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PubMed1985

Clinical data quality: impact on revenue.

In large measure, individual hospitals' survival and growth in a prospective payment environment depend on management's abilities to develop both an aggressive strategic plan and short-term monitoring systems to ensure the quality of the clinical data. In general, managers should capitalize on factors favorable to the hospital, minimize the impact of unfavorable factors, and position the organization to respond favorably to expect future changes. To accomplish these three goals, the quality of the clinical data must be analyzed and linked to revenue expectations. Then, if needed, the next step is to implement short-term actions to improve any data deficiencies that are identified. In this way, the hospital's clinical data, which are an accurate reflection of services rendered, can be used with confidence for long-term strategic and financial planning.

Data Collection↗
PubMed1996

Analyses of data quality in registries concerning diabetes mellitus--a comparison between a population based hospital discharge and an insulin prescription registry.

To evaluate the data quality in the Danish National Registry of Patients (DNRP) and the Prescription Registry in the country of Northern Jutland (487,000 inhabitants) concerning insulin dependent diabetes mellitus (IDDM) and insulin treated diabetes mellitus, a comparison between data in the two registries was made. From the Regional Hospital Registry in the County of Northern Jutland, containing discharge diagnoses from all admissions to hospitals in the county, we identified all patients with the IDDM diagnosis between 1987 and 1993. From the Regional Prescription Registry all insulin prescriptions taken up at pharmacies in the county in 1993 were identified. All persons were identified by their individual identification number (CPR-number), and a record linkage between the two data sources was made. The predictive value of an IDDM-registration in the DNRP was 96% and the corresponding completeness 91%. In the Prescription Registry the completeness was 96%. Both registries seem to be valuable study bases for epidemiological research in diabetes mellitus.

Denmark↗
PubMedDate not supplied

Establishing sensitivity requirements for environmental analyses from project data quality objectives.

This article proposes a simple strategy for establishing sensitivity requirements (quantitation limits) for environmental chemical analyses when the primary data quality objective is to determine if a contaminant of concern is greater or less than an action level (e.g., an environmental "cleanup goal," regulatory limit, or risk-based decision limit). The approach assumes that the contaminant concentrations are normally distributed with constant variance (i.e., the variance is not significantly dependent upon concentration near the action level). When the total or "field" portion of the measurement uncertainty can be estimated, the relative uncertainty at the laboratory's quantitation limit can be used to determine requirements for analytical sensitivity. If only the laboratory component of the total uncertainty is known, the approach can be used to identify analytical methods or laboratories that will not satisfy objectives for sensitivity (e.g., when selecting methodology during project planning).

Environmental Health↗
PubMed1991

Employers making use of price, quality data.

In efforts they hope will reduce costs, healthcare purchasers are becoming more active in helping beneficiaries choose providers. Such efforts include increased use of widely available price and quality data. Quaker Oats, for example, publishes hospital price guides, while Navistar International and Hershey Foods are developing their own provider networks.

Community Participation↗
PubMed1994

Data quality in a DRG-based information system.

The aim of this study initiated in May 1990 was to evaluate the quality of the medical data collected from the main hospital of the "Hospices Civils de Lyon", Edouard Herriot Hospital. We studied a random sample of 593 discharge abstracts from 12 wards of the hospital. Quality control was performed by checking multi-hospitalized patients' personal data, checking that each discharge abstract was exhaustive, examining the quality of abstracting, studying diagnoses and medical procedures coding, and checking data entry. Assessment of personal data showed a 4.4% error rate. It was mainly accounted for by spelling mistakes in surnames and first names, and mistakes in dates of birth. The quality of a discharge abstract was estimated according to the two purposes of the medical information system: description of hospital morbidity per patient and Diagnosis Related Group's case mix. Error rates in discharge abstracts were expressed in two ways: an overall rate for errors of concordance between Discharge Abstracts and Medical Records, and a specific rate for errors modifying classification in Diagnosis Related Groups (DRG). For abstracting medical information, these error rates were 11.5% (SE +/- 2.2) and 7.5% (SE +/- 1.9) respectively. For coding diagnoses and procedures, they were 11.4% (SE +/- 1.5) and 1.3% (SE +/- 0.5) respectively. For data entry on the computerized data base, the error rate was 2% (SE +/- 0.5) and 0.2% (SE +/- 0.05). Quality control must be performed regularly because it demonstrates the degree of participation from health care teams and the coherence of the database.(ABSTRACT TRUNCATED AT 250 WORDS)

Data Collection↗
PubMed1986

Design and data quality of a mixed longitudinal study to elucidate the role of dietary calcium and phosphorus on bone mineralization in pre-, peri-, and postmenopausal women.

The study design and data quality control of an ongoing study (10 yr duration) in a few hundred women are presented. Good variables with respect to their longitudinal usefulness are: body weight, body height, and span-width. Reasonable variables are the bone parameters of the radius (BMC, BW, and BMC/BW). Poor variables are: dietary calcium and phosphorus intake, dietary calcium-to-phosphorus ratio, urinary calcium-to-creatinine ratio, urinary sodium-to-creatinine ratio, hematocrit, serum alkaline phosphatase activity, serum gamma-GT activity, and serum parathyroid-hormone concentration. Bad variables are: urinary phosphorus-to-creatinine ratio, urinary hydroxyproline-to-creatinine ratio, creatinine clearance, hemoglobin, MCHC, serum calcium, serum ionized calcium, serum phosphorus, serum total protein, serum albumin, and serum creatinine. In conclusion, it is possible to relate bone loss to food intake and to changes in anthropometric variables on an individual basis. However, quantification of the metabolic process is not possible.

Anthropometry↗
PubMed1985

Data quality and DRGs: an assessment of the reliability of federal beneficiary discharge data in selected Manhattan hospitals.

New York County Health Services Review Organization (NYCHSRO), the physicians' professional standards review organization of Manhattan, examined whether diagnostic coding errors identified in Manhattan hospitals would affect reimbursement under a diagnostic-related group (DRG) method of financing inpatient services. A sampling of 1,027 Medicare and Medicaid cases representing discharges from 18 Manhattan hospitals during 1982 and 1983 revealed incorrect DRG assignment for 17.5% of patient record abstracts, but these appear to have been unsystematic rather than deliberate errors. The difference between estimated reimbursement based on original and reabstracted records was not statistically significant either in the aggregate or for specific hospitals. It is emphasized that while New York State's Prospective Hospital Reimbursement Methodology (in effect during the study period) is not solely dependent upon DRG's case-mix is one of several factors used to make adjustments to existing per diem rates. A key recommendation is that hospitals conduct internal monitorings with all involved departments to improve the quality of the data abstracting process.

Diagnosis-Related Groups↗
PubMedDate not supplied

Outpatient satisfaction: validation of a French-language questionnaire: data quality and identification of associated factors.

OBJECTIVES: Following 1996 legislation requiring French hospitals to assess patient satisfaction, this study developed and validated a brief French-language multidimensional questionnaire designed to measure outpatient satisfaction with hospital visits and compared data quality for two patient-satisfaction survey methods. DESIGN: Authors developed a 19-item questionnaire following a strict procedure (identification of dimensions to explore, formulation, and selection of items). SETTING: Validation data were obtained from patients of six outpatient clinics in a teaching hospital. PARTICIPANTS: 586 consenting eligible patients were randomized to receive the questionnaire 2 weeks after their visit with one of two survey methods: a mailed self-administered questionnaire or a telephone interview. RESULTS: The response rate (79%) was not significantly different between the two survey methods. The risk of having one or more missing values was higher in the mail survey group (odds ratio, 1.65; 95% confidence interval, 1.03-2.63), but mail respondents were less likely to use the "extremely positive" response category. Principal component analysis identified four factors that accounted for 56% of the variance: interpersonal skills and information transfer, physical surroundings, convenience, and appointment delay. Patients' comments on open-ended questions validated the semantic content of the factorial construct. The internal consistency coefficient was greater than 0.70 for three of four subscales. Patient background characteristics accounted for less than 10% of the factorial score variance. Patient satisfaction was correlated with age, type of visit, and, to a lesser extent, gender and education level. CONCLUSION: This easily administered, multidimensional out-patient-satisfaction questionnaire provided encouraging preliminary psychometric characteristics.

Adolescent↗
PubMed1999

Follow-up procedures in EPIC-Germany--data quality aspects. European Prospective Investigation into Cancer and Nutrition.

With 475,000 participants throughout Europe, EPIC is one of the largest cohort studies investigating the association between diet and cancer and other chronic diseases. The German part of EPIC comprises about 53,000 participants in Potsdam (n = 27,616) and in Heidelberg (n = 25,546). In the German study centers, follow-up started in 1998 and will be continued in 2-year intervals over the next 10-15 years. To ensure high follow-up data quality at an European level, an international working group developed guidelines for endpoint data collection in every country. A follow-up phase in Germany comprises mailing of a questionnaire, tracing of individuals to whom mail could not be delivered, obtaining information on deceased participants including cause of death, and verifying self- reported diagnoses. Furthermore, activities aimed at motivating study participants are part of the follow-up. The first round of follow-up of those who entered the study in 1994 and 1995 included 8, 706 participants in Potsdam and 6,289 in Heidelberg. Due to a comprehensive and intensive reminder and tracing system, vital status of the study subjects is known from almost 100% in Potsdam and 99% in Heidelberg. Two years after baseline examination, and with twice as many addresses in Potsdam as in Heidelberg, addresses had to be traced or checked via population registry (13 versus 6%). Tracing, the application of different mailing strategies, and intensive reminder activities resulted in a 95% return of the questionnaire in Potsdam and 90% in Heidelberg. The system of follow-up data entry and control, including completion of missing information via telephone, verification of self-reports and causes of death, has been set up for EPIC-Germany and works efficiently and successfully. The aim of this paper is to describe the follow-up procedures in EPIC-Germany with a focus on the generation of valid and complete outcome data.

Cohort Studies↗
PubMedDate not supplied

Improved quality data systems through the use of standard electronic data deliverables (EDDs) and environmental data assessment software.

One of the challenges facing professionals in the environmental arena today is the collection and assessment of large amounts of environmental analytical data. The assessment of the quality of that data is essential as multi-million dollar decisions for environmental site cleanups and/or long term monitoring efforts are made based on the analytical results. Also critical to environmental programs is the sharing and access of data across multiple data users. The ability to share data allows for better use of the limited resources available to clean up and monitor contaminated environmental sites. Standardization of electronic deliverables allows for collection of data from multiple data collectors into a single database for use by numerous data users and stakeholders on a project. This paper discusses the benefits of using a standard EDD deliverable format and use of environmental data assessment software tools to do project planning and data assessment throughout the duration of the environmental project.

Database Management Systems↗
PubMed1998

Vision 2006 brings data quality management into view.

One of the goals of the vision 2006 initiative has been to identify new career opportunities for HIM professionals. Recently, a group of AHIMA volunteers has provided a framework and tools for developing data quality management expertise. Here's an overview of their work and a look at resources for those who want to learn more.

Information Management↗
PubMed1995

Exploratory research synthesis--methodological considerations for addressing limitations in data quality.

Exploratory meta-analysis or research synthesis has been advocated as a way of developing important hypotheses for further study. An exploratory research synthesis was conducted on the carotid endarterectomy (CE) literature to illustrate this method. The CE scientific literature is similar to that of many other new medical interventions because it contains numerous limitations to data quality. Exploratory research synthesis of such literature necessitates a number of methodological and statistical considerations to address these limitations, including the problems of missing data, appropriate unit of analysis, nonnormal distribution of outcomes, and lack of controlled studies. Strengths and limitations of the exploratory research synthesis approach are discussed within the context of public policy decisions for assessing medical technologies.

Data Collection↗
PubMed1998

Methods for testing data quality, scaling assumptions, and reliability: the IQOLA Project approach. International Quality of Life Assessment.

Following the translation development stage, the second research stage of the IQOLA Project tests the assumptions underlying item scoring and scale construction. This article provides detailed information on the research methods used by the IQOLA Project to evaluate data quality, scaling and scoring assumptions, and the reliability of the SF-36 scales. Tests include evaluation of item and scale-level descriptive statistics; examination of the equality of item-scale correlations, item internal consistency and item discriminant validity; and estimation of scale score reliability using internal consistency and test-retest methods. Results from these tests are used to determine if standard algorithms for the construction and scoring of the eight SF-36 scales can be used in each country and to provide information that can be used in translation improvement.

Activities of Daily Living↗
PubMed1988

DRG 468: an analysis of data quality.

Washington State's Medical Assistance Program personnel conducted a retrospective review of medical records assigned to DRG 468 to determine the reasons for incorrect/improper DRG 468 assignment. The results of their coding validation review provide insight into the origins of lapses in data quality.

Abstracting and Indexing↗
PubMed2001

Meeting data quality objectives with interval information.

Immunoassay test kits are promising technologies for measuring analytes under field conditions. Frequently, these field-test kits report the analyte concentrations as falling in an interval between minimum and maximum values. Many project managers use field-test kits only for screening purposes when characterizing waste sites because the results are presented as semiquantitative intervals. However, field-test kits that report results as intervals can also be used to make project-related decisions in compliance with false-rejection and false-acceptance decision error rates established during a quantitative data quality objective process. Sampling and analysis plans can be developed that rely on field-test kits to meet certain data needs of site remediation projects.

Data Collection↗
PubMed1990

Multicentric quality assurance in cardiac surgery. QUADRA study of the German Society for Thoracic and Cardiovascular Surgery (QUADRA: quality data retrospective analysis).

A method for initiating quality assurance in cardiac surgery was developed multicentrically by a commission of the German Society for Thoracic and Cardiovascular Surgery (QUADRA Study). To appraise the quality of cardiosurgical action, variables were compiled from the preoperative, intraoperative, and postoperative treatment course. The data collection was carried out at the same time as treatment. On the basis of unicentric data profiles, multicentric hospital profiles, and problem profiles, a quality comparison could be carried out and the variability of cardiosurgical action which may occasion interventions could be identified. A reduction of perioperative blood consumption during the study period could be observed in four out of five hospitals as the first result. The data collection also revealed epidemiological features. On average, women were older than men at the time of the heart-valve and coronary operations. To ensure data validity and the organization of quality assurance, a documentation assistant and a specially trained physician are necessary at every cardiovascular surgery center. The multicentric external comparison is indispensable and must be carried out by means of a central data collection, for which intrumental and staff capacities are also to be provided. With modern methods of data processing, an additional and new approach to the improvement of quality in cardiac surgery can thus be made.

Blood Transfusion↗
PubMed1994

The effect of information systems architecture on health care data quality.

A rapidly increasing number of health care provider institutions is dealing with data architecture design issues that directly affect the quality of data within their heterogeneous information systems. These problems result from a failure to recognize that they are actually managing a loosely distributed yet integrated database among their many information system platforms. Understanding the issues surrounding data integration, the application available interface standards, and the tools available for implementation is critical to operating a successful distributed health care information systems environment today.

Architecture↗