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PubMed1994

Data quality in computerized patient records. Analysis of a haematology biopsy report database.

This paper addresses the problem of data quality in electronic patient records using a computerized haematology biopsy report system as an example. Physicians extracted five parameters from a traditional free text cytology report and encoded these parameters thus producing a computer processable report. The parameters were 1) the organ biopsied, 2) quality of specimen, 3) cytological diagnosis including 4) a modifier code for the main diagnosis code (i.e. status post chemotherapy, Y-code) and 5) an additional key describing the degree of remission obtained after chemotherapy of acute leukemias. From the various steps involved in generating the electronic record we selected two critical ones: encoding of free text terms by physician staff; entering of the coded terms into a computer by lab staff. We analyzed the rates of correct, incorrect and missing codes for each of the five parameters. Our findings indicate that in this model of an electronic patient record: 1) there is significant inaccuracy of physicians during the process of encoding the free text report with error rates between 3.2 and 28% and omission rates up to 64%. 2) lab staff entering these coded data into the computer introduce additional errors (0-7.8%) but rarely miss correctly encoded data (0-0.9%). 3) introducing a revised coding system data quality improved significantly (p < or = 0.001) with a fivefold increase of correct and a 75% reduction of missing codes. 4) the clinical relevance of the diagnoses encoded as perceived by clinicians is a significant factor affecting error and omission rates.(ABSTRACT TRUNCATED AT 250 WORDS)

Adult↗
PubMed2005

The data quality of haematological malignancy ICD-10 diagnoses in a population-based hospital discharge registry.

The objectives of this study were to estimate the data quality of haematological malignancy diagnoses in a hospital discharge registry, and to quantify the impact of any misclassification of diagnoses on survival estimates. We included all patients > or = 15 years living in North Jutland County, Denmark with a first-time discharge diagnosis of a haematological malignancy registered in the Hospital Discharge Registry and the Danish Cancer Registry, the reference standard, from 1994 to 1999. We estimated completeness and positive predictive value (PPV) of haematological malignancies and specific subcategories, as a measure of data quality, and compared mortality rates based on data from the two registries by Cox regression analysis. Completeness in the Hospital Discharge Registry for all haematological malignancies was 91.5% (95% confidence interval (CI) 89.6-93.1) and PPV was 84.5% (95% CI 82.2-86.5). Reviews of the pathological files showed misclassified cases in both registries and thus indicated that both completeness and PPV of the Hospital Discharge Registry were underestimates. Mortality rate ratio for all haematological malignancies when registered in the Hospital Discharge Registry compared with being registered in the Danish Cancer Registry was 0.98 (95% CI 0.88-1.09). Discharge data had some misclassifications but these had no major impact on survival estimates.

Adolescent↗
PubMed2005

Victorian Emergency Minimum Dataset: factors that impact upon the data quality.

OBJECTIVE: The Victorian Emergency Minimum Dataset (VEMD) records details of approximately 80% of Victoria's ED presentations. Its usefulness for quality assurance and research relies on the data being both complete and accurate. We aimed to determine the factors that impact adversely on the collection of high-quality VEMD data. METHODS: The study was a voluntary, anonymous, cross-sectional survey of a range of ED staff (medical, nursing, clerical) who collect and enter data into the VEMD. Nine of the 28 hospitals that contribute to the VEMD were surveyed. The questionnaire was purpose-designed and self-administered. RESULTS: A total of 218 staff participated (response rate 95%). Six different software types were used, with 40% of respondents using the Pickware (MCAT) system. There was no consistency of ED personnel for the completion of specific data fields. One hundred and twenty-six (56%) respondents had heard of the VEMD, 67 (29%) had had its structure and purpose explained and 65 (30%) had been trained to enter data. Ninety-seven (45%) respondents knew what the VEMD data was used for, 38 (17%) knew they could request VEMD data for their own use and 17 (7.8%) had done so. Time constraints, software problems and lack of formal orientation and training in data entry were reported as the most important factors impacting adversely upon quality data entry. CONCLUSION: Staff knowledge of the VEMD system and its uses are poor. Numerous factors impact on the quality of data entered and interventions aimed at improving staff education, training and feedback and software are indicated.

Attitude of Health Personnel↗
PubMedDate not supplied

From figures to facts: data quality managers emerge as knowledge leaders.

It is no surprise that data quality managers are being recognized as knowledge leaders--they turn patient care data into valuable information for research, quality reviews, and planning. This article explores how this position is emerging in the HIM field and becoming a key player in quality care.

Benchmarking↗
PubMed2004

Data quality management in pharmacovigilance.

Pharmacovigilance relies on information gathered from the collection of individual case safety reports and other pharmacoepidemiological data. Even given the inherent limitations of spontaneous reports, the usefulness of this data source can be improved with good data quality management. Although under-reporting cannot be remedied this way, the negative impact of incomplete reports, which is another serious problem in pharmacovigilance, can be reduced. Quality management consists of quality planning, quality control, quality assurance and quality improvements. The pharmacovigilance data processing cycle starts with data collection and, in computerised systems, data entry; the next step is data storage and maintenance; followed by data selection, retrieval and manipulation. The resulting data output is analysed and assessed. Finally, conclusions are drawn and decisions made. The increased knowledge feeds back into the data processing cycle. Focussing on the first three steps of the data processing cycle, the different quality dimensions associated with these steps are described in this review, together with examples relevant to pharmacovigilance data. Functioning, well documented, and transparent quality management systems will benefit not only those involved in data collection, management and output production, but, ultimately, also the pharmacovigilance end users, the patients.

Adverse Drug Reaction Reporting Systems↗
PubMed2006

Health-based screening levels to evaluate U.S. Geological Survey ground water quality data.

Federal and state drinking-water standards and guidelines do not exist for many contaminants analyzed by the U.S. Geological Survey's National Water-Quality Assessment Program, limiting the ability to evaluate the potential human-health relevance of water-quality findings. Health-based screening levels (HBSLs) were developed collaboratively to supplement existing drinking-water standards and guidelines as part of a six-year, multi-agency pilot study. The pilot study focused on ground water samples collected prior to treatment or blending in areas of New Jersey where groundwater is the principal source of drinking water. This article describes how HBSLs were developed and demonstrates the use of HBSLs as a tool for evaluating water-quality data in a human-health context. HBSLs were calculated using standard U.S. Environmental Protection Agency (USEPA) methodologies and toxicity information. New HBSLs were calculated for 12 of 32 contaminants without existing USEPA drinking-water standards or guidelines, increasing the number of unregulated contaminants (those without maximum contaminant levels (MCLs)) with human-health benchmarks. Concentrations of 70 of the 78 detected contaminants with human-health benchmarks were less than MCLs or HBSLs, including all 12 contaminants with new HBSLs, suggesting that most contaminant concentrations were not of potential human-health concern. HBSLs were applied to a state-scale groundwater data set in this study, but HBSLs also may be applied to regional and national evaluations of water-quality data. HBSLs fulfill a critical need for federal, state, and local agencies, water utilities, and others who seek tools for evaluating the occurrence of contaminants without drinking-water standards or guidelines.

Data Collection↗
PubMed1998

Study finds purchasers aren't using providers' outcomes data, quality report cards.

Are your data collection efforts for naught? A recent study shows that employers and purchasers overlook provider outcomes data and quality report cards when choosing health plans and providers--opting instead for customer satisfaction, HEDIS, and cost data. Here are the details on the study, plus advice from one of the study's authors.

Consumer Behavior↗
PubMed1991

The time interval between death and next-of-kin contact and its effects on response rates and data quality.

The relation of the interval of time between death and next-of-kin contact to outcome variables including response rates and data quality was examined in a nationally representative sample of 17,713 deaths of persons 25 years of age or older that occurred in the United States in 1986. For most of the outcome variables examined, the length of time had little effect, although there was a small decrease in the response rate and a small increase in the refusal rate for contact 40 or more weeks after death. The small decrease in the response rate and small increase in the refusal rate for the longest interval examined held for most decedent background characteristics examined (age, race, cause of death, and type of informant.) Authorizations to contact health care facilities signed by the respondents decreased slightly as the interval increased. The rate of returned mailed questionnaires passing quality and consistency edits increased slightly with time since death. Substantive responses (versus blanks, don't knows, etc.) decreased as time since death increased. Certain questions such as those on income and birth control pill use showed a decrease in response with time since death. Overall, the effects of longer time intervals between death and next-of-kin contact were less than expected on response rates and data quality, although our findings may reflect the high overall response rate, 90.5%, leaving little opportunity for significant areas of nonresponse.

Adult↗
PubMed2005

Protocols for the assurance of microarray data quality and process control.

Microarrays represent a powerful technology that provides the ability to simultaneously measure the expression of thousands of genes. However, it is a multi-step process with numerous potential sources of variation that can compromise data analysis and interpretation if left uncontrolled, necessitating the development of quality control protocols to ensure assay consistency and high-quality data. In response to emerging standards, such as the minimum information about a microarray experiment standard, tools are required to ascertain the quality and reproducibility of results within and across studies. To this end, an intralaboratory quality control protocol for two color, spotted microarrays was developed using cDNA microarrays from in vivo and in vitro dose-response and time-course studies. The protocol combines: (i) diagnostic plots monitoring the degree of feature saturation, global feature and background intensities, and feature misalignments with (ii) plots monitoring the intensity distributions within arrays with (iii) a support vector machine (SVM) model. The protocol is applicable to any laboratory with sufficient datasets to establish historical high- and low-quality data.

Artificial Intelligence↗
PubMedDate not supplied

Building data quality into clinical trials.

Meaningful data begin with the collection process. Pharmaceutical companies are using several different strategies in clinical trials to ensure the highest quality of data. This article will examine these approaches, with an emphasis on case report form development through database release.

Abstracting and Indexing↗
PubMed2004

Data quality aspects of a database for abdominal septic shock patients.

Since many years, medical researchers have investigated the mechanisms that may cause a septic shock. Despite many approaches that analyzed smaller parts of the relevant data or single variables, respectively, no larger database with all the possible relevant data existed. Our work was to bridge this gap. We built a large database for abdominal septic shock patients. While building it, we were confronted with many problems concerning the database realization and the data quality. Thus, we will demonstrate how we built our database and how we assured data quality. This is of interest for all medical or computer scientists who are concerned with building medical databases with retrospective data, e.g. for data mining purposes.

Abdomen↗
PubMed2001

Is shorter always better? Relative importance of questionnaire length and cognitive ease on response rates and data quality for two dietary questionnaires.

In this study, the authors sought to determine the effects of length and clarity on response rates and data quality for two food frequency questionnaires (FFQs): the newly developed 36-page Diet History Questionnaire (DHQ), designed to be cognitively easier for respondents, and a 16-page FFQ developed earlier for the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. The PLCO Trial is a 23-year randomized controlled clinical trial begun in 1992. The sample for this substudy, which was conducted from January to April of 1998, consisted of 900 control and 450 screened PLCO participants aged 55-74 years. Controls received either the DHQ or the PLCO FFQ by mail. Screenees, who had previously completed the PLCO FFQ at baseline, were administered the DHQ. Among controls, the response rate for both FFQs was 82%. Average amounts of time needed by controls to complete the DHQ and the PLCO FFQ were 68 minutes and 39 minutes, respectively. Percentages of missing or uninterpretable responses were similar between instruments for questions on frequency of intake but were approximately 3 and 9 percentage points lower (p < or = 0.001) in the DHQ for questions on portion size and use of vitamin/mineral supplements, respectively. Among screenees, response rates for the DHQ and the PLCO FFQ were 84% and 89%, respectively, and analyses of questions on portion size and supplement use showed few differences. These data indicated that the shorter FFQ was not better from the perspective of response rate and data quality, and that clarity and ease of administration may compensate for questionnaire length.

Aged↗
PubMed1997

Data quality of administratively collected hospital discharge data for liver cirrhosis epidemiology.

We estimated the validity, i.e., whether the diagnostic criteria were fulfilled for the patients registered with the diagnosis of liver cirrhosis in a Danish hospital discharge registry, and the completeness, i.e., whether all patients with liver cirrhosis were included in the registry. Information in the regional hospital discharge registry in the Country of Aarhus, Denmark was compared with hospital records and information in a pathology registry. 85.4% of the patients registered with a diagnosis of liver cirrhosis fulfilled the diagnostic criteria for the diagnosis (validity). 93.2% of the patients registered with biopsy proven liver cirrhosis in the pathology registry were found in the discharge registry (completeness) with a diagnosis of liver cirrhosis. The hospital discharge registry showed relatively few misclassifications and the Danish National Registry of Patients (NRP), which is based on the regional registries, may provide a unique study base for future research.

Biopsy↗
PubMed1998

The Primary Care Assessment Survey: tests of data quality and measurement performance.

OBJECTIVES: The authors examine the data quality and measurement performance of the Primary Care Assessment Survey (PCAS), a patient-completed questionnaire that operationalizes formal definitions of primary care, including the definition recently proposed by the Institute of Medicine Committee on the Future of Primary Care. METHODS: The PCAS measures seven domains of care through 11 summary scales: accessibility (organizational, financial), continuity (longitudinal, visit-based), comprehensiveness (contextual knowledge of patient, preventive counseling), integration, clinical interaction (clinician-patient communication, thoroughness of physical examinations), interpersonal treatment, and trust. Data from a study of Massachusetts state employees (n = 6094) were used to evaluate key measurement properties of the 11 PCAS scales. Analyses were performed on the combined population and for each of the 16 subgroups defined according to sociodemographic and health characteristics. RESULTS: The 11 PCAS scales demonstrated consistently strong measurement characteristics across all subgroups of this adult population. Tests of scaling assumptions for summated rating scales were well satisfied by all Likert-scaled measures. Assessment of data completeness, scale score dispersion characteristics, and inter-scale correlations provide strong evidence for the soundness of all scales, and for the value of separately measuring and interpreting these concepts. CONCLUSIONS: With public and private sector policies increasingly emphasizing the importance of primary care, the need for tools to evaluate and improve primary care performance is clear. The PCAS has excellent measurement properties, and performs consistently well across varied segments of the adult population. Widespread application of an assessment methodology, such as the PCAS, will afford an empiric basis through which to measure, monitor, and continuously improve primary care.

Adult↗
PubMed1986

Impact of the medical record credential on data quality.

This research study was funded by the Foundation of Record Education of the American Medical Record Association. The Department of Medical Record Administration at the University of Illinois, Chicago, was awarded the grant in the summer of 1984. The purpose of the study was to evaluate the quality of coded and abstracted medical record data and to determine if a relationship existed between data quality and the professional credentials of the individuals who manage and/or supervise the collection of these data. The study consisted of three phases: a telephone interview conducted of 83 hospitals, recoding and reabstracting of medical records performed by the staffs of 59 hospitals, and 34 random on-site visits for a reliability check. Data was collected and analyzed from each of the three phases of the study.

Abstracting and Indexing↗