Why Is the Electronic Health Record So Challenging for Research and Clinical Care?

BACKGROUND: The electronic health record (EHR) has become increasingly ubiquitous. At the same time, health professionals have been turning to this resource for access to data that is needed for the delivery of health care and for clinical research. There is little doubt that the EHR has made both of these functions easier than earlier days when we relied on paper-based clinical records. Coupled with modern database and data warehouse systems, high-speed networks, and the ability to share clinical data with others are large number of challenges that arguably limit the optimal use of the EHR OBJECTIVES: Our goal was to provide an exhaustive reference for those who use the EHR in clinical and research contexts, but also for health information systems professionals as they design, implement, and maintain EHR systems. METHODS: This study includes a panel of 24 biomedical informatics researchers, information technology professionals, and clinicians, all of whom have extensive experience in design, implementation, and maintenance of EHR systems, or in using the EHR as clinicians or researchers. All members of the panel are affiliated with Penn Medicine at the University of Pennsylvania and have experience with a variety of different EHR platforms and systems and how they have evolved over time. RESULTS: Each of the authors has shared their knowledge and experience in using the EHR in a suite of 20 short essays, each representing a specific challenge and classified according to a functional hierarchy of interlocking facets such as usability and usefulness, data quality, standards, governance, data integration, clinical care, and clinical research. CONCLUSION: We provide here a set of perspectives on the challenges posed by the EHR to clinical and research users.

Development of a Coronavirus Disease 2019 (COVID-19) Application Ontology for the Accrual to Clinical Trials (ACT) network.

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that are critical to COVID-19 research. The ontology contains over 50 000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for severe acute respiratory syndrome coronavirus 2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of 9 academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

Development of a COVID-19 Application Ontology for the ACT Network.

Clinical data networks that leverage large volumes of data in electronic health records (EHRs) are significant resources for research on coronavirus disease 2019 (COVID-19). Data harmonization is a key challenge in seamless use of multisite EHRs for COVID-19 research. We developed a COVID-19 application ontology in the national Accrual to Clinical Trials (ACT) network that enables harmonization of data elements that that are critical to COVID-19 research. The ontology contains over 50,000 concepts in the domains of diagnosis, procedures, medications, and laboratory tests. In particular, it has computational phenotypes to characterize the course of illness and outcomes, derived terms, and harmonized value sets for SARS-CoV-2 laboratory tests. The ontology was deployed and validated on the ACT COVID-19 network that consists of nine academic health centers with data on 14.5M patients. This ontology, which is freely available to the entire research community on GitHub at https://github.com/shyamvis/ACT-COVID-Ontology, will be useful for harmonizing EHRs for COVID-19 research beyond the ACT network.

Risk of Persistent Opioid Use following Major Surgery in Matched Samples of Patients with and without Cancer.

BACKGROUND: The opioid crisis has reached epidemic proportions, yet risk of persistent opioid use following curative intent surgery for cancer and factors influencing this risk are not well understood. METHODS: We used electronic health record data from 3,901 adult patients who received a prescription for an opioid analgesic related to hysterectomy or large bowel surgery from January 1, 2013, through June 30, 2018. Patients with and without a cancer diagnosis were matched on the basis of demographic, clinical, and procedural variables and compared for persistent opioid use. RESULTS: Cancer diagnosis was associated with greater risk for persistent opioid use after hysterectomy [18.9% vs. 9.6%; adjusted OR (aOR), 2.26; 95% confidence interval (CI), 1.38-3.69; P = 0.001], but not after large bowel surgery (28.3% vs. 24.1%; aOR 1.25; 95% CI, 0.97-1.59; P = 0.09). In the cancer hysterectomy cohort, persistent opioid use was associated with cancer stage (increased rates among those with stage III cancer compared with stage I) and use of neoadjuvant or adjuvant chemotherapy; however, these factors were not associated with persistent opioid use in the large bowel cohort. CONCLUSIONS: Patients with cancer may have an increased risk of persistent opioid use following hysterectomy. IMPACT: Risks and benefits of opioid analgesia for surgical pain among patients with cancer undergoing hysterectomy should be carefully considered.

Strategies for high-throughput comparative modeling: applications to leverage analysis in structural genomics and protein family organization.

The technological breakthroughs in structural genomics were designed to facilitate the solution of a sufficient number of structures, so that as many protein sequences as possible can be structurally characterized with the aid of comparative modeling. The leverage of a solved structure is the number and quality of the models that can be produced using the structure as a template for modeling and may be viewed as the "currency" with which the success of a structural genomics endeavor can be measured. Moreover, the models obtained in this way should be valuable to all biologists. To this end, at the Northeast Structural Genomics Consortium (NESG), a modular computational pipeline for automated high-throughput leverage analysis was devised and used to assess the leverage of the 186 unique NESG structures solved during the first phase of the Protein Structure Initiative (January 2000 to July 2005). Here, the results of this analysis are presented. The number of sequences in the nonredundant protein sequence database covered by quality models produced by the pipeline is approximately 39,000, so that the average leverage is approximately 210 models per structure. Interestingly, only 7900 of these models fulfill the stringent modeling criterion of being at least 30% sequence-identical to the corresponding NESG structures. This study shows how high-throughput modeling increases the efficiency of structure determination efforts by providing enhanced coverage of protein structure space. In addition, the approach is useful in refining the boundaries of structural domains within larger protein sequences, subclassifying sequence diverse protein families, and defining structure-based strategies specific to a particular family.

The role of electrostatics in protein-membrane interactions.

Many experimental, structural and computational studies have established the importance of nonspecific electrostatics as a driving force for peripheral membrane association. Here we focus on this component of protein/membrane interactions by using examples ranging from phosphoinositide signaling to retroviral assembly. We stress the utility of the collaboration of experiment and theory in identifying and quantifying the role of electrostatics not only in contributing to membrane association, but also in affecting subcellular targeting, in the control of membrane binding, and in the organization of proteins and lipids at membrane surfaces.

Solution structure of Archaeglobus fulgidis peptidyl-tRNA hydrolase (Pth2) provides evidence for an extensive conserved family of Pth2 enzymes in archea, bacteria, and eukaryotes.

The solution structure of protein AF2095 from the thermophilic archaea Archaeglobus fulgidis, a 123-residue (13.6-kDa) protein, has been determined by NMR methods. The structure of AF2095 is comprised of four alpha-helices and a mixed beta-sheet consisting of four parallel and anti-parallel beta-strands, where the alpha-helices sandwich the beta-sheet. Sequence and structural comparison of AF2095 with proteins from Homo sapiens, Methanocaldococcus jannaschii, and Sulfolobus solfataricus reveals that AF2095 is a peptidyl-tRNA hydrolase (Pth2). This structural comparison also identifies putative catalytic residues and a tRNA interaction region for AF2095. The structure of AF2095 is also similar to the structure of protein TA0108 from archaea Thermoplasma acidophilum, which is deposited in the Protein Data Bank but not functionally annotated. The NMR structure of AF2095 has been further leveraged to obtain good-quality structural models for 55 other proteins. Although earlier studies have proposed that the Pth2 protein family is restricted to archeal and eukaryotic organisms, the similarity of the AF2095 structure to human Pth2, the conservation of key active-site residues, and the good quality of the resulting homology models demonstrate a large family of homologous Pth2 proteins that are conserved in eukaryotic, archaeal, and bacterial organisms, providing novel insights in the evolution of the Pth and Pth2 enzyme families.

Structure-based assessment of missense mutations in human BRCA1: implications for breast and ovarian cancer predisposition.

The BRCA1 gene from individuals at risk of breast and ovarian cancers can be screened for the presence of mutations. However, the cancer association of most alleles carrying missense mutations is unknown, thus creating significant problems for genetic counseling. To increase our ability to identify cancer-associated mutations in BRCA1, we set out to use the principles of protein three-dimensional structure as well as the correlation between the cancer-associated mutations and those that abolish transcriptional activation. Thirty-one of 37 missense mutations of known impact on the transcriptional activation function of BRCA1 are readily rationalized in structural terms. Loss-of-function mutations involve nonconservative changes in the core of the BRCA1 C-terminus (BRCT) fold or are localized in a groove that presumably forms a binding site involved in the transcriptional activation by BRCA1; mutations that do not abolish transcriptional activation are either conservative changes in the core or are on the surface outside of the putative binding site. Next, structure-based rules for predicting functional consequences of a given missense mutation were applied to 57 germ-line BRCA1 variants of unknown cancer association. Such a structure-based approach may be helpful in an integrated effort to identify mutations that predispose individuals to cancer.

MODBASE, a database of annotated comparative protein structure models, and associated resources.

MODBASE (http://salilab.org/modbase) is a relational database of annotated comparative protein structure models for all available protein sequences matched to at least one known protein structure. The models are calculated by MODPIPE, an automated modeling pipeline that relies on the MODELLER package for fold assignment, sequence-structure alignment, model building and model assessment (http:/salilab.org/modeller). MODBASE uses the MySQL relational database management system for flexible querying and CHIMERA for viewing the sequences and structures (http://www.cgl.ucsf.edu/chimera/). MODBASE is updated regularly to reflect the growth in protein sequence and structure databases, as well as improvements in the software for calculating the models. For ease of access, MODBASE is organized into different data sets. The largest data set contains 1,26,629 models for domains in 659,495 out of 1,182,126 unique protein sequences in the complete Swiss-Prot/TrEMBL database (August 25, 2003); only models based on alignments with significant similarity scores and models assessed to have the correct fold despite insignificant alignments are included. Another model data set supports target selection and structure-based annotation by the New York Structural Genomics Research Consortium; e.g. the 53 new structures produced by the consortium allowed us to characterize structurally 24,113 sequences. MODBASE also contains binding site predictions for small ligands and a set of predicted interactions between pairs of modeled sequences from the same genome. Our other resources associated with MODBASE include a comprehensive database of multiple protein structure alignments (DBALI, http://salilab.org/dbali) as well as web servers for automated comparative modeling with MODPIPE (MODWEB, http://salilab. org/modweb), modeling of loops in protein structures (MODLOOP, http://salilab.org/modloop) and predicting functional consequences of single nucleotide polymorphisms (SNPWEB, http://salilab. org/snpweb).

Tools for comparative protein structure modeling and analysis.

The following resources for comparative protein structure modeling and analysis are described (http://salilab.org): MODELLER, a program for comparative modeling by satisfaction of spatial restraints; MODWEB, a web server for automated comparative modeling that relies on PSI-BLAST, IMPALA and MODELLER; MODLOOP, a web server for automated loop modeling that relies on MODELLER; MOULDER, a CPU intensive protocol of MODWEB for building comparative models based on distant known structures; MODBASE, a comprehensive database of annotated comparative models for all sequences detectably related to a known structure; MODVIEW, a Netscape plugin for Linux that integrates viewing of multiple sequences and structures; and SNPWEB, a web server for structure-based prediction of the functional impact of a single amino acid substitution.