GCI D’CLENZ HEALTH DATACLEANSING SOLUTIONS

Enhance Care, Optimize Health Outcomes and Minimize Loss of Reimbursements with High Quality Data

www.gcinfosys.com

There can be many reasons for lack of consistent and unusable patient data, from simple human errors to non-application of standards in the process of capturing and maintaining such data. Complete and accurate patient profile is paramount to ensure correct patient identification and provision of proper care leading to better patient health outcomes. With funding and reimbursement for health services being increasingly benchmarked to outcomes and related measures, importance rendered to accuracy of patient profile is progressively finding place at the highest level of management decision making processes.

To overcome the problems associated to bad data, health organizations need to put in place sound technological foundations to identify and eliminate data quality issues.

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. In built reporting tool and analytical engines provide data and information to better manage patient and business processes.

In built reporting tool and analytical engines provide data and information to better manage patient and business processes.

GCI D’CLENZ HEALTHDATA CLEANSINGSOLUTIONSEnhance Care, Optimize Health Outcomes and Minimize Loss of Reimbursements withHigh Quality Data

D’CLENZ PATIENT DATAPROFILING

Profiling the patient data is essentially a process of understanding the database being analyzed on a 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. The scope of validation will be, though not limited to, will include

• Values:• Structure:• Relationship:• Duplication:

data complete, accurate, and standardized? pattern matching the accepted norms? expected relationship between data evident?validation to identify and extract all duplicates

A comprehensive toolkit to identify varying data quality issues can be configured to provide a detailed as-is status of the patient data quality in tables, graphs, charts and supporting views for assessment and analysis.

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 validation can be performed using multiple algorithms and analyzed to extract inappropriate data using Fuzzy Logic that applies less than strict conditions to extract patient data that seemingly are duplicated, incorrect or incomplete. The validation that can be applied to the analysis process is almost unlimited and comprehensive in nature. The use of Soundex Algorithm is exceptionally useful in extracting analyzing patient names with similar sounding vocals but with differing spelling in English. This is especially useful in dealing with Arabic names. Using the preconfigured set of reports generated during the profiling stage, the validation exercise will identify and highlight the lags in the quality of patient data within the database. Using the Analytics Tool, extensive checks and views of the extracted data; slice and dice, pivot, drill down, group, sort etc can be performed.

D’CLENZ PATIENT DATAVALIDATION

This is especially useful in dealing with Arabic names.

Cleansing: Managing Duplicate Patient Data

Erroneously merged patient records can also be demerged.

The process searches and identifies patient data validity, extracts possible duplicates for further analysis and action. The use of multiple algorithms including but not limited to Soundex Algorithm; using Phonetic Matching, is used to detect duplicate patent records. Once identified as duplicates, these records are merged into a single valid record. Erroneously merged patient records can also be demerged. The options of merging and demerging patient records can be performed thru flexible auto merge or manual options.

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.

D’CLENZ PATIENT DATACLEANSING

An integral part of patient data cleansing is an activity that involves the Normalizing Data within the database. The process of Normalizing can be one of many ways and one that demands the will and want of the institutions to move forward with patient data of good quality. The scope of Patient Data Normalization can begin with standardizing MRN field; ensuring number of name fields; limits on fields with numeric values eg postcode, telephone numbers etc,; auto filling alternate calendar dates eg Gregorian to Arabic and vice versa; and a whole lot of other possibilities. D’Clenz provides full functionality and flexibility to help manage comprehensive Patient Data Normalization.

Cleansing: Normalizing Patient Data

Cleansing: Unifying Patient DataThe institutions may operate multiple systems and may as a result have multiple patient records references and numbers. D’Clenz allows various methods and processes to unify these patient records references, consolidating to single Master Index yet maintaining references to silo databases.

D’Clenz also provides the option of allowing the healthcare institutions to enhance the quality of the Patient Demographic Profiles within the database in many ways. Updating data like addresses, postcodes, contacts, etc can all be done thru various methods; one for example using external databases to verify and update postcodes. Enhancing the quality of patient data thru, completeness, accuracy, consistency, uniformity and standardization/normalization argues well in providing better patient care going forward in addition to ensuring efficacy in management and administration.

Cleansing: Enriching Patient Profiles

Cleansing: Monitoring Patient DataThe continued and consistent monitoring of cleansed data will ensure the integrity of patient data on an ongoing basis. D’Clenz provides a number a ways in which automatic and manual monitoring processes can be put in place to ensure continue maintenance of high quality patient data.

GCI suite of offerings includes sound reporting and information intelligence tools and engines that help deliver data, reports, information and intelligence to the appropriate users on a timed or on request basis. These offerings wills serve well to complement D’Clenz Data Cleansing solutions in the healthcare institutions pursuit in ensuring comprehensive data integrity on an ongoing basis within the Patient Profile database.

GCI REPORTING ANDDAILY BUSINESSINTELLIGENCE TOOLS

For a fuss-free analysis of your patient data, please call GCI RCM SDN BHD on +603 2776 6867, write to us reach-us@gcinfosys.com or visit www.gcinfosys.com