ABSTRACT
This project consisted of evaluating a computer-assisted instruction (CAI) module for a patient controlled Analgesia (PCA) pump. Evaluations of the CAI included a pre and post written knowledge test to measure the subject's knowledge about the PCA pump before and after completing the CAI. User satisfaction of the CAI was measured using the Questionnaire for User Interaction Satisfaction (QUIS). Eight subject improved their post-test score over their pre-test score. Analysis using the Wilcoxon-Signed Ranks Test produced a maximum signed rank sum of 36, which indicates effectiveness with significance at the 0.005 level. The overall QUIS score was 6.53 out of a possible score of 9. A score of 6.53 indicates a moderate level of satisfaction. Observation while completing the CAI indicated satisfaction with the CAI. While there was satisfaction with the CAI, the nurses noted some problems with the ability to correct errors and with system speed. Overall, the nurses felt the CAI would be beneficial if the problems noted could be fixed.
Subject(s)
Analgesics/administration & dosage , Computer-Assisted Instruction , Infusion Pumps , Adult , Humans , Surveys and Questionnaires , Task Performance and Analysis , UtahABSTRACT
Paper-based clinical practice standards usually pertain to a single diagnosis or clinical condition. When a computerized provider order entry system applies multiple paper-based practice standards to one patient, it generates an order list containing redundant orders. Nurses respond to redundant orders on the basis of their level of nursing expertise with clinical care, computers, and practice standard domains. In this project, orders from three practice standards were manually combined to create a single order list, resulting in 15 duplicate and overlapping orders. A relational database was developed to test the automated removal of redundant orders. As expected, the automatically evaluated order list contained only one of each duplicate order and only the parent of each overlapping order group. Orders were then grouped by related system or function for readability. It is possible to automate the removal of duplicate and overlapping orders from an order list before display to the nurse.
Subject(s)
Database Management Systems/standards , Decision Support Systems, Clinical/organization & administration , Medical Records Systems, Computerized/organization & administration , Nurse's Role , Algorithms , Attitude of Health Personnel , Attitude to Computers , Clinical Competence/standards , Decision Trees , Guideline Adherence/standards , Health Services Needs and Demand , Humans , Intuition , Judgment , Nursing Assessment , Nursing Evaluation Research , Nursing Staff/education , Nursing Staff/psychology , Point-of-Care Systems/standards , Practice Guidelines as Topic , Systematized Nomenclature of Medicine , Vocabulary, ControlledABSTRACT
OBJECTIVE: To develop a model to store information in an electronic medical record (EMR) for the management of transplant patients. The model for storing donor information must be designed to allow clinicians to access donor information from the transplant recipient's record and to allow donor data to be stored without needlessly proliferating new Logical Observation Identifier Names and Codes (LOINC) codes for already-coded laboratory tests. DESIGN: Information required to manage transplant patients requires the use of a donor's medical information while caring for the transplant patient. Three strategies were considered: (1) link the transplant patient's EMR to the donor's EMR; (2) use pre-coordinated observation identifiers (i.e., LOINC codes with *(wedge)DONOR specified in the system axes) to identify donor data stored in the transplant patient's EMR; and (3) use an information model that allows donor information to be stored in the transplant patient's record by allowing the "source" of the data (donor) and the "name" of the result (e.g., blood type) to be post-coordinated in the transplant patient's EMR. RESULTS: We selected the third strategy and implemented a flexible post-coordinated information model. There was no need to create new LOINC codes for already-coded laboratory tests. The model required that the data structure in the EMR allow for the storage of the "subject" of the test. CONCLUSION: The selected strategy met our design requirements and provided an extendable information model to store donor data. This model can be used whenever it is necessary to refer to one patient's data from another patient's EMR.
Subject(s)
Medical Records Systems, Computerized , Tissue Donors , Tissue and Organ Procurement/organization & administration , Databases as Topic , Forms and Records Control , Humans , Information Management , Logical Observation Identifiers Names and Codes , Organ Transplantation , Tissue Donors/classification , Tissue Donors/statistics & numerical dataABSTRACT
The objectives of this project were to 1) use a multi-disciplinary pain assessment information model to identify concepts required in Logical Observation Identifiers, Names and Codes (LOINC), 2) submit the proposed LOINC codes to the LOINC committee, and 3) have LOINC names and codes created for others to use.
Subject(s)
Logical Observation Identifiers Names and Codes , Pain Measurement/classification , Humans , Information ScienceABSTRACT
Information required to manage transplant patients and donors is complex, voluminous and requires the reporting and use of one person's medical information within another person's record. One strategy using a vocabulary model (i.e., LOINC codes with *DONOR specified in the system axes) will lead to problems with combinatorial explosion. After evaluating workflow processes, data collection forms, decision support and functional requirements, we designed and implemented an extendable information model to support the process of care following liver transplantation.
