Workplace risk determination and scoring system and method

Abstract

A system and method for the collection and processing of workplace, public and private data to predict and score risk incident frequency and severity for a commercial client. In one embodiment, the risk assessment may be performed using one or more machine learning techniques.

1 . A computer-implemented method, comprising:
 capturing, by a data capture module, one or more pieces of internal data for an entity about a workplace hazard and one or more pieces of external data about the workplace hazard for the entity that together form a plurality of data points for the workplace hazard risk of the entity;   feeding the data points into a plurality of compliance modules;   processing, by each of the plurality of compliance modules, the plurality of data points to generate a safety compliance factor that assesses a compliance of the entity for workplace and a safety behavior factor that assesses a set of behaviors of the entity, and a module significance factor that assesses a significance of each compliance module relative to a workplace hazard incident probability, and wherein the compliance modules use the safety compliance factors, safety behavior factors, and module significance factors to each generate a safety factor;   generating, by a risk score generation module, an evolving risk score of the probability and severity of the workplace hazard for the entity based on the safety factors for each of the compliance modules for the entity; and   generating, by a user interface generation module, a user interface that displays the evolving risk score to an authorized user of the entity.   
     
     
         2 . The method of  claim 1 , wherein each of the plurality of data points grade risk and predict probability of an injury from the workplace hazard for the entity. 
     
     
         3 . The method of  claim 1 , wherein the risk score generation module uses a recursive machine learning process to generate the evolving risk score. 
     
     
         4 . The method of  claim 1 , wherein risk score generation module is configured to weight all of the safety factors for all of the compliance modules, and wherein the evolving risk score is updated when more data points are introduced. 
     
     
         5 . The method of  claim 1 , wherein the plurality of compliance modules further comprises a safety compliance module that generates a safety module compliance factor, an environmental conditions module that generates an environmental conditions factor, a personnel health conditions module that generates a personnel health conditions factor, a personnel geospatial monitoring module that generates a personnel geospatial monitoring factor and a public safety and risk module that generates a public safety and risk factor and wherein generating the risk score further comprises weighting each of the safety module compliance factor, the environmental conditions factor, the personnel health conditions factor, the personnel geospatial monitoring factor and the public safety and risk factor to generate the risk score. 
     
     
         6 . The method of  claim 1 , wherein performing the scoring process further comprises receiving, at an incidence probability and consequence prediction module, each of the factors from each of the compliance modules, weighing each of the factors with a location weighting factor and an organizational saturation factor, wherein the location weighting factor is equal to a number of humans at each jobsite of the entity as a proportion of the total humans employed by the entity and the organizational saturation factor measures a use of technology by the humans in the entity for workplace hazard compliance. 
     
     
         7 . The method of  claim 6 , wherein generating the risk score further comprises generating a letter grade indicative of the probability and severity of the workplace hazard for the entity. 
     
     
         8 . The method of  claim 3 , wherein the performing the scoring process using the recursive machine learning further comprises feeding back the weighted safety factors into the recursive machine learning process and reweighting the safety factors from each compliance module based on the fed back weighted safety factors. 
     
     
         9 . The method of  claim 6 , wherein the incidence probability and consequence module further configures the processor to feed back the weighted safety factors into the recursive machine learning process and reweight the safety factors from each compliance module based on the fed back weighted safety factors. 
     
     
         10 . A computing device comprising:
 one or more processors; and   a memory including instructions that, when executed by the one or more processors, cause the one or more processors to:
 determine a workplace hazard incident probability and severity for an entity; 
 capture one or more pieces of internal data for an entity about a workplace hazard and one or more pieces of external data about the workplace hazard for the entity that together form a plurality of data points for the workplace hazard risk of the entity; 
 feed the data points into a plurality of compliance modules; 
 wherein each compliance module is configured to process the data points to generate a safety compliance factor that assesses a compliance of the entity for workplace risk, a safety behavior factor that assesses a set of behaviors of the entity for workplace risk against a similar sized company, and a module significance factor that assesses a significance of the particular compliance module relative to a workplace hazard incident probability, wherein each compliance module generates a safety factor; 
 generate an evolving risk score for a workplace risk probability indicator and a workplace risk severity indicator for the entity based on the safety factors for each of the compliance modules for the entity; and 
 generate a user interface that displays the evolving risk score to an authorized user of the entity. 
   
     
     
         11 . The computing device of  claim 10 , wherein each of the plurality of data points grade risk and predict probability of an injury from the workplace hazard for the entity. 
     
     
         12 . The computing device of  claim 10 , wherein the risk score generation module uses a recursive machine learning process to generate the evolving risk score. 
     
     
         13 . The computing device of  claim 10 , wherein each of the compliance modules use the safety compliance factors, safety behavior factors, and module significance factors to each generate the safety factors. 
     
     
         14 . The computing device of  claim 10 , wherein generating the evolving risk score further includes weighing all of the safety factors for all of the compliance modules, and wherein the evolving risk score is updated when more data points are introduced. 
     
     
         15 . The computing device of  claim 10 , wherein each compliance module is further configured to generate a module factor and wherein the incidence probability and consequence module is further configured to combine each of the module factors for each of the plurality of compliance modules to generate the risk score. 
     
     
         16 . The computing device of  claim 10 , wherein the instructions further cause the processor to:
 receive each of the module factors from each of the compliance modules, weigh each of the module factors with a location weighting factor and an organizational saturation factor, wherein the location weighting factor is equal to a number of humans at each jobsite of the entity as a proportion of the total humans employed by the entity and the organizational saturation factor measures a use of technology by the humans in the entity for workplace hazard compliance.   
     
     
         17 . The computing device of  claim 16 , wherein the instructions further cause the processor to:
 generate a letter grade indicative of the probability and severity of the workplace hazard for the entity.   
     
     
         18 . The computing device of  claim 10 , further comprising one or more computing devices each having a display that displays the user interface with the risk score. 
     
     
         19 . The system of  claim 10 , wherein the instructions further cause the processor to:
 determine a set of new weighting factors and feeding back the set of new weighting factors and wherein weighing each of the factors in the incidence probability and consequence prediction module further comprises weighting each of the factors using the set of new weighting factors.   
     
     
         20 . The system of  claim 10 , wherein the instructions further cause the processor to:
 determine a set of new weighting factors, to feed back the set of new weighting factors and to weight each of the module factors using the set of new weighting factors.