\nAfter reviewing the Practicum Weekly Resources, record a response to the following in your Journal:<\/p>\n
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)
\nJournal Assignment?Part 2<\/p>\n
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)
\nDowding, 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<\/p>\n
Journal Assignment?Part 3<\/p>\n
Practicum Onsite Visits<\/p>\n
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.<\/p>\n
Practicum Weekly Resources
\nReadings<\/p>\n
HIMSS Position Statement Talking Point. (2011). Transforming nursing through technology and informatics. Retrieved from http:\/\/www.himss.org\/files\/HIMSSorg\/handouts\/HIMSSNIPositionStatementTalkingPoints.pdf
\nThis 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.<\/p>\n
HIMSS Position Statement. (2011). Transforming nursing through technology and informatics. Retrieved from http:\/\/www.himss.org\/files\/HIMSSorg\/handouts\/HIMSSPositionStatementTransformingNursingPracticethroughTechnologyInformatics.pdf
\n 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.<\/p>\n
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 http:\/\/himss.files.cms-plus.com\/HIMSSorg\/Content\/files\/Code%2019_Informatics%20role%20in%20the%20future%20of%20nursing_Sugrue_JHIM_S11.pdf<\/p>\n
Guest Editorial
\nUsing Health Information Technology
\nto Support Evidence-Based Practice
\nThe use of information technology is pervasive in our society,
\na trend which is increasingly being reflected in healthcare environments
\nwith the rise in the use of electronic health records
\n(EHRs) and associated patient portals for patients to access
\ntheir health records. For example, in 2009, the U.S. government
\nprovided $19 billion through the American Recovery and
\nReinvestment Act to healthcare organizations with the goal of
\npromoting the uptake and use of EHRs (Blumenthal, 2009).
\nThe rise in the use of personal devices and smart phone apps
\nto monitor health, together with the use of social media and
\nonline forums to discuss healthcare issues, also is increasing.
\nThis rise in the availability of technology, both in healthcare
\nsettings and in the wider population provides unique opportunities
\nfor supporting evidence-based practice (EBP) across
\nhealthcare settings. Evidence-based practice has been defined
\nas ?a life-long problem-solving approach to the delivery of care
\nthat integrates the best evidence from well-designed studies
\nand evidence-based theories (i.e., external evidence) with a clinician?s
\nexpertise, which includes internal evidence gathered
\nfrom a thorough patient assessment and patient data, and a patient?s
\npreferences and values? (Melnyk & Newhouse, 2014, p.
\n347). Arguably, the use of EBP and associated methods of systematically
\nreviewing and appraising evidence for quality have
\nbeen made possible due to the ability of technology to support
\nthe storage, annotation and retrieval of research studies quickly
\nand easily.
\nIn the early days of EBP, a significant focus was on teaching
\nclinicians the information search, retrieval and appraisal skills
\nthey required to be able to identify evidence they needed ?at
\nthe bedside.? However, a number of studies have consistently
\nhighlighted how clinicians rely on other sources, such as peer
\nadvice, to support their decisions mainly due to the number
\nof barriers encountered (e.g., such as time and resources available)
\nin trying to access evidence electronically and interpret it
\nappropriately in a busy healthcare environment. One solution
\nto this problem is to develop functionality within EHR systems
\nto provide evidence-based advice and guidance to clinicians at
\nthe point of care. For example, some systems use ?info buttons?
\nthat are located in the EHR system (e.g., for instance in
\nthe order entry module), which when clicked on by a clinician,
\nprovide context specific information related to the location such
\nas the results of a PubMed search for a specific question related
\nto the drug order (Bakken et al., 2008). However, while
\novercoming some of the major barriers faced by clinicians in
\naccessing up-to-date clinical information, this solution does not
\nhelp clinicians in terms of guidance for care delivery.
\nClinical guidelines summarize the best research evidence
\nand is often integrated with expert advice to provide an overview
\nof the care that should be provided for a group of patients either
\nin a particular healthcare context or across the healthcare
\ncontinuum. However, as above, the implementation and adoption
\nof guidelines into care has been problematic; guideline
\ndocuments are often lengthy and complex, and clinicians may
\nneed to remember the guidelines for a number of different
\npatient conditions. Increasingly, the recommendations from
\nevidence-based guidelines are being integrated into EHR systems
\nto provide a structure and content for what information
\nshould be collected, giving alerts or prompts to clinicians on
\nappropriate diagnoses and interventions that should be chosen
\n(Savinon, Taylor, Canty-Mitchell, & Blood-Siegfried, 2012). A
\nfurther development in EHR systems is that of clinical decision
\nsupport which ?provide clinicians with patient specific assessments
\nor recommendations to aid clinical decision making?
\n(Kawamoto, Houlihan, Balas, & Lobach, 2005, p. 765). Similar
\nto the integration of guidelines into the EHR, clinical decision
\nsupport works by matching patient characteristics to a computerized
\nknowledgebase using decision rules or algorithms,
\nresulting in a provision of guidance for action (Dowding et al.,
\n2009). Both the integration of clinical guidelines and the use
\nof clinical decision support in EHR systems has been shown to
\nimprove clinicians? evidence-based practice behaviors, such as
\nincreased adherence to guideline recommendations and improvements
\nin health prevention interventions (Garg et al.,
\n2005; McGinn et al., 2013; Savinon et al., 2012). Through the
\ncomputerization of guidelines and the development of decision
\nsupport, the ability of clinicians to access and use the best
\nevidence from high-quality studies and combine that with data
\nfrom the patient assessment and other relevant data is increasing
\nrapidly. However, existing technologies are currently failing
\nto take advantage of the broader availability of data from patients
\nto inform their decisions, and often ignore one element
\nof evidence-based decisions completely: that of the patient?s
\nvalues and beliefs.
\nIndividuals have the capacity to generate vast quantities of
\nhealth-related data through their day-to-day activities; existing
\nsmart phone health apps enable users to monitor a number
\nhealth indicators such as diet, exercise, blood pressure, glucose
\nmeasurements, and have the potential to be automatically
\nshared with their healthcare provider?s EHR system to provide
\nup to date data, which can be monitored over time to detect
\ntrends in health status. This moves beyond the current state,
\nwhere all data incorporated into an EHR is normally generated
\nWorldviews on Evidence-Based Nursing, 2015; 12:3, 129?130. 129
\nC 2015 Sigma Theta Tau International
\nEditorial
\nby the healthcare providers and is often relevant to a single
\npoint in time or episode of care. There is also increasing focus
\non making use of ?big data? and precision medicine, linking
\ndata about patients (e.g., genetic, physiological, and behavioral)
\nin a way to be able to predict with precision what interventions
\nwould work for individual patients in a particular context. The
\nability to collect data about all patients (regardless of whether
\nor not they are in a clinical trial), learn what works for whom,
\nand then feed it back into sophisticated decision support systems
\nis where we can use technology to both use evidence
\nto inform practice and generate evidence from that practice
\n(Bakken, 2001; Yu, 2015).
\nInvolving patients in decisions about their health care and
\nensuring that those decisions take patients? values and preferences
\ninto account is the final component of an evidence-based
\ndecision. Decision aids have been used as a way of explaining
\ndecision options to patients, exploring the benefits and
\nharms of those options, and encouraging them to consider the
\noptions in relation to their own values or preferences. Using
\ndecision aids has been shown to improve patients? knowledge
\nabout the decisions, feel more informed about their choices,
\nhave more realistic expectations about the benefits and harms
\nof different options and participate more in the decision process
\n(Stacey et al., 2014). However, the use of such decision
\naids in routine medical practice is still limited; a recent IOM
\ndiscussion paper highlights that there are significant barriers
\nto the widespread adoption of shared decision making approaches
\nin medical practice; one of the key barriers is the
\nlack of integration of tools (such as decision aids) into existing
\nhealthcare information systems and providing the infrastructure
\n(in terms of relevant and appropriate information,
\nand organization of workflows) to support patients? decisions
\n(Alston et al., 2014).
\nSo where are we in terms of effectively utilizing technology
\nto support evidence-based decision making in healthcare
\norganizations? We have developed ways of providing evidence
\nto clinicians at the point of decision making as well as techniques
\nthat encourage and support them to make intervention
\ndecisions based on evidence-based guidelines. However,
\nwe need to ensure we work with technology developers to incorporate
\npatients? preferences and values into those systems,
\nand utilize existing data across the spectrum from community
\nto hospital to ensure that healthcare decisions are truly
\nreflective of evidence, expertise, and patient preferences and
\nvalues. WVN
\nDawn Dowding, PhD, RN,
\nEditorial Board Member
\n(E-mail: dd2724@columbia.edu)
\nReferences
\nAlston, C., Berger, Z., Brownlee, S., Elwyn, G., Fowler Jr., F. J.,
\nHall, L. K., . . . Henderson, D. (2014). Shared decision-making
\nstrategies for best care: Patient decision aids. Washington DC: Institute
\nof Medicine.
\nBakken, S. (2001). An informatics infrastructure is essential for
\nevidence-based practice. Journal of the American Medical Informatics
\nAssociation, 8(3), 199?201.
\nBakken, S., Currie, L. M., Lee, N. J., Roberts, W. D., Collins, S.
\nA., & Cimino, J. J. (2008). Integrating evidence into clinical
\ninformation systems for nursing decision support. International
\nJournal of Medical Informatics, 77(6), 413?420.
\nBlumenthal, D. (2009). Stimulating the adoption of health information
\ntechnology. New England Journal of Medicine, 360, 1477?
\n1479.
\nDowding, D., Randell, R., Mitchell, N., Foster, R., Thompson, C.,
\nLattimer, V., & Cullum, N. (2009). Experience and nurses use of
\ncomputerised decision support systems. Studies in Health Technology
\nand Informatics, 146, 506?510.
\nGarg, A., Adhikari, N., McDonald, H., Rosas-Arellano, M., Devereaux,
\nP., Beyene, P., . . . Haynes, B. (2005). Effect of computerized
\nclinical decision support systems on practitioner
\nperformance and patient outcomes. A systematic review. JAMA,
\n293(10), 1223?1238.
\nKawamoto, K., Houlihan, C., Balas, E., & Lobach, D. (2005). Improving
\nclinical practice using clinical decision support systems:
\nA systematic review of trials to identify features critical to success.
\nBritish Medical Journal, 330, 765?768.
\nMcGinn, T. G., McCullagh, L., Kannry, J. L., Knaus, M., Sofianou,
\nA., Wisnivesky, J., & Mann, D. M. (2013). Efficacy of an evidencebased
\nclinical decision support in primary care practices. A Randomized
\nclinical trial. JAMA Internal Medicine, 173(13), 1584?
\n1591.
\nMelnyk, B. M., & Newhouse, R. (2014). Evidence-based practice
\nversus evidence-informed practice: A debate that could stall forward
\nmomentum in improving healthcare quality, safety, patient
\noutcomes and costs. Worldviews on Evidence-Based Nursing, 11(6),
\n347?349.
\nSavinon, C., Taylor, J. S., Canty-Mitchell, J., & Blood-Siegfried, J.
\n(2012). Childhood obesity: Can electronic medical records customized
\nwith clinical practice guidelines improve screening and
\ndiagnosis? Journal of the American Academy of Nurse Practitioners,
\n24(8), 463?471.
\nStacey, D., Legar ? e, F., Col, N., Bennett, C., Barry, M., Eden, K., . . . ?
\nWu, J. (2014). Decision aids for people facing health treatment
\nor screening decisions. Cochrane Database of Systematic Reviews
\n1, Art. No.: CD001431. doi: 10.1002\/14651858.CD001431.pub4
\nYu, P. (2015). Big data and the promise of precision medicine
\nin cancer. The ASCO Post, 6(4). Retrieved from: http:\/\/www.
\nascopost.com\/issues\/march-10,-2015\/big-data-and-the-promiseof-precision-medicine-in-cancer.aspx
\ndoi 10.1111\/wvn.12093
\nWVN 2015;12:129?130
\n130 Worldviews on Evidence-Based Nursing, 2015; 12:3, 129?130.
\nC 2015 Sigma Theta Tau International
\nCopyright of Worldviews on Evidence-Based Nursing is the property of Wiley-Blackwell and
\nits content may not be copied or emailed to multiple sites or posted to a listserv without the
\ncopyright holder’s express written permission. However, users may print, download, or email
\narticles for individual use.<\/p>\n
\nWeek 5 Journal Assignment?Part 1:<\/p>\n
\nWeek 5 Journal Assignment?Part 2 (Practicum Journal Article Summary): <\/p>\n
\nWeek 5 Journal Assignment?Part 3 (Practicum Onsite Visits):<\/p>\n","protected":false},"excerpt":{"rendered":"
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