Clinical risk model

Abstract

A model-assisted system and method for predicting health care services. In one implementation, a model-assisted system may comprise a least one processor programmed to access a database storing a medical record associated with a patient and analyze the medical record to identify a characteristic of the patient. The processor may determine a patient risk level indicating a likelihood that the patient will require a health care service within a predetermined time period; compare the patient risk level to a predetermined risk threshold; and generate a report indicating a recommended intervention for the patient. The processor may further determine a calibration factor indicating a difference between an average patient risk level and an average actual healthcare service usage for a first group of patients; and determine, based on the calibration factor, a bias relative to a second group of patients.

1 . A model-assisted system for predicting health care services, the system comprising:
 at least one processor programmed to:
 access a database storing medical records associated with a plurality of patients; 
 analyze a medical record associated with a patient of the plurality of patients to identify a characteristic of the patient; 
 determine, based on the patient characteristic and using a trained machine learning model, a patient risk level indicating a likelihood that the patient will require a health care service within a predetermined time period, the machine learning model being trained based on clinical factors weighted based on logistic regression; 
 compare the patient risk level to a predetermined risk threshold; and 
 generate, based on the comparison, a report indicating a recommended intervention for the patient; 
 determine a calibration factor indicating a difference between an average patient risk level and an average actual healthcare service usage for a first group of the plurality of patients; and 
 determine, based on the calibration factor, a bias associated with the first group relative to a second group of the plurality of patients. 
   
     
     
         2 . The system of  claim 1 , wherein the medical record comprises structured data associated with the patient and analyzing the medical record comprises analyzing the structured data. 
     
     
         3 . The system of  claim 1 , wherein the medical record comprises unstructured data associated with the patient and analyzing the medical record comprises analyzing the unstructured data. 
     
     
         4 . The system of  claim 1 , wherein the patient characteristic comprises prior use of medical services by the patient. 
     
     
         5 . The system of  claim 1 , wherein the patient characteristic comprises an indication of a medical diagnosis for the patient. 
     
     
         6 . The system of  claim 1 , wherein the patient characteristic comprises at least one of a laboratory or diagnostic test result for the patient. 
     
     
         7 . The system of  claim 1 , wherein the at least one processor is further configured to schedule the recommended intervention for the patient. 
     
     
         8 . The system of  claim 10 , wherein the at least one processor is further configured to generate, based on the patient risk level, a priority level for the patient, and wherein the recommended intervention is scheduled based on the priority level. 
     
     
         9 . The system of  claim 1 , wherein the at least one processor is further configured to:
 generate reports indicating recommended interventions for a plurality of patients; and   schedule, based on the reports, the recommended interventions for the plurality of patients within the predetermined time period.   
     
     
         10 . The system of  claim 1 , wherein the at least one processor is further configured to generate a report indicating the bias. 
     
     
         11 . The system of  claim 1 , wherein the first group comprises patients having a first ethnicity and the second group comprises patients having a second ethnicity. 
     
     
         12 . The system of  claim 1 , wherein the at least one processor is further configured to determine a confidence interval representing a range of values for the calibration factor associated with a particular degree of confidence. 
     
     
         13 . A computer-assisted method for predicting health care services, the method comprising:
 accessing a database storing a medical record associated with a patient;   analyzing the medical record to identify a characteristic associated with the patient;   determining, based on the patient characteristic and using a trained machine learning model, a patient risk level indicating a likelihood that the patient will require a health care service within a predetermined time period, the machine learning model being trained based on clinical factors weighted based on logistic regression;   comparing the patient risk level to a predetermined risk threshold; and   generating, based on the comparison, a report indicating a recommended intervention for the patient;   determining a calibration factor indicating a difference between an average patient risk level and an average actual healthcare service usage for a first group of the plurality of patients; and   determining, based on the calibration factor, a bias associated with the first group relative to a second group of the plurality of patients.   
     
     
         14 . The method of  claim 13 , wherein the medical record comprises structured data associated with the patient and analyzing the medical record comprises analyzing the structured data. 
     
     
         15 . The method of  claim 13 , wherein the medical record comprises unstructured data associated with the patient and analyzing the medical record comprises analyzing the unstructured data. 
     
     
         16 . The method of  claim 13 , wherein the patient characteristic comprises prior use of medical services by the patient. 
     
     
         17 . The method of  claim 13 , wherein the patient characteristic comprises an indication of a medical diagnosis for the patient. 
     
     
         18 . The method of  claim 13 , wherein the patient characteristic comprises at least one of a laboratory or diagnostic test result for the patient. 
     
     
         19 . The method of  claim 13 , wherein the method further comprises scheduling the recommended intervention for the patient. 
     
     
         20 . The method of  claim 19 , wherein the method further comprises generating, based on the patient risk level, a priority level for the patient, and wherein the recommended intervention is scheduled based on the priority level. 
     
     
         21 - 27 . (canceled)