PATIENT FALL LIKELIHOOD AND SEVERITY
An example system for estimating an injury risk relating to a fall for a patient can include: at least one processor; and memory encoding instructions which, when executed by the at least one processor, cause the at least one processor to: access first data associated with an estimated likelihood of a fall for the patient; access second data associated with an estimated severity of a fall for the patient; and calculate a score based upon the first data and the second data.
1 . A system for estimating an injury risk relating to a fall for a patient, the system comprising:
at least one processor; and
memory encoding instructions which, when executed by the at least one processor, cause the at least one processor to:
access first data associated with an estimated likelihood of a fall for the patient;
access second data associated with an estimated severity of a fall for the patient; and
calculate a score based upon the first data and the second data.
2 . The system of claim 1 , wherein the memory encodes further instructions which, when executed by the at least one processor, cause the at least one processor to:
access demographic data associated with the patient;
access health state data associated with the patient; and
use the demographic data and the health state data to provide the estimated severity of a fall for the patient.
3 . The system of claim 2 , wherein the memory encodes further instructions which, when executed by the at least one processor, cause the at least one processor to:
access activity data for the patient; and
use the demographic data, the health state data, and the activity data to provide the estimated severity of a fall for the patient.
4 . The system of claim 1 , wherein the memory encodes further instructions which, when executed by the at least one processor, cause the at least one processor to:
develop an immediate risk of fall model using the first data;
develop an attribute risk of fall model using the first data; and
use the immediate risk of fall model and the attribute risk of fall model to provide the estimated likelihood of a fall for the patient.
5 . The system of claim 1 , wherein the memory encodes further instructions which, when executed by the at least one processor, cause the at least one processor to provide an alert when the score exceeds a threshold.
6 . The system of claim 5 , wherein the alert provides additional contextual information about the patient. The system of claim 5 , wherein the alert is configurable by the caregiver.
8 . The system of claim 7 , wherein the memory encodes further instructions which, when executed by the at least one processor, cause the at least one processor to allow the caregiver to configure alerting based upon the threshold or a change in the score.
9 . The system of claim 7 , wherein the memory encodes further instructions which, when executed by the at least one processor, cause the at least one processor to allow the caregiver to configure alerting based upon who is alerted, when alerts are sent, and how alerts are delivered.
10 . The system of claim 1 , wherein the memory encodes further instructions which, when executed by the at least one processor, cause the at least one processor to generate a user interface to display the score.
11 . The system of claim 1 , wherein the memory encodes further instructions which, when executed by the at least one processor, cause the at least one processor to generate a user interface to display the score, a fall likelihood score, and a fall severity score.
12 . A method for estimating an injury risk relating to a fall for a patient, the method comprising:
accessing first data associated with an estimated likelihood of a fall for the patient;
accessing second data associated with an estimated severity of a fall for the patient; and
calculating a score based upon the first data and the second data.
13 . The method of claim 12 , further comprising:
accessing demographic data associated with the patient;
accessing health state data associated with the patient; and
using the demographic data and the health state data to provide the estimated severity of a fall for the patient.
14 . The method of claim 13 , further comprising:
accessing activity data for the patient; and
using the demographic data, the health state data, and the activity data to provide the estimated severity of a fall for the patient.
15 . The method of claim 12 , further comprising:
developing an immediate risk of fall model using the first data;
developing an attribute risk of fall model using the first data; and
using the immediate risk of fall model and the attribute risk of fall model to provide the estimated likelihood of a fall for the patient.
16 . The method of claim 12 , further comprising providing an alert when the score exceeds a threshold.
17 . The method of claim 16 , wherein the alert provides additional contextual information about the patient.
18 . The method of claim 17 , further comprising allowing the caregiver to configure alerting based upon the threshold or a change in the score.
19 . The method of claim 17 , further comprising allowing the caregiver to configure alerting based upon who is alerted, when alerts are sent, and how alerts are delivered.
20 . The method of claim 12 , further comprising generating a user interface to display the score, a fall likelihood score, and a fall severity score.