Machine learning system and method for predicting caregiver attrition

1. A method of providing business intelligence for in-home patient care, comprising:

analyzing data associated with employment history, work performance, work satisfaction of a set of caregivers responsible for providing in-home patient care, and interactions between individual patients and particular caregivers to generate analyzed data, wherein analyzing data comprises: 1) analyzing vocal communications occurring in interactions between patients and caregivers during in-home patient care monitored via audio or audiovisual sensors disposed in homes of patients while individual patients were being cared for by particular caregivers and 2) analyzing data associated with feedback from family members of patients regarding a quality of care provided by caregivers to patients;

predicting an attrition risk for each caregiver utilizing a machine learning system trained to determine an attrition risk probability for each caregiver based on features of the analyzed data; and

generating, based on the attrition risk, a user interface having graphical elements representing a risk of attrition of individual caregivers providing in-home care of patients.

2. The method of claim 1 , wherein analyzing data associated with interactions of individual patients with particular caregivers comprises identifying difficult patients for caregivers to care for.

3. The method of claim 1 , further comprising analyzing data associated at least one biometric sensor of a mobile device of at least one patient to provide biometric data indicative of individual patient stress associated with working with particular caregivers.

4. The method of claim 1 , further comprising analyzing data associated at least one biometric sensor of a mobile device of at least one caregiver to provide biometric data indicative of stress of particular caregivers associated with working with individual patients.

5. The method of claim 1 , wherein the audio or audiovisual sensors are disposed in smartwatches of at least one of the caregivers.

6. The method of claim 1 , wherein the audio or audiovisual sensors are disposed in a smartwatch of at least one of the patients.

7. The method of claim 1 , further comprising analyzing data based at least in part on at least one of web browsing behavior of the caregiver and social network behavior of the caregiver.

8. The method of claim 1 , wherein the data comprises data based at least in part on at least one biometric sensor of a mobile device of at least one caregiver to provide biometric data indicative of stress of a caregiver associated with working with individual patients.

9. The method of claim 1 , wherein the audio or audiovisual sensors disposed in a home of a patient comprises sensor data from one or more stationary audio or audiovisual sensors disposed in a home of a patient being cared for by the caregiver.

10. The method of claim 1 , wherein the data associated with interactions between individual patients and particular caregivers includes information derived from biometric sensor readings of patients taken during in-home visits by caregivers.

11. The method of claim 10 , wherein the data associated with interactions between individual patients and particular caregivers includes data from smart watches of caregivers that includes at least one of microphone data and biometric data.

12. The method of claim 1 , wherein data associated with interactions between individual patients and particular caregivers includes data from smart watches of patients that includes at least one of microphone data and biometric data.

13. A system for providing business intelligence for in-home patient care, comprising:

a database of stored information regarding caregivers providing in-home care services, the stored information including data associated with interactions between individual patients and particular caregivers including at least data associated with feedback from family members of patients regarding a quality of care provided by caregivers to patients and information associated with caregiver employment data, caregiver work performance, and caregiver work satisfaction;

a machine learning system trained to generate an output indicative of a risk of attrition of individual caregivers based on features of the stored information in the database; and

the system being configured to generate an output user interface, based on the output of the machine learning system, indicative of the risk of attrition for one or more caregivers in a set of caregivers;

wherein the data associated with interactions between individual patients and particular caregivers comprises 1) biometric data regarding stress on the part of particular caregivers during interactions with individual patients and 2) information derived from biometric sensor readings of patients taken during in-home visits by caregivers.

14. The system of claim 13 , wherein the data associated with interactions between individual patients and particular caregivers includes: 1) conversations monitored between individual patients and particular caregivers 2) information on caregiver work performance and satisfaction from a caregiver, from the patient being cared for by the caregiver, and from family members of the patient being cared for and 3) feedback from caregivers regarding specific patients served by the caregivers.

15. The system of claim 13 , wherein the data associated with interactions between individual patients and particular caregivers includes data from smart watches of caregivers that includes at least one of microphone data and biometric data.

16. The system of claim 13 , wherein data associated with interactions between individual patients and particular caregivers includes data from smart watches of patients that includes at least one of microphone data and biometric data.