Medical failure pattern search engine

Abstract

A patient safety search engine and alarm processor is programmed to repetitively search the electronic medical records of all patients in a hospital system to automatically provide early detection of patients with evolving pathophysiologic cascades, and in particular the cascades of evolving death, such as cascades of septic shock. The search engine also searches for a wide range of evolving pathophysiologic failures which are commonly fatal if detected too late. An alarm processor is provided which is programmed to provide an alarm upon the detection of a cascade or failure. The processor is further be programmed to provide an image of the cascade, and to determine, the severity of the cascade, and the time of onset of the cascade in relation to the timing and type of procedures and treatment and the increased cost associated with the cascade. Discretionary real-time system-wide searches for a wide range of clinical failure patterns or images within the hospital system may be performed using the disclosed patient safety search engine.

1 . A hospital monitoring system comprising:
 a database comprising an electronic medical record repository of data wherein the electronic medical record repository of data contains the data of a plurality of patients in at least one hospital; and   a processor programmed with instructions for:
 searching the database for at least one complex cascade pattern within the data of the plurality of patients and to identify one or more patients generating the at least one complex cascade pattern. 
   
     
     
         2 . The hospital monitoring system of  claim 1 , comprising an alarm processor programmed with instructions to provide an automatic alarm upon the detection of the complex cascade pattern. 
     
     
         3 . The hospital monitoring system of  claim 1 , wherein the complex cascade pattern comprises a cascade of at least one of sepsis, severe sepsis, septic shock, microcirculatory failure, and shock. 
     
     
         4 . The hospital monitoring system of  claim 1 , wherein the complex cascade pattern comprises at least a plurality of linked perturbations and/or trends of physiologic and laboratory data creating a progressively enlarging aggregation of progressively greater numbers of perturbed physiologic and laboratory data. 
     
     
         5 . The hospital monitoring system of  claim 1 , further programmed to determine at least one characteristic of the complex cascade pattern, the characteristic comprising at least one of a severity of the complex cascade pattern, a duration of the complex cascade pattern, a time of onset of the complex cascade pattern, a maturity of the complex cascade pattern, a timing relationship of the complex cascade pattern to other events or another complex cascade pattern, a cost associated with the complex cascade pattern, a global pattern of the complex cascade pattern, a time of termination of the complex cascade pattern, components of the complex cascade pattern, a state of evolution of the complex cascade pattern, a length of stay subsequent to, or in association with the complex cascade pattern, or treatments associated with the complex cascade pattern. 
     
     
         6 . The hospital monitoring system of  claim 5 , wherein the at least one characteristic of the complex cascade pattern is defined by at least one of a number of perturbations and/or trends which comprise the complex cascade pattern, a severity of the perturbations and/or trends, a number of systems affected by the complex cascade pattern, a presence, number and/or severity of failure of compensation in response to perturbations associated with the complex cascade pattern. 
     
     
         7 . The hospital monitoring system of  claim 1 , wherein the processor comprises instructions for determining a rate of growth of the complex cascade pattern. 
     
     
         8 . The hospital monitoring system of  claim 1 , wherein the processor comprises instructions for determining the rate of growth of the complex cascade pattern by at least one of the increase in number and/or severity of new perturbations being added per unit time, the increase number of systems affected, and the increase number of perturbations present in different systems. 
     
     
         9 . The hospital monitoring system of  claim 1 , wherein the processor comprises instructions for detecting events or components that are temporally and/or spatially associated with the complex cascade pattern but that are not part of the complex cascade pattern. 
     
     
         10 . The hospital monitoring system of  claim 1 , wherein the processor comprises instructions for converting the electronic medical records into a format favorable for searching for the complex cascade pattern. 
     
     
         11 . The hospital monitoring system of  claim 10 , wherein the format comprises sequential and timed variations comprised of at least positive variations and negative variations of the data. 
     
     
         12 . The hospital monitoring system of  claim 10 , wherein the electronic medical record repository of data comprises data from a plurality hospitals, and wherein the processor is programmed comprises instructions for identifying the patients that are generating complex cascade patterns and the hospital in which they are located. 
     
     
         13 . A patient data processing system comprising a processor programmed to:
 convert the electronic medical records of at least one hospital into sequential and timed trends comprised of at least positive trends and negative trends of both the physiologic parameters and the laboratory data,   detect relational trends comprised of a combination of positive and/or negative trends,   detect complex cascade patterns comprised of a plurality of combinations of relational trends,   automatically output a display of the image of the detected complex cascade,   automatically output a warning indicating the detection of the complex cascade and the identification of the patient generating the complex cascade,   track the growth or decline of the complex cascade and output an indication indicative of growth or decline.   
     
     
         14 . The patient data processing system of  claim 13 , wherein the complex cascade pattern is indicative of physiologic failure. 
     
     
         15 . The patient data processing system of  claim 13 , wherein the physiologic failure is at least one of sepsis, severe sepsis, septic shock, and microcirculatory failure, a shock cascade, and a septic shock cascade. 
     
     
         16 . The patient data processing system of  claim 13 , wherein the processor comprises instructions for to determining and outputting an indication of the type of the cascade detected. 
     
     
         17 . The patient data processing system of  claim 13 , wherein the processor comprises instructions for to determining and outputting at least an indication of the timing and type of the trends along the cascade. 
     
     
         18 . The patient data processing system of  claim 13 , wherein the processor comprises instructions for to determining and outputting at least an indication of the length of the cascade. 
     
     
         19 . The patient data processing system of  claim 13 , wherein the processor comprises instructions for to detecting the onset of therapy determining and outputting at least an indication of timing of therapy in relation to the cascade. 
     
     
         20 . A patient data processing system for processing electronic medical records of a hospital, comprising a processor programmed to:
 generate a time-series of data of at least a portion of a plurality of patients in the hospital, including at least data relating to the physiologic state and/or care of each patient;   convert the datasets, including at least the monitored datasets and laboratory datasets into parallel and overlapping time series;   identify occurrences comprising inflammatory occurrences, metabolic occurrences, volumetric occurrences, hemodynamic occurrences, therapy occurrences, hematologic occurrences, or respiratory occurrences;   identify the timing of the occurrences;   identify at least one relational pattern of occurrences along a plurality of time series that is indicative of failure cascade of at least one of a sepsis cascade, a pulmonary embolism cascade, a metabolic cascade, or a microcirculatory failure cascade; and   output an alarm when the failure cascade is detected.