Daniella Meeker

Neural prosthetic control signals from plan activity

The prospect of assisting disabled patients by translating neural activity from the brain into control signals for prosthetic devices, has flourished in recent years. Current systems rely on neural activity present during natural arm movements. We propose here that neural activity present before or even without natural arm movements can provide an important, and potentially advantageous, source of control signals. To demonstrate how control signals can be derived from such plan activity we performed a computational study with neural activity previously recorded from the posterior parietal cortex of rhesus monkeys planning arm movements. We employed maximum likelihood decoders to estimate movement direction and to drive finite state machines governing when to move. Performance exceeded 90% with as few as 40 neurons.

Time of Day and the Decision to Prescribe Antibiotics

Clinicians make many patient care decisions each day. The cumulative cognitive demand of these decisions may erode clinicians’ abilities to resist making potentially inappropriate choices. Psychologists, who refer to the erosion of self-control after making repeated decisions as decision fatigue,1,2 have found evidence that it affects nonmedical professionals. For example, as court sessions wear on, judges are more likely to deny parole, the “easier” or “safer” option.3

Characterizing treatment pathways at scale using the OHDSI network

Observational research promises to complement experimental research by providing large, diverse populations that would be infeasible for an experiment. Observational research can test its own clinical hypotheses, and observational studies also can contribute to the design of experiments and inform the generalizability of experimental research. Understanding the diversity of populations and the variance in care is one component. In this study, the Observational Health Data Sciences and Informatics (OHDSI) collaboration created an international data network with 11 data sources from four countries, including electronic health records and administrative claims data on 250 million patients. All data were mapped to common data standards, patient privacy was maintained by using a distributed model, and results were aggregated centrally. Treatment pathways were elucidated for type 2 diabetes mellitus, hypertension, and depression. The pathways revealed that the world is moving toward more consistent therapy over time across diseases and across locations, but significant heterogeneity remains among sources, pointing to challenges in generalizing clinical trial results. Diabetes favored a single first-line medication, metformin, to a much greater extent than hypertension or depression. About 10% of diabetes and depression patients and almost 25% of hypertension patients followed a treatment pathway that was unique within the cohort. Aside from factors such as sample size and underlying population (academic medical center versus general population), electronic health records data and administrative claims data revealed similar results. Large-scale international observational research is feasible.

Recording advances for neural prosthetics

An important challenge for neural prosthetics research is to record from populations of neurons over long periods of time, ideally for the lifetime of the patient. Two new advances toward this goal are described, the use of local field potentials (LFPs) and autonomously positioned recording electrodes. LFPs are the composite extracellular potential field from several hundreds of neurons around the electrode tip. LFP recordings can be maintained for longer periods of time than single cell recordings. We find that similar information can be decoded from LFP and spike recordings, with better performance for state decodes with LFPs and, depending on the area, equivalent or slightly less than equivalent performance for signaling the direction of planned movements. Movable electrodes in microdrives can be adjusted in the tissue to optimize recordings, but their movements must be automated to be a practical benefit to patients. We have developed automation algorithms and a meso-scale autonomous electrode testbed, and demonstrated that this system can autonomously isolate and maintain the recorded signal quality of single cells in the cortex of awake, behaving monkeys. These two advances show promise for developing very long term recording for neural prosthetic applications.

pSCANNER: patient-centered Scalable National Network for Effectiveness Research

This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administrations 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses.

Variations in network boundary and type: A study of adolescent peer influences

Abstract This study compares variation in network boundary and network type on network indicators such as degree and estimates of social influences on adolescent substance use. We compare associations between individual use and peer use of tobacco and alcohol when network boundary (e.g., classroom, entire grade in school, and community) and relational type (elicited by asking whom students: (a) are friends with, (b) admire, (c) think will succeed, (d) would like to have a romantic relationship with, and (e) think are popular) are varied. Additionally, we estimate Exponential Random Graph Models (ERGMs) for 232 networks to obtain a homophily estimate for smoking and drinking. Data were collected from a cross-sectional sample of 1707 adolescents in five high schools in one school district in Los Angeles, CA. Results of logistic regression models show that associations were strongest when the boundary condition was least constrained and that associations were stronger for friendship networks than for other ones. Additionally, ERGM estimations show that grade-level friendship networks returned significant homophily effects more frequently than the classroom networks. This study validates existing theoretical approaches to the network study of social influence as well as ways to estimate them. We recommend researchers use as broad a boundary as possible when collecting network data, but observe that for some research purposes more narrow boundaries may be preferred.

Transparent Reporting of Data Quality in Distributed Data Networks

Introduction: Poor data quality can be a serious threat to the validity and generalizability of clinical research findings. The growing availability of electronic administrative and clinical data is accompanied by a growing concern about the quality of these data for observational research and other analytic purposes. Currently, there are no widely accepted guidelines for reporting quality results that would enable investigators and consumers to independently determine if a data source is fit for use to support analytic inferences and reliable evidence generation. Model and Methods: We developed a conceptual model that captures the flow of data from data originator across successive data stewards and finally to the data consumer. This “data lifecycle” model illustrates how data quality issues can result in data being returned back to previous data custodians. We highlight the potential risks of poor data quality on clinical practice and research results. Because of the need to ensure transparent reporting of a data quality issues, we created a unifying data-quality reporting framework and a complementary set of 20 data-quality reporting recommendations for studies that use observational clinical and administrative data for secondary data analysis. We obtained stakeholder input on the perceived value of each recommendation by soliciting public comments via two face-to-face meetings of informatics and comparative-effectiveness investigators, through multiple public webinars targeted to the health services research community, and with an open access online wiki. Recommendations: Our recommendations propose reporting on both general and analysis-specific data quality features. The goals of these recommendations are to improve the reporting of data quality measures for studies that use observational clinical and administrative data, to ensure transparency and consistency in computing data quality measures, and to facilitate best practices and trust in the new clinical discoveries based on secondary use of observational data.

Use of behavioral economics and social psychology to improve treatment of acute respiratory infections (BEARI): rationale and design of a cluster randomized controlled trial [1RC4AG039115-01] - study protocol and baseline practice and provider characteristics

BackgroundInappropriate antibiotic prescribing for nonbacterial infections leads to increases in the costs of care, antibiotic resistance among bacteria, and adverse drug events. Acute respiratory infections (ARIs) are the most common reason for inappropriate antibiotic use. Most prior efforts to decrease inappropriate antibiotic prescribing for ARIs (e.g., educational or informational interventions) have relied on the implicit assumption that clinicians inappropriately prescribe antibiotics because they are unaware of guideline recommendations for ARIs. If lack of guideline awareness is not the reason for inappropriate prescribing, educational interventions may have limited impact on prescribing rates. Instead, interventions that apply social psychological and behavioral economic principles may be more effective in deterring inappropriate antibiotic prescribing for ARIs by well-informed clinicians.Methods/designThe Application of Behavioral Economics to Improve the Treatment of Acute Respiratory Infections (BEARI) Trial is a multisite, cluster-randomized controlled trial with practice as the unit of randomization. The primary aim is to test the ability of three interventions based on behavioral economic principles to reduce the rate of inappropriate antibiotic prescribing for ARIs. We randomized practices in a 2 × 2 × 2 factorial design to receive up to three interventions for non-antibiotic-appropriate diagnoses: 1) Accountable Justifications: When prescribing an antibiotic for an ARI, clinicians are prompted to record an explicit justification that appears in the patient electronic health record; 2) Suggested Alternatives: Through computerized clinical decision support, clinicians prescribing an antibiotic for an ARI receive a list of non-antibiotic treatment choices (including prescription options) prior to completing the antibiotic prescription; and 3) Peer Comparison: Each provider’s rate of inappropriate antibiotic prescribing relative to top-performing peers is reported back to the provider periodically by email. We enrolled 269 clinicians (practicing attending physicians or advanced practice nurses) in 49 participating clinic sites and collected baseline data. The primary outcome is the antibiotic prescribing rate for office visits with non-antibiotic-appropriate ARI diagnoses. Secondary outcomes will examine antibiotic prescribing more broadly. The 18-month intervention period will be followed by a one year follow-up period to measure persistence of effects after interventions cease.DiscussionThe ongoing BEARI Trial will evaluate the effectiveness of behavioral economic strategies in reducing inappropriate prescribing of antibiotics.Trials registrationClinicalTrials.gov: NCT01454947

A Harmonized Data Quality Assessment Terminology and Framework for the Secondary Use of Electronic Health Record Data.

Objective: Harmonized data quality (DQ) assessment terms, methods, and reporting practices can establish a common understanding of the strengths and limitations of electronic health record (EHR) data for operational analytics, quality improvement, and research. Existing published DQ terms were harmonized to a comprehensive unified terminology with definitions and examples and organized into a conceptual framework to support a common approach to defining whether EHR data is ‘fit’ for specific uses. Materials and Methods: DQ publications, informatics and analytics experts, managers of established DQ programs, and operational manuals from several mature EHR-based research networks were reviewed to identify potential DQ terms and categories. Two face-to-face stakeholder meetings were used to vet an initial set of DQ terms and definitions that were grouped into an overall conceptual framework. Feedback received from data producers and users was used to construct a draft set of harmonized DQ terms and categories. Multiple rounds of iterative refinement resulted in a set of terms and organizing framework consisting of DQ categories, subcategories, terms, definitions, and examples. The harmonized terminology and logical framework’s inclusiveness was evaluated against ten published DQ terminologies. Results: Existing DQ terms were harmonized and organized into a framework by defining three DQ categories: (1) Conformance (2) Completeness and (3) Plausibility and two DQ assessment contexts: (1) Verification and (2) Validation. Conformance and Plausibility categories were further divided into subcategories. Each category and subcategory was defined with respect to whether the data may be verified with organizational data, or validated against an accepted gold standard, depending on proposed context and uses. The coverage of the harmonized DQ terminology was validated by successfully aligning to multiple published DQ terminologies. Discussion: Existing DQ concepts, community input, and expert review informed the development of a distinct set of terms, organized into categories and subcategories. The resulting DQ terms successfully encompassed a wide range of disparate DQ terminologies. Operational definitions were developed to provide guidance for implementing DQ assessment procedures. The resulting structure is an inclusive DQ framework for standardizing DQ assessment and reporting. While our analysis focused on the DQ issues often found in EHR data, the new terminology may be applicable to a wide range of electronic health data such as administrative, research, and patient-reported data. Conclusion: A consistent, common DQ terminology, organized into a logical framework, is an initial step in enabling data owners and users, patients, and policy makers to evaluate and communicate data quality findings in a well-defined manner with a shared vocabulary. Future work will leverage the framework and terminology to develop reusable data quality assessment and reporting methods.

Identifying appropriate reference data models for comparative effectiveness research (CER) studies based on data from clinical information systems.

Introduction: The need for a common format for electronic exchange of clinical data prompted federal endorsement of applicable standards. However, despite obvious similarities, a consensus standard has not yet been selected in the comparative effectiveness research (CER) community. Methods: Using qualitative metrics for data retrieval and information loss across a variety of CER topic areas, we compare several existing models from a representative sample of organizations associated with clinical research: the Observational Medical Outcomes Partnership (OMOP), Biomedical Research Integrated Domain Group, the Clinical Data Interchange Standards Consortium, and the US Food and Drug Administration. Results: While the models examined captured a majority of the data elements that are useful for CER studies, data elements related to insurance benefit design and plans were most detailed in OMOP’s CDM version 4.0. Standardized vocabularies that facilitate semantic interoperability were included in the OMOP and US Food and Drug Administration Mini-Sentinel data models, but are left to the discretion of the end-user in Biomedical Research Integrated Domain Group and Analysis Data Model, limiting reuse opportunities. Among the challenges we encountered was the need to model data specific to a local setting. This was handled by extending the standard data models. Discussion: We found that the Common Data Model from the OMOP met the broadest complement of CER objectives. Minimal information loss occurred in mapping data from institution-specific data warehouses onto the data models from the standards we assessed. However, to support certain scenarios, we found a need to enhance existing data dictionaries with local, institution-specific information.