Accurate data has become essential to operate successfully in a fast-changing healthcare environment where payment increasingly is based on performance and providing high-quality care. Correct identification and access to the patient’s full medical history is of paramount importance if hospitals are to provide internationally acceptable care for their patients. Duplicate patients records are often created when patients are inadvertently added to a system multiple times or when data is entered incorrectly. The duplicate records result in inconsistent and incomplete medical records, loss of reimbursement, administrative inefficiencies, resource drains, and most significantly negative health outcomes, serious privacy breaches and legal ramifications.
Our data cleansing services helps to eliminate all data quality issues thereby ensuring accuracy, consistency and data integrity. The data cleansing process is as below:
- Assessment & Reporting: Using an extract of the facility’s MPI, our team develops SQL queries so as to perform an initial data analysis for the purpose of producing estimates regarding suspected erroneous records including duplicates, overlaps, overlays and data quality issues.
- Clean-Up: A radical Clean-Up process of the MPI is essential to eliminate duplicate medical records and all other errors. This is a highly skilled and laborious process involving electronic systems and physical records. Our team of professionals work very closely with HIM department and all the other entities involved accomplishing the process.
As this is not just an IT project, we believe success can only be achieved by bringing together people having the needed skill sets, efficient processes and applicable policies. A team of resources acquainted with record matching and validation methods supported by detailed, comprehensive and well informative IT diagnoses reports, guided and managed by experienced professionals in data cleaning is essential to assure that all the related clean-up activities are accomplished on time in accordance with best practices.
- Monitoring & Recommendation: The current data quality issues and duplicates are the result of human errors. Thus, irrespective of the system used such errors are expected to exist always. No doubt, to mitigate this, the hospital requires adequate staff training and organizational policies and procedures. However, we believe that continuous monitoring using specialized tools is the best answer to this problem.
To overcome the challenges of duplication and overlapping of data, it is important to deploy the right tool for effective data matching and cleansing. The tool that we have deployed is the D’ CLENZ data cleansing solution from GCI where the data cleansing exercise is one that can be performed on a multiple cycle basis.
D’Clenz is a bundle suite of tools, solutions and services that provides a one-stop offering to health institutions and systems to profile, analyze, cleanse, normalize, unify and enrich their patient data profiles. The offering further provides solutions and services to monitor the patient database to ensure that it remains consistent and relevant on an ongoing basis.
Profiling the patient data is essentially a process of understanding the database being analyzed on an as-is basis. The objective will be to define the scope of data validation work that will require to be performed. Once the scope is outline, the set of data validation rules are defined using the solution’s validation engine.
Based on a predefined set of rules and conditions, the current database is accessed, data is extracted in the formats required and uploaded into the validation engine. The process of validation is performed by the engine within the specifications derived at the Data Profiling stage.
The data cleansing exercise is one that can be performed on a multiple cycle basis; an initial step of data cleansing will result in the generation of a set of reports that will highlight the range and types of data issues. This first cycle of cleansing will then be reviewed where full detailed history of pre and post status of data analysis and cleansing is maintained. Further continuous cycles are performed on a step basis on the new sets of cleansed data until satisfactory level of data quality is achieved. Full audit trail of all cycles and history of all data changes and movements are maintained.
The following processes are operations that are performed on the database to correct, remove, replace, normalize, unify, modify and enrich inaccurate or duplicate patient records.
- Cleansing: Managing duplicate patient data
- Cleansing: Normalizing patient data
- Cleansing: Unifying patient data
- Cleansing: Monitoring patient data
- Cleansing: Enriching patient profiles
- Identifying and cleaning up of data quality issues
- Resolving medical records duplicates (physical and electronic)
- Eliminating any junk data caused from the old legacy system
- Time and cost saving of registration and validation of records on the new system
- Maintaining organization’s quality recommendations
- Achieving data accuracy, integrity and consistency