Article Preview
TopBackground
Organizational issues are frequently encountered barriers to the implementation and adoption of clinical decision support (CDS) systems in health care settings. According to the Agency for Healthcare Research and Quality (AHRQ), failure to understand organizational and cultural issues may affect the adoption and use of CDS systems (HHS, 2009). Implicit in the AHRQ statement is that CDS adoption and use can significantly impact the quality and performance of health care through the influence of select organizational factors. Recent research suggests that structural differences in the health care organization may explain greater performance variance than patient factors alone (Soban & Yano, 2005). In particular, organizational factors can serve as inhibitors or facilitators in the adoption and implementation of any new technology, such as a clinical decision support system or the conceptually similar clinical information system (CIS) (Weiner, Savitz, Bernard, & Pucci, 2004).
The Chronic Care Model (CCM) describes clinical decision support as a practice to promote clinical care that is consistent with scientific evidence and patient preferences, and it involves efforts to embed evidence-based guidelines into daily clinical practice, share evidence-based guidelines, and enhance provider decision making through proven provider education methods (Haggstrom, 2010; Sperl-Hillen et al., 2004). CCM defines a clinical information system as a set of tools and processes enabling the organization of patient and population data in order to facilitate efficient and effective care. CIS tools include encounter reminders, flowcharts, tracking lists of high-risk patients due to lack of screening adherence, follow-up, or other recommendations (Haggstrom, 2010; Sperl-Hillen et al., 2004). Henceforth, the authors will use the composite term CDS/IS or simply CDS as indicative of a combined concept of comprehensive capability in this area.
A close examination of CDS use in community health centers reveals that approximately 40% (or 3,160) of all 7,900 CHCs in the United States have some form of Electronic Health Record (EHR) in use today (Lardiere, 2010). The EHR will be an essential component in the eventual deployment of specialized clinical decision support systems supporting disease-specific target areas. Seventy percent of the community health centers with EHRs (2,212) also use some form of clinical decision support such as electronic dashboards, data repositories, tele-health technologies, kiosks, or other technologies (Lardiere, 2010). However, less than 28% of all 7,900 CHCs use some form of clinical decision support for practices such as cancer screening. These statistics are reinforced by the 2009 Commonwealth Fund National Survey of Federally Qualified Health Centers (Abrams et al., 2010). The survey of 1000 community health centers found that despite 40% of the community health centers having electronic medical record capability, the capacity for more advanced health information technology (e.g., electronically ordering prescriptions and tests, creating and maintaining patient registries, tracking patients and tests, and providing alerts or prompts) varied tremendously among centers (Abrams et al., 2010). Organizational factors, such as budget priorities and technology affordability, remain inhibiting factors to widespread CDS adoption and use (Lardiere, 2010, Abrams et al., 2010).
A number of incentives motivate health care organizations to adopt computerized clinical decision support (CDS), including cost savings, clinical performance improvement, improved clinician decision making, adherence to evidence-based guidelines, medical error reduction, and more efficient information transfer (Bates et al., 2001; Bates et al., 1999; Doebbeling, Chou, & Tierney, 2006; Reid et al., 2005). CDS has also been shown to have a positive impact on reducing health disparities (HHS, 2010). However, slower adoption of CDS within institutions that provide care to historically underserved populations could result in even greater health disparities (Shields et al., 2007).