A conceptual model for translating omic data into clinical action

Interpreting and visualizing pathway analyses using embedding representations with PAVER

Omics studies use large-scale high-throughput data to explain changes underlying different traits or conditions. However, omics analysis often results in long lists of pathways that are difficult to interpret. Therefore, it is of interest to describe a tool named PAVER (Pathway Analysis Visualization with Embedding Representations) for large scale genomic analysis. PAVER curates similar pathways into groups, identifies the pathway most representative of each group, and provides publication-ready intuitive visualizations. PAVER clusters pathways defined by their vector embedding representations and then identifies the term most cosine similar to its respective cluster's average embedding. PAVER can integrate multiple pathway analyses, highlight relevant biological insights, and work with any pathway database.

Data structuring may prevent ambiguity and improve personalized medical prognosis

Topics expected to influence personalized medicine (PM), where medical decisions, practices, and treatments are tailored to the individual patient, are reviewed. Lack of discrimination due to different biological conditions that express similar values of numerical variables (ambiguity) is regarded to be a major potential barrier for PM. This material explores possible causes and sources of ambiguity and offers suggestions for mitigating the impacts of uncertainties. Three causes of ambiguity are identified: (1) delayed adoption of innovations, (2) inadequate emphases, and (3) inadequate processes used when new medical practices are developed and validated. One example of the first problem is the relative lack of medical research on "compositional data" -the type that characterizes leukocyte data. This omission results in erroneous use of data abundantly utilized in medicine, such as the blood cell differential. Emphasis on data output ‒not biomedical interpretation that facilitates the use of clinical data‒ exemplifies the second type of problems. Reliance on tools generated in other fields (but not validated within biomedical contexts) describes the last limitation. Because reductionism is associated with these problems, non-reductionist alternatives are reviewed as potential remedies. Data structuring (converting data into information) is considered a key element that may promote PM. To illustrate a process that includes data-information-knowledge and decision-making, previously published data on COVID-19 are utilized. It is suggested that ambiguity may be prevented or ameliorated. Provided that validations are grounded on biomedical knowledge, approaches that describe certain criteria - such as non-overlapping data intervals of patients that experience different outcomes, immunologically interpretable data, and distinct graphic patterns - can inform, at personalized bases, earlier and/or with fewer observations.

Multi-Cellular Immunological Interactions Associated With COVID-19 Infections

To rapidly prognosticate and generate hypotheses on pathogenesis, leukocyte multi-cellularity was evaluated in SARS-CoV-2 infected patients treated in India or the United States (152 individuals, 384 temporal observations). Within hospital (<90-day) death or discharge were retrospectively predicted based on the admission complete blood cell counts (CBC). Two methods were applied: (i) a "reductionist" one, which analyzes each cell type separately, and (ii) a "non-reductionist" method, which estimates multi-cellularity. The second approach uses a proprietary software package that detects distinct data patterns generated by complex and hypothetical indicators and reveals each data pattern's immunological content and associated outcome(s). In the Indian population, the analysis of isolated cell types did not separate survivors from non-survivors. In contrast, multi-cellular data patterns differentiated six groups of patients, including, in two groups, 95.5% of all survivors. Some data structures revealed one data point-wide line of observations, which informed at a personalized level and identified 97.8% of all non-survivors. Discovery was also fostered: some non-survivors were characterized by low monocyte/lymphocyte ratio levels. When both populations were analyzed with the non-reductionist method, they displayed results that suggested survivors and non-survivors differed immunologically as early as hospitalization day 1.

Building an information system to facilitate pharmacogenomics clinical translation with clinical decision support

Pharmacogenomics clinical decision support (PGx-CDS) is an important tool to incorporate PGx information into existing clinical workflows and facilitate PGx clinical translation. However, due to the lack of a computable formalization to represent the primary PGx knowledge, the complexity of genomics information and the lag of current commercial electronic health record (EHR) system for precision medicine, it is difficult to develop computerized PGx-CDS. Therefore, we explored a novel approach to build an information system, named the Pharmacogenomics Clinical Translation Platform (PCTP), for PGx clinical implementation. The PCTP can represent, store, and manage the primary PGx knowledge in a structured and computable format. Moreover, it has the potential to provide various PGx-CDS services and simplify the integration of PGx-CDS into EHRs.

Assessing the readiness of Turkish health information systems for integrating genetic/genomic patient data: System architecture and available terminologies, legislative, and protection of personal data

Advances in genetic/genomic research and translational studies drive the progress on molecular diagnosis, personalised treatment, and monitoring. Healthcare professionals and governments are encouraged to set administrative regulations and implement structured and interoperable representation to utilise the genetic/genomic data, which will support precision medicine approaches through Health Information Systems (HIS). Clear regulations and careful legislation are also crucial for the security and privacy of genetic/genomic test data. In this article, we present a review of the National Health Information System of Turkey (NHIS-T) about interoperable health data representation for genetic tests. We discuss the content of rules and regulations related to genetic/genomic testing and structured data representation in Turkey. A brief comparison of the Turkish "Law on the Protection of Personal Data" (LPPD) in genetic/genomic data privacy with its counterparts is presented. The final discussion about the shortcomings of Turkey is transferable to health information systems worldwide. Constructing a national reference database and IT infrastructure to enable data integration and exchange between genomic data, metadata, and health records will improve genetics studies' utility and outcomes. The critical success factors behind integration are establishing broadly accepted terminologies and government guidance. The governments should set clear a transparent policy defining the legal and ethical framework, workforce training, clinical decision-support tools, public engagement, and education concurrently.

Pharmacogenomic clinical decision support design and multi-site process outcomes analysis in the eMERGE Network

To better understand the real-world effects of pharmacogenomic (PGx) alerts, this study aimed to characterize alert design within the eMERGE Network, and to establish a method for sharing PGx alert response data for aggregate analysis. Seven eMERGE sites submitted design details and established an alert logging data dictionary. Six sites participated in a pilot study, sharing alert response data from their electronic health record systems. PGx alert design varied, with some consensus around the use of active, post-test alerts to convey Clinical Pharmacogenetics Implementation Consortium recommendations. Sites successfully shared response data, with wide variation in acceptance and follow rates. Results reflect the lack of standardization in PGx alert design. Standards and/or larger studies will be necessary to fully understand PGx impact. This study demonstrated a method for sharing PGx alert response data and established that variation in system design is a significant barrier for multi-site analyses.

Genomic Information for Clinicians in the Electronic Health Record: Lessons Learned From the Clinical Genome Resource Project and the Electronic Medical Records and Genomics Network

Genomic knowledge is being translated into clinical care. To fully realize the value, it is critical to place credible information in the hands of clinicians in time to support clinical decision making. The electronic health record is an essential component of clinician workflow. Utilizing the electronic health record to present information to support the use of genomic medicine in clinical care to improve outcomes represents a tremendous opportunity. However, there are numerous barriers that prevent the effective use of the electronic health record for this purpose. The electronic health record working groups of the Electronic Medical Records and Genomics (eMERGE) Network and the Clinical Genome Resource (ClinGen) project, along with other groups, have been defining these barriers, to allow the development of solutions that can be tested using implementation pilots. In this paper, we present “lessons learned” from these efforts to inform future efforts leading to the development of effective and sustainable solutions that will support the realization of genomic medicine.

Precision medicine: a call for increased pharmacogenomic education

Precision medicine is an emerging model of care where providers consider patients' genetic profiles, lifestyles and environments to offer more precise therapy. The potential of precision medicine is boundless as interdisciplinary teams utilize genetic technologies to improve patient outcomes. The integration of precision medicine into healthcare faces many barriers, including a lack of standardization and reimbursement concerns. This article argues that increased pharmacogenetics education and system-wide implementation is necessary to overcome some of these challenges. Extensive expansion of pharmacogenomics education is a step toward producing knowledgeable clinicians who are poised to apply its methodology and champion for patient-centered care.

Patient-Centered Precision Health In A Learning Health Care System: Geisinger's Genomic Medicine Experience

Health care delivery is increasingly influenced by the emerging concepts of precision health and the learning health care system. Although not synonymous with precision health, genomics is a key enabler of individualized care. Delivering patient-centered, genomics-informed care based on individual-level data in the current national landscape of health care delivery is a daunting challenge. Problems to overcome include data generation, analysis, storage, and transfer; knowledge management and representation for patients and providers at the point of care; process management; and outcomes definition, collection, and analysis. Development, testing, and implementation of a genomics-informed program requires multidisciplinary collaboration and building the concepts of precision health into a multilevel implementation framework. Using the principles of a learning health care system provides a promising solution. This article describes the implementation of population-based genomic medicine in an integrated learning health care system-a working example of a precision health program.

Bringing 3D tumor models to the clinic - predictive value for personalized medicine

Current decision-guiding algorithms in cancer drug treatment are based on decades of research and numerous clinical trials. For the majority of patients, this data is successfully applied for a systemic disease management. For a number of patients however, treatment stratification according to clinically based risk criteria will not be sufficient. The most effective treatment options are ideally identified prior to the start of clinical drug therapy. This review will discuss the implementation of three-dimensional (3D) cell culture models as a preclinical testing paradigm for the efficacy of clinical cancer treatment. Patient tumor-derived cells in 3D cultures duplicate the individual tumor microenvironment with a minimum of confounding factors. Clinical implementation of such personalized tumor models requires a high quality of methodological and clinical validation comparable to other biomarkers. A non-systematic literature search demonstrated the small number of prospective studies that have been conducted in this area of research. This may explain the current reluctance of many physicians and insurance providers in implementing this type of assay into the clinical diagnostic routine despite potential benefit for patients. Achieving valid and reproducible results with a high level of evidence is central in improving the acceptance of preclinical 3D tumor models.

How to Become a Smart Patient in the Era of Precision Medicine?

The objective of this paper is to define the definition of smart patients, summarize the existing foundation, and explore the approaches and system participation model of how to become a smart patient. Here a thorough review of the literature was conducted to make theory derivation processes of the smart patient; "data, information, knowledge, and wisdom (DIKW) framework" was performed to construct the model of how smart patients participate in the medical process. The smart patient can take an active role and fully participate in their own health management; DIKW system model provides a theoretical framework and practical model of smart patients; patient education is the key to the realization of smart patients. The conclusion is that the smart patient is attainable and he or she is not merely a patient but more importantly a captain and global manager of one's own health management, a partner of medical practitioner, and also a supervisor of medical behavior. Smart patients can actively participate in their healthcare and assume higher levels of responsibility for their own health and wellness which can facilitate the development of precision medicine and its widespread practice.

Informatics in neurocritical care: new ideas for Big Data

PURPOSE OF REVIEW

Big data is the new hype in business and healthcare. Data storage and processing has become cheap, fast, and easy. Business analysts and scientists are trying to design methods to mine these data for hidden knowledge. Neurocritical care is a field that typically produces large amounts of patient-related data, and these data are increasingly being digitized and stored. This review will try to look beyond the hype, and focus on possible applications in neurointensive care amenable to Big Data research that can potentially improve patient care.

RECENT FINDINGS

The first challenge in Big Data research will be the development of large, multicenter, and high-quality databases. These databases could be used to further investigate recent findings from mathematical models, developed in smaller datasets. Randomized clinical trials and Big Data research are complementary. Big Data research might be used to identify subgroups of patients that could benefit most from a certain intervention, or can be an alternative in areas where randomized clinical trials are not possible.

SUMMARY

The processing and the analysis of the large amount of patient-related information stored in clinical databases is beyond normal human cognitive ability. Big Data research applications have the potential to discover new medical knowledge, and improve care in the neurointensive care unit.

Big data from small samples: Informatics of next-generation sequencing in cytopathology

The rapid adoption of next-generation sequencing (NGS) in clinical molecular laboratories has redefined the practice of cytopathology. Instead of simply being used as a diagnostic tool, cytopathology has evolved into a practice providing important genomic information that guides clinical management. The recent emphasis on maximizing limited-volume cytology samples for ancillary molecular studies, including NGS, requires cytopathologists not only to be more involved in specimen collection and processing techniques but also to be aware of downstream testing and informatics issues. For the integration of molecular informatics into the clinical workflow, it is important to understand the computational components of the NGS workflow by which raw sequence data are transformed into clinically actionable genomic information and to address the challenges of having a robust and sustainable informatics infrastructure for NGS-based testing in a clinical environment. Adapting to needs ranging from specimen procurement to report delivery is crucial for the optimal utilization of cytology specimens to accommodate requests from clinicians to improve patient care. This review presents a broad overview of the various aspects of informatics in the context of NGS-based testing of cytology specimens. Cancer Cytopathol 2017;125:236-244. © 2016 American Cancer Society.

Electronic Health Records: Then, Now, and in the Future

OBJECTIVES

Describe the state of Electronic Health Records (EHRs) in 1992 and their evolution by 2015 and where EHRs are expected to be in 25 years. Further to discuss the expectations for EHRs in 1992 and explore which of them were realized and what events accelerated or disrupted/derailed how EHRs evolved.

METHODS

Literature search based on "Electronic Health Record", "Medical Record", and "Medical Chart" using Medline, Google, Wikipedia Medical, and Cochrane Libraries resulted in an initial review of 2,356 abstracts and other information in papers and books. Additional papers and books were identified through the review of references cited in the initial review.

RESULTS

By 1992, hardware had become more affordable, powerful, and compact and the use of personal computers, local area networks, and the Internet provided faster and easier access to medical information. EHRs were initially developed and used at academic medical facilities but since most have been replaced by large vendor EHRs. While EHR use has increased and clinicians are being prepared to practice in an EHR-mediated world, technical issues have been overshadowed by procedural, professional, social, political, and especially ethical issues as well as the need for compliance with standards and information security. There have been enormous advancements that have taken place, but many of the early expectations for EHRs have not been realized and current EHRs still do not meet the needs of today's rapidly changing healthcare environment.

CONCLUSION

The current use of EHRs initiated by new technology would have been hard to foresee. Current and new EHR technology will help to provide international standards for interoperable applications that use health, social, economic, behavioral, and environmental data to communicate, interpret, and act intelligently upon complex healthcare information to foster precision medicine and a learning health system.

The genomic CDS sandbox: An assessment among domain experts

Genomics is a promising tool that is becoming more widely available to improve the care and treatment of individuals. While there is much assertion, genomics will most certainly require the use of clinical decision support (CDS) to be fully realized in the routine clinical setting. The National Human Genome Research Institute (NHGRI) of the National Institutes of Health recently convened an in-person, multi-day meeting on this topic. It was widely recognized that there is a need to promote the innovation and development of resources for genomic CDS such as a CDS sandbox. The purpose of this study was to evaluate a proposed approach for such a genomic CDS sandbox among domain experts and potential users. Survey results indicate a significant interest and desire for a genomic CDS sandbox environment among domain experts. These results will be used to guide the development of a genomic CDS sandbox.