D'Clenz Patient Data Cleansing

Patient Data Cleansing

"Erroneously merged patient records can also be demerged."


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.

Processes

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.

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.

The 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.

The 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.

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