Products

Over the existence of the past several years, Kreara has built innovative products that enable cutting-edge analysis, relationship building, modeling and accurate predicting, building and deploying of systems-biology models etc. Our products are very user-friendly and adept at dealing with complexity. They help users conduct virtual experiments and use the results in an intuitive manner. Our products include an electronic CRF platform, an electronic randomization frame work, an artificial neural network for cancer prognosis prediction, and a SAS programming quality control framework.

Sopanam

Sopanam is a client specific web based tracking application, developed in-house by Kreara Solutions Pvt Ltd with in-built audit trails which helps follow the progress of SAS programming and quality checking tasks.

This application can be used to assign project managers and team members to different projects and also to assign specific SAS programming tasks to team members.

This application helps in tracking the progress of the SAS programming tasks by project managers at Kreara and the client representatives. We further intend to include capturing of review comments through this application thereby enhancing its capabilities to a Project Management and QC comments tracking tool.

The user friendly interface can be set up separately for different clients, thereby ensuring information security for each client. Such a tracking system not only helps easy assignment, logging-in of the status of tasks but also helps concerned personnel follow up progress of projects easily.

EMR

An electronic medical record (EMR) is a computerized medical record created in an organization that delivers care, such as a hospital or physician's office. Electronic medical records tend to be a part of a local stand-alone health information system that allows storage, retrieval and modification of records. The various types of records that could be captured as part of an EMR system will include the following and more.

• Patient Registration/Demographics which will output a unique Patient ID
• Medical History of the patient
• Individual Patient Encounter Details
• Details of Prescription/Drug Dispensed
• Hospital/Care Center Details
• Doctor Database which will include qualifications and specializations
• Support Staff Details
• Laboratory Encounters and Results
• Periodic vaccination details outside the health centers

Currently most of the systems that capture health records stay in the form of disparate data centers and the government does not have any effective mechanism to consolidate this data into a state data center. A major issue is that the private health care providers till date does not form part of the state health reporting system without which the health statistics will not be complete. The following diagram envisions a data architecture which will enable the state to consolidate the data from the following.

Public Hospitals – This will include the Primary Health Centers, Taluk Hospitals, District Hospitals, General Hospitals, Medical Colleges and specialty clinics like RCC, SCT, SAT etc.

Private Hospitals – Each of these hospitals store the health records in the form of a paper record or a custom made database with no interface into the state health database. An efficient EMR system should envisage a method to translate the private healthcare database into the central database.

Laboratory Data - There are thousands of private labs and testing centers across the state whose data is also kept individually as disparate data sources. This EMR system will also adopt an architecture where in lab data will be transferred in batches to the state data center.

Medical Stores – The cases of drug abuse and shortage of critical illness drugs has been grabbing a lot of media attention. Hence it is equally important that the drug dispensed to each of the patients be brought into the state database.

Once effective data capture and consolidation is implemented, a periodic automated reporting and statistics dashboard will be created which could be reviewed all the way from the local body level to the state level. A predictive analytics mechanism could also be developed in the future to help the Government with early interventions to prevent the outbreak of communicable diseases which has been seriously affecting the productivity of the population of the state.