et al. (2012,p667). However, LOINC has not been adopted internationally yet, which is why it cannot be regarded as a fully successful set of standards for the time being.
More specific standards have been proposed as well. For instance, in the US, a set of data standards was recommended for pediatric cardiology (Boris et al.,2017,p3). Similarly, there are standards meant especially for clinical research, even though there are no unified and universally accepted ones (Richesson & Nadkarni,2011,p341). For example, the US Department of Health and Human Services standards for race, ethnicity, and language has been developed to facilitate health research that focuses on ethnic and racial groups, as well as care disparities (Dorsey & Graham,2011,p 2378; Holland & Palaniappan, 2012,p400). The primary advantage of such sets is that they acknowledge the need for standards in particular areas of healthcare.
Additionally, it should be noted that the significance of nursing knowledge and its perspective on data standards are also taken into account. For instance, the Nursing Management Minimum Data Set is an attempt of the United States to provide the data standards and terminology that would be suited individually for nursing (McCormick et al.,2015,par20). Thus, certain steps are being made towards the creation of nursing data standards, even though there is no universal set for the time being.
All the above-presented examples indicate that the need for data standards in nursing and healthcare is acknowledged and appropriately addressed, but they also show that no fully successful, universal set has been developed yet. The slow nature of this process can be explained by several factors. Firstly, the health IT industry is not very mature for the time being (Alkraiji et al.,2011,p356), which is why there is a lack of research and experience that can be used as a basis for relevant data standards. Secondly, the creation of data standards is complicated and requires the consideration of multiple elements and perspectives. At the very least, it needs the determination of specific terminology and syntax and the development of procedures for data collection and recording (Dorsey & Graham,2011,p 2378; Kush & Goldman,2014,p2164). Additionally, the process requires cooperation and communication between nursing and healthcare specialists and organizations around the world (Richesson & Chute,2015,p493). Their combined efforts should be employed to analyze the existing standards for their viability and make decisions about the most useful ones (González-Ferrer & Peleg,2015,pp134-135). Thus, while this solution is obvious, it is difficult to arrange and requires the review and inclusion of multiple perspectives, which explains the slow progress towards the development of nursing standards.
However, it should also be noted that the creation of standards is not the only difficulty in this field. Their adoption maybe even more problematic as evidenced by the studies which consider the attempts of individual hospitals or countries to focus on the use of specific standards. Such case studies allow making some conclusions about the potential difficulties that need to be taken into account in the future when attempting to address the need for data standards in nursing.
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The most prominent issues include the challenge of integrating the standards, the rigidity of existing systems, bureaucracy, and the lack of support (Alkraiji et al.,2011,pp353-354). The absence of infrastructure and resistance to change are also significant barriers (Alkraiji, Jackson & Murray,2016,p660). A very important problem is funding; standardization is unlikely to be associated with financial benefits, which is why healthcare organizations tend to find other IT-related investment areas (Alkraiji et al.,2011,p346; Rocca-Serra et al.,2015,pp13-14). Similarly, even though it is crucial for the well-being of patients, nursing data is not particularly profitable, which is why it is often overlooked (Westra et al.,2015b,p306). Finally, there is the issue of the lack of consistent policies on the topic (Alkraiji, Jackson, & Murray,2013,pp7-8). In summary, the need for introducing standards in practice promises multiple challenges for nurses.
Still, the existing research allows making some recommendations on the topic. An appropriate solution to the adoption issues is the development of governmental policies that would promote the implementation of standards (Alkraiji et al.,2011,p347; McCormick et al.,2015,par33). The governmental intervention would ensure the presence of both external pressure and support, which are required for successful standards adoption and can deal with the problems of system rigidity, bureaucracy, and resistance to change (Alkraiji et al.,2011,pp353-354). Said policies should also be aimed at resolving the funding concerns; it appears that the government should attempt to provide the necessary funds in this regard (Alkraiji et al.,2011,p346; Delaney & Weaver,2017,p616). In defense of this kind of spending, it can be stated that standardization of data in healthcare is likely to have immense long-lasting outcomes for research and practice, even though no immediate gains can be offered (Rocca-Serra et al.,2015,pp13-14). Therefore, in the long run, the investment is likely to have positive outcomes.
Other important strategies are also noteworthy. Researchers suggest that the study of data standards and their implementation should be carried out to provide advice on the topic and assist in the development of best practices (Alkraiji et al.,2011,p347). Additionally, it is necessary to ensure the engagement of providers in the adoption process (Alkraiji et al.,2011,pp354). This approach will help to reduce resistance to change and involve important perspectives of the people who use standards, which should help to refine the latter.
Based on the final recommendation, it can be suggested that the primary solution to the challenge of standardization consists of cooperation (Delaney & Weaver,2017,p616; Richesson & Chute,2015,p493). Consequently, the role of nursing leaders should be noted. They can contribute by investigating the best practices in the field, supporting and engaging professionals, and promoting communication between different groups of stakeholders (Westra et al.,2015b,p307). Nursing informatics experts and other nurses are also capable of promoting progress in the field of nursing data standardization. In particular, their knowledge and experiences should be taken into account, and their advocacy can foster the development and implementation of standards (Sensmeier,2015,p26). In summary, to both develop and adopt nursing data standards, cooperation is required. However, given the need for such standards, the necessary efforts are fully justified.
Nowadays, healthcare and nursing depend on data to a noticeable extent, which increases the significance of health IT. For the improved use of IT, data standards are required. In the field of nursing and healthcare, data standards promise multiple positive outcomes, including the enhanced consistency of data, facilitated knowledge exchange, and better care quality management. Some additional benefits, including the simpler integration and use of IT, are also noteworthy. Finally, nursing research necessitates improved data standardization. The benefits of data standards explain the need for them in nursing; additionally, their absence is associated with problems, which is why the healthcare community attempts to develop the standards that can be employed in the field. However, no unified data standards for nursing or healthcare exist for the time being.
The reasons for the issue can be connected to the fact that the process of developing the mentioned standards is very complex. Additionally, the adoption of standards in practice is also associated with multiple problems and challenges. In order the resolve them, it is necessary to ensure the cooperation of nurses, nursing leaders, nurse informaticists, as well as other stakeholders, including governments. Only united efforts can lead to the development of appropriate data standards, which are expected to have extremely beneficial outcomes for nursing and healthcare in general.
Alkraiji, A., Jackson, T., & Murray, I. (2011). Health data standards and adoption process. Campus-Wide Information Systems, 28(5), 345-359. Web.
Alkraiji, A., Jackson, T., & Murray, I. (2013). Barriers to the widespread adoption of health data standards: An exploratory qualitative study in tertiary healthcare organizations in Saudi Arabia. Journal of Medical Systems, 37(2), 1-13. Web.
Alkraiji, A., Jackson, T., & Murray, I. (2016). Factors impacting the adoption decision of health data standards in tertiary healthcare organisations in Saudi Arabia. Journal of Enterprise Information Management, 29(5), 650-676. Web.
Boris, J. R., Béland, M. J., Bergensen, L. J., Colan, S. D., Dangel, J., Daniels, C. J.,… Gray, D. T. (2017). 2017 AHA/ACC key data elements and definitions for ambulatory electronic health records in pediatric and congenital cardiology. Journal of the American College of Cardiology, 70(8), 1029-1095. Web.
Delaney, C., & Weaver, C. (2017). Nursing Knowledge and the 2017 Big Data Science Summit. CIN: Computers, Informatics, Nursing, 35(12), 615-616. Web.
Dorsey, R., & Graham, G. (2011). New HHS data standards for race, ethnicity, sex, primary language, and disability status. JAMA, 306(21), 2378-2379. Web.
González-Ferrer, A., & Peleg, M. (2015). Understanding requirements of clinical data standards for developing interoperable knowledge-based DSS: A case study. Computer Standards & Interfaces, 42, 125-136. Web.
Holland, A., & Palaniappan, L. (2012). Problems with the collection and interpretation of Asian-American health data: Omission, aggregation, and extrapolation. Annals of Epidemiology, 22(6), 397-405. Web.
Kush, R., & Goldman, M. (2014). Fostering responsible data sharing through standards. New England Journal of Medicine, 370(23), 2163-2165. Web.
Mathys, T., & Boulos, M. (2011). Geospatial resources for supporting data standards, guidance and best practice in health informatics. BMC Research Notes, 4(1), 1-18. Web.
McCormick, K., Sensmeier, J., Dykes, P., Grace, E., Matney, S., Schwartz, K., & Weston, M. (2015). Exemplars for advancing standardized terminology in nursing to achieve sharable, comparable quality data based upon evidence. Online Journal of Nursing Informatics (OJNI), 19(2). Web.
Richesson, R., & Chute, C. (2015). Health information technology data standards get down to business: Maturation within domains and the emergence of interoperability. Journal of the American Medical Informatics Association, 22, 492–494. Web.
Richesson, R., & Nadkarni, P. (2011). Data standards for clinical research data collection forms: Current status and challenges. Journal of the American Medical Informatics Association, 18(3), 341-346. Web.
Rocca-Serra, P., Salek, R. M., Arita, M., Correa, E., Dayalan, S., Gonzalez-Beltran, A.,… Koulman, A. (2016). Data standards can boost metabolomics research, and if there is a will, there is a way. Metabolomics, 12(1). Web.
Sensmeier, J. (2015). Big data and the future of nursing knowledge. Nursing Management (Springhouse), 46(4), 22-27. Web.
Vreeman, D., Chiaravalloti, M., Hook, J., & McDonald, C. (2012). Enabling international adoption of LOINC through translation. Journal of Biomedical Informatics, 45(4), 667-673. Web.
Westra, B. L., Latimer, G. E., Matney, S. A., Park, J. I., Sensmeier, J., Simpson, R. L.,… Delaney, C. W. (2015a). A national action plan for sharable and comparable nursing data to support practice and translational research for transforming health care. Journal of the American Medical Informatics Association, 22, 600–607. Web.
Westra, B., Clancy, T., Sensmeier, J., Warren, J., Weaver, C., & Delaney, C. (2015b). Nursing Knowledge. Nursing Administration Quarterly, 39(4), 304-310. Web.
Zhang, Y., Qiu, M., Tsai, C., Hassan, M., & Alamri, A. (2017). Health-CPS: Healthcare cyber-physical system assisted by cloud and big data. IEEE Systems Journal, 11(1), 88-95. Web.