USE OF CLINICAL LABORATORY DATA TO IDENTIFY INPATIENT HOSPITAL COMPLICATIONS

• US 20100023342A1
• Filed: 07/25/2008
• Published: 01/28/2010
• Est. Priority Date: 07/25/2008
• Status: Abandoned Application

• Current Assignee: Carefusion 303 Incorporated (Becton Dickinson & Co)
• Original Assignee: Cardinal Health 303 Incorporated (Cardinal Health, Inc.)
• Inventors: Darin, Robert, Johannes, Richard
• Application Number:
  US12/180,428
• Publication Number:
  US 20100023342A1
• Time in Patent Office: Days
• US Class Current: 705/2
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G16H 10/40 for data related to laboratory analysis, e.g. patient specimen analysis
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment

Abstract

Described herein are systems and methods for identification of complications by identifying physiological changes associated with these complications that are reflected in laboratory results. In some embodiments, systems and methods are described for identifying trends in the laboratory data over time and associating those trends with degrees of risk or complication factors that are established through retroactive analysis of similar laboratory data across a plurality of care facilities or patient groups. In some embodiments, a benchmark may be established, based on laboratory results, against which care facilities can retrospectively compare patient outcomes within their own facility or among various patient groups.

Claims

1. A method of identifying complications of a patient of a care facility, the method comprising:

obtaining at least a first set of laboratory data of a first patient and a second set of laboratory data of the first patient;

determining a data trend that represents a difference between the first set of data and the second set of data;

obtaining at least a first set of laboratory data of a second patient;

comparing at least part of the first set of laboratory data of the second patient with the data trend to calculate trends associated with a plurality of complications;

assigning a risk value, based on the data trend, that represents a likelihood that the patient has developed or will develop at least one complication; and

outputting, to an output module, the risk value and the at least one complication.

2. The method of claim 1, wherein the calculated trends are calculated based on the laboratory data from a plurality of patients at a plurality of care facilities.

3. The method of claim 1, wherein the first and second sets of data of the first patient include data from at least one of a blood urea nitrogen (BUN), creatinine, glucose, albumin, potassium, bilirubin, creatinine phosphokinase (CPK), hemoglobin, sodium, and white blood cell (WBC) test.

4. The method of claim 1, wherein the complications associated with the calculated trends include at least one of acute renal failure, anemia, heart failure, myocardial infarction, nonhemorrhagic stroke, pancreas disease, renal disease, asthma, acute liver disease, arterial and/or aneurysm complications, fractures, gastro-intestinal tract complications or obstruction, liver and/or biliary disorder, arrhythmia, chronic lung disorders, and pneumonia.

5. The method of claim 1, wherein the first set of data of the first patient represents a baseline value of laboratory data for the first patient upon admittance of the patient to a care facility.

6. The method of claim 5, wherein the risk value assigned to the second patient comprises a low, moderate, or high likelihood designation based on a comparison of the data trend with the baseline value.

7. The method of claim 6, wherein the low likelihood designation is assigned to a patient whose data trend has progressed to between about 25% and about 40% from the baseline value to a threshold value associated with a presence of a complication.

8. The method of claim 6, wherein the moderate likelihood designation is assigned to a patient whose data trend has progressed to between about 35% and about 60% from the baseline value to a threshold value associated with a presence of a complication.

9. The method of claim 6, wherein the high likelihood designation is assigned to a patient whose data trend has progressed between about 50% and about 75% from the baseline value to a threshold value associated with a presence of a complication.

10. The method of claim 1, wherein the risk value assigned to the second patient comprises a percentage of the likelihood that the patient has developed or will develop a complication.

11. A method of identifying the likelihood of complications of a patient, the method comprising:

obtaining at least a set of the patient'"'"'s laboratory data;

comparing at least a part of the patient'"'"'s laboratory data to threshold values associated with a plurality of complications, the threshold values being based on laboratory data from a plurality of patients at a plurality of care facilities;

assigning a risk value, based on the patient'"'"'s laboratory data in view of the threshold values, that represents a likelihood that the patient has developed or will develop at least one complication; and

outputting, to an output module, the risk value and the at least one complication.

12. The method of claim 11, wherein the patient'"'"'s laboratory data include data from at least one of a blood urea nitrogen (BUN), creatinine, glucose, albumin, potassium, bilirubin, creatinine phosphokinase (CPK), hemoglobin, sodium, and white blood cell (WBC) test.

13. The method of claim 11, wherein the complications associated with the threshold values include at least one of acute renal failure, anemia, heart failure, myocardial infarction, nonhemorrhagic stroke, pancreas disease, renal disease, asthma, acute liver disease, arterial and/or aneurysm complications, fractures, gastrointestinal tract complications or obstruction, liver and/or biliary disorder, arrhythmia, chronic lung disorders, and pneumonia.

14. The method of claim 11, wherein the risk value assigned to the patient comprises a low, moderate, or high likelihood designation based on a comparison of the patient'"'"'s laboratory data with the threshold value.

15. The method of claim 11, wherein the risk value assigned to the patient comprises a percentage of the likelihood that the patient has developed or will develop a complication.

16. A system, for determining a likelihood of a patient outcome, comprising:

a database, comprising at least one of a result and a trend of laboratory data, the at least one of the result and the trend being associated with an outcome of a plurality of patients;

a processor that receives laboratory results for a patient, compares the results with the at least one of the result and the trend to determine a likelihood of the patient'"'"'s outcome, and outputs a signal indicative of the likelihood; and

an output module, connected to the processor, configured to receive the signal and to communicate the likelihood of the patient'"'"'s outcome.

17. The system of claim 16, wherein the outcome comprises whether the patient has developed or will develop a complication.

18. A computer readable medium, comprising instructions that, when executed, cause a computing device to perform a method comprising:

obtaining at least a set of a patient'"'"'s laboratory data;

comparing at least part of the patient'"'"'s laboratory data to threshold values associated with a plurality of complications, the threshold values being based on laboratory data from a plurality of patients at a plurality of care facilities;

assigning a risk value, based on the patient'"'"'s laboratory data in view of the threshold values, that represents a likelihood that the patient has developed or will develop at least one complication; and

generating an output signal indicative of the risk value and the at least one complication.