Methods Inf Med 2011; 50(02): 115-123
DOI: 10.3414/ME09-01-0062
Original Articles
Schattauer GmbH

Long-term Impact of Physician Encoding on Detail and Number of Recorded Diagnoses

Support by a Speciality Specific List of Diseases
H. Prins
1   School of Health Care, Windesheim University of Applied Sciences, Zwolle, The Netherlands
,
A. Hasman
2   Department of Medical Informatics, Academic Medical Center – University of Amsterdam, Amsterdam, The Netherlands
› Author Affiliations
Further Information

Publication History

received: 10 July 2009

accepted: 04 January 2010

Publication Date:
18 January 2018 (online)

Summary

Objectives: To improve the recording of diagnostic discharge data, pediatricians encoded diagnostic information as part of discharge letter writing supported by a pediatric list of ICD-9-CM-based codes. We evaluated the effect of this new policy on level of detail and number of recorded diagnoses.

Methods: We compared proportions of specific principal diagnoses and numbers of secondary diagnoses of the four years before with the eight years after introduction.

Results: Immediately after introduction, half of the diagnoses for which both generic and specific codes existed was coded specific. In later years this proportion remained stable at 0.35 (p < 0.05). Diagnoses that fall under the pediatrician’s own subspecialty had more often a specific code than diagnoses that do not. The mean number of secondary diagnoses per admission increased from 0.7 before introduction to 1.4 in the third year after introduction (p < 0.05) but gradually fell back to 0.7. This increase and decrease was mainly due to diagnoses that did not fall under the pediatrician’s own subspecialty. The extra codes in individual discharge summaries had added informational value.

Conclusions: Discharge letter-linked encoding by pediatricians supported by a pediatric list of diseases leads initially to increased detail and number of diagnoses with added informational value. When attention diminishes, especially the level of detail and number of secondary diagnoses that do not fall under one’s own subspecialty decrease. The level of detail of principal diagnoses remains stable because of the advantage for pediatricians of having specific diagnostic codes falling under their own subspecialty.

 
  • References

  • 1 De Coster C, Quan H, Finlayson A. et al. Identifying priorities in methodological research using ICD-9-CM and ICD-10 administrative data: report from an international consortium. BMC Health Serv Res 2006; 6: 77.
  • 2 National Center for Health Statistics.. International Classification of Diseases, 9th Revision, Clinical Modification, 6th edition. Hayattsville, Maryland: NCHS; 2003
  • 3 World Health Organization.. International Statistical Classification of Diseases and Related Health Problems; Tenth Revision. Geneva: World Health Organization; 1992
  • 4 McKee M. Routine data: a resource for clinical audit?. Quality in Health Care 1993 pp 104-111.
  • 5 Safran C, Chute CG. Exploration and exploitation of clinical databases. International Journal of BioMedical Computing 1995; 39: 151-156.
  • 6 Wyatt J. Acquisition and use of clinical data for audit and research. Journal of Evaluation in Clinical Practice 1995; 1: 15-27.
  • 7 Iezzoni LI. Assessing quality using administrative data. Annals of Internal Medicine 1997; 127: 666-674.
  • 8 Coory M, Thompson B, Baade P, Fritschi L. Utility of routine data sources for feedback on the quality of cancer care: an assessment based on clinical practice guidelines. BMC Health Serv Res 2009; 9: 84.
  • 9 Rosamond WD, Chambless LE, Sorlie PD. et al. Trends in the sensitivity, positive predictive value, false-positive rate, and comparability ratio of hospital discharge diagnosis codes for acute myocardial infarction in four US communities, 1987– 2000. Am J Epidemiol 2004; 160: 1137-1146.
  • 10 Tunkel AR, Hartman BJ, Kaplan SL. et al. Practice guidelines for the management of bacterial meningitis. Clin Infect Dis 2004; 39: 1267-1284.
  • 11 Lazzarini L, Toti M, Fabris P. et al. Clinical features of bacterial meningitis in Italy: a multicenter prospective observational study. J Chemother 2008; 20: 478-487.
  • 12 Prins H, Kruisinga FH, Buller HA, ZwetslootSchonk JH. Availability and usability of data for medical practice assessment. Int J Qual Health Care 2002; 14: 127-137.
  • 13 Naessens JM, Huschka TR. Distinguishing hospital complications of care from pre-existing conditions. International Journal for Quality in Health Care 2004; 16 (01) i27-i35.
  • 14 Pine M, Jordan HS, Elixhauser A. et al. Modifying ICD-9-CM coding of secondary diagnoses to improve risk-adjustment of inpatient mortality rates. Medical Decision Making 2009; 29: 69-81.
  • 15 Quan H, Parsons GA, Ghali WA. Assessing accuracy of diagnosis-type indicators for flagging complications in administrative data. Journal of Clinical Epidemiology 2004; 57: 366-372.
  • 16 Prins H, Buller HA, Zwetsloot-Schonk JH. Redesign of Diagnostic Coding in Pediatrics: From Form-based to Dicharge Letter Linked. Perspectives in Health Information Management 2004; 1: 1-10.
  • 17 Prins H, Buller HA, Zwetsloot-Schonk JH. Effect of discharge letter-linked diagnosis registration on data quality. Int J Qual Health Care 2000; 12: 47-57.
  • 18 Cleary R, Beard R, Coles J. et al. Comparative hospital databases: value for management and quality. Qual Health Care 1994; 3: 3-10.
  • 19 Pietila K, Tenkanen L, Manttari M, Manninen V. How to define coronary heart disease in register-based follow-up studies: experience from the Helsinki Heart Study. Annals of Medicine 1997; 29: 253-259.
  • 20 Tai D, Dick P, To T, Wright JG. Development of pediatric comorbidity prediction model. Arch Pediatr Adolesc Med 2006; 160: 293-299.
  • 21 Berg M. Patient care information systems and health care work: a sociotechnical approach. Int J Med Inf 1999; 55: 87-101.
  • 22 Kronman MP, Hall M, Slonim AD, Shah SS. Charges and lengths of stay attributable to adverse patient-care events using pediatric-specific quality indicators: a multicenter study of freestanding children’s hospitals. Pediatrics 2008; 121: e1653-e1659.
  • 23 Surjan G. Questions on validity of International Classification of Diseases-coded diagnoses. Int J Med Inform 1999; 54: 77-95.
  • 24 O’Malley KJ, Cook KF, Price MD, Wildes KR, Hurdle JF, Ashton CM. Measuring diagnoses: ICD code accuracy. Health Services Research 2005; 40: 1620-1639.
  • 25 Santos S, Murphy G, Baxter K, Robinson KM. Organisational factors affecting the quality of hospital clinical coding. HIM J 2008; 37: 25-37.
  • 26 De Lusignan S, Hague N, Brown A, Majeed A. An educational intervention to improve data recording in the management of ischaemic heart disease in primary care. J Public Health (Oxf) 2004; 26: 34-37.
  • 27 De Lusignan S, Stephens PN, Adal N, Majeed A. Does feedback improve the quality of computerized medical records in primary care?. J Am Med Inform Assoc 2002; 9: 395-401.