A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources.

OBJECTIVE: The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled Developing a Clinical Genomic Informatics Research Agenda . The meeting\u27s goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. MATERIALS AND METHODS: Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting\u27s goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. RESULTS: Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. DISCUSSION: Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them

Towards identifying intervention arms in randomized controlled trials: Extracting coordinating constructions

AbstractBackground: Large numbers of reports of randomized controlled trials (RCTs) are published each year, and it is becoming increasingly difficult for clinicians practicing evidence-based medicine to find answers to clinical questions. The automatic machine extraction of RCT experimental details, including design methodology and outcomes, could help clinicians and reviewers locate relevant studies more rapidly and easily. Aim: This paper investigates how the comparison of interventions is documented in the abstracts of published RCTs. The ultimate goal is to use automated text mining to locate each intervention arm of a trial. This preliminary work aims to identify coordinating constructions, which are prevalent in the expression of intervention comparisons. Methods and results: An analysis of the types of constructs that describe the allocation of intervention arms is conducted, revealing that the compared interventions are predominantly embedded in coordinating constructions. A method is developed for identifying the descriptions of the assignment of treatment arms in clinical trials, using a full sentence parser to locate coordinating constructions and a statistical classifier for labeling positive examples. Predicate-argument structures are used along with other linguistic features with a maximum entropy classifier. An F-score of 0.78 is obtained for labeling relevant coordinating constructions in an independent test set. Conclusions: The intervention arms of a randomized controlled trials can be identified by machine extraction incorporating syntactic features derived from full sentence parsing

The Effect of Risk Attitude and Uncertainty Comfort on Primary Care Physicians' Use of Electronic Information Resources

Background: Clinicians use information regularly in clinical care. New electronic information resources provided in push, pull, and prompting formats have potential to improve information support but have not been designed for individualization. Physicians with differing risk status use healthcare resources differently often without an improvement in outcomes.Questions: Do physicians who are risk seeking or risk avoiding and comfortable or uncomfortable with uncertainty use or prefer electronic information resources differently when answering simulated clinical questions and can the processes be modeled with existing theoretical models?Design: Cohort study.Methods: Primary care physicians in Canada and the United States were screened for risk status. Those with high and low scores on 2 validated scales answered 23 multiple-choice questions and searched for information using their own electronic resources for 2 of these questions. They also answered 2 other questions using information from 2 electronic information sources: PIER© and Clinical Evidence© .Results: The physicians did not differ for number of correct answers according to risk status although the number of correct answers was low and not substantially higher than chance. Their searching process was consistent with 2 information-seeking models from information science (modified Wilson Problem Solving and Card/Pirolli Information Foraging/Information Scent models). Few differences were seen for any electronic searching or information use outcome based on risk status although those physicians who were comfortable with uncertainty used more searching heuristics and spent less effort on direct searching. More than 20% of answers were changed after searching—almost the same number going from incorrect to correct and from correct to incorrect. These changes from a correct to incorrect answer indicate that some electronic information resources may not be ideal for direct clinical care or integration into electronic medical record systems.Conclusions: Risk status may not be a major factor in the design of electronic information resources for primary care physicians. More research needs to be done to determine which computerized information resources and which features of these resources are associated with obtaining and maintaining correct answers to clinical questions

Are decision trees a feasible knowledge representation to guide extraction of critical information from randomized controlled trial reports?

<p>Abstract</p> <p>Background</p> <p>This paper proposes the use of decision trees as the basis for automatically extracting information from published randomized controlled trial (RCT) reports. An exploratory analysis of RCT abstracts is undertaken to investigate the feasibility of using decision trees as a semantic structure. Quality-of-paper measures are also examined.</p> <p>Methods</p> <p>A subset of 455 abstracts (randomly selected from a set of 7620 retrieved from Medline from 1998 – 2006) are examined for the quality of RCT reporting, the identifiability of RCTs from abstracts, and the completeness and complexity of RCT abstracts with respect to key decision tree elements. Abstracts were manually assigned to 6 sub-groups distinguishing whether they were primary RCTs versus other design types. For primary RCT studies, we analyzed and annotated the reporting of intervention comparison, population assignment and outcome values. To measure completeness, the frequencies by which complete intervention, population and outcome information are reported in abstracts were measured. A qualitative examination of the reporting language was conducted.</p> <p>Results</p> <p>Decision tree elements are manually identifiable in the majority of primary RCT abstracts. 73.8% of a random subset was primary studies with a single population assigned to two or more interventions. 68% of these primary RCT abstracts were structured. 63% contained pharmaceutical interventions. 84% reported the total number of study subjects. In a subset of 21 abstracts examined, 71% reported numerical outcome values.</p> <p>Conclusion</p> <p>The manual identifiability of decision tree elements in the abstract suggests that decision trees could be a suitable construct to guide machine summarisation of RCTs. The presence of decision tree elements could also act as an indicator for RCT report quality in terms of completeness and uniformity.</p

Doctor of Philosophy

dissertationInadequate care coordination has been identified as a significant problem in patient care, resulting in diminished satisfaction, increased cost, and reduced quality of care. Comprising an estimated 15.6% (approximately 11 million) of the pediatric population, children and youth with special health care needs (CYSHCN) are "those who have or are at increased risk for a chronic physical, developmental, behavioral, or emotional condition and who also require health and related services of a type or amount beyond that required by children generally". Caring for CYSHCN is often highly complex, time-, effort-, and resource-intensive, due to complex healthcare conditions, comorbidities, and age of patients. Current electronic health record (EHR) and personal health record (PHR) systems do not adequately support the needs of care coordination. The reasons for this include lack of appropriate tools to support complex care coordination tasks, poor usability, and gaps in information essential for providing team-based patient care. The issues are further amplified while coordinating care for CYSHCN because their health records tend to be voluminous, involve a large care team, and are distributed over multiple systems typically with little to no interoperability. To develop tools that promote effective and efficient care coordination, designers must first understand what information is needed, who needs it, when they need it, and how it can be made available. Our first study focused on identifying and describing information needs and associated goals related to coordinating care for CYSHCN. We found that a critical information goal for care coordination is care networking, which includes building a patient's care team; knowing team member identities, roles, and contact information; and sharing pertinent information with the team to coordinate care. In our second study, we designed and developed two versions of a patient-, family-, and clinician-facing tool to support care networking. We then conducted a formative evaluation and compared the usability, usefulness, and efficiency of the two versions. To enable such tools to help with management of information critical to care coordination, information for care networking needs to be obtained from all information sources involved in the patient's care. In our third study, we identified and assessed prevalent and emerging national data standards to support electronic exchange and extraction of patient care team related data. The findings and innovations from this research are envisioned to help guide the design and development of next generation clinician- and patient-/family-facing applications to support care coordination of complex pediatric patients

Utilization of automated location tracking for clinical workflow analytics and visualization

abstract: The analysis of clinical workflow offers many challenges to clinical stakeholders and researchers, especially in environments characterized by dynamic and concurrent processes. Workflow analysis in such environments is essential for monitoring performance and finding bottlenecks and sources of error. Clinical workflow analysis has been enhanced with the inclusion of modern technologies. One such intervention is automated location tracking which is a system that detects the movement of clinicians and equipment. Utilizing the data produced from automated location tracking technologies can lead to the development of novel workflow analytics that can be used to complement more traditional approaches such as ethnography and grounded-theory based qualitative methods. The goals of this research are to: (i) develop a series of analytic techniques to derive deeper workflow-related insight in an emergency department setting, (ii) overlay data from disparate sources (quantitative and qualitative) to develop strategies that facilitate workflow redesign, and (iii) incorporate visual analytics methods to improve the targeted visual feedback received by providers based on the findings. The overarching purpose is to create a framework to demonstrate the utility of automated location tracking data used in conjunction with clinical data like EHR logs and its vital role in the future of clinical workflow analysis/analytics. This document is categorized based on two primary aims of the research. The first aim deals with the use of automated location tracking data to develop a novel methodological/exploratory framework for clinical workflow. The second aim is to overlay the quantitative data generated from the previous aim on data from qualitative observation and shadowing studies (mixed methods) to develop a deeper view of clinical workflow that can be used to facilitate workflow redesign. The final sections of the document speculate on the direction of this work where the potential of this research in the creation of fully integrated clinical environments i.e. environments with state-of-the-art location tracking and other data collection mechanisms, is discussed. The main purpose of this research is to demonstrate ways by which clinical processes can be continuously monitored allowing for proactive adaptations in the face of technological and process changes to minimize any negative impact on the quality of patient care and provider satisfaction.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201