The 3Rs program: the Colorado experience.

COPIC Insurance Company is a Colorado-based medical liability carrier. From October of 2000 to the present, it has employed an early intervention program called the 3Rs program standing for recognize, respond, and resolve. The program emphasizes disclosure, transparency, apology, and patient benefits. The origins of the program, its operational aspects, and results of implementation are discussed.

Patient perspectives of patient-provider communication after adverse events.

OBJECTIVE: To explore patient perceptions of patient-provider communication after an actual adverse medical event because prior patient error studies are rarely based on real situations. DESIGN: We conducted four patient focus groups using a semi-structured guide. We analyzed transcripts using an editing approach to identify themes. SETTING: Three sites in Colorado. STUDY PARTICIPANTS: participants were recruited from statewide post-injury program. Purposeful sampling began with patients in a geographic location; we contacted every other patient (up to 50). Twenty-two patients initially agreed to participate; 16 adults participated, representing 13 cases. RESULTS: Complex issues and processes were involved in resolution attempts. Effective communication was an important factor in whether professional relationships continued after an adverse event. The communication nature and quality influenced whether patients defined event as 'honest mistake' or 'error'. Two types of trauma (physical and emotional) were expected and found. A third (financial) uncovered and proved in some cases the most salient factor influencing patients' subsequent actions. Caring, honest, quick, personal, and repeated provider responses were linked to patient satisfaction. CONCLUSIONS: Provider communication timeliness and quality were important influences on patients' responses to adverse events. Confronting an adverse medical event collaboratively helped both patients and providers with patients' emotional, physical, and financial trauma and minimized the anger and frustration commonly experienced. Health organizations, providers, investigators, and policymakers should consider the patient experience when developing provider training or evaluating processes in patient resolution.

Developing and testing a model to predict outcomes of organizational change.

OBJECTIVE: To test the effectiveness of a Bayesian model employing subjective probability estimates for predicting success and failure of health care improvement projects. DATA SOURCES: Experts' subjective assessment data for model development and independent retrospective data on 221 healthcare improvement projects in the United States, Canada, and The Netherlands collected between 1996 and 2000 for validation. METHODS: A panel of theoretical and practical experts and literature in organizational change were used to identify factors predicting the outcome of improvement efforts. A Bayesian model was developed to estimate probability of successful change using subjective estimates of likelihood ratios and prior odds elicited from the panel of experts. A subsequent retrospective empirical analysis of change efforts in 198 health care organizations was performed to validate the model. Logistic regression and ROC analysis were used to evaluate the model's performance using three alternative definitions of success. DATA COLLECTION: For the model development, experts' subjective assessments were elicited using an integrative group process. For the validation study, a staff person intimately involved in each improvement project responded to a written survey asking questions about model factors and project outcomes. RESULTS: Logistic regression chi-square statistics and areas under the ROC curve demonstrated a high level of model performance in predicting success. Chi-square statistics were significant at the 0.001 level and areas under the ROC curve were greater than 0.84. CONCLUSIONS: A subjective Bayesian model was effective in predicting the outcome of actual improvement projects. Additional prospective evaluations as well as testing the impact of this model as an intervention are warranted.