American Health Information Management Association’s

/in /by

In order for the flow of patient care to run efficiently and effectively from the point of admission to discharge.  Clinical and Administrative health care teams greatly rely on the availability of complete and accurate data. Complete and accurate data reflects a level of “quality data” within the health record and personal health record to examine, diagnosis, devise a health care plan and monitor the patient’s outcome/or response to the prescribed health care plan. As a participating member of the American Health Information Management Association (AHIMA), you were appointed to serve on a global health workforce aimed at developing a Data Quality Management System similar to that of AHIMA’s DQM Model for assist in modernizing health information infrastructures in other countries. The final product to submit is a proposed Health Record Content & Documentation Checklists & Procedures that include the following:

  • Compare and contrast the American Health Information Management Association’s (AHIMA’s) Data Quality Management Model (DQM) (http://bok.ahima.org/doc?oid=107773#.V_GvvfArKUk) in comparison to the Canadian Institute for Health Information (CIHI) Data Quality Framework (DQF) aka Six Dimensions of Quality (http://www.ec.gc.ca/inrp-npri/default.asp?lang=En&n=23EAF55A-1)
  • Assess the effectiveness of both models by developing two (2) separate data quality checklists based on the AHIMA DQM Model and CIHI Data Quality Framework, to randomly evaluate a sample number of inpatient health records. The desired results of an assessment, is a tool for the development of a succinct data quality management system. The checklist must assess each data quality characteristic within the two models and include at least two (2) measures to assess each data quality characteristics, a checkbox for each measure along with a comment box to record any findings, recommendations and/or notes. Using the provided Data Quality checklist as a sample below to assist you in the development of your data quality checklists.