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1.  Development of Clinical Decision Support Alerts for Pharmacogenomic Incidental Findings from Exome Sequencing 
Purpose
Electronic health records (EHRs) and their associated decision support tools are potentially important means of disseminating a patient’s pharmacogenomic profile to his or her health-care providers. We sought to create a proof-of-concept decision support alert system generated from pharmacogenomic incidental findings from exome sequencing.
Methods
A pipeline for alerts from exome sequencing tests was created for patients in the New EXome Technology in (NEXT) Medicine study at the University of Washington. Decision support rules using discrete, machine-readable incidental finding results were programmed into a commercial EHR rules engine. An evaluation plan to monitor the alerts in real medical interactions was established.
Results
Alerts were created for 48 actionable pharmacogenomic variants in 11 genes and were launched on 24 September 2014 for University of Washington inpatient care. Of the 94 participants enrolled in the NEXT Medicine study, 49 had one or more pharmacogenomic variants identified for return.
Conclusion
Reflections on the process reveal that while incidental findings can be used to generate decision support alerts, substantial resources are required to ensure that each alert is consistent with rapidly evolving pharmacogenomic literature and is customized to fit in the clinical workflow unique to each incidental finding.
doi:10.1038/gim.2015.5
PMCID: PMC4976082  PMID: 25741865
Genomic medicine; pharmacogenomics; electronic medical records; clinical decision support; clinical informatics
2.  CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record 
Objective Clinicians’ ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS).
Materials and Methods The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement.
Results There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information.
Conclusion Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended.
doi:10.1093/jamia/ocv065
PMCID: PMC5009914  PMID: 26142422
genetics; electronic health records; translational research; clinical decision support; survey
3.  Modeling the costs of clinical decision support for genomic precision medicine 
Clinical decision support (CDS) within the electronic health record represents a promising mechanism to provide important genomic findings within clinical workflows. To better understand the current and possible future costs of genomic CDS, we leveraged our local CDS experience to assemble a simple model with inputs such as initial cost and numbers of patients, rules, and institutions. Our model assumed efficiencies of scale and allowed us to perform a one-way sensitivity analysis of the impact of each model input. The number of patients with genomic results per institution was the only single variable that could decrease the cost of CDS per useful alert below projected genomic sequencing costs. Because of the prohibitive upfront cost of sequencing large numbers of individuals, increasing the number of institutions using genomic CDS and improving the efficiency of sharing CDS infrastructure represent the most promising paths to making genomic CDS cost-effective.
PMCID: PMC5001767  PMID: 27570652
4.  Making Pharmacogenomic-based Prescribing Alerts More Effective: A Scenario-based Pilot Study with Physicians 
To facilitate personalized drug dosing (PDD), this pilot study explored the communication effectiveness and clinical impact of using a prototype clinical decision support (CDS) system embedded in an electronic health record (EHR) to deliver pharmacogenomic (PGx) information to physicians. We employed a conceptual framework and measurement model to access the impact of physician characteristics (previous experience, awareness, relative advantage, perceived usefulness), technology characteristics (methods of implementation-semi-active/active, actionability-low/high) and a task characteristic (drug prescribed) on communication effectiveness (usefulness, confidence in prescribing decision), and clinical impact (uptake, prescribing intent, change in drug dosing). Physicians performed prescribing tasks using five simulated clinical case scenarios, presented in random order within the prototype PGx-CDS system. Twenty-two physicians completed the study. The proportion of physicians that saw a relative advantage to using PGx-CDS was 83% at the start and 94% at the conclusion of our study. Physicians used semi-active alerts 74%-88% of the time. There was no association between previous experience with, awareness of, and belief in a relative advantage of using PGx-CDS and improved uptake. The proportion of physicians reporting confidence in their prescribing decisions decreased significantly after using the prototype PGx-CDS system (p=0.02). Despite decreases in confidence, physicians perceived a relative advantage to using PGx-CDS, viewed semi-active alerts on most occasions, and more frequently changed doses toward doses supported by published evidence. Specifically, sixty-five percent of physicians reduced their dosing, significantly for capecitabine (p=0.002) and mercaptopurine/thioguanine (p=0.03). These findings suggest a need to improve our prototype such that PGx CDS content is more useful and delivered in a way that improves physician's confidence in their prescribing decisions. The greatest increases in communication effectiveness and clinical impact of PGx-CDS are likely to be realized through continued focus on content, content delivery, and tailoring to physician characteristics.
Graphical Abstract
doi:10.1016/j.jbi.2015.04.011
PMCID: PMC4704108  PMID: 25957826
5.  Pragmatic and Ethical Challenges of Incorporating the Genome into the Electronic Medical Record 
Current genetic medicine reports  2014;2(4):201-211.
Recent successes in the use of gene sequencing for patient care highlight the potential of genomic medicine. For genomics to become a part of usual care, pertinent elements of a patient's genomic test must be communicated to the most appropriate care providers. Electronic medical records may serve as a useful tool for storing and disseminating genomic data. Yet, the structure of existing EMRs and the nature of genomic data pose a number of pragmatic and ethical challenges in their integration. Through a review of the recent genome-EMR integration literature, we explore concrete examples of these challenges, categorized under four key questions: What data will we store? How will we store it? How will we use it? How will we protect it? We conclude that genome-EMR integration requires a rigorous, multi-faceted and interdisciplinary approach of study. Problems facing the field are numerous, but few are intractable.
doi:10.1007/s40142-014-0051-9
PMCID: PMC4489153  PMID: 26146597
Genomic medicine; clinical genomics; personalized medicine; pharmacogenomics; pharmacogenetics; electronic medical records; clinical decision support; translational research; implementation science; individualized medicine; Computerized Prescriber Order Entry; clinical informatics; genotype
6.  A Nationwide Surveyof Trauma Center Information Technology Leverage Capacity for Mental Health Comorbidity Screening 
Background
Despite evidence that electronic medical records (EMR) information technology innovations can enhance the quality of trauma center care, few investigations have systematically assessed United States (US) trauma center EMR capacity, particularly for screening of mental health comorbidities.
Study Design
Trauma programs at all US Level I and II trauma centers were contacted and asked to complete a survey regarding health information technology (IT) and EMR capacity.
Results
Three hundred and ninety one of 525 (74%) US Level I and II trauma centers responded to the survey. More than 90% of trauma centers report the ability to create custom patient tracking lists in their EMR. Forty-seven percent of centers were interested in automating a blood alcohol content (BAC) screening process, while only 14% report successfully using their EMR to perform this task. Marked variation was observed across trauma center sites with regard to the types of EMR systems employed as well as rates of adoption and turnover of EMR systems.
Conclusions
Most US Level I and II trauma centers have installed EMR systems, however marked heterogeneity exists with regard to EMR type, available features, and turnover. A minority of centers have leveraged their EMR for screening of mental health comorbidities among trauma inpatients. Greater attention to effective EMR use is warranted from trauma accreditation organizations.
doi:10.1016/j.jamcollsurg.2014.02.032
PMCID: PMC4160658  PMID: 25151344
7.  Usability Evaluation of Pharmacogenomics Clinical Decision Support Aids and Clinical Knowledge Resources in a Computerized Provider Order Entry System: A Mixed Methods Approach 
Background
Pharmacogenomics (PGx) is positioned to have a widespread impact on the practice of medicine, yet physician acceptance is low. The presentation of context-specific PGx information, in the form of clinical decision support (CDS) alerts embedded in a computerized provider order entry (CPOE) system, can aid uptake. Usability evaluations can inform optimal design, which, in turn, can spur adoption.
Objectives
The study objectives were to: 1) evaluate an early prototype, commercial CPOE system with PGx-CDS alerts in a simulated environment, 2) identify potential improvements to the system user interface, and 3) understand the contexts under which PGx knowledge embedded in an electronic health record is useful to prescribers.
Methods
Using a mixed methods approach, we presented seven cardiologists and three oncologists with five hypothetical clinical case scenarios. Each scenario featured a drug for which a gene encoding drug metabolizing enzyme required consideration of dosage adjustment. We used Morae® to capture comments and on-screen movements as participants prescribed each drug. In addition to PGx-CDS alerts, ‘Infobutton®’ and ‘Evidence’ icons provided participants with clinical knowledge resources to aid decision-making.
Results
Nine themes emerged. Five suggested minor improvements to the CPOE user interface; two suggested presenting PGx information through PGx-CDS alerts using an ‘Infobutton’ or ‘Evidence’ icon. The remaining themes were strong recommendations to provide succinct, relevant guidelines and dosing recommendations of phenotypic information from credible and trustworthy sources; any more information was overwhelming. Participants’ median rating of PGx-CDS system usability was 2 on a Likert scale ranging from 1 (strongly agree) to 7 (strongly disagree).
Conclusions
Usability evaluation results suggest that participants considered PGx information important for improving prescribing decisions; and that they would incorporate PGx-CDS when information is presented in relevant and useful ways.
doi:10.1016/j.ijmedinf.2014.04.008
PMCID: PMC4095746  PMID: 24874987
Clinical decision support systems; Clinical knowledge resources (not a MeSH term); Medical order entry systems; Pharmacogenetics; User-Computer Interface
8.  Characterizing Secondary Use of Clinical Data 
The increasing reliance on electronic health data has created new opportunities for the secondary use of clinical data to impact practice. We analyzed the secondary uses of clinical data at the University of Washington (UW) to better understand the types of users and uses as well as the benefits and limitations of these electronic data. At the UW, a diverse population is utilizing different elements of clinical data to conduct a wide· variety of studies. Investigators are using clinical data to explore research questions, determine study feasibility and to reduce the burden of manual chart abstraction. Discovered limitations include difficult-to-use data formatting, researchers’ lack of understanding about the data structure and organization resulting in mistrust, and difficulty generalizing data to fit needs of many specialized users.
PMCID: PMC4525245  PMID: 26306247
9.  Refining the Structure and Content of Clinical Genomic Reports 
To effectively articulate the results of exome and genome sequencing we refined the structure and content of molecular test reports. To communicate results of a randomized control trial aimed at the evaluation of exome sequencing for clinical medicine, we developed a structured narrative report. With feedback from genetics and non-genetics professionals, we developed separate indication-specific and incidental findings reports. Standard test report elements were supplemented with research study-specific language, which highlighted the limitations of exome sequencing and provided detailed, structured results, and interpretations. The report format we developed to communicate research results can easily be transformed for clinical use by removal of research-specific statements and disclaimers. The development of clinical reports for exome sequencing has shown that accurate and open communication between the clinician and laboratory is ideally an ongoing process to address the increasing complexity of molecular genetic testing.
doi:10.1002/ajmg.c.31395
PMCID: PMC4077592  PMID: 24616401
exome sequencing; Clinical Laboratory Improvement Amendments (CLIA); College of American Pathologists (CAP); incidental findings; laboratory report
10.  Automating Data Abstraction in a Quality Improvement Platform for Surgical and Interventional Procedures 
eGEMs  2014;2(1):1114.
Objective:
This paper describes a text processing system designed to automate the manual data abstraction process in a quality improvement (QI) program. The Surgical Care and Outcomes Assessment Program (SCOAP) is a clinician-led, statewide performance benchmarking QI platform for surgical and interventional procedures. The data elements abstracted as part of this program cover a wide range of clinical information from patient medical history to details of surgical interventions.
Methods:
Statistical and rule-based extractors were developed to automatically abstract data elements. A preprocessing pipeline was created to chunk free-text notes into its sections, sentences, and tokens. The information extracted in this preprocessing step was used by the statistical and rule-based extractors as features.
Findings:
Performance results for 25 extractors (14 statistical, 11 rule based) are presented. The average f1-scores for 11 rule-based extractors and 14 statistical extractors are 0.785 (min=0.576,max=0.931,std-dev=0.113) and 0.812 (min=0.571,max=0.993,std-dev=0.135) respectively.
Discussion:
Our error analysis revealed that most extraction errors were due either to data imbalance in the data set or the way the gold standard had been created.
Conclusion:
As future work, more experiments will be conducted with a more comprehensive data set from multiple institutions contributing to the QI project.
doi:10.13063/2327-9214.1114
PMCID: PMC4371448  PMID: 25848598
Natural language processing; Quality improvement; SCOAP CERTAIN
11.  A Template for Authoring and Adapting Genomic Medicine Content in the eMERGE Infobutton Project 
The Electronic Medical Records and Genomics (eMERGE) Network is a national consortium that is developing methods and best practices for using the electronic health record (EHR) for genomic medicine and research. We conducted a multi-site survey of information resources to support integration of pharmacogenomics into clinical care. This work aimed to: (a) characterize the diversity of information resource implementation strategies among eMERGE institutions; (b) develop a master template containing content topics of important for genomic medicine (as identified by the DISCERN-Genetics tool); and (c) assess the coverage of content topics among information resources developed by eMERGE institutions. Given that a standard implementation does not exist and sites relied on a diversity of information resources, we identified a need for a national effort to efficiently produce sharable genomic medicine resources capable of being accessed from the EHR. We discuss future areas of work to prepare institutions to use infobuttons for distributing standardized genomic content.
PMCID: PMC4419923  PMID: 25954402
12.  Comparative effectiveness of next generation genomic sequencing for disease diagnosis: Design of a randomized controlled trial in patients with colorectal cancer/polyposis syndromes✩ 
Whole exome and whole genome sequencing are applications of next generation sequencing transforming clinical care, but there is little evidence whether these tests improve patient outcomes or if they are cost effective compared to current standard of care. These gaps in knowledge can be addressed by comparative effectiveness and patient-centered outcomes research. We designed a randomized controlled trial that incorporates these research methods to evaluate whole exome sequencing compared to usual care in patients being evaluated for hereditary colorectal cancer and polyposis syndromes. Approximately 220 patients will be randomized and followed for 12 months after return of genomic findings. Patients will receive findings associated with colorectal cancer in a first return of result visit, and findings not associated with colorectal cancer (incidental findings) during a second return of result visit. The primary outcome is efficacy to detect mutations associated with these syndromes; secondary outcomes include psychosocial impact, cost-effectiveness and comparative costs. The secondary outcomes will be obtained via surveys before and after each return visit. The expected challenges in conducting this randomized controlled trial include the relatively low prevalence of genetic disease, difficult interpretation of some genetic variants, and uncertainty about which incidental findings should be returned to patients. The approaches utilized in this study may help guide other investigators in clinical genomics to identify useful outcome measures and strategies to address comparative effectiveness questions about the clinical implementation of genomic sequencing in clinical care.
doi:10.1016/j.cct.2014.06.016
PMCID: PMC4175052  PMID: 24997220
Comparative effectiveness research; Genomics; Next generation sequencing; Randomized clinical trial; Outcomes research; Whole exome sequencing
13.  Achieving and Sustaining Automated Health Data Linkages for Learning Systems: Barriers and Solutions 
EGEMS  2014;2(2):1069.
Introduction:
Delivering more appropriate, safer, and highly effective health care is the goal of a learning health care system. The Agency for Healthcare Research and Quality (AHRQ) funded enhanced registry projects: (1) to create and analyze valid data for comparative effectiveness research (CER); and (2) to enhance the ability to monitor and advance clinical quality improvement (QI). This case report describes barriers and solutions from one state-wide enhanced registry project.
Methods:
The Comparative Effectiveness Research and Translation Network (CERTAIN) deployed the commercially available Amalga Unified Intelligence System™ (Amalga) as a central data repository to enhance an existing QI registry (the Automation Project). An eight-step implementation process included hospital recruitment, technical electronic health record (EHR) review, hospital-specific interface planning, data ingestion, and validation. Data ownership and security protocols were established, along with formal methods to separate data management for QI purposes and research purposes. Sustainability would come from lowered chart review costs and the hospital’s desire to invest in the infrastructure after trying it.
Findings:
CERTAIN approached 19 hospitals in Washington State operating within 12 unaffiliated health care systems for the Automation Project. Five of the 19 completed all implementation steps. Four hospitals did not participate due to lack of perceived institutional value. Ten hospitals did not participate because their information technology (IT) departments were oversubscribed (e.g., too busy with Meaningful Use upgrades). One organization representing 22 additional hospitals expressed interest, but was unable to overcome data governance barriers in time. Questions about data use for QI versus research were resolved in a widely adopted project framework. Hospitals restricted data delivery to a subset of patients, introducing substantial technical challenges. Overcoming challenges of idiosyncratic EHR implementations required each hospital to devote more IT resources than were predicted. Cost savings did not meet projections because of the increased IT resource requirements and a different source of lowered chart review costs.
Discussion:
CERTAIN succeeded in recruiting unaffiliated hospitals into the Automation Project to create an enhanced registry to achieve AHRQ goals. This case report describes several distinct barriers to central data aggregation for QI and CER across unaffiliated hospitals: (1) competition for limited on-site IT expertise, (2) concerns about data use for QI versus research, (3) restrictions on data automation to a defined subset of patients, and (4) unpredictable resource needs because of idiosyncrasies among unaffiliated hospitals in how EHR data are coded, stored, and made available for transmission—even between hospitals using the same vendor’s EHR. Therefore, even a fully optimized automation infrastructure would still not achieve complete automation. The Automation Project was unable to align sufficiently with internal hospital objectives, so it could not show a compelling case for sustainability.
doi:10.13063/2327-9214.1069
PMCID: PMC4371442  PMID: 25848606
CERTAIN; Informatics; Quality Improvement; Research Networks; Sustainability
14.  Availability of Structured and Unstructured Clinical Data for Comparative Effectiveness Research and Quality Improvement: A Multisite Assessment 
EGEMS  2014;2(1):1079.
Introduction:
A key attribute of a learning health care system is the ability to collect and analyze routinely collected clinical data in order to quickly generate new clinical evidence, and to monitor the quality of the care provided. To achieve this vision, clinical data must be easy to extract and stored in computer readable formats. We conducted this study across multiple organizations to assess the availability of such data specifically for comparative effectiveness research (CER) and quality improvement (QI) on surgical procedures.
Setting:
This study was conducted in the context of the data needed for the already established Surgical Care and Outcomes Assessment Program (SCOAP), a clinician-led, performance benchmarking, and QI registry for surgical and interventional procedures in Washington State.
Methods:
We selected six hospitals, managed by two Health Information Technology (HIT) groups, and assessed the ease of automated extraction of the data required to complete the SCOAP data collection forms. Each data element was classified as easy, moderate, or complex to extract.
Results:
Overall, a significant proportion of the data required to automatically complete the SCOAP forms was not stored in structured computer-readable formats, with more than 75 percent of all data elements being classified as moderately complex or complex to extract. The distribution differed significantly between the health care systems studied.
Conclusions:
Although highly desirable, a learning health care system does not automatically emerge from the implementation of electronic health records (EHRs). Innovative methods to improve the structured capture of clinical data are needed to facilitate the use of routinely collected clinical data for patient phenotyping.
doi:10.13063/2327-9214.1079
PMCID: PMC4371483  PMID: 25848594
Learning Health System; Comparative Effectiveness; Data Use and Qualit
15.  Characterizing Data Discovery and End-User Computing Needs in Clinical Translational Science 
In this paper, the authors present the results of a qualitative case-study seeking to characterize data discovery needs and barriers of principal investigators and research support staff in clinical translational science. Several implications for designing and implementing translational research systems have emerged through the authors’ analysis. The results also illustrate the benefits of forming early partnerships with scientists to better understand their workflow processes and end-user computing practices in accessing data for research. The authors use this user-centered, iterative development approach to guide the implementation and extension of i2b2, a system they have adapted to support cross-institutional aggregate anonymized clinical data querying. With ongoing evaluation, the goal is to maximize the utility and extension of this system and develop an interface that appropriately fits the swiftly evolving needs of clinical translational scientists.
doi:10.4018/joeuc.2011100102
PMCID: PMC3983692  PMID: 24729759
Biomedical Research; Clinical Data Discovery; Clinical Translational Science; End-User Scientific Computing; Federated Querying; Patient Information Systems; User Needs
16.  A survey of informatics approaches to whole-exome and whole-genome clinical reporting in the electronic health record 
Purpose
Genome-scale clinical sequencing is being adopted more broadly in medical practice. The National Institutes of Health developed the Clinical Sequencing Exploratory Research (CSER) program to guide implementation and dissemination of best practices for the integration of sequencing into clinical care. This study describes and compares the state of the art of incorporating whole-exome and whole-genome sequencing results into the electronic health record, including approaches to decision support across the six current CSER sites.
Methods
The CSER Medical Record Working Group collaboratively developed and completed an in-depth survey to assess the communication of genome-scale data into the electronic health record. We summarized commonalities and divergent approaches.
Results
Despite common sequencing platform (Illumina) adoptions, there is a great diversity of approaches to annotation tools and workflow, as well as to report generation. At all sites, reports are human-readable structured documents available as passive decision support in the electronic health record. Active decision support is in early implementation at two sites.
Conclusion
The parallel efforts across CSER sites in the creation of systems for report generation and integration of reports into the electronic health record, as well as the lack of standardized approaches to interfacing with variant databases to create active clinical decision support, create opportunities for cross-site and vendor collaborations.
doi:10.1038/gim.2013.120
PMCID: PMC3951437  PMID: 24071794
clinical decision support; clinical sequencing; decision support rules; electronic health record; electronic medical record; next-generation sequencing
18.  AMIA Board white paper: definition of biomedical informatics and specification of core competencies for graduate education in the discipline 
The AMIA biomedical informatics (BMI) core competencies have been designed to support and guide graduate education in BMI, the core scientific discipline underlying the breadth of the field's research, practice, and education. The core definition of BMI adopted by AMIA specifies that BMI is ‘the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem solving and decision making, motivated by efforts to improve human health.’ Application areas range from bioinformatics to clinical and public health informatics and span the spectrum from the molecular to population levels of health and biomedicine. The shared core informatics competencies of BMI draw on the practical experience of many specific informatics sub-disciplines. The AMIA BMI analysis highlights the central shared set of competencies that should guide curriculum design and that graduate students should be expected to master.
doi:10.1136/amiajnl-2012-001053
PMCID: PMC3534470  PMID: 22683918
President and CEO; preparedness; wireless; preferences; population health; primary care; collaborative technologies; knowledge representations; knowledge acquisition and knowledge management; controlled terminologies and vocabularies; ontologies; AMIA
19.  Personalized medicine: challenges and opportunities for translational bioinformatics 
Personalized medicine  2013;10(5):453-462.
Personalized medicine can be defined broadly as a model of healthcare that is predictive, personalized, preventive and participatory. Two US President’s Council of Advisors on Science and Technology reports illustrate challenges in personalized medicine (in a 2008 report) and in use of health information technology (in a 2010 report). Translational bioinformatics is a field that can help address these challenges and is defined by the American Medical Informatics Association as “the development of storage, analytic and interpretive methods to optimize the transformation of increasing voluminous biomedical data into proactive, predictive, preventative and participatory health.” This article discusses barriers to implementing genomics applications and current progress toward overcoming barriers, describes lessons learned from early experiences of institutions engaged in personalized medicine and provides example areas for translational bioinformatics research inquiry.
PMCID: PMC3770190  PMID: 24039624
biobanks; clinical decision support; computational analyses; education; electronic health records; implementation challenges; individual research results; personalized medicine; translational bioinformatics; translational research
20.  Preparing Electronic Clinical Data for Quality Improvement and Comparative Effectiveness Research: The SCOAP CERTAIN Automation and Validation Project 
EGEMS  2013;1(1):1025.
Background:
The field of clinical research informatics includes creation of clinical data repositories (CDRs) used to conduct quality improvement (QI) activities and comparative effectiveness research (CER). Ideally, CDR data are accurately and directly abstracted from disparate electronic health records (EHRs), across diverse health-systems.
Objective:
Investigators from Washington State’s Surgical Care Outcomes and Assessment Program (SCOAP) Comparative Effectiveness Research Translation Network (CERTAIN) are creating such a CDR. This manuscript describes the automation and validation methods used to create this digital infrastructure.
Methods:
SCOAP is a QI benchmarking initiative. Data are manually abstracted from EHRs and entered into a data management system. CERTAIN investigators are now deploying Caradigm’s Amalga™ tool to facilitate automated abstraction of data from multiple, disparate EHRs. Concordance is calculated to compare data automatically to manually abstracted. Performance measures are calculated between Amalga and each parent EHR. Validation takes place in repeated loops, with improvements made over time. When automated abstraction reaches the current benchmark for abstraction accuracy - 95% - itwill ‘go-live’ at each site.
Progress to Date:
A technical analysis was completed at 14 sites. Five sites are contributing; the remaining sites prioritized meeting Meaningful Use criteria. Participating sites are contributing 15–18 unique data feeds, totaling 13 surgical registry use cases. Common feeds are registration, laboratory, transcription/dictation, radiology, and medications. Approximately 50% of 1,320 designated data elements are being automatically abstracted—25% from structured data; 25% from text mining.
Conclusion:
In semi-automating data abstraction and conducting a rigorous validation, CERTAIN investigators will semi-automate data collection to conduct QI and CER, while advancing the Learning Healthcare System.
doi:10.13063/2327-9214.1025
PMCID: PMC4371452  PMID: 25848565
CERTAIN; Informatics; Quality Improvement; Comparative Effectiveness; Health Information Technology
21.  Deriving rules and assertions from pharmacogenomics knowledge resources in support of patient drug metabolism efficacy predictions 
Objective
Pharmacogenomics evaluations of variability in drug metabolic processes may be useful for making individual drug response predictions. We present an approach to deriving ‘phenotype scores’ based on existing pharmacogenomics knowledge and a patient's genomics data. Pharmacogenomics plays an important role in the bioactivation of tamoxifen, a prodrug administered to patients for breast cancer treatment. Tamoxifen is therefore considered a model for many drugs requiring bioactivation. We investigate whether this knowledge-based approach can be applied to produce a phenotype score that is predictive of the endoxifen/N-desmethyltamoxifen (NDM) plasma concentration ratio in patients taking tamoxifen.
Materials and methods
We implement a knowledge-based model for calculating phenotype scores from patient-specific genotype data. These data include allelic variants of genes encoding enzymes involved in the bioactivation of tamoxifen. We performed quantile linear regression to evaluate whether six phenotype scoring algorithms are predictive of patient endoxifen/NDM plasma concentration ratio, and validate our scoring methods.
Results
Our model illustrates a knowledge-based approach to predict drug metabolism efficacy given patient genomics data. Results showed that for one phenotype scoring algorithm, scores were weakly correlated with patient endoxifen/NDM plasma concentration ratios. This algorithm performed better than simple metrics for variation in individual and multiple genes.
Discussion
We discuss advantages of the model, challenges to its implementation in a personalized medicine context, and provide example future directions.
Conclusions
We demonstrate the utility of our model in a tamoxifen case study context. We also provide evidence that more complicated polygenic models are needed to represent heterogeneity in clinical outcomes.
doi:10.1136/amiajnl-2011-000405
PMCID: PMC3422817  PMID: 22539082
Pharmacogenetics; pharmacogenomics; pharmacokinetics; knowledge bases; computer reasoning; genotype; genetic variation; predictive genetic testing; cytochrome P-450 enzyme system; tamoxifen; computer reasoning; knowledge bases; genomics; electronic health records; clinical decision support; developing/using computerized provider order entry; classical experimental and quasi-experimental study methods (lab and field); developing/using clinical decision support (other than diagnostic) and guideline systems; other specific ehr applications (results review; medication administration; disease progression; system implementation and management issues; surveys and needs analysis; qualitative/ethnographic field study
22.  A survey of informatics platforms that enable distributed comparative effectiveness research using multi-institutional heterogeneous clinical data 
Medical care  2012;50(Suppl):S49-S59.
Comparative Effectiveness Research (CER) has the potential to transform the current healthcare delivery system by identifying the most effective medical and surgical treatments, diagnostic tests, disease prevention methods and ways to deliver care for specific clinical conditions. To be successful, such research requires the identification, capture, aggregation, integration, and analysis of disparate data sources held by different institutions with diverse representations of the relevant clinical events. In an effort to address these diverse demands, there have been multiple new designs and implementations of informatics platforms that provide access to electronic clinical data and the governance infrastructure required for inter-institutional CER. The goal of this manuscript is to help investigators understand why these informatics platforms are required and to compare and contrast six, large-scale, recently funded, CER-focused informatics platform development efforts. We utilized an 8-dimension, socio-technical model of health information technology use to help guide our work. We identified six generic steps that are necessary in any distributed, multi-institutional CER project: data identification, extraction, modeling, aggregation, analysis, and dissemination. We expect that over the next several years these projects will provide answers to many important, and heretofore unanswerable, clinical research questions.
doi:10.1097/MLR.0b013e318259c02b
PMCID: PMC3415281  PMID: 22692259
Methods; Comparative Effectiveness Research; Organization and Administration; Medical Informatics; Methods
23.  Developing a Prototype System for Integrating Pharmacogenomics Findings into Clinical Practice 
Journal of personalized medicine  2012;2(4):241-256.
Findings from pharmacogenomics (PGx) studies have the potential to be applied to individualize drug therapy to improve efficacy and reduce adverse drug events. Researchers have identified factors influencing uptake of genomics in medicine, but little is known about the specific technical barriers to incorporating PGx into existing clinical frameworks. We present the design and development of a prototype PGx clinical decision support (CDS) system that builds on existing clinical infrastructure and incorporates semi-active and active CDS. Informing this work, we updated previous evaluations of PGx knowledge characteristics, and of how the CDS capabilities of three local clinical systems align with data and functional requirements for PGx CDS. We summarize characteristics of PGx knowledge and technical needs for implementing PGx CDS within existing clinical frameworks. PGx decision support rules derived from FDA drug labels primarily involve drug metabolizing genes, vary in maturity, and the majority support the post-analytic phase of genetic testing. Computerized provider order entry capabilities are key functional requirements for PGx CDS and were best supported by one of the three systems we evaluated. We identified two technical needs when building on this system, the need for (1) new or existing standards for data exchange to connect clinical data to PGx knowledge, and (2) a method for implementing semi-active CDS. Our analyses enhance our understanding of principles for designing and implementing CDS for drug therapy individualization and our current understanding of PGx characteristics in a clinical context. Characteristics of PGx knowledge and capabilities of current clinical systems can help govern decisions about CDS implementation, and can help guide decisions made by groups that develop and maintain knowledge resources such that delivery of content for clinical care is supported.
doi:10.3390/jpm2040241
PMCID: PMC3670105  PMID: 23741623
electronic health records; clinical decision support systems; pharmacogenomics; personalized medicine; computerized provider order entry; knowledge representation
24.  Learning virulent proteins from integrated query networks 
BMC Bioinformatics  2012;13:321.
Background
Methods of weakening and attenuating pathogens’ abilities to infect and propagate in a host, thus allowing the natural immune system to more easily decimate invaders, have gained attention as alternatives to broad-spectrum targeting approaches. The following work describes a technique to identifying proteins involved in virulence by relying on latent information computationally gathered across biological repositories, applicable to both generic and specific virulence categories.
Results
A lightweight method for data integration is used, which links information regarding a protein via a path-based query graph. A method of weighting is then applied to query graphs that can serve as input to various statistical classification methods for discrimination, and the combined usage of both data integration and learning methods are tested against the problem of both generalized and specific virulence function prediction.
Conclusions
This approach improves coverage of functional data over a protein. Moreover, while depending largely on noisy and potentially non-curated data from public sources, we find it outperforms other techniques to identification of general virulence factors and baseline remote homology detection methods for specific virulence categories.
doi:10.1186/1471-2105-13-321
PMCID: PMC3560104  PMID: 23198735
25.  Developing a Prototype System for Integrating Pharmacogenomics Findings into Clinical Practice  
Journal of Personalized Medicine  2012;2(4):241-256.
Findings from pharmacogenomics (PGx) studies have the potential to be applied to individualize drug therapy to improve efficacy and reduce adverse drug events. Researchers have identified factors influencing uptake of genomics in medicine, but little is known about the specific technical barriers to incorporating PGx into existing clinical frameworks. We present the design and development of a prototype PGx clinical decision support (CDS) system that builds on existing clinical infrastructure and incorporates semi-active and active CDS. Informing this work, we updated previous evaluations of PGx knowledge characteristics, and of how the CDS capabilities of three local clinical systems align with data and functional requirements for PGx CDS. We summarize characteristics of PGx knowledge and technical needs for implementing PGx CDS within existing clinical frameworks. PGx decision support rules derived from FDA drug labels primarily involve drug metabolizing genes, vary in maturity, and the majority support the post-analytic phase of genetic testing. Computerized provider order entry capabilities are key functional requirements for PGx CDS and were best supported by one of the three systems we evaluated. We identified two technical needs when building on this system, the need for (1) new or existing standards for data exchange to connect clinical data to PGx knowledge, and (2) a method for implementing semi-active CDS. Our analyses enhance our understanding of principles for designing and implementing CDS for drug therapy individualization and our current understanding of PGx characteristics in a clinical context. Characteristics of PGx knowledge and capabilities of current clinical systems can help govern decisions about CDS implementation, and can help guide decisions made by groups that develop and maintain knowledge resources such that delivery of content for clinical care is supported.
doi:10.3390/jpm2040241
PMCID: PMC3670105  PMID: 23741623
electronic health records; clinical decision support systems; pharmacogenomics; personalized medicine; computerized provider order entry; knowledge representation

Results 1-25 (52)