Methods Inf Med 2003; 42(03): 236-242
DOI: 10.1055/s-0038-1634356
Original article
Schattauer GmbH

Managers Reports of Automated Coding System Adoption and Effects on Data Quality

A National Assessment
D. P. Lorence
1   Dept. of Health Policy and Administration and School of Information Sciences and Technology, Pennsylvania State University, University Park, PA, USA
,
R. Jameson
2   CDS Research Group, Chicago, IL, USA
› Institutsangaben
Weitere Informationen

Publikationsverlauf

Received 15. Oktober 2001

Accepted 13. September 2002

Publikationsdatum:
07. Februar 2018 (online)

Summary

Objective: Assessment of the adoption of automated classification (encoder) systems in healthcare settings and related effects on perceived data quality.

Methods: Survey of all U.S. accredited medical records managers, summarizing their reports of automated encoding systems and data quality change following adoption of systems.

Results: Significant improvement in data was seen from adoption of automated encoding systems, though variation existed across regions and key demographic variables.

Conclusion: At a national level, there is a need to minimize data quality variation and ensure some degree of nationwide uniformity in the performance of coding systems. If healthcare providers are expected to trust coded data for comparative purposes, there will be a like need for more uniform and standardized system-based performance benchmarks.

 
  • References

  • 1 Soot LC, Moneta GL, Edwards JM. Vascular surgery and the internet: a poor source of patient-oriented information. J Vasc Surg. 1999; 30: 84-91.
  • 2 P.L. 104-191. Health insurance portability and accountability Act of 1996, Public Law 104-91, 104th Congress,. 1996
  • 3 Hasman A, De Bruijn LM, Arends JW. Evaluation of a method that supports pathology report coding. Methods Inf Med 2001; 40: 293-7.
  • 4 Nilsson G, Petersson H, Ahlfeldt H, Strender LE. Evaluation of three swedish ICD-10 primary care versions: reliability and ease of use in diagnostic coding. Methods Inf Med 2000; 39: 325-31.
  • 5 Lowe HJ, Antipov I, Hersh W, Smith CA, Mail-hot M. Automated semantic indexing of imaging reports to support retrieval of medical images in the multimedia electronic medical record. Methods Inf Med 1999; 38: 303-7.
  • 6 Griffiths KM, Christensen H. Quality of web based information on treatment of depression: cross sectional survey. BMJ. 2000; 321: 1511-5.
  • 7 Orman LV. Database audit and control strategies. Information Technology and Management. 2001; 2 (Suppl. 01) 27-51.
  • 8 The American Health Information Management Association (US). Census of US health information managers, benchmarks in health information management. Chicago: AHIMA; 2000
  • 9 Lorence D. Benchmarking professional practice issues: a preview. JAHIMA. 1998; 69 (Suppl. 10) 53-6.
  • 10 Interstudy. The national hmo financial database. St. Paul, MN: InterStudy Publications; 1999
  • 11 Dataquest. Market statistics report. San Jose, CA: Dataquest Products; 1999
  • 12 American Hospital Association (US). AHA annual survey database. Chicago, IL: Health Forum, American Hospital Association; 1998
  • 13 Health Resources and Services Administration (US). The area resource file. Washington, DC: Dept. of HHD; 1998
  • 14 Hsia D, Krushat W, Fagan A, Tebbutt J, Kusserow R. Accuracy of diagnostic coding for Medicare patients under the prospective-payment system. N England J Med. 1988; 318 (Suppl. 06) 352-5.
  • 15 Gribben B, Coster G, Pringle M, Simon J. Noninvasive methods for measuring data quality in general practice. N Z Med J 2001; 114 1125 30-2.
  • 16 Fisher E, Whaley F, Krushat W, Malenka D, Fleming C, Baron J, Hsia D. The accuracy of Medicare’s hospital claims data: progress has been made, but problems remain. Am J Public Health. 1992; 82 (Suppl. 02) 243-8.
  • 17 Lorence D. National implementation patterns of computer-based patient records. In: Proceedings of the National Conference on Health Statistics. Washington, DC: NCHVS; 1999
  • 18 Wennberg JE. Dartmouth Atlas of Health Care. Hanover, NH: Center for the Evaluative Clinical Sciences, Dartmouth Medical School; 1997
  • 19 Wennberg JE. Dartmouth Atlas of Health Care. Hanover, NH: Center for the Evaluative Clinical Sciences, Dartmouth Medical School; 1997
  • 20 Chambers L. The use of guidelines improved the quality of economic analysis. Evidence-based Healthcare 1999; 3 (Suppl. 02) 48-58.
  • 21 Ellis J, Mulligan I, Rowe J, Sackett DL. Inpatient general medicine is evidence based. Lancet 1995; 346: 407-10.