Gene Hart

A computerized system to facilitate medical record abstraction in cancer research (United States)

Objective: To implement a computerized system to gather and transmit medical record information from six sites to a centralized database for two cancer prevention studies. Methods: Microsoft Access 97 was selected as the application for the system. Sites purchased Access and hardware meeting technical specifications required for the system. A developer worked with the lead investigator and medical record abstractors to develop a ‘back-end’ database to hold the desired data while maintaining a user-friendly ‘front-end’ interface. Abstractors trained on a paper version of the abstraction form were then trained to use the system. Meeting minutes and technical notes were used in summarizing the approach and process. Observations were collected through discussions. Results: We overcame multiple obstacles to develop computerized systems supporting medical record data collection at multiple sites. Although system development slowed implementation of the study, the system produced data for cleaning and analysis immediately. Overall the approach decreased the time from study implementation to manuscript submission. Development time for a second system was substantially reduced. Conclusions: Computerized systems for medical record abstraction at multiple sites convey substantial benefit. We present a schematic approach to facilitate development of similar systems in the future.

PS2-43: VDW Data Sources: Group Health

Background The Virtual Data Warehouse (VDW) was created as a mechanism for producing comparable data across sites for purposes of proposing and conducting research. It is “virtual” in the sense that the data remain at the local sites; there is no multi-site physical database at a centralized data coordinating center. At the core of the VDW are a series of standardized file definitions. Content areas and data elements that are commonly required for research studies are identified, and data dictionaries are created for each of the content areas, specifying a common format for each of the elements—variable name, label, description, code values, and value labels. Local site programmers have mapped the data elements from their HMO’s data systems into this standardized set of variable definitions, names, and codes, as well as onto standardized SAS file formats. This common structure of the VDW files enables a SAS analyst at one site to write one program to extract and/or analyze data at all participating sites. Methods This poster demonstrates the wide range of data sources used at Group Health to feed information into our local implementation of the VDW datasets. Results The Group Health local implementation of the VDW contains detailed medical information on Group Health members. These files contain details on 143 million pharmacy dispensings (1977–2011), nearly 98 million unique medical encounters (1993–2011), including .75 million hospitalizations, 68 million ambulatory visits, 141 million diagnoses, and 224 million procedures. We have some 11 million Vital Signs observations, and 82 million lab results. The VDW Enrollment and Demographic files are derived from several historical and current membership files; the VDW Pharmacy and utilization files are derived from internal Group Health systems plus claims files; the VDW tumor data is derived from the Puget Sound SEER Registry. Conclusions The VDW at Group Health provides an easily employed unified central repository of data from all available source files. This resource enables the sharing of compatible data in multi-site studies, and also improves programming efficiency, accuracy, and completeness for local single site studies by expending resources to link these legacy systems only once.

PS1-01: Infrastructure Setup for the Informatics for Integrating Biology and the Bedside Framework

Background/Aims Informatics for Integrating Biology and the Bedside (I2B2) is a data querying tool enjoying increased use among HMORN members. While I2B2 uses standard software and includes installation documentation from the developers, many parts of the installation and configuration process are challenging to implement. Simplifying the initial setup removes a significant barrier to adoption. Methods A combination of simplifying assumptions (limiting choices) and scripts (reducing manual file edits) were developed to reduce time-consuming and error-prone steps in the I2B2 installation process. Results Following the procedure developed, a new I2B2 installation can be a predictable and straightforward process. Conclusions Simplifying the installation and initial configuration of the I2B2 infrastructure provides multiple benefits. If your organization is new to I2B2, you will be able to reach the goal of using I2B2 more quickly by following our process. If you have a need to create multiple I2B2 instances - to test different data loading approaches or different ontologies - our process will allow you to quickly create a development or a test/quality assurance installation of I2B2.

PS1-20: Developing an Ontology: Informatics for Integrating Biology & the Bedside (I2B2) and Structuring the Virtual Data Warehouse (VDW) Content Areas

Background/Aims Informatics for Integrating Biology & the Bedside (I2B2) offers a dynamic graphical interface for querying VDW data. Part of the I2B2 effort involves designing the drop-down menu of standardized nomenclatures provided by coding schemes such as CPT-4, ICD-9, and RxNorm, by which the user queries the data. This menu of content hierarchies can be complex in terms of the number of nodes and levels of nested folders. In addition, there often isn’t an established way to organize these codes into categorization scheme that allows for a clean and organized querying interface. Methods We will demonstrate the use of SAS and SQL to craft these hierarchies and how to leverage the resources provided by the Unified Medical Language System (UMLS) along with resources that may be available at your local site. Results We have been able to develop an ontology that spans many of the VDW content areas including Procedures, Diagnoses, Pharmacy Fills, Vitals, Demographics, Tumor, and Enrollment. Conclusions It is possible to build an ontology that organizes the various coding schemes using SAS and SQL, sourcing public resources such as UMLS. Therefore, our knowledge about building an ontology can be readily transferred across HMORN sites and other consults, empowering additional I2B2 deployments. The development of an ontology for VDW subject areas may have additional uses insofar as organizing standard nomenclatures in a programmatic way is useful.

PS1-38: Benefits of Best Practice for Multi-Site National Drug Code Identification

Background Prescription drugs are identified in the VDW pharmacy file by National Drug Code (NDC.) Although the NDC constitutes a standard nomenclature, there is a no single, comprehensive resource for codes, either in the HMORN or beyond. Instead, each site maintains an “everNDC” file based on its own internal and external sources. Therefore, to identify NDC codes for drugs of interest for a multi-site study, the recommended process (VDW EverNDC Data Structure, Version 3) involves the collection and compilation of codes from each site. This is a frequent and time-consuming practice. We will present an exploration of the benefits of this approach in the experience of the Mental Health Research Network (MHRN.) Methods We will compare the results of the recommended practice to those from a shortcut limited to RXnorm (RXN), an open-source library from the National Library of Medicine and First DataBank (FDB), a proprietary database licensed by Group Health, for antidepressants, lithium, anticonvulsant mood stabilizers, first and second generation antipsychotics, benzodiazepines, and stimulants. Results In our process of identifying antidepressant NDCs from a list of 32 active ingredient names, RXN and FDB generated a list of 15401 codes. At Group Health (GH), our historical NDC file had 1653 additional codes. However, these additional codes only match 35 fills in our pharmacy file, the most recent being in 2002. For comparison, the full list of antidepressant NDC codes yielded an average of 480,000 fills per year at GH over the period 2000–2009. From nine other sites, we received an additional 1329 codes, increasing the number of codes by about 8%. Conclusions Our initial results indicate that for antidepressants, site-specific NDC codes at GH represent rare and older prescriptions. The RXN/FDB method yielded ~90% of the total NDC codes identified at our site, and >99.99 % of the prescriptions identified. We need to confirm this across other sites and other drug classes, but it is possible that a simplified process for defining a list of NDC codes for a drug class would be suitable for many multi-site studies.

C-A5-04: A Simple, Accurate SAS Algorithm for Electronic Abstraction of Race from Digitized Progress Notes

Background and Aims: Individual-level race/ethnicity is important for research into causes and consequences of health disparities. For various non-research reasons, it has rarely been collected on enrollees in integrated delivery systems. Individual-level race/ethnicity can be found in medical record documentation. Manual abstraction on large numbers of medical records is costly. We developed a simple SAS algorithm for electronic abstraction of white and African American race from digitized progress notes and evaluated its accuracy by comparing electronically abstracted race with other data sources. Methods: A simple SAS algorithm, based on text search strings (e.g. white male, African American woman), scanned digitized progress notes for provider face-to-face visits from 2005 through July 2009 in Kaiser Permanente Georgia’s (KPG) and Group Health Cooperative’s (GHC) electronic medical record systems. White and African American race was abstracted. If the patient had more than 1 visit with abstracted race, the patient was classified using the earliest visit. Abstracted race was linked at the individual-level to survey datasets with self-reported race (2005 survey of working age adults, 2007 survey of adults with hypertension, 2000–2005 Medicare surveys) and mother’s race on 2000–2006 birth certificates. White and African American race was abstracted from GHC progress notes from 2005 through July 2009 using the same algorithm and compared to self-reported race on health risk appraisals. Accuracy of the SAS algorithm was assessed by overall proportion matching race from the other datasets, Cohen’s kappa, and McNemar’s test. Results: White or African American race was electronically abstracted for 56,261 KPG and 6,427 GHC enrollees. Abstracted race matched race from the other datasets in 97–99% of enrollees. Cohen’s kappas were highly significant (p<0.05), ranging from 0.939 ± 0.013 (N=657 matches with hypertension survey records) to 0.994 ± 0.006 (N=518 matches with Medicare surveys). McNemar’s tests were marginally significant for several datasets; and, misclassification was not systematically biased toward white or African American race. Conclusions: The SAS algorithm was highly accurate in electronically abstracting white and African American race from digitized progress notes of provider visits at KPG and GHC. We are expanding the evaluation to include additional sites and additional race/ ethnic categories (e.g. Asian, Hispanic).