Standardized nursing terminology

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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

Citation

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.

Author’s Bio

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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