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At least 37 records · Page 2Linked to original sources
PubMed1979

Significance of data collection. Viewpoint - academia.

The academic view of the collection of data relating to the health of workers must reflect a concern for the value of the data for research purposes. Most routine data systems do not provide information of research quality, and often are not appropriate or sufficiently detailed to allow specific hypotheses to be tested. the cost of serving research purposes in such systems is likely to be prohibitive. However, the monitoring of illness in groups of employees can and should serve as a most valuable research resource when viewed as a screening and detection mechanism. Therefore, a data collection system should be carefully designed with research needs in mind, so that it will be appropriately sensitive and consistent.

Epidemiologic Methods↗
PubMedDate not supplied

Data collection as an educational process.

Data collection, an experiential method for learning about the research process, may affect the attitudes and future research involvement of nurse data collectors. This study describes the effects of the data collection experience on nurses' perceptions of nursing research and the research environment and on their plans for research involvement. Implications of using the data collection experience as an educational process about research are included.

Adult↗
PubMed1988

[Use of a computer system in perinatal data collection].

We present a computer system for obstetrical and perinatological data collection. All deliveries of the years 1984, 1985, and 1986 have been collected with this data collection program, so that there is how a data base containing 7,975 data records. A relational data base was chosen for data collection and storage. Either off-line or on-line data collection is possible by medical users via menu monitoring. Various possibilities of use are described.

Birth Weight↗
PubMed1990

A standardized data collection tool.

To successfully integrate data collection into the staff's responsibilities, the process must be simple, concise, and easy to use. The data collection tool described in this article includes all the important information at a glance, permits easy comparison with projected and actual thresholds, and analysis of data with follow-up action. It reduces the volumes of paperwork required in many systems. Since the indicators are written on only one paper, there is a reduction in transcription time. Additionally, one paper that contains a sample of twenty should be adequate for a unit-based indicator. Use of this tool reduces the number of papers that must be handled by the QA coordinator as well. Finally, the tool is flexible enough to use in a variety of settings.

Data Collection↗
PubMed1991

Use of a microcomputer database system in a statewide effort for data collection in medical genetics.

The Genetics Office Automation System (GOAS) is a database management system for the collection and reporting of medical genetics data. We have previously reported on its implementation in a single university center [1,2]. We report here on its implementation in a coordinated data collection effort for the State of Missouri. We discuss the current status of the data collection activities and procedures to share data collected at an individual center with state, regional, and national data collection efforts.

Data Collection↗
PubMed1989

The Mid-Atlantic Oncology Program's comparison of two data collection methods.

The Mid-Atlantic Oncology Program (MAOP) compared clinical trial data collected by circuit riding data managers (CDMs) from the MAOP statistical center with data collected by local data managers (LDMs) from clinics and practices, the latter being the standard data capture method in cancer clinical trials. LDMs and CDMs filled out identical study forms, using the same patient charts, for randomly selected patients on MAOP protocols. All coded answers on the forms were compared by one of the authors (DJ) and discrepant items were resolved in a blinded manner by the local MAOP physician acting as the principal investigator (PI). Thirty-three patient charts were reviewed with 53 pairs of forms completed and 1023 pairs of codes compared. A total of 129 (13%) pairs of codes were considered discrepant. Of the 100 discrepancies resolved (29 items were answered differently by the CDM, LDM, and PI) the PI's answers matched 66 of 100 codes as recorded by the CDMs and 34 of 100 codes as recorded by the LDMs. This results in chi 2 = 9.61 (p less than 0.005), demonstrating a significant difference between the frequency with which the PI's answers matched data collected by the CDMs and LDMs. It was also determined that CDMs consistently coded toxicities as more severe than did LDMs and were more often correct. Given the results of this study, CDMs should be considered an acceptable alternative to LDMs in the context of regional programs.

Bias↗
PubMed1986

Simplified data collection and analysis in a Schistosoma mansoni endemic area.

Proper data collection and analysis is one of the important factors to successfully carry out the surveys in Schistosoma mansoni endemic areas. The data needed can be divided into three main categories i.e. pre-survey, during the survey and follow up data. Various factors are considered important for correct data collection. Without proper data collection and analysis, future planning of the programme, monitoring of the operation, modifications, correct interpretation of the results and evaluation of the programme cannot be done. Hence correct decision on the future of the programme cannot be made by professional, technical and administrative authorities. Data collection forms could be designed as required, keeping in mind the objectives of the programme.

Animals↗
PubMed1983

Developing a data-collection system.

Desirable characteristics for a hospital pharmacy financial data-collection system are described. Data collection should follow standardized methods, require minimum personnel time, monitor financial performance, high-light trends, and use standard reporting periods and nomenclature. When automated data-collection methods are not available for pharmacy departments, manual systems should be developed so that totals correspond to automated hospital reports. Data should be collected for expenses (drug and supply costs; personnel costs; equipment purchase, lease, and maintenance; and purchased services), revenues, workload, transfers, credits, drug use, and hospital indicators. For data to be useful as a management tool, they must be reported in a timely fashion and in a manner that is easy to analyze. Reports to hospital administration should emphasize summary data, and details should support the summary. An adequate system for collection, collation, and reporting of financial data is essential for sound management of a hospital pharmacy department.

Costs and Cost Analysis↗
PubMed1989

Quality control of validity of data collected in clinical trials. EORTC Study Group on Data Management (SGDM).

In a study initiated by the EORTC Study Group on Data Management, 15 site visits to main participating centers in ongoing cancer clinical trials have been carried out over a 1 year period. The aim was to evaluate the quality level of EORTC clinical trial data, to find out the order of magnitude of possible problems encountered and to test a technique to objectively assess the quality of data. The process of data collection and the quality of data transfer from hospital charts to EORTC case report forms (CRF) were checked. The data quality was scored and the causes of incorrectness were evaluated. Percentages of correct data ranged from 78% up to 98%; 11/15 centers had greater than 90% correct data. The median rate of error encountered in key data was 2.8% (range 0.5-7%). The main source of error was incorrect transfer of the information recorded in the patient chart to the CRF. Equally good overall results have been observed in the centers where data managers fill in the forms (DM) and those centers without an administrative trial structure (PH). The mean percentage of correct data for both types of centers is 91.4%. The wider range in percentage for incorrect data (DM mean value 3.0%, range 0.5-7%; PH mean value 2.3%, range 1.4-3.1) suggests the important impact of the knowledge and experience of the people involved in data management. The data quality evaluation was hampered by the impossibility of checking part of the data present on the CRF, 0.4-14.5%. Besides knowledge and experience, the main aspects influencing good data quality appeared to be the efficacy of the internal organization and good local data monitoring. The importance of the design of CRFs was also highlighted. As this study was run for on-going protocols, the site visiting team had the opportunity to point out and report to the trial coordinator all shortcomings and controversial points that could thus be corrected during the course of the trial.

Clinical Trials as Topic↗
PubMed1992

[Computerization of data collection in epidemiological studies].

Data collected in epidemiological studies on large cohorts need to be evaluated through the organization of a specific computer laboratory in order to make all procedures simple and fast. This paper describes the basic elements composing a computer laboratory and discusses its implementation in epidemiological investigations.

Breast Neoplasms↗
PubMed1988

Interobserver variability in collecting data from medical records.

As part of a quality assurance program for monitoring medical efficacy of laboratory tests, three registered nurses collected data from the records of over 3000 patients who had plasma parathyroid hormone measurements. At the close of this investigation, medical records were reabstracted to assess the consistency of data collection. Overall agreement between data entries varied from 99.5% to 93.4% to 82% for 1672 numerical entries, 940 interpretative entries, and 50 classification codes, respectively. Statistics for 68 variables showed 3% poor agreement, 10% slight to fair agreement, 15% moderate agreement, 24% substantial agreement, and 48% almost perfect agreement. Since interobserver variability is a potential source of bias, statistical analysis of reabstracted data is a useful way to evaluate data consistency.

Abstracting and Indexing↗
PubMed1990

Prehospital data entry compliance by paramedics after institution of a comprehensive EMS data collection tool.

OBJECTIVE: To determine the completeness of data entry by paramedics after an extensive modification of the prehospital first-care form in an urban emergency medical services (EMS) system. DESIGN: Comprehensive medical information was added to the EMS data collection tool used by a metropolitan fire department. We evaluated the frequency of failure to enter data pertaining to medical assessment and/or treatment of victims of cardiac arrest after implementation of the system. RESULTS: Failure to enter data in the first month was compared with two subsequent two-month blocks. A high rate of noncompliance existed in the first month (all medical data were missing in 24.6%). However, the subsequent two months revealed a marked decline in noncompliance (4.4%, P less than .001). This decline was maintained after a three-month interim (5.0%, P less than .001). CONCLUSION: Data entry noncompliance can be a significant problem after implementation of a new prehospital data collection system. However, compliance can be markedly improved over a relatively short period. Because EMS system evaluation is based on data collected in the field. EMS researchers and administrators must be aware of the data entry compliance rate in their system when attempting to make conclusions from such information.

Adolescent↗
PubMed1987

Using administrative data for longitudinal research: comparisons with primary data collection.

This paper discusses the advantages and disadvantages of using administrative data for longitudinal research, focusing on loss to follow-up. Comparisons between research relying on primary data collection and that using data bases are made. After development of a suitable framework, follow-up in several well-known projects based on primary data collection (the Seven Countries project on coronary heart disease, the Massachusetts research on long-term care and the Pittsburgh clinical trial of tonsillectomy) is compared with follow-up using the Health Services Commission data base in Manitoba, Canada. Overall follow-up in the Manitoba research compares favorably with participation and follow-up rates in other studies based on primary data collection. Initial nonresponse and nonlocation are major problems with studies using primary data; failure to locate earlier respondents in subsequent waves results in a wide range of overall response rates. Data bases do not require researchers to contact individuals and hence follow-up is simplified. Eight year follow-up rates in the Manitoba data base are almost always over 80% and often over 90%. Because records can be flexibly summarized for each individual over time, data bases facilitate certain types of longitudinal studies which would be difficult, if not impossible, to perform using other methodologies. If the desired data are available and recorded with acceptable accuracy, administrative data banks hold considerable promise for the health care researcher.

Adolescent↗
PubMed1987

Italian multicenter case-control study of clinically diagnosed Alzheimer disease: strategies and instruments for data collection.

Instruments and methods used for data collection in the Italian multicenter case-control study of clinically diagnosed Alzheimer disease are illustrated. Details of design of the questionnaire, choice and training of the interviewers, choice of the respondent, and assessment of the quality of data collected are discussed. The text of the questionnaire is included.

Alzheimer Disease↗
PubMed1991

Evaluation of a computerized field data collection system for health surveys.

A customized field data collection system (FDCS) has been developed for a hand-held computer to collect and check questionnaire data. The data quality, preparation time, and user acceptability of the system were evaluated during a malaria morbidity survey in Bakau, the Gambia. Eight field-workers collected data with either the FDCS or on paper questionnaire forms in alternate weeks over a 6-week period. Significantly fewer item errors occurred with the FDCS, and by the end of the survey period interview times were significantly less with the FDCS than with the paper and pencil questionnaire. Advanced appropriate technology may have a useful role in providing accurate and rapid information, particularly in overcoming bottlenecks in data processing, and in obviating the need for costly expertise and equipment. In developing countries this could help to improve the quality of data on health care.

Data Collection↗
PubMed1989

The Sunnybrook Neurotrauma Assessment Record: improving trauma data collection.

A neurotrauma assessment record has been designed to aid in data collection and clinical documentation of patients with multiple injuries. The record collects data concerning patient demography, trauma and medical history, neurological and systemic examinations, investigations, and treatment planning. It consists of two pages and the clinician need only circle listed choices, write focused comments, or draw on provided diagrams. It obviates the narrative record of the history and physical examination. We reviewed the written records of 100 consecutive polytraumatized patients seen in the Trauma Room before institution of the form, transcribing their information onto the form. These were compared to a second series of 100 consecutive patients who were evaluated following the introduction of the neurotrauma form as the initial assessment record. Seventy-seven of these patients were evaluated by the Neurosurgical service. Overall, the quality and completeness of recording improved dramatically. The neurotrauma assessment record ensures more complete recording of information during initial patient assessment, allows easy transfer to computerized databases, and may assist academic centres in performing clinical research.

Humans↗
PubMed1988

The Hollywood surgical-audit programme: a computer-based discharge and data-collection system for surgical audit.

This article describes the development of a computer-based system for the prospective collection of data for surgical audit and peer review with the generation of a surgical discharge letter. The software has been developed for an IBM personal computer and is suitable for any compatible computer. The system has potential advantages for teaching hospitals as it enables patients to obtain a definitive discharge letter on their discharge from hospital. It is also of potential benefit to surgeons who wish to collect clinical data and to audit the quality of their surgical practice.

Computers↗
PubMedDate not supplied

Automated data collection and analysis system for MOSFET radiation detectors.

Metal oxide semiconductor field effect transistors (MOSFET) have been used as radiation dosimeters. Because of their small detector size, minimal power requirements, and signal integration characteristics, they offer unique possibilities as real-time dose monitors in radiotherapy. An automated data collection and analysis system for use with MOSFET radiation dosimeters has been designed and built. The objective was to design a system which can acquire and process the MOSFET signals in real time, in any radiation field encountered in radiotherapy. In particular, major problems have been solved arising from the intrinsic drifts of the MOSFET signal during low dose rate measurements. These signal drifts are significant when the MOSFET detector is used in applications such as on-line monitoring of radiation dose delivery in brachytherapy or radioimmunotherapy. The data collection and analysis system includes a portable IBM-compatible personal computer fitted with digital-to-analog and analog-to-digital converter boards. A single-chip programmable current supply is used to power the MOSFET dosimeters. Intrinsic and extrinsic drifts in signal due to ion diffusion and electron tunneling are corrected by deconvolution of the collected data in real time or after data collection. The data acquisition system and signal-processing methodologies are described.

Data Collection↗