Model Formulation: Defining and Improving Data Quality in Medical Registries: A Literature Review, Case Study, and Generic Framework

Over the past years the number of medical registries has increased sharply. Their value strongly depends on the quality of the data contained in the registry. To optimize data quality, special procedures have to be followed. A literature review and a case study of data quality formed the basis for the development of a framework of procedures for data quality assurance in medical registries. Procedures in the framework have been divided into procedures for the co-ordinating center of the registry (central) and procedures for the centers where the data are collected (local). These central and local procedures are further subdivided into (a) the prevention of insufficient data quality, (b) the detection of imperfect data and their causes, and (c) actions to be taken / corrections. The framework can be used to set up a new registry or to identify procedures in existing registries that need adjustment to improve data quality.

[1]  S. Lemeshow,et al.  Mortality Probability Models (MPM II) based on an international cohort of intensive care unit patients. , 1993, JAMA.

[2]  J. Gassman,et al.  Data quality assurance, monitoring, and reporting. , 1995, Controlled clinical trials.

[3]  Phillip Cykana,et al.  DoD Guidelines on Data Quality Management , 1996, IQ.

[4]  Richard G. Mathieu,et al.  Data Quality in the Database Systems Course , 1998, Data Qual..

[5]  D. Solomon,et al.  Evaluation and implementation of public health registries. , 1991, Public health reports.

[6]  J Goldberg,et al.  Registry evaluation methods: a review and case study. , 1980, Epidemiologic reviews.

[7]  Kathleen V. Diegert,et al.  A Hierarchical Approach to Improving Data Quality , 1998, Data Qual..

[8]  S Lemeshow,et al.  The Logistic Organ Dysfunction system. A new way to assess organ dysfunction in the intensive care unit. ICU Scoring Group. , 1996, JAMA.

[9]  E van der Schueren,et al.  Quality control of validity of data collected in clinical trials. EORTC Study Group on Data Management (SGDM). , 1989, European journal of cancer & clinical oncology.

[10]  D. Marsh,et al.  Quality of data in the Manchester orthopaedic database. , 1992, BMJ.

[11]  B. Hawkins,et al.  Data collection and transcription. , 1995, Controlled clinical trials.

[12]  A. G. Duchene,et al.  An examination of the efficiency of some quality assurance methods commonly employed in clinical trials. , 1990, Statistics in medicine.

[13]  W. Hogan,et al.  The accuracy of medication data in an outpatient electronic medical record. , 1996, Journal of the American Medical Informatics Association : JAMIA.

[14]  Paul L. Canner,et al.  Quality assurance and monitoring in the Hypertension Prevention Trial , 1989 .

[15]  W. Knaus.  The APACHE III Prognostic System , 1992 .

[16]  J Passchier,et al.  [Medical registries: goal, methods and utilization]. , 1992, Nederlands tijdschrift voor geneeskunde.

[17]  D. Seddon,et al.  Data quality in population-based cancer registration: an assessment of the Merseyside and Cheshire Cancer Registry. , 1997, British Journal of Cancer.

[18]  Michael M. Wagner,et al.  Review: Accuracy of Data in Computer-based Patient Records , 1997, J. Am. Medical Informatics Assoc..

[19]  S Day,et al.  Double data entry: what value, what price? , 1998, Controlled clinical trials.

[20]  Peter J. Haug,et al.  Research Paper: Assessing the Quality of Clinical Data in a Computer-based Record for Calculating the Pneumonia Severity Index , 2000, J. Am. Medical Informatics Assoc..

[21]  Giri Kumar Tayi,et al.  Examining data quality , 1998, CACM.

[22]  M. Lehtonen,et al.  Data quality and quality control of a population-based cancer registry. Experience in Finland. , 1994, Acta oncologica.

[23]  J. L. Gall,et al.  APACHE II--a severity of disease classification system. , 1986, Critical care medicine.

[24]  J. Wyatt.  Acquisition and use of clinical data for audit and research. , 1995, Journal of evaluation in clinical practice.

[25]  S. Lemeshow,et al.  A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. , 1993, JAMA.

[26]  J. Teperi,et al.  Multi method approach to the assessment of data quality in the Finnish Medical Birth Registry. , 1993, Journal of epidemiology and community health.

[27]  A L Shroyer,et al.  Updates to the Data Quality Review Program: the Society of Thoracic Surgeons Adult Cardiac National Database. , 1998, The Annals of thoracic surgery.

[28]  L. Lorenzoni,et al.  The quality of abstracting medical information from the medical record: the impact of training programmes. , 1999, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[29]  A. Lewis Bastian.  Data validation , 1970, SIGFIDET '70.

[30]  S L George,et al.  Guidelines for quality assurance in multicenter trials: a position paper. , 1998, Controlled clinical trials.

[31]  D. Winchester,et al.  A national quality improvement effort: Cancer registry data , 1995, Journal of surgical oncology.

[32]  J. Cutler,et al.  Quality assurance and monitoring in the Hypertension Prevention Trial. Hypertension Prevention Trial Research Group. , 1989, Controlled clinical trials.

[33]  E van der Putten,et al.  A pilot study on the quality of data management in a cancer clinical trial. , 1987, Controlled clinical trials.

[34]  Richard Y. Wang,et al.  Toward total data quality management (TDQM) , 1993 .

[35]  D H Christiansen,et al.  Computer-assisted data collection in multicenter epidemiologic research. The Atherosclerosis Risk in Communities Study. , 1990, Controlled clinical trials.

[36]  H T Sorensen,et al.  A framework for evaluation of secondary data sources for epidemiological research. , 1996, International journal of epidemiology.

[37]  J. Wittes,et al.  Quality control of dietary data collection in the CARDIA study. , 1992, Controlled clinical trials.

[38]  A L Shroyer,et al.  Data quality review program: the Society of Thoracic Surgeons Adult Cardiac National Database. , 1996, The Annals of thoracic surgery.

[39]  J D Horbar,et al.  An assessment of data quality in the Vermont-Oxford Trials Network database. , 1995, Controlled clinical trials.

[40]  D. Goldhill,et al.  APACHE II, data accuracy and outcome prediction , 1998, Anaesthesia.

[41]  Richard Y. Wang,et al.  Anchoring data quality dimensions in ontological foundations , 1996, CACM.

[42]  R Fretschner,et al.  Patient data management systems in critical care. , 2001, Journal of the American Society of Nephrology : JASN.

[43]  G. Maudsley,et al.  What lessons can be learned for cancer registration quality assurance from data users? Skin cancer as an example. , 1999, International journal of epidemiology.

[44]  C. Schmid,et al.  58A Measuring the impact of the control rate in meta-analysis of clinical trials , 1995 .

[45]  Corinne Alberti,et al.  The Logistic Organ Dysfunction system. A new way to assess organ dysfunction in the intensive care unit. ICU Scoring Group. , 1996, JAMA.

[46]  M Gissler,et al.  Data quality after restructuring a national medical registry. , 1995, Scandinavian journal of social medicine.

[47]  Perry L. Miller,et al.  Research Paper: Exploring the Degree of Concordance of Coded and Textual Data in Answering Clinical Queries from a Clinical Data Repository , 2000, J. Am. Medical Informatics Assoc..

[48]  B. Lind,et al.  Quality assurance and quality control in longitudinal studies. , 1998, Epidemiologic reviews.

[49]  W. Knaus,et al.  The APACHE III prognostic system. Risk prediction of hospital mortality for critically ill hospitalized adults. , 1991, Chest.

[50]  B A Blumenstein.  Verifying keyed medical research data. , 1993, Statistics in medicine.

[51]  Jeremy Wyatt Dm Mrcp.  Acquisition and use of clinical data for audit and research. , 1995 .

[52]  Stanley Lemeshow,et al.  The Logistic Organ Dysfunction System , 1997 .