The cumulative influence of conflict on nursing home staff.

Nursing staff burnout is a significant challenge in the delivery of nursing home care. Using a representative sample of nursing staff working within the nursing home setting, our analysis addressed the influence of conflict with residents' families on the burnout experience of staff. Through the use of computer simulation modeling we were able to assess the cumulative effects of conflict between staff and families. Findings indicated that conflict with the residents' families increased both burnout and dissatisfaction among nursing staff. The burnout experience of nursing staff peaked with initial episodes of conflict, then leveled off as simulated conflict with family members continued. Because previous research has indicated that burnout tends to peak early in nurses' career cycle, the finding that initial episodes of conflict have a strong influence on nursing staff burnout highlights the importance of interpersonal conflict within nursing homes in both individual and institutional outcomes.

Preventing technology-induced errors in healthcare: the role of simulation.

We describe a novel approach to the study and prediction of technology-induced error in healthcare. The objective of our approach is to identify and reduce the potential for error so that the benefits of introducing information technology, such as Computerized Physician Order Entry (CPOE) or Electronic Health Records (EHRs), are maximized. The approach involves four phases. In Phase 1, we typically conduct small scale clinical simulations to assess whether or not the use of a new information technology can introduce error. (Human subjects are involved and user-system interactions are recorded.) In Phase 2, we analyze the results from Phase 1 to identify statistically significant relationships between usability issues and the occurrence of error (e.g., medication error). In Phase 3, we enter the results from Phase 2 into computer-based simulation models to explore the potential impact of the technology over time and across user populations. In Phase 4, we conduct naturalistic studies to examine whether or not the predictions made in Phases 2 and 3 apply to the real world. In closing, we discuss how the approach can be used to increase the safety of health information systems.

The need for organizational change in patient safety initiatives.

OBJECTIVES: This study describes a computer simulation model that has been developed to explore organizational changes required to improve patient safety based on a medication error reporting system. METHODS: Model parameters for the simulation model were estimated from data submitted to the MEDMARX medication error reporting system from 570 healthcare facilities in the U.S. The model's results were validated with data from the Pittsburgh Regional Healthcare Initiative consisting of 44 hospitals in Pennsylvania that have adopted the MEDMARX medication error reporting system. The model was used to examine the effects of organizational changes in response to the error reporting system. Four interventions were simulated involving the implementation of computerized physician order entry, decision support systems and a clinical pharmacist on hospital rounds. CONCLUSIONS: Results of the analysis indicate that improved patient safety requires more than clinical initiatives and voluntary reporting of errors. Organizational change is essential for significant improvements in patient safety. In order to be successful, these initiatives must be designed and implemented through organizational support structures and institutionalized through enhanced education, training, and implementation of information technology that improves work flow capabilities.

Modeling the costs and outcomes of cardiovascular surgery.

Coronary artery bypass graft (CABG) operations consume more health care resources than any other single procedure. The objective of this study was to develop a computer simulation model that can be used to predict costs and patient outcomes of CABG surgery. The analysis is based on a systems dynamic model developed using STELLA software. Two sets of data from Medicare patients who underwent CABG operations at Methodist Hospital of Indiana were used to construct and validate the model. The model predictions of length of hospital stay, use of specialists in caring for patients, costs and postoperative functional status are reasonably close to actual data on patients who underwent CABG surgery. The analysis indicates the most important factors affecting costs and outcomes are gender, age, whether or not the surgery is a reoperation and whether the patient experiences postoperative complications. The model can be used to predict costs and outcomes for a patient population from a small set of preoperative characteristics (i.e., age, gender, DRG, whether the surgery is a reoperation, and the patient's operative status). A second potential use of the model is to answer clinical questions such as do the costs and risks of CABG operations outweigh the benefits for patients with certain risk factors.