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Biomedical subjects

C L Berthelsen

Publications and source records attributed to C L Berthelsen.

8 recordsLinked to original sources

Automated personal health inventory for dentistry: a pilot study.

BACKGROUND: The authors conducted a study to investigate the feasibility of having patients enter their health histories, or HHs, directly into a computer so the HHs then can be transferred into computer-based patient records. The authors examined a patient-completed, pen-based computerized HH questionnaire to determine if it is acceptable to patients, if patients answer sensitive questions on the HH questionnaire more forthrightly using a computer than a pen and paper, and if the availability of explanations and examples provided for each question on the computer questionnaire results in more accurate responses than on the paper version. METHODS: Fifty subjects completed two almost identical versions of a 78-item HH questionnaire, completing either the pen-based, computerized version first or the paper version first. After the subjects finished the questionnaires, they completed an opinion survey about using the computer to provide their HHs. RESULTS: Subjects responded favorably to the use of a pen-based computer questionnaire to provide their HH; 73 percent indicated that they would prefer to use it in the future rather than complete a paper questionnaire. The authors found that the overall reliability of answers was 93 percent with an average of 5.4 inconsistent answers between the two HH questionnaires. CONCLUSIONS: HHs can be collected efficiently and reliably from patients using a computer. It is important, however, that oral health care professionals review the data provided on HHs with their patients regardless of method used to collect them. CLINICAL IMPLICATIONS: Practices can expand the use of computers into more areas of patient care by having patients complete a computerized HH questionnaire. Computerized data capture is more legible, complete and efficient than a paper HH and can be imported directly into clinical data systems, thus avoiding data entry.

Adolescent↗

Application of statistical inference techniques in health information management.

We have demonstrated that objective comparisons can be made using accepted statistical techniques. We have also shown that you can apply tests which don't meet the basic assumptions and still obtain valid results, in most cases. This robustness of statistics tests is particularly helpful with the type of data and analysis that health information management professionals typically deal with, where exactness of the results is not crucial. You can perform a quick analysis using simple statistical tools and obtain a P value that is fairly close to what it would be if you selected the tests more stringently. The examples of inferential statistics in this article demonstrate how to select tests based on characteristics of the data and how to interpret the results. The kinds of statistical analysis that can be performed in health information management are numerous. Below are some other ideas on how to use inferential statistics in HIM practice. 1. Set up an ordinal scale to evaluate coding accuracy to evaluate coders: Score 1 means the correct code was assigned for the principal diagnosis and only minor errors in coding among secondary diagnoses. Score 2 means the correct code was assigned for the principal diagnosis, but there are omissions or major errors among secondary diagnoses. Score 3 means a minor error in coding the principal diagnosis and only minor errors in secondary diagnoses. Score 4 means a minor error in coding the principal diagnosis and major errors or omissions in secondary diagnoses.(ABSTRACT TRUNCATED AT 250 WORDS)

Analysis of Variance↗

Evaluation of an SQL model of the HELP patient database.

We tested a new model of the HELP patient database that makes use of relational tables to store patient data and provides access to data using SQL (Structured Query Language). The SQL database required more storage space and had many more physical records than the HELP database, but it was faster and more efficient in storing data than the standard HELP utilities. The HELP utilities used disk space more efficiently and were faster than the SQL tools when retrieving data for typical clinical reports. However, the SQL model provides networking capabilities, general report writing tools, detailed user documentation, and an ability for creating secondary indexes that offset its poorer performance.

Database Management Systems↗