Methods, systems, and devices for determining mutli-party collocation
AssigneeCerner Innovation, Inc.
InventorsDaniel Aycock, Mario Manese, Colin Wurtz
Life of this patent
Estimating the term…
Abstract
Methods, systems, and computer-readable media are provided for analyzing amounts of time spent by clinicians caring for patients. Clinician locations, patient locations, patient data, and clinician electronic health record activity may be used to facilitate generation of one or more predictive models. Such predictive models will provide, among other things, a decision support tool for scheduling patient-clinician appointments to minimize, at least partially, patient wait times.
Claims20
A system comprising:
- a wireless communication device for communicating a device identifier;
- a plurality of wireless receivers, wherein each of the plurality of wireless receivers for receiving the device identifier communicated by the wireless communication device, determine a signal strength, and generate a time stamp associated with the receipt of the device identifier;
- one or more processors;
- and
- computer readable instructions that cause the one or more processors to perform operations comprising:
- receiving the device identifier, the signal strengths, and the associated time stamp from a set of the plurality of wireless receivers;
- based on the signal strengths and the associated time stamp, determining a location of the wireless communication device at a first time;
- based on the location of the wireless communication device, determine that a provider associated with the wireless communication device is at the determined location;
- and
- based on the time stamp, the determined location, and the provider determine that the provider and a patient are collocated.
One or more computer storage media having computer-executable instructions embodied thereon that, when executed, facilitate a method of time data analysis, the method comprising:
- receiving time data, the time data comprising an amount of time a set of patients and a set of clinicians were collocated;
- receiving patient data for the set of patients;
- tuning a predictive model by supplying the predictive model the time data and the patient data corresponding to input variables in the predictive model;
- receiving a clinic appointment request for a particular patient;
- and
- in response to receiving the new appointment request utilizing the tuned predictive model to identify one or more recommended appointment times and durations.
A computer implemented method for using broadcast identifiers to predictively generate optimized scheduling, the method comprising:
- receiving a plurality of device identifiers, signal strengths, corresponding time stamps, and wireless receiver identifiers from a set of wireless receivers for receiving device identifiers broadcast by a set of devices, each device of the set of devices associated with a health care clinician;
- segmenting the plurality of device identifiers, signal strengths, corresponding time stamps, and wireless receiver identifiers by device identifier thereby generating a per-device set of data for each device identifier comprising a plurality of signal strengths, corresponding time stamps, and wireless receiver identifiers;
- determining, for each per-device set of data, a location of the device associated with the device identifier at each time stamp based on the signal strengths and the wireless receiver identifiers, thereby generating an enhanced per-device set of data;
- receiving patient data for a set of patients associated with the health care provider from an electronic health records (EHR) system, wherein the set of patients are identified at least partially based on the location of the device at each time stamp;
- training a predictive model by providing the clinician locations, patient location, and patient data as training inputs to the predictive model, wherein the predictive model generates appointment durations and appointment slots as output;
- and
- receiving an appointment request for a particular patient;
- receiving the particular patient's patient data from the EHR system;
- scheduling the appointment for a length of time determined by the predictive model.
Dependencies are inferred from the claim text.