Point Of Data Entry Research Articles

Methods for Collection of Participant-aided Sociograms for the Study of Social, Sexual and Substance-using Networks Among Young Men Who Have Sex with Men.

In this study, we adapted and tested a participant-aided sociogram approach for the study of the social, sexual, and substance use networks of young men who have sex with men (YMSM); a population of increasing and disproportionate risk of HIV infection. We used a combination of two interviewer-administered procedures: completion of a pre-numbered list form to enumerate alters and to capture alter attributes; and a participant-aided sociogram to capture respondent report of interactions between alters on an erasable whiteboard. We followed the collection of alter interactions via the sociogram with a traditional matrix-based tie elicitation approach for a sub-sample of respondents for comparison purposes. Digital photographs of each network drawn on the whiteboard serve as the raw data for entry into a database in which group interactions are stored. Visual feedback of the network was created at the point of data entry, using NetDraw network visualization software for comparison to the network structure elicited via the sociogram. In a sample of 175 YMSM, we found this approach to be feasible and reliable, with high rates of participation among those eligible for the study and substantial agreement between the participant-aided sociogram in comparison to a traditional matrix-based approach. We believe that key strengths of this approach are the engagement and maintenance of participant attention and reduction of participant burden for alter tie elicitation. A key weakness is the challenge of entry of interview-based list form and sociogram data into the database. Our experience suggests that this approach to data collection is feasible and particularly appropriate for an adolescent and young adult population. This builds on and advances visualization-based approaches to social network data collection.

The Roles of Users' Personal Characteristics and Organisational Support in the Attitude towards Using ERP Systems in a Spanish Public Hospital

Enterprise resources planning (ERP) systems enable central and integrative control over all processes throughout an organisation by ensuring one data entry point and the use of a common database. T his paper analyses the attitude of healthcare personnel towards the use of an ERP system in a Spanish public hospital, identifying influencing factors. This research is based on a regression analysis of latent variables using the optimisation technique of partial least squares. We propose a research model including possible relationships among different constructs using the technology acceptance model. Our results show that the personal characteristics of potential users are key factors in explaining attitude towards using ERP systems.

Abstract P147: Telestroke Software Offers Improved and Novel Methods of Studying Physician Decision Making

Introduction: Data quality in stroke registries is typically dependent upon some form of chart review and manual data abstraction. The retrospective nature of this process is inherently prone to incomplete and inaccurate data collection with limited insight into the process of physician decision making. Hypothesis: New software packages accompanying telestroke systems will dramatically improve the quality of data by automating the abstraction process and providing real-time access to electronic databases. Methods: Telestroke systems provide web-based programs that record various levels of data. InTouch Technologies, Inc. currently provides StrokeRESPOND v3.0, a web-based program that facilitates telestroke consultation by organizing elements of the physician-patient encounter, including history, vitals, physical exam, laboratory results, and radiographs, and by generating a consultation note. Many data elements captured in the user interface mirror traditional metrics of acute stroke care research and can be de-identified and then directly transferred into an electronic database. The “forced choice” (aka hard-stop) design of data entry and elimination of secondhand abstraction can minimize data corruption and loss. Further, because each point of data entry and manipulation is time-stamped, powerful metadata_“data about data”_can be explored. By analyzing the sequence and patterns of clinical information entry and utilization, the actual thought process of the physician user can be investigated and provide new insights into stroke treatment. Optimization of acute stroke management, a complicated protocol, can be driven by identification of physician decision making patterns associated with multiple outcomes, including higher rates of treatment and faster treatment times. Conclusions: Specialized software programs will improve registry data collection, completeness and accuracy. The generation of metadata offers exciting, new avenues of research. Prospective stroke research using this methodology will require the collaboration of multiple academic institutions and industry partners.

Pilot Testing of a Population‐based Surgical Survey Tool in Sierra Leone

The prevalence of surgical diseases in low income countries is thought to be very large, but to date no population-based survey has documented the need. The Surgeons OverSeas Assessment of Surgical Need (SOSAS) is a survey tool programmed for use with iPads to measure the prevalence of surgical conditions. To assess the appropriateness and utility of SOSAS, a pilot test was undertaken in Sierra Leone. Local medical students were trained in sampling, interviewing, and SOSAS specifics. Five clusters of 10 households were randomly selected and 100 individuals were interviewed. Problems with the tool, iPad use, and respondent answers were collected. Daily debriefings with the enumerators aimed to identify problems and ways for improvement. Administering SOSAS via iPads was found to be easy and facilitated data entry. Quick analysis of the data allowed for rapid feedback. Although the survey has 450 possible data entry points, by using conditional formatting, the enumerators were able to collect household demographics and interview two randomly selected household members in an average of 25min. The survey methodology was acceptable, with a response rate of 96%. Five major sections were amended after the pilot. Pilot testing of SOSAS showed that a population-based survey measuring the prevalence of surgical disease could be undertaken in a low income country. It is recommended that SOSAS be used with a larger sample size to calculate the prevalence of surgical disease in low income countries.

Interface Design and Human Factors Considerations for Model-Based Tight Glycemic Control in Critical Care

Tight glycemic control (TGC) has shown benefits but has been difficult to implement. Model-based methods and computerized protocols offer the opportunity to improve TGC quality and compliance. This research presents an interface design to maximize compliance, minimize real and perceived clinical effort, and minimize error based on simple human factors and end user input. The graphical user interface (GUI) design is presented by construction based on a series of simple, short design criteria based on fundamental human factors engineering and includes the use of user feedback and focus groups comprising nursing staff at Christchurch Hospital. The overall design maximizes ease of use and minimizes (unnecessary) interaction and use. It is coupled to a protocol that allows nurse staff to select measurement intervals and thus self-manage workload. The overall GUI design is presented and requires only one data entry point per intervention cycle. The design and main interface are heavily focused on the nurse end users who are the predominant users, while additional detailed and longitudinal data, which are of interest to doctors guiding overall patient care, are available via tabs. This dichotomy of needs and interests based on the end user's immediate focus and goals shows how interfaces must adapt to offer different information to multiple types of users. The interface is designed to minimize real and perceived clinical effort, and ongoing pilot trials have reported high levels of acceptance. The overall design principles, approach, and testing methods are based on fundamental human factors principles designed to reduce user effort and error and are readily generalizable.

Integrating clinical research with the Healthcare Enterprise: From the RE-USE project to the EHR4CR platform

BackgroundThere are different approaches for repurposing clinical data collected in the Electronic Healthcare Record (EHR) for use in clinical research. Semantic integration of “siloed” applications across domain boundaries is the raison d’être of the standards-based profiles developed by the Integrating the Healthcare Enterprise (IHE) initiative – an initiative by healthcare professionals and industry promoting the coordinated use of established standards such as DICOM and HL7 to address specific clinical needs in support of optimal patient care. In particular, the combination of two IHE profiles – the integration profile “Retrieve Form for Data Capture” (RFD), and the IHE content profile “Clinical Research Document” (CRD) – offers a straightforward approach to repurposing EHR data by enabling the pre-population of the case report forms (eCRF) used for clinical research data capture by Clinical Data Management Systems (CDMS) with previously collected EHR data. ObjectiveImplement an alternative solution of the RFD–CRD integration profile centered around two approaches: (i) Use of the EHR as the single-source data-entry and persistence point in order to ensure that all the clinical data for a given patient could be found in a single source irrespective of the data collection context, i.e. patient care or clinical research; and (ii) Maximize the automatic pre-population process through the use of a semantic interoperability services that identify duplicate or semantically-equivalent eCRF/EHR data elements as they were collected in the EHR context. MethodsThe RE-USE architecture and associated profiles are focused on defining a set of scalable, standards-based, IHE-compliant profiles that can enable single-source data collection/entry and cross-system data reuse through semantic integration. Specifically, data reuse is realized through the semantic mapping of data collection fields in electronic Case Report Forms (eCRFs) to data elements previously defined as part of patient care-centric templates in the EHR context. The approach was evaluated in the context of a multi-center clinical trial conducted in a large, multi-disciplinary hospital with an installed EHR. ResultsData elements of seven eCRFs used in a multi-center clinical trial were mapped to data elements of patient care-centric templates in use in the EHR at the George Pompidou hospital. 13.4% of the data elements of the eCRFs were found to be represented in EHR templates and were therefore candidate for pre-population. During the execution phase of the clinical study, the semantic mapping architecture enabled data persisted in the EHR context as part of clinical care to be used to pre-populate eCRFS for use without secondary data entry. To ensure that the pre-populated data is viable for use in the clinical research context, all pre-populated eCRF data needs to be first approved by a trial investigator prior to being persisted in a research data store within a CDMS. ConclusionSingle-source data entry in the clinical care context for use in the clinical research context – a process enabled through the use of the EHR as single point of data entry, can – if demonstrated to be a viable strategy – not only significantly reduce data collection efforts while simultaneously increasing data collection accuracy secondary to elimination of transcription or double-entry errors between the two contexts but also ensure that all the clinical data for a given patient, irrespective of the data collection context, are available in the EHR for decision support and treatment planning.The RE-USE approach used mapping algorithms to identify semantic coherence between clinical care and clinical research data elements and pre-populate eCRFs. The RE-USE project utilized SNOMED International v.3.5 as its “pivot reference terminology” to support EHR-to-eCRF mapping, a decision that likely enhanced the “recall” of the mapping algorithms. The RE-USE results demonstrate the difficult challenges involved in semantic integration between the clinical care and clinical research contexts.

CerFix

We present CerFix, a data cleaning system that finds certain fixes for tuples at the point of data entry, i.e. , fixes that are guaranteed correct. It is based on master data, editing rules and certain regions. Given some attributes of an input tuple that are validated (assured correct), editing rules tell us what other attributes to fix and how to correct them with master data. A certain region is a set of attributes that, if validated, warrant a certain fix for the entire tuple. We demonstrate the following facilities provided by CerFix: (1) a region finder to identify certain regions; (2) a data monitor to find certain fixes for input tuples, by guiding users to validate a minimal number of attributes; and (3) an auditing module to show what attributes are fixed and where the correct values come from.

Using Web Technology to Support Population-Based Diabetes Care

Managed clinical networks have been used to coordinate chronic disease management across geographical regions in the United Kingdom. Our objective was to review how clinical networks and multidisciplinary team-working can be supported by Web-based information technology while clinical requirements continually change. A Web-based population information system was developed and implemented in November 2000. The system incorporates local guidelines and shared clinical information based upon a national dataset for multispecialty use. Automated data linkages were developed to link to the master index database, biochemistry, eye screening, and general practice systems and hospital diabetes clinics. Web-based data collection forms were developed where computer systems did not exist. The experience over the first 10 years (to October 2010) was reviewed. The number of people with diabetes in Tayside increased from 9694 (2.5% prevalence) in 2001 to 18,355 (4.6%) in 2010. The user base remained stable (~400 users), showing a high level of clinical utility was maintained. Automated processes support a single point of data entry with 10,350 clinical messages containing 40,463 data items sent to external systems during year 10. The system supported quality improvement of diabetes care; for example, foot risk recording increased from 36% in 2007 to 73.3% in 2010. Shared-care datasets can improve communication between health care service providers. Web-based technology can support clinical networks in providing comprehensive, seamless care across a geographical region for people with diabetes. While health care requirements evolve, technology can adapt, remain usable, and contribute significantly to quality improvement and working practice.

Practical application of ontologies to annotate and analyse large scale raw mouse phenotype data

BackgroundLarge-scale international projects are underway to generate collections of knockout mouse mutants and subsequently to perform high throughput phenotype assessments, raising new challenges for computational researchers due to the complexity and scale of the phenotype data. Phenotypes can be described using ontologies in two differing methodologies. Traditionally an individual phenotypic character has either been defined using a single compound term, originating from a species-specific dedicated phenotype ontology, or alternatively by a combinatorial annotation, using concepts from a range of disparate ontologies, to define a phenotypic character as an entity with an associated quality (EQ). Both methods have their merits, which include the dedicated approach allowing use of community standard terminology, and the combinatorial approach facilitating cross-species phenotypic statement comparisons. Previously databases have favoured one approach over another. The EUMODIC project will generate large amounts of mouse phenotype data, generated as a result of the execution of a set of Standard Operating Procedures (SOPs) and will implement both ontological approaches to capture the phenotype data generated.ResultsFor all SOPs a four-tier annotation is made: a high-level description of the SOP, to broadly define the type of data generated by the SOP; individual parameter annotation using the EQ model; annotation of the qualitative data generated for each mouse; and the annotation of mutant lines after statistical analysis. The qualitative assessments of phenodeviance are made at the point of data entry, using child PATO qualities to the parameter quality. To facilitate data querying by scientists more familiar with single compound terms to describe phenotypes, the mappings between the Mammalian Phenotype (MP) ontology and the EQ PATO model are exploited to allow querying via MP terms.ConclusionWell-annotated and comparable phenotype databases can be achieved through the use of ontologically derived comparable phenotypic statements and have been implemented here by means of OBO compatible EQ annotations. The implementation we describe also sees scientists working seamlessly with ontologies through the assessment of qualitative phenotypes in terms of PATO qualities and the ability to query the database using community-accepted compound MP terms. This work represents the first time the combinatorial and single-dedicated approaches have both been implemented to annotate a phenotypic dataset.

A new scoring system in Cystic Fibrosis: statistical tools for database analysis – a preliminary report

BackgroundCystic fibrosis is the most common fatal genetic disorder in the Caucasian population. Scoring systems for assessment of Cystic fibrosis disease severity have been used for almost 50 years, without being adapted to the milder phenotype of the disease in the 21st century. The aim of this current project is to develop a new scoring system using a database and employing various statistical tools. This study protocol reports the development of the statistical tools in order to create such a scoring system.MethodsThe evaluation is based on the Cystic Fibrosis database from the cohort at the Royal Children's Hospital in Melbourne. Initially, unsupervised clustering of the all data records was performed using a range of clustering algorithms. In particular incremental clustering algorithms were used. The clusters obtained were characterised using rules from decision trees and the results examined by clinicians. In order to obtain a clearer definition of classes expert opinion of each individual's clinical severity was sought. After data preparation including expert-opinion of an individual's clinical severity on a 3 point-scale (mild, moderate and severe disease), two multivariate techniques were used throughout the analysis to establish a method that would have a better success in feature selection and model derivation: 'Canonical Analysis of Principal Coordinates' and 'Linear Discriminant Analysis'. A 3-step procedure was performed with (1) selection of features, (2) extracting 5 severity classes out of a 3 severity class as defined per expert-opinion and (3) establishment of calibration datasets.Results(1) Feature selection: CAP has a more effective "modelling" focus than DA.(2) Extraction of 5 severity classes: after variables were identified as important in discriminating contiguous CF severity groups on the 3-point scale as mild/moderate and moderate/severe, Discriminant Function (DF) was used to determine the new groups mild, intermediate moderate, moderate, intermediate severe and severe disease. (3) Generated confusion tables showed a misclassification rate of 19.1% for males and 16.5% for females, with a majority of misallocations into adjacent severity classes particularly for males.ConclusionOur preliminary data show that using CAP for detection of selection features and Linear DA to derive the actual model in a CF database might be helpful in developing a scoring system. However, there are several limitations, particularly more data entry points are needed to finalize a score and the statistical tools have further to be refined and validated, with re-running the statistical methods in the larger dataset.

Development of a Functional, Internet-Accessible Department of Surgery Outcomes Database

The need for surgical outcomes data is increasing due to pressure from insurance companies, patients, and the need for surgeons to keep their own "report card". Current data management systems are limited by inability to stratify outcomes based on patients, surgeons, and differences in surgical technique. Surgeons along with research and informatics personnel from an academic, hospital-based Department of Surgery and a state university's Department of Information Technology formed a partnership to develop a dynamic, internet-based, clinical data warehouse. A five-component model was used: data dictionary development, web application creation, participating center education and management, statistics applications, and data interpretation. A data dictionary was developed from a list of data elements to address needs of research, quality assurance, industry, and centers of excellence. A user-friendly web interface was developed with menu-driven check boxes, multiple electronic data entry points, direct downloads from hospital billing information, and web-based patient portals. Data were collected on a Health Insurance Portability and Accountability Act-compliant server with a secure firewall. Protected health information was de-identified. Data management strategies included automated auditing, on-site training, a trouble-shooting hotline, and Institutional Review Board oversight. Real-time, daily, monthly, and quarterly data reports were generated. Fifty-eight publications and 109 abstracts have been generated from the database during its development and implementation. Seven national academic departments now use the database to track patient outcomes. The development of a robust surgical outcomes database requires a combination of clinical, informatics, and research expertise. Benefits of surgeon involvement in outcomes research include: tracking individual performance, patient safety, surgical research, legal defense, and the ability to provide accurate information to patient and payers.

An Electronic Medical Records System for Clinical Research and the EMR–EDC Interface

The electronic medical record (EMR), sometimes called electronic health record (EHR) or electronic patient record (EPR), has become one of the most important new technologies in healthcare. Electronic storage and exchange of health-care information has been of interest worldwide for years, but recent reports on medical error rates and national mandates for conversion from handwritten documents have heightened its importance. There have been many published EMR implementations in many different subspecialties. Most focus on improvements in efficiency, patient experience, and care. The Medical Records Institute has published the results of their survey of EHR usage,1 and it demonstrates that usage has been predominately in the ambulatory care sector, with a focus on clinical workflow and quality of care. Few published articles have focused on systems designed for use in ophthalmology, despite a history of the use of such systems dating back to Alcon’s IVY system in the 1990s (Alcon, Ltd., Fort Worth, TX). Published reports from users in the field of ophthalmology have all focused on custom systems, because no commercial product was suitable for their purposes.2 However, commercial systems are widely used and DeBry3 has provided a good overview of the systems available as well as the considerations that are important for individual practices. The focus of these reports is on cost savings, time savings, and quality of care. Concurrent with the increased use of EMR for clinical care, there has been an increase in the use of electronic systems for capture of data in clinical research and clinical trials.4 Many trials still require researchers to enter data manually on both a paper clinical record and a paper case report form (CRF), from which it is transcribed into an electronic database. With this process data are transcribed twice, once from the encounter note to the CRF, and again into the database, providing multiple potential sources for error. Databases tend to be developed independently for each study making cross-study research difficult and therefore infrequent. Retrospective studies require review of the paper record and recompiling the data of interest. As clinical trials have become more complicated, expensive, regulated, and increasingly monitored, clinical research organizations (CROs) and research coordinating centers have expanded and evolved, especially in terms of data collection and monitoring.5 With the advent of the internet, CROs and coordinating centers have moved to electronic data capture (EDC),6,7 which enables data entry for clinical studies to be performed on-site in real time by the researcher. This eliminates one transcription step, but still keeps data in discrete databases and provides no method for retrospective studies. With the increase in EMR implementation, there has been a subsequent increase in interest in using these systems for clinical research and clinical trials. As researchers have had to enter data into two electronic systems, EMR and EDC, speculation on the feasibility of merging these two technologies has begun.8,9 Bleicher8 and the document from the eClinical Forum (eCF)9 discuss some of the different ways that these two systems can be interfaced. In this report, we summarize a system we have built in a research environment at the National Eye Institute (NEI), National Institutes of Health (NIH). Both the eCF and Bleicher8,9 discuss the need for using the investigator as the single point of data entry and the need for electronic data transfer to a data-coordinating center to eliminate transcription errors. The data collection system would have to be highly customizable, to allow for the inclusion of elements for specific clinical trials. In addition, with all the data collected in a single system, it would allow for retrospective studies including data from all patients. We chose to build such a system based on the commercially available EMR from Next-Gen, Inc. (Horsham, PA; formerly MicroMed). In use at the NEI since 2002, the system has become the universal method of clinical research data collection in the outpatient clinic and currently contains data from almost 30,000 patient visits. Although the system has provided developmental challenges, we have implemented it as both a record of patient care and a platform for prospective and retrospective studies.

The HANDS project: studying and refining the automated collection of a cross-setting clinical data set.

The consistent availability of a core set of clinical nursing data is essential to promote quality patient care. Although important work to improve terminology and enhance comparability of data is underway, the efforts do not address the immediate need for useful nursing data sets and valid methods of collection at the point of data entry. The Hands-on Automated Nursing Data System (HANDS) project is dedicated to refining a feasible methodology for gathering, storing, and retrieving a standardized nursing data set. To date the project team has developed and tested a prototype research tool that is automated and contains the structured terminologies (North American Nursing Diagnosis Association, Nursing Outcomes Classification, and Nursing Interventions Classification) to represent nursing diagnoses, outcomes, and interventions, respectively. The Phase I project development activities are reported in this article, along with Phase II and III plans for testing and refining the methodology under actual clinical conditions. Results and lessons learned during Phase I are reported.

Development of a paperless, Y2K compliant exposure tracking database at Los Alamos National Laboratory.

The Industrial Hygiene and Safety Group at Los Alamos National Laboratory (LANL) developed a database application known as IH DataView, which manages industrial hygiene monitoring data. IH DataView replaces a LANL legacy system, IHSD, that restricted user access to a single point of data entry needed enhancements that support new operational requirements, and was not Year 2000 (Y2K) compliant. IH DataView features a comprehensive suite of data collection and tracking capabilities. Through the use of Oracle database management and application development tools, the system is Y2K compliant and Web enabled for easy deployment and user access via the Internet. System accessibility is particularly important because LANL operations are spread over 43 square miles, and industrial hygienists (IHs) located across the laboratory will use the system. IH DataView shows promise of being useful in the future because it eliminates these problems. It has a flexible architecture and sophisticated capability to collect, track, and analyze data in easy-to-use form.

Digital radiography of the chest

Digital radiography is an appropriate method for both bedside and in-department chest radiographs. Its major advantage in bedside chest radiography is its control of the displayed optical density of these radiographs. With dynamic range control processing, it improves the visibility of tubes and lines superimposed on the mediastinal tissues. When used for in-department chest radiography, it may offer slight advantages in the evaluation of disease in the mediastinum, but in general is equivalent to film-screen chest radiography. The main reasons for using digital chest radiography for in-department chest radiographs relate mainly to its use as a data entry point method of projection radiography for high-quality teleradiology or for its use in a picture archiving and communication system. Apart from these advantages, there is no reason to change from conventional to digital chest radiographs. Digital radiographs are, with certain systems, printed at smaller than life size. Because of this, there is a necessary period of learning as radiologists adjust to the new image size. The most important change in radiologists' work pattern appears to be the need to sit closer to the film. Findings of disease are smaller, but, with experience, just as easy to see.

Light absorption and pigment content in natural populations and cultures of a non-gas vacuolate cyanobacterium OsciUatoria bourrellyi (=Tychomema bourrellyi)

Vertical profiles of temperature, the flux density and spectral composition of irradiance and the vertical distribution of Oscillatoria bourrellyi were measured in the North and South Basins of Windermere in the English Lake District At the population maximum (8–14 m) the photon flux was 0.3–14 μmol m −2 s −1 with the waveband 512–580 nm contributing 54–60% of the photosyn-thetically active irradiance. Samples of O.bourrcllyi taken concurrently from 0 and 12 m were analysed to determine the absorption properties of the populations and the phycoerythrin-related fluorescence of individual filaments. The 12 m populations were distinguished from the surface populations by higher beam absorption coefficients at all wavelengths throughout the visible spectrum. These differences were accentuated when the absorption characteristics were calculated using in situ irradiance profiles. The deeper populations consistently absorbed a greater fraction of the available irradiance than the shallow populations. This was due to an overall increase in the total pigment per cell rather than the differential synthesis of phycobiliprotein pigments. These observations were confirmed by both laboratory and field experiments using cultures of O. bourrellyi In these experiments low white light was sufficient to induce the synthesis of both phycoerythnn and phycocyanin as well as chlorophyll Mean individual filament fluorescence also distinguished populations from different depths These measurements further demonstrated that filaments within a population located at a discrete depth have a wide range of fluorescence. This variation increased with decreasing light intensity and suggests that phycoerythrin could be used as a cellular marker to determine the provenance of individual filaments The benefits of photo-adaptation in O.bourreUyi are analysed in relation to the variable underwater light climate of Windermere, UK Their ecological significance for this alga during periods of intense but intermittent stratification in a nutrient-limited environment are discussed. The ecological implications of both intra- and inter-specific variations in the absorption properties of phytoplankton are discussed in relation to the use of simple community or populations means as the data entry points for models of photosynthetic production.

Over-friendly software

A user friendly system which incorporates full screen edit, controlled data entry points and explicitly stated data entry formats, guides a user's data entry and processing steps W the narrowest limits. This specification is, however, the antithesis of sub sequent flexibility. Also, too much prompting can become a tortuous path for the experienced user. The article discusses the nature of a truly user friendly system, one which affords both distance as well as intimacy.

Microcomputer data entry in epidemiological research

The use of an inexpensive mark sense card reader as a data entry point for a Radio Shack TRS-80, Model I microcomputer is described. IBM-like cards marked in pencil with epidemiological data from a public health survey are fed into the reader interfaced to the microcomputer. The complete procedure and a computer program which accepts the card data input, deciphers it, and stores it in manageable form on diskette are explained in detail.

TRACKING THE ISSN

During a two year study ending in February 1978, nearly 13,000 serial publications received at Colorado State University were examined to determine if they contained an ISSN printed on or in each piece. Approximately 25% of the total population of titles contained an ISSN in one or more of seven dominant locations, representing 5O countries and the United Nations. It was concluded that reliance on the ISSN printed on each serial piece as a primary data entry point into an automated serials subsystem is not recommended at this time; nevertheless, the future looks optimistic.

Accuracy and Timeliness in Large-Scale Data-Entry Subsystems

Errors generated at the input to automated information systems threaten the utility of the outputs produced by such systems—i.e., the classic GIGO (Garbage In—Garbage Out) problem. Large amounts of effort and attendant costs are involved in dealing with these errors once they are in the system; however, more attention should be given to a coordinated solution of the problem at the source—the data entry point. A large-scale data-entry subsystem model is presented, together with statistics on error and timeliness. The concept of a Source Data Entry Module (SDEM) is presented along with considerations of design criteria and a design approach. An example is given, paralleling a testbed for which data collection is planned. It is suggested that a generalized SDEM design may be achieveable, providing a basis for the solution of a wide range of automated information system data entry problems.