Kathryn L. Jackson

Design and implementation of a privacy preserving electronic health record linkage tool in Chicago

OBJECTIVE To design and implement a tool that creates a secure, privacy preserving linkage of electronic health record (EHR) data across multiple sites in a large metropolitan area in the United States (Chicago, IL), for use in clinical research. METHODS The authors developed and distributed a software application that performs standardized data cleaning, preprocessing, and hashing of patient identifiers to remove all protected health information. The application creates seeded hash code combinations of patient identifiers using a Health Insurance Portability and Accountability Act compliant SHA-512 algorithm that minimizes re-identification risk. The authors subsequently linked individual records using a central honest broker with an algorithm that assigns weights to hash combinations in order to generate high specificity matches. RESULTS The software application successfully linked and de-duplicated 7 million records across 6 institutions, resulting in a cohort of 5 million unique records. Using a manually reconciled set of 11 292 patients as a gold standard, the software achieved a sensitivity of 96% and a specificity of 100%, with a majority of the missed matches accounted for by patients with both a missing social security number and last name change. Using 3 disease examples, it is demonstrated that the software can reduce duplication of patient records across sites by as much as 28%. CONCLUSIONS Software that standardizes the assignment of a unique seeded hash identifier merged through an agreed upon third-party honest broker can enable large-scale secure linkage of EHR data for epidemiologic and public health research. The software algorithm can improve future epidemiologic research by providing more comprehensive data given that patients may make use of multiple healthcare systems.

Novel pathways in the pathobiology of human abdominal aortic aneurysms

Objectives: Abdominal aortic aneurysm (AAA), a dilatation of the infrarenal aorta, typically affects males >65 years. The pathobiological mechanisms of human AAA are poorly understood. The goal of this study was to identify novel pathways involved in the development of AAAs. Methods: A custom-designed ‘AAA-chip’ was used to assay 43 of the differentially expressed genes identified in a previously published microarray study between AAA (n = 15) and control (n = 15) infrarenal abdominal aorta. Protein analyses were performed on selected genes. Results: Altogether 38 of the 43 genes on the ‘AAA-chip’ showed significantly different expression. Novel validated genes in AAA pathobiology included ADCY7, ARL4C, BLNK, FOSB, GATM, LYZ, MFGE8, PRUNE2, PTPRC, SMTN, TMODI and TPM2. These genes represent a wide range of biological functions, such as calcium signaling, development and differentiation, as well as cell adhesion not previously implicated in AAA pathobiology. Protein analyses for GATM, CD4, CXCR4, BLNK, PLEK, LYZ, FOSB, DUSP6, ITGA5 and PTPRC confirmed the mRNA findings. Conclusion: The results provide new directions for future research into AAA pathogenesis to study the role of novel genes confirmed here. New treatments and diagnostic tools for AAA could potentially be identified by studying these novel pathways.

An evaluation of recurrent diabetic ketoacidosis, fragmentation of care, and mortality across Chicago, Illinois

OBJECTIVE A portion of patients with diabetes are repeatedly hospitalized for diabetic ketoacidosis (DKA), termed recurrent DKA, which is associated with poorer clinical outcomes. This study evaluated recurrent DKA, fragmentation of care, and mortality throughout six institutions in the Chicago area. RESEARCH DESIGN AND METHODS A deidentified Health Insurance Portability and Accountability Act–compliant data set from six institutions (HealthLNK) was used to identify 3,615 patients with DKA (ICD-9 250.1x) from 2006 to 2012, representing 5,591 inpatient admissions for DKA. Demographic and clinical data were queried. Recurrence was defined as more than one DKA episode, and fragmentation of health care was defined as admission at more than one site. RESULTS Of the 3,615 patients, 780 (21.6%) had recurrent DKA. Patients with four or more DKAs (n = 211) represented 5.8% of the total DKA group but accounted for 26.3% (n = 1,470) of the encounters. Of the 780 recurrent patients, 125 (16%) were hospitalized at more than one hospital. These patients were more likely to recur (odds ratio [OR] 2.96; 95% CI 1.99, 4.39; P < 0.0001) and had an average of 1.88-times the encounters than nonfragmented patients. Although only 13.6% of patients died of any cause during the study period, odds of death increased with age (OR 1.06; 95% CI 1.05, 1.07; P < 0.001) and number of DKA encounters (OR 1.28; 95% CI 1.04, 1.58; P = 0.02) after adjustment for age, sex, insurance, race, fragmentation, and DKA visit count. This study was limited by lack of medical record–level data, including comorbidities without ICD-9 codes. CONCLUSIONS Recurrent DKA was common and associated with increased fragmentation of health care and increased mortality. Further research is needed on potential interventions in this unique population.

Disease Outcomes and Care Fragmentation Among Patients With Systemic Lupus Erythematosus

To examine the impact of care fragmentation across multiple health care institutions on disease outcomes in patients with systemic lupus erythematosus (SLE).

Engaging Primary Care Practices in Studies of Improvement: Did You Budget Enough for Practice Recruitment?

PURPOSE The methods and costs to enroll small primary care practices in large, regional quality improvement initiatives are unknown. We describe the recruitment approach, cost, and resources required to recruit and enroll 500 practices in the Northwest and Midwest regional cooperatives participating in the Agency for Healthcare Research and Quality (AHRQ)-funded initiative, EvidenceNOW: Advancing Heart Health in Primary Care. METHODS The project management team of each cooperative tracked data on recruitment methods used for identifying and connecting with practices. We developed a cost-of-recruitment template and used it to record personnel time and associated costs of travel and communication materials. RESULTS A total of 3,669 practices were contacted during the 14- to 18-month recruitment period, resulting in 484 enrolled practices across the 6 states served by the 2 cooperatives. The average number of interactions per enrolled practice was 7, with a total of 29,100 hours and a total cost of

Creation of an Accurate Algorithm to Detect Snellen Best Documented Visual Acuity from Ophthalmology Electronic Health Record Notes

2.675 million, or

Performance of an electronic health record-based phenotype algorithm to identify community associated methicillin-resistant Staphylococcus aureus cases and controls for genetic association studies

5,529 per enrolled practice. Prior partnerships predicted recruiting almost 1 in 3 of these practices as contrasted to 1 in 20 practices without a previous relationship or warm hand-off. CONCLUSIONS Recruitment of practices for large-scale practice quality improvement transformation initiatives is difficult and costly. The cost of recruiting practices without existing partnerships is expensive, costing 7 times more than reaching out to familiar practices. Investigators initiating and studying practice quality improvement initiatives should budget adequate funds to support high-touch recruitment strategies, including building trusted relationships over a long time frame, for a year or more.

A case study evaluating the portability of an executable computable phenotype algorithm across multiple institutions and electronic health record environments

Background Visual acuity is the primary measure used in ophthalmology to determine how well a patient can see. Visual acuity for a single eye may be recorded in multiple ways for a single patient visit (eg, Snellen vs. Jäger units vs. font print size), and be recorded for either distance or near vision. Capturing the best documented visual acuity (BDVA) of each eye in an individual patient visit is an important step for making electronic ophthalmology clinical notes useful in research. Objective Currently, there is limited methodology for capturing BDVA in an efficient and accurate manner from electronic health record (EHR) notes. We developed an algorithm to detect BDVA for right and left eyes from defined fields within electronic ophthalmology clinical notes. Methods We designed an algorithm to detect the BDVA from defined fields within 295,218 ophthalmology clinical notes with visual acuity data present. About 5668 unique responses were identified and an algorithm was developed to map all of the unique responses to a structured list of Snellen visual acuities. Results Visual acuity was captured from a total of 295,218 ophthalmology clinical notes during the study dates. The algorithm identified all visual acuities in the defined visual acuity section for each eye and returned a single BDVA for each eye. A clinician chart review of 100 random patient notes showed a 99% accuracy detecting BDVA from these records and 1% observed error. Conclusions Our algorithm successfully captures best documented Snellen distance visual acuity from ophthalmology clinical notes and transforms a variety of inputs into a structured Snellen equivalent list. Our work, to the best of our knowledge, represents the first attempt at capturing visual acuity accurately from large numbers of electronic ophthalmology notes. Use of this algorithm can benefit research groups interested in assessing visual acuity for patient centered outcome. All codes used for this study are currently available, and will be made available online at https://phekb.org.

457 Algorithms to identify systemic lupus erythematosus (sle) from electronic health record (ehr) data

BackgroundCommunity associated methicillin-resistant Staphylococcus aureus (CA-MRSA) is one of the most common causes of skin and soft tissue infections in the United States, and a variety of genetic host factors are suspected to be risk factors for recurrent infection. Based on the CDC definition, we have developed and validated an electronic health record (EHR) based CA-MRSA phenotype algorithm utilizing both structured and unstructured data.MethodsThe algorithm was validated at three eMERGE consortium sites, and positive predictive value, negative predictive value and sensitivity, were calculated. The algorithm was then run and data collected across seven total sites. The resulting data was used in GWAS analysis.ResultsAcross seven sites, the CA-MRSA phenotype algorithm identified a total of 349 cases and 7761 controls among the genotyped European and African American biobank populations. PPV ranged from 68 to 100% for cases and 96 to 100% for controls; sensitivity ranged from 94 to 100% for cases and 75 to 100% for controls. Frequency of cases in the populations varied widely by site. There were no plausible GWAS-significant (p < 5 E −8) findings.ConclusionsDifferences in EHR data representation and screening patterns across sites may have affected identification of cases and controls and accounted for varying frequencies across sites. Future work identifying these patterns is necessary.

32 Disease outcomes and care fragmentation in systemic lupus erythematosus (sle)

Electronic health record (EHR) algorithms for defining patient cohorts are commonly shared as free-text descriptions that require human intervention both to interpret and implement. We developed the Phenotype Execution and Modeling Architecture (PhEMA, http://projectphema.org) to author and execute standardized computable phenotype algorithms. With PhEMA, we converted an algorithm for benign prostatic hyperplasia, developed for the electronic Medical Records and Genomics network (eMERGE), into a standards-based computable format. Eight sites (7 within eMERGE) received the computable algorithm, and 6 successfully executed it against local data warehouses and/or i2b2 instances. Blinded random chart review of cases selected by the computable algorithm shows PPV ≥90%, and 3 out of 5 sites had >90% overlap of selected cases when comparing the computable algorithm to their original eMERGE implementation. This case study demonstrates potential use of PhEMA computable representations to automate phenotyping across different EHR systems, but also highlights some ongoing challenges.