Identify a health information technology system, explain how it improves healthcare outcomes. Identify the organization you work for uses this system.

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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 en- vironments 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. govern- ment 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 oppor- tunities 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 clin- ician’s expertise, which includes internal evidence gathered from a thorough patient assessment and patient data, and a pa- tient’s preferences and values” (Melnyk & Newhouse, 2014, p. 347). Arguably, the use of EBP and associated methods of sys- tematically 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 avail- able) 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 but- tons” 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 re- lated 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 ei- ther in a particular healthcare context or across the healthcare continuum. However, as above, the implementation and adop- tion 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 sys- tems 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 assess- ments 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 com- puterized 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 im- provements 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 deci- sion 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 increas- ing rapidly. However, existing technologies are currently failing to take advantage of the broader availability of data from pa- tients 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, glu- cose 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

Editorial

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 sys- tems 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 prefer- ences into account is the final component of an evidence-based decision. Decision aids have been used as a way of explain- ing 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 pro- cess (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 barri- ers to the widespread adoption of shared decision making ap- proaches in medical practice; one of the key barriers is the lack of integration of tools (such as decision aids) into exist- ing healthcare information systems and providing the infras- tructure (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 technol- ogy 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 tech- niques that encourage and support them to make interven- tion decisions based on evidence-based guidelines. However, we need to ensure we work with technology developers to in- corporate patients’ preferences and values into those systems, and utilize existing data across the spectrum from commu- nity 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

(E-mail: [email protected])

References 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: In- stitute of Medicine.

Bakken, S. (2001). An informatics infrastructure is essential for evidence-based practice. Journal of the American Medical Infor- matics 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 infor- mation technology. New England Journal of Medicine, 360, 1477– 1479.

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 Tech- nology and Informatics, 146, 506–510.

Garg, A., Adhikari, N., McDonald, H., Rosas-Arellano, M., Dev- ereaux, P., Beyene, P., . . . Haynes, B. (2005). Effect of com- puterized 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). Im- proving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to suc- cess. 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 evidence- based clinical decision support in primary care practices. A Ran- domized clinical trial. JAMA Internal Medicine, 173(13), 1584– 1591.

Melnyk, B. M., & Newhouse, R. (2014). Evidence-based practice versus evidence-informed practice: A debate that could stall for- ward momentum in improving healthcare quality, safety, patient outcomes and costs. Worldviews on Evidence-Based Nursing, 11(6), 347–349.

Savinon, C., Taylor, J. S., Canty-Mitchell, J., & Blood-Siegfried, J. (2012). Childhood obesity: Can electronic medical records cus- tomized with clinical practice guidelines improve screening and diagnosis? Journal of the American Academy of Nurse Practitioners, 24(8), 463–471.

Stacey, D., Légaré, 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. ascopost.com/issues/march-10,-2015/big-data-and-the-promise- of-precision-medicine-in-cancer.aspx

doi 10.1111/wvn.12093 WVN 2015;12:129–130

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