The Patient-Centered Outcomes Research Network Antibiotics and Childhood Growth Study: Implementing Patient Data Linkage.

PCORnet, the Patient-Centered Outcomes Research Network, is comprised of health systems and health plans that transform electronic health records (EHRs) and claims data to a common data model (CDM) to facilitate real-world clinical research. Because patients receive health care in multiple care delivery settings, linking health records across systems and health plan claims would provide a more comprehensive and accurate picture of health care for patients. The current study expanded on a PCORnet Antibiotics and Childhood Growth (ABX) study to (1) identify and implement a privacy-preserving patient linkage solution among a clinical data research network and a health plan network within the ABX Study, and (2) assess overlap in prescribed and dispensed antibiotics and additional data gained from claims among the linked patients. This manuscript describes the linkage process and resulting overlap analysis. The authors identified 549 patients from the EHR record study cohort who had claims records with the health plan. Sixty percent (n = 329) of patients had consistent antibiotic exposure data across the 2 sources, indicating antibiotic exposure (44.3%) or nonexposure (15.7%). Among total antibiotic prescribing records, 43.1% had a matched claims record for dispensing within 60 days. Among antibiotic dispense records 26.5% were not associated with a prescribing record in the linked health systems. These findings showcase the feasibility of linking health plan claims data to PCORnet CDM in a privacy-preserving manner while also demonstrating continued gaps in data that may occur. The study highlights the importance of combining multiple health data sources for comprehensive clinical research.

The Hispanic Paradox in Patients With Liver Cirrhosis: Current Evidence From a Large Regional Retrospective Cohort Study.

BACKGROUND: Despite lower socioeconomic status, Hispanics in the United States paradoxically maintain equal or higher average survival rates compared to non-Hispanic Whites (NHW). METHODS: We used multivariable Cox regression to assess whether this "Hispanic paradox" applies to patients with liver cirrhosis using a retrospective cohort of twenty 121 patients in a Chicago-wide electronic health record database. RESULTS: Our study population included 3279 (16%) Hispanics, 9150 (45%) NHW, 4432 (22%) African Americans, 529 (3%) Asians, and 2731 (14%) of other races/ethnic groups. Compared to Hispanics, NHW (hazard ratio [HR] 1.26; 95% confidence interval [CI], 1.16-1.37), African American (HR 1.26; 95% CI, 1.15-1.39), and other races/ethnic groups (HR 1.55; 95% CI, 1.40-1.71) had an increased risk of death despite adjustment for age, sex, insurance status, etiology of cirrhosis, and comorbidities. On stratified analyses, a mortality advantage for Hispanics compared to NHW was seen for alcohol cirrhosis (HR for NHW 1.35; 95% CI, 1.19-1.52), hepatitis B (HR for NHW 1.35; 95% CI, 0.98-1.87), hepatitis C (HR for NHW 1.21; 95% CI, 1.06-1.38), and nonalcoholic steatohepatitis (HR for NHW 1.14; 95% CI, 0.94-1.39). There was no advantage associated with Hispanic race over NHW in cases of hepatocellular carcinoma or cholestatic liver disease. CONCLUSIONS: Hispanic patients with cirrhosis experience a survival advantage over many other racial groups despite adjustment for multiple covariates.

A Novel Patient Recruitment Strategy: Patient Selection Directly from the Community through Linkage to Clinical Data.

OBJECTIVE: This article presents and describes our methods in developing a novel strategy for recruitment of underrepresented, community-based participants, for pragmatic research studies leveraging routinely collected electronic health record (EHR) data. METHODS: We designed a new approach for recruiting eligible patients from the community, while also leveraging affiliated health systems to extract clinical data for community participants. The strategy involves methods for data collection, linkage, and tracking. In this workflow, potential participants are identified in the community and surveyed regarding eligibility. These data are then encrypted and deidentified via a hashing algorithm for linkage of the community participant back to a record at a clinical site. The linkage allows for eligibility verification and automated follow-up. Longitudinal data are collected by querying the EHR data and surveying the community participant directly. We discuss this strategy within the context of two national research projects, a clinical trial and an observational cohort study. CONCLUSION: The community-based recruitment strategy is a novel, low-touch, clinical trial enrollment method to engage a diverse set of participants. Direct outreach to community participants, while utilizing EHR data for clinical information and follow-up, allows for efficient recruitment and follow-up strategies. This new strategy for recruitment links data reported from community participants to clinical data in the EHR and allows for eligibility verification and automated follow-up. The workflow has the potential to improve recruitment efficiency and engage traditionally underrepresented individuals in research.

The Building Blocks of Interoperability. A Multisite Analysis of Patient Demographic Attributes Available for Matching.

BACKGROUND: Patient matching is a key barrier to achieving interoperability. Patient demographic elements must be consistently collected over time and region to be valuable elements for patient matching. OBJECTIVES: We sought to determine what patient demographic attributes are collected at multiple institutions in the United States and see how their availability changes over time and across clinical sites. METHODS: We compiled a list of 36 demographic elements that stakeholders previously identified as essential patient demographic attributes that should be collected for the purpose of linking patient records. We studied a convenience sample of 9 health care systems from geographically distinct sites around the country. We identified changes in the availability of individual patient demographic attributes over time and across clinical sites. RESULTS: Several attributes were consistently available over the study period (2005-2014) including last name (99.96%), first name (99.95%), date of birth (98.82%), gender/sex (99.73%), postal code (94.71%), and full street address (94.65%). Other attributes changed significantly from 2005-2014: Social security number (SSN) availability declined from 83.3% to 50.44% (p<0.0001). Email address availability increased from 8.94% up to 54% availability (p<0.0001). Work phone number increased from 20.61% to 52.33% (p<0.0001). CONCLUSIONS: Overall, first name, last name, date of birth, gender/sex and address were widely collected across institutional sites and over time. Availability of emerging attributes such as email and phone numbers are increasing while SSN use is declining. Understanding the relative availability of patient attributes can inform strategies for optimal matching in healthcare.

Surveying a New Multi-Institution Clinical Data Research Network.

Cultivated by the Patient-Centered Outcomes Research Network (PCORnet), thirteen regional clinical data research networks (CDRNs) are taking shape across the U.S. The PCORnet common data model was carefully planned, and the data marts assembled by the more than 80 data-contributing institutions (nodes) are undergoing, in 2016-2017, a series of data characterization cycles. PCORnet will adjudge each node's-and thereby, in a significant way, each CDRN's-readiness or unreadiness for multi-institution research. Certifying each node's quality and fidelity is of course essential. But in understanding network readiness there is an additional, vital dimension-one that has received too little attention. It is the development of knowledge about the nature of a CDRN's data, in its federated sense. With visualizations, how might one grasp the meta-data of a CDRN? We outline an approach that builds upon the HealthLNK Data Repository, a forerunner to the Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) CDRN.

Distance from hospital impacts adverse event detection after outpatient endoscopy.

BACKGROUND AND AIMS: Monitoring adverse events (AEs) after GI endoscopy is an endorsed quality measure but is challenging to implement in practice. Patients with major AEs may seek care elsewhere after endoscopy. We aimed to determine the hospital utilization patterns of patients with AEs after ambulatory endoscopy. METHODS: We used the HealthLNK Data Repository, which uses a software application for integration of deidentified, patient-level clinical data across institutions. Data for patients undergoing outpatient endoscopy from 2010 to 2011 at 5 Chicago-area hospitals were used. Early mortality was defined as death no more than 2 months after the outpatient procedure. AEs were defined as a hospital admission for perforation, bleeding, or pancreatitis the same or following month after endoscopy. RESULTS: During the study period, 42,842 outpatient procedures were performed in 22,898 unique individuals. Early mortality occurred in 86 patients (.4%). Per-patient mortality was greatest after outpatient ERCP (2.5%, P < .0001). Of 86 patients with early mortality, 36 (42%) were not hospitalized at the index hospital after endoscopy. Patients who did not return to the index hospital lived farther from the index hospital (P = .02). In total, 8.3% of ambulatory endoscopies were associated with potential endoscopy-related AEs. The observed rate of potential AEs trended downward as patients' home zip codes moved farther from the index hospital (P = .01). CONCLUSIONS: Nearly half of patients who die soon after outpatient endoscopy are not hospitalized at their index hospital after endoscopy. The observed AE rate was higher for patients living closer to the index hospital, suggesting that patients who live farther away are less likely to return to the index hospital for emergency care. Novel methods to efficiently track outcomes after outpatient endoscopy are needed.

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.

Using Monte Carlo/Gaussian Based Small Area Estimates to Predict Where Medicaid Patients Reside.

Electronic Health Records (EHR) are rapidly becoming accepted as tools for planning and population health1,2. With the national dialogue around Medicaid expansion12, the role of EHR data has become even more important. For their potential to be fully realized and contribute to these discussions, techniques for creating accurate small area estimates is vital. As such, we examined the efficacy of developing small area estimates for Medicaid patients in two locations, Albuquerque and Chicago, by using a Monte Carlo/Gaussian technique that has worked in accurately locating registered voters in North Carolina11. The Albuquerque data, which includes patient address, will first be used to assess the accuracy of the methodology. Subsequently, it will be combined with the EHR data from Chicago to develop a regression that predicts Medicaid patients by US Block Group. We seek to create a tool that is effective in translating EHR data's potential for population health studies.

Patient-Centered Outcomes Research in Practice: The CAPriCORN Infrastructure.

CAPriCORN, the Chicago Area Patient Centered Outcomes Research Network, is one of the eleven PCORI-funded Clinical Data Research Networks. A collaboration of six academic medical centers, a Chicago public hospital, two VA hospitals and a network of federally qualified health centers, CAPriCORN addresses the needs of a diverse community and overlapping populations. To capture complete medical records without compromising patient privacy and confidentiality, the network created policies and mechanisms for patient consultation, central IRB approval, de-identification, de-duplication, and integration of patient data by study cohort, randomization and sampling, re-identification for consent by providers and patients, and communication with patients to elicit patient-reported outcomes through validated instruments. The paper describes these policies and mechanisms and discusses two case studies to prove the feasibility and effectiveness of the network.

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.

CAPriCORN: Chicago Area Patient-Centered Outcomes Research Network.

The Chicago Area Patient-Centered Outcomes Research Network (CAPriCORN) represents an unprecedented collaboration across diverse healthcare institutions including private, county, and state hospitals and health systems, a consortium of Federally Qualified Health Centers, and two Department of Veterans Affairs hospitals. CAPriCORN builds on the strengths of our institutions to develop a cross-cutting infrastructure for sustainable and patient-centered comparative effectiveness research in Chicago. Unique aspects include collaboration with the University HealthSystem Consortium to aggregate data across sites, a centralized communication center to integrate patient recruitment with the data infrastructure, and a centralized institutional review board to ensure a strong and efficient human subject protection program. With coordination by the Chicago Community Trust and the Illinois Medical District Commission, CAPriCORN will model how healthcare institutions can overcome barriers of data integration, marketplace competition, and care fragmentation to develop, test, and implement strategies to improve care for diverse populations and reduce health disparities.

Density functional theory study of small nickel clusters.

The stable geometries and atomization energies for the clusters Ni( n ) (n = 2-5) are predicted with all-electron density functional theory (DFT), using the BMK hybrid functional and a Gaussian basis set. Possible isomers and several spin states of these nickel clusters are considered systematically. The ground spin state and the lowest energy isomers are identified for each cluster size. The results are compared to available experimental and other theoretical data. The molecular orbitals of the largest cluster are plotted for all spin states. The relative stabilities of these states are interpreted in terms of superatom orbitals and no-pair bonding.

Potential energy curves and electronic structure of 3d transition metal hydrides and their cations.

We investigate gas-phase neutral and cationic hydrides formed by 3d transition metals from Sc to Cu with density functional theory (DFT) methods. The performance of two exchange-correlation functionals, Boese-Martin for kinetics (BMK) and Tao-Perdew-Staroverov-Scuseria (TPSS), in predicting bond lengths and energetics, electronic structures, dipole moments, and ionization potentials is evaluated in comparison with available experimental data. To ensure a unique self-consistent field (SCF) solution, we use stability analysis, Fermi smearing, and continuity analysis of the potential energy curves. Broken-symmetry approach was adapted in order to get the qualitatively correct description of the bond dissociation. We found that on average BMK predicted values of dissociation energies and ionization potentials are closer to experiment than those obtained with high level wave function theory methods. This agreement deteriorates quickly when the fraction of the Hartree-Fock exchange in DFT functional is decreased. Natural bond orbital (NBO) population analysis was used to describe the details of chemical bonding in the systems studied. The multireference character in the wave function description of the hydrides is reproduced in broken-symmetry DFT description, as evidenced by NBO analysis. We also propose a new scheme to correct for spin contamination arising in broken-symmetry DFT approach. Unlike conventional schemes, our spin correction is introduced for each spin-polarized electron pair individually and therefore is expected to yield more accurate energy values. We derive an expression to extract the energy of the pure singlet state from the energy of the broken-symmetry DFT description of the low spin state and the energies of the high spin states (pentuplet and two spin-contaminated triplets in the case of two spin-polarized electron pairs). The high spin states are build with canonical natural orbitals and do not require SCF convergence.