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PubMed1996

Expert systems for the evaluation of data quality for establishing the Recommended Dietary Allowances.

In view of the important role that nutrient intake assessments play in establishing the Recommended Dietary Allowances (RDAs), the quality of food composition data must be assured for accuracy and representativeness. Assurance of data quality requires the definition of critical parameters in the data generation process and the evaluation of specific data for foods and components according to these parameters. An expert systems approach for evaluating the quality of analytical data has been developed by scientists at the Beltsville Human Nutrition Research Center to determine the quality of food composition data for five parameters: sampling plan, sample handling, number of samples, analytical method and analytical quality control. A rating scale for each parameter was developed with 0 representing poor or inadequately documented data and 3 representing optimal data. Specific criteria for each parameter and rating have been developed and incorporated into expert systems software to facilitate the objective assignment of ratings for each data source by the reviewer. After all ratings for a specific food-nutrient combination are assigned, the system calculates a composite score called the "confidence code" which indicates to the user the relative level of confidence in the data. By identifying and rating the important steps in the data generation process, one can begin to partition the possible sources of error or variability in the process. Limitation of the data set relative to specific purposes (e.g., setting the RDAs) can be identified. The evaluation process can provide the basis for focussed research to improve the most critical areas of the data generation process. A similar process could be established to evaluate the quality of analytical data for clinical measurements used to establish the RDAs.

Documentation↗
PubMed1994

Data quality assurance measures (DQAMs) for electronic death investigation data.

Data quality assurance measures (DQAMs) involve manual and computerized procedures to ensure that essential death investigation data have been collected, essential data have been entered into a data base, electronic data accurately reflect the original information, data entries are consistent with one another, words are spelled correctly, numbers and values are entered correctly, and coding is consistent if codes are used. Quality assurance of death investigation data is essential to ensure accuracy in computer-generated office documents and data used for research and public health purposes. A basic approach to the development of DQAMs is discussed, and specific death investigation variables are presented that lend themselves to quality assurance measures.

Coroners and Medical Examiners↗
PubMed2001

Data quality probes--a synergistic method for quality monitoring of electronic medical record data accuracy and healthcare provision.

Increasing reliance is being placed on electronic medical records to support clinical care and achieve improved quality standards. In order for clinical information systems (CIS) to deliver, the data within needs to be complete, consistent and accurate. This data of course only forms part of the process in delivering quality health care during the clinician-patient encounter. This paper outlines a method of assessing the quality of the processes involved in healthcare provision and data quality within a CIS. It proposes a principle of Data Quality Probes (DQP) that can be used to assess the performance of the whole encounter system. The main feature of this is the generation of a query which clinical knowledge predicts should not retrieve any cases in a system performing flawlessly. This approach is applied practically within the paradigm of a UK family practice testing the DQP that only patients who have had a hysterectomy should be prescribed unopposed oestrogen hormone replacement therapy.

Estrogen Replacement Therapy↗
PubMed1994

Data quality and quality control of a population-based cancer registry. Experience in Finland.

Cancer registries should pay great attention to the quality of their data, both in terms of completeness (all cancer patients in the population are registered) and accuracy (data on individual cancer patients must be correct). In addition to technical measures in the data processing, different types of checks and comparisons should be routine practice. Active research policy and ambitious, research-oriented staff with competence in medicine, biostatistics and computer science are essential in terms of maintaining good data quality.

Finland↗
PubMed2001

The use of monetary incentives in a community survey: impact on response rates, data quality, and cost.

OBJECTIVES: To assess the effect of incentive size on response rates, data quality, and cost in a digestive health status mail survey of a community sample of health plan enrollees. DATA SOURCES/SETTING: The study population was selected from a database of enrollees in various health plans obligated to receive care at Park Nicollet Clinic-HealthSystem Minnesota, a large, multispecialty group in Minneapolis, Minnesota, and the nearby suburbs. STUDY DESIGN: A total of 1,800 HealthSystem Minnesota enrollees were randomly assigned to receive a survey with an incentive of $5 or $2. The response rates for each incentive level were determined. Data quality, as indicated by item nonresponse and scale scores, was measured. Total cost and cost per completed survey were calculated. PRINCIPAL FINDINGS: The response rate among enrollees receiving $5 (74.3 percent) was significantly higher than among those receiving $2 (67.4 percent); differences were more pronounced in the first wave of data collection. Data quality did not differ between the two incentive groups. The total cost per completed survey was higher in the $5 condition than in the $2 condition. CONCLUSIONS: A $5 incentive resulted in a higher response rate among a community patient sample with one mailing than did a $2 incentive. However, the response rates in the $2 condition approached the level of the $5 incentive, and costs were significantly lower when the full follow-up protocol was completed. Response rates were marginally increased by follow-up phone calls. The incentive level did not influence data quality. The results suggest if a survey budget is limited and a timeline is not critical, a $2 incentive provides an affordable means of increasing participation.

Adult↗
PubMed2006

The Swedish Heart Surgery Register: data quality for proximal thoracic aortic operations.

OBJECTIVES: To review the data quality and validity in the nationwide Swedish Heart Surgery register for patients operated on the proximal thoracic aorta. DESIGN: Medical records from a random sample of 300 patients in The Swedish Heart Surgery register were reviewed with register data items systematically re-reported. Variable reporting frequency, proportion of adequately reported data, and number and correctness of diagnostic and procedural codes were analysed. RESULTS: After exclusions, 251 patients (84%) remained in the analysis. Reporting frequency for individual items varied from 12% to 100% (median 61%). For core variables, reporting frequency was 96%-100%. In 40 of 43 (93%) reviewed variables, registry data were at least 85% correct. A total of 485 diagnoses and 673 procedures were reported, compared to 617 diagnoses and 758 procedures identified in the review process. CONCLUSIONS: The register data quality and validity for patients operated on the proximal thoracic aorta was satisfactory overall, but need further improvement for complications. The register coverage and completeness was very high. Register-based reports should be accompanied by review of data quality.

Aorta, Thoracic↗
PubMed2003

Three steps to data quality.

BACKGROUND: The quality of data in general practice clinical information systems varies enormously. This variability jeopardizes the proposed national strategy for an integrated care records service and the capacity of primary care organisations to respond coherently to the demands of clinical governance and the proposed quality-based general practice contract. This is apparent in the difficulty in automating the audit process and in comparing aggregated data from different practices. In an attempt to provide data of adequate quality to support such operational needs, increasing emphasis is being placed on the standardisation of data recording. OBJECTIVE: To develop a conceptual framework to facilitate the recording of standardised data within primary care. METHOD: A multiprofessional group of primary care members from the South Thames Research Network examined leading guidelines for best practice. Using the nominal group technique the group prioritized the information needs of primary care organisations for managing coronary heart disease according to current evidence. RESULTS: Information needs identified were prioritized and stratified into a functional framework. CONCLUSION: It has been possible within the context of a primary care research network to produce a framework for standardising data collection. Motivation of front-line clinicians was achieved through the incorporation of their views into the synthesis of the dataset.

Comorbidity↗
PubMedDate not supplied

Data quality objectives in environmental research planning.

This paper presents highlights of a Data Quality Objectives course relating the Environmental Protection Agency's (EPA) seven step research planning process to research efforts at the U.S. EPA National Health and Environmental Effects Research Laboratory, Mid-Continent Ecology Division, in Duluth, Minnesota. Introductory materials were derived from "Guidance for the Data Quality Objectives Process, EPA QA/G-4." Case studies illustrate decisions that were made during the systematic planning process and subsequent experimentation. This paper demonstrates how the Data Quality Objectives Process clearly links research goals and objectives with the final product. Application of the process to environmental research ensures that environmental research data are of known, credible, defensible and usable quality.

Animals↗
PubMed1998

Aspects of data quality in the new millennium.

A major topic of concern for the health information management (HIM) professional today is the quality of health care data. Although the coding professional within the HIM department has responsibility for assigning accurate clinical codes, often there are discrepancies or areas that need improvement. What ways are in place now to ensure quality clinical data? Quality programs and projects that can lead us into the next millennium are important to the HIM profession, administrators, health care payers, and state agencies. The challenges that we face are not without solutions. Sharing information and solutions is important for the individual and the profession as a whole. The article discusses four current but different ways in which data quality is looked at and addressed.

Abstracting and Indexing↗
PubMed2003

Continuous monitoring of intracranial compliance after severe head injury: relation to data quality, intracranial pressure and brain tissue PO2.

The objective of the present study was to test the new continuous intracranial compliance (cICC) device in terms of data quality, relationship to intracranial pressure (ICP) and brain tissue oxygenation (PtiO2). A total of 10 adult patients with severe traumatic brain injury underwent computerized monitoring of arterial blood pressure, ICP, cerebral perfusion pressure, end-tidal CO2, cICC and PtiO2 providing a total of 1726 h of data. (1) The data quality assessed by calculating the 'time of good data quality' (TGDQ, %), i.e. the median duration of artefact-free time as a percentage of total monitoring time reached 98 and 99% for ICP and PtiO2, while cICC measurements were free of artefacts in only 81%. (2) Individual regression analysis showed broad scattered correlation between cICC and ICP ranging from low (r = 0.05) to high (r = 0.52) correlation coefficients. (3) From 225 episodes of increased ICP (ICP > 20 mmHg > 10 min), only 37 were correctly predicted by a preceding decline in cICC to pathological values (< 0.5 ml/mmHg). (4) In all episodes of cerebral hypoxia (PtiO2 < 10 mmHg > 10 min), cICC was not pathologically altered. Based on the present results, we conclude that the current hardware and software version of the cICC monitoring system is unsatisfactory concerning data quality, prediction of increased ICP and revelance of cerebral hypoxic episodes.

Adult↗
PubMed2004

Problems with primary care data quality: osteoporosis as an exemplar.

OBJECTIVE: To report problems implementing a data quality programme in osteoporosis. DESIGN: Analysis of data extracted using Morbidity Information Query and Export Syntax (MIQUEST) from participating general practices' systems and recommendations of practitioners who attended an action research workshop. SETTING: Computerised general practices using different Read code versions to record structured data. PARTICIPANTS: 78 practices predominantly from London and the south east, with representation from north east, north west and south west England. MAIN OUTCOME MEASURES: Patients at risk can be represented in many ways within structured data. Although fracture data exists, it is unclear which are fragility fractures. T-scores, the gold standard for measuring bone density, cannot be extracted using the UK's standard data extraction tool, MIQUEST; instead manual searches had to be implemented. There is a hundredfold variation in data recording levels between practices. Therapy is more frequently recorded than diagnosis. A multidisciplinary forum of experienced practitioners proposed that a limited list of codes should be used. CONCLUSIONS: There is variability in inter-practice data quality. Some clinically important codes are lacking, and there are multiple ways that the same clinical concept can be represented. Different practice computer systems have different versions of Read code, making some data incompatible. Manual searching is still required to find data. Clinicians with an understanding of what data are clinically relevant need to have a stronger voice in the production of codes, and in the creation of recommended lists.

Accidental Falls↗
PubMedDate not supplied

In search of representativeness: evolving the environmental data quality model.

Environmental regulatory policy states a goal of "sound science." The practice of good science is founded on the systematic identification and management of uncertainties; i.e., knowledge gaps that compromise our ability to make accurate predictions. Predicting the consequences of decisions about risk and risk reduction at contaminated sites requires an accurate model of the nature and extent of site contamination, which in turn requires measuring contaminant concentrations in complex environmental matrices. Perfecting analytical tests to perform those measurements has consumed tremendous regulatory attention for the past 20-30 years. Yet, despite great improvements in environmental analytical capability, complaints about inadequate data quality still abound. This paper argues that the first generation data quality model that equated environmental data quality with analytical quality was a useful starting point, but it is insufficient because it is blind to the repercussions of multifaceted issues collectively termed "representativeness." To achieve policy goals of "sound science" in environmental restoration projects, the environmental data quality model must be updated to recognize and manage the uncertainties involved in generating representative data from heterogeneous environmental matrices.

Environmental Monitoring↗
PubMed2003

Managers reports of automated coding system adoption and effects on data quality.

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.

Attitude of Health Personnel↗
PubMed2005

Ensuring the Quality of Aggregated General Practice Data: Lessons from the Primary Care Data Quality Programme (PCDQ).

BACKGROUND: There are large numbers of schemes that collect and aggregate data from primary care computer systems into large databases. These data are then used for market and academic research. How the data is aggregated, cleaned and processed is usually opaque. Making the method transparent allows researchers to compare methods, and users of the output to better understand the strengths and weaknesses of the data.Objectives To define the stages of the process of aggregating, processing and cleaning clinical data from multiple data sources. METHODS: Identify errors in design, collection, staging, integration and analysis. RESULTS: An eight step process defined: (1) Design (2) DATA: entry, (3) Extraction, (4) Migration, (5) Integration, (6) Cleaning, (7) Processing, and (8) Analysis. CONCLUSIONS: This eight step method provides a taxonomy to enable researchers to compare their methods of data process and aggregation.

Computer Systems↗
PubMed1988

Record linkage studies for postmarketing drug surveillance: data quality and validity considerations.

Large automated databases are the source of information for many record linkage studies, including postmarketing drug surveillance. Despite this reliance on prerecorded data, there have been few attempts to assess data quality and validity. This article presents some of the basic data quality and validity issues in applying record linkage methods to postmarketing surveillance. Studies based on prerecorded data, as in most record linkage studies, have all the inherent problems of the data from which they are derived. Sources of threats to the validity of record linkage studies include the completeness of data, the ability to accurately identify and follow the records of individuals through time and place, and the validity of data. This article also describes techniques for evaluating data quality and validity. Postmarketing surveillance could benefit from more attention to identifying and solving the problems associated with record linkage studies.

Database Management Systems↗
PubMedDate not supplied

An electronic menstrual cycle calendar: comparison of data quality with a paper version.

OBJECTIVES: This pilot study compared a prototype electronic menstrual calendar on a handheld computer with a paper calendar for data quality and participants' perceptions. DESIGN: Twenty-three women completed identical information about menstrual bleeding and symptoms using paper and electronic calendars for 1 month each. RESULTS: Use of the paper calendar resulted in more missing data than the electronic calendar for bleeding characteristics (13% vs. 4%) and symptoms (35% vs. 4%). The electronic calendar's ability to log data entries revealed retrospective entry for 61% of the data. Total data entry and cleaning time was reduced by 81% with the electronic calendar. Overall, participants preferred the electronic (70%) to the paper (22%) calendar. CONCLUSIONS: Data quality with conventional paper calendars may be poorer than recognized. The data-logging feature, unique to the electronic calendar, is critical for assessing data quality. Electronic menstrual calendars can be useful data collection tools for research in women's health.

Computers↗
PubMed2005

Automation of cDNA microarray hybridization and washing yields improved data quality.

Microarray technology allows the analysis of whole-genome transcription within a single hybridization, and has become a standard research tool. It is extremely important to minimize variation in order to obtain high quality microarray data that can be compared among experiments and laboratories. The majority of facilities implement manual hybridization approaches for microarray studies. We developed an automated method for cDNA microarray hybridization that uses equivalent pre-hybridization, hybridization and washing conditions to the suggested manual protocol. The automated method significantly decreased variability across microarray slides compared to manual hybridization. Although normalized signal intensities for buffer-only spots across the chips were identical, significantly reduced variation and inter-quartile ranges were obtained using the automated workstation. This decreased variation led to improved correlation among technical replicates across slides in both the Cy3 and Cy5 channels.

Animals↗
PubMed2006

Impact of on-site initiation visits on patient recruitment and data quality in a randomized trial of adjuvant chemotherapy for breast cancer.

PURPOSE: To provide empirical evidence on the impact of on-site initiation visits on the following outcomes: patient recruitment, quantity and quality of data submitted to the trial coordinating office, and patients' follow-up time. PATIENTS AND METHODS: This methodological study was performed as part of a randomized trial comparing two combination chemotherapies for adjuvant treatment of breast cancer. Centers participating to the trial were randomized to either receive systematic on-site visits (Visited group), or not (Non-visited group). RESULTS: The study was terminated after two years, while the main randomized trial continued. Of the 135 centers that had expressed an interest in the trial, only 69 randomized at least one patient (35/68 in the Visited group, 34/67 in the Non-visited group). Almost two-thirds of the patients were entered by 17 centers (10 in the Visited group, seven in the Non-visited group) that accrued more than 10 patients each. None of the prespecified outcomes favored the group of centers submitted to on-site initiation visits (ie, mean number of queries par patient: 6.1 +/- 9.7 versus 5.4 +/- 6.4, respectively for the Visited and Non-visited groups). Spontaneous transmittal of case report forms, although required by protocol, was low in both randomized groups (mean number of pages per patient: 1.5 +/- 2.0 versus 2.1 +/- 2.3, respectively), with investigators submitting about one-third of the expected forms on time (29% and 39%, respectively). LIMITATIONS: This study could not evaluate the impact of repeated on-site visits on clinical outcomes. CONCLUSION: Systematic on-site initiation visits did not contribute significantly to this clinical trial.

Antineoplastic Agents, Phytogenic↗