How does your HIM department measure up? A national benchmarking survey of acute care facilities will give you some answers. Take a look at your colleagues’ practice patterns. HIM professionals often are asked to provide information on how their department practices compare to those in similar organizations. The information requested concerns how tasks are performed, various quality measures, and productivity standards. Unfortunately, there is little data available in professional literature to answer these questions. Benchmarking fills this need by determining what best practices are and how they are achieved.
To be done well, investigation of best practices requires site visits and personal interviews. But because time and money often are not available to conduct such detailed assessments, members of the HIM and Systems Division of the Ohio State University Health Sciences Center, with the assistance of an AHIMA grant, conducted a survey to collect baseline information on HIM hospital department practices. A descriptive research study was designed to obtain initial data on HIM practices in acute care facilities. The objectives of the survey were to: obtain descriptive information on HIM professionals working in acute care determine turnaround times for HIM department functions determine standard procedures for discharge analysis determine productivity standards for HIM department functions describe the extent to which electronic storage is used determine which department functions are outsourced determine the status of implementation of electronic signature, optical imaging, and universal chart order We used the AHIMA membership list to identify HIM directors in acute care facilities. From this list, we randomly selected 1,000 practitioners to participate in the study.
In January 1998, the survey was mailed to these practitioners and 200 useable surveys were returned. Because only 20 percent of the surveys were returned, we assessed the validity of the conclusions drawn from survey results. One way to determine whether survey respondents match the underlying population parameters is to conduct a Chi Square Goodness of Fit Test, which compares how closely the observed values of a variable match the values derived from a theoretical distribution. A non-significant test result indicates that the respondent profile matches that of the population from which it was drawn. This test was conducted on two variables?the respondent’s hospital size and his or her credential (see table 1).
The results were not statistically significant for hospital size, meaning that our sample matched the population on the hospital size variable. However, the result was statistically significant for the credential variable, indicating that the proportion of RHITs and RHIAs in the sample did not match the demographics of AHIMA membership. Therefore, results may be generalized only to practice patterns by hospital size and not by credential. Other limitations of this research include non-response bias and misinterpretation of survey questions.
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