Creating user-friendly databases with Microsoft Access

Data entry can be tedious and is fraught with potential for errors that affect study findings. Researchers can minimise entry errors and streamline data entry by using some of the popular software packages on the market. Joanne Kraenzle Schneider and colleagues describe one way to create a user-friendly database that minimises entry errors by using Microsoft (MS) Access.     

Reducing quantitative data errors: tips for clinical researchers.

Data errors, or missing and inaccurate data, can compromise research findings. Steps can be taken before and during data collection, as well as during data entry that will minimize data errors. The purpose of this article is to discuss ways to reduce quantitative data errors through thoughtful planning of the data collection form, careful reviewing of completed data forms in the field and laboratory, and consideration of accurate and efficient data entry with thorough verification methods. Clinical researchers are provided with tips and examples for reducing data errors.     

Palm computer demonstrates a fast and accurate means of burn data collection

Manual biomedical data collection and entry of the data into a personal computer is time-consuming and can be prone to errors. The purpose of this study was to compare data entry into a hand-held computer versus hand written data followed by entry of the data into a personal computer. A Palm (3Com Palm IIIx, Santa, Clara, Calif) computer with a custom menu-driven program was used for the entry and retrieval of burn-related variables. These variables were also used to create an identical sheet that was filled in by hand. Identical data were retrieved twice from 110 charts 48 hours apart and then used to create an Excel (Microsoft, Redmond, Wash) spreadsheet. One time data were recorded by the Palm entry method, and the other time the data were handwritten. The method of retrieval was alternated between the Palm system and handwritten system every 10 charts. The total time required to log data and to generate an Excel spreadsheet was recorded and used as a study endpoint. The total time for the Palm method of data collection and downloading to a personal computer was 23% faster than hand recording with the personal computer entry method (P < 0.05), and 58% fewer errors were generated with the Palm method.) The Palm is a faster and more accurate means of data collection than a handwritten technique.     

Double Data Entry: What Value,What Price?

We challenge the notion that double data entry is either sufficient or necessary to ensure good-quality data in clinical trials. Although we do not completely reject that notion, we quantify some of the effects that poor quality data have on final study results in terms of estimation, significance testing, and power. By introducing digit errors into simulated blood pressure measurements we demonstrate that simple range checks allow us to detect (and therefore correct) the main errors that impact the final study results and conclusions. The errors that cannot easily be detected by such range checks, although possibly numerous, are shown to be of little importance in drawing the correct conclusions from the statistical analysis of data. Exploratory data analysis cannot identify all errors that a second data entry would detect, but on the other hand, not all errors that are found by exploratory data analysis are detectable by double data entry. Double data entry is concerned solely with ensuring, to a high degree of certainty, that what is recorded on the case record form is transcribed into the database. Exploratory data analysis looks beyond the case record form to challenge the plausibility of the written data. In this sense, the second entering of data has some benefit, but the use of exploratory data analysis methods, either as data entry is ongoing or at the end of data entry and as the first stage in an analysis strategy, should always be mandatory.     

Adverse drug reaction and medication error reporting by pharmacy students

BACKGROUND: The reporting of adverse drug reactions (ADRs) and medication errors is the responsibility of all who are involved, particularly pharmacists. Since pharmacists are often privy to information surrounding ADRs and medication errors, it is of utmost importance that they are educated regarding the procedures of reporting. OBJECTIVE: To determine pharmacy students' knowledge of and ability to report ADRs and medication errors. METHODS: A total of 1322 students from 9 colleges of pharmacy were surveyed. RESULTS: The largest group of respondents was fifth-year pharmacy students (38%) followed by third-, fourth-, and sixth-year students (28%, 26%, and 8%, respectively). The majority of students reported learning about ADR and medication error reporting programs via didactic experiences. In comparison, fewer students cited alternative mechanisms of learning, including experiential rotations and work experience. Overall, respondents demonstrated the most experience with MedWatch and the least experience with the Vaccine Adverse Event Reporting System (VAERS). As students progressed through pharmacy curricula, there was a positive trend in the ability to locate and complete MedWatch forms. For VAERS and Medication Error Reporting (MER) program forms, however, this positive trend was broken at year 4. For all programs, significantly fewer students demonstrated appropriate use of the forms compared with those indicating familiarity with the programs. CONCLUSIONS: This study demonstrated that students are becoming familiar with ADR and MER programs via the college curriculum; however, there is opportunity for greater exposure and understanding. Colleges of pharmacy should continually seek methods to strengthen the education provided to pharmacy students regarding these programs.     

A comparison of error detection rates between the reading aloud method and the double data entry method

Data entry and its verification are important steps in the process of data management in clinical studies. In Japan, a kind of visual comparison called the reading aloud (RA) method is often used as an alternative to or in addition to the double data entry (DDE) method. In a typical RA method, one operator reads previously keyed data aloud while looking at a printed sheet or computer screen, and another operator compares the voice with the corresponding data recorded on case report forms (CRFs) to confirm whether the data are the same. We compared the efficiency of the RA method with that of the DDE method in the data management system of the Japanese Registry of Renal Transplantation. Efficiency was evaluated in terms of error detection rate and expended time. Five hundred sixty CRFs were randomly allocated to two operators for single data entry. Two types of DDE and RA methods were performed. Single data entry errors were detected in 358 of 104,720 fields (per-field error rate=0.34%). Error detection rates were 88.3% for the DDE method performed by a different operator, 69.0% for the DDE method performed by the same operator, 59.5% for the RA method performed by a different operator, and 39.9% for the RA method performed by the same operator. The differences in these rates were significant (p<0.001) between the two verification methods as well as between the types of operator (same or different). The total expended times were 74.8 hours for the DDE method and 57.9 hours for the RA method. These results suggest that in detecting errors of single data entry, the RA method is inferior to the DDE method, while its time cost is lower.     

A comparison of RN students in two types of baccalaureate completion programs

The purpose of this study was to compare perceptions of nursing programs and attitudes toward professional nursing behaviors in entering and exiting registered nurse students enrolled in generic and second step baccalaureate degree completion programs. The convenience sample of 233 registered nurse students was drawn from eight baccalaureate nursing programs in a metropolitan area. Data included a student profile, perceptions of program in which enrolled, a professional attitude scale, and demographics. Data were factor analyzed to cluster items into distinct empirical groups and then examined by analysis of variance using program type and entry/exit status as covariates. Factor analysis of program perception data delineated six principal factors. On two of the factors, overall nursing role attainment and communication ability, entering second step students held significantly higher expectations. However, responses of exiting students as to how well expectations had been met were similar between the two program types. Similar analysis of the professional attitude scale differentiated five factors. Results showed that among students entering both program types, second step entrants had significantly more professional attitudes (p less than .01). Differences between the exiting groups were not significant. This study suggests that professional attitude formation is not dependent upon type of baccalaureate completion program in which enrolled but may relate to type of prior nursing education.     

Data collection in a multisite project: Teleform

Data collection, entry, validation, and management are salient time- and resource-consuming dimensions of all research projects. This is especially true for multisite studies, which pose unique, additional challenges because of their research design requirements. To save time and reduce the number of manual data entry errors, automated processing systems are becoming more widely used. Our research team chose Teleform for data entry and collection for the Smoking Cessation or Reduction in Pregnancy Trial study. This article presents our experience with this new technology.     

A quantifiable alternative to double data entry

Recent articles in this journal have questioned the effectiveness of double data entry to enhance the quality of clinical trials data entered into the computer from case report forms. Although double data entry is widely used, no definitive agreement has been reached as to how to model and quantify the time/cost involved to perform double data entry and the exact gain in data quality derived from its use. In this paper, an alternative to the verification step of double data entry is described. This alternative procedure involves the verification of a sample of records from the data set created from the single entry of the data into the computer. The sampling procedure used for this alternative procedure is known in the statistical quality control literature as a continuous sampling plan. This type of secondary check of the data has the following advantages: (1) it is easily performed by data entry personnel, (2) the cost/time necessary to perform the check along with the gain in quality of the data set can be computed, and (3) the sampling plan can be constructed to attain data quality goals set by the data management staff. Examples of how to select the parameters for this type of data quality assurance procedure are given in the paper.     

Facilitating the Nurse Practitioner's Research Role: Using a Microcomputer for Data Entry in Clinical Settings

Data coding and entry can be a tedious and error-prone component of research. Statistical packages for a microcomputer are now available that enable the researcher to create data entry forms in a spread sheet format. In this article the use of software that can be used for data entry, manipulation, and analysis is discussed. In a practice setting these features are particularly useful, because the nurse practitioner can take the microcomputer to the clinical facility and enter data directly from client-provider interactions or from records. In clinical settings where client data are entered directly into a computer database as part of assessment and care giving, data can be downloaded onto this or similar programs for manipulation and analysis.     

Reducing medical errors through barcoding at the point of care.

Medical errors are a major concern in health care today. Errors in point-of-care testing (POCT) are particularly problematic because the test is conducted by clinical operators at the site of patient care and immediate medical action is taken on the results prior to review by the laboratory. The Performance Improvement Program at Baystate Health System, Springfield, Massachusetts, noted a number of identification errors occurring with glucose and blood gas POCT devices. Incorrect patient account numbers that were attached to POCT results prevented the results from being transmitted to the patient's medical record and appropriately billed. In the worst case, they could lead to results being transferred to the wrong patient's chart and inappropriate medical treatment. Our first action was to lock-out operators who repeatedly made identification errors (3-Strike Rule), requiring operators to be counseled and retrained after their third error. The 3-Strike Rule significantly decreased our glucose meter errors (p = 0.014) but did not have an impact on the rate of our blood gas errors (p = 0.378). Neither device approached our ultimate goal of zero tolerance. A Failure Mode and Effects Analysis (FMEA) was conducted to determine the various processes that could lead to an identification error. A primary source of system failure was the manual entry of 14 digits for each test, five numbers for operator and nine numbers for patient account identification. Patient barcoding was implemented to automate the data entry process, and after an initial familiarization period, resulted in significant improvements in error rates for both the glucose (p = 0.0007) and blood gas devices (p = 0.048). Despite the improvements, error rates with barcoding still did not achieve zero errors. Operators continued to utilize manual data entry when the barcode scan was unsuccessful or unavailable, and some patients were found to have incorrect patient account numbers due to hospital transfer, multiple wristbands on a single patient, and selection of expired account numbers from previous hospitalizations when printing the barcoded wristbands. Barcoding can thus improve the incidence of identification errors, but hospitals need to take additional steps to ensure successful barcode scanning and to verify that patient wristbands contain correct information. Implementation of patient barcoding was successful in significantly reducing identification errors with POCT, improving patient care, and enhancing interdisciplinary communication.     

Comparison of hip center estimation using in-vivo and ex-vivo measurements from the same subject

Objective. To evaluate the accuracy of locating the hip center (HIPC) by comparing in-vivo estimations to posthumous HIPC measurement in the same subject.

Background. Numerous techniques have been used to locate the HIPC in living subjects and in cadavers. There are no published reports, however, which have compared measurements of the HIPC obtained in-vivo and ex-vivo for the same subject.

Design and methods. Bilateral hip centers (HIPC) were estimated for an elderly male patient seven times over 32 months in a gait laboratory using a technique which combines kinematic data and static pointing. The patient's pelvis was recovered posthumously and analytical photogrammetry used to estimate the HIPC locations. Published methods which scale anatomical measurements to locate the HIPC were compared to our ex-vivo and in-vivo measurements.

Results. HIPC precision was 0.94 cm for the in-vivo measurements (N = 7 trial days) and 0.15 cm for the ex-vivo measurements (N = 6 photogrammetry trials). The resultant error between ex-vivo and in-vivo estimates of HIPC position was 1.17 cm. Resultant errors in locating the HIPC using published anatomical scaling factors ranged between 1.48 cm and 5.37 cm.

Conclusions. Although we have been unable to compare in-vivo and ex-vivo HIPC measurements for more than one subject, results of our analysis suggest that using anatomical scaling to locate the HIPC may result in unsatisfactory subject-specific estimates of the HIPC.     

Patient identification errors: The detective in the laboratory

BackgroundThe eradication of errors regarding patients' identification is one of the main goals for safety improvement. As clinical laboratory intervenes in 70% of clinical decisions, laboratory safety is crucial in patient safety. We studied the number of Laboratory Information System (LIS) demographic data errors registered in our laboratory during one year.MethodsThe laboratory attends a variety of inpatients and outpatients. The demographic data of outpatients is registered in the LIS, when they present to the laboratory front desk. The requests from the primary care centers (PCC) are made electronically by the general practitioner. A manual step is always done at the PCC to conciliate the patient identification number in the electronic request with the one in the LIS. Manual registration is done through hospital information system demographic data capture when patient's medical record number is registered in LIS. Laboratory report is always sent out electronically to the patient's electronic medical record. Daily, every demographic data in LIS is manually compared to the request form to detect potential errors.ResultsFewer errors were committed when electronic order was used. There was great error variability between PCC when using the electronic order.ConclusionsLIS demographic data manual registration errors depended on patient origin and test requesting method. Even when using the electronic approach, errors were detected. There was a great variability between PCC even when using this electronic modality; this suggests that the number of errors is still dependent on the personnel in charge of the technology.     

Eliciting remote data entry system requirements for the collection of cancer clinical trial data

Information regarding the clinical course of a patient in a clinical trial is critical for cancer research. Nurses and clinical research associates must locate data stored in many formats and transfer data to paper case report forms. These forms are associated with a unique protocol document that specifies the set of data to be collected. This paper-based process involves a series of abstractions that pose a significant potential for error. Remote data entry has been advocated to facilitate and streamline the collection of clinical trial data. A systematic process to elicit system requirements from end-users is described as an approach to integrate computer technology with cancer research. Nurses and clinical research associates, as end-users of remote data entry, can provide invaluable input into the requirements necessary to develop an efficient and practical system. By incorporating this input, remote data entry can offer a potential benefit for preserving data integrity.     

Continuity of care practices and substance use disorder patients' engagement in continuing care

BACKGROUND: Substance use disorder (SUD) patients who engage in more continuing care have better outcomes, but information on practices associated with greater patient engagement and retention in continuing care remains elusive. OBJECTIVES: The objectives of this study were to determine if staff's continuity of care practices predict patients' engagement in continuing care in the 6 months after discharge from intensive SUD treatment and to determine if the impact of continuity of care practices on patients' engagement in continuing care differs for patients treated in inpatient/residential versus outpatient programs. RESEARCH DESIGN: Staff in 28 Veterans Affairs (VA) intensive SUD treatment programs with varying continuity of care practices provided data on 878 patients' alcohol and drug problems at treatment entry. At discharge, staff provided data on patients' motivation, treatment intensity, and on the continuity of care practices they used with each patient. VA administrative databases supplied data on patients' subsequent engagement in continuing care. Mixed-effects modeling was used to examine predictors of patients' engagement in care. RESULTS: Patients in outpatient programs who received more continuity of care engaged in continuing care significantly longer. More highly motivated outpatients, those with fewer alcohol problems at treatment entry, and patients who used VA services in the year before treatment also remained in continuing care longer. These findings did not hold for patients treated in inpatient/residential programs. CONCLUSIONS: Continuity of care practices predicted engagement in continuing care only for patients treated in outpatient SUD programs. More research is needed to identify effective continuity of care practices for patients treated in inpatient/residential programs.     

Is double data entry necessary? The CHART trials

There is some controversy over the need for double data entry in clinical trials. In particular, does the number and types of errors identified with this approach justify the extra effort involved? We report the results of a study carried out to address this question. Our main outcome measure was the frequency and types of errors involved in the entry of data for the CHART (continuous, hyperfractionated, accelerated radio-therapy) trials. Data were reentered for a sample of 44 patients by a data manager other than the one making the initial entry. The second entry was then compared with the first entry. The error rate for the two entries combined was 14 per 10,000 data items (fields) (95% confidence interval 10,19). The error rate for the initial entry alone was 15 per 10,000 fields (95% confidence interval 9.5, 22), and the vital/important error rate (defined as any error on a principal outcome measure or a major error on any other endpoint or variable) was 2.5 per 10,000 fields (95% confidence interval 0.68, 6.4). On this evidence double data entry is not performed for the CHART trials.     

The use of machine learning program LERS-LB 2.5 in knowledge acquisition for expert system development in nursing.

LERS-LB (Learning from Examples using Rough Sets Lower Boundaries) is a computer program based on rough set theory for knowledge acquisition, which extracts patterns from real-world data in generating production rules for expert system development. From LERS-LB evaluation of an SPSS-X data file containing data for recovery room patients, it was concluded that both statistical data files and existing databases can be converted to decision-table format needed by LERS-LB, but it is less desirable to work with statistical files than a well-developed database. It was also concluded that choosing a well-developed database and checking it thoroughly for accuracy and completeness should be done before running LERS-LB, or other learning programs, to avoid problems with data errors. Using rough set theory and a technique called 'dropping conditions' LERS-LB offers, at least in theory, a possible method for identifying which data items are critical to nursing practice. Further research and continued LERS-LB program enhancements still may help with identifying critical data items versus redundant data for nursing practice. LERS-LB, and other learning programs, offer techniques which will help reduce the knowledge acquisition bottleneck in nursing expert system development. It is doubtful, however, that learning programs will eliminate the need for involving domain experts in evaluating rules and expert systems for clinical decision support.