SYSTEM, METHOD AND COMPUTER PROGRAM PRODUCT FOR PERFORMING AUTOMATIC SURVEILLANCE AND TRACKING OF ADVERSE EVENTS

• US 20090216555A1
• Filed: 02/22/2008
• Published: 08/27/2009
• Est. Priority Date: 02/22/2008
• Status: Abandoned Application

• Current Assignee: Change Healthcare LLC (UnitedHealth Group Incorporated)
• Original Assignee: McKesson Automation Incorporated (Omnicell Inc)
• Inventors: Coultas, Anne, Elder, Patricia, Mitchell, Andrea Claire, Bulger, Deborah J., Gasowski, Alice M., Aller, Kathleen C., Yeager, Timothy
• Application Number:
  US12/035,478
• Publication Number:
  US 20090216555A1
• Time in Patent Office: Days
• US Class Current: 705/3
• CPC Class Codes: G06Q 10/00 Administration; Management
• CPC Class Codes: G16H 10/60 for patient-specific data, e.g. for electronic patient records
• CPC Class Codes: G16H 20/10 relating to drugs or medications, e.g. for ensuring correct administration to patients
• CPC Class Codes: G16H 70/40 relating to drugs, e.g. their side effects or intended usage

Abstract

A system, method and computer program product are provided for automatically detecting and tracking adverse events. The system may include one or more data sources configured to provide a combination of clinical, operational and financial data associated with each of a plurality of patients. The system may further include a network entity configured to receive at least part of the combination of data and to apply one or more trigger rules to the combinations of data to identify one or more suspected adverse events. The network entity may further be configured to receive an indication of one or more confirmed adverse events from among the suspected adverse events, and to automatically compile at least part of the combination of data associated with respective patients in association with which the confirmed adverse events occurred. The system may further include a user device configured to enable performance of a root cause analysis of the compiled data.

Claims

1. A system comprising:

one or more data sources configured to provide a combination of clinical, operational and financial data associated with respective patients of a plurality of patients;

a network entity in electronic communication with respective data sources in order to receive at least part of the combinations of data, said network entity configured to;

apply one or more trigger rules to the combinations of data received in order to identify one or more suspected adverse events;

receive an indication of one or more confirmed adverse events from among the one or more suspected adverse events; and

automatically compile at least part of the combination of data associated with respective patients in association with which the confirmed adverse events occurred; and

a user device in electronic communication with the network entity, said user device configured to enable performance of a root cause analysis of the compiled data.

2. The system of claim 1, wherein the one or more suspected adverse events comprise one or more suspected adverse drug events, and wherein the one or more confirmed adverse events comprise one or more confirmed adverse drug events.

3. The system of claim 2, wherein the combination of data comprises data associated with one or more of patient demographics, one or more drugs administered, one or more lab results received, one or more procedures, one or more patient conditions, date and time of admittance, date and time of discharge, and one or more costs incurred.

4. The system of claim 3, wherein the data associated with one or more drugs administered comprises a time and a dosage of respective drugs administered.

5. The system of claim 2, wherein the network entity is configured to apply at least one trigger rule that is configured to determine whether a particular sequence of events occurred with respect to the patient.

6. The system of claim 2, wherein the network entity is configured to apply at least one trigger rule that is configured to determine whether one or more events occurred at a specific time with respect to the occurrence of another event.

7. The system of claim 2, wherein the one or more confirmed adverse drug events are determined based at least in part on a causality factor and a severity level associated with respective suspected adverse drug events, and wherein the network entity is configured to compile data comprising the causality factor and severity level associated with respective confirmed adverse drug events.

8. The system of claim 2, wherein the network entity is further configured to:

receive an indication of one or more confirmed adverse drug events not associated with the one or more suspected adverse drug events.

9. The system of claim 2, wherein the network entity is further configured to:

automatically compile at least part of the combination of data associated with respective patients in association with which the suspected adverse drug events occurred.

10. The system of claim 9, wherein in order to compile at least part of the combination of data associated with respective patients in association with which suspected and confirmed adverse drug events occurred, the network entity is further configured to create a Medication Safety Scorecard comprising one or more performance metrics associated with the one or more suspected and confirmed adverse drug events.

11. The system of claim 10, wherein the one or more performance metrics comprise one or more of a rate of suspected and confirmed adverse drug events, a length of stay associated with suspected and confirmed adverse drug events, an excess cost associated with suspected and confirmed adverse drug events, a percentage of returns to an emergency department with a confirmed adverse drug event, a percentage of readmissions with a confirmed adverse drug event, and an average number of days from a confirmed adverse drug event occurrence to an adverse drug event review.

12. The system of claim 11, wherein in order to enable performance of a root cause analysis of the compiled data, the user device is further configured to evaluate data underlying the one or more performance metrics in order to analyze one or more factors contributing to or associated with respective adverse drug events.

13. The system of claim 12, wherein the one or more factors comprise one or more of a facility in which the patient is located, a department in which the patient is located, a month in which the suspected or confirmed adverse drug event occurred, a day on which the suspected or confirmed adverse drug event occurred, a time at which the suspected or confirmed adverse drug event occurred, and a physician responsible for administering care for the patient.

14. The system of claim 12, wherein in order to enable performance of a root cause analysis of the compiled data, the user device is further configured to analyze one or more events leading up to respective adverse drug events and one or more trends associated with suspected and confirmed adverse drug events.

15. The system of claim 2, wherein at least one trigger rule has been identified as having a probability of accurately identifying an adverse drug event that exceeds a predefined threshold, and wherein the network entity is further configured to:

generate an alert when a suspected adverse drug event is identified as a result of applying the identified trigger rule.

16. A method comprising:

receiving a combination of clinical, operational and financial data associated with respective patients of a plurality of patients;

applying one or more trigger rules to the combinations of data in order to identify one or more suspected adverse events, wherein at least one trigger rule is configured to determine whether a particular sequence of events has occurred with respect to the patient;

receiving an indication of one or more confirmed adverse events from among the one or more suspected adverse events; and

automatically compiling at least part of the combination of data associated with respective patients in association with which the confirmed adverse events occurred, thereby facilitating performance of a root cause analysis of the compiled data.

17. The method of claim 16, wherein the one or more suspected adverse events comprise one or more suspected adverse drug events, and wherein the one or more confirmed adverse events comprise one or more confirmed adverse drug events.

18. The method of claim 17, wherein the combination of data comprises data associated with one or more of patient demographics, a time and a dosage associated with one or more drugs administered, one or more lab results received, one or more procedures, one or more patient conditions, date and time of admittance, date and time of discharge, and one or more costs incurred.

19. The method of claim 18, wherein the at least one trigger rule is configured to determine whether a second drug was administered or a particular lab result was received following the administering of a first drug.

20. The method of claim 18, wherein at least another one of the trigger rules is configured to determine whether one or more events occurred at a specific time with respect to the occurrence of another event.

21. The method of claim 20, wherein the at least another one of the trigger rules is configured to determine whether a drug was administered or a lab result was received more than a predetermined period of time after the patient was admitted.

22. The method of claim 17 further comprising:

receiving a definition of a trigger rule.

23. A computer program product for comprising at least one computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions comprising:

a first executable portion for receiving a combination of clinical, operational and financial data associated with respective patients of a plurality of patients;

a second executable portion for applying one or more trigger rules to the combinations of data in order to identify one or more suspected adverse events, wherein at least one trigger rule is configured to determine whether a particular sequence of events has occurred with respect to the patient;

a third executable portion for receiving an indication of one or more confirmed adverse events from among the one or more suspected adverse events; and

a fourth executable portion for automatically compiling at least part of the combination of data associated with respective patients in association with which the confirmed adverse events occurred, thereby facilitating performance of a root cause analysis of the compiled data.

24. The computer program product of claim 23, wherein the one or more suspected adverse events comprise one or more suspected adverse drug events, and wherein the one or more confirmed adverse events comprise one or more confirmed adverse drug events.

25. The computer program product of claim 24, wherein the combination of data comprises data associated with one or more of patient demographics, a time and a dosage associated with one or more drugs administered, one or more lab results received, one or more procedures performed, one or more patient conditions, date and time of admittance, date and time of discharge, and one or more costs incurred.

26. The computer program product of claim 25, wherein at least another one of the trigger rules is configured to determine whether one or more events occurred at a specific time with respect to the occurrence of another event.

27. A method comprising:

receiving a combination of clinical, operational and financial data associated with respective patients of a plurality of patients;

applying one or more trigger rules to the combinations of data in order to identify one or more suspected adverse events;

receiving an indication of one or more confirmed adverse events from among the one or more suspected events; and

automatically compiling at least part of the combination of data associated with respective patients in association with which the confirmed adverse events occurred; and

automatically generating one or more performance metrics based at least in part on the compiled data, wherein the performance metrics comprise a cost or a length of stay associated with confirmed adverse events corresponding to one or more severity levels, event categories, or drug classes.

28. The method of claim 27, wherein the one or more suspected adverse events comprise one or more suspected adverse drug events, and wherein the one or more confirmed adverse events comprise one or more confirmed adverse drug events.

30. The method of claim 28 further comprising:

automatically compiling at least part of the combination of data associated with respective patients in association with which the suspected adverse drug events occurred.

31. The method of claim 30, wherein automatically generating the one or more performance metrics further comprises automatically generating one or more of a rate of suspected and confirmed adverse drug events, an overall length of stay associated with suspected and confirmed adverse drug events, an overall excess cost associated with suspected and confirmed adverse drug events, a percentage of returns to an emergency department with a confirmed adverse drug event, a percentage of readmissions with a confirmed adverse drug event, and an average number of days from a confirmed adverse drug event occurrence to an adverse drug event review.

29. The method of claim 29, wherein the one or more confirmed adverse drug events are determined based at least in part on a causality factor and a severity level associated with respective suspected adverse drug events, and wherein automatically compiling data comprises compiling the causality factor and severity level associated with respective confirmed adverse drug events.