Electronic medical record (EMR) systems have been in use for more than 2 decades. Studies documenting nursing satisfaction with an EMR system, the benefits of an EMR, implementation barriers, user acceptance, the importance of staff buy-in, and the importance of attitudes toward and expectations from user buy-in are in the literature. Central to many studies is the importance of nursing staff buy-in to the successful implementation and ongoing use of an EMR, as well as the dependency of buy-in on staff attitudes and expectations. Buy-in is a precursor to effective use. Consequently, staff buy-in is a prerequisite to collecting and making optimum use of the data contained in an EMR. Data collected from an EMR containing rich, accurate documentation of nursing interventions and patient responses support evidence-based practice changes and documentation of the import of the care provided by nurses.

Cognitive artifacts--information displays that inform thought processes and increase knowledge--fulfill a fundamental role in distributed cognition. Cognitive work--the mental processes of selecting and evaluating data, reasoning, and making decisions--is guided and informed by cognitive artifacts, especially in clinical areas. The importance of cognitive artifacts to cognitive work suggests the need to study and comprehensively understand cognitive artifacts prepared and used by the clinical nurses and how these documents influence and guide nursing practice. This article identifies and describes the attributes of effectively constructed cognitive artifacts using the concept analysis process described by Walker and Avant.

Objective To develop and test a statistical model which correctly predicts the approval of outpatient referrals when reviewed by a specialty service based on nine discriminating variables. Design Retrospective cross-sectional study. Setting Large public county hospital system in a southern US city. Participants Written documents and associated data from 500 random adult referrals made by primary care providers to various specialty services during the course of one month. Main outcome measures The resulting correct prediction rates obtained by the model. Results The model correctly predicted 78.6% of approved referrals using all nine discriminating variables, 75.3% of approved referrals using all variables in a stepwise manner and 74.7% of approved referrals using only the referral total word count as a single discriminating variable. Conclusions Three iterations of the model correctly predicted at least 75% of the approved referrals in the validation set. A correct prediction of whether or not a referral will be approved can be made in three out of four cases.