Among the Resources in this module is the Rutherford (2008) article Standardized Nursing Language: What Does It Mean for Nursing Practice? In this article, the author recounts a visit to a local hospital to view the recent implementation of a new coding system.
During the visit, one of the nurses commented to her, “We document our care using standardized nursing languages but we don’t fully understand why we do” (Rutherford, 2008, para. 1).
How would you respond to a comment such as this one?
In a 2- to 3-page paper, address the following:
5051 wk 5 references for assignment
Inclusion of Recognized Terminologies Supporting Nursing Practice within Electronic Health Records and Other Health Information Technology Solutions – ANA Position Statement. (n.d.). ANA. https://www.nursingworld.org/practice-policy/nursing-excellence/official-position-statements/id/Inclusion-of-Recognized-Terminologies-Supporting-Nursing-Practice-within-Electronic-Health-Records/
Macieira, T. G. R., Smith, M. B., Davis, N., Yao, Y., Wilkie, D. J., Lopez, K. D., & Keenan, G. (2018). Evidence of Progress in Making Nursing Practice Visible Using Standardized Nursing Data: a Systematic Review. AMIA … Annual Symposium Proceedings. AMIA Symposium, 2017, 1205–1214. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5977718/
Standard Nursing terminologies
Standard Nursing Terminologies: A Landscape Analysis MBL Technologies, Clinovations, Contract # GS35F0475X Task Order # HHSP2332015004726. (2017). https://www.healthit.gov/sites/default/files/snt_final_05302017.pdf
Standardized nursing language
Rutherford, M. A. (2008). Standardized Nursing Language: What Does It Mean for Nursing Practice? Online Journal of Issues in Nursing, 13(1), 1–12. https://doi-org.ezp.waldenulibrary.org/10.3912/OJIN.Vol13No01PPT05
ATTACHED THE ARTICLE SEPERATELY BUT THIS IS THE REFERENCE
Big data means big potential
Welcome | HealthLeaders Media. (n.d.). Www.Healthleadersmedia.Com. https://www.healthleadersmedia.com/welcome-ad?toURL=/nursing/big-data-means-big-potential-challenges-nurse-execs
Hitchhikers guide to nursing informatics
Topaz, M. (2013). The Hitchhiker’s Guide to nursing informatics theory: using the Data-Knowledge-Information-Wisdom framework to guide informatics research. Online Journal of Nursing Informatics, 17(3), 1–5.
ATTACHED THE ARTICLE SEPERATELY BUT THIS IS THE REFERENCE
Big data anylitics
Wang, Y. Kung, L., & Byrd, T. A. (2018). Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126(1), 3–13. doi:10.1016/j.techfore.2015.12.019
ATTACHED THE ARTICLE SEPERATELY BUT THIS IS THE REFERENCE
You tube video
Big Data in Health Informatics. (2014). [YouTube Video]. In YouTube. https://www.youtube.com/watch?v=4W6zGmH_pOw
In a 2- to 3-page paper, address the following: · Explain how you would inform this nurse (and others) of the importance of standardized nursing terminologies. · Describe the benefits and challenges of implementing standardized nursing terminologies in nursing practice. Be specific and provide examples. · Be sure to support your paper with peer-reviewed research on standardized nursing terminologies that you consulted from the Walden Library.
Points Range: 77 (77%) – 85 (85%)
The responses accurately and thoroughly explain in detail how to inform the nurse in the scenario, as well as others, on the importance of standardized nursing terminologies. The responses accurately and thoroughly describe in detail the benefits and challenges of implementing standardized nursing terminologies in nursing practice, with sufficient supporting evidence and detailed examples. Responses are fully supported as evidenced by 3 or more accurate, peer-reviewed research sources on standardized nursing terminologies.
Written Expression and Formatting – Paragraph Development and Organization: Paragraphs make clear points that support well developed ideas, flow logically, and demonstrate continuity of ideas. Sentences are carefully focused–neither long and rambling nor short and lacking substance.
Points Range: 5 (5%) – 5 (5%)
Paragraphs and sentences follow writing standards for flow, continuity, and clarity.
Written Expression and Formatting – English writing standards: Correct grammar, mechanics, and proper punctuation
Points Range: 5 (5%) – 5 (5%)
Uses correct grammar, spelling, and punctuation with no errors.
Written Expression and Formatting – The paper follows correct APA format for title page, headings, font, spacing, margins, indentations, page numbers, running head, parenthetical/in-text citations, and reference list.
Points Range: 5 (5%) – 5 (5%)
Uses correct APA format with no errors.
o jni.o rg http://o jni.o rg/is s ues /?p=2852
The Hitchhiker ’s Guide to nursing informatics theory: using the Data-Knowledge-Information-Wisdom framework to guide informatics research
by Maxim To paz, PhD Student, RN, MA
Invited Guest Edito r
To paz, M. (2013). Invited Edito rial: T he Hitchhiker ’s Guide to nursing inf o rmatics theo ry: using the Data- Kno wledge- Inf o rmatio n- Wisdo m f ramewo rk to guide inf o rmatics research. Online Journal of Nursing Informatics (OJNI), 17 (3). Available at http://o jni.o rg/issues/?p=2852
Editorial T heo ry is o ne o f the f undamental blo cks o f each scientif ic discipline. It is impo ssible to imagine bio lo gy witho ut the theo ry o f Evo lutio n o r physics witho ut the theo ry o f Relativity. Nursing inf o rmatics, a relatively new discipline, is also thirsty f o r its o wn theo ry. Ho wever, it is challenging to f ind literature that pro vides clear theo retical guidance f o r nurse inf o maticians. In this co mmentary, I will brief ly o verview a theo retical f ramewo rk that has high po tential to serve as o ne o f the f o undatio ns f o r nursing inf o rmatics. I will also argue that to apply the described f ramewo rk, it needs to be merged with a nursing specif ic theo ry. I will pro vide an example o f my dissertatio n wo rk to illustrate the necessary merge. T his co mmentary might be used as a theo retical blueprint – o r the Hitchhiker ’s Guide- to guide nursing inf o rmatics research and practice.
The Data-Inf ormation-Knowledge-Wisdom f ramework
Nursing inf o rmatics was created by the merge o f three well established scientif ic f ields: Inf o rmatio n science, Co mputer science and Nursing science. One o f the mo st co mpelling def initio ns o f the discipline states: “Nursing inf o rmatics science and practice integrates nursing, its inf o rmatio n and kno wledge and their management with inf o rmatio n and co mmunicatio n techno lo gies to pro mo te the health o f peo ple, f amilies and co mmunities wo rldwide” (Internatio nal Medical Inf o rmatics Asso ciatio n – Nursing Wo rking Gro up, 2010). Unf o rtunately, very f ew attempts were made to generate a bro ad theo retical f ramewo rk f o r nursing inf o rmatics. T here are several challenges to generate such f ramewo rk. First, the interdisciplinary nature o f nursing inf o rmatics demands the use o f bro ad eno ugh theo retical f ramewo rk to enco mpass all the disciplines. Also , the required theo retical f ramewo rk sho uld co nsider the practice/applicatio n do main; the implementatio n o f nursing inf o rmatics in real healthcare settings. Recently, it was suggested that the Data- Inf o rmatio n- Kno wledge- Wisdo m (DIKW) f ramewo rk has a high po tential to address these challenges and this f ramewo rk was ado pted by the American Nurses Asso ciatio n (American Nurses Asso ciatio n, 2008; Matney, Brewster, Sward, Clo yes, & Staggers, 2011).
Histo rically, the develo pment o f the DIKW f ramewo rk was urged by a search f o r a new theo retical mo del explaining the emerging f ield o f Nursing Inf o rmatics in 1980- 90s. In their seminal wo rk, Graves and Co rco ran (1989) def ined that data, information, and knowledge are f undamental co ncepts f o r the discipline. T heir f ramewo rk was widely accepted by the internatio nal nursing co mmunity (Matney et al., 2011; McGo nigle & Mastrian, 2011). In 2008, the American Nurses Asso ciatio n revised the Sco pe and Standards f o r nursing inf o rmatics to include an additio nal co ncept, wisdo m (American Nurses Asso ciatio n, 2008). Recently, Matney and co lleagues (2011) have expanded o n the co mpo nents o f the DIKW f ramewo rk:
Dat a: are the smallest co mpo nents o f the DIKW f ramewo rk. T hey are co mmo nly presented as discrete f acts; pro duct o f o bservatio n with little interpretatio n (Matney et al., 2011). T hese are the discrete f acto rs describing the patient o r his/her enviro nment. Examples include patient’s medical diagno sis (e.g. Internatio nal Statistical Classif icatio n o f Diseases (ICD- 9) diagno sis # 428.0: Co ngestive heart f ailure, unspecif ied) o r living status (e.g. living alo ne; living with f amily; living in a retirement co mmunity; etc.). A single piece o f data, datum, o f ten has little meaning in iso latio n.
Inf ormat ion: might be tho ught o f as “data + meaning” (Matney et al., 2011). Inf o rmatio n is o f ten co nstructed by co mbining dif f erent data po ints into a meaningf ul picture, given certain co ntext. Inf o rmatio n is a co ntinuum o f pro gressively develo ping and clustered data; it answers questio ns such as “who ”, “what”, “where”, and “when”. Fo r example, a co mbinatio n o f patient’s ICD- 9 diagno sis # 428.0 “Co ngestive heart f ailure, unspecif ied” and living status “living alo ne” has a certain meaning in a co ntext o f an o lder adult.
Knowle dge : is inf o rmatio n that has been synthesized so that relatio ns and interactio ns are def ined and f o rmalized; it is build o f meaningf ul inf o rmatio n co nstructed o f discrete data po ints (Matney et al., 2011). Kno wledge is o f ten af f ected by assumptio ns and central theo ries o f a scientif ic discipline and is derived by disco vering patterns o f relatio nships between dif f erent clusters o f inf o rmatio n. Kno wledge answers questio ns o f “why” o r “ho w”. Fo r healthcare pro f essio nals, the co mbinatio n o f dif f erent inf o rmatio n clusters, such as the ICD- 9 diagno sis # 428.0 “Co ngestive heart f ailure, unspecif ied” + living status “living alo ne” with an additio nal inf o rmatio n that an o lder man (78 years o ld) was just discharged f ro m ho spital to ho me with a co mplicated new medicatio n regimen (e.g. blo o d thinners) might indicate that this perso n is at a high risk f o r drug- related adverse ef f ects (e.g. bleeding).
Wisdom: is an appro priate use o f kno wledge to manage and so lve human pro blems (American Nurses Asso ciatio n, 2008; Matney et al., 2011). Wisdo m implies a f o rm o f ethics, o r kno wing why certain things o r pro cedures sho uld o r sho uld no t be implemented in healthcare practice. In nursing, wisdo m guides the nurse in reco gnizing the situatio n at hand based o n patients’ values, nurse’s experience, and healthcare kno wledge. Co mbining all these co mpo nents, the nurse decides o n a nursing interventio n o r actio n. Benner (2000) presents wisdo m as a clinical judgment integrating intuitio n, emo tio ns and the senses. Using the previo us examples, wisdo m will be displayed when the ho mecare nurse will co nsider prio ritizing the elderly heart f ailure patient using blo o d thinners f o r an immediate interventio n, such as a f irst nursing visit within the f irst ho urs o f discharge f ro m ho spital to assure appro priate use o f medicatio ns.
T he bo undaries o f the DIKW f ramewo rk co mpo nents are no t strict; rather, they are interrelated and there is a “co nstant f lux” between the f ramewo rk parts. Simply put, data is used to generate inf o rmatio n and kno wledge while the derived new kno wledge co upled with wisdo m, might trigger assessment o f new data elements (Matney et al., 2011).
Applying the Data-Inf ormation-Knowledge-Wisdom f ramework to guide inf ormatics research
T he DIKW f ramewo rk do es no t pro po se any relatio ns between the distinct data elements that lead to the generatio n o f meaningf ul information and knowledge. To acco mplish that, a discipline specif ic theo ry is required in co mbinatio n with the DIKW f ramewo rk. To illustrate that, I will use a practical example f ro m my dissertatio n f o cusing o n identif ying patients’ risk f o r po o r o utco mes during transitio n f ro m ho spital to ho mecare.
In my dissertatio n, I have cho sen to use the nursing specif ic Transitio ns theo ry (Meleis, 2010) to describe the transitio n o f interest (patient’s transitio n f ro m ho spital to ho me). As nurses f requently study and manage vario us types o f transitio ns (e.g. immigratio n transitio n, health- illness transitio n, administrative transitio n, etc), Transitio ns theo ry has been easily ado pted and welco med in nursing research, educatio n, and practice (Im, 2011; Meleis, Sawyer, Im, Messias, & Schumacher, 2000). In my dissertatio n, the Transitio ns theo ry helps me to analyze the dif f erent elements af f ecting transitio n f ro m ho spital to ho me. Fo r example, the Transitio ns theo ry suggests that several perso nal co nditio ns (such as the high level o f f amily suppo rt) might f acilitate ho spital to ho me transitio ns f o r o lder adults and sho uld be measured. T hus, the discipline specif ic theo ry serves as the glue that binds all the distinct data po ints (e.g. caregiver ’s availability to assist with patient’s basic needs) to gether to pro duce meaningf ul information (e.g. the level o f f amily suppo rt). T his information is then synthesized and used – with the help o f Transitio ns theo ry- to build knowledge abo ut the specif ic pheno meno n. T his example illustrates the DIK aspects o f the DIKW f ramewo rk in the co ntext o f Transitio ns theo ry.
T he wisdom co mpo nent o f the DIKW f ramewo rk is o f ten addressed by the clinicians in the f ield. Fo r example, the f inal pro duct o f my dissertatio n will be a decisio n suppo rt to o l helping ho mecare clinicians with identif icatio n o f patients’ risk f o r po o r o utco mes. When using the to o l in practice, the clinicians will have to act acco rding to a specif ic kno wledge present in each clinical situatio n (e.g. ethics, clinical practice regulatio ns in each particular state in the US etc.). In o ther wo rds, the clinicians will use their wisdom to interpret suggestio ns and make clinical judgments using inf o rmatio n received f ro m the decisio n suppo rt to o l. Figure I presents the po ssible interplay between the discipline specif ic theo ry (Transitio ns theo ry) and dif f erent co mpo nents o f the DIKW f ramewo rk.
Figure I: Combining t he discipline specif ic and DIKW t heoret ical f rameworks
In summary, this edito rial presents a po ssible theo retical blueprint f o r nursing and healthcare inf o rmatics researchers that intend to use the DIKW f ramewo rk. T he co mbinatio n o f discipline specif ic theo ries and the DIKW f ramewo rk o f f ers a usef ul to o l to examine the theo retical aspects and guide the practical applicatio n o f inf o rmatics research.
Acknowle dgme nt : I wanted to thank my academic adviser, Dr. K. Bo wles PhD, RN, FAAN, FACMI, f o r her guidance o n the presented wo rk. Also , I wanted to thank Charlene Ro nquillo , RN, MSN, PhD student (University o f British Co lumbia, Canada) f o r her review and co mments o n this manuscript.
References American Nurses Asso ciatio n. (2008). Nursing informatics: Scope and standards of practice. Silver Spring, MD: nursesbo o ks.o rg.
Benner, P. (2000). T he wisdo m o f o ur practice. The American Journal of Nursing, 100 (10), 99- 101, 103, 105.
Graves, J. R., & Co rco ran, S. (1989). T he study o f nursing inf o rmatics. Image–the Journal of Nursing Scholarship, 21(4), 227- 231.
Im, E. O. (2011). Transitio ns theo ry: A trajecto ry o f theo retical develo pment in nursing. Nursing Outlook, 59(5), 278- 285.e2. do i: 10.1016/j.o utlo o k.2011.03.008
Internatio nal Medical Inf o rmatics Asso ciatio n – Nursing Wo rking Gro up. (2010). IMIA def initio n o f nursing inf o rmatics updated. Retrieved 01/02, 2013, f ro m http://imianews.wo rdpress.co m/2009/08/24/imia- ni- def initio n- o f – nursing- inf o rmatics- updated/
Matney, S., Brewster, P. J., Sward, K. A., Clo yes, K. G., & Staggers, N. (2011). Philo so phical appro aches to the nursing inf o rmatics data- inf o rmatio n- kno wledge- wisdo m f ramewo rk. Advances in Nursing Science, 34(1), 6- 18.
McGo nigle, D., & Mastrian, K. (2011). Nursing informatics and the foundation of knowledge Jo nes & Bartlett Learning.
Meleis, A. (2010). Transitions theory: Middle range and situation specific theories in nursing research and practice Springer Publishing Co mpany.
Meleis, A., Sawyer, L. M., Im, E. – ., Messias, D. K. H., & Schumacher, K. (2000). Experiencing transitio ns: An emerging middle- range theo ry. Advances in Nursing Science, 23 (1), 12- 28.
Maxim Topaz, RN, MA, Doctoral student
Maxim To paz, MA, RN, is a Spencer Scho lar, a Fulbright Fello w and a PhD Student in Nursing at the University o f Pennsylvania. He earned his Bachelo rs in Nursing and Masters in Gero nto lo gy (cum laude) f ro m the University o f Haif a, Israel.
Bac k to Is s ue Ind e x
In the past, Maxim was invo lved in nursing practice and educatio n in Israel. In his current wo rk, Maxim f o cuses o n Electro nic Medical Reco rds, Clinical Decisio n Suppo rt and Standardized Termino lo gies. Maxim has mo re than a do zen o f publicatio ns in healthcare inf o rmatics http://scho lar.go o gle.co m/citatio ns? hl=en&user=7MxxJ2UAAAAJ&view_o p=list_wo rks&pagesize=100. Currently, he serves as a Chair o f the Students’ gro up with Internatio nal Medical Inf o rmatics Asso ciatio n Nursing Inf o rmatics Special Interest Gro up (IMIA- NISIG). Also , Maxim serves as a member o f the Student Edito rial Bo ard with the Jo urnal o f American Medical Inf o rmatics Asso ciatio n. Additio nally, Maxim is invo lved in several inf o rmatics o riented po licy making ef f o rts with the Of f ice o f Natio nal Co o rdinato r f o r Health Inf o rmatio n Techno lo gy (ONC) in the U.S. and the Israeli Ministry o f Health, Department o f Inf o rmatio n Techno lo gy. Maxim is recipient o f several inf o rmatics awards, f o r example the PhD Student Inf o rmatics Metho do lo gist award f ro m received at the First Internatio nal Co nf erence o n Research Metho ds f o r Standardized Termino lo gies http://o mahasystempartnership.o rg/internatio nal- co nf erence- o n- research- metho ds- f o r- standardized- termino lo gies/co nf erence- metho do lo gist- awards/.
“I am thrilled to be invo lved in the expanding and f ast- paced f ield o f healthcare inf o rmatics. Nurses- the largest secto r o f healthcare pro viders wo rldwide- are in the midst o f health inf o rmatio n techno lo gy revo lutio n. Nursing inf o rmatics has a high po tential to impro ve patient o utco mes, increase the quality o f healthcare and bridge the gap between healthcare science and practice.”
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Technological Forecasting & Social Change 126 (2018) 3–13
Contents lists available at ScienceDirect
Technological Forecasting & Social Change
Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations
Yichuan Wang a,⁎, LeeAnn Kung b, Terry Anthony Byrd a a Raymond J. Harbert College of Business, Auburn University, 405 W. Magnolia Ave., Auburn, AL 36849, USA b Rohrer College of Business, Rowan University, 201 Mullica Hill Road, Glassboro, NJ 08028, USA
⁎ Corresponding author. E-mail addresses: [email protected] (Y. Wang), k
[email protected] (T.A. Byrd).
http://dx.doi.org/10.1016/j.techfore.2015.12.019 0040-1625/© 2016 Elsevier Inc. All rights reserved.
a b s t r a c t
a r t i c l e i n f o
Article history: Received 17 June 2015 Received in revised form 11 November 2015 Accepted 12 December 2015 Available online 26 February 2016
To date, health care industry has not fully grasped the potential benefits to be gained from big data analytics. While the constantly growing body of academic research on big data analytics is mostly technology oriented, a better understanding of the strategic implications of big data is urgently needed. To address this lack, this study examines the historical development, architectural design and component functionalities of big data ana- lytics. From content analysis of 26 big data implementation cases in healthcare, we were able to identify five big data analytics capabilities: analytical capability for patterns of care, unstructured data analytical capability, deci- sion support capability, predictive capability, and traceability. We also mapped the benefits driven by big data an- alytics in terms of information technology (IT) infrastructure, operational, organizational, managerial and strategic areas. In addition, we recommend five strategies for healthcare organizations that are considering to adopt big data analytics technologies. Our findings will help healthcare organizations understand the big data an- alytics capabilities and potential benefits and support them seeking to formulate more effective data-driven an- alytics strategies.
© 2016 Elsevier Inc. All rights reserved.
Keywords: Big data analytics Big data analytics architecture Big data analytics capabilities Business value of information technology (IT) Health care
Information technology (IT)-related challenges such as inadequate integration of healthcare systems and poor healthcare information management are seriously hampering efforts to transform IT value to business value in the U.S. healthcare sector (Bodenheimer, 2005; Grantmakers In Health, 2012; Herrick et al., 2010; The Kaiser Family Foundation, 2012). The high volume digital flood of information that is being generated at ever-higher velocities and varieties in healthcare adds complexity to the equation. The consequences are unnecessary in- creases in medical costs and time for both patients and healthcare ser- vice providers. Thus, healthcare organizations are seeking effective IT artifacts that will enable them to consolidate organizational resources to deliver a high quality patient experience, improve organizational per- formance, and maybe even create new, more effective data-driven busi- ness models (Agarwal et al., 2010; Goh et al., 2011; Ker et al., 2014).
One promising breakthrough is the application of big data analytics. Big data analytics that is evolved from business intelligence and decision support systems enable healthcare organizations to analyze an im- mense volume, variety and velocity of data across a wide range of healthcare networks to support evidence-based decision making and action taking (Watson, 2014; Raghupathi and Raghupathi, 2014). Big
[email protected] (L. Kung),
data analytics encompasses the various analytical techniques such as descriptive analytics and mining/predictive analytics that are ideal for analyzing a large proportion of text-based health documents and other unstructured clinical data (e.g., physician's written notes and pre- scriptions and medical imaging) (Groves et al., 2013). New database management systems such as MongoDB, MarkLogic and Apache Cassandra for data integration and retrieval, allow data being trans- ferred between traditional and new operating systems. To store the huge volume and various formats of data, there are Apache HBase and NoSQL systems. These big data analytics tools with sophisticated func- tionalities facilitate clinical information integration and provide fresh business insights to help healthcare organizations meet patients' needs and future market trends, and thus improve quality of care and fi- nancial performance (Jiang et al., 2014; Murdoch and Detsky, 2013; Wang et al., 2015).
A technological understanding of big data analytics has been studied well by computer scientists (see a systemic review of big data research from Wamba et al., 2015). Yet, healthcare organizations continue to struggle to gain the benefits from their investments on big data analyt- ics and some of them are skeptical about its power, although they invest in big data analytics in hope for healthcare transformation (Murdoch and Detsky, 2013; Shah and Pathak, 2014). Evidence shows that only 42% of healthcare organizations surveyed are adopting rigorous analyt- ics approaches to support their decision-making process; only 16% of them have substantial experience using analytics across a broad range of functions (Cortada et al., 2012). This implies that healthcare
4 Y. Wang et al. / Technological Forecasting & Social Change 126 (2018) 3–13
practitioners still vaguely understand how big data analytics can create value for their organizations (Sharma et al., 2014). As such, there is an urgent need to understand the managerial, economic, and strategic im- pact of big data analytics and explore its potential benefits driven by big data analytics. This will enable healthcare practitioners to fully seize the power of big data analytics.
To this end, two main goals of this study are: first, to identify big data analytics capabilities; and second, to explore the potential benefits it may bring. By doing so, we hope to give healthcare organization a more current comprehensive understanding of big data analytics and how it helps to transform organizations. In this paper, we begin by pro- viding the historical context and developing big data analytics architec- ture in healthcare, and then move on to conceptualizing big data analytics capabilities and potential benefits in healthcare. We conduct- ed a content analysis of 26 big data implementation cases in health care which lead to the identification of five major big data analytics ca- pabilities and potential benefits derived from its application. In conclud- ing sections, we present several strategies for being successful with big data analytics in healthcare settings as well as the limitations of this study, and direction of future research.
2.1. Big data analytics: past and present
The history of big data analytics is inextricably linked with that of data science. The term “big data” was used for the first time in 1997 by Michael Cox and David Ellsworth in a paper presented at an IEEE con- ference to explain the visualization of data and the challenges it posed for computer systems (Cox and Ellsworth, 1997). By the end of the 1990s, the rapid IT innovations and technology improvements had en- abled generation of large amount of data but little useable information in comparison. Concepts of business intelligence (BI) created to empha- size the importance of collection, integration, analysis, and interpreta- tion of business information and how this set of process can help businesses make more appropriate decisions and obtain a better under- standing of market behaviors and trends.
The period of 2001 to 2008 was the evolutionary stage for big data development. Big data was first defined in terms of its volume, veloc- ity, and variety (3Vs), after which it became possible to develop more sophisticated software to fulfill the needs of handling informa- tion explosion accordingly. Software and application developments like Extensible Markup Language (XML) Web services, database management systems, and Hadoop added analytics modules and functions to core modules that focused on enhancing usability for end users, and enabled users to process huge amounts of data across and within organizations collaboratively and in real-time. At the same time, healthcare organizations were starting to digitize their medical records and aggregate clinical data in huge electronic data- bases. This development made the health data storable, usable, searchable, and actionable, and helped healthcare providers practice more effective medicine.
At the beginning of 2009, big data analytics entered the revolution- ary stage (Bryant et al., 2008). Not only had big-data computing become a breakthrough innovation for business intelligence, but also re- searchers were predicting that data management and its techniques were about to shift from structured data into unstructured data, and from a static terminal environment to a ubiquitous cloud-based envi- ronment. Big data analytics computing pioneer industries such as banks and e-commerce were beginning to have an impact on improving business processes and workforce effectiveness, reducing enterprise costs and attracting new customers. In regards to healthcare industry, as of 2011, stored health care data had reached 150 exabytes (1 EB = 1018 bytes) worldwide, mainly in the form of electronic health records (Institute for Health Technology Transformation, 2013). However, most of the potential value creation is still in its infancy, because
predictive modeling and simulation techniques for analyzing healthcare data as a whole have not yet been adequately developed.
More recent trend of big data analytics technology has been towards the use of cloud in conjunction with data. Enterprises have increasingly adopted a “big data in the cloud” solution such as software-as-a-service (SaaS) that offers an attractive alternative with lower cost. According to the Gartner's, 2013 IT trend prediction, taking advantage of cloud com- puting services for big data analytics systems that support a real-time analytic capability and cost-effective storage will become a preferred IT solution by 2016. The main trend in the healthcare industry is a shift in data type from structure-based to semi-structured based (e.g., home monitoring, telehealth, sensor-based wireless devices) and unstructured data (e.g., transcribed notes, images, and video). The in- creasing use of sensors and remote monitors is a key factor supporting the rise of home healthcare services, meaning that the amount of data being generated from sensors will continue to grow significantly. This will in turn improve the quality of healthcare services through more ac- curate analysis and prediction.
2.2. Big data analytics architecture
To reach our goals of this study which are to describe the big data an- alytics capability profile and its potential benefits, it is necessary to un- derstand its architecture, components and functionalities. The first action taken is to explore best practice of big data analytics architecture in healthcare. We invited four IT experts (two practitioners and two ac- ademics) to participate in a five-round evaluation process which includ- ed brainstorming and discussions. The resulted big data analytics architecture is rooted in the concept of data life cycle framework that starts with data capture, proceeds via data transformation, and culmi- nates with data consumption. Fig. 1 depicts the proposed best practice big data analytics architecture that is loosely comprised of five major ar- chitectural layers: (1) data, (2) data aggregation, (3) analytics, (4) infor- mation exploration, and (5) data governance. These logical layers make up the big data analytics components that perform specific functions, and will therefore enable healthcare managers to understand how to transform the healthcare data from various sources into meaningful clinical information through big data implementations.
2.2.1. Data layer This layer includes all the data sources necessary to provide the
insights required to support daily operations and solve business problems. Data is divided into structured data such as traditional electronic healthcare records (EHRs), semi-structured data such as the logs of health monitoring devices, and unstructured data such as clinical images. These clinical data are collected from various in- ternal or external locations, and will be stored immediately into ap- propriate databases, depending on the content format.
2.2.2. Data aggregation layer This layer is responsible for handling data from the various data
sources. In this layer, data will be intelligently digested by performing three steps: data acquisition, transformation, and storage. The primary goal of data acquisition is to read data provided from various communi- cation channels, frequencies, sizes, and formats. This step is often a major obstacle in the early stages of implementing big data analytics, because these incoming data characteristics might vary considerably. Here, the cost may well exceed the budget available for establishing new data warehouses, and extending their capacity to avoid workload bottlenecks. During the transformation step, the transformation engine must be capable of moving, cleaning, splitting, translating, merging, sorting, and validating data. For example, structured data such as that typically contained in an eclectic medical record might be extracted from healthcare information systems and subsequently converted into a specific standard data format, sorted by the specified criterion (e.g., patient name, location, or medical history), and then the record
Fig. 1. Big data analytics architecture in health care.
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validated against data quality rules. Finally, the data are loaded into the target databases such as Hadoop distributed file systems (HDFS) or in a Hadoop cloud for further processing and analysis. The data storage prin- ciples are based on compliance regulations, data governance policies and access controls. Data storage methods can be implemented and completed in batch processes or in real time.
2.2.3. Analytics layer This layer is responsible for processing all kinds of data and
performing appropriate analyses. In this layer, data analysis can be di- vided into three major components: Hadoop Map/Reduce, stream com- puting, and in-database analytics, depending on the type of data and the purpose of the analysis. Mapreduce is the most commonly used pro- gramming model in big data analytics which provides the ability to pro- cess large volumes of data in batch form cost-effectively, as well as allowing the analysis of both unstructured and structured data in a mas- sively parallel processing (MPP) environment. Stream computing can support high performance stream data processing in near real time or real time. With a real time analysis, users can track data in motion, re- spond to unexpected events as they happen and quickly determine next-best actions. For example, in the case of healthcare fraud detection, stream computing is an important analytical tool that assists in predicting the likelihood of illegal transactions or deliberate misuse of customer accounts. Transactions and accounts will be analyzed in real time and alarms generated immediately to prevent myriad frauds across healthcare sectors. In-database analytics refers to a data mining approach built on an analytic platform that allows data to be processed within the data warehouse. This component provides high-speed paral- lel processing, scalability, and optimization features geared toward big data analytics, and offers a secure environment for confidential enter- prise information. However, the results provided from in-database ana- lytics are neither current nor real time and it is therefore likely to generate reports with a static prediction. Typically, this analytic compo- nent in healthcare organizations is useful for supporting preventative healthcare practice and improving pharmaceutical management. The analytics layer also provides exceptional support for evidence based
medical practices by analyzing EHRs, patterns of care, care experience, and individual patients' habits and medical histories.
2.2.4. Information exploration layer This layer generates outputs such as various visualization reports,
real-time information monitoring, and meaningful business insights de- rived from the analytics layer to users in the organization. Similar to tra- ditional business intelligence platforms, reporting is a critical big data analytics feature that allows data to be visualized in a useful way to sup- port users' daily operations and help managers to make faster, better decisions. However, the most important output for health care may well be its real-time monitoring of information such as alerts and proac- tive notifications, real time data navigation, and operational key perfor- mance indicators (KPIs). This information is analyzed from sources such as smart phones and personal medical devices and can be sent to inter- ested users or made available in the form of dashboards in real time for monitoring patients' health and preventing accidental medical events.
2.2.5. Data governance layer This layer is comprised of master data management (MDM), data
life-cycle management, and data security and privacy management. This layer emphasizes the “how-to” as in how to harness data in the or- ganization. The first component of data governance, master data man- agement, is regarded as the processes, governance, policies, standards, and tools for managing data. Data is properly standardized, removed, and incorporated in order to create the immediacy, completeness, accu- racy, and availability of master data for supporting data analysis and de- cision making. The second component, data life-cycle management, is the process of managing business information throughout its lifecycle, from archiving data, through maintaining data warehouse, testing and delivering different application systems, to deleting and disposing of data. By managing data effectively over its lifetime, firms are better equipped to provide competitive offerings to meet market needs and support business goals with lower timeline overruns and cost. The third component, data security and privacy management, is the plat- form for providing enterprise-level data activities in terms of discovery, configuration assessment, monitoring, auditing, and protection (IBM,
6 Y. Wang et al. / Technological Forecasting & Social Change 126 (2018) 3–13
2012). Due to the nature of complexity in data management, organiza- tions have to face ethical, legal, and regulatory challenges with data gov- ernance (Phillips-Wren et al., 2015). Particularly in healthcare industry, it is essential to implement rigorous data rules and control mechanisms for highly sensitive clinical data to prevent security breaches and pro- tect patient privacy. By adopting suitable policies, standards, and com- pliance requirements to restrict users' permissions will ensure the new system satisfies healthcare regulations and creates a safe environ- ment for the proper use of patient information.
2.3. Big data analytics capability
Several definitions for big data analytics capability have been de- veloped in the literature (see Table 1). In general, big data analytics capability refers to the ability to manage a huge volume of disparate data to allow users to implement data analysis and reaction (Hurwitz et al., 2013). Wixom et al. (2013) indicate that big data analytics ca- pability for maximizing enterprise business value should encompass speed to insight which is the ability to transform raw data into usable information and pervasive use which is the ability to use business analytics across the enterprise. With a lens of analytics adoption, LaLalle et al. (2011) categorize big data analytics capability into three levels: aspirational, experienced, and transformed. The former two levels of analytics capabilities focus on using business analytics technologies to achieve cost reduction and operation optimization. The last level of capability is aimed to drive customer profitability and making targeted investments in niche analytics.
Moreover, with a view of adoption benefit, Simon (2013) defines big data analytics capability as the ability to gather enormous variety of data – structured, unstructured and semi-structured data – from current and former customers to gain useful knowledge to support bet- ter decision-making, to predict customer behavior via predictive analyt- ics software, and to retain valuable customers by providing real-time offers. Based on the resource-based view, Cosic et al. (2012) define big data analytics capability as “the ability to utilize resources to perform a business analytics task, based on the interaction between IT assets and other firm resources (p. 4)”.
In this study, we define big data analytics capability through an in- formation lifecycle management (ILM) view. Storage Networking Industry Association (2009) describes ILM as “the policies, processes, practices, services and tools used to align the business value of informa- tion with the most appropriate and cost-effective infrastructure from the time when information is created through its final disposition (p. 2).” Generally, data regardless of its structure in a system has been followed this cycle, starting with collection, through repository and pro- cess, and ending up with dissemination of data. The concept of ILM helps us to understand all the phases of information life cycle in busi- ness analytics architecture (Jagadish et al., 2014). Therefore, with a
Table 1 The definition of big data analytics capability from prior research.
Sources Viewpoints Definitions
Cosic et al. (2012) Resource based view • The ability to ut Hurwitz et al. (2013) 3V of big data • The ability to m
reaction LaLalle et al. (2011) Analytics adoption • Achieve cost red
• Drive customer Simon (2013) Adoption benefit • The ability to ga
customer servic Trkman et al. (2010) Business process • Analytics in plan
• Analytics in sou • Analytics in mak • Analytics in deli
Wixom et al. (2013) Business value • Speed to insight • Pervasive use
view of ILM, we define big data analytics capability in the context of health care as
the ability to acquire, store, process and analyze large amount of health data in various forms, and deliver meaningful information to users that allows them to discover business values and insights in a timely fashion.
2.4. Conceptualizing the potential benefit of big data analytics
To capture the potential benefits from big data analytics, a multidi- mensional benefit framework (see Table 2), including IT infrastructure benefits, operational benefits, organizational benefits, managerial bene- fits, and strategic benefits (Shang and Seddon, 2002) was used to classi- fy the statements related to the benefits from the collected 26 big data cases in health care. We choose Shang & Seddon's framework to classify the potential benefits of big data analytics for three reasons. First, our exploratory work is to provide a specific set of benefit sub-dimensions in the big analytics context. This framework will help us to identify the benefits of big data analytics into proper categories. Second, this framework is designed for managers to assess the benefits of their com- panies' enterprise systems. It has been refined by many studies related to ERP systems and specific information system (IS) architectures (Esteves, 2009; Gefen and Ragowsky, 2005; Mueller et al., 2010). In this regard, this framework is suitable as a more generic and systemic model for categorizing the benefits of big data analytics system. Third, this framework also provides a clear guide for assessing and classifying benefits from enterprise systems. This guide also suggests the ways how to validate the IS benefit framework through implementation cases, which is helpful for our study.
3. Research methods
To reach our goals of this study, we used a quantitative approach, more specifically, a multiple cases content analysis to gain understand- ing and categorization of big data analytics capabilities and potential benefits derived from its application. The cases collection, approach and procedures for analyzing the cases are described in the following subsections.
3.1. Cases collection
Our cases were drawn from current and past big data projects mate- rial from multiple sources such as practical journals, print publications, case collections, and reports from companies, vendors, consultants or analysts. The absence of academic discussion in our case collection is due to the incipient nature of such in the field of healthcare. The follow- ing case selection criteria were applied: (1) the case presents an actual implementation of big data platforms or initiatives, and (2) it clearly
ilize resources to perform a business analytics task anage a huge volume of disparate data to allow users to implement data analysis and
uction and operation optimization profitability and making targeted investments in niche analytics ther enormous variety of data from customers to gain business insights to optimize e
rce e ver
Table 2 The overview of enterprise systems' multidimensional benefit framework.
Benefit dimension Description Sub-dimensions
IT infrastructure benefits Sharable and reusable IT resources that provide a foundation for present and future business applications
• Building business flexibility for current and future changes
• IT cost reduction • Increased IT infrastructure capability
Operational benefits The benefits obtained from the improvement of operational activities • Cost reduction • Cycle time reduction • Productivity improvement • Quality improvement • Customer service improvement
Managerial benefits The benefits obtained from business management activities which involve allocation and control of the firms' resources, monitoring of operations and supporting of business strategic decisions
• Better resource management • Improved decision making and planning • Performance improvement
Strategic benefits The benefits obtained from strategic activities which involve long-range planning regarding high-level decisions
• Support for business growth • Support for business alliance • Building for business innovations • Building cost leadership • Generating product differentiation • Building external linkages
Organizational benefits The benefits arise when the use of an enterprise system benefits an organization in terms of focus, cohesion, learning, and execution of its chosen strategies.
• Changing work patterns • Facilitating organizational learning • Empowerment • Building common vision
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describes the software they introduce and benefits obtaining from the implementation. We excluded reports from one particular vendor due to their connection to one of our experts who were invited for the eval- uation. We were able to collect 26 big data cases specifically related to the healthcare industries. Of these cases, 14 (53.8%) were collected from the materials released by vendors or companies, 2 cases (7.7%) from journal databases, and 10 cases (38.4%) from print publications, in- cluding healthcare institute reports and case collections. Categorizing by region, 17 cases were collected from Northern America, 7 cases from Europe, and others from Asia-Pacific region. The cases we used are listed in Appendix A.
3.2. Research approach and process
We applied content analysis to gain insights from the cases collected. Content analysis is a method for extracting various themes and topics from text, and it can be understood as, “an empirically grounded meth- od, exploratory in process, and predictive or inferential in intent.” Spe- cifically, this study followed inductive content analysis, because the knowledge about big data implementation in health care is fragmented (Raghupathi and Raghupathi, 2014). A three-phase research process for inductive content analysis (i.e., preparation, organizing, and reporting) suggested by Elo and Kyngäs (2008) was performed in order to ensure a better understanding of big data analytics capabilities and benefits in the healthcare context.
The preparation phase starts with selecting the “themes” (informa- tive and persuasive nature of case material), which can be sentences, paragraphs, or a portion of a page (Elo and Kyngäs, 2008). For this study, themes from case materials were captured by a senior consultant who has over 15 years working experience with a multinational tech- nology and consulting corporation headquartered in the United States, and currently is involved in several big data analytics projects. The senior consultant manually …
Standardized Nursing Language: What Does It Mean for Nursing Practice?
Use of a standardized nursing language for documentation of nursing care is vital both to the nursing profession and to the bedside/direct care nurse. The purpose of this article is to provide examples of the usefulness of standardized languages to direct care/bedside nurses. Currently, the American Nurses Association has approved thirteen standardized languages that support nursing practice, only ten of which are considered languages specific to nursing care. The purpose of this article is to offer a definition of standardized language in nursing, to describe how standardized nursing languages are applied in the clinical setting, and to explain the benefits of standardizing nursing languages. These benefits include: better communication among nurses and other health care providers, increased visibility of nursing interventions, improved patient care, enhanced data collection to evaluate nursing care outcomes, greater adherence to standards of care, and facilitated assessment of nursing competency. Implications of standardized language for nursing education, research, and administration are also presented.
Keywords: North American Nursing Diagnosis Association (NANDA); Nursing Intervention Classification (NIC); Nursing Outcome Classification (NOC); nursing judgments; patient care; quality care; standardized nursing language; communication
Citation: Rutherford, M., (Jan. 31, 2008) "Standardized Nursing Language: What Does It Mean for Nursing Practice? "OJIN: The Online Journal of Issues in Nursing. Vol. 13 No. 1.
Recently a visit was made by the author to the labor and delivery unit of a local community hospital to observe the nurses' recent implementation of the Nursing Intervention Classification (NIC) (McCloskey-Dochterman & Bulechek, 2004) and the Nursing Outcome Classification (NOC) (Moorehead, Johnson, & Maas, 2004) systems for nursing care documentation within their electronic health care records system. �it is impossible for medicine, nursing, or any health care-related discipline to implement the use of [electronic documentation] without having a standardized language or vocabulary to describe key components of the care process. During the conversation, one nurse made a statement that was somewhat alarming, saying, "We document our care using standardized nursing languages but we don't fully understand why we do." The statement led the author to wonder how many practicing nurses might benefit from an article explaining how standardized nursing languages will improve patient care and play an important role in building a body of evidence-based outcomes for nursing.
Most articles in the nursing literature that reference standardized nursing languages are related to research or are scholarly discussions addressing the fine points surrounding the development or evaluation of these languages. Although the value of a specific, standardized nursing language may be addressed, there often is limited, in-depth discussion about the application to nursing practice.
Practicing nurses need to know why it is important to document care using standardized nursing languages, especially as more and more organizations are moving to electronic documentation (ED) and the use of electronic health records. In fact, it is impossible for medicine, nursing, or any health care-related discipline to implement the use of ED without having a standardized language or vocabulary to describe key components of the care process. It is important to understand the many ways in which utilization of nursing languages will provide benefits to nursing practice and patient outcomes.
Norma Lang has stated, "If we cannot name it, we cannot control it, practice it, teach it, finance it, or put it into public policy" (Clark & Lang, 1992, p. 109). Although nursing care has historically been associated with medical diagnoses, �today nursing needs a unique language to express what it does so that nurses can be compensated for the care provided. nurses need an explicit language to better establish their standards and influence the regulations that guide their practice.
A standardized nursing language should be defined so that nursing care can be communicated accurately among nurses and other health care providers. Once standardized, a term can be measured and coded. Measurement of the nursing care through a standardized vocabulary by way of an ED will lead to the development of large databases. From these databases, evidence-based standards can be developed to validate the contribution of nurses to patient outcomes.
The purpose of this article is to offer a definition of standardized language in nursing, to describe how standardized nursing languages are applied in the clinical arena, and to explain the benefits of standardizing nursing languages. These benefits include: better communication among nurses and other health care providers, increased visibility of nursing interventions, improved patient care, enhanced data collection to evaluate nursing care outcomes, greater adherence to standards of care, and facilitated assessment of nursing competency. Implications of standardized language for nursing education, research, and administration are also presented.
Keenan (1999) observed that throughout history nurses have documented nursing care using individual and unit-specific methods; consequently, there is a wide range of terminology to describe the same care. Although there are other more complex explanations, Keenan supplies a straightforward definition of standardized nursing language as a "common language, readily understood by all nurses, to describe care" (Keenan, p. 12). The Association of Perioperative Registered Nurses (AORN) (n.d.) adds a dimension by explaining that a standardized language "provides nurses with a common means of communication." Both convey the idea that nurses need to agree upon a common terminology to describe assessments, interventions, and outcomes related to the documentation of nursing care. In this way, nurses from different units, hospitals, geographic areas, or countries will be able to use commonly understood terminology to identify the specific problem or intervention implied and the outcome observed. Standardizing the language of care (developing a taxonomy) with commonly accepted definitions of terms allows a discipline to use an electronic documentation system.
Consider, for example, documentation related to vaginal bleeding for a postpartum, obstetrical patient. Most nurses document the amount as small, moderate, or large. But exactly how much is small, moderate, or large? Is small considered an area the size of a fifty-cent piece on the pad? Or is it an area the size of a grapefruit? Patients benefit when nurses are precise in the definition and communication of their assessments which dictate the type and amount of nursing care necessary to effectively treat the patient.
The Duke University School of Nursing website < www.nursing.duke.edu> has a list of guidelines for the nurse to use for evaluation of a standardized nursing language. The language should facilitate communication among nurses, be complete and concise, facilitate comparisons across settings and locales, support the visibility of nursing, and evaluate the effectiveness of nursing care through the measurement of nursing outcomes. In addition to these guidelines the language should describe nursing outcomes by use of a computer-compatible coding system so a comprehensive analysis of the data can be accomplished.
The Committee for Nursing Practice Information Infrastructure (CNPII of the American Nurses Association (ANA) has recognized thirteen standardized languages, one of which has been retired. Two are minimum data sets, seven are nursing specific, and two are interdisciplinary. The ANA (2006b) Recognized Terminologies and Data Element Sets outlines the components of each of these languages.
The submission of a language for recognition by CNPPII is a voluntary process for the developers. This terminology is evaluated by the committee to determine if it meets a set of criteria. "The criteria, which are updated periodically, state that the data set, classification, or nomenclature must provide a rationale for its development and support the nursing process by providing clinically useful terminology. The concepts must be clear and ambiguous, and there must be documentation of utility in practice, as well as validity, and reliability. Additionally, there must be a named group who will be responsible for maintaining and revising the system" (Thede & Sewell, 2010, p. 293).
Another ANA committee, the Nursing Information and Data Set Evaluation Center (NIDSEC), evaluates implementation of a terminology by a vendor. This approval is similar to obtaining the good seal of approval from Good Housekeeping or the United Laboratories (UL) seal on products. The approval signifies that the documentation in the standardized language supports the documentation of nursing practice and conforms to standards pertaining to computerized information systems. The language is evaluated against standards that follow the Joint Commission's model for evaluation. The language must support documentation on a nursing information system (NIS) or computerized patient record system (CPR). The criteria used by the ANA to evaluate how the standardized language(s) are implemented, include how the terms can be connected, how easily the records can be stored and retrieved, and how well the security and confidentiality of the records are maintained. The recognition is valid for three years. A new application must be submitted at the end of the three years for further recognition. Some, but not all of the standardized languages are copyrighted. (The previous paragraphs were updated 2/23/09. See previous content.)
Vendors may also have their software packages evaluated by NIDSEC. The evaluation is a type of quality control on the vendor. An application packet must be purchased, priced at $100, then the fee for the evaluation is $20,000 (American Nurses Association, 2004). The only product currently recognized is Cerner Corporation CareNet Solutions (American Nurses Association, 2004). The recognition signifies that the software in the Cerner system has met the standards set by NIDSEC. The direct care/bedside nurse must understand the importance of the inclusion of standardized nursing languages in the software sold by vendors and demand the use of a standardized nursing language in these systems.
The use of standardized nursing languages has many advantages for the direct care/bedside nurse. These include: better communication among nurses and other health care providers, increased visibility of nursing interventions, improved patient care, enhanced data collection to evaluate nursing care outcomes, greater adherence to standards of care, and facilitated assessment of nursing competency. These advantages for the bedside/direct care nurse are discussed below.
Improved communication with other nurses, health care professionals, and administrators of the institutions in which nurses work is a key benefit of using a standardized nursing language. Physicians realized the value of a standardized language in 1893 (The International Statistical Classification of Diseases and Related Health Problems, 2003) with the beginning of the standardization of medical diagnosis that has become the International Classification of Diseases (ICD-10) (Clark & Phil, 1999). A more recent language, the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV), provides a common language for mental disorders. When an obstetrician lists "failure to progress" on a patient's chart or a psychiatrist names the diagnosis "paranoid schizophrenia, chronic," other physicians, health care practitioners, and third-party payers understand the patient's diagnosis.
Improved communication with other nurses, health care professionals, and administrators of the institutions in which nurses work is a key benefit of using a standardized nursing language. ICD-10 and DSM-IV are coded by a system of numbers for input into computers. The IDC-10 is a coding system used mainly for billing purposes by organizations and practitioners while the DSM-IV is a categorization system for psychiatric diagnoses. The DSM-IV categories have an ICD-10 counterpart code that is used for billing purposes.
Nurses lacked a standardized language to communicate their practice until the North American Nursing Diagnosis (NANDA), was introduced in 1973. Since then several more languages have been developed. The Nursing Minimum Data Set (NMDS) was developed in 1988 (Prophet & Delaney, 1998) followed by the Nursing Management Minimum Data Set (NMMDS) in 1989 (Huber, Schumacher, & Delaney, 1997). The Clinical Care Classification (CCC) was developed in 1991 for use in hospitals, ambulatory care clinics, and other settings (Saba, 2003). The standardized language developed for home, public health, and school health is the Omaha System (The Omaha System, 2004). The Nursing Intervention Classification (NIC) was published for the first time in 1992; it is currently in its fourth edition (McCloskey-Dochterman & Bulachek, 2004). The most current edition of the Nursing Outcomes Classification system (NOC), as of this writing, is the third edition published in 2004 (Moorhead, Johnson, & Maas, 2004). Both are used across a number of settings.
Use of standardized nursing languages promises to enhance communication of nursing care nationally and internationally. This is important because it will alert nurses to helpful interventions that may not be in current use in their areas. Two presentations at the NANDA, NIC, NOC 2004 Conference illustrated the use of a standardized nursing language in other countries (Baena de Morales Lopes, Jose dos Reis, & Higa, 2004; Lee, 2004). Lee (2004) used 360 nurse experts in quality assurance to identify five patient outcomes from the NOC (Johnson, Maas, & Moorhead, 2000) criteria to evaluate the quality of nursing care in Korean hospitals. The five NOC outcomes selected by the nurse experts as standards to evaluate the quality of care were vital signs status; knowledge: infection control; pain control behavior; safety behavior: fall prevention; and infection status.
Baena de Morales Lopes et al. (2004) identified the major nursing diagnoses and interventions in a protocol used for victims of sexual violence in Sao Paulo, Brazil. The major nursing diagnoses identified were: rape-trauma syndrome, acute pain, fear/anxiety, risk for infection, impaired skin integrity, and altered comfort. Through the use of these nursing diagnoses, specific interventions were identified, such as administration of appropriate medications with explanations of expected side effects, emotional support, helping the client to a shower and clean clothes, and referrals to needed agencies. The authors used these diagnoses in providing care for 748 clients and concluded that use of the nursing diagnoses contributed to the establishment of bonds with their clients. These are just two examples illustrating how a standardized language has been used across nursing specialties and around the world.
Nurses need to express exactly what it is that they do for patients. Nurses need to express exactly what it is that they do for patients. Pearson (2003) has stated, "Nursing has a long tradition of over-reliance on handing down both information and knowledge by word-of-mouth" (p. 271). Because nurses use informal notes to verbally report to one another, rather than patient records and care plans, their work remains invisible. Pearson states that at the present time the preponderance of care documentation focuses on protection from litigation rather than patient care provided. He anticipates that use of computerized nursing documentation systems, located close to the patient, will lead to more patient-centered and consistent documentation. Increased sensitivity to the nursing care activities provided by these computerized documentation systems will help highlight the contribution of nurses to patient outcomes, making nursing more visible.
Nursing practice, in addition to the interventions, treatments, and procedures, includes the use of observation skills and experience to make nursing judgments about patient care. Because nurses use informal notes to verbally report to one another, rather than patient records and care plans, their work remains invisible. Interventions that should be undertaken to in support nursing judgments and that demonstrate the depth of nursing judgment are built into the standardized nursing languages. For example, one activity listed under labor induction in the NIC language is that of re-evaluating cervical status and verifying presentation before initiating further induction measures (McCloskey-Dochterman & Bulechek, 2004). This activity guides the nurse to assess the dilatation and effacement of the cervix and presentation of the fetus, before making a judgment about continuing the induction procedure.
LaDuke (2000) provides an additional example of using the NIC to make nursing interventions visible. For example, LaDuke noted that the intervention of emotional support, described by McCloskey-Dochterman & Bulechek (2004) requires "interpersonal skills, critical thinking and time" (LaDuke, p. 43). NIC identifies emotional support as a specific intervention, provides a distinct definition for it, and lists specific activities to provide emotional support. Identification of emotional support as a specific intervention gives nurses a standardized nursing language to describe the specific activities necessary for the intervention of emotional support.
The use of a standardized nursing language can improve patient care. Cavendish (2001) surveyed sixty-four members of the National Association of School Nurses to obtain their perceptions of the most frequent complaints for abdominal pain. They used the NIC and NOC to determine the interventions and outcomes of children after acute abdomen had been ruled out. Nurses identified the chief complaints of the children, the most frequent etiology, the most frequent pain management activities from the NIC, and the change in NOC outcomes after intervention.
The three chief complaints were nausea, headache, and vomiting; the character of the pain was described as crampy/mild or moderate; and the three most identified etiologies were psychosocial problems, viral syndromes, and relationship to menses. The psychosocial problems included test anxiety, separation anxiety, and interpersonal problems. Nutrition accounted for a large number of abdominal complaints, such as skipping meals, eating junk food, and food intolerances. Cultural backgrounds of the children, such as the practice of fasting during Ramadan, were identified as causes for abdominal complaints.
The three top pain management activities from NIC were: observe for nonverbal cues of discomfort, perform comprehensive assessment of pain (location, characteristics, duration, frequency, quality, severity, precipitating factors), and reduce or eliminate factors that precipitate/increase pain experience (e.g., fear, fatigue, and lack of knowledge) (Cavendish, 2001). Cavendish described a decrease in symptoms, based on the Nursing Outcomes Classification Symptom Severity Indicators, following the intervention. Symptom intensity decreased 6.25%, symptom persistence decreased 4.69%, symptom frequency decreased 6.25%, and associated discomfort decreased 41.06% (p. 272). Similar studies are needed to provide evidence that specific nursing interventions improve patient outcomes.
The use of a standardized language to record nursing care can provide the consistency necessary to compare the quality of outcomes for various nursing interventions across settings.The use of a standardized language to record nursing care can provide the consistency necessary to compare the quality of outcomes for various nursing interventions across settings. As stated earlier, more organizations are moving to electronic documentation (ED) and electronic health records. When the nursing care data stored in these computer systems are in a standardized nursing language, large local, state, and national data repositories can be constructed that will facilitate benchmarking with other hospitals and settings that provide nursing care. The National Quality Forum (NQF) (NQF, 2006), is in the process of developing national standards for the measurement and reporting of health care performance data. The Nursing Care Measures Project is one of the 24 projects on which the NQF is developing consensus-based, national standards to use as mechanisms for quality improvement and measurement initiatives to improve American health care. The NQF has stated, "Given the importance of nursing care, the absence of standardized nursing care performance measures is a major void in healthcare quality assurance and work system performance"(NQF, May 2003, p. 1).
Patient outcomes are also related to the uniqueness of the individual, the care given by other health care professionals, and the environment in which the care is provided. The American Nurses Association's National Center for Nursing Quality (NCNQ) maintains a database called the National Database of Nursing Quality Indicators™ (NDNQI)® (American Nurses Association, 2006a). This database collects nurse-sensitive and unit-specific indicators from health care organizations, compares this data with organizations of similar size having similar units, and sends the comparison findings back to the participating organization. This activity facilitates longitudinal benchmarking as the database has been ongoing since the early 1990's (National Database, 2004).
The already-mentioned NOC system outcomes are nurse-sensitive outcomes, which means the they are sensitive to those interventions performed primarily by nurses (Moorehead et al., 2004). Because the NOC system measures nursing outcomes on a numerical rating scale, it, too, facilitates the benchmarking of nursing practices across facilities, regions, and countries. The current edition of NOC (2004), which assesses the impact of nursing care on the individual, the family, and the community, contains 330 outcomes classified in seven domains and 29 classes.
A NOC outcome common to nurses who work with elderly patients who have a swallowing impairment is aspiration prevention (Moorehead et al., 2004). Patient behaviors indicating this outcome include identifying risk factors, avoiding risk factors, positioning self upright for eating/drinking, and choosing liquids and foods of proper consistency. Rating each indictor on a scale from one (never demonstrated) to five (consistently demonstrated) helps track risk for aspiration in individuals at various stages of illness during the hospitalization. It also gives an indication of a person's compliance in following the prevention measures and the nurse's success in patient education.
A NOC outcome that labor nurses frequently use is pain level (Moorehead et al., 2004), related to the severity and intensity of pain a woman experiences with contractions. The pain level can be assessed before and after the use of coping techniques such as breathing exercises and repositioning. Indicators for this specific pain outcome include: reported pain, moaning and crying, facial expressions of pain, restlessness, narrowed focus, respiratory rate, pulse rate, blood pressure, and perspiration (p. 421) and are rated on a scale from severe ( 1) to none ( 5). The difference between the numerical ratings for each indicator before and after use of the coping techniques estimates the success of the intervention in achieving the outcome of reducing the pain level for laboring mothers.
Related to the quality of nursing care is the level of adherence to the standards of care for a given patient population. The NIC and NOC standardized nursing language systems are based on both the input of expert nurses and the standards of care from various professional organizations. For example, the NIC intervention of electronic fetal monitoring: intrapartum (McCloskey-Dochterman & Bulechek, 2004) is supported by publications of expert authors and researchers in the field of fetal monitoring and by standards of care from the Association of Women's Health, Obstetric and Neonatal Nurses (AWHONN). The first activity listed under electronic fetal monitoring: intrapartum is to verify maternal and fetal heart rates before initiation of electronic fetal monitoring (p. 328), which is understood to be one of the gold standards for electronic fetal monitoring. There are several reasons why both heart rates need to be identified. The nurse must be sure that it is the fetal heart rate being monitored and not the heart rate of the mother. Moreover, it is important to ascertain the exact position of the fetus before positioning the fetal monitor's transducer. This illustration exemplifies how important standards are reinforced by the NIC activities.
Standardized language can also be used to assess nursing competency. Health care facilities are required to demonstrate the competence of staff for the Joint Commission. The nursing interventions delineated in standardized nursing languages can be used as a standard by which to assess nurse competency in the performance of these interventions. A Midwestern hospital is already doing this (Nolan, 2004). Using an example from the NIC system, specifically intrapartal care (McCloskey-Dochterman & Bulechek, 2004), a nurse's competency can be established by a preceptor's watching to see whether the nurse is performing the recommended activities, such as a vaginal examination or the assessment of the fetus presentation. The preceptor can also evaluate the nurse's teaching skills regarding what the patient should expect during labor, using the activities listed under the teaching intervention.
In addition to enhancing the care provided by direct care nurses, standardized language has implications for nursing education, research, and administration. Nurse educators can use the knowledge inherent in standardized nursing languages to educate future nurses. Such a system can be used to describe the unique roles of the nurse. Nurse educators can teach students to use systems such as the CCC and Omaha System when in the community health fields, or the use of the NANDA, NIC, NOC terminology when in the acute care setting. References to the primary resources upon which each intervention is based are listed at the end of each individual intervention to provide information supporting each intervention. By referring to the references associated with these nursing standards, nurse educators can role model the use of standardized language to help students recognize the body of knowledge upon which the standards are built. Tying the standardized language to education and practice will enhance its implementation and expand practicing nurses' knowledge of interventions, outcomes, and languages. Armed with an appreciation of the value of standardized language, students can champion further development and use of the standardized nursing languages once they enter professional practice.
The use of standardized languages can provide a launching point for conducting research on standardized languages. The research conducted by the two teams of educators at the University of Iowa on the NIC and NOC are excellent examples of the research that can be done on the standardized nursing languages using computerized databases designed for research (McCloskey-Dochterman & Bulechek, 2004; Moorehead et al., 2004).
Nursing research performed with�larger sample sizes�using databases may reveal more powerful patterns with stronger implications for practice than can past research that depended on small samples. Although nursing researchers have traditionally used historic data (data describing completed activities), computerized documentation based on a standardized language can enable researchers and quality improvement staff to use "real-time" data. This data is more readily accessible and retrievable as compared to the traditional, time-consuming task of sifting through stacks of charts for the needed information.
When the bedside nurse documents via a nursing information system …
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