Methods to Facilitate the Capture, Use, and Reuse of Structured and Unstructured Clinical Data.

Electronic health records (EHRs) have great potential to improve quality of care and to support clinical and translational research. While EHRs are being increasingly implemented in U.S. hospitals and clinics, their anticipated benefits have been largely unachieved or underachieved. Among many factors, tedious documentation requirements and the lack of effective information retrieval tools to access and reuse data are two key reasons accounting for this deficiency. In this dissertation, I describe my research on developing novel methods to facilitate the capture, use, and reuse of both structured and unstructured clinical data. Specifically, I develop a framework to investigate potential issues in this research topic, with a focus on three significant challenges. The first challenge is structured data entry (SDE), which can be facilitated by four effective strategies based on my systematic review. I further propose a multi-strategy model to guide the development of future SDE applications. In the follow-up study, I focus on workflow integration and evaluate the feasibility of using EHR audit trail logs for clinical workflow analysis. The second challenge is the use of clinical narratives, which can be supported by my innovative information retrieval (IR) technique called “semantically-based query recommendation (SBQR)”. My user experiment shows that SBQR can help improve the perceived performance of a medical IR system, and may work better on search tasks with average difficulty. The third challenge involves reusing EHR data as a reference standard to benchmark the quality of other health-related information. My study assesses the readability of trial descriptions on ClinicalTrials.gov and found that trial descriptions are very hard to read, even harder than clinical notes. My dissertation has several contributions. First, it conducts pioneer studies with innovative methods to improve the capture, use, and reuse of clinical data. Second, my dissertation provides successful examples for investigators who would like to conduct interdisciplinary research in the field of health informatics. Third, the framework of my research can be a great tool to generate future research agenda in clinical documentation and EHRs. I will continue exploring innovative and effective methods to maximize the value of EHRs.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/135845/1/tzuyu_1.pd

Review of health information technology usability study methodologies

Usability factors are a major obstacle to health information technology (IT) adoption. The purpose of this paper is to review and categorize health IT usability study methods and to provide practical guidance on health IT usability evaluation. 2025 references were initially retrieved from the Medline database from 2003 to 2009 that evaluated health IT used by clinicians. Titles and abstracts were first reviewed for inclusion. Full-text articles were then examined to identify final eligibility studies. 629 studies were categorized into the five stages of an integrated usability specification and evaluation framework that was based on a usability model and the system development life cycle (SDLC)-associated stages of evaluation. Theoretical and methodological aspects of 319 studies were extracted in greater detail and studies that focused on system validation (SDLC stage 2) were not assessed further. The number of studies by stage was: stage 1, task-based or user–task interaction, n=42; stage 2, system–task interaction, n=310; stage 3, user–task–system interaction, n=69; stage 4, user–task–system–environment interaction, n=54; and stage 5, user–task–system–environment interaction in routine use, n=199. The studies applied a variety of quantitative and qualitative approaches. Methodological issues included lack of theoretical framework/model, lack of details regarding qualitative study approaches, single evaluation focus, environmental factors not evaluated in the early stages, and guideline adherence as the primary outcome for decision support system evaluations. Based on the findings, a three-level stratified view of health IT usability evaluation is proposed and methodological guidance is offered based upon the type of interaction that is of primary interest in the evaluation

Providing better clinical reference sources at the point of care

Physicians frequently need clinical reference material at the time they are taking care of patients, but despite the widespread availability of seemingly appropriate resources, physicians do not consistently avail themselves. Previous research has shown that the main obstacle to resource use is lack of time to search, followed by inability to find the information even with extended searching. Using a randomly selected sample of previously published questions (Clinical Questions Collection at the National Library of Medicine), current online electronic clinical reference resources were searched. Search times, success rates, and result quality were compared. Retrieval of drug information was dominated by navigation time, whereas searches for other materials were dominated by search time. It was rare that actually reading the material required significant time. Based on the results, an improved interface for accessing clinical reference data is proposed. Key features include a "Fast" interface that minimizes navigation time to easily found resources and a "Flex" interface that minimizes search time for more arcane material.Master of Science in Information Scienc

Master of Science

thesisAnnually, 46 million patients, or 37% of patients seen in the emergency department (ED), receive laboratory testing in the U.S.; thus, making efficient lab order and result management critical to improving ED throughput, clinical efficacy, and safety. In order to manage labs and other processes, electronic emergency department tracking systems (EDTS) or electronic whiteboards have evolved features that support clinical, operational, and administrative needs. EDTSs have often augmented manual data entry with interfaces and/or integration with other systems such as registration, laboratory, radiology, and clinical information systems (CIS). One such integration evaluated in this study, EDTS/CIS context sharing, was added to automatically pass all necessary user, patient, and application parameters between the two systems in order to open the CIS lab module for a selected patient when the user is notified in the EDTS that laboratory test results for that patient are available for review. Therefore, context sharing eliminated multiple user steps needed to log-on, search, select, and navigate to the lab viewing module in order to view a patient's lab results. This study evaluates the effects of adding EDTS/CIS context sharing to an EDTS with lab notifications on ED process times. These effects were measured utilizing a pre- and post-intervention design for all ED encounters where specific common labs were resulted. A method of analyzing CIS audit logs in combination with EDTS and laboratory information system timestamps was implemented to measure patient management processes for quality improvement. After adding context sharing to lab notification features, the median interval between the availability of lab results and review of those results by the ordering provider decreased from 22.7 min., by 25% or 5.7 min. (p-value < 0.001), to 17.0 min. However, median time from resulting of labs to patient discharge were essentially unchanged, decreasing from 106.6 min. to 105.0 min. (p-value = 0.080). The proportion of lab results reviewed by physicians in the CIS integrated with the EDTS increased from 66% to 86% after the intervention (p-value < 0.001). EDTS/CIS context sharing and passive lab notification features improved the timeliness and completion of lab result review in the CIS and increased system adoption in this setting. However, reductions in the time intervals to review of lab results in the CIS did not result in an operationally or statistically significant improvement in time to discharge after the availability of results

Three analytics-based essays examining the use and impact of Intelligent Voice Assistants (IVA) and Health Information Technologies (HIT) in service contexts

Recent advancements in information technology (IT) innovation, such as artificial intelligence (AI) and machine learning (ML), are changing the dynamics in the service sector by driving smart reinvention of service tasks and processes. Additionally, organisations are leveraging the capabilities of emerging information systems (IS) to make their services more efficient and customer centric. However, the decision to use recent advancements in IT can be challenging for organizations since the required initial investment for implementation is often high and the economic value and impact on service performance cannot be gauged with certainty (Kwon et al. 2015). This forces many organizations to prioritise which IT functionalities may best be suited for their needs. To support the decision making process of organizations, regarding the adoption and use of innovative IT, scholars in the information systems (IS) and related fields are called to improve knowledge and understanding about various IT components and functionalities as well as their corresponding impact on individual users and organizations. Scholars are also expected to provide the means by which businesses can meaningfully predict the potential impact and economic value of innovative IT (Ravichandran 2018). In this three essay dissertation, we investigate how the use of various components and functionalities of innovative information systems can individually (or together) impact the quality of service delivered to end consumers. The essays are broadly based on the intersection of artificial intelligence (AI), machine learning(ML) and services. In the first study, we found that during encounters between eService consumers and Intelligent Voice Assistants (IVAs), typically powered by artificial intelligence, machine learning and natural language processing, the following dimensions are important for the perceived quality of service: IVA interactivity, IVA personalization, IVA flexibility, IVA assurance and IVA reliability. Among the five dimensions of IVA encounter, we found that IVA interactivity, IVA personalization and IVA reliability had positive impacts on the effective use of IVAs. In study 2, we investigated performance of hospitals in the health service sector. We proposed a smart decision support system (DSS) for predicting the performance of hospitals based on the Health Information Technology (HIT) functionalities as applied and used in these hospitals for patient care and in improving hospital performance. We found that the predictive performance of our proposed smart DSS was most accurate when HIT functionalities were used in certain bundles than in isolation. In study 3, we investigated the effect of hospital heterogeneity on the accuracy of prediction of our proposed smart DSS as we recognize that not all hospitals have the same set of context, opportunity, location and constraints. We found that the following sources of variations in hospitals had significant moderator effects on the accurate prediction of our smart DSS: hospital size, ownership, region, location (urban/rural) and complexity of cases treated. In summary, this dissertation contributes to the IS literature by providing insight into the emergent use of artificial intelligence and machine learning technologies as part of IS/IT solutions in both consumer-oriented services and the healthcare sector

Efficient Decision Support Systems

This series is directed to diverse managerial professionals who are leading the transformation of individual domains by using expert information and domain knowledge to drive decision support systems (DSSs). The series offers a broad range of subjects addressed in specific areas such as health care, business management, banking, agriculture, environmental improvement, natural resource and spatial management, aviation administration, and hybrid applications of information technology aimed to interdisciplinary issues. This book series is composed of three volumes: Volume 1 consists of general concepts and methodology of DSSs; Volume 2 consists of applications of DSSs in the biomedical domain; Volume 3 consists of hybrid applications of DSSs in multidisciplinary domains. The book is shaped decision support strategies in the new infrastructure that assists the readers in full use of the creative technology to manipulate input data and to transform information into useful decisions for decision makers