Claims:We Claim:

1. A method for generating one or more actions in a healthcare domain, the method comprising:
retrieving, by a clinical action generating system (102), data (208) comprising patient-specific information (214) and surveyed information (216) from a plurality of data sources (101), wherein the data (208) is associated with a plurality of patients;
analyzing, by the clinical action generating system (102), the data (208) to classify each of the plurality of patients into a predefined disease category and a medical finding category;
generating, by the clinical action generating system (102):
a set of profiles for each of the plurality of patients based on the data (208),
a plurality of clusters based on the classification of each of the plurality of patients and the set of profiles, wherein each cluster comprises two or more patients having similarity in at least one of the predefined disease category, the medical finding category, and the set of profiles,
a trend model based on the plurality of clusters, wherein the trend model comprises a trend of a plurality of diseases and a rate of recovery of the plurality of diseases based on existing procedures and existing medications associated with each of the plurality of the patients, and
a plurality of scores corresponding to the plurality of clusters based on the trend model;
ranking, by the clinical action generating system (102), the plurality of clusters based on the plurality of scores; and
generating, by the clinical action generating system (102), one or more actions based on the ranking, wherein the one or more actions comprises a new procedure and a new medication.

2. The method as claimed in clam 1, wherein the plurality of data sources (101) comprises at least one of clinical sources, hospice item set (HIS), legacy system, social media, blogs and journals.

3. The method as claimed in clam 1, wherein:
the patient-specific information (214) comprises at least one of diagnosis summary, health parameters or results of a medical test conducted upon the patient, and
the surveyed information (216) comprises at least one of sample size of a population, criticality of patients, post-medication observations or other findings by the research practitioners and doctors.

4. The method as claimed in clam 1 further comprising building, by the clinical action generating system (102), a learning model based on the patient-specific information (214) and the surveyed information (216).

5. The method as claimed in clam 1 further comprising a data exchange policy for exchanging the patient-specific information between a plurality of devices associated with a plurality of entities, wherein the plurality of entities indicates healthcare institutions.

6. The method as claimed in clam 1 further comprising extracting, by the clinical action generating system (102), etiological features from the data (208), wherein the etiological features indicate one or more causes for the plurality of diseases present in the predefined disease category.

7. The method as claimed in clam 1, wherein the set of profiles comprises at least one of a persona, a family profile and a genetic profile.

8. The method as claimed in claim 1 further comprising creating, by the clinical action generating system (102), a knowledge database based on the data (208) retrieved, the set of profiles, the plurality of clusters and the trend model.

9. The method as claimed in claim 1 further comprising:
receiving, by the clinical action generating system (102), a query from a user, wherein the query is formed using one or more Boolean operators;
executing, by the clinical action generating system (102), the query to retrieve two or more clusters from the plurality of clusters stored in the knowledge database, wherein the two or more clusters retrieved are related to each other; and
correlating, by the clinical action generating system (102), the two or more clusters to generate one or more correlated-clusters, wherein the one or more correlated-clusters is generated based on at least one of symptoms, observations, procedures, medication outcomes, rate of change of test parameters and health parameters.

10. A clinical action generating system (102) for generating one or more actions in a healthcare domain, the system comprising:
a processor; and
a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, causes the processor to:
retrieve data (208) comprising patient-specific information (214) and surveyed information (216) from a plurality of data sources (101), wherein the data (208) is associated with a plurality of patients;
analyze the data (208) to classify each of the plurality of patients into a predefined disease category and a medical finding category;
generate:
a set of profiles for each of the plurality of patients based on the data (208),
a plurality of clusters based on the classification of each of the plurality of patients and the set of profiles, wherein each cluster comprises two or more patients having similarity in at least one of the predefined disease category, the medical finding category, and the set of profiles,
a trend model based on the plurality of clusters, wherein the trend model comprises a trend of a plurality of diseases and a rate of recovery of the plurality of diseases based on existing procedures and existing medications associated with each of the plurality of patients, and
a plurality of scores corresponding to the plurality of clusters based on the trend model;
rank the plurality of clusters based on the plurality of scores; and
generate one or more actions based on the ranking, wherein the one or more actions comprises a new procedure and a new medication.

11. The clinical action generating system (102) as claimed in clam 10, wherein the plurality of data sources (101) comprises at least one of clinical sources, hospice item set (HIS), legacy system, social media, blogs and journals.

12. The clinical action generating system (102) as claimed in clam 10, wherein:
the patient-specific information (214) further comprises at least one of diagnosis summary, health parameters and results of a medical test conducted upon a patient, and
the surveyed information (216) comprises at least one of sample size of a population, criticality of patients, observations been made after certain medication and other findings by the research practitioners and doctors.

13. The clinical action generating system (102) as claimed in clam 10, wherein the processor is further configured to build a learning model based on the patient-specific information (214) and the surveyed information (216).

14. The clinical action generating system (102) as claimed in clam 10, wherein the processor further facilitates a data exchange policy for exchanging the patient-specific information between a plurality of devices associated with a plurality of entities, wherein the plurality of entities indicates healthcare institutions.

15. The clinical action generating system (102) as claimed in clam 10, wherein the processor is further configured to extract etiological features from the data (208), wherein the etiological features indicate one or more causes for the plurality of diseases present in the predefined disease category.

16. The clinical action generating system (102) as claimed in clam 10, wherein the set of profiles comprises at least one of a persona, a family profile and a genetic profile.
17. The clinical action generating system (102) as claimed in claim 10, wherein the processor is further configured to create a knowledge database based on the data (208) retrieved, the set of profiles, the plurality of clusters and the trend model.

18. The clinical action generating system (102) as claimed in claim 10, wherein the processor is further configured to:
receive a query from a user, wherein the query is formed using one or more Boolean operators;
execute the query to retrieve two or more clusters from the plurality of clusters stored in the knowledge database, wherein the two or more clusters retrieved are related to each other; and
correlate the two or more clusters to generate one or more correlated-clusters, wherein the one or more correlated-clusters is generated based on at least one of symptoms, observations, procedures, medication outcomes, rate of change of test parameters and health parameters.

Dated this 30th day of August, 2016

Swetha SN
Of K&S Partners
Agent for the Applicant
, Description:TECHNICAL FIELD

The present disclosure relates in general to data processing. More particularly, but not exclusively, the present disclosure discloses a method and system for generating clinical actions in a healthcare domain.