Clinical Knowledge Model for the Prevention of Healthcare-Associated Venous Thromboembolism.

Knowledge models inform organizational behavior through the logical association of documentation processes, definitions, data elements, and value sets. The development of a well-designed knowledge model allows for the reuse of electronic health record data to promote efficiency in practice, data interoperability, and the extensibility of data to new capabilities or functionality such as clinical decision support, quality improvement, and research. The purpose of this article is to describe the development and validation of a knowledge model for healthcare-associated venous thromboembolism prevention. The team used FloMap, an Internet-based survey resource, to compare metadata from six healthcare organizations to an initial draft model. The team used consensus decision-making over time to compare survey results. The resulting model included seven panels, 41 questions, and 231 values. A second validation step included completion of an Internet-based survey with 26 staff nurse respondents representing 15 healthcare organizations, two electronic health record vendors, and one academic institution. The final knowledge model contained nine Logical Observation Identifiers Names and Codes panels, 32 concepts, and 195 values representing an additional six panels (groupings), 15 concepts (questions), and the specification of 195 values (answers). The final model is useful for consistent documentation to demonstrate the contribution of nursing practice to the prevention of venous thromboembolism.

Implementation of a two-specimen requirement for verification of ABO/Rh for blood transfusion.

BACKGROUND: This study presents our implementation of a two-specimen requirement with no prior record of ABO/Rh to verify patients' blood type before transfusion. MATERIALS AND METHODS: Blood type verification was introduced, discussed, approved, and implemented over a 12-month period (May 2007 to May 2008). Potential barriers and impact on benchmark indicators were identified and tracked. RESULTS: Inpatient identification and/or specimen labeling for nursing and laboratory phlebotomists baseline corrected error rates were 1:467 and 1:5555, respectively. This study therefore sought and obtained approval to initiate a new policy of blood type verification before blood transfusion. Compliance in turnaround time (TAT) before and after implementation for completion of STAT type and screen/crossmatch within 60 minutes worsened marginally, from 90% to 80%. The impact on use of O-, uncrossmatched blood was found to be manageable. Seven (of 25 total) recorded electronic complaints were received after implementation. The corrected error rate for nurse phlebotomy draws after implementation was 1:630. CONCLUSION: Despite the lack of an instigating event, verification of blood type before blood transfusion was successfully implemented. An impact on resources and benchmark indicators such as TAT can be anticipated and managed. Further process improvement efforts will be needed to ensure safety (e.g., at time of blood transfusion) for patients receiving blood transfusions. ABO/Rh verification may be necessary even after future implementation of bar coding and/or RFID chips, because human errors continue to occur even with systems improvements.