Incorporating visualized data completeness information in an open and interoperable GIS map interface

2011 ◽  
Vol 34 (6) ◽  
pp. 733-745 ◽  
Author(s):  
Jung-Hong Hong ◽  
Hsiung-Peng Liao
Keyword(s):  
Data Completeness

Monitoring and auditing the transfer of syndromic surveillance data to ensure data completeness

2011 ◽  
Vol 4 (0) ◽  
Author(s):  
Zachary Faigen ◽  
Anikah Salim ◽  
Kishok Rojohn ◽  
Ajit Isaac ◽  
Sherry Adams
Keyword(s):  
Syndromic Surveillance ◽  
Surveillance Data ◽  
Data Completeness

Assessing Real-World Medication Data Completeness

2021 ◽  
pp. 103847
Author(s):  
Laura Evans ◽  
Jack W. London ◽  
Matvey B. Palchuk
Keyword(s):  
Real World ◽  
Data Completeness

Linking Provider Specialty and Outpatient Diagnoses in Medicare Claims Data: Data Quality Implications

2021 ◽  
Vol 12 (04) ◽  
pp. 729-736
Author(s):  
Vojtech Huser ◽  
Nick D. Williams ◽  
Craig S. Mayer
Keyword(s):  
Data Quality ◽  
Claims Data ◽  
Medical Toxicology ◽  
Health Care Research ◽  
Real World Data ◽  
Full Spectrum ◽  
Data Completeness ◽  
Medicare Claims ◽  
Diagnosis Codes ◽  
Medicare Claims Data

Abstract Background With increasing use of real world data in observational health care research, data quality assessment of these data is equally gaining in importance. Electronic health record (EHR) or claims datasets can differ significantly in the spectrum of care covered by the data. Objective In our study, we link provider specialty with diagnoses (encoded in International Classification of Diseases) with a motivation to characterize data completeness. Methods We develop a set of measures that determine diagnostic span of a specialty (how many distinct diagnosis codes are generated by a specialty) and specialty span of a diagnosis (how many specialties diagnose a given condition). We also analyze ranked lists for both measures. As use case, we apply these measures to outpatient Medicare claims data from 2016 (3.5 billion diagnosis–specialty pairs). We analyze 82 distinct specialties present in Medicare claims (using Medicare list of specialties derived from level III Healthcare Provider Taxonomy Codes). Results A typical specialty diagnoses on average 4,046 distinct diagnosis codes. It can range from 33 codes for medical toxicology to 25,475 codes for internal medicine. Specialties with large visit volume tend to have large diagnostic span. Median specialty span of a diagnosis code is 8 specialties with a range from 1 to 82 specialties. In total, 13.5% of all observed diagnoses are generated exclusively by a single specialty. Quantitative cumulative rankings reveal that some diagnosis codes can be dominated by few specialties. Using such diagnoses in cohort or outcome definitions may thus be vulnerable to incomplete specialty coverage of a given dataset. Conclusion We propose specialty fingerprinting as a method to assess data completeness component of data quality. Datasets covering a full spectrum of care can be used to generate reference benchmark data that can quantify relative importance of a specialty in constructing diagnostic history elements of computable phenotype definitions.

scholarly journals The Application of Public Data in Forensic Real Estate Valuations: Problems, Limitations, Opportunities

2019 ◽  
Vol 14 (1) ◽  
pp. 24-29
Author(s):  
A. Yu. Butyrin ◽  
V. M. Kruglyakova ◽  
I. A. Shipilova
Keyword(s):  
Real Estate ◽  
Initial Data ◽  
The Body ◽  
Legal Authority ◽  
Data Completeness ◽  
Legal Proceedings ◽  
Public Data ◽  
Judicial Proceedings ◽  
Forensic Expert

One of the problems with the practice of legal proceedings is the determination of the legal authority limits of a forensic expert when he forms an array of initial data, the structure and content of which would provide a full and comprehensive research of the issues put before him by the body (person) having appointed the examination. The possibility of the specifed initial data completeness ensuring is suggested and substantiated in the article, arguments arising from the provisions of the law concerning neutralization of the traditional criticism directions of the expert in this part during his interrogation in the course of judicial proceedings on his conclusion are adduced.

A program to analyze the distributions of unmeasured reflections

2011 ◽  
Vol 44 (4) ◽  
pp. 865-872 ◽  
Author(s):  
Ludmila Urzhumtseva ◽  
Alexandre Urzhumtsev
Keyword(s):  
Spatial Distribution ◽  
Missing Data ◽  
Computer Program ◽  
Diffraction Data ◽  
Reciprocal Space ◽  
Data Set ◽  
Data Completeness ◽  
Significant Time ◽  
Cumulative Data ◽  
Structure Solution

Crystallographic Fourier maps may contain barely interpretable or non-interpretable regions if these maps are calculated with an incomplete set of diffraction data. Even a small percentage of missing data may be crucial if these data are distributed non-uniformly and form connected regions of reciprocal space. Significant time and effort can be lost trying to interpret poor maps, in improving them by phase refinement or in fighting against artefacts, whilst the problem could in fact be solved by completing the data set. To characterize the distribution of missing reflections, several types of diagrams have been suggested in addition to the usual plots of completeness in resolution shells and cumulative data completeness. A computer program,FOBSCOM, has been developed to analyze the spatial distribution of unmeasured diffraction data, to search for connected regions of unmeasured reflections and to obtain numeric characteristics of these regions. By performing this analysis, the program could help to save time during structure solution for a number of projects. It can also provide information about a possible overestimation of the map quality and model-biased features when calculated values are used to replace unmeasured data.

scholarly journals Data quality audit of a clinical quality registry: a generic framework and case study of the Australian and New Zealand Hip Fracture Registry

2019 ◽  
Vol 8 (3) ◽  
pp. e000490 ◽  
Author(s):  
Aidan Christopher Tan ◽  
Elizabeth Armstrong ◽  
Jacqueline Close ◽  
Ian Andrew Harris
Keyword(s):  
Quality Improvement ◽  
New Zealand ◽  
Hip Fracture ◽  
Data Quality ◽  
Data Abstraction ◽  
Data Set ◽  
Clinical Quality ◽  
Data Completeness ◽  
Quality Registry ◽  
Generic Framework

ObjectivesThe value of a clinical quality registry is contingent on the quality of its data. This study aims to pilot methodology for data quality audits of the Australian and New Zealand Hip Fracture Registry, a clinical quality registry of hip fracture clinical care and secondary fracture prevention.MethodsA data quality audit was performed by independently replicating the data collection and entry process for 163 randomly selected patient records from three contributing hospitals, and then comparing the replicated data set to the registry data set. Data agreement, as a proxy indicator of data accuracy, and data completeness were assessed.ResultsAn overall data agreement of 82.3% and overall data completeness of 95.6% were found, reflecting a moderate level of data accuracy and a very high level of data completeness. Half of all data disagreements were caused by information discrepancies, a quarter by missing discrepancies and a quarter by time, date and number discrepancies. Transcription discrepancies only accounted for 1 in every 50 data disagreements. The sources of inaccurate and incomplete data have been identified with the intention of implementing data quality improvement.ConclusionsRegular audits of data abstraction are necessary to improve data quality, assure data validity and reliability and guarantee the integrity and credibility of registry outputs. A generic framework and model for data quality audits of clinical quality registries is proposed, consisting of a three-step data abstraction audit, registry coverage audit and four-step data quality improvement process. Factors to consider for data abstraction audits include: central, remote or local implementation; single-stage or multistage random sampling; absolute, proportional, combination or alternative sample size calculation; data quality indicators; regular or ad hoc frequency; and qualitative assessment.

scholarly journals Completeness and accuracy of anthropometric measurements in electronic medical records for children attending primary care

2018 ◽  
Vol 25 (1) ◽  
pp. 19-26 ◽  
Author(s):  
Sarah Carsley ◽  
Catherine S. Birken ◽  
Patricia C. Parkin ◽  
Eleanor Pullenayegum ◽  
Karen Tu
Keyword(s):  
Primary Care ◽  
Body Mass Index ◽  
Electronic Medical Records ◽  
Body Mass ◽  
Medical Records ◽  
Data Access ◽  
World Health ◽  
Z Score ◽  
Data Completeness ◽  
Weight Data

BackgroundElectronic medical records (EMRs) from primary care may be a feasible source of height and weight data. However, the use of EMRs in research has been impeded by lack of standardisation of EMRs systems, data access and concerns about the quality of the data.ObjectivesThe study objectives were to determine the data completeness and accuracy of child heights and weights collected in primary care EMRs, and to identify factors associated with these data quality attributes.MethodsA cross-sectional study examining height and weight data for children <19 years from EMRs through the Electronic Medical Record Administrative data Linked Database (EMRALD), a network of family practices across the province of Ontario. Body mass index z-scores were calculated using the World Health Organization Growth Standards and Reference.ResultsA total of 54,964 children were identified from EMRALD. Overall, 93% had at least one complete set of growth measurements to calculate a body mass index (BMI) z-score. 66.2% of all primary care visits had complete BMI z-score data. After stratifying by visit type 89.9% of well-child visits and 33.9% of sick visits had complete BMI z-score data; incomplete BMI z-score was mainly due to missing height measurements. Only 2.7% of BMI z-score data were excluded due to implausible values.ConclusionsData completeness at well-child visits and overall data accuracy were greater than 90%. EMRs may be a valid source of data to provide estimates of obesity in children who attend primary care.

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