Amy Butani

Managing protected health information in distributed research network environments: automated review to facilitate collaboration

BackgroundMulti-site health sciences research is becoming more common, as it enables investigation of rare outcomes and diseases and new healthcare innovations. Multi-site research usually involves the transfer of large amounts of research data between collaborators, which increases the potential for accidental disclosures of protected health information (PHI). Standard protocols for preventing release of PHI are extremely vulnerable to human error, particularly when the shared data sets are large.MethodsTo address this problem, we developed an automated program (SAS macro) to identify possible PHI in research data before it is transferred between research sites. The macro reviews all data in a designated directory to identify suspicious variable names and data patterns. The macro looks for variables that may contain personal identifiers such as medical record numbers and social security numbers. In addition, the macro identifies dates and numbers that may identify people who belong to small groups, who may be identifiable even in the absences of traditional identifiers.ResultsEvaluation of the macro on 100 sample research data sets indicated a recall of 0.98 and precision of 0.81.ConclusionsWhen implemented consistently, the macro has the potential to streamline the PHI review process and significantly reduce accidental PHI disclosures.

Managing personal health information in distributed research network environments

BackgroundStudying rare outcomes, new interventions and diverse populations often requires collaborations across multiple health research partners. However, transferring healthcare research data from one institution to another can increase the risk of data privacy and security breaches.MethodsA working group of multi-site research programmers evaluated the need for tools to support data security and data privacy. The group determined that data privacy support tools should: 1) allow for a range of allowable Protected Health Information (PHI); 2) clearly identify what type of data should be protected under the Health Insurance Portability and Accountability Act (HIPAA); and 3) help analysts identify which protected health information data elements are allowable in a given project and how they should be protected during data transfer. Based on these requirements we developed two performance support tools to support data programmers and site analysts in exchanging research data.ResultsThe first tool, a workplan template, guides the lead programmer through effectively communicating the details of multi-site programming, including how to run the program, what output the program will create, and whether the output is expected to contain protected health information. The second performance support tool is a checklist that site analysts can use to ensure that multi-site program output conforms to expectations and does not contain protected health information beyond what is allowed under the multi-site research agreements.ConclusionsTogether the two tools create a formal multi-site programming workflow designed to reduce the chance of accidental PHI disclosure.

PS2-55: VDW Data Sources: HealthPartners Research Foundation

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 HealthPartners Research Foundation to feed information into our local implementation of the VDW datasets. Results The HealthPartners Research Foundation local implementation of the VDW contains detailed medical information on HealthPartners members and patients. These files contain details on 69 million pharmacy dispensings (2000–2011), nearly 58 million unique medical encounters (2000–2011), including 14 million diagnoses, and 20 million procedures. We have some 9 million Vital Signs observations, and 26 million lab results. The VDW Enrollment and Demographic files are derived from several historical and current membership/patient files; the VDW Pharmacy and utilization files are derived from internal HealthPartners systems plus claims files; the VDW tumor data is retrieved from our owned Cancer Registry. Conclusions The VDW at HealthPartners Research Foundation 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.

PS3-10: Patient of Two Professions: Medicine and Dentistry.

Background/Aims The literature in both medicine and dentistry has increasingly focused on conditions relevant to both professions. However, research addressing these conditions is limited by the scope of available data, the size of their samples, and data sets that can be combined. The aim of this project is to create a Dental VDW using data from the three HMORN members that have both medical and dental data (i.e., Kaiser Permanente Northwest, Marshfield, and HealthPartners Institute). Methods A team of investigators and VDW programmers was formed. Initial meetings, aimed at coming up with a common design that would accommodate the dental data from all three sites, were held. Each site has a different electronic dental record system and different ways of coding diagnoses and identifying risk. The project has the further challenge that it was being built in anticipation of future projects and so does not have any project related funding. Results Twelve content areas were identified. Of those, four of the tables have been designed and are having test data loaded into them. In order to simplify these early steps they are designed based on the VDW medical model. The existing ‘Demographics’ table will accommodate both medical and dental patients. ‘Dental Utilization’ will hold data common to all the procedures within an encounter and is very similar to the comparable VDW table. Where dental data starts to diverge from medical is the ‘Dental Diagnosis and Procedures’ table. This is because each dental procedure is related to a specific tooth and surface and can have its own diagnosis code. The ‘Provider’ table is similar to the medical VDW but with different provider specialties. Conclusions The HMORN is working to create a Dental VDW within an existing Medical VDW that will support efforts to conduct research that crosses the divide that currently exists between medical and dental research. It will be a multi-directional system, allowing medical data within dental studies and dental data within medical studies.

PS1-40: The VDW Vital Signs File: 2011 Quality Assurance Activities

Background/Aims The VDW Vitals Workgroup specifies data standards for the four traditional clinical vital signs (body temperature, blood pressure, respirations, heart rate) and other physiological measures that are collected routinely during clinic visits, such as height and weight. Data specifications changed considerably between the older Version 2, and the current Version 3. The Vitals Workgroup recently completed quality assurance review of the vital signs data from the sites participating in the VDW. The aim of this review is to ensure that sites have standardized their data tables according to Workgroup Version 3 specifications, data are within plausible ranges, and to assess the abundance of data. Methods The Vitals Workgroup developed and distributed a SAS program to all HMORN sites collecting quality information, abundance (counts), and distributions of vitals measurements for the years 2001 through 2010. We checked for data anomalies and summarized the distributions of height, weight, and systolic-and diastolic blood pressure according to site, age, gender, and year. Results Data were received from 12 of the 15 HMORN sites. The 3 sites not reporting do not have a Vitals Table. Variable lengths were often set to the SAS default instead of the specified length. Seven sites had instances of diastolic blood pressure greater than the corresponding systolic blood pressure. One site had blood pressure measurements skewed considerably lower than the other 11. Data were highly abundant, although the years of data availability vary considerably across sites. Discussion Several sites had correctable errors such as incorrect formats and data types. The nature of errors suggested that some sites do not have on-going internal quality assurance procedures in place. The data are sufficiently clean for simple counts, but quality and abundance of data needs to be evaluated at the site level prior to use of data for research.