Journal Information
Vol. 12. Issue 1.
Pages 9-21 (January - February 1998)
Vol. 12. Issue 1.
Pages 9-21 (January - February 1998)
Open Access
Análisis automatizado de la calidad del conjunto mínimo de datos básicos. Implicaciones para los sistemas de ajuste de riesgos
Visits
9041
J. Librero, R. Ordiñana, S. Peiró*
Institut Valencià d'Estudis en Salut Pública (IVESP). Institut d'Investigació en Serveis de Salut (IISS), Valencia
This item has received

Under a Creative Commons license
Article information
Resumen
Fundamentos

Junto a la edad del paciente, el diagnóstico principal, los diagnósticos secundarios (comorbilidad y complicaciones) y los procedimientos realizados son las variables críticas para el ajuste de riesgos. De ahí la importancia de su correcta incorporación al CMBD. Sin embargo, diversos trabajos, especialmente en Estados Unidos, pero también en España, han puesto en evidencia importantes problemas de calidad en estos datos, dificultades para su mejora y las limitaciones que ello conlleva para evaluar la calidad o la eficiencia de los hospitales. El objetivo de este trabajo es realizar una aproximación a la calidad de la información administrativa y clínica recogida en el CMBD del Servei Valencià de la Salut (SVS) mediante un proceso automatizado de análisis de los datos del propio CMBD, y discutir tanto sus implicaciones para la gestión, como las posibles estrategias de mejora.

Material y método

Se realizó un análisis automatizado de la calidad del CMBD 1994 del SVS (20 hospitales, 241.341 altas), utilizando indicadores de cumplimentación válida de los valores de los campos, relaciones entre campos del mismo episodio, relaciones entre variables en diferentes episodios y volumen y especificidad de la información clínica.

Resultados

El CMBD analizado contiene escasos errores en las variables administrativas, con excepción de la residencia, pero presenta importantes problemas de volumen y especificidad de la información clínica, así como una alta variabilidad en su cumplimentación y calidad en diferentes hospitales.

Conclusiones

La calidad de los datos clínicos del CMBD pueden suponer sesgos en su utilización con finalidades de gestión o evaluación de la calidad, así como en los estudios epidemiológicos, de evaluación de tecnologías o utilización de servicios.

Palabras clave:
Calidad datos diagnósticos
Bases de datos clínicas
Estadísticas hospitalarias
Summary
Setting

Together with the age of the patient, the main diagnosis, secondary diagnosis (comorbility and complications) and the procedures performed are the critical variables for risk-adjusting. Therefore, its correct incorporation to CMBD is of great importance. However, several studies, especially in the United States, but also in Spain, have made evident the existence of important problems of quality in these data, difficulties for its improvement and the limitations which this has to assess the quality or the efficiency of hospitals. The objective of this study is to approach the quality of administrative and clinical collected in the CMBD of the Valencian Health Service (VHS) using an automatized process of analysis of data from the same CMBD, and discuss the implications for its management, as well as possible improvement strategies.

Material and method

An automatized analysis of the quality of CMBD 1994 of the VHS (20 hospitals, 241,341 admissions) was performed, using indicators of valid fulfilling of field values, relationship between fields of the same episode, relationship between variables in different episodes and volume and specificity of clinical information.

Results

The analysed CMBD contains few errors in management variables, with the exception of residence, but it shows important problems of volume and specificity of clinical information, as well as a high variability in its fulfilling and quality in different hospitals.

Conclusions

The quality of the clinical data of CMBD may be biased in its use with management aims or when assessing quality, as well as in epidemiological studies, evaluation of technology or use of services.

Key words:
Diagnosis data quality
Clinical databases
Hospital statistics
Full text is only aviable in PDF
Bibliografía
[1.]
L.I. Iezzoni.
Risk adjustment for measuring health care outcomes.
[2.]
Department of Health, Education, and Welfare.
National Committee on Vital and Health Statistics. Uniform Hospital Discharge Data Minimum Data Set. DHEW Pub. No. (PHS) 80–1157.
U.S. Department of Health, Education, and Welfare, (1980),
[3.]
H.D. Doremus, E.M. Michenzi.
Data Quality: An illustration of its potential impact upon Diagnosis-Related Group's Case Mix Index and Reimbursement.
Medical Care, 21 (1983), pp. 1001-1002
[4.]
D.C. Hsia, W.M. Krushat, A.B. Fagan, J.A. Tebbutt, R.P. Kusserow.
Accuracy of Diagnostic Coding for Medicare Patients under the Prospective-Payment System.
N Engl J Med, 318 (1988), pp. 352-355
[5.]
L.I. Iezzoni, S. Burnside, L. Sickles, M.A. Moskowitz, E. Sawitz, P.A. Levine.
Coding of Acute Myocardial Infarction: Clinical and Policy Implications.
Ann Intern Med, 109 (1988), pp. 745-751
[6.]
F.L. Waterstraat, J. Barlow, F. Newman.
Diagnostic Coding Quality and Its Impact on Healthcare Reimbursement: Research Perspectives.
Journal of the American Record Association, 61 (1990), pp. 52-59
[7.]
L.I. Iezzoni.
Using Administrative Diagnostic Data to Assess the Quality of Hospital Care: The Pitfalls and Potential of ICD-9-CM.
International Journal of Technology Assessment in Health Care, 6 (1991), pp. 272-281
[8.]
R.F. Corn.
The sensitivity of prospective hospital reimbursement to errors in patient data.
Inquiry, 18 (1991), pp. 351-360
[9.]
D.C. Hsia, C.A. Ahern, B.P. Ritchie, L.M. Moscoe, W.M. Krushat.
Medicare Reimbursement Accuracy under the Prospective Payment System, 1985 to 1988.
Journal of the American Medical Association, 268 (1992), pp. 896-899
[10.]
J. Green, N. Wintfeld.
How Accurate are Hospital Discharge Data for Evaluating Effectiveness of Care?.
Medical Care, 31 (1993), pp. 719-731
[11.]
A. Guilabert, J.J. Pérez López, V. Almela, V. Company.
Calidad de datos y grupos relacionados con el diagnóstico.
Rev Calidad Asistencial, 5 (1995), pp. 287-293
[12.]
Health System International, Inc. Diagnosis Related Groups Fifth Revision. Definitions Manual.
[13.]
Clasificación Internacional de Enfermedades 9a revisión Modificación Clínica. Madrid: Instituto Nacional de la Salud; 1989.
[14.]
Dirección para la Gestión de la Asistencia Especializada.
La medición de la calidad del conjunto mínimo básico de datos de la Comunidad Valenciana (CMBD-CV).
Servicio Valenciano de Salud, (1994),
[15.]
Institute of Medicine.
Reliability of Hospital Discharge Abstracts.
National Academy of Sciences, (1977),
[16.]
Institute of Medicine.
Reliability of Medicare Hospital Discharge Records.
National Academy of Sciences, (1977),
[17.]
Institute of Medicine.
Reliability of National Hospital Discharge Survey Data.
National Academy of Sciences, (1980),
[18.]
L.K. Demlo, P.M. Campbell, S.S. Brown.
Reliability of information abstracted from patient's medical records.
Medical Care, 16 (1978), pp. 995
[19.]
L.K. Demlo, P.M. Campbell.
Improving hospital discharge data: Lessons from the National Hospital Discharge Survey.
Medical Care, 19 (1981), pp. 1030-1040
[20.]
S.E. Williams, P. Latessa.
Improving the quality of discharge data.
pp. 241
[21.]
S.S. Lloyd, J.P. Rissing.
Physician and Coding Errors in Patient Records.
JAMA, 254 (1985), pp. 1330-1336
[22.]
L.A. Schraffenberger.
Coding errors encountered in DRG study.
Journal of the American Medical Record Association, 57 (1986), pp. 15-17
[23.]
T.J. Marrie, H. Durant, E. Sealy.
Pneumonia – The quality of medical records data.
Medical Care, 25 (1987), pp. 20-24
[24.]
E.S. Fisher, F.S. Whaley, W.M. Krushat, D.J. Malenka, C. Fleming, J.A. Baron, et al.
The Accuracy of Medicare's Hospital Claims Data. Progress Has Been Made, But Problems Remain.
American Journal of Public Health, 82 (1992), pp. 243-248
[25.]
A.R. Assaf, K.L. Lapane, J.L. McKenney, R.A. Carleton.
Possible influence of the prospective payment system on the assignment of discharge diagnoses for coronary heart disease.
N Engl J Med, 329 (1993), pp. 931-935
[26.]
D.J. Malenka, D. McLerran, N. Roos, E.S. Fisher, J.E. Wennberg.
Using administrative data to describe casemix: a comparison with the medical record.
Journal of Clinical Epidemiology, 47 (1994), pp. 1027-1032
[27.]
P.S. Romano, H.S. Luft.
Getting the Most Out of Messy Data: Problems and Approaches for Dealing with Large Administrative Data Sets. Proceedings of Medical Effectiveness Research Data Methods Conference.
Agency for Health Care Policy and Research, Public Health Service, (1992),
[28.]
A.R. Feinstein.
ICD, POR, and DRG. Unsolved Scientific Problems in the Nosology of Clinical Medicine.
Archives of Internal Medicine, 148 (1988), pp. 2269-2274
[29.]
L.I. Iezzoni, S. Burnside, L. Sickles, M.A. Moskowitz, E. Sawitz, P.A. Levine.
Coding of Acute Myocardial Infarction: Clinical and Policy Implications.
Ann Intern Med, 109 (1988), pp. 745-751
[30.]
G.D. Schiff, A.S. Yaacoub.
The diagnostic coding of miocardial infarction.
Ann Intern Med, 110 (1989), pp. 243
[31.]
R.A. Bright, J. Avorn, D.E. Everitt.
Medicaid Data as a Resource for Epidemiologic Studies: Strengths and Limitations.
Journal of Clinical Epidemiology, 42 (1989), pp. 937-945
[32.]
G.L. Burke, S.A. Edlavitch, R.S. Crow.
The effects of diagnostic criteria on trends in coronary heart disease morbidity: the Minnesota Heart Survey.
Journal of Clinical Epidemiology, 42 (1989), pp. 17-24
[33.]
V.V. Ozonoff, S. Tan-Torres, C.W. Barber.
Assessment of E-coding practices and costs in Massachussets hospitals.
Public Health Reports, 108 (1993), pp. 633-636
[34.]
L.F. McMahon, H.L. Smits.
Can Medicare Prospective Payment Survive the ICD9-CM Disease Classification System?.
Ann Intern Med, 104 (1986), pp. 562-566
[35.]
L. Compañ, E. Portella, AM. García.
¿Cuánto y cómo estamos utilizando la encuesta de morbilidad hospitalaria?.
Gac Sanit, 9 (1995), pp. 354-362
[36.]
J. Martín, C. Cavero, J.M. Tejedor, J.M. Martín.
Validez del libro de registro en el estudio de morbilidad atendida por un hospital general.
Revista de Sanidad e Higiene Pública, 65 (1991), pp. 413-419
[37.]
C. Bischofberger, A. Otero.
Análisis de los principales errores que se producen en el informe de alta y en el libro de registro de un hospital.
Med Clin (Barc), 98 (1992), pp. 565-567
[38.]
R. Martínez, F. García.
Estadísticas de morbilidad hospitalaria: exactitud del diagnóstico notificado en el libro de registro de altas.
Med Clin (Barc), 96 (1991), pp. 765-768
[39.]
A. Alberquillla, M. Ugalde, J.M. Pérez, J.M. Rivera.
El libro de registro de enfermos ¿Un instrumento útil como fuente de información sanitaria?.
Revista de Sanidad e Higiene Pública, 65 (1991), pp. 147-154
[40.]
C.A. González, A. Agudo, J. Costa, L. Mir, J. Romagosa, A. Sicras.
Validez del diagnóstico principal de alta hospitalaria.
Med Clin (Barc), 89 (1987), pp. 269-271
[41.]
A. Sicras.
Concordancia del diagnóstico principal de alta hospitalaria.
Gac Sanit, 21 (1990), pp. 252-253
[42.]
R.M. Massanari.
Reliability of reporting nosocomial infections in the discharge abstract and implication for receipt of reveneues on prospective payment.
American Journal of Public Health, 77 (1987), pp. 561-564
[43.]
N.F. Holderman.
DRG 48: an analysis of data quality.
Journal of American Medical Record Association, 59 (1988), pp. 30-33
[44.]
J.S. Gonnella, M.C. Hornbrook, D.Z. Louis.
Staging of Disease: A Case-Mix Measurement.
Journal of the American Medical Association, 251 (1984), pp. 637-644
[45.]
R.A. Israel.
The International Classification of Diseases: Two Hundred Years of Development.
Public Health Reports, 93 (1978), pp. 150-152
[46.]
R.A. Israel.
The history of the International Classification of Diseases.
Health Bulletin, 49 (1991), pp. 62-66
[47.]
K.M. Weigel, C.A. Lewis.
Forum: In Sickness and in Health-The Role of the ICD in the United States Health Care Data and ICD-10.
Topics in Health Record Management, 12 (1991), pp. 70-82
[48.]
S. O'Gara.
Data Sets and Coding Guidelines: Sequencing vs.Classification Rules.
Journal of the American Medical Record Review Association, 61 (1990), pp. 20-21
[49.]
K.H. Sheehy.
White Paper: Coding and Classification Systems-Implications for the Profession.
Journal of the American Medical Record Association, 62 (1991), pp. 44-49
[50.]
B. Steinwald, L.A. Dummit.
Hospital Case-Mix Change.
Sicker Patients or DRG Creep?» Health Affairs, 8 (1989), pp. 35-47
[51.]
M.E. Charlson, P. Pompei, K.L. Ales, R. MacKenzie.
A new method of classifying prognostic comorbidity in longitudinal studies: development and validation.
Journal of Chronic Diseases, 40 (1987), pp. 373-383
[52.]
S. Greenfield, H.U. Aronow, R.M. Elashoff, D. Watanabe.
Flaws in Mortality Data: The Hazards of Ignoring Comorbid Disease.
Journal of the American Medical Association, 260 (1988), pp. 2253-2255
[53.]
L.I. Iezzoni, S.M. Foley, J. Daley, J. Hughes, E.S. Fisher, T. Heeren.
Comorbidities, Complications, and Coding Bias: does the Number of Diagnosis Codes Matter in Predicting In-Hospital Mortality?.
JAMA, 267 (1992), pp. 2197-2203
[54.]
S.F. Jencks, D.K. Williams, T.L. Kay.
Assessing Hospital-Associated Deaths from Discharge Data: The Role of Length of Stay and Comorbidities.
JAMA, 260 (1988), pp. 2240-2246
[55.]
S.F. Jencks.
Accuracy in Recorded Diagnoses.
JAMA, 267 (1992), pp. 2238-2239
[56.]
National Committee on Vital and Health Statistics, Subcommittee on Ambulatory and Hospital Care Statistics.
Proposed Revision to the Uniform Hospital Discharge Data Set.
National Committee on Vital and Health Statistics, (1992),
[57.]
L.L. Roos, J.P. Nicol, S.M. Cageorge.
Using Administrative Data for Longitudinal Research: Comparisons with Primary Data Collection.
Journal of Chronic Disease, 40 (1987), pp. 41-49
[58.]
L.L. Roos, N.P. Roos, S.M. Sharp.
Monitoring Adverse Outcomes of Surgery Using Administrative Data.
Health Care Financing Review, (1987), pp. 5-16
[59.]
L.L. Roos, S.M. Sharp, M.M. Cohen, A. Wajda.
Risk Adjustment in Claims-Based Research: The Search for Efficient Approaches».
Journal of Clinical Epidemiology, 42 (1989), pp. 1193-1206
[60.]
S.I. DesHarnais, J.D. Chesney, R.T. Wroblewski, S.T. Fleming, L.F. McMahon.
The Risk-Adjusted Mortality Index: A New Measure of Hospital Performance.
Medical Care, 26 (1988), pp. 1129-1148
[61.]
S.I. DesHarnais, L.F. McMahon, R.T. Wroblewski, A.J. Hogan.
Measuring Hospital Performance: The Development and Validation of Risk Adjusted Indexes of Mortality, Readmissions, and Complications.
Medical Care, 28 (1990), pp. 1127-1141
[62.]
S.I. DesHarnais, L.F. McMahon, R.T. Wroblewski.
Measuring Outcomes of Hospital Care Using Multiple Risk-Adjusted Indexes.
Health Services Research, 26 (1991), pp. 425-445
[63.]
L.I. Iezzoni, S.M. Foley, T. Heeren, J. Daley, C.C. Duncan, E.S. Fisher, et al.
A Method for Screening the Quality of Hospital Care Using Administrative Data: Preliminary Validation Results.
Quality Review Bulletin, 18 (1992), pp. 361-371
[64.]
L.I. Iezzoni, J. Daley, T. Heeren, S.M. Foley, J.S. Hughes, E.S. Fisher, et al.
Using Administrative Data to Screen Hospitals for High Complication Rates.
Inquiry, (1994),
[65.]
D.W. Simborg.
DRG Creep: A New Hospital-Acquired Disease.
N Eng J Med, 304 (1981), pp. 1602-1604
[66.]
B. Reid.
The Impact of Different Coding Systems on DRG Assignment and Data.
Health Policy, 17 (1991), pp. 133-149
[67.]
M.G. Gildford, R.M. Coffey.
Change in the Medicare Case-Mix Index in the 1980s and the Effect of the Prospective Payment System.
Health Services Research, 27 (1992), pp. 385-415
[68.]
D.M. Cutler.
The incidence of adverse medical outcomes under prospective payment.
Econometrica, 63 (1995), pp. 29-50
[69.]
R.B. Fetter, Y. Shin, J.L. Freeman, R.F. Averill, J.D. Thompson.
Case Mix Definition by Diagnosis Related Groups.
Medical Care, 18 (1980), pp. 1-53
[70.]
M.C. Hornbrook.
Hospital Case Mix: Its Definition, Measurement and Use: Part I. The Conceptual Framework.
Medical Care Review, 39 (1982), pp. 1-43
[71.]
M.C. Hornbrook.
Hospital Case Mix: Its Definition, Measurement and Use: Part II Review of Alternative Measures.
Medical Care Review, 39 (1982), pp. 73-123
[72.]
M.S. Blumberg.
Risk Adjusting Health Care Outcomes: A Methodologic Review.
Medical Care Review, 43 (1986), pp. 351-393
[73.]
L.F. McMahon, J.E. Billi.
Measurement of Severity of Illness and the Medicare Prospective Payment System: State of the Art and Future Directions.
Journal of General Internal Medicine, 3 (1988), pp. 482-490
[74.]
L.I. Iezzoni.
Risk Adjustment for Medical Outcomes Studies.
Medical Effectiveness Research Data Methods, pp. 83-97
[75.]
S. Peiró.
Limitaciones en la medición de los resultados de la atención hospitalaria. Implicaciones para la gestión. En: Instrumentos para la gestión en sanidad.
pp. 57-101
Copyright © 1998. Sociedad Española de Salud Pública y Administración Sanitaria
Download PDF
Idiomas
Gaceta Sanitaria
Article options
Tools
es en

¿Es usted profesional sanitario apto para prescribir o dispensar medicamentos?

Are you a health professional able to prescribe or dispense drugs?