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At least 127 records · Page 7Linked to original sources

PubMed1997

Computerized data collection in the operating room during coronary artery bypass surgery: a comparison to the hand-written anesthesia record.

OBJECTIVE: To investigate variability between hand-written and computerized anesthesia records and evaluate any associated bias. DESIGN AND MEASUREMENTS: A computer system that was used to collect intraoperative data for a study of hemodynamic management during coronary artery bypass graft surgery is described. The system collected and recorded hemodynamic data automatically downloaded from the anesthesia monitor as well as surgical events and drug administration data entered through menu options. The system then combined, summarized, and graphed the data as well as formatted it for export to a commercially available database program. In a sample of 14 patients, blood pressure data collected by the computer system was compared with the blood pressure data charted in the hand-written anesthesia record. MAIN RESULTS: Although general linear models controlling for within-patient variation and randomization assignment for mean arterial pressure range on cardiopulmonary bypass showed a significant relationship; low R2 values indicated that much of the variability could not be explained and that there was, therefore, poor agreement between the two records. Furthermore, a systematic bias in the hand-written anesthesia record was found when the computer system record was compared with the hand-written record and to the difference of the two records, so that extremes seen in the computer system record tended to be minimized in the hand-written anesthesia record. CONCLUSIONS: Because of the lack of explained variability between the computer system and hand-written anesthesia records and the bias in the hand-written anesthesia record, the hand-written anesthesia record should not be relied on as a source of accurate data for research purposes.

Anesthesia↗

PubMed1999

Supporting practice-based audit: a price to be paid for collecting data.

BACKGROUND: There has been considerable investment by health authorities in the funding of support staff whose job is to collect data for audit purposes. It is important to understand what costs are involved in such a data collection exercise. The cost advantages of using existing practice staff or externally funded staff are not known. AIM: To assess the cost of transposing data on workload to computer software for audit purposes and retrieving data on five chronic diseases from case records. METHOD: Four audit support staff monitored the time taken to collect specific data as part of a broad audit programme in 12 training practices within one health board area in the West of Scotland in 1997. The time taken was used to estimate comparative costs for using a receptionist or practice nurse for carrying out a similar exercise. RESULTS: Average costs for collecting data per 1000 patients for waiting time, appointments, recall, and telephone audits were 5.24 Pounds for reception staff, 5.64 Pounds for audit support staff, and 9.68 Pounds for a practice nurse. The average cost for collecting data per patient with diabetes, asthma, epilepsy, hypertension, or rheumatoid arthritis was 1.48 Pounds for reception staff, 1.60 Pounds for audit support staff, and 2.74 Pounds for a practice nurse. CONCLUSIONS: The cost of collecting data varies considerably depending on which staff are chosen for the purpose. Practices should consider carefully how best to collect data for audit in terms of cost.

Allied Health Personnel↗

PubMed1999

Profile of patients presenting for cataract surgery in the UK: national data collection.

AIMS/METHODS: A national data collection exercise was carried out in more than 100 hospital eye service units within the UK to provide clinical and administrative information on patients undergoing cataract surgery. This included patient clinical data such as visual acuity at the time of wait listing and at the time of admission for surgery, presence of other eye disorders, other serious medical disorders, and data on waiting time and type of admission. RESULTS: The profiles of the 18 454 patients aged 50 years or older are reported. Findings of particular note were as follows. At the time of wait listing for cataract surgery 31% had visual acuity of 6/12 or better, 54% had visual acuity between 6/18 and 6/60, and 15% had less than 6/60 vision. Considering those who had visual acuity of 6/12 or better at the time of wait listing, by the time of admission for surgery, the vision deteriorated to 6/18-6/60 in 33% and in a further 3% the vision deteriorated to below 6/60. In patients with moderately poor visual acuity (<6/12-6/60) at the time of wait listing, 13% had less than 6/60 vision by the time of admission for surgery. CONCLUSION: This type of data collection and reporting exercise provides new material that can be used in the planning and provision of cataract surgery services in the UK.

Age Distribution↗

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↗

PubMedDate not supplied

Windows-based guided data capture software for mass-scale thermophysical and thermochemical property data collection.

Guided data capture software (GDC) is described for mass-scale abstraction from the literature of experimental thermophysical and thermochemical property data for organic chemical systems involving one, two, and three components, chemical reactions, and chemical equilibria. Property values are captured with a strictly hierarchical system based upon rigorous application of the thermodynamic constraints of the Gibbs phase rule with full traceability to source documents. Key features of the program and its adherence to scientific principles are described with particular emphasis on data-quality issues, both in terms of data accuracy and database integrity.

Journal Article↗

PubMed2001

Modeling the effects of epidemics on routinely collected data.

The use of routinely collected data, such as absenteeism, to provide an early warning of an epidemic will depend on better understanding of the effects of epidemics on such data. We reviewed studies in behavioral medicine and health psychology in order to build a model relating known factors related to human health information and treatment seeking behavior and effects on routinely collected data. This review and modeling effort may be useful to researchers in early detection, simulation, and response policy analysis.

Data Collection↗

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↗

PubMed1986

Data collection, analysis, and interpretation in ambulatory care research: uses of the epidemiologic method.

In summary, in the collection, analysis, and interpretation of study data, individuals planning and analyzing the results of their own investigations or those of others must be particularly careful about the potential pitfalls that may occur. Considerable time and effort beforehand should be given to the type of study design (observational versus randomized trial) to be utilized; the scope of the investigation to be carried out, be it of a small descriptive scale or a large collaborative effort; attention to detail in the development of the data-collection instruments; and extensive training and continued monitoring of the data abstractors. Awareness of these factors should enhance data collection efforts, the quality of the collected data, and any interpretation thereof.

Ambulatory Care↗

PubMed2003

Handheld computers. A feasible alternative to paper forms for field data collection.

Recent advances in handheld computer hardware and software may provide alternatives to paper-based data collection methods. The authors compared data collected with paper forms to data collected with handheld computer-based forms in a field observation study of alcohol purchase attempts at 47 community festivals in a large metropolitan area. Agreement between data collected with paper forms and data collected with handheld computers was greater than 95%. Computer-based forms handled branching patterns better and yielded data that were immediately available for analyses. Paper forms handled written comments better. Handheld computers are a feasible alternative to paper forms for field data collection.

Adult↗

PubMed2005

Factors to weigh when considering electronic data collection.

Researchers are increasingly considering the adoption of electronic data-collection methods--which entail the use of the Web, e-mail, and desktop and handheld computers--for surveys, diaries, research instruments, and focus groups. Based on prior research findings on electronic data collection, the authors delineate the key factors, which incorporate population, data, and resource characteristics, that guide researchers in selecting the data-collection method most appropriate for their research question. Population factors to consider in data-collection decisions are age, gender, socio-economic status, and access to technology. Key data factors influencing the selection of collection method include sensitivity of the topic, time sensitivity, longitudinal data, and contextual data. Data-collection decisions are also based on financial, time, and technological resources. Technology and demographics will continue to change and affect data-collection methods and possibilities. By examining these key factors, however, researchers will be able to reach data-collection decisions that are appropriate for each project.

Computer Simulation↗

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↗

PubMed1994

[Participant observation as data collection method in nursing research].

Participant observation as data collection method in nursing research is explored in this article. The scientific basis of observation method comes from symbolic interactionism. Nature and epistemological starting points of the knowledge produced by observational method can vary depending on the purpose of the study. Participant observation is a process, which aims at scientific research through observer's presence in social situations. Fieldwork should be carefully prepared because the main problem in participant observation is observer's influence on phenomenon which is being studied. In the beginning of the field-work researcher tries to get a general view what happens in the situation. After the analysis the researcher can focus his observations according to his research problems and finally observe selectively. The data consists of record-keeping, descriptions and field-work note book. Participant observation is the most subjective observation method of data collection. By using triangulation a researcher can avoid his personal influence on results and make the validity of his study better. The researcher must take ethical issues into consideration when collecting data by participant observation.

Data Collection↗

PubMed2001

Data collection with adolescents.

Adolescence spans a wide time period during which development and its context influence behaviour and the strategies used for data collection. This article addresses problems that can be anticipated in data collection with adolescents. Problems are presented that are associated with adolescent developmental capacities, adolescent developmental change and stability, gender and ethnicity. Some problems are specific to certain settings or to certain data collection methods. The application of theoretical and empirical knowledge about adolescent development can strengthen data collection instruments and procedures. Strategies to enhance data collection include carefully defining adolescence and tailoring procedures and protocols based on a theoretical understanding of the biological, cognitive and emotional development of adolescents in conjunction with an understanding of the characteristics of the research setting. Adolescent research settings can pose challenges. Investigators must understand clinic routines in evaluating the feasibility of implementing protocols and collecting data where there are distractions and time constraints. In education settings, strategies must be used to collect data in a group context where peer pressure can affect responses. In home settings, ingrained response patterns and family norms can also impede obtaining valid perspectives from adolescents. Ethnicity as well may play an important role that must be addressed when designing and implementing data collection with adolescents. Procedures and instruments selected to collect data need to address variations in ethnicity.

Adolescent↗

PubMedDate not supplied

Measurement issues related to data collection on the World Wide Web.

As the World Wide Web has become more prominent as a mode of communication, it has opened up new possibilities for research data collection. This article identifies measurement issues that occur with Internet data collection that are relevant to qualitative and quantitative research approaches as they occurred in a triangulated Internet study of perimenopausal women with migraine headaches. Issues associated with quantitative data collection over the Internet include (a) selecting and designing Internet data collection protocols that adequately address study aims while also taking advantage of the Internet, (b) ensuring the reliability and validity of Internet data collected, (c) adapting quantitative paper-and-pencil data collection protocols for the Internet, (d) making Internet data collection practical for respondents and researchers, and (e) ensuring the quality of quantitative data collected. Qualitative data collection over the Internet needs to remain true to the philosophical stance of the qualitative approach selected. Researcher expertise in qualitative data collection must be combined with expertise in computer technology and information services if data are to be of ultimate quality The advantages and limitations of collecting qualitative data in real time or at a later time are explored, as well as approaches to enhance qualitative data collection over the Internet. It was concluded that like any research approach or method, Internet data collection requires considerable creativity, expertise, and planning to take advantage of the technology for the collection of reliable and valid research data.

Climacteric↗

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↗

PubMedDate not supplied

A web-based data collection system for clinical studies using Java.

Data collection in multi-centre controlled clinical trials is a major problem. We describe a web-based documentation system for data collection via the Internet we have developed in Java. It keeps both the platform independent program (a Java applet) and the database on a single server to which the participating centres have access at any time. It is based on a three-tier model where client requests are handled and monitored by an application server (a Java application). We show the advantages such a system has over the HTML/CGI combination which is often used for database access, as well as possible extensions, e.g. connection to a data dictionary. Finally, we discuss the security problems which arise by the use of the Internet for data collection.

Data Collection↗

PubMedDate not supplied

Medical record confidentiality and data collection: current dilemmas.

Author discusses current data collection and data disclosure practices that threaten medical record confidentiality. She raises concerns about the reidentification of ostensibly deidentified patient information and discusses measures for protecting confidentiality in a research setting.

Biomedical Research↗