Construction of a Retrospective Cohort to Observe 10-Year Urologic Cancer Treatment Trends at the Biggest Medical Center of South Korea

Purpose: To construct a urologic cancer database using a standardized, reproducible method, and to assess preliminary characteristics of this cohort.Materials and Methods: Patients with prostate, bladder, and kidney cancers who were enrolled with diagnostic codes in the electronic medical record (EMR) at Asan Medical Center from 2007–2016 were included. Research Electronic Data Capture (REDCap) was used to design the Asan Medical Center-Urologic Cancer Database (AMC-UCD). The process included developing a data dictionary, applying branching logic, mapping clinical data warehouse structures, alpha testing, clinical record summary testing, creating “standards of procedure,” importing data, and entering data. Descriptive statistics were used to identify rates of surgeries and numbers of patients.Results: Clinical variables (n=407) were selected to develop a data dictionary from REDCap. In total, 20,198 urologic cancer patients visited our institution from 2007–2016 (bladder cancer, 4,616; kidney cancer, 5,750; prostate cancer, 10,330). The overall numbers of patients and surgeries increased over time, with robotic surgeries rapidly growing over a decade. The most common treatment for urologic cancer was surgery, followed by chemotherapy and radiation therapy.Conclusions: Using a standardized method, the AMC-UCD fosters multidisciplinary research. This constructed database provides access to clinical statistics to effectively assist research. Preliminary data should be refined through EMR chart review. The successful organization of data from 2007–2016 provides a framework for future periods of investigation and prospective models.

Clinical Annotations for Prostate Cancer Research: Defining Data Elements, Creating a Reproducible Analytical Pipeline, and Assessing Data Quality

Background: Routine clinical data from clinical charts are indispensable for retrospective and prospective observational studies and clinical trials. Their reproducibility is often not assessed. Objective: To develop a prostate cancer-specific database with a defined source hierarchy for clinical annotations in conjunction with molecular profiling and to evaluate data reproducibility. Design, setting, and participants: For men with prostate cancer and clinical-grade paired tumor-normal sequencing, we performed team-based retrospective data collection from the electronic medical record at a comprehensive cancer center. We developed an open-source R package for data processing. We assessed reproducibility using blinded repeat annotation by a reference medical oncologist. Outcome measurements and statistical analysis: We evaluated completeness of data elements, reproducibility of team-based annotation compared to the reference, and impact of measurement error on bias in survival analyses. Results and limitations: Data elements on demographics, diagnosis and staging, disease state at the time of procuring a genomically characterized sample, and clinical outcomes were piloted and then abstracted for 2,261 patients (with 2,631 samples). Completeness of data elements was generally high. Comparing to the repeat annotation by a medical oncologist blinded to the database (100 patients/samples), reproducibility of annotations was high to very high; T stage, metastasis date, and presence and date of castration resistance had lower reproducibility. Impact of measurement error on estimates for strong prognostic factors was modest. Conclusions: With a prostate cancer-specific data dictionary and quality control measures, manual clinical annotations by a multidisciplinary team can be scalable and reproducible. The data dictionary and the R package for reproducible data processing are freely available to increase data quality in clinical prostate cancer research.

A review of the inclusion of equity stratifiers for the measurement of health inequalities within health and social care data collections in Ireland

Background Health equity differs from the concept of health inequality by taking into consideration the fairness of an inequality. Inequities may be culturally specific, based on social relations within a society. Measuring these inequities often requires grouping individuals. These groupings can be termed equity stratifiers. The most common groupings affected by health inequalities are summarised by the acronym PROGRESS (Place of residence, Race, Occupation, Gender, Religion, Education, Socioeconomic status, Social capital). The aim of this review was to examine the use of equity stratifiers in routinely collected health and social care data collections in Ireland. Methods One hundred and twenty data collections were identified from the Health Information and Quality Authority (HIQA) document, “Catalogue of national health and social care data collections: Version 3.0”. Managers of all the data collections included were contacted and a data dictionary was requested where one was not available via the HIQA website. Each of the data dictionaries available was reviewed to identify the equity stratifiers recorded. Results Eighty-three of the 120 data collections were considered eligible to be included for review. Twenty-nine data dictionaries were made available. There was neither a data dictionary available nor a response to our query from data collection managers for twenty-three (27.7%) of the data collections eligible for inclusion. Data dictionaries were from national data collections, regional data collections and national surveys. All data dictionaries contained at least one of the PROGRESS equity stratifiers. National surveys included more equity stratifiers compared with national and regional data collections. Definitions used for recording social groups for the stratifiers examined lacked consistency. Conclusions While there has been much discussion on tackling health inequalities in Ireland in recent years, health and social care data collections do not always record the social groupings that are most commonly affected. In order to address this, it is necessary to consider which equity stratifiers should be used for the Irish population and, subsequently, for agreed stratifiers to be incorporated into routine health data collection. These are lessons that can be shared internationally as other countries begin to address deficits in their use of equity stratifiers.

New Zealand Attitudes and Values Study Data Dictionary

This is the data dictionary for the New Zealand Attitudes and Values Study (NZAVS). The document lists the scales (and the number of items, and design origin, etc.) included in each yearly wave of the NZAVS. This document thus allows comparison of the measures and instruments included across different waves. This document is also our master planning document, and lists the current measures intended for the coming wave of data collection. This document also contains the database keys for each wave of the NZAVS. Each spreadsheet list the item content contained in a given wave of the NZAVS. Items are listed in the order that they appear in the master SPSS and Mplus data files. Long variable names (used in SPSS and Excel versions of the NZAVS data files) and eight-character variable names (used in the tab-delimited Mplus NZAVS data files) are also provided. These spreadsheets are thus designed as a reference guide for the NZAVS SPSS, Excel and tab-delimited Mplus data files. The data files themselves are not available online, but may be made available upon request, to qualified researchers for the purpose of collaborative research.

Application Design For Office Stationary Inventory Management Based On Vb.Net At PT. Hunu Osias Padmada Eara Karawang

The availability of office stationery is very important to make the required work smooth. In order for ATK management to be handled properly, an application is needed to manage ATK inventory data using a technology-based information system so that it becomes a complete information system. The method used by the author in conducting this research is the Waterfall model system development method starting from the specification of user requirements, planning, modeling, and coding. The method of specification of user requirements includes interviews, observations, literature studies, and documentation studies. The planning method includes the tasks to be carried out including the risks that may occur and the work schedule. Modeling methods include the design of flow documents, DFD, data dictionary, and ERD. The coding includes a program designer with the VB.Net programming language with SQL Server 2008 database and application testing using black box testing, namely this test is intended to determine whether the functions, inputs and outputs of the software are in accordance with the required specifications. To simplify reporting and recording so that errors do not occur, it is necessary to design and implement a system that can facilitate inter-related functions within the company by using Microsoft Visual Studio 2010 (VB.Net) and Microsoft SQL Server 2008. With the implementation of this system, it is expected will be useful for the company in its operations in the future.

Suitability of Electronic Health Record Data for Computational Phenotyping of Diabetes Mellitus at Nairobi Hospital, Nairobi City County, Kenya

This research aims to determine the applicability of routine healthcare in clinical informatics research. One of the key areas of research in precision medicine is computational phenotyping from longitudinal Electronic Health Record (EHR) data. The objective of this research was to determine how the interplay of EHR software design, the use of a data dictionary, the process of data collection, and the training and motivation of the human resource involved in the collection and entry of data into the EHR affect the quality of EHR data thus the suitability of such data for utility in computational phenotyping of diabetes mellitus. This research employed a prospective/retrospective study design at the diabetes clinic in Nairobi Hospital. The first source of data was from interviews with 32 staff; nurses, doctors, and health record officers using a referenced peer-reviewed usability questionnaire. Thereafter, a sample of EHR data collected during routine care between January 2012 and December 2016 was also analyzed by looking into the quality of clusters identified in the data using a density-based clustering algorithm and Statistical Package for Social Sciences (SPSS) version 21. Regression analysis shows that software design and the utility of a data dictionary explained 50.7% and 32.3% respectively in the improvement of the suitability of EHR data for computational phenotyping of diabetes mellitus. Also, EHR software was rated useful (82%) in accomplishing users’ daily tasks. However, EHR data were found to be unsuitable for utility in computational phenotyping of diabetes. Despite the fact that 88% of EHR data were clustered as noise, the clustering algorithm identified a total of 23 clusters from the diabetes dataset. However, with improved quality of EHR data, sub-phenotyping tasks would be achievable. This research concludes that the poor quality of EHR data is a result of employees’ unmet intrinsic factors of motivation.

Development of a pharmacoeconomic registry: an example using hormonal contraceptives

Background Disease-specific registries, documenting costs and probabilities from pharmacoeconomic studies along with health state utility values from quality-of-life studies could serve as a resource to guide researchers in evaluating the published literature and in the conduct of future economic evaluations for their own research. Registries cataloging economic evaluations currently exist, however they are restricted by the type of economic evaluations they include. There is a need for intervention-specific registries, that document all types of complete and partial economic evaluations and auxiliary information such as quality of life studies. The objective of this study is to describe the development of a pharmacoeconomic registry and provide best practices using an example of hormonal contraceptives. Methods An expert panel consisting of researchers with expertise in pharmacoeconomics and outcomes research was convened and the clinical focus of the registry was finalized after extensive discussion. A list of key continuous, categorical and descriptive variables was developed to capture all relevant data with each variable defined in a data dictionary. A web-based data collection tool was designed to capture and store the resulting metadata. A keyword based search strategy was developed to retrieve the published sources of literature. Finally, articles were screened for relevancy and data was extracted to populate the registry. Expert opinions were taken from the panel at each stage to arrive at consensus and ensure validity of the registry. Results The registry focused on economic evaluation literature of hormonal contraceptives used for contraception. The registry consisted of 65 articles comprising of 22 cost-effectiveness analyses, 9 cost-utility analyses, 7 cost-benefit analyses, 1 cost-minimization, 14 cost analyses, 10 cost of illness studies and 2 quality of life studies. The best practices followed in the development of the registry were summarized as recommendations. The completed registry, data dictionary and associated data files can be accessed in the supplementary information files. Conclusion This registry is a comprehensive database of economic evaluations, including costs, clinical probabilities and health-state utility estimates. The collated data captured from published information in this registry can be used to identify trends in the literature, conduct systematic reviews and meta-analysis and develop novel pharmacoeconomic models.