Extracting Physician Group Intelligence from Electronic Health Records to Support Evidence Based Medicine

Evidence-based medicine employs expert opinion and clinical data to inform clinical decision making. The objective of this study is to determine whether it is possible to complement these sources of evidence with information about physician “group intelligence” that exists in electronic health records. Specifically, we measured laboratory test “repeat intervals”, defined as the amount of time it takes for a physician to repeat a test that was previously ordered for the same patient. Our assumption is that while the result of a test is a direct measure of one marker of a patient's health, the physician's decision to order the test is based on multiple factors including past experience, available treatment options, and information about the patient that might not be coded in the electronic health record. By examining repeat intervals in aggregate over large numbers of patients, we show that it is possible to 1) determine what laboratory test results physicians consider “normal”, 2) identify subpopulations of patients that deviate from the norm, and 3) identify situations where laboratory tests are over-ordered. We used laboratory tests as just one example of how physician group intelligence can be used to support evidence based medicine in a way that is automated and continually updated

Electronic Patient-Physician Communication: Problems and Promise

A critical mass of Internet users will soon enable wide diffusion of electronic communication within medical practice. E-mail between physicians and patients offers important opportunities for better communication. Linking patients and physicians through e-mail may increase the involvement of patients in supervising and documenting their own health care, processes that may activate patients and contribute to improved health. These new linkages may have profound implications for the patientphysician relationship. Although the federal government proposes regulation of telemedicine technologies and medical software, communications technologies are evolving under less scrutiny. Unless these technologies are implemented with substantial forethought, they may disturb delicate balances in the patient-physician relationship, widen social disparities in health outcomes, and create barriers to access to health care. This paper seeks to identify the promise and pitfalls of electronic patient-pbysician communication before such technology becomes widely distributed. A research agenda is proposed that would provide data that are useful for careful shaping of the communications infrastructure. The paper addresses the need to 1) define appropriate use of the various modes of patient-physician communication, 2) ensure the security and confidentiality of patient information, 3) create user interfaces that guide patients in effective use of the technology, 4) proactively assess medicolegal liability, and 5) ensure access to the technology by a multicultural, multilingual population with varying degrees of literacy.History of Scienc

EHRtemporalVariability: delineating temporal data-set shifts in electronic health records

[EN] Background: Temporal variability in health-care processes or protocols is intrinsic to medicine. Such variability can potentially introduce dataset shifts, a data quality issue when reusing electronic health records (EHRs) for secondary purposes. Temporal data-set shifts can present as trends, as well as abrupt or seasonal changes in the statistical distributions of data over time. The latter are particularly complicated to address in multimodal and highly coded data. These changes, if not delineated, can harm population and data-driven research, such as machine learning. Given that biomedical research repositories are increasingly being populated with large sets of historical data from EHRs, there is a need for specific software methods to help delineate temporal data-set shifts to ensure reliable data reuse. Results: EHRtemporalVariability is an open-source R package and Shiny app designed to explore and identify temporal data-set shifts. EHRtemporalVariability estimates the statistical distributions of coded and numerical data over time; projects their temporal evolution through non-parametric information geometric temporal plots; and enables the exploration of changes in variables through data temporal heat maps. We demonstrate the capability of EHRtemporalVariability to delineate data-set shifts in three impact case studies, one of which is available for reproducibility. Conclusions: EHRtemporalVariability enables the exploration and identification of data-set shifts, contributing to the broad examination and repurposing of large, longitudinal data sets. Our goal is to help ensure reliable data reuse for a wide range of biomedical data users. EHRtemporalVariability is designed for technical users who are programmatically utilizing the R package, as well as users who are not familiar with programming via the Shiny user interface.This work was supported by Universitat Politecnica de Valencia grant PAID-00-17, Generalitat Valenciana grant BEST/2018, and projects H2020-SC1-2016-CNECT No. 727560 and H2020-SC1-BHC-2018-2020 No. 825750Sáez Silvestre, C.; Gutiérrez-Sacristán, A.; Kohane, I.; Garcia-Gomez, JM.; Avillach, P. (2020). EHRtemporalVariability: delineating temporal data-set shifts in electronic health records. GigaScience. 9(8):1-7. https://doi.org/10.1093/gigascience/giaa079S1798Gewin, V. (2016). Data sharing: An open mind on open data. Nature, 529(7584), 117-119. doi:10.1038/nj7584-117aKatzan, I. L., & Rudick, R. A. (2012). Time to Integrate Clinical and Research Informatics. Science Translational Medicine, 4(162). doi:10.1126/scitranslmed.3004583Zhu, L., & Zheng, W. J. (2018). Informatics, Data Science, and Artificial Intelligence. JAMA, 320(11), 1103. doi:10.1001/jama.2018.8211Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. New England Journal of Medicine, 380(14), 1347-1358. doi:10.1056/nejmra1814259Andreu-Perez, J., Poon, C. C. Y., Merrifield, R. D., Wong, S. T. C., & Yang, G.-Z. (2015). Big Data for Health. IEEE Journal of Biomedical and Health Informatics, 19(4), 1193-1208. doi:10.1109/jbhi.2015.2450362Sáez, C., Rodrigues, P. P., Gama, J., Robles, M., & García-Gómez, J. M. (2014). Probabilistic change detection and visualization methods for the assessment of temporal stability in biomedical data quality. Data Mining and Knowledge Discovery, 29(4), 950-975. doi:10.1007/s10618-014-0378-6Schlegel, D. R., & Ficheur, G. (2017). Secondary Use of Patient Data: Review of the Literature Published in 2016. Yearbook of Medical Informatics, 26(01), 68-71. doi:10.15265/iy-2017-032Agniel, D., Kohane, I. S., & Weber, G. M. (2018). Biases in electronic health record data due to processes within the healthcare system: retrospective observational study. BMJ, k1479. doi:10.1136/bmj.k1479Sáez, C., & García-Gómez, J. M. (2018). Kinematics of Big Biomedical Data to characterize temporal variability and seasonality of data repositories: Functional Data Analysis of data temporal evolution over non-parametric statistical manifolds. International Journal of Medical Informatics, 119, 109-124. doi:10.1016/j.ijmedinf.2018.09.015Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., Johnson, W. E., … Irizarry, R. A. (2010). Tackling the widespread and critical impact of batch effects in high-throughput data. Nature Reviews Genetics, 11(10), 733-739. doi:10.1038/nrg2825Goh, W. W. B., Wang, W., & Wong, L. (2017). Why Batch Effects Matter in Omics Data, and How to Avoid Them. Trends in Biotechnology, 35(6), 498-507. doi:10.1016/j.tibtech.2017.02.012Sáez, C., Zurriaga, O., Pérez-Panadés, J., Melchor, I., Robles, M., & García-Gómez, J. M. (2016). Applying probabilistic temporal and multisite data quality control methods to a public health mortality registry in Spain: a systematic approach to quality control of repositories. Journal of the American Medical Informatics Association, 23(6), 1085-1095. doi:10.1093/jamia/ocw010Wright, A., Ash, J. S., Aaron, S., Ai, A., Hickman, T.-T. T., Wiesen, J. F., … Sittig, D. F. (2018). Best practices for preventing malfunctions in rule-based clinical decision support alerts and reminders: Results of a Delphi study. International Journal of Medical Informatics, 118, 78-85. doi:10.1016/j.ijmedinf.2018.08.001Moreno-Torres, J. G., Raeder, T., Alaiz-Rodríguez, R., Chawla, N. V., & Herrera, F. (2012). A unifying view on dataset shift in classification. Pattern Recognition, 45(1), 521-530. doi:10.1016/j.patcog.2011.06.019Svolba, G., & Bauer, P. (1999). Statistical Quality Control in Clinical Trials. Controlled Clinical Trials, 20(6), 519-530. doi:10.1016/s0197-2456(99)00029-xBray, F., & Parkin, D. M. (2009). Evaluation of data quality in the cancer registry: Principles and methods. Part I: Comparability, validity and timeliness. European Journal of Cancer, 45(5), 747-755. doi:10.1016/j.ejca.2008.11.032Springate, D. A., Parisi, R., Olier, I., Reeves, D., & Kontopantelis, E. (2017). rEHR: An R package for manipulating and analysing Electronic Health Record data. PLOS ONE, 12(2), e0171784. doi:10.1371/journal.pone.0171784Choi, L., Carroll, R. J., Beck, C., Mosley, J. D., Roden, D. M., Denny, J. C., & Van Driest, S. L. (2018). Evaluating statistical approaches to leverage large clinical datasets for uncovering therapeutic and adverse medication effects. 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Hippocrates revisited? Old ideals and new realities

Individual genomics has arrived, personal decisions to make use of it are a new reality. What are the implications for the patient–physician relationship? In this article we address three factors that call the traditional concept of confidentiality into question. First, the illusion of absolute data safety, as shown by medical informatics. Second, data sharing as a standard practice in genomics research. Comprehensive data sets are widely accessible. Third, genotyping has become a service that is directly available to consumers. The availability and accessibility of personal health data strongly suggest that the roles in the clinical encounter need to be remodeled. The old ideal of physicians as keepers of confidential information is outstripped by the reality of individuals who decide themselves about the way of using their data

Reproducibility of gene expression across generations of Affymetrix microarrays

BACKGROUND: The development of large-scale gene expression profiling technologies is rapidly changing the norms of biological investigation. But the rapid pace of change itself presents challenges. Commercial microarrays are regularly modified to incorporate new genes and improved target sequences. Although the ability to compare datasets across generations is crucial for any long-term research project, to date no means to allow such comparisons have been developed. In this study the reproducibility of gene expression levels across two generations of Affymetrix GeneChips(® )(HuGeneFL and HG-U95A) was measured. RESULTS: Correlation coefficients were computed for gene expression values across chip generations based on different measures of similarity. Comparing the absolute calls assigned to the individual probe sets across the generations found them to be largely unchanged. CONCLUSION: We show that experimental replicates are highly reproducible, but that reproducibility across generations depends on the degree of similarity of the probe sets and the expression level of the corresponding transcript

An international effort towards developing standards for best practices in analysis, interpretation and reporting of clinical genome sequencing results in the CLARITY Challenge

This is an Open Access article distributed under the terms of the Creative Commons Attribution License.-- et al.[Background]: There is tremendous potential for genome sequencing to improve clinical diagnosis and care once it becomes routinely accessible, but this will require formalizing research methods into clinical best practices in the areas of sequence data generation, analysis, interpretation and reporting. The CLARITY Challenge was designed to spur convergence in methods for diagnosing genetic disease starting from clinical case history and genome sequencing data. DNA samples were obtained from three families with heritable genetic disorders and genomic sequence data were donated by sequencing platform vendors. The challenge was to analyze and interpret these data with the goals of identifying disease-causing variants and reporting the findings in a clinically useful format. Participating contestant groups were solicited broadly, and an independent panel of judges evaluated their performance. [Results]: A total of 30 international groups were engaged. The entries reveal a general convergence of practices on most elements of the analysis and interpretation process. However, even given this commonality of approach, only two groups identified the consensus candidate variants in all disease cases, demonstrating a need for consistent fine-tuning of the generally accepted methods. There was greater diversity of the final clinical report content and in the patient consenting process, demonstrating that these areas require additional exploration and standardization. [Conclusions]: The CLARITY Challenge provides a comprehensive assessment of current practices for using genome sequencing to diagnose and report genetic diseases. There is remarkable convergence in bioinformatic techniques, but medical interpretation and reporting are areas that require further development by many groups.This work was supported by funds provided through the Gene Partnership and the Manton Center for Orphan Disease Research at Boston Children’s Hospital and the Center for Biomedical Informatics at Harvard Medical School and by generous donations in-kind of genomic sequencing services by Life Technologies (Carlsbad, CA, USA) and Complete Genomics (Mountain View, CA, USA).Peer Reviewe

Peripheral blood gene expression signature differentiates children with autism from unaffected siblings

Autism spectrum disorder (ASD) is one of the most prevalent neurodevelopmental disorders with high heritability, yet a majority of genetic contribution to pathophysiology is not known. Siblings of individuals with ASD are at increased risk for ASD and autistic traits, but the genetic contribution for simplex families is estimated to be less when compared to multiplex families. To explore the genomic (dis-) similarity between proband and unaffected sibling in simplex families, we used genome-wide gene expression profiles of blood from 20 proband-unaffected sibling pairs and 18 unrelated control individuals. The global gene expression profiles of unaffected siblings were more similar to those from probands as they shared genetic and environmental background. A total of 189 genes were significantly differentially expressed between proband-sib pairs (nominal p < 0.01) after controlling for age, sex, and family effects. Probands and siblings were distinguished into two groups by cluster analysis with these genes. Overall, unaffected siblings were equally distant from the centroid of probands and from that of unrelated controls with the differentially expressed genes. Interestingly, five of 20 siblings had gene expression profiles that were more similar to unrelated controls than to their matched probands. In summary, we found a set of genes that distinguished probands from the unaffected siblings, and a subgroup of unaffected siblings who were more similar to probands. The pathways that characterized probands compared to siblings using peripheral blood gene expression profiles were the up-regulation of ribosomal, spliceosomal, and mitochondrial pathways, and the down-regulation of neuroreceptor-ligand, immune response and calcium signaling pathways. Further integrative study with structural genetic variations such as de novo mutations, rare variants, and copy number variations would clarify whether these transcriptomic changes are structural or environmental in origin.Simons Foundatio

Macroporous nanowire nanoelectronic scaffolds for synthetic tissues

available in PMC 2013 April 11.The development of three-dimensional (3D) synthetic biomaterials as structural and bioactive scaffolds is central to fields ranging from cellular biophysics to regenerative medicine. As of yet, these scaffolds cannot electrically probe the physicochemical and biological microenvironments throughout their 3D and macroporous interior, although this capability could have a marked impact in both electronics and biomaterials. Here, we address this challenge using macroporous, flexible and free-standing nanowire nanoelectronic scaffolds (nanoES), and their hybrids with synthetic or natural biomaterials. 3D macroporous nanoES mimic the structure of natural tissue scaffolds, and they were formed by self-organization of coplanar reticular networks with built-in strain and by manipulation of 2D mesh matrices. NanoES exhibited robust electronic properties and have been used alone or combined with other biomaterials as biocompatible extracellular scaffolds for 3D culture of neurons, cardiomyocytes and smooth muscle cells. Furthermore, we show the integrated sensory capability of the nanoES by real-time monitoring of the local electrical activity within 3D nanoES/cardiomyocyte constructs, the response of 3D-nanoES-based neural and cardiac tissue models to drugs, and distinct pH changes inside and outside tubular vascular smooth muscle constructs.National Institutes of Health (U.S.) (Director’s Pioneer award)McKnight Foundation (Technological Innovations in Neurosciences Award)Boston Children's Hospital (Biotechnology Research Endowment)National Institutes of Health (U.S.) (DE013023)National Institutes of Health (U.S.) (DE016516

Near-infrared-actuated devices for remotely controlled drug delivery

A reservoir that could be remotely triggered to release a drug would enable the patient or physician to achieve on-demand, reproducible, repeated, and tunable dosing. Such a device would allow precise adjustment of dosage to desired effect, with a consequent minimization of toxicity, and could obviate repeated drug administrations or device implantations, enhancing patient compliance. It should exhibit low off-state leakage to minimize basal effects, and tunable on-state release profiles that could be adjusted from pulsatile to sustained in real time. Despite the clear clinical need for a device that meets these criteria, none has been reported to date to our knowledge. To address this deficiency, we developed an implantable reservoir capped by a nanocomposite membrane whose permeability was modulated by irradiation with a near-infrared laser. Irradiated devices could exhibit sustained on-state drug release for at least 3 h, and could reproducibly deliver short pulses over at least 10 cycles, with an on/off ratio of 30. Devices containing aspart, a fast-acting insulin analog, could achieve glycemic control after s.c. implantation in diabetic rats, with reproducible dosing controlled by the intensity and timing of irradiation over a 2-wk period. These devices can be loaded with a wide range of drug types, and therefore represent a platform technology that might be used to address a wide variety of clinical indications

Early Detection of Poor Adherers to Statins: Applying Individualized Surveillance to Pay for Performance

Background: Medication nonadherence costs $300 billion annually in the US. Medicare Advantage plans have a financial incentive to increase medication adherence among members because the Centers for Medicare and Medicaid Services (CMS) now awards substantive bonus payments to such plans, based in part on population adherence to chronic medications. We sought to build an individualized surveillance model that detects early which beneficiaries will fall below the CMS adherence threshold. Methods: This was a retrospective study of over 210,000 beneficiaries initiating statins, in a database of private insurance claims, from 2008-2011. A logistic regression model was constructed to use statin adherence from initiation to day 90 to predict beneficiaries who would not meet the CMS measure of proportion of days covered 0.8 or above, from day 91 to 365. The model controlled for 15 additional characteristics. In a sensitivity analysis, we varied the number of days of adherence data used for prediction. Results: Lower adherence in the first 90 days was the strongest predictor of one-year nonadherence, with an odds ratio of 25.0 (95% confidence interval 23.7-26.5) for poor adherence at one year. The model had an area under the receiver operating characteristic curve of 0.80. Sensitivity analysis revealed that predictions of comparable accuracy could be made only 40 days after statin initiation. When members with 30-day supplies for their first statin fill had predictions made at 40 days, and members with 90-day supplies for their first fill had predictions made at 100 days, poor adherence could be predicted with 86% positive predictive value. Conclusions: To preserve their Medicare Star ratings, plan managers should identify or develop effective programs to improve adherence. An individualized surveillance approach can be used to target members who would most benefit, recognizing the tradeoff between improved model performance over time and the advantage of earlier detection