Literature DB >> 10435841

The quality of abstracting medical information from the medical record: the impact of training programmes.

L Lorenzoni1, R Da Cas, U L Aparo.   

Abstract

OBJECTIVE: To evaluate the impact of a programme of training, education and awareness on the quality of the data collected through discharge abstracts. STUDY
DESIGN: Three random samples of hospital discharge abstracts relating to three different periods were studied. Quality control to evaluate the impact of systematic training and education activities was performed by checking the quality of abstracting medical records.
SETTING: The study was carried out at the Istituto Dermopatico dell'Immacolata, a research hospital in Rome, Italy; it has 335 beds specializing in dermatology and vascular surgery. MEASURES: Error rates in discharge abstracts were subdivided into six categories: wrong selection of the principal diagnosis (type A); low specificity of the principal diagnosis (type B); incomplete reporting of secondary diagnoses (type C); wrong selection of the principal procedure (type D); low specificity of the principal procedure (type E); incomplete reporting of procedures (type F). A specific rate of errors modifying classification in diagnosis related groups was then estimated.
RESULTS: Error types A, B and F dropped from 8.5% to 1.3%, from 15.8% to 1.6% and from 22% to 2.6% respectively. Error type D and E were zero in the third period of analysis (September-October 1997) compared with a rate of 0.7% and 4.1% in the third quarter of 1994. Error type C showed a slight decrease from 31.8% in 1994 to 27.2% in 1997. All differences in error types except incomplete reporting of secondary diagnoses were statistically significant. Five and a half per cent of cases were assigned to a different diagnoses related group after re-abstracting in 1997 as compared to 24.3% in the third quarter of 1994 and 23.8% in the first quarter of 1995. DISCUSSION: Training and continuous monitoring, and feedback of information to departments have proved to be successful in improving the quality of abstracting information at patient level from the medical record. The effort to increase administrative data quality at hospital level will facilitate the use of those data sets for internal quality management activities and for population-based quality of care studies.

Entities:  

Mesh:

Year:  1999        PMID: 10435841     DOI: 10.1093/intqhc/11.3.209

Source DB:  PubMed          Journal:  Int J Qual Health Care        ISSN: 1353-4505            Impact factor:   2.038


  10 in total

Review 1.  Defining and improving data quality in medical registries: a literature review, case study, and generic framework.

Authors:  Danielle G T Arts; Nicolette F De Keizer; Gert-Jan Scheffer
Journal:  J Am Med Inform Assoc       Date:  2002 Nov-Dec       Impact factor: 4.497

2.  Redesign of diagnostic coding in pediatrics: from form-based to discharge letter linked.

Authors:  Hilco Prins; Hans Büller; Betty Zwetsloot-Schonk
Journal:  Perspect Health Inf Manag       Date:  2004-12-07

3.  Analysis of data errors in clinical research databases.

Authors:  Saveli I Goldberg; Andrzej Niemierko; Alexander Turchin
Journal:  AMIA Annu Symp Proc       Date:  2008-11-06

4.  Evaluation of the CCAM Hierarchy and Semi Structured Code for Retrieving Relevant Procedures in a Hospital Case Mix Database.

Authors:  Cédric Bousquet; Béatrice Trombert; Julien Souvignet; Eric Sadou; Jean-Marie Rodrigues
Journal:  AMIA Annu Symp Proc       Date:  2010-11-13

5.  Coder perspectives on physician-related barriers to producing high-quality administrative data: a qualitative study.

Authors:  Karen L Tang; Kelsey Lucyk; Hude Quan
Journal:  CMAJ Open       Date:  2017-08-15

6.  [Validity of clinical records and information systems in studies of health-care delivery in primary care].

Authors:  V Pedrera-Carbonell; V Gil-Guillén; D Orozco-Beltrán; I Prieto Erades; G Schwarz-Chavarri; Mi Moya-García
Journal:  Aten Primaria       Date:  2005-12       Impact factor: 1.137

7.  Do coder characteristics influence validity of ICD-10 hospital discharge data?

Authors:  Deirdre A Hennessy; Hude Quan; Peter D Faris; Cynthia A Beck
Journal:  BMC Health Serv Res       Date:  2010-04-21       Impact factor: 2.655

8.  Inaccuracy in self-report of fractures may underestimate association with health outcomes when compared with medical record based fracture registry.

Authors:  Kristin Siggeirsdottir; Thor Aspelund; Gunnar Sigurdsson; Brynjolfur Mogensen; Milan Chang; Birna Jonsdottir; Gudny Eiriksdottir; Lenore J Launer; Tamara B Harris; Brynjolfur Y Jonsson; Vilmundur Gudnason
Journal:  Eur J Epidemiol       Date:  2007-07-25       Impact factor: 8.082

9.  Quantifying data quality for clinical trials using electronic data capture.

Authors:  Meredith L Nahm; Carl F Pieper; Maureen M Cunningham
Journal:  PLoS One       Date:  2008-08-25       Impact factor: 3.240

10.  Coding reliability and agreement of International Classification of Disease, 10th revision (ICD-10) codes in emergency department data.

Authors:  Mingkai Peng; Cathy Eastwood; Alicia Boxill; Rachel Joy Jolley; Laura Rutherford; Karen Carlson; Stafford Dean; Hude Quan
Journal:  Int J Popul Data Sci       Date:  2018-07-26
  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.