Satyender Goel

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.

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.

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

AbstractThe stable geometries and atomization energies for the clusters Nin (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. FigureThe stable geometries and atomization energies for the clusters Nin (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 systematically considered. 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

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.

Pairwise Spin-Contamination Correction Method and DFT Study of MnH and H2 Dissociation Curves

A clear advantage of broken symmetry (BS) unrestricted density functional theory DFT is qualitatively correct description of bond dissociation process, but its disadvantage is that spin-polarized Slater determinant is no longer a pure spin state (a.k.a. spin contamination). We propose a new approach to eliminate the spin-contamination, based on canonical Natural Orbitals (NO). We derive an expression to extract the energy of the pure singlet state given in terms of energy of BS DFT solution, the occupation number of the bonding NO, and the energy of the higher state built on these bonding and antibonding NOs (as opposed to self-consistent Kohn-Sham orbitals). Thus, unlike spin-contamination correction schemes by Noodleman and Yamaguchi, spin-correction is introduced for each correlated electron pair individually and thus expected to give more accurate results. We validate this approach on two examples, a simple diatomic H2 and transition metal hydride MnH.

SMCQL: secure querying for federated databases

People and machines are recording data at an unprecedented rate. At the same time, progress has been slow in making data available for open science and other research initiatives. Many of these efforts are stymied by privacy concerns and regulatory compliance issues. For example, numerous hospitals are interested in combining their patient records with those of other healthcare sites for clinical data research, but they cannot disclose the contents of their databases without violating patient confidentiality. We propose a novel generalization of federated database systems called a private data network (PDN), and it is designed for querying over the collective data of mutually distrustful parties. In a PDN, participants do not reveal their raw data, nor do they encrypt and upload it to the cloud. Rather, they perform secure multiparty computation (SMC) with other federation members to produce query results over the data of both parties. Here, a user submits their SQL query to an honest broker that plans and coordinates its distributed execution using SMC. Within SMC, the participating database providers compute a joint function with an output that is only revealed to the user and the honest broker. The databases computing the query learn nothing about the inputs provided by their peers, nor can they see the output of the groups computation. This capability comes at a high cost-SMC programs typically have runtimes that are orders of magnitude slower than their insecure counterparts. We address this challenge with a query planner that automatically identifies the minimal set of coordination points between parties in a given query plan. The planner translates these distributed steps into SMC as needed and feeds the secure code into our query executor. Our framework, SMCQL, plans and executes PDN queries. We are preparing SMCQL for an open-source release.People and machines are collecting data at an unprecedented rate. Despite this newfound abundance of data, progress has been slow in sharing it for open science, business, and other data-intensive endeavors. Many such efforts are stymied by privacy concerns and regulatory compliance issues. For example, many hospitals are interested in pooling their medical records for research, but none may disclose arbitrary patient records to researchers or other healthcare providers. In this context we propose the Private Data Network (PDN), a federated database for querying over the collective data of mutually distrustful parties. In a PDN, each member database does not reveal its tuples to its peers nor to the query writer. Instead, the user submits a query to an honest broker that plans and coordinates its execution over multiple private databases using secure multiparty computation (SMC). Here, each databases query execution is oblivious, and its program counters and memory traces are agnostic to the inputs of others. We introduce a framework for executing PDN queries named SMCQL. This system translates SQL statements into SMC primitives to compute query results over the union of its source databases without revealing sensitive information about individual tuples to peer data providers or the honest broker. Only the honest broker and the querier receive the results of a PDN query. For fast, secure query evaluation, we explore a heuristics-driven optimizer that minimizes the PDNs use of secure computation and partitions its query evaluation into scalable slices.

Big Data: What Is It and What Does It Mean for Cardiovascular Research and Prevention Policy

Over the past decade, there has been explosive growth in the amount of healthcare-related data generated and interest in harnessing this data for research purposes and informing public policy. Outside of healthcare, specialized software has been developed to tackle the problems that voluminous data creates, and these techniques could be applicable in several areas of cardiovascular research. Cardiovascular risk analysis may benefit from the inclusion of patient genetic and health record data, while cardiovascular epidemiology could benefit from crowd-sourced environmental data. Some of the most significant advances may come from the ability to predict and respond to events in real-time—such as assessing the impact of new public policy at the community level on a weekly basis through electronic health records or monitoring a patient’s cardiovascular health remotely with a smartphone.

Mortality Risk Factors Among Patients With Cirrhosis and a Low Model for End-Stage Liver Disease Sodium Score (≤15): An Analysis of Liver Transplant Allocation Policy Using Aggregated Electronic Health Record Data

Although the Model for End‐Stage Liver Disease sodium (MELD Na) score is now used for liver transplant allocation in the United States, mortality prediction may be underestimated by the score. Using aggregated electronic health record data from 7834 adult patients with cirrhosis, we determined whether the cause of cirrhosis or cirrhosis complications was associated with an increased risk of death among patients with a MELD Na score ≤15 and whether patients with the greatest risk of death could benefit from liver transplantation (LT). Over median follow‐up of 2.3 years, 3715 patients had a maximum MELD Na score ≤15. Overall, 3.4% were waitlisted for LT. Severe hypoalbuminemia, hepatorenal syndrome, and hepatic hydrothorax conferred the greatest risk of death independent of MELD Na score with 1‐year predicted mortality >14%. Approximately 10% possessed these risk factors. Of these high‐risk patients, only 4% were waitlisted for LT, despite no difference in nonliver comorbidities between waitlisted patients and those not listed. In addition, risk factors for death among waitlisted patients were the same as those for patients not waitlisted, although the effect of malnutrition was significantly greater for waitlisted patients (hazard ratio 8.65 [95% CI 2.57–29.11] vs. 1.47 [95% CI 1.08–1.98]). Using the MELD Na score for allocation may continue to limit access to LT.

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.