Practicum Experience: Week 5/ Journal Assignment?Part 1

After reviewing the Practicum Weekly Resources, record a response to the following in your Journal:

In each of the articles, four areas are highlighted as relevant and needing action for the effective growth of the field of informatics?leadership, education, practice, and policy. For each of these areas, identify specific ways that you personally can make an impact. (See articles below in required resources list)
Journal Assignment?Part 2

Note: Each week, you are responsible for locating a scholarly journal article in the Walden Library related to your area(s) of interest. Include in your Journal the reference in proper APA format and provide a brief summary of the article. (See Below and attached PDF file)
Dowding, D. (2015). Using Health Information Technology to Support Evidence-Based Practice. Worldviews On Evidence-Based Nursing / Sigma Theta Tau International, Honor Society Of Nursing, 12(3), 129-130. doi:10.1111/wvn.12093

Journal Assignment?Part 3

Practicum Onsite Visits

Summarize the key activities of your visits to your Practicum site (as appropriate), including with whom you met, what you did, and what you gained from the experience.

Practicum Weekly Resources

HIMSS Position Statement Talking Point. (2011). Transforming nursing through technology and informatics. Retrieved from
This article provides recommendations for achieving health care delivery transformation. Specifically, the article recommends actions to be taken in the domains of leadership, education, practice, and policy.

HIMSS Position Statement. (2011). Transforming nursing through technology and informatics. Retrieved from
The authors of this position statement draw attention to the need for transforming health care by improving the current regulatory, business, and organizational conditions. The statement identifies actions that should be taken on the part of academia, government, businesses, health care organizations, professional associations, consumers, and the insurance industry.

Sugrue, M. (2011). Informatics? role in the future of nursing: Health IT and health professional organizations at the crossroad. Journal of Healthcare Information Management, 25(3), 12?14. Retrieved from

Guest Editorial
Using Health Information Technology
to Support Evidence-Based Practice
The use of information technology is pervasive in our society,
a trend which is increasingly being reflected in healthcare environments
with the rise in the use of electronic health records
(EHRs) and associated patient portals for patients to access
their health records. For example, in 2009, the U.S. government
provided $19 billion through the American Recovery and
Reinvestment Act to healthcare organizations with the goal of
promoting the uptake and use of EHRs (Blumenthal, 2009).
The rise in the use of personal devices and smart phone apps
to monitor health, together with the use of social media and
online forums to discuss healthcare issues, also is increasing.
This rise in the availability of technology, both in healthcare
settings and in the wider population provides unique opportunities
for supporting evidence-based practice (EBP) across
healthcare settings. Evidence-based practice has been defined
as ?a life-long problem-solving approach to the delivery of care
that integrates the best evidence from well-designed studies
and evidence-based theories (i.e., external evidence) with a clinician?s
expertise, which includes internal evidence gathered
from a thorough patient assessment and patient data, and a patient?s
preferences and values? (Melnyk & Newhouse, 2014, p.
347). Arguably, the use of EBP and associated methods of systematically
reviewing and appraising evidence for quality have
been made possible due to the ability of technology to support
the storage, annotation and retrieval of research studies quickly
and easily.
In the early days of EBP, a significant focus was on teaching
clinicians the information search, retrieval and appraisal skills
they required to be able to identify evidence they needed ?at
the bedside.? However, a number of studies have consistently
highlighted how clinicians rely on other sources, such as peer
advice, to support their decisions mainly due to the number
of barriers encountered (e.g., such as time and resources available)
in trying to access evidence electronically and interpret it
appropriately in a busy healthcare environment. One solution
to this problem is to develop functionality within EHR systems
to provide evidence-based advice and guidance to clinicians at
the point of care. For example, some systems use ?info buttons?
that are located in the EHR system (e.g., for instance in
the order entry module), which when clicked on by a clinician,
provide context specific information related to the location such
as the results of a PubMed search for a specific question related
to the drug order (Bakken et al., 2008). However, while
overcoming some of the major barriers faced by clinicians in
accessing up-to-date clinical information, this solution does not
help clinicians in terms of guidance for care delivery.
Clinical guidelines summarize the best research evidence
and is often integrated with expert advice to provide an overview
of the care that should be provided for a group of patients either
in a particular healthcare context or across the healthcare
continuum. However, as above, the implementation and adoption
of guidelines into care has been problematic; guideline
documents are often lengthy and complex, and clinicians may
need to remember the guidelines for a number of different
patient conditions. Increasingly, the recommendations from
evidence-based guidelines are being integrated into EHR systems
to provide a structure and content for what information
should be collected, giving alerts or prompts to clinicians on
appropriate diagnoses and interventions that should be chosen
(Savinon, Taylor, Canty-Mitchell, & Blood-Siegfried, 2012). A
further development in EHR systems is that of clinical decision
support which ?provide clinicians with patient specific assessments
or recommendations to aid clinical decision making?
(Kawamoto, Houlihan, Balas, & Lobach, 2005, p. 765). Similar
to the integration of guidelines into the EHR, clinical decision
support works by matching patient characteristics to a computerized
knowledgebase using decision rules or algorithms,
resulting in a provision of guidance for action (Dowding et al.,
2009). Both the integration of clinical guidelines and the use
of clinical decision support in EHR systems has been shown to
improve clinicians? evidence-based practice behaviors, such as
increased adherence to guideline recommendations and improvements
in health prevention interventions (Garg et al.,
2005; McGinn et al., 2013; Savinon et al., 2012). Through the
computerization of guidelines and the development of decision
support, the ability of clinicians to access and use the best
evidence from high-quality studies and combine that with data
from the patient assessment and other relevant data is increasing
rapidly. However, existing technologies are currently failing
to take advantage of the broader availability of data from patients
to inform their decisions, and often ignore one element
of evidence-based decisions completely: that of the patient?s
values and beliefs.
Individuals have the capacity to generate vast quantities of
health-related data through their day-to-day activities; existing
smart phone health apps enable users to monitor a number
health indicators such as diet, exercise, blood pressure, glucose
measurements, and have the potential to be automatically
shared with their healthcare provider?s EHR system to provide
up to date data, which can be monitored over time to detect
trends in health status. This moves beyond the current state,
where all data incorporated into an EHR is normally generated
Worldviews on Evidence-Based Nursing, 2015; 12:3, 129?130. 129
C 2015 Sigma Theta Tau International
by the healthcare providers and is often relevant to a single
point in time or episode of care. There is also increasing focus
on making use of ?big data? and precision medicine, linking
data about patients (e.g., genetic, physiological, and behavioral)
in a way to be able to predict with precision what interventions
would work for individual patients in a particular context. The
ability to collect data about all patients (regardless of whether
or not they are in a clinical trial), learn what works for whom,
and then feed it back into sophisticated decision support systems
is where we can use technology to both use evidence
to inform practice and generate evidence from that practice
(Bakken, 2001; Yu, 2015).
Involving patients in decisions about their health care and
ensuring that those decisions take patients? values and preferences
into account is the final component of an evidence-based
decision. Decision aids have been used as a way of explaining
decision options to patients, exploring the benefits and
harms of those options, and encouraging them to consider the
options in relation to their own values or preferences. Using
decision aids has been shown to improve patients? knowledge
about the decisions, feel more informed about their choices,
have more realistic expectations about the benefits and harms
of different options and participate more in the decision process
(Stacey et al., 2014). However, the use of such decision
aids in routine medical practice is still limited; a recent IOM
discussion paper highlights that there are significant barriers
to the widespread adoption of shared decision making approaches
in medical practice; one of the key barriers is the
lack of integration of tools (such as decision aids) into existing
healthcare information systems and providing the infrastructure
(in terms of relevant and appropriate information,
and organization of workflows) to support patients? decisions
(Alston et al., 2014).
So where are we in terms of effectively utilizing technology
to support evidence-based decision making in healthcare
organizations? We have developed ways of providing evidence
to clinicians at the point of decision making as well as techniques
that encourage and support them to make intervention
decisions based on evidence-based guidelines. However,
we need to ensure we work with technology developers to incorporate
patients? preferences and values into those systems,
and utilize existing data across the spectrum from community
to hospital to ensure that healthcare decisions are truly
reflective of evidence, expertise, and patient preferences and
values. WVN
Dawn Dowding, PhD, RN,
Editorial Board Member
Alston, C., Berger, Z., Brownlee, S., Elwyn, G., Fowler Jr., F. J.,
Hall, L. K., . . . Henderson, D. (2014). Shared decision-making
strategies for best care: Patient decision aids. Washington DC: Institute
of Medicine.
Bakken, S. (2001). An informatics infrastructure is essential for
evidence-based practice. Journal of the American Medical Informatics
Association, 8(3), 199?201.
Bakken, S., Currie, L. M., Lee, N. J., Roberts, W. D., Collins, S.
A., & Cimino, J. J. (2008). Integrating evidence into clinical
information systems for nursing decision support. International
Journal of Medical Informatics, 77(6), 413?420.
Blumenthal, D. (2009). Stimulating the adoption of health information
technology. New England Journal of Medicine, 360, 1477?
Dowding, D., Randell, R., Mitchell, N., Foster, R., Thompson, C.,
Lattimer, V., & Cullum, N. (2009). Experience and nurses use of
computerised decision support systems. Studies in Health Technology
and Informatics, 146, 506?510.
Garg, A., Adhikari, N., McDonald, H., Rosas-Arellano, M., Devereaux,
P., Beyene, P., . . . Haynes, B. (2005). Effect of computerized
clinical decision support systems on practitioner
performance and patient outcomes. A systematic review. JAMA,
293(10), 1223?1238.
Kawamoto, K., Houlihan, C., Balas, E., & Lobach, D. (2005). Improving
clinical practice using clinical decision support systems:
A systematic review of trials to identify features critical to success.
British Medical Journal, 330, 765?768.
McGinn, T. G., McCullagh, L., Kannry, J. L., Knaus, M., Sofianou,
A., Wisnivesky, J., & Mann, D. M. (2013). Efficacy of an evidencebased
clinical decision support in primary care practices. A Randomized
clinical trial. JAMA Internal Medicine, 173(13), 1584?
Melnyk, B. M., & Newhouse, R. (2014). Evidence-based practice
versus evidence-informed practice: A debate that could stall forward
momentum in improving healthcare quality, safety, patient
outcomes and costs. Worldviews on Evidence-Based Nursing, 11(6),
Savinon, C., Taylor, J. S., Canty-Mitchell, J., & Blood-Siegfried, J.
(2012). Childhood obesity: Can electronic medical records customized
with clinical practice guidelines improve screening and
diagnosis? Journal of the American Academy of Nurse Practitioners,
24(8), 463?471.
Stacey, D., Legar ? e, F., Col, N., Bennett, C., Barry, M., Eden, K., . . . ?
Wu, J. (2014). Decision aids for people facing health treatment
or screening decisions. Cochrane Database of Systematic Reviews
1, Art. No.: CD001431. doi: 10.1002/14651858.CD001431.pub4
Yu, P. (2015). Big data and the promise of precision medicine
in cancer. The ASCO Post, 6(4). Retrieved from: http://www.,-2015/big-data-and-the-promiseof-precision-medicine-in-cancer.aspx
doi 10.1111/wvn.12093
WVN 2015;12:129?130
130 Worldviews on Evidence-Based Nursing, 2015; 12:3, 129?130.
C 2015 Sigma Theta Tau International
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Week 5 Journal Assignment?Part 1:

Week 5 Journal Assignment?Part 2 (Practicum Journal Article Summary):

Week 5 Journal Assignment?Part 3 (Practicum Onsite Visits):

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