CA3-04: Repurposing the Virtual Data Warehouse as a Reporting Tool

Background/Aims The creation and implementation of a research Virtual Data Warehouse (VDW) has shown demonstrable benefits for healthcare researchers. However, the VDW's potential may be diminished to the extent its accessibility is limited outside of the research sphere. The most obvious limitation is in cases where VDW data can only be accessed via statistical software packages (such as SAS) or the use of Structured Query Language (SQL). This may inhibit access to data by interested, authorized parties who lack training in those computer languages. The potential exists for a research site to leverage the VDW into a more open and accessible reporting system, provided that its implementation meets the following criteria: its data is hosted on a relational database management system (RDBMS); its design schema reasonably well adheres to the principles of data normalization; the site implementing the VDW has access to a standardized library of medical terminology that can be used to validate data and display it in a hierarchical manner. The aim is to leverage the existing VDW data model to produce a flexible and easily accessible reporting system. Methods Data from a research site's VDW meeting the above criteria was conformed to the fact/dimension data model pioneered by Ralph Kimball and widely used in the "business intelligence" technology field. Existing VDW subject matters (patients, encounters, diagnoses and procedures) were converted to a fact table format through the creation of views. Hierarchical representations of key attributes (such as diagnosis and procedure codes, date of service and patient location) were extracted from the Metathesaurus of the Unified Medical Language System (UMLS) to populate dimension tables. Results Using the above approach, the research site was able to create a data "cube" with an intuitive interface that facilitates data browsing, navigation of code hierarchy trees, unlimited pivoting of rows and columns, and the ability to view summary measures at various levels of aggregation. Discussion With a modest amount of development and access to medical data libraries, a research site's Virtual Data Warehouse VDW can be leveraged into a user-friendly data resource with modern reporting capabilities.     

PS2-56: Building the Virtual Data Warehouse Using Data from a Healthcare Billing System

Background Healthcare utilization data, specifically diagnoses and procedures, can be processed through different administrative systems. Billing data are generated by the care provider while claims data are generated by the payer. These two sources can represent the data content differently and incorporating the data from these systems into the VDW has presented a unique set of challenges. We present options for reconciling these two data sources to create a VDW that includes all patients including those outside of the HMO member realm. Methods Henry Ford Health System (HFHS) captures patients, care providers, procedures, diagnoses and medical supply information through a proprietary system. Outpatient billing information is entered by clinic staff using an optical scanning device and clinic-specific forms. Other additional procedures and/or supplies are entered into the system using a transaction capture application. Ancillary services such as imaging and pathology are imported into the billing system from their proprietary information systems. The main function of this system is to generate bills for services performed. The data elements are standardized to contain required justification for reimbursement from all payer types. Standard codes sets, ICD9, HCPCS and CPT4, are required. The data can also be used to evaluate workload and staffing levels, project future needs and characterize trends in service demands. HFHS uses this data source to build VDW files. Results Major differences between claims and billing data exist. Claims data are based on health plan contract with bundled procedures, coverage exclusions and deductibles affecting content. Billing data are based solely on services provided. The potential for overwhelming amounts of data in billing sources is possible due to the level of detail; however, this same level of detail is a rich source for specific care components that would not otherwise be present in claims records. Conclusion Billing data are less likely to be compromised by contract restrictions, bundling of different procedures under one code or rejected payments. It is often more granular than claims-based data. Billing data is a reliable source for the VDW utilization data area because it is a more accurate representation of the delivery of health care.     

CB6-04: Standardizing Naming Conventions and Document Templates for Virtual Data Warehouse

Background/Aims SAS Programmers support their Virtual Data Warehouse (VDW) in many venues. These include collaboration and deliverables for multiple projects both internal and external. Such requests include ad-hoc, feasibility and grants proposal data requests from both internal and external investigators and researchers. Different VDW workgroups and VDW site programmers use different naming conventions and standards; this adds to the confusion and produces an unclean environment. SAS Programmers have to make changes to programs over time as the data requirements continually change and must be maintained and accurate per update. The aim of this paper is to suggest a unified method to ensure a cohesive culture amongst the VDW participants by standardized naming conventions. This paper also focuses on setting guidelines for VDW workgroup members and multi-site study programmers regarding creating documents, complying with standard templates, and ensuring guidelines. Methods We suggest standardization of: 1. SAS Program names 2. SAS Datasets names 3. SAS Library names 4. SAS Variable names 5. SAS Logs and Outputs names 6. Multisite SAS programs header template 7. Directory structures (folder names on Windows) for projects 8. Common template for CRN portal documents (Minutes of Meetings, Work plans, Guidelines, etc) consisting of label, header and footer information 9. Processes for multisite programmers and for SAS programmer working on multisite studies. Results will provide a standardized naming convention process for VDW multi-site programmers to follow before sending out programs. Provided are guidelines for standard naming conventions for VDW SAS programmers at each site, to configure their environment to work on multiple data requests efficiently. Also provided are document templates that assist in making the document more informative and containing revision control. Discussion The common standardized process in conjunction with consistent naming conventions for the HMORN VDW members will benefit current and new programmers collaborating on multi-site studies and multiple data requests. The aim is to establish standardized naming conventions at all HMORN participating sites. This provides a better understanding and allows for a unified culture.     

Underreporting of computed tomography and magnetic resonance imaging procedures in inpatient claims data

BACKGROUND: ICD-9-CM procedure codes in inpatient claims data are used for a wide range of purposes, such as monitoring utilization, costs, and quality, and adjusting for patient risk. However, many procedures may be underreported because they are not required for reimbursement via Diagnosis-Related Group (DRG) assignment (non-DRG procedures). OBJECTIVES: This study examined the extent and variability of ICD-9-CM procedure code reporting for 2 commonly employed non-DRG imaging procedures, computerized tomography (CT) and magnetic resonance imaging (MRI). RESEARCH DESIGN: Using nonfederal hospital inpatient claims (n = 56,091) from Washington State Inpatient Data for 1997, ICD-9-CM procedure and Universal Billing revenue codes for CT and MRI were compared by payer and hospital characteristics. RESULTS: When compared with revenue codes, ICD-9-CM procedure coding was found to be considerably underreported and variable, with only 33% of CT and 43% of MRI procedures being recorded. Moreover, the frequency of underreporting of both procedures did not appear to be random, with 31 of 72 hospitals that reported revenue codes for the CT not recording any ICD-9-CM codes for the procedure. Of the 48 hospitals that reported revenue codes for the MRI, 15 failed to record any ICD-9-CM codes that indicated its use. Statistically significant differences in median coding frequencies by teaching and rural status were found for both procedures, while ownership was an important factor in CT reporting variability. CONCLUSIONS: This nonrandom variability in reporting can potentially bias utilization studies as well as risk-adjustment outcome estimates of techniques that rely on reporting of these procedures (eg, APR-DRG and AHRQ CCS). An effort to define a universally agreed upon list of non-DRG procedures to be coded in all US hospitals would greatly improve the capacity of health services researchers to conduct important utilization, outcome and policy studies.     

PS2-51: Utilization Quality Assurance: Are We Better Yet?

Background The HMORN Virtual Data Warehouse (VDW) Utilization files are used in almost every VDW research project for a range of purposes including selecting study populations, building disease registries, measuring health status, and evaluating resource use and appropriateness of care. Utilization data, including encounter, diagnosis and procedure data, comes from multiple data sources including legacy data, electronic health records, and claims. Because the data come from many sources and require complicated processes of standardization, the VDW tables can be very complex to build, potentially leading to inconsistencies across sites. Our objective was to assess, document, and improve the overall quality, availability, and completeness of the VDW Utilization data. To understand whether our QA approach was effective, we compared the current data quality to quality assurance data collected in 2009. Methods The HMORN Utilization Work Group, together with KP CESR staff, developed quality assurance programs to build summary tables for participating sites. The Utilization Work Group then combined the summary tables from each site and graphically compared utilization rates, diagnosis capture, and other statistics across HMORN sites to provide a more complete picture of the variability across sites and identify potential outliers that may indicate data quality concerns. Results Overall, we found that VDW Utilization data quality has improved considerably since 2009, as demonstrated by the reduction in variability across sites. In particular, rates of hospitalization, inpatient days, and doctor's office visits are considerably more consistent across time and sites. Residual differences likely reflect real-world variation in membership composition and standards of care. In addition, we identified areas with persistent variability that indicate a need for further exploration, such as rates of dialysis and out-of-office encounters. Finally, we also found between-site differences in the interpretation of the Utilization specification, such as the designation of principal and primary diagnoses. Conclusions Identification and resolution of data quality problems through frequent use of the data, cross-site quality checks, QA programs that produce traffic light (pass/warning/fail) reports, and sites sharing ETL (Extract, Transform, Load) code have considerably improved the data quality of the VDW utilization files.     

PS2-59: It Takes a Village to Raise a VDW Data Set: Creating Partnerships to Improve Research Data

Background Healthcare data is highly complex, and considerable effort is required to create rich data resources that are reliable, user-friendly and represent valid utilization. Challenges include identifying appropriate sources, interpreting the data in a given source, matching data between sources, and transforming source data to meet desired specifications. These challenges are shared by all healthcare analytic groups, including research, finance, HEDIS reporting, care delivery and membership management. Creating partnerships for data development both within and across institutions could make the process more efficient by pooling specialized knowledge and providing opportunities to share both development strategies and data products. Objective: Within KPMAS, our goal was to facilitate development of the Virtual Data Warehouse (VDW) by tapping into existing knowledge about source data and strategies for validating and refining data. Across KP sites, our goal was to reduce the effort required to build and use the VDW by sharing code and analytic infrastructure among sites using highly similar data sources. Methods We used a combined systems and human factors approach to identify opportunities for partnership. Within KPMAS, we invited non-research analysts with similar data needs to participate in discovery activities prior to building the VDW. As an incentive to help us find data solutions, we provided all regional analysts with access to the resulting Regional Data Warehouse. Across KP sites, we initiated and led two workgroups focused on code sharing and sharing potential infrastructure solutions to common problems. Results We developed a decision tree that guides the developer through the process of developing relevant data partnerships. As a result of our data partnerships, we developed several shared KPMAS data products including a table that identifies voided patient encounters that should be excluded from analysis and a table that flags procedure records that do not represent valid utilization. Working across Kaiser Permanente sites, at least three sites are now using common code to produce VDW data. Conclusions Sharing code and data products within the organization and across Kaiser Permanente sites has reduced the burden of developing the VDW, increased data quality and shared efforts to reduce costs.     

药物干扰检验结果的信息系统开发

目的 开发药物干扰检验结果信息的检索系统.方法 以LOINC代码、临床检验项目分类与代码、国际疾病分类代码-10(ICD-10)、<药理学>作为基础数据标准化编码来源;收集近年来有关药物影响检验结果的文献,分析整理获取药物干扰信息数据,采用Microsoft Access2003建立信息数据库和Borland Delphi7.0程序设计开发检索系统.结果 建立了信息检索系统,为临床提供一种信息检索工具.结论 软件界面美观,操作简便,实用性强,可在临床广泛应用.     

Accuracy of E-codes Assigned to Emergency Department Records

Objective: To determine the accuracy of ICD-9-CM external-cause-of-injury codes (e-codes) assigned to the medical records of injured patients treated in an ED and released.
Methods: A comparison was made of routine coding and expert recoding of medical records generated in the ED for a convenience sample of patients treated for injuries within 24 hours of injury occurrence and subsequently released from the ED. The medical record was handwritten and subsequently coded by three medical records coders (MRCs). The e-coded charts were sent to an external medical record consultant (expert), who was blinded to the codes previously assigned. The expert reading was used as the criterion standard. Accuracy was measured using a kappa statistic, and errors were described.
Results: Of 126 available patient charts, 108 (85.7%) were assigned e-codes by MRCs. The expert assigned two codes to (double-coded) 67 patients, while the MRCs double-coded only one patient. The additional code was usually a "place of occurrence code." In 60 cases (55.6%), the expert code exactly matched the MRC code; kappa = 0.462. Of the 48 mismatches (44.4%), 20 (41.7%) were e-coded in the wrong category, 20 (41.7%) were e-coded in the correct category but with incorrect specificity of information, either too specific or not specific enough, and eight (16.6%) had combined coding errors.
Conclusion: The accuracy of e-codes assigned to ED records was moderate in this single institution analysis. Errors were predominantly related to the specificity of the code, but some e-codes were in the wrong category. There are implications for injury surveillance and research. E-code assignment must be standardized and applied uniformly to obtain accurate codes. Automation of e-coding could improve accuracy and consistency of codes. National and international epidemiologic studies of cause of injury among ED patients will be severely hampered until e-code assignment can be better standardized.     

PS2-58: A Survey of HMORN VDW Tumor Data Sources

Background There are currently fifteen HMORN sites that have a VDW tumor table. The VDW tumor specifications standardize this data. However, there is variation in each site's cancer registry, and hence the types of data that can be accessed. Methods During 2011 two metadata surveys have been sent out to the VDW implementation group. The participation rate has been over a dozen sites. The surveys ascertain each site's registry source type, (internal local registry, external central registry, such as SEER, NPCR, State, etc), the type of software used at the registry, whether all patients are captured by the registry, whether NAACCR is used to format and populate VDW tumor, the table update frequency, and other factors. Results All responding sites populate their data by means of a cancer registry All but two sites maintain their own facility registry. Just about half the respondents claimed to be a SEER site, Six sites use the NAACCR manual as a dictionary to aid the population of VDW tumor, four sites use the FORDS manual, Planned VDW Tumor file update schedules vary a great deal, from weekly to annually, with four sites each reporting annual and monthly update schedules, There is a diverse collection of software vendors used to collect cancer data at the registries, Geographic central registry coverage area may miss some patients treated for cancer at some sites. Conclusion It is instructive to understand the diversity in tumor data sources. Different central registry requirements dictate which fields are available to the site and may vary in content. While the data is standardized across sites by a common specification, there may still be nuances between sites; understanding the source data will help researchers and programmers navigate these nuances, and help shape requests for new fields or derivations.     

PS2-49: The VDW Census File: Strengths, Issues, and Recommendations for the Future

Background/Aims The VDW Census file contains information about our members such as income and education. The information is estimated based on data collected by the decennial census for the geographic areas around our members' addresses (identified by geocodes).The VDW Census file was initially developed based on 2000 census data. Data from the 2010 census is now being released, and in many cases, the 2010 census data structure looks different than the 2000 census. For example, income and education were no longer collected in the 2010 census, but have been moved to a supplementary survey (American Community Survey, or ACS) collected over a different time period. Methods The VDW Census workgroup reviewed the changes in the 2010 census data and compared the available information to the data in the existing VDW Census file. Many of the fields from the 'long-form' in the 2000 census were moved to the ACS. Where possible, the workgroup mapped the existing VDW Census fields to fields with similar, if not equivalent, data in the new census data structure. The workgroup also considered whether new data available from the ACS might be of interest to HMORN investigators. Where major changes in information availability were anticipated, the workgroup used online surveys to solicit feedback from a larger group of HMORN analysts and investigators. Results Our primary goal was to identify changes to the specification that would allow updating the Census file to 2010 data. Our secondary goal was to identify that would allow sites to maintain multiple sets of census data to accommodate projects with different time periods. To accommodate multiple years of census data and reduce file sizes, we recommend splitting the VDW Census file into two files: one tracking members' geocoded addresses, and another containing the census data for all available geocodes. Conclusions We found significant changes in the 2010 census data that require considerable changes to the VDW Census file. Once agreement has been reached on the new Census specification, a subcommittee will develop code to build a new VDW Census data file and recommendations on building a file containing members' geocodes over time.     

Comparison and validity of procedures coded With ICD-9-CM and ICD-10-CA/CCI

BACKGROUND: The use of health administrative data in health services research is facilitated by standardized classification systems, such as the International Classification of Diseases (ICD). Canada, among other countries, recently introduced the tenth version of ICD and its accompanying Canadian Classification of Interventions (CCI). It is imperative to assess errors that could occur in administrative data due to the introduction of the new coding system. OBJECTIVE: To evaluate the validity of procedure coding in hospital discharge data, comparing CCI with ICD-9-CM. RESEARCH DESIGN: Trained reviewers examined 4008 randomly selected charts from 4 teaching hospitals in Alberta, Canada, for the presence of 30 procedures. The charts, already coded using CCI, were recoded using ICD-9-CM. Comprehensive lists of procedure codes in both systems were identified using literature, health records technicians, surgeons and online resources. MEASURES: Three databases were created for the same hospital discharge record, including CCI, ICD-9-CM, and chart review data. Sensitivity, specificity, positive predictive value, negative predictive value and kappa scores were calculated. RESULTS: Compared with the chart review data, ICD-9-CM data under-reported 17 procedures, over-reported 12, and equivalently reported 1. CCI data under-reported 19 procedures, over-reported 9, and equivalently reported 2. Kappa value was within 0.1 difference between ICD-9-CM and CCI for 14 procedures. CONCLUSIONS: Both ICD-9-CM and CCI coded the more major or invasive procedures reasonably well, but were not valid for less invasive or minor procedures. CCI can be used by health services and population health researchers with as much confidence as ICD-9-CM.     

Performance of International Classification Of Diseases, 9th Revision, Clinical Modification codes as an adverse drug event surveillance system

BACKGROUND: Adverse drug events (ADEs) are one of the most frequent causes of iatrogenic injury. Because International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes are routinely assigned to inpatient discharges, they could provide a method to detect ADEs within a hospital, a state, and the nation. OBJECTIVE: The objective of this study was to determine validity of selected ICD-9-CM codes in identifying inpatient ADEs. RESEARCH DESIGN: An expert panel identified 416 ICD-9-CM codes to represent ADEs (flagged ADE codes). Retrospective chart review using a structured tool was performed to ascertain code performance in detecting ADEs. SUBJECTS: Subjects included 3103 inpatients from all 41 acute care hospitals in Utah in 2001: 1961 inpatients sampled randomly (random sample) and 1142 inpatients sampled from the discharge records with at least one flagged ADE code (flagged sample). MEASURES: Measures were ADEs identified by structured review. RESULTS: The flagged sample yielded 1122 flagged ADE codes recorded in patient charts with 704 representing ADEs (63%). Two hundred eighty-six of the 704 verified ADE codes (41%) were determined to be inpatient ADEs. In the random sample, 32 of 58 ADEs (55%) causing hospital admission were detected by the ADE-flagged codes. Only 23 of 224 inpatient ADEs had been assigned a flagged ADE code (10%). CONCLUSIONS: Flagged ADE codes have an overall positive predictive value of 63% and detect just over half of ADEs causing hospital admission. These codes have a positive predictive value of 25% for inpatient ADEs but detect only 10% of overall inpatient ADEs. Flagged ADE codes provide an imperfect but immediately available ADE surveillance system.     

Quality of diagnosis and procedure coding in ICD-10 administrative data

OBJECTIVES: The International Classification of Disease, 10th Revision (ICD-10) was introduced worldwide beginning in the late 1990s. Because there have been no published data on the quality of coding using ICD-10, the aim of our analysis is to assess the quality of ICD-10 coding in routinely collected hospital discharge data from Australia, which began using ICD-10 in 1998. METHODS: Audit data from the years 1998-1999 (n = 7004) and 2000-2001 (n = 7631), excluding same-day chemotherapy and dialysis cases, were used in data analysis. Quality measures included prevalence comparisons, sensitivity, positive predictive value (PPV), and the kappa statistic. RESULTS: Comparison of the audit sample to public hospital discharges showed little difference in age and gender, with audited cases more likely to be overnight stays. There was no difference in the median number of hospital assigned diagnosis and procedure codes per discharge. Agreement of the principal diagnosis code was 85% at the 3-digit level and 79% at the 4-digit level in 1998-1999; this rate had improved to 87% and 81% in 2000-2001. Principal procedure code agreement was 85% in 1998-1999 and 83% in 2000-2001 at the 5-digit level, and 81% and 80% at the 7-digit level, respectively. Specific major diagnoses, comorbid diagnoses, major procedures, and minor procedures showed good-to-excellent coding quality. CONCLUSIONS: The transition to ICD-10 has occurred with no loss of data quality, with data showing a high level of reliability and adherence to coding standards. When consideration is given to the nature of the analysis, administrative data can provide highly reliable population-based estimates of hospitalization rates.     

改善医保数据质量的标准化流程建设:2013-2015年河南省某县新型农村合作医疗数据质量的评价和改进

目的 对2013-2015年河南省某县的新型农村合作医疗数据进行质量评价和改进,形成改善医保数据质量的标准操作程序,为其他地区数据收集和整理提供思路.方法 首先评价原始数据库的完整性和内部一致性,包括缺失值、异常值和极值检查,同一信息的多个来源间比对,逻辑核查及查重,并且以同样的内容为目标,对数据库中能够补充或修订的内...