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At least 1,189 records · Page 66Linked to original sources
PubMed2001

Using data mining to find fraud in HCFA health care claims.

Data mining can be/used to detect health care fraud and abuse through visualization of very large data sets to isolate new and unusual patterns of activity. Data mining has allowed better direction and use of health care fraud detection and investigative resources by recognizing and quantifying the underlying indicators of fraudulent claims, fraudulent providers, and fraudulent beneficiaries. A large amount of work must be performed prior to the actual data mining. These precursory tasks include: customer discussions, data extraction and cleaning, transformation of the database, and auditing (basic statistics and visualization of the information) of the data. This paper describes the tasks performed in support of a project for HCFA (Health Care Financing Administration).

Centers for Medicare and Medicaid Services, U.S.↗
PubMed2002

[System of centralized control of tuberculosis in the armed forces of the Russian Federation].

Tuberculosis belongs to the group of controllable diseases. Epidemic control should characterize not only the effectiveness of each of anti-tubercular measures individually but the result of their complex conduction. The specialists of Central Military Clinical Tubercular Hospital have developed the main principles of All-Army system of tuberculosis centralized control (monitoring), the technology of information collection, storage and analysis. It consists of three levels: the territorial (garrison), the specific (district, naval) and the all-army. The All-Army register of tubercular patients (servicemen under the contract and RF MD pensioners) is complied.

Chronic Disease↗
PubMed2003

A dynamic Web application within an n-tier architecture: a Multi-Source Information System for end-stage renal disease.

A Multi-Source Information System (MSIS) has been designed for the Renal Epidemiology and Information Network (REIN) dedicated to End-Stage Renal Disease. Interoperability has been considered at 4 levels: semantics, network, formats and contents. An n-tier architecture has been chosen at the network level. It is made out of a universal client, a dynamic Web server connected to a production database and to a data warehouse. The MSIS is patient-oriented, based on a regional organization. Its implementation in the context of a regional experimentation is presented with insights on the design and underlying technologies. The n-tier architecture is a robust model and flexible enough to aggregate multiple information sources and integrate modular developments. The data warehouse is dedicated to support health care decision-making.

Data Display↗
PubMed2003

A preprocessing method for improving data mining techniques. Application to a large medical diabetes database.

The Knowledge Discovery in Databases (KDD) methodology seems to be attractive on the analyze of large clinical databases. In the KDD process, the preprocessing step (data cleaning and handling of missing values) is paramount since it conditions the quality of the results obtained by data mining procedures and represents about 80% of the whole project time. The aims of the present study were to analyze this step and provide tools to handle inconsistent data and missing values. We have broken down the process into 3 main stages: data cleaning--explanatory study of missing values--choice of the procedure used for handling missing values. The data cleaning stage was based on a system of logical rules to correct mistakes and on cluster analysis to discard the poorly filled files. The missing-data mechanism was analyzed by means of multivariate statistical procedures. Two methods to deal with missing values were compared: imputation by the most common value (mode) and imputation using decision trees. This study was performed on a large medical diabetes database (23,601 patients) including numerous missing values. A system of logical rules allowed to correct mistakes on essential parameters (for example, the type of diabetes). Cluster analysis allowed to identify 10% of poorly filled files. After multivariate analysis, the missing-data mechanism could be considered as random. For variables with low number of missing values (< 10%) and categories (< 4), imputation using decision trees provided better results than imputation by mode.

Data Interpretation, Statistical↗
PubMed2004

Organizing literature information for clinical decision support.

Answers to clinical questions occurring during healthcare practitioner/patient interaction can be often found in National Library of Medicine's (NLM) databases. The recent advances in wireless handheld computers promise to make them a widely used tool to deliver needed information to the practitioner at the point of service. This paper addresses challenges in organizing and presenting information obtained from NLM's MEDLINE database of indexed citations in a way that will help practitioners reduce literature search time on handheld computers. We study two clustering algorithms and two methods of labeling document clusters.

Algorithms↗
PubMed2006

Evidence in pharmacovigilance: extracting adverse drug reactions articles from MEDLINE to link them to case databases.

Literature, specifically MEDLINE, is among the main sources of information used to detect whether a drug may be responsible for Adverse Drug Reactions cases. The aim of our work is to automate the search of publications that correspond to a given Adverse Drug Reactions case: (i) by defining a general pattern for the queries used to search MEDLINE and (ii) by determining the threshold number of publications capable to confirm or infirm the Adverse Drug Reaction. We applied our algorithm to a set of 620 cases from a French pharmacovigilance database. We obtained a precision of 93%, recall 70%. We determined a threshold of 3 publications to confirm an Adverse Drug Reaction case.

Adverse Drug Reaction Reporting Systems↗
PubMed1994

Medical data capture and display: the importance of clinicians' workstation design.

The Department of Veterans Affairs is developing, testing and evaluating the benefits of physicians' workstations as an aid to medical data capture in an outpatient clinic setting. The physician's workstation uses a graphical user interface to aid the clinician in recording encounter data. Various input devices including keyboard, mouse, pen, voice, barcode reader, and tablet are available on the workstations, and user preferences will be examined. Access to general services such as electronic mail and reference databases is also available. The workstation provides a wide variety of patient specific data from the hospital information system, including image data. The single data collection process by the clinician will also provide data for the cost recovery process.

Computer Peripherals↗
PubMedDate not supplied

Automation of measurements and interventions in the systematic care of postoperative cardiac surgical patients.

Since July of 1967, a computer-based system has been employed in the observation and treatment of 8500 patients following cardiac surgical procedures. Of the 12 beds in the University of Alabama Hospital Cardiac Surgical Intensive Care Unit, 10 are equipped with biomedical instrumentation interfaced with digital computer systems. These systems perform the automatic acquisition, display, storage, retrieval, and charting of measurements; computation and evaluation of acid-base balance from manually entered blood gas data; analysis of the data for the detection and treatment of impaired cardiac performance; automatic control of blood infusion and vasodilating agents by closed-loop feedback control techniques; hourly evaluation of chest tube drainage patterns to detect excessive blood loss in the early hours following operation; and computation of the infusion rate (ml/hr) of pharmacologic agents according to a specified dosage (microgram/kg/min). Automatic control of left atrial pressure by blood infusion has been applied to 8000 patients during the past 10 years. Regulation of mean arterial pressure by computer-controlled infusion of vasodilating agents has been performed in 400 patients in the last 2 years. The use of the system has contributed to the reduction of patient time spent in the unit to 24 hours or less for the majority of the patients.

Cardiac Surgical Procedures↗
PubMed1997

An empirical study of the Health Status Questionnaire System for use in patient-computer interaction.

Patient involvement in the health care process is very important to any attempt to improve health care quality and patient satisfaction. Although many computerized medical record systems have been introduced, physicians are the only players in the process of data collection and interpretation. A computerized version of the Health Status Questionnaire has been developed to provide a simple, inexpensive method of direct patient entry into the medical record. The system philosophy emphasizes user-centered design and an empirical study was conducted with one hundred twelve outpatients to evaluate the interface aspects of the system as well as the hardware preference of the patients. Statistical analysis indicate that the patients involved in the study rated the user interface of the Health Status Questionnaire System highly. The study also revealed that a considerable number of the general population still have negative preconceptions about their ability to handle a computer or similar looking machinery. When they were asked to use a desktop computer with a mouse, 26 out of 50 patients refused, while 61 out of 62 agreed to use a hand-held pen computer.

Attitude to Computers↗
PubMedDate not supplied

Data organization for soil metabolism and soil degradation studies with microsoft Excel spreadsheets.

Certain steps for soil metabolism and soil degradation studies are repetitive and must transpire for each study. These steps are the data on initial combustion of soil, the extraction data, volatile-recovery data, and postextraction soil combustion data. A summary page that includes all these data has been developed and used successfully to document all the raw data associated with these steps on one form. Weights, volumes, and liquid scintillation data can then be entered into an Excel spreadsheet formatted to accept and process these raw data to provide the calculated data required for reporting. This information is centrally located for each sampling time period, which facilitates and streamlines quality assurance (QA) review of this study file. An explanation of the process and examples of the forms is provided.

Data Collection↗
PubMed1999

Kleisli: a new tool for data integration in biology.

One of the central problems in bioinformatics is data retrieval and integration. The existing biological databases are geographically distributed across the Internet, complex and heterogeneous in data types and data structures, and constantly changing. With the current rapid growth of biomedical data, the challenge is how large volumes of data retrieved from multiple databases can be transformed and integrated automatically and flexibly. This article describes a powerful new tool, the Kleisli system, for complex queries across multiple databases and data integration.

Computational Biology↗
PubMed1994

An information model for medical events.

Information gathered during the healthcare process is lost when forced into rigidly structured record-oriented databases. By contrast, content can be difficult to manipulate if stored as unstructured text. Spurred by the requirements of electronic publishing, military procurement and the Internet, new robust standards for structuring documents have been developed and deployed. These standards can provide a foundation for a document-based Electronic Medical Record System. In order to fully exploit this added flexibility, an information model is necessary to define both the direct and contextual content of documents. Once context, as well as fact, are recorded in formal structures, inferential techniques can either selectively extract knowledge and data from documents or aggregate data to create summaries so that all interested and authorized parties have a better chance of meeting their information needs from a single, permanent data source.

Humans↗
PubMed1999

Medical student database development: a model for record management in a multi-departmental setting.

Student records flow through medical school offices at a rapid rate. Much of this data is often tracked on paper, spread across multiple departments. The Medical Student Informatics Group at the University of Utah School of Medicine identified offices and organizations documenting student information. We assessed departmental needs, identified records, and researched database software available within the private sector and academic community. Although a host of database applications exist, few publications discuss database models for storage and retrieval of student records. We developed and deployed an Internet based application to meet current requirements, and allow for future expandability. During a test period, users were polled regarding utility, security, stability, ease of use, data accuracy, and potential project expansion. Feedback demonstrated widespread approval, and considerable interest in additional feature development. This experience suggests that many medical schools would benefit from centralized database management of student records.

Consumer Behavior↗
PubMed2002

Genew: the human gene nomenclature database.

Genew, the Human Gene Nomenclature Database, is the only resource that provides data for all human genes which have approved symbols. It is managed by the HUGO Gene Nomenclature Committee (HGNC) as a confidential database, containing over 16 000 records, 80% of which are represented on the Web by searchable text files. The data in Genew are highly curated by HGNC editors and gene records can be searched on the Web by symbol or name to directly retrieve information on gene symbol, gene name, cytogenetic location, OMIM number and PubMed ID. Data are integrated with other human gene databases, e.g. GDB, LocusLink and SWISS-PROT, and approved gene symbols are carefully co-ordinated with the Mouse Genome Database (MGD). Approved gene symbols are available for querying and browsing at http://www.gene.ucl.ac.uk/cgi-bin/nomenclature/searchgenes.pl.

Confidentiality↗