The HMO Research Network (HMORN) Virtual Data Warehouse (VDW) is a public, non-proprietary, research-focused data model implemented at 17 health care systems across the United States. The HMORN has created a governance structure and specified policies concerning the VDW’s content, development, implementation, and quality assurance. Data extracted from the VDW have been used by thousands of studies published in peer-reviewed journal articles. Advances in software supporting care delivery and claims processing and the availability of new data sources have greatly expanded the data available for research, but substantially increased the complexity of data management. The VDW data model incorporates software and data advances to ensure that comprehensive, up-to-date data of known quality are available for research. VDW governance works to accommodate new data and system complexities. This article highlights the HMORN VDW data model, its governance principles, data content, and quality assurance procedures. Our goal is to share the VDW data model and its operations to those wishing to implement a distributed interoperable health care data system.

Study Objective To determine the association between Anticholinergic Cognitive Burden (ACB) score and both cognitive impairment and health care utilization among a diverse ambulatory older adult population. Design Retrospective cohort study. Data Source Medication exposure and other clinical data were extracted from the Regenstrief Medical Record System (RMRS), and cognitive diagnosis was derived from a dementia screening and diagnosis study. Patients A total of 3344 community-dwelling older adults (age 65 yrs and older) who were enrolled in a previously published dementia screening and diagnosis study; of these, 3127 were determined to have no cognitive impairment, and 217 were determined to have cognitive impairment. Measurements and Main Results The study followed a two-phase screening and comprehensive neuropsychiatric examination to determine a cognitive diagnosis, which defined cognitive impairment as dementia or mild cognitive impairment. The ACB scale was used to identify anticholinergics dispensed in the 12 months prior to screening. A total daily ACB score was calculated by using pharmacy dispensing data from RMRS; each anticholinergic was multiplied by 1, 2, or 3 consistent with anticholinergic burden defined by the ACB scale. The sum of all ACB medications was divided by the number of days with any medication dispensed to achieve the total daily ACB score. Health care utilization included visits to inpatient, outpatient, and the emergency department, and it was determined by using visit data from the RMRS. The overall population had a mean age of 71.5 years, 71% were female, and 58% were African American. Each 1-point increase in mean total daily ACB score was associated with increasing risk of cognitive impairment (odds ratio [OR] 1.13, 95% confidence interval [CI] 1.004–1.27, p=0.043). Each 1-point increase in mean total daily ACB score increased the likelihood of inpatient admission (OR 1.11, 95% CI 1.02–1.29, p=0.014) and number of outpatient visits after adjusting for demographic characteristics, number of chronic conditions, and prior visit history (estimate 0.382, standard error [SE] 0.113; p=0.001). The number of visits to the emergency department was also significantly different after similar adjustments (estimate 0.046, SE 0.023, p=0.043). Conclusion Increasing total ACB score was correlated with an increased risk for cognitive impairment and more frequent health care utilization. Future work should study interventions that safely reduce ACB and evaluate the impact on brain health and health care costs.

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Purpose To validate an algorithm based upon International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) codes for acute myocardial infarction (AMI) documented within the Mini-Sentinel Distributed Database (MSDD). Methods Using an ICD-9-CM-based algorithm (hospitalized patients with 410.x0 or 410.x1 in primary position), we identified a random sample of potential cases of AMI in 2009 from 4 Data Partners participating in the Mini-Sentinel Program. Cardiologist reviewers used information abstracted from hospital records to assess the likelihood of an AMI diagnosis based on criteria from the joint European Society of Cardiology and American College of Cardiology Global Task Force. Positive predictive values (PPVs) of the ICD-9-based algorithm were calculated. Results Of the 153 potential cases of AMI identified, hospital records for 143 (93%) were retrieved and abstracted. Overall, the PPV was 86.0% (95% confidence interval; 79.2%, 91.2%). PPVs ranged from 76.3% to 94.3% across the 4 Data Partners. Conclusions The overall PPV of potential AMI cases, as identified using an ICD-9-CM-based algorithm, may be acceptable for safety surveillance; however, PPVs do vary across Data Partners. This validation effort provides a contemporary estimate of the reliability of this algorithm for use in future surveillance efforts conducted using the FDA’s MSDD.

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