using the data information knowledge wisdom continuum

Running head: USING THE DATA INFORMATION KNOWLEDGE WISDOM 1

Using the Data Information Knowledge Wisdom Continuum

Lori Dixon

Walden University

Transforming Nursing & Healthcare Through Information Technology

NURS-6051-12

June 29, 2014

USING THE DATA INFORMATION KNOWLEDGE WISDOM 2

Using the Data/Information/Knowledge/Wisdom Continuum

Obesity has reached epidemic levels in the United States, and it is passed down from the

parents to their children. Lifetime changes to create healthy lifestyles in families need to be

addressed, and teach them to maintain healthier lives. The purpose of this paper is to use the

framework of the Data, Information, Knowledge, and Wisdom (DIKW) Continuum. Data is

distinct fields of information; such as a weight is 200 lbs. Information is the organization of data

that is interpreted and organized. Knowledge is finding the relationships between the

information that has been obtained. Wisdom is using the knowledge that is obtained to solve

health problems (Bickford, 2008).

Clinical Question

The United States spends over $147 million a year on treating obesity and related diseases.

There are 78 million obese adults and 12.5 million obese children, and the number of obese

individuals is increasing by 10-20% over the next 20 years (Zamosky, 2013). Children learn

from their parents’ bad nutritional habits and lack of activity. How do we teach parents and

children to live a healthy lifestyle, and then maintain that lifestyle? I chose this questions

because I have lived it all of my life. My parents are not overweight, but I was overweight from

the time I was a toddler. I see others all around me that struggle with their weight, and have not

found a solution to being healthy. We measure obesity by BMI and weight, but the more

important issues are eating healthy and active lifestyle. My goal in researching data on this topic

is to find treatment programs where patients will make changes in their habits for a lifetime.

Data to Information


To find data concerning my research question, I used the Walden Library and Internet. I

searched for articles based on the topic. The Nursing Databases - CINAHL & MEDLINE

Simultaneous Search was used with the keywords of obesity and nursing. I filtered this search

by searching the period of 2010 to 2014. I reviewed the titles of articles and the abstracts to find

articles I wanted to read, and I thought applied to my research question. Once I found the

articles to read, I would download them and read each article highlighting the data points that

applied. In addition, information within the articles would interest me, I would use a Google

scholar search to find articles not included in my initial search.

Data becomes information when it is interpreted and organized. Data being entered into

an electronic health record (EHR) should be organized across health systems to interpret the data

gathered to improve population health. In 2009, the Pediatric EHR Data Sharing Network

(PEDSNet) formed a consortium with six hospitals and one university system. Four of the six

hospitals used EPIC, one used Cerner Millennium, and one used Allscripts (Bailey et al., 2013).

The ability to aggregate data across several hospitals provides a large sample to monitor and

research childhood obesity. These systems are also used for adults, and the same data could be

extracted and organized to understand obesity better obesity in adults. The hospitals extracted

information from all outpatient visits, except surgery and emergency. The data extracted

included; height and weight, sex and age, visit date and department specialty and all diagnoses

(Bailey et al., 2013). This type of project could be expanded to develop questions regarding

characteristics or data elements of obese families. Data elements that could be included would

be family members and relationship to each other, weight and height of each member, and diet

and activity.

Information to Knowledge


Knowledge is finding the relationships between the data elements that have been

organized into information. In the PEDSNet study, the information about height and weight was

calculated to find the body mass index (BMI). Children with a BMI in the 95 percentile only

20% had any diagnosis recorded (Bailey et al., 2013). By looking at this information in a large

sample, they were able to identify deficits in diagnosing pediatric patients. In an obese

population, it would be expected to see comorbidities such as hypertension and diabetes. In this

population of children it was found that there was a relationship between obesity and acute

leukemia, multiple sclerosis, and chromosomal anomalies (Bailey et al., 2013).

Knowledge to Wisdom

Once the characteristics of the obese population are identified as knowledge, the next step is

taking that knowledge and finding the treatments that will answer the research question. This

lead to another article search for obesity and mobile applications and the result was five articles

that I reviewed. Children love playing video games, and a review was done on mobile

applications to treat obesity in children. “HyperAnt” was the top application after the review, it

provided information about activity and food choices. It did not allow any user entry of data,

and the child could not set personal goals. Overall the use of applications engaged children

about their health, but they lacked evidence-based information, and the author suggested the app

developers’ work with clinical experts (Schoffman, Turner-McGrievy, & Wilcox, 2013).

A research study was done in Australia on a mobile app called “TXT2BFiT” for young

adults. The program combined self-report, coaching calls, and text messages with education for

the participant. The article did not include any final outcomes and is still in process (Hebden et

al., 2013). Due to the research study being ongoing, this did not appear to be the solution to my

question.


The final article was about obesity rehabilitation and using mobile technology. The

treatment was long-term with an inpatient and outpatient combination. The patient was admitted

for 30 days to a multi-disciplinary team, and the treatment focus was on changing dysfunctional

behaviors. After inpatient discharge, the patient was admitted to outpatient, and to sustain

weight-loss long-term, extensive psychotherapeutic intervention and is multidisciplinary. The

program uses mobile devices, video conferencing with clinicians the patient worked in the

hospital, and call coaching. There are five components that need to be included in a mobile

application; self-monitoring, counselor feedback and communication, social support, structured

program, and individually tailored program (Castelnuovo et al., 2014).

Conclusion

The increasing use of the EHR provides the data that nursing informatics can access to

research patient information related to a disease process. By organizing the data into a structured

format, researchers are able to collate data to find new treatments. For example, the PEDSNet

study pulled together the information to monitor obesity in children. Reviewing the common

data elements in the population the research has an understanding of cause, and can work on

solutions to create health changes. To create solutions to change the health behavior of obese

children and adults, the treatment must be long-term and multidisciplinary. The use of video

conferencing and mobile technology will also decrease the cost of healthcare by decreasing the

comorbidities of obesity.


References

Bailey, L. C., Milov, D. E., Kelleher, K., Kahn, M. G., Del Beccaro, M., Yu, F., ... Forrest,

C. B. (2013, June). Multi-institutional sharing of electronic health record data to

assess childhood obesity. PLOS ONE , 8(6), 1-8. doi:

10.1371/journal.pone.0066192

Bickford, C. J. (Ed.). (2008). Nursing informatics: Scope and standards of practice.

Silver Spring, MD: Nursesbooks.org.

Castelnuovo, G., Mauro Manzoni, G., Pietrabissa, G., Corti, S., Giusti, E., Molinari, E., &

Simpson, S. (2014, May 20). Obesity and outpatient rehabilitation using mobile

technologies: the potential mHealth approach. Frontiers in Psychology, 5, 1-11.

doi:10.3389/fpsyg.2014.00559

Hebden, L., Balestracci, K., McGeechan, K., Denney-Wilson, E., Harris, M., Bauman,

A., & Allman-Farinelli, M. (2013). ‘TXT2BFiT’ a mobile phone-based healthy

lifestyle program for preventing unhealthy weight gain in young adults: study

protocol for a randomized controlled trial. Trials, 14, 1-9. Retrieved from

http://www.trialsjournal.com/content/14/1/75

Schoffman, D. E., Turner-McGrievy, G., & Wilcox, S. (2013, September). Mobile apps

for pediatric obesity prevention and treatment, healthy eating, and physical

activity promotion: just fun and games? Transl Behav Med, 3, 320–325. doi:

10.1007/s13142-013-0206-3

Zamosky, L. (2013, February 25th). The obesity epidemic. MEDICAL ECONOMICS, 14-

17. Retrieved from MedicalEconomics.com

using the data information knowledge wisdom continuum

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