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At least 19 recordsLinked to original sources
PubMed1998

The Genome Sequence DataBase (GSDB): improving data quality and data access.

In 1997 the primary focus of the Genome Sequence DataBase (GSDB; www. ncgr.org/gsdb ) located at the National Center for Genome Resources was to improve data quality and accessibility. Efforts to increase the quality of data within the database included two major projects; one to identify and remove all vector contamination from sequences in the database and one to create premier sequence sets (including both alignments and discontiguous sequences). Data accessibility was improved during the course of the last year in several ways. First, a graphical database sequence viewer was made available to researchers. Second, an update process was implemented for the web-based query tool, Maestro. Third, a web-based tool, Excerpt, was developed to retrieve selected regions of any sequence in the database. And lastly, a GSDB flatfile that contains annotation unique to GSDB (e.g., sequence analysis and alignment data) was developed. Additionally, the GSDB web site provides a tool for the detection of matrix attachment regions (MARs), which can be used to identify regions of high coding potential. The ultimate goal of this work is to make GSDB a more useful resource for genomic comparison studies and gene level studies by improving data quality and by providing data access capabilities that are consistent with the needs of both types of studies.

Base Sequence↗
PubMed1995

Data quality assurance, monitoring, and reporting.

In conclusion, the quality assurance and monitoring program is an integral and continuing part of study operations. A system must be devised and implemented by the coordinating center investigators, with the endorsement of the study leadership and support of the field site and resource center personnel. Proactive mechanisms for promoting high-quality data acquisition and reporting must be implemented. Data quality monitoring must address the entire process by which the data are gathered, transmitted, stored, and analyzed. Data quality should be monitored continually, with summary reports prepared and distributed to the study leadership. Appropriate training and certification enhance data quality, and site visits allow data collection and storage processes to be observed directly. The quality assurance and monitoring system must be documented. It should be flexible enough so that new means of quality assurance or monitoring can be added when necessary during the course of the study. At the completion of the study, quality monitoring results should be summarized in a final report regarding the level of quality achieved by the study investigators and personnel. Finally, for a quality assurance and monitoring program to be successful, the coordinating center investigators and personnel must provide prompt feedback and suggestions for corrective action whenever a data quality problem is discovered. This need can be met only when the coordinating center staff understand data quality goals and are up to date with all phases of data management and reporting. Delays in initiating any stage of data management and quality monitoring may result in uncorrectable data problems. Thus, knowledgeable and efficient coordinating center personnel are essential to achieving good data quality studywide.

Clinical Trials as Topic↗
PubMed1996

Extreme longevity in five countries: presentation of trends with special attention to issues of data quality.

"Data on the maximum age at death and other indicators of extreme longevity are assembled for five countries (Sweden, England & Wales, France, Japan, and the United States) over various time periods. The raw data are shown in both graphical and tabular formats. Two types of measures are presented: the extreme ages at death reported for a given year (i.e., the maximum, second, third, and fourth highest), and the upper percentiles of the age distribution of deaths by year. The analysis demonstrates that the upper tail of the age distribution of deaths has moved steadily higher over a period of at least 130 years in Sweden. Similar trends are observed (over shorter time periods) for other countries, although in many cases it is argued that the raw data are flawed due to misstatement (in particular, exaggeration) of age at death." (SUMMARY IN FRE)

Americas↗
PubMed2001

Non-invasive methods for measuring data quality in general practice.

AIM: To develop non-invasive methods of measuring the quality of data recorded in general practice. METHODS: Laboratory and pharmaceutical claims data from fourteen practices (44 doctors) from the FirstHealth network of general practices were examined to determine the extent to which valid minimum bounds on expected rates of diagnosis coding could be established. These were compared with recorded rates in patient notes to measure completeness of diagnosis recording. Data completeness was measured for demographic data and a marker for the accuracy of gender coding was developed from diagnosis data. RESULTS: Minimum rates of diagnosis could be established for asthma, diabetes (NIDDM and IDDM), ischaemic heart disease, hypothyroidism, bipolar affective disorder and Parkinson's disease. Minimum bounds for the number of patients requiring monitoring of warfarin and digoxin levels were also established. These expected minimum rates were combined with measures of completeness of age, gender, ethnicity and smoking data, and a gender coding accuracy measure, to produce a set of fourteen data quality indicators. Pass/fail thresholds on each indicator were set and each of the fourteen practices was scored on the number of passes they achieved. The scores ranged from three to nine out of fourteen passses. CONCLUSIONS: Non-invasive data quality measures may be useful in providing feedback to general practitioners as part of a data quality improvement cycle. The sensitivity of this method will decline as data quality improves.

Data Collection↗
PubMed1980

Pathology data quality assurance and data retrieval at the National Center for Toxicological Research.

Automated experimental and pathology data collection and reporting systems are utilized at the National Center for Toxicological Research to increase the efficiency of the variety of research studies being performed. In order for the ED01 study to be conducted in an effective manner, usage of these automated systems was considered advantageous due to the study size, approximately 24,000 mice. The large volume of pathology and other experiment data generated in the study required detailed quality assurance and data retrieval, and the use of automated systems allowed this to be performed much more easily than with manual methods. This paper addresses the methods employed to ensure quality and to provide the data retrieval capabilities necessary for conducting and analyzing the ED01 study.

Government Agencies↗
PubMed2000

Effect of discharge letter-linked diagnosis registration on data quality.

OBJECTIVE: Diagnostic data are essential for the assessment of medical practice: they are needed for retrieval of clinical cases and describing co-morbidity and complications. In most Western countries, diagnosis registration in hospital information systems is based mainly on completing forms after patient discharge. As this registration plays no role in patient care, data quality is usually unsatisfactory. To improve data quality, we redesigned the process of diagnosis registration at a paediatric department, and now paediatricians provide diagnoses with codes in a separate registration heading of the discharge letter. We compared the quality of this discharge letter-linked diagnosis registration with the quality of the previous form based registration. DESIGN: Retrospective study with blinded before and after measurement. Re-abstracted diagnosis descriptions of the text of discharge letters were taken as gold standard. SETTING: A paediatric department in an academic medical centre. STUDY PARTICIPANTS: From each registration period, 60 admissions were selected randomly. Mean age of the patients was 4.5 (SD +/- 5.5) and 5.2 (SD +/- 5.2) years for the old and new situation respectively. Mean length of stay was 8.8 (SD +/- 11.0) and 7.2 (SD +/- 12.4) days. INTERVENTION: Discharge letter-linked diagnosis registration. MAIN OUTCOME MEASURES: Completeness and accuracy, both at three-digit level of ICD-9-CM. RESULTS: Completeness of form-based diagnosis registration was 51% (95% CI, 44-58%) and of discharge letter-linked diagnosis registration 54% (95% CI, 47-60%). Accuracy was 65% (95% CI, 58-72%) and 67% (95% CI, 60-74%) respectively. CONCLUSIONS: The discharge letter-linked diagnosis registration does not provide a better basis for assessment of medical practice than the form based diagnosis registration.

Abstracting and Indexing↗
PubMedDate not supplied

Reversal of the decline in breastfeeding in Peninsular Malaysia? Ethnic and educational differentials and data quality issues.

Data from the First and Second Malaysian Family Life Surveys in 1976 and 1988, respectively, are analyzed to examine long-term trends in breastfeeding in Peninsular Malaysia, educational and ethnic differences therein, and the quality of retrospective data on infant feeding. The steady decrease between the mid-1950's and mid-1970's in breastfeeding was reversed to become a nearly monotonic increase since 1975. Part of the change is attributable to the changing composition of the Malaysian population. Over time, the percentages of births to subgroups with higher rates of breastfeeding--particularly Malays and more highly educated women--have increased. However, there is also evidence of changes in rates of breastfeeding within these subgroups. Many Malaysian infants have a total duration of breastfeeding (including with supplementation) considerably shorter than WHO's recommended four months of exclusive (unsupplemented) breastfeeding. Moreover, nearly all breastfed infants are first given supplementary food or beverage shortly after birth. Breastfeeding promotion efforts in Malaysia need to emphasize the appropriate timing of and types of supplementary feeding.

Adolescent↗
PubMedDate not supplied

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.

Critical Care↗
PubMed1989

Monitoring data quality through comparisons between data systems.

Since its creation in 1960, the National Center for Health Statistics (NCHS) has placed a high value on the quality of the information and statistics collected and published by its data systems. An important component of a comprehensive data quality monitoring system for a statistical agency such as NCHS is the comparison of statistics between data systems. Between-data-systems comparisons are used to monitor the consistency and comparability of statistics across data systems and between NCHS data systems and outside sources of data. This paper focuses on between-data-systems comparisons and describes a number of methodological analyses that can be used to evaluate data quality. The methodological analyses presented include the evaluation of time trends, the estimation of survey method effects, the evaluation of response error, the measurement of definitional and concept effects, and the detection of inconsistencies between data systems. The paper concludes with a discussion of the complexities of the between-data-systems comparisons and an assessment of the net benefits from such comparisons.

Data Collection↗
PubMed1994

Quality data: what are they?

Nowadays, quality has become a very important factor in almost all areas of endeavour. The data generated from tests for the assessment of potentially toxic chemicals is obviously no exception. It is necessary, therefore, that quality systems be developed to ensure that the data generated to support these tests are of good quality. An acceptable quality system should require that, where applicable, the tests be performed according to defined guidelines. Once defined guidelines have been identified for the type of test to be performed, it is then necessary to design a plan which describes how, when, where and by whom the data will be generated. If at all possible, the data should be generated according to written standard procedures which provide for the production of data to the same quality standard. The data should be generated and collected by properly trained staff using data collection systems (paper or electronic media) which ensure the accuracy, reliability and integrity of the data recorded. The data must then be recorded in such a way as to ensure that they are reported completely clearly and accurately. The report, whether it be in the form of scientific article, monograph or formal study report, should present the data in a consistent manner and allow for adequate reconstruction of the events which took place during the test. Finally, the report and the data supporting it should be verified to ensure that the test was carried out according to the relevant guidelines (if used), that the study plan was correctly followed and finally that all data were properly generated and accurately reported in the report.(ABSTRACT TRUNCATED AT 250 WORDS)

Databases, Factual↗
PubMedDate not supplied

Ensuring data quality in medical research through an integrated data management system.

An effective data management system ensures high quality research data by making certain of the proper execution of the study design. This paper presents the components of a data management system and describes procedures for use in each component of the system to obtain high quality data. We discuss the interrelationship among the components of the data management system and the relationship of the data management system to other parts of the research project. We identify underlying principles in design and implementation of a data management system to ensure high quality data.

Computers↗
PubMed1987

Managing data quality through automation.

Traditional definitions of data quality deal primarily with individual data sets and the data collection process. Today's standards for ensuring data quality have not changed with respect to the desired results, but have simply been expanded to take advantage of modern technology. Computers are used to acquire, review, store, analyze, and report data. Because each of these steps can be automated, the need for human intervention and manual review is minimized. As a result, the potential for invalid data to reach the data analysis stage has increased significantly. To reduce this potential, efforts must be devoted to developing automated procedures that cover every conceivable validation possibility. Relationships between data and data sets must be well defined [1], and data base support that facilitates ready access to the data for the purpose of analysis must be provided. For small data sets, automation may therefore be impractical; but for large, interrelated data sets, automation is highly desirable. Computer automation has therefore expanded the traditional concept of ensuring data quality to include a complex array of interrelated tasks that must be properly managed to achieve the desired results.

Animals↗
PubMed2003

Use of a three-color cDNA microarray platform to measure and control support-bound probe for improved data quality and reproducibility.

Construction methodologies for cDNA microarrays lack the ability to determine array integrity prior to hybridization, leaving the array itself a source of uncontrolled experimental variation. We solved this problem through development of a three-color cDNA array platform whereby printed probes are tagged with fluorescein and are compatible with Cy3 and Cy5 target labeling dyes when using confocal laser scanners possessing narrow bandwidths. Here we use this approach to: (i) develop a tracking system to monitor the printing of probe plates at predicted coordinates; (ii) define the quantity of immobilized probe necessary for quality hybridized array data to establish pre-hybridization array selection criteria; (iii) investigate factors that influence probe availability for hybridization; and (iv) explore the feasibility of hybridized data filtering using element fluorescein intensity. A direct and significant relationship (R2 = 0.73, P < 0.001) between pre-hybridization average fluorescein intensity and subsequent hybridized replicate consistency was observed, illustrating that data quality can be improved by selecting arrays that meet defined pre-hybridization criteria. Furthermore, we demonstrate that our three-color approach provides a means to filter spots possessing insufficient bound probe from hybridized data sets to further improve data quality. Collectively, this strategy will improve microarray data and increase its utility as a sensitive screening tool.

Color↗
PubMed2002

Data quality probes-exploiting and improving the quality of electronic patient record data and patient care.

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 excellence the data within it needs to be complete, consistent and accurate. This capture of data is critical but forms only part of the procedure in delivering quality health care during the clinician-patient encounter. A number of processes are involved in this encounter, each of which has to be performed flawlessly to deliver a perfect outcome. This paper outlines a method of assessing the quality of these processes involved in healthcare provision and data quality within a CIS. It proposes the principle of Data Quality Probes (DQP) 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. Any cases retrieved (which fail the DQP) indicate an error in either data quality or clinical judgment. This approach is applied practically within the paradigm of a UK family practice testing the hypothesis that a series DQPs can provide a valuable method for monitoring both the data accuracy of a CIS and the provision of quality patient care.

Delivery of Health Care↗
PubMed1993

[Data quality in the MORBUS Sentinel Project--expiratory wheezing in infants].

Since 1991 the MORBUS project is being conducted to establish and run a sentinel network of 100 general and paediatric practices in three regions of Germany. A number of health conditions have been and will be monitored consecutively with special emphasis on environmentally determined health problems. From March to June, 1991, 1054 contacts of 1- and 2-year-old children with expiratory wheezing were reported. Quality of these event data was assessed by means of internal completeness. Important clinical information was missing in about 10% of all cases without evidence for regional differentiations. Data quality by this criterion was better in first contact than in re-contact cases (9.7% vs 18.1% missing). Questions concerning the parents (allergies, smoking, education) were less frequently answered (up to 24% missing) than questions of obvious medical relevance to the child. Completeness of parental information varied considerably between regions. There was no association between the medical specialty of the doctors and the quality of their data. In a longitudinal view, there was a slightly positive trend over time in the proportion of clinically incomplete case reports at borderline statistical significance (p = 0.057). Apart from these minor findings then, there was an overall good consistency of completeness in the MORBUS data on expiratory wheezing. By optimizing questionnaires and data transmission, it should be possible to increase the data quality even further.

Air Pollutants↗