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The SAGE (Standards-Based Active Guideline Environment) project was formed to create a methodology and infrastructure required to demonstrate integration of decision-support technology for guideline-based care in commercial clinical information systems. This paper describes the development and innovative features of the SAGE Guideline Model and reports our experience encoding four guidelines. Innovations include methods for integrating guideline-based decision support with clinical workflow and employment of enterprise order sets. Using SAGE, a clinician informatician can encode computable guideline content as recommendation sets using only standard terminologies and standards-based patient information models. The SAGE Model supports encoding large portions of guideline knowledge as re-usable declarative evidence statements and supports querying external knowledge sources. [ABSTRACT FROM AUTHOR]

Information technology can support the implementation of clinical research findings in practice settings. Technology can address the quality gap in health care by providing automated decision support to clinicians that integrates guideline knowledge with electronic patient data to present real-time, patient-specific recommendations. However, technical success in implementing decision support systems may not translate directly into system use by clinicians. Successful technology integration into clinical work settings requires explicit attention to the organizational context. We describe the application of a "sociotechnical" approach to integration of ATHENA DSS, a decision support system for the treatment of hypertension, into geographically dispersed primary care clinics. We applied an iterative technical design in response to organizational input and obtained ongoing endorsements of the project by the organization's administrative and clinical leadership. Conscious attention to organizational context at the time of development, deployment, and maintenance of the system was associated with extensive clinician use of the system. [ABSTRACT FROM AUTHOR]

The Institute of Medicine recently issued a landmark report on medical error. In the penumbra of this report, every aspect of health care is subject to new scrutiny regarding patient safety. Informatics technology can support patient safety by correcting problems inherent in older technology; however, new information technology can also contribute to new sources of error. We report here a categorization of possible errors that may arise in deploying a system designed to give guideline-based advice on prescribing drugs, an approach to anticipating these errors in an automated guideline system, and design features to minimize errors and thereby maximize patient safety. Our guideline implementation system, based on the EON architecture, provides a framework for a knowledge base that is sufficiently comprehensive to incorporate safety information, and that is easily reviewed and updated by clinician-experts. [ABSTRACT FROM AUTHOR]

This paper presents an effort by the World Health Organization (WHO) to integrate the reference classifications of the Family of International Classifications (ICD, ICF, and ICHI) into a unified digital framework. The integration was accomplished via an expanded Content Model and a single Foundation that hosts all entities from these classifications, allowing the traditional use cases of individual classifications to be retained while enhancing their combined use. The harmonized WHO-FIC Content Model and the unified Foundation has streamlined the content management, enhanced the web-based tool functionalities, and provided opportunities for linkage with external terminologies and ontologies. This integration promises reduced maintenance cost, seamless joint application, complete representation of health-related concepts while enabling better interoperability with other informatics infrastructures.

The Family of International Classifications of the World Health Organization (WHO-FIC) currently includes three reference classifications, namely International Classification of Diseases (ICD), International Classification of Functioning, Disability, and Health (ICF), and International Classification of Health Interventions (ICHI). Recently, the three classifications have been incorporated into a single WHO-FIC Foundation that serves as the repository of all concepts in the classifications. Each classification serves a specific classification need. However, they share some common concepts that are present, in different forms, in two or all of them. For the WHO-FIC Foundation to be a logically consistent repository without duplicates, these common concepts must be reconciled. One important set of shared concepts is the representation of human anatomy entities, which are not always modeled in the same way and with the same level of detail. To understand the relationships among the three anatomical representations, an effort is needed to compare them, identifying common areas, gaps, and compatible and incompatible modeling. The work presented here contributes to this effort, focusing on the anatomy representations in ICF and ICD-11. For this aim, three experts were asked to identify, for each entity in the ICF Body Structures, one or more entities in the ICD-11 Anatomic Detail that could be considered identical, broader or narrower. To do this, they used a specifically developed web application, which also automatically identified the most obvious equivalences. A total of 631 maps were independently identified by the three mappers for 218 ICF Body Structures, with an interobserver agreement of 93.5%. Together with 113 maps identified by the software, they were then consolidated into 434 relations. The results highlight some differences between the two classifications: in general, ICF is less detailed than ICD-11; ICF favors lumping of structures; in very few cases, the two classifications follow different anatomic models. For these issues, solutions have to be found that are compliant with the WHO approach to classification modeling and maintenance., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Della Mea et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: The study has dual objectives. Our first objective (1) is to develop a community-of-practice-based evaluation methodology for knowledge-intensive computational methods. We target a whitebox analysis of the computational methods to gain insight on their functional features and inner workings. In more detail, we aim to answer evaluation questions on (i) support offered by computational methods for functional features within the application domain; and (ii) in-depth characterizations of the underlying computational processes, models, data and knowledge of the computational methods. Our second objective (2) involves applying the evaluation methodology to answer questions (i) and (ii) for knowledge-intensive clinical decision support (CDS) methods, which operationalize clinical knowledge as computer interpretable guidelines (CIG); we focus on multimorbidity CIG-based clinical decision support (MGCDS) methods that target multimorbidity treatment plans., Materials and Methods: Our methodology directly involves the research community of practice in (a) identifying functional features within the application domain; (b) defining exemplar case studies covering these features; and (c) solving the case studies using their developed computational methods-research groups detail their solutions and functional feature support in solution reports. Next, the study authors (d) perform a qualitative analysis of the solution reports, identifying and characterizing common themes (or dimensions) among the computational methods. This methodology is well suited to perform whitebox analysis, as it directly involves the respective developers in studying inner workings and feature support of computational methods. Moreover, the established evaluation parameters (e.g., features, case studies, themes) constitute a re-usable benchmark framework, which can be used to evaluate new computational methods as they are developed. We applied our community-of-practice-based evaluation methodology on MGCDS methods., Results: Six research groups submitted comprehensive solution reports for the exemplar case studies. Solutions for two of these case studies were reported by all groups. We identified four evaluation dimensions: detection of adverse interactions, management strategy representation, implementation paradigms, and human-in-the-loop support. Based on our whitebox analysis, we present answers to the evaluation questions (i) and (ii) for MGCDS methods., Discussion: The proposed evaluation methodology includes features of illuminative and comparison-based approaches; focusing on understanding rather than judging/scoring or identifying gaps in current methods. It involves answering evaluation questions with direct involvement of the research community of practice, who participate in setting up evaluation parameters and solving exemplar case studies. Our methodology was successfully applied to evaluate six MGCDS knowledge-intensive computational methods. We established that, while the evaluated methods provide a multifaceted set of solutions with different benefits and drawbacks, no single MGCDS method currently provides a comprehensive solution for MGCDS., Conclusion: We posit that our evaluation methodology, applied here to gain new insights into MGCDS, can be used to assess other types of knowledge-intensive computational methods and answer other types of evaluation questions. Our case studies can be accessed at our GitHub repository (https://github.com/william-vw/MGCDS)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Making recommendations from clinical practice guidelines (CPGs) computable for clinical decision support (CDS) has typically been a laborious and costly process. Identifying domain-specific regularities helps clinicians and knowledge engineers conceptualize, extract, and encode evidence-based recommendations. Based on our work to provide complex CDS in the management of multiple chronic diseases, we propose nine chronic disease CPG structural patterns, discuss considerations in representing the necessary knowledge, and illustrate them with the solutions that our CDS system provides., (©2022 AMIA - All rights reserved.)

Multimorbidity, the coexistence of two or more health conditions, has become more prevalent as mortality rates in many countries have declined and their populations have aged. Multimorbidity presents significant difficulties for Clinical Decision Support Systems (CDSS), particularly in cases where recommendations from relevant clinical guidelines offer conflicting advice. A number of research groups are developing computer-interpretable guideline (CIG) modeling formalisms that integrate recommendations from multiple Clinical Practice Guidelines (CPGs) for knowledge-based multimorbidity decision support. In this paper we describe work towards the development of a framework for comparing the different approaches to multimorbidity CIG-based clinical decision support (MGCDS). We present (1) a set of features for MGCDS, which were derived using a literature review and evaluated by physicians using a survey, and (2) a set of benchmarking case studies, which illustrate the clinical application of these features. This work represents the first necessary step in a broader research program aimed at the development of a benchmark framework that allows for standardized and comparable MGCDS evaluations, which will facilitate the assessment of functionalities of MGCDS, as well as highlight important gaps in the state-of-the-art. We also outline our future work on developing the framework, specifically, (3) a standard for reporting MGCDS solutions for the benchmark case studies, and (4) criteria for evaluating these MGCDS solutions. We plan to conduct a large-scale comparison study of existing MGCDS based on the comparative framework., (©2021 AMIA - All rights reserved.)

Background: The proportion of hospital deaths has declined in the past few decades, while the proportions of nursing home deaths have increased. This trend of increasing deaths in long-term care facilities underlines the importance of improving end-of-life care provisions in these settings to meet individual preferences and needs. Under these circumstances, a comprehensive understanding of end-of-life care preferences in local nursing home residents can help healthcare professionals and policymakers develop strategies to increase the advance directive completion rate and quality of care. This study aimed to explore and compare advance directive and end-of-life care preferences of nursing home residents in Hong Kong and Taiwan., Methods: A structured questionnaire was developed by the research team to investigate advance directive and end-of-life care preferences in older Chinese nursing home residents. Nursing home residents with frail or pre-frail status and over the age of 64 were invited to participate in the study, and information on demographics, functional status, advance directive experiences, and end-of-life care expectations was collected through questionnaire interviews., Results: A total of 325 eligible participants from 32 facilities completed the survey, including 238 older residents in Hong Kong and 87 in Taiwan. A significantly lower proportion of the Hong Kong residents had completed an advance directive compared with the Taiwanese (3 vs. 13%, p = 0.001). Among participants who did not have an advance directive, 46% of the Taiwanese participants said they would consider completing one in the future, compared with 20% of the Hong Kong participants (p < 0.001). A total of 79% of the Hong Kong participants and 80% of the Taiwanese participants responded that prolonging life in the given hypothetical dying scenario was "not important" (p = 0.76). Only 14% of participants in Hong Kong and 18% of participants in Taiwan reported prior occurrence of end-of-life care discussions with family members or health professionals (p = 0.37)., Conclusions: This paper adds evidence in support of improving end-of-life communication and the advance directive completion rate in nursing homes in Hong Kong and Taiwan. Further research is necessary to explore cross-cultural differences in end-of-life preferences and its applications in predicting decision-making and the quality of end-of-life care., (© 2021. The Author(s).)

Deconvolution of mouse transcriptomic data is challenged by the fact that mouse models carry various genetic and physiological perturbations, making it questionable to assume fixed cell types and cell type marker genes for different data set scenarios. We developed a Semi-Supervised Mouse data Deconvolution (SSMD) method to study the mouse tissue microenvironment. SSMD is featured by (i) a novel nonparametric method to discover data set-specific cell type signature genes; (ii) a community detection approach for fixing cell types and their marker genes; (iii) a constrained matrix decomposition method to solve cell type relative proportions that is robust to diverse experimental platforms. In summary, SSMD addressed several key challenges in the deconvolution of mouse tissue data, including: (i) varied cell types and marker genes caused by highly divergent genotypic and phenotypic conditions of mouse experiment; (ii) diverse experimental platforms of mouse transcriptomics data; (iii) small sample size and limited training data source and (iv) capable to estimate the proportion of 35 cell types in blood, inflammatory, central nervous or hematopoietic systems. In silico and experimental validation of SSMD demonstrated its high sensitivity and accuracy in identifying (sub) cell types and predicting cell proportions comparing with state-of-the-arts methods. A user-friendly R package and a web server of SSMD are released via https://github.com/xiaoyulu95/SSMD., (© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Patients with chronic multimorbidity are becoming more common as life expectancy increases, making it necessary for physicians to develop complex management plans. We are looking at the patient management process as a goal-attainment problem. Hence, our aim is to develop a goal-oriented methodology for providing decision support for managing patients with multimorbidity continuously, as the patient's health state is progressing and new goals arise (e.g., treat ulcer, prevent osteoporosis). Our methodology allows us to detect and mitigate inconsistencies among guideline recommendations stemming from multiple clinical guidelines, while consulting medical ontologies and terminologies and relying on patient information standards. This methodology and its implementation as a decision-support system, called GoCom, starts with computer-interpretable clinical guidelines (CIGs) for single problems that are formalized using the PROforma CIG language. We previously published the architecture of the system as well as a CIG elicitation guide for enriching PROforma tasks with properties referring to vocabulary codes of goals and physiological effects of management plans. In this paper, we provide a formalization of the conceptual model of GoCom that generates, for each morbidity of the patient, a patient-specific goal tree that results from the PROforma engine's enactment of the CIG with the patient's data. We also present the "Controller" algorithm that drives the GoCom system. Given a new problem that a patient develops, the Controller detects inconsistencies among goals pertaining to different comorbid problems and consults the CIGs to generate alternative non-conflicted and goal-oriented management plans that address the multiple goals simultaneously. In this stage of our research, the inconsistencies that can be detected are of two types - starting vs. stopping medications that belong to the same medication class hierarchy, and detecting opposing physiological effect goals that are specified in concurrent CIGs (e.g., decreased blood pressure vs. increased blood pressure). However, the design of GoCom is modular and generic and allows the future introduction of additional interaction detection and mitigation strategies. Moreover, GoCom generates explanations of the alternative non-conflicted management plans, based on recommendations stemming from the clinical guidelines and reasoning patterns. GoCom's functionality was evaluated using three cases of multimorbidity interactions that were checked by our three clinicians. Usefulness was evaluated with two studies. The first evaluation was a pilot study with ten 6th year medical students and the second evaluation was done with 27 6th medical students and interns. The participants solved complex realistic cases of multimorbidity patients: with and without decision-support, two cases in the first evaluation and 6 cases in the second evaluation. Use of GoCom increased completeness of the patient management plans produced by the medical students from 0.44 to 0.71 (P-value of 0.0005) in the first evaluation, and from 0.31 to 0.78 (P-value < 0.0001) in the second evaluation. Correctness in the first evaluation was very high with (0.98) or without the system (0.91), with non-significant difference (P-value ≥ 0.17). In the second evaluation, use of GoCom increased correctness from 0.68 to 0.83 (P-value of 0.001). In addition, GoCom's explanation and visualization were perceived as useful by the vast majority of participants. While GoCom's detection of goal interactions is currently limited to detection of starting vs. stopping the same medication or medication subclasses and detecting conflicting physiological effects of concurrent medications, the evaluation demonstrated potential of the system for improving clinical decision-making for multimorbidity patients., (Copyright © 2020 Elsevier Inc. All rights reserved.)

An overarching WHO-FIC Content Model will allow uniform modeling of classifications in the WHO Family of International Classifications (WHO-FIC) and promote their joint use. We provide an initial conceptualization of such a model.

Software testing of knowledge-based clinical decision support systems is challenging, labor intensive, and expensive; yet, testing is necessary since clinical applications have heightened consequences. Thoughtful test case selection improves testing coverage while minimizing testing burden. ATHENA-CDS is a knowledge-based system that provides guideline-based recommendations for chronic medical conditions. Using the ATHENA-CDS diabetes knowledgebase, we demonstrate a generalizable approach for selecting test cases using rules/ filters to create a set of paths that mimics the system's logic. Test cases are allocated to paths using a proportion heuristic. Using data from the electronic health record, we found 1,086 cases with glycemic control above target goals. We created a total of 48 filters and 50 unique system paths, which were used to allocate 200 test cases. We show that our method generates a comprehensive set of test cases that provides adequate coverage for the testing of a knowledge-based CDS.

Computer-interpretable guidelines (CIGs) are based on clinical practice guidelines, which typically address a single morbidity. However, most of the aging population suffers from multiple morbidities. Currently, there is no demonstrated effective mechanism that integrates recommendations from multiple CIGs. We are developing a goal-based method that utilizes knowledge of drugs' physiological effects and therapeutic usage to combine knowledge from CIGs. It incrementally detects interactions and plans non-contradicting therapies. Our algorithm uses patterns to check consistency and respond to events, including data enquiries, diagnoses, adverse events, recommended medications, tests, and goals. Our method utilizes existing standards and CIG tools, including the Fast Healthcare Interoperability Resources (FHIR) patient data model, SNOMED-CT, and the PROforma CIG formalism with its Alium knowledge-engineering environment and PROforma enactment engine. We demonstrate our approach using a case study involving two clinical guidelines with templates for responding to a new goal and to a medication request that causes an inconsistency which can be automatically detected and resolved based on the knowledge of the two CIGs.

Background: Structured data acquisition is a common task that is widely performed in biomedicine. However, current solutions for this task are far from providing a means to structure data in such a way that it can be automatically employed in decision making (e.g., in our example application domain of clinical functional assessment, for determining eligibility for disability benefits) based on conclusions derived from acquired data (e.g., assessment of impaired motor function). To use data in these settings, we need it structured in a way that can be exploited by automated reasoning systems, for instance, in the Web Ontology Language (OWL); the de facto ontology language for the Web., Results: We tackle the problem of generating Web-based assessment forms from OWL ontologies, and aggregating input gathered through these forms as an ontology of "semantically-enriched" form data that can be queried using an RDF query language, such as SPARQL. We developed an ontology-based structured data acquisition system, which we present through its specific application to the clinical functional assessment domain. We found that data gathered through our system is highly amenable to automatic analysis using queries., Conclusions: We demonstrated how ontologies can be used to help structuring Web-based forms and to semantically enrich the data elements of the acquired structured data. The ontologies associated with the enriched data elements enable automated inferences and provide a rich vocabulary for performing queries.

Clinicians and clinical decision-support systems often follow pharmacotherapy recommendations for patients based on clinical practice guidelines (CPGs). In multimorbid patients, these recommendations can potentially have clinically significant drug-drug interactions (DDIs). In this study, we describe and validate a method for programmatically detecting DDIs among CPG recommendations. The system extracts pharmacotherapy intervention recommendations from narrative CPGs, normalizes the terms, creates a mapping of drugs and drug classes, and then identifies occurrences of DDIs between CPG pairs. We used this system to analyze 75 CPGs written by authoring entities in the United States that discuss outpatient management of common chronic diseases. Using a reference list of high-risk DDIs, we identified 2198 of these DDIs in 638 CPG pairs (46 unique CPGs). Only 9 high-risk DDIs were discussed by both CPGs in a pairing. In 69 of the pairings, neither CPG had a pharmacologic reference or a warning of the possibility of a DDI.

The International Classification of Diseases (ICD) is the de facto standard international classification for mortality reporting and for many epidemiological, clinical, and financial use cases. The next version of ICD, ICD-11, will be submitted for approval by the World Health Assembly in 2018. Unlike previous versions of ICD, where coders mostly select single codes from pre-enumerated disease and disorder codes, ICD-11 coding will allow extensive use of multiple codes to give more detailed disease descriptions. For example, "severe malignant neoplasms of left breast" may be coded using the combination of a "stem code" (e.g., code for malignant neoplasms of breast) with a variety of "extension codes" (e.g., codes for laterality and severity). The use of multiple codes (a process called post-coordination), while avoiding the pitfall of having to pre-enumerate vast number of possible disease and qualifier combinations, risks the creation of meaningless expressions that combine stem codes with inappropriate qualifiers. To prevent that from happening, "sanctioning rules" that define legal combinations are necessary. In this work, we developed a crowdsourcing method for obtaining sanctioning rules for the post-coordination of concepts in ICD-11. Our method utilized the hierarchical structures in the domain to improve the accuracy of the sanctioning rules and to lower the crowdsourcing cost. We used Bayesian networks to model crowd workers' skills, the accuracy of their responses, and our confidence in the acquired sanctioning rules. We applied reinforcement learning to develop an agent that constantly adjusted the confidence cutoffs during the crowdsourcing process to maximize the overall quality of sanctioning rules under a fixed budget. Finally, we performed formative evaluations using a skin-disease branch of the draft ICD-11 and demonstrated that the crowd-sourced sanctioning rules replicated those defined by an expert dermatologist with high precision and recall. This work demonstrated that a crowdsourcing approach could offer a reasonably efficient method for generating a first draft of sanctioning rules that subject matter experts could verify and edit, thus relieving them of the tedium and cost of formulating the initial set of rules., (Copyright © 2017 Elsevier Inc. All rights reserved.)

As utilization of clinical decision support (CDS) increases, it is important to continue the development and refinement of methods to accurately translate the intention of clinical practice guidelines (CPG) into a computable form. In this study, we validate and extend the 13 steps that Shiffman et al. 5 identified for translating CPG knowledge for use in CDS. During an implementation project of ATHENA-CDS, we encoded complex CPG recommendations for five common chronic conditions for integration into an existing clinical dashboard. Major decisions made during the implementation process were recorded and categorized according to the 13 steps. During the implementation period, we categorized 119 decisions and identified 8 new categories required to complete the project. We provide details on an updated model that outlines all of the steps used to translate CPG knowledge into a CDS integrated with existing health information technology.

Through close analysis of two pairs of systems that implement the automated evaluation of performance measures (PMs) and guideline-based clinical decision support (CDS), we contrast differences in their knowledge encoding and necessary changes to a CDS system that provides management recommendations for patients failing performance measures. We trace the sources of differences to the implementation environments and goals of PMs and CDS.

Clinical decision support (CDS) systems with complex logic are being developed. Ensuring the quality of CDS is imperative, but there is no consensus on testing standards. We tested ATHENA-HTN CDS after encoding updated hypertension guidelines into the system. A logic flow and a complexity analysis of the encoding were performed to guide testing. 100 test cases were selected to test the major pathways in the CDS logic flow, and the effectiveness of the testing was analyzed. The encoding contained 26 decision points and 3120 possible output combinations. The 100 cases selected tested all of the major pathways in the logic, but only 1% of the possible output combinations. Test case selection is one of the most challenging aspects in CDS testing and has a major impact on testing coverage. A test selection strategy should take into account the complexity of the system, identification of major logic pathways, and available resources.

Decision support tools increasingly integrate clinical knowledge such as medication indications and contraindications with electronic health record (EHR) data to support clinical care and patient safety. The availability of this encoded information and patient data provides an opportunity to develop measures of clinical decision complexity that may be of value for quality improvement and research efforts. We investigated the feasibility of using encoded clinical knowledge and EHR data to develop a measure of comorbidity interrelatedness (the degree to which patients' co-occurring conditions interact to generate clinical complexity). Using a common clinical scenario-decisions about blood pressure medications in patients with hypertension-we quantified comorbidity interrelatedness by calculating the number of indications and contraindications to blood pressure medications that are generated by patients' comorbidities (e.g., diabetes, gout, depression). We examined properties of comorbidity interrelatedness using data from a decision support system for hypertension in the Veterans Affairs Health Care System.

We developed a method to evaluate the extent to which the International Classification of Function, Disability, and Health (ICF) and SNOMED CT cover concepts used in the disability listing criteria of the U.S. Social Security Administration's "Blue Book." First we decomposed the criteria into their constituent concepts and relationships. We defined different types of mappings and manually mapped the recognized concepts and relationships to either ICF or SNOMED CT. We defined various metrics for measuring the coverage of each terminology, taking into account the effects of inexact matches and frequency of occurrence. We validated our method by mapping the terms in the disability criteria of Adult Listings, Chapter 12 (Mental Disorders). SNOMED CT dominates ICF in almost all the metrics that we have computed. The method is applicable for determining any terminology's coverage of eligibility criteria.

Background & Problems: Terminally ill patients frequently suffer from physical distress and a limited ability to perform activities of daily living. Bathing is a particularly troublesome problem for these patients, as the limitations of the home environment increase the challenges for caregivers to bathe terminally ill patients at home., Purpose: This study was developed to alter the current bathing methods of terminally ill patients in order to improve their physical comfort and to help caregivers better facilitate home-bathing care for their patients., Resolutions: A "portable shower bed" was introduced to allow patients to bathe in their own beds. The tools used to evaluate results were the Bathing and Physical Comfort Scale and the self-developed Bathing Satisfaction Questionnaire., Results: A total of 90% of participants expressed satisfaction with the portable shower bed. Furthermore, the average physical comfort score of participants improved from 3.9 to 6.13., Conclusion: The innovative portable shower bed is inexpensive, practical, foldable, easy to operate, safe, and comfortable. The bed significantly reduces the difficulties faced by caregivers in helping terminally ill patients bathe in home environments. This shower bed reduces the burden on caregivers by improving bathing care and the physical comfort of terminally ill patients.

To date, the scientific process for generating, interpreting, and applying knowledge has received less informatics attention than operational processes for conducting clinical studies. The activities of these scientific processes - the science of clinical research - are centered on the study protocol, which is the abstract representation of the scientific design of a clinical study. The Ontology of Clinical Research (OCRe) is an OWL 2 model of the entities and relationships of study design protocols for the purpose of computationally supporting the design and analysis of human studies. OCRe's modeling is independent of any specific study design or clinical domain. It includes a study design typology and a specialized module called ERGO Annotation for capturing the meaning of eligibility criteria. In this paper, we describe the key informatics use cases of each phase of a study's scientific lifecycle, present OCRe and the principles behind its modeling, and describe applications of OCRe and associated technologies to a range of clinical research use cases. OCRe captures the central semantics that underlies the scientific processes of clinical research and can serve as an informatics foundation for supporting the entire range of knowledge activities that constitute the science of clinical research., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Two of the four WNK (with no lysine (K)) protein kinases are associated with a heritable form of ion imbalance culminating in hypertension. WNK1 affects ion transport in part through activation of the closely related Ste20 family protein kinases oxidative stress-responsive 1 (OSR1) and STE20/SPS1-related proline-, alanine-rich kinase (SPAK). Once activated by WNK1, OSR1 and SPAK phosphorylate and stimulate the sodium, potassium, two chloride co-transporters, NKCC1 and NKCC2, and also affect other related ion co-transporters. We find that WNK1 and OSR1 co-localize on cytoplasmic puncta in HeLa and other cell types. We show that the C-terminal region of WNK1 including a coiled coil is sufficient to localize the fragment in a manner similar to the full-length protein, but some other fragments lacking this region are mislocalized. Photobleaching experiments indicate that both hypertonic and hypotonic conditions reduce the mobility of GFP-WNK1 in cells. The four WNK family members can phosphorylate the activation loop of OSR1 to increase its activity with similar kinetic constants. C-terminal fragments of WNK1 that contain three RFXV interaction motifs can bind OSR1, block activation of OSR1 by sorbitol, and prevent the OSR1-induced enhancement of ion co-transporter activity in cells, further supporting the conclusion that association with WNK1 is required for OSR1 activation and function at least in some contexts. C-terminal WNK1 fragments can be phosphorylated by OSR1, suggesting that OSR1 catalyzes feedback phosphorylation of WNK1.

An abstraction network is an auxiliary network of nodes and links that provides a compact, high-level view of an ontology. Such a view lends support to ontology orientation, comprehension, and quality-assurance efforts. A methodology is presented for deriving a kind of abstraction network, called a partial-area taxonomy, for the Ontology of Clinical Research (OCRe). OCRe was selected as a representative of ontologies implemented using the Web Ontology Language (OWL) based on shared domains. The derivation of the partial-area taxonomy for the Entity hierarchy of OCRe is described. Utilizing the visualization of the content and structure of the hierarchy provided by the taxonomy, the Entity hierarchy is audited, and several errors and inconsistencies in OCRe's modeling of its domain are exposed. After appropriate corrections are made to OCRe, a new partial-area taxonomy is derived. The generalizability of the paradigm of the derivation methodology to various families of biomedical ontologies is discussed.

Human studies are one of the most valuable sources of knowledge in biomedical research, but data about their design and results are currently widely dispersed in siloed systems. Federation of these data is needed to facilitate large-scale data analysis to realize the goals of evidence-based medicine. The Human Studies Database project has developed an informatics infrastructure for federated query of human studies databases, using a generalizable approach to ontology-based data access. Our approach has three main components. First, the Ontology of Clinical Research (OCRe) provides the reference semantics. Second, a data model, automatically derived from OCRe into XSD, maintains semantic synchrony of the underlying representations while facilitating data acquisition using common XML technologies. Finally, the Query Integrator issues queries distributed over the data, OCRe, and other ontologies such as SNOMED in BioPortal. We report on a demonstration of this infrastructure on data acquired from institutional systems and from ClinicalTrials.gov.

Effective clinical text processing requires accurate extraction and representation of temporal expressions. Multiple temporal information extraction models were developed but a similar need for extracting temporal expressions in eligibility criteria (e.g., for eligibility determination) remains. We identified the temporal knowledge representation requirements of eligibility criteria by reviewing 100 temporal criteria. We developed EliXR-TIME, a frame-based representation designed to support semantic annotation for temporal expressions in eligibility criteria by reusing applicable classes from well-known clinical temporal knowledge representations. We used EliXR-TIME to analyze a training set of 50 new temporal eligibility criteria. We evaluated EliXR-TIME using an additional random sample of 20 eligibility criteria with temporal expressions that have no overlap with the training data, yielding 92.7% (76 / 82) inter-coder agreement on sentence chunking and 72% (72 / 100) agreement on semantic annotation. We conclude that this knowledge representation can facilitate semantic annotation of the temporal expressions in eligibility criteria.

Formalizing eligibility criteria in a computer-interpretable language would facilitate eligibility determination for study subjects and the identification of studies on similar patient populations. Because such formalization is extremely labor intensive, we transform the problem from one of fully capturing the semantics of criteria directly in a formal expression language to one of annotating free-text criteria in a format called ERGO annotation. The annotation can be done manually, or it can be partially automated using natural-language processing techniques. We evaluated our approach in three ways. First, we assessed the extent to which ERGO annotations capture the semantics of 1000 eligibility criteria randomly drawn from ClinicalTrials.gov. Second, we demonstrated the practicality of the annotation process in a feasibility study. Finally, we demonstrate the computability of ERGO annotation by using it to (1) structure a library of eligibility criteria, (2) search for studies enrolling specified study populations, and (3) screen patients for potential eligibility for a study. We therefore demonstrate a new and practical method for incrementally capturing the semantics of free-text eligibility criteria into computable form., (Copyright © 2010 Elsevier Inc. All rights reserved.)

WNK [with no lysine (K)] protein kinases are found in all sequenced multicellular and many unicellular organisms. WNKs influence ion balance. Two WNK family members are associated with a single gene form of hypertension. RNA interference screens have implicated WNKs in survival and growth, and WNK1 is essential for viability of mice. We found that the majority of WNK1 is localized on cytoplasmic puncta in resting cells. During cell division, WNK1 localizes to mitotic spindles. Therefore, we analyzed mitotic phenotypes in WNK1 knockdown cells. A large percentage of WNK1 knockdown cells fail to complete cell division, displaying defects in mitotic spindles and also in abscission and cell survival. One of the best-characterized WNK1 targets is the protein kinase OSR1 (oxidative stress responsive 1). OSR1 regulates ion cotransporters, is activated in response to osmotic stress by WNK family members, and is largely associated with WNK1. In resting cells, the majority of OSR1, like WNK1, is on cytoplasmic puncta. OSR1 is also in nuclei. In contrast to WNK1, however, OSR1 does not concentrate around spindles during mitosis and does not show a WNK1-like localization pattern in mitotic cells. Knockdown of OSR1 has only a modest effect on cell survival and does not lead to spindle defects. We conclude that decreased cell survival associated with loss of WNK1 is attributable to defects in chromosome segregation and abscission and is independent of the effector kinase OSR1.

Standards-based, computable knowledge representations for eligibility criteria are increasingly needed to provide computer-based decision support for automated research participant screening, clinical evidence application, and clinical research knowledge management. We surveyed the literature and identified five aspects of eligibility criteria knowledge representation that contribute to the various research and clinical applications: the intended use of computable eligibility criteria, the classification of eligibility criteria, the expression language for representing eligibility rules, the encoding of eligibility concepts, and the modeling of patient data. We consider three of these aspects (expression language, codification of eligibility concepts, and patient data modeling) to be essential constructs of a formal knowledge representation for eligibility criteria. The requirements for each of the three knowledge constructs vary for different use cases, which therefore should inform the development and choice of the constructs toward cost-effective knowledge representation efforts. We discuss the implications of our findings for standardization efforts toward knowledge representation for sharable and computable eligibility criteria.

Background: Opioid prescribing for chronic pain is common and controversial, but recommended clinical practices are followed inconsistently in many clinical settings. Strategies for increasing adherence to clinical practice guideline recommendations are needed to increase effectiveness and reduce negative consequences of opioid prescribing in chronic pain patients., Methods: Here we describe the process and outcomes of a project to operationalize the 2003 VA/DOD Clinical Practice Guideline for Opioid Therapy for Chronic Non-Cancer Pain into a computerized decision support system (DSS) to encourage good opioid prescribing practices during primary care visits. We based the DSS on the existing ATHENA-DSS. We used an iterative process of design, testing, and revision of the DSS by a diverse team including guideline authors, medical informatics experts, clinical content experts, and end-users to convert the written clinical practice guideline into a computable algorithm to generate patient-specific recommendations for care based upon existing information in the electronic medical record (EMR), and a set of clinical tools., Results: The iterative revision process identified numerous and varied problems with the initially designed system despite diverse expert participation in the design process. The process of operationalizing the guideline identified areas in which the guideline was vague, left decisions to clinical judgment, or required clarification of detail to insure safe clinical implementation. The revisions led to workable solutions to problems, defined the limits of the DSS and its utility in clinical practice, improved integration into clinical workflow, and improved the clarity and accuracy of system recommendations and tools., Conclusions: Use of this iterative process led to development of a multifunctional DSS that met the approval of the clinical practice guideline authors, content experts, and clinicians involved in testing. The process and experiences described provide a model for development of other DSSs that translate written guidelines into actionable, real-time clinical recommendations.

Measuring quality in clinical care is a time-consuming manual task. The vast amounts of clinical data collected through electronic medical records (EMRs) create an opportunity to develop tools that automatically assess quality indicators; however, the diversity of EMR implementations limits the ability to implement general, reusable methods. We evaluate an ontology-based virtual medical record (VMR) approach as a standardized, sharable methodology for defining data abstractions needed for quality of care assessment. Using a set of cancer quality indicators, we conducted a requirements analysis for modeling these abstractions with an OWL-based VMR. We found that the VMR approach needs to be extended to support population-based aggregations of clinical events, models of intended versus completed actions, and models of workflow and delivery systems. Incorporating the patient perspective on quality also requires additional extension of the VMR. We are using these results to create a virtual quality record based on EMR data.

Worldwide developments concerning infectious diseases and bioterrorism are driving forces for improving aberrancy detection in public health surveillance. The performance of an aberrancy detection algorithm can be measured in terms of sensitivity, specificity and timeliness. However, these metrics are probabilistically dependent variables and there is always a trade-off between them. This situation raises the question of how to quantify this tradeoff. The answer to this question depends on the characteristics of the specific disease under surveillance, the characteristics of data used for surveillance, and the algorithmic properties of detection methods. In practice, the evidence describing the relative performance of different algorithms remains fragmented and mainly qualitative. In this paper, we consider the development and evaluation of a Bayesian network framework for analysis of performance measures of aberrancy detection algorithms. This framework enables principled comparison of algorithms and identification of suitable algorithms for use in specific public health surveillance settings.

Objective: Transforming narrative guidelines into a computer-interpretable formalism is still a bottleneck in the development of decision-support systems. Our goal was to support this step by providing computer-interpretable templates for representing guideline knowledge using clinical abstractions that are appropriate for particular guideline sub-domains., Methods and Materials: We analyzed guidelines taken from the sub-domains of screening and immunization guidelines to find repeatable clinical abstractions and structured them as design templates to support encoding of these guidelines in a computer-interpretable format. To find guidelines for analysis and validation, we (1) searched the National Guideline Clearinghouse for screening guidelines in internal medicine, that have a clinical algorithm, and which were published during 2002-5 and (2) used adult and childhood immunization guidelines developed by Center of Disease Control and Prevention (CDC) and the Institute for Clinical Systems Improvement., Results: We developed two visual templates that structure screening guidelines as algorithms of guideline steps used for screening and data collection and used them to represent the guidelines collected. We validated the computability of the screening templates by executing a screening guideline in a workflow engine. We validated the computability of immunization templates by writing code that, based on represented knowledge, computes immunization due dates and by creating an algorithm that translates the knowledge into computer-interpretable guidelines., Conclusion: We have demonstrated that our templates could be effectively applied to screening and immunization guidelines to produce computer-interpretable representations using domain-level abstractions.

The Open Biomedical Ontologies (OBO) Foundry is a coordinated community-wide effort to develop ontologies that support the annotation and integration of scientific data. In work supported by the National Database of Autism Research (NDAR), we are developing an ontology of autism that extends the ontologies available in the OBO Foundry. We undertook a systematic literature review to identify domain terms and relationships relevant to autism phenotypes. To enable user queries and inferences about such phenotypes using data in the NDAR repository, we augmented the domain ontology with an information model. In this paper, we show how our approach, using a combination of description logic and rule-based reasoning, enables high-level phenotypic abstractions to be inferred from subject-specific data. Our integrated domain ontologyinformation model approach allows scientific data repositories to be augmented with rule-based abstractions that facilitate the ability of researchers to undertake data analysis.

Interoperable support of electronic health records and clinical decision support technology are central to the vision of sustainable information infrastructure. Efforts to implement interoperable clinical guidelines for immunization practice have been sparse. We used the SAGE knowledge workbench to develop a knowledge base to provide immunization decision support in primary care. We translated the written clinical guideline into a structured decision logic format. The semantic content to completely capture CDC clinical decision logic required 197 separate concepts but was completely captured with SNOMED CT and LOINC. Although 88% of concepts employed precoordinated codes, 6% of guideline concepts required expanded vocabulary services employing Boolean logical definition using two or more SNOMED concepts. Postcoordination requirements were modest, representing just 6% of guideline semantic concepts. We conclude that creation of interoperable knowledge bases employing clinical vocabulary standards is achievable and realistic. Employment of information model (HL7 RIM) and vocabulary (SNOMED CT, LOINC) standards is a necessary and feasible requirement to achieve interoperability in clinical decision support.

A major portion of patient care planning occurs during the process of writing orders. Computerized order entry can present collections of predefined orders to the user during the ordering process. These order sets are useful for promoting standards of care, and provide one element of structured clinical knowledge to be used by Computerized Provider Order Entry (CPOE) systems at the point of care. Since the creation, confirmation and maintenance of order sets is resource intensive, sharing order sets is a useful goal. We describe a standard representation of order sets that supports maintenance, sharing and interoperation of pre-defined order sets. A dialogue within the HL7 community seeks to harmonize this proposal with the Clinical Document Architecture and the HL7 Reference Information Model.

Developing computer-interpretable clinical practice guidelines (CPGs) to provide decision support for guideline-based care is an extremely labor-intensive task. In the EON/ATHENA and SAGE projects, we formulated substantial portions of CPGs as computable statements that express declarative relationships between patient conditions and possible interventions. We developed query and expression languages that allow a decision-support system (DSS) to evaluate these statements in specific patient situations. A DSS can use these guideline statements in multiple ways, including: (1) as inputs for determining preferred alternatives in decision-making, and (2) as a way to provide targeted commentaries in the clinical information system. The use of these declarative statements significantly reduces the modeling expertise and effort required to create and maintain computer-interpretable knowledge bases for decision-support purpose. We discuss possible implications for sharing of such knowledge bases.

Prostate cancer is initially responsive to androgen ablation, but prostate cancer tumors invariably progress to an androgen-independent state that is ultimately lethal. The onset of the androgen-independent prostate cancer is often associated with up-regulation of the androgen receptor that can cause antagonists to exhibit agonistic activity, which could lead to the failure of androgen ablation therapy. We describe a unique protein-DOC-2/DAB2 (differentially expressed in ovarian cancer-2/disabled 2)-that antagonizes androgen receptor-mediated cell growth in prostate cancer cells via interaction with c-Src protein. This interaction causes inactivation of Erk and Akt proteins critical for proliferation and survival of prostate cancer cells. However, DOC-2/DAB2 does not change the capacity of androgen receptor to regulate the transcription of androgen-responsive reporter genes, indicating that DOC-2/DAB2 selectively inhibits androgen receptor-mediated cell growth in androgen-independent prostate cancer by disrupting the androgen receptor/c-Src complex. In normal prostatic epithelia, DOC-2/DAB2 protein levels are more abundant than androgen receptor protein levels and reduced endogenous DOC-2/DAB2 protein levels in these cells by DOC-2/DAB2 RNA interference result in enhancing androgen receptor-mediated cell growth. We conclude that DOC-2/DAB2 can modulate androgen receptor-mediated cell growth in both normal and malignant prostatic epithelial cells and the outcome of this study could evolve into a new therapeutic strategy of prostate cancer.

The down-regulation of DOC-2/DAB2 gene, which encodes a unique phosphoprotein modulating signal pathways elicited by exogenous stimuli, is often associated with several cancer types; however, the underlying mechanism is still unknown. Dramatically different expression levels of DOC-2/DAB2 mRNA and protein are observed among several human transitional cell carcinoma (TCC) cell lines, suggesting that transcriptional regulation may play a role in these cells. In this study, we have shown that the histone acetylation status associated with the 5' upstream regulatory sequence of DOC-2/DAB2 gene is one of the key determinants for its gene expression. In addition, GATA6 but not other GATA family members, such as GATA2 and GATA4, can specifically induce DOC-2/DAB2 promoter activity, although GATA transcription factors share a very similar DNA-binding sequence. We also show that increased histone acetylation and the presence of GATA6 have a synergistic effect on DOC-2/DAB2 promoter activity, which results in the elevation of DOC-2/DAB2 protein expression. Thus, we conclude that transcriptional regulation of DOC-2/DAB2 gene in human TCC is determined by histone acetylation and a specific transcription factor (i.e., GATA6), which underlie the reduced DOC-2/DAB2 protein expression in TCC cells.

Human DAB2IP (hDAB2IP), a novel GTPase-activating protein modulating the Ras-mediated signaling and tumor necrosis factor-mediated apoptosis, is a potent growth inhibitor in human prostate cancer (PCa). Loss of hDAB2IP expression in PCa is due to altered epigenetic regulation (i.e. DNA methylation and histone modification) of its promoter region. The elevated polycomb Ezh2, a histone methyltransferase, has been associated with PCa progression. In this study, we have demonstrated that an increased Ezh2 expression in normal prostatic epithelial cells can suppress hDAB2IP gene expression. In contrast, knocking down the endogenous Ezh2 levels in PCa by a specific small interfering RNA can increase hDAB2IP expression. The association of Ezh2 complex (including Eed and Suz12) with hDAB2IP gene promoter is also detected in PCa cells but not in normal prostatic epithelial cells. Increased Ezh2 expression in normal prostatic epithelial cells by cDNA transfection facilitates the recruitment of other components of Ezh2 complex to the hDAB2IP promoter region accompanied with the increased levels of methyl histone H3 (H3) and histone deacetylase (HDAC1). Consistently, data from PCa cells transfected with Ezh2 small interfering RNA demonstrated that reduced Ezh2 levels resulted in the dissociation of Ezh2 complex accompanied with decreased levels of both methyl H3 and HDAC1 from hDAB2IP gene promoter. We further unveiled that the methylation status of Lys-27 but not Lys-9 of H3 in hDAB2IP promoter region is consistent with the hDAB2IP levels in both normal prostatic epithelial cells and PCa cells. Together, we conclude that hDAB2IP gene is a target gene of Ezh2 in prostatic epithelium, which provides an underlying mechanism of the down-regulation of hDAB2IP gene in PCa.

Decision-support systems based on clinical practice guidelines can support physicians and other healthcare personnel in the process of following best practice consistently. A knowledge-based approach to represent guidelines makes it possible to encode computer-interpretable guidelines in a formal manner,perform consistency checks, and use the guidelines directly in decision-support systems.Decision-support authors and guideline users require guidelines in human-readable formats in addition to computer-interpretable ones (e.g., for guideline review and quality assurance). We propose a new document-oriented information architecture that combines knowledge-representation models with electronic and paper documents. The approach integrates decision-support modes with standard document formats to create a combined clinical-guideline model that supports on-line viewing, printing, and decision support.

We have developed the GLIF3 Guideline Execution Engine (GLEE) as a tool for executing guidelines encoded in the GLIF3 format. In addition to serving as an interface to the GLIF3 guideline representation model to support the specified functions, GLEE provides defined interfaces to electronic medical records (EMRs) and other clinical applications to facilitate its integration with the clinical information system at a local institution. The execution model of GLEE takes the "system suggests, user controls" approach. A tracing system is used to record an individual patient's state when a guideline is applied to that patient. GLEE can also support an event-driven execution model once it is linked to the clinical event monitor in a local environment. Evaluation has shown that GLEE can be used effectively for proper execution of guidelines encoded in the GLIF3 format. When using it to execute each guideline in the evaluation, GLEE's performance duplicated that of the reference systems implementing the same guideline but taking different approaches. The execution flexibility and generality provided by GLEE, and its integration with a local environment, need to be further evaluated in clinical settings. Integration of GLEE with a specific event-monitoring and order-entry environment is the next step of our work to demonstrate its use for clinical decision support. Potential uses of GLEE also include quality assurance, guideline development, and medical education.

The success of clinical decision-support systems requires that they are seamlessly integrated into clinical workflow. In the SAGE project, which aims to create the technological infra-structure for implementing computable clinical practice guide-lines in enterprise settings, we created a deployment-driven methodology for developing guideline knowledge bases. It involves (1) identification of usage scenarios of guideline-based care in clinical workflow, (2) distillation and disambiguation of guideline knowledge relevant to these usage scenarios, (3) formalization of data elements and vocabulary used in the guideline, and (4) encoding of usage scenarios and guideline knowledge using an executable guideline model. This methodology makes explicit the points in the care process where guideline-based decision aids are appropriate and the roles of clinicians for whom the guideline-based assistance is intended. We have evaluated the methodology by simulating the deployment of an immunization guideline in a real clinical information system and by reconstructing the workflow context of a deployed decision-support system for guideline-based care. We discuss the implication of deployment-driven guideline encoding for sharability of executable guidelines.

The goal of shareable, executable clinical guidelines is both worthwhile and challenging. One of the largest hurdles is that of representing the necessary clinical information in a precise and shareable manner. Standard terminologies and common information models, such as the HL7 RIM, are necessary, they are not sufficient. In addition, common detailed clinical models are needed to give precise semantics and to make the task of mapping between models manageable. We discuss the experience of the SAGE project related to detailed clinical models.

The SAGE (Standards-Based Sharable Active Guideline Environment) project is a collaboration among research groups at six institutions in the US. The ultimate goal of the project is to create an infrastructure that will allow execution of standards-based clinical practice guidelines across heterogeneous clinical information systems. This paper describes the design goals of the SAGE guideline model in the context of the technological infrastructure and guideline modeling methodology that the project is developing.

An important step in building guideline-based clinical care systems is encoding guidelines. Protégé-2000, developed in our laboratory, is a general-purpose knowledge-acquisition tool that facilitates domain experts and developers to record, browse and maintain domain knowledge in knowledge bases. In this poster we illustrate a knowledge-acquisition wizard that we built around Protégé-2000. The wizard provides an environment that is more intuitive to domain specialists to enter knowledge, and domain specialists and practitioners to review the knowledge entered.

Protégé-2000 is an open-source tool that assists users in the construction of large electronic knowledge bases. It has an intuitive user interface that enables developers to create and edit domain ontologies. Numerous plugins provide alternative visualization mechanisms, enable management of multiple ontologies, allow the use of interference engines and problem solvers with Protégé ontologies, and provide other functionality. The Protégé user community has more than 7000 members.