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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↗
PubMed2000

Validation of an instrument for injury data collection in rugby union.

OBJECTIVE: To provide the basis for collecting rugby union injury data using a rigorously validated injury report form. METHODS: Seven stages were used to assess face, content, and criterion validity of the rugby union injury report form. A 22 member panel plus four sporting bodies assessed the form for face validity, and an expert panel assessed it for content and criterion validity. Panel members were consulted until consensus was reached. A yardstick developed by an expert panel using the Delphi technique was used to assess the reliability of the form. An independent panel of 10 viewed a series of five videotaped injuries, three times over a five week period to assess inter-rater and intrarater reliability. The form was then trialed by 40 people in situ during four games. RESULTS: The rugby union injury report form for games and training was developed, and the face, content, and criterion validity successfully assessed. A seven step protocol to create a yardstick was also developed to assist in the validation process. Both inter-rater and intrarater reliability results indicated a 98% agreement. The 40 trialists who completed forms in situ during four games were found to have an inter-rater reliability agreement of 98% for nine injuries. CONCLUSIONS: A measurement instrument for injury data collection in rugby union was successfully developed and validated, providing researchers with a basis for future studies in this area. A procedure to develop future injury data collection instruments in other sports was also developed.

Data Collection↗
PubMed2006

Brief report: beyond clinical experience: features of data collection and interpretation that contribute to diagnostic accuracy.

BACKGROUND: Clinical experience, features of data collection process, or both, affect diagnostic accuracy, but their respective role is unclear. OBJECTIVE, DESIGN: Prospective, observational study, to determine the respective contribution of clinical experience and data collection features to diagnostic accuracy. METHODS: Six Internists, 6 second year internal medicine residents, and 6 senior medical students worked up the same 7 cases with a standardized patient. Each encounter was audiotaped and immediately assessed by the subjects who indicated the reasons underlying their data collection. We analyzed the encounters according to diagnostic accuracy, information collected, organ systems explored, diagnoses evaluated, and final decisions made, and we determined predictors of diagnostic accuracy by logistic regression models. RESULTS: Several features significantly predicted diagnostic accuracy after correction for clinical experience: early exploration of correct diagnosis (odds ratio [OR] 24.35) or of relevant diagnostic hypotheses (OR 2.22) to frame clinical data collection, larger number of diagnostic hypotheses evaluated (OR 1.08), and collection of relevant clinical data (OR 1.19). CONCLUSION: Some features of data collection and interpretation are related to diagnostic accuracy beyond clinical experience and should be explicitly included in clinical training and modeled by clinical teachers. Thoroughness in data collection should not be considered a privileged way to diagnostic success.

Clinical Competence↗
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↗
PubMed2001

The Southwestern Ontario Joint Replacement Pilot Project: electronic point-of-care data collection. Southwestern Ontario Study Group.

OBJECTIVE: To pilot a provincial joint replacement registry using electronic point-of-care data collection. DESIGN: Data collection study. SETTING: Southwestern Ontario, which has a population base of 3.5 million people. PARTICIPANTS: Eighteen orthopedic surgeons. METHOD: Information on total hip and knee replacements was obtained by the orthopedic surgeons over a 6-month period. Information was obtained in paper form and electronically on hand-held computers. MAIN OUTCOME MEASURES: Patient demographics, waiting times from referral to operation, patient satisfaction and relevance and value of electronic records compared with paper records. MAIN RESULTS: Data were collected on 815 total hip and knee arthroplasties. A slightly greater number of hips required revision than knees. The majority of patients were in the 60 to 90-year age range. With respect to the waiting time from referral to operation 10% of patients waited less than 5 weeks, 50% waited less than 30 weeks, and 90% waited less than 59 weeks. There was a high level of patient satisfaction with the operation and with hospital care received. Most surgeons found that the gathering and use of data electronically was relevant and easy. The electronic data were more timely, accurate and complete than paper records. CONCLUSION: Electronic point-of-care data collection is appropriate, particularly in high-volume, high-cost surgical interventions such as total joint replacements.

Adult↗
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↗
PubMed2005

Using randomized controlled trial data, the agreement between retrospectively and prospectively collected data comprising the pneumonia severity index was substantial.

OBJECTIVE: To assess the agreement between prospectively and retrospectively determined variables comprising the Pneumonia Severity Index (PSI), assignment to PSI risk class, and designation as low risk, based on these two methods of data collection. STUDY DESIGN AND SETTING: We analyzed data from a randomized trial of patients with community-acquired pneumonia managed in 32 hospital emergency departments (EDs). For all enrolled patients, the 20 PSI variables were collected prospectively by ED providers and retrospectively by medical record abstractors. We examined the agreement for each of the 20 PSI variables, assignment to the five PSI risk classes, and classification of patients as low (classes I-III) vs. high (classes IV and V) risk. Agreement was measured using total percent agreement and the kappa statistic. RESULTS: Among the 3,220 enrolled patients, percent agreement was >90% for 18 of the 20 variables comprising the PSI, with most unweighted kappa's being >0.6. Agreement was substantial for assignment to PSI risk class (percent agreement: 92.7%; weighted kappa: 0.79) and for classification as low vs. high risk (percent agreement: 88.5%; unweighted kappa: 0.74). CONCLUSION: There was substantial agreement between retrospective and prospective assignment to PSI risk class, classification as low vs. high risk, and the determination of most individual variables that constitute the PSI.

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↗
PubMed1994

Tutorial: planning for data collection. Part I: Asking the right question.

While data collection is fundamental to any scientifically based effort, health care improvement teams often report that it is one of the most frustrating aspects of their work. Groups that do succeed in gathering some data can also sometimes find that the data they have worked so hard to obtain does not really help them or deepen their understanding of the process. The root of many of these problems can be traced to insufficient planning for data collection and to fundamental misunderstandings about the eventual goals of such studies. In this tutorial, we will explore a systematic way of thinking through the planning for data collection that will result in a higher probability of generating useful information.

Data Collection↗
PubMed2005

Guidelines were developed for data collection from medical records for use in retrospective analyses.

OBJECTIVE: To construct a set of guidelines for data collection from medical records. STUDY DESIGN AND SETTING: Retrospective analysis of clinical data is often performed by physician-scientists. In such research, the source of clinical data is the patient's medical record; however, medical records are intended for patient care and the data are not systematically recorded for research purposes. We drew on recommendations in the literature and our own experience with a retrospective cohort study that uses a DNA bank to construct guidelines for data collection from medical records. RESULTS: The guidelines incorporate a number of strategies for accurate data collection, which are discussed and illustrated by application. CONCLUSION: With guidelines for data collection, the quality of research data is enhanced. A well-designed case record form and a handbook for standardized data collection are essential for training the data collectors and for ensuring fastidious searching of the record; however, certain kinds of information are not always well documented in patient records. Consequently, it is essential to perform a pilot study to assess the study design and to use additional questionnaires. Correct interpretation of clinical outcomes documented in the medical records often necessitates an independent adjudication committee to prevent bias in outcome definition.

Bias↗
PubMed2004

Assessment, measurement and data collection tools.

The terms 'measurement' and 'assessment' are often used interchangeably, especially when referring to the tools used to collect information. This leads to unclear thinking, and often to poor selection of a 'measure' or 'assessment'. This editorial suggests that we should distinguish between the identification of the data needed for some purpose, the methods used to collect data, and the use made of (interpretation of) the data collected. This would focus more attention on the three most important questions to consider when collecting data, whether in day-to-day clinical practice or in research: Why should the data be collected? How should the data be collected? and How should the results be interpreted?

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↗
PubMed1998

Structured data collection improves the diagnosis of acute appendicitis.

BACKGROUND: Structured preoperative data collection and computer-assisted methods are claimed to improve diagnostic accuracy in patients with acute abdominal pain. The aim of this study was to evaluate a possible age- and sex-related effect of using structured data collection in the preoperative diagnosis of patients with suspected acute appendicitis. METHODS: Between 1989 and 1994, clinical and demographic data from 1764 consecutive patients were recorded. In 1990 and 1992, various detailed symptom, clinical and laboratory data were collected prospectively on a structured registration form. Age- and sex-specific diagnostic accuracy as well as perforation rate were calculated for each year. RESULTS: Diagnostic accuracy increased significantly by 5 (95 per cent confidence interval (c.i.) 1-9) per cent when structured data registration was applied. In female patients aged between 13 and 40 years, diagnostic accuracy increased by 16 (95 per cent c.i. 8-24) per cent. Significant changes in diagnostic accuracy were not seen in other subgroups. Perforation rates remained unchanged during the entire study period. CONCLUSION: In this population-based study, diagnostic accuracy in patients operated on for suspected acute appendicitis increased for all patients when structured preoperative data collection was used. However, the only subgroup with a significant increase in diagnostic accuracy was female patients aged between 13 and 40 years. Perforation rate was unaffected by structured data collection.

Acute Disease↗