Abstract: A computer-implemented method includes displaying a number of questions on a stack of overlapping graphical objects that dynamically indicates a status of the number of remaining questions, determining an output based on answers input by a user, and displaying an indication of the output.

Abstract: A system trains a predictive model based on training data including records of non-public healthcare insurance claims and public healthcare fee schedule data. The system determines a multiplier for each of multiple healthcare facilities based on the predictive model. The system then receives, from a consumer device, a query for a particular cost estimate of a healthcare service provided by the multiple healthcare facilities. The query includes constraints for a preferred geographic region or a preferred insurer. The system predicts a first cost estimate for the healthcare service at a first healthcare facility based on a first multiplier and a second cost estimate for the healthcare service at a second healthcare facility based on second multiplier. The system then causes the consumer device to display query results including indications of the first and second cost estimates.

Abstract: The disclosed embodiments include a method performed by server computer(s). The method includes obtaining private healthcare insurance claims data and public healthcare procedure code data from one or more data source(s), generating a model based the training data, and determining a multiplier for each of the many facilities based on the model. The model provides predictive results for variability between healthcare facilities thereby enabling consumer research in light of considerations in facility variability. The multiplier is indicative of a value used to scale the public healthcare procedure code data for a facility such that a healthcare service associated with the facility can be estimated based on the multiplier.

Abstract: Disclosed are systems and techniques for providing a master health entity index and a data analysis mechanism designed for entity cohort discovery and entity profiling. The entity can be a healthcare facility that diagnoses or treats health conditions and diseases (e.g., hospital, clinic), individuals (e.g., providers, patients, caregivers), healthcare data (e.g., medical conditions, treatments, diagnostic studies, health outcomes), etc. For example, a data analysis mechanism may identify distinctive patient cohorts based on what happened to patients in a hospital and why the occurrence happened, glean insight from the activities of the distinctive patient cohorts, and assist with additional patients with the gleaned insight.

Abstract: Introduced are methods and systems for gathering information from a healthcare consumer using context-based questions, and progressively presenting information associated with a ranked list of suggested healthcare providers. First, the server gathers information associated with the healthcare consumer using context-based questions. Each question is formulated based on the answers the healthcare consumer has provided to previous questions, so that minimal and necessary questions are asked. Second, the server progressively sends to a healthcare consumer device, information associated with a ranked list of suggested healthcare providers. The first information tier sent to the healthcare consumer device includes a graphic representing the match strength between the healthcare consumer and the healthcare provider, and the number of similar healthcare consumers the healthcare provider has treated. Based on the healthcare consumer interest, the server displays additional information to the healthcare consumer.