Understanding the acceptability of a computer decision support system in pediatric primary care

Objective Individual users' attitudes and opinions help predict successful adoption of health information technology (HIT) into practice; however, little is known about pediatric users' acceptance of HIT for medical decision-making at the point of care. Materials and methods We wished to examine the attitudes and opinions of pediatric users' toward the Child Health Improvement through Computer Automation (CHICA) system, a computer decision support system linked to an electronic health record in four community pediatric clinics. Surveys were administered in 2011 and 2012 to all users to measure CHICA's acceptability and users' satisfaction with it. Free text comments were analyzed for themes to understand areas of potential technical refinement. Results 70 participants completed the survey in 2011 (100% response rate) and 64 of 66 (97% response rate) in 2012. Initially, satisfaction with CHICA was mixed. In general, users felt the system held promise; however various critiques reflected difficulties understanding integrated technical aspects of how CHICA worked, as well as concern with the format and wording on generated forms for families and users. In the subsequent year, users' ratings reflected improved satisfaction and acceptance. Comments also reflected a deeper understanding of the system's logic, often accompanied by suggestions on potential refinements to make CHICA more useful at the point of care. Conclusions Pediatric users appreciate the system's automation and enhancements that allow relevant and meaningful clinical data to be accessible at point of care. Understanding users' acceptability and satisfaction is critical for ongoing refinement of HIT to ensure successful adoption into practice

Doctor of Philosophy

dissertationFamily health history (FHH) is an independent risk factor for predicting an individual's chance of developing selected chronic diseases. Though various FHH tools have been developed, many research questions remain to be addressed. Before FHH can be used as an effective risk assessment tool in public health screenings or population-based research, it is important to understand the quality of collected data and evaluate risk prediction models. No literature has been identified whereby risks are predicted by applying machine learning solely on FHH. This dissertation addressed several questions. First, using mixed methods, we defined 50 requirements for documenting FHH for a population-based study. Second, we examined the accuracy of self- and proxy-reported FHH data in the Health Family Tree database, by comparing the disease and risk factor rates generated from this database with rates recorded in a cancer registry and standard public health surveys. The rates generated from the Health Family Tree were statistically lower than those from public sources (exceptions: stroke rates were the same, exercise rates were higher). Third, we validated the Health Family Tree risk predictive algorithm. The very high risk (≥2) predicted the risk of all concerned diseases for adult population (20 ~ 99 years of age), and the predictability remained when using disease rates from public sources as the reference in the relative risk model. The referent population used to establish the expected rate of disease impacted risk classification: the lower expected disease rates generated by the Health Family Tree, in comparison to the rates from public iv sources, caused more persons to be classified at high risk. Finally, we constructed and evaluated new predictive models using three machine learning classifiers (logistic regression, Bayesian networks, and support vector machine). A limited set of information about first-degree relatives was used to predict future disease. In summary, combining FHH with valid risk algorithms provide a low cost tool for identifying persons at risk for common diseases. These findings may be especially useful when developing strategies to screen populations for common diseases and identifying those at highest risk for public health interventions or population-based research

The Potential of Clinical Decision Support Systems for Prevention, Diagnosis, and Monitoring of Allergic Diseases

Clinical decision support systems (CDSS) aid health care professionals (HCP) in evaluating large sets of information and taking informed decisions during their clinical routine. CDSS are becoming particularly important in the perspective of precision medicine, when HCP need to consider growing amounts of data to create precise patient profiles for personalized diagnosis, treatment and outcome monitoring. In allergy care, several CDSS are being developed and investigated, mainly for respiratory allergic diseases. Although the proposed solutions address different stakeholders, the majority aims at facilitating evidence-based and shared decision-making, incorporating guidelines, and real-time clinical data. We offer here an overview on existing tools, new developments and novel concepts and discuss the potential of digital CDSS in improving prevention, diagnosis and monitoring of allergic diseases

Repeatable and reusable research - Exploring the needs of users for a Data Portal for Disease Phenotyping

Background: Big data research in the field of health sciences is hindered by a lack of agreement on how to identify and define different conditions and their medications. This means that researchers and health professionals often have different phenotype definitions for the same condition. This lack of agreement makes it hard to compare different study findings and hinders the ability to conduct repeatable and reusable research. Objective: This thesis aims to examine the requirements of various users, such as researchers, clinicians, machine learning experts, and managers, for both new and existing data portals for phenotypes (concept libraries). Methods: Exploratory sequential mixed methods were used in this thesis to look at which concept libraries are available, how they are used, what their characteristics are, where there are gaps, and what needs to be done in the future from the point of view of the people who use them. This thesis consists of three phases: 1) two qualitative studies, including one-to-one interviews with researchers, clinicians, machine learning experts, and senior research managers in health data science, as well as focus group discussions with researchers working with the Secured Anonymized Information Linkage databank, 2) the creation of an email survey (i.e., the Concept Library Usability Scale), and 3) a quantitative study with researchers, health professionals, and clinicians. Results: Most of the participants thought that the prototype concept library would be a very helpful resource for conducting repeatable research, but they specified that many requirements are needed before its development. Although all the participants stated that they were aware of some existing concept libraries, most of them expressed negative perceptions about them. The participants mentioned several facilitators that would encourage them to: 1) share their work, such as receiving citations from other researchers; and 2) reuse the work of others, such as saving a lot of time and effort, which they frequently spend on creating new code lists from scratch. They also pointed out several barriers that could inhibit them from: 1) sharing their work, such as concerns about intellectual property (e.g., if they shared their methods before publication, other researchers would use them as their own); and 2) reusing others' work, such as a lack of confidence in the quality and validity of their code lists. Participants suggested some developments that they would like to see happen in order to make research that is done with routine data more reproducible, such as the availability of a drive for more transparency in research methods documentation, such as publishing complete phenotype definitions and clear code lists. Conclusions: The findings of this thesis indicated that most participants valued a concept library for phenotypes. However, only half of the participants felt that they would contribute by providing definitions for the concept library, and they reported many barriers regarding sharing their work on a publicly accessible platform such as the CALIBER research platform. Analysis of interviews, focus group discussions, and qualitative studies revealed that different users have different requirements, facilitators, barriers, and concerns about concept libraries. This work was to investigate if we should develop concept libraries in Kuwait to facilitate the development of improved data sharing. However, at the end of this thesis the recommendation is this would be unlikely to be cost effective or highly valued by users and investment in open access research publications may be of more value to the Kuwait research/academic community

Patient Health Record Systems Scope and Functionalities: Literature Review and Future Directions

Background: A new generation of user-centric information systems is emerging in health care as patient health record (PHR) systems. These systems create a platform supporting the new vision of health services that empowers patients and enables patient-provider communication, with the goal of improving health outcomes and reducing costs. This evolution has generated new sets of data and capabilities, providing opportunities and challenges at the user, system, and industry levels. Objective: The objective of our study was to assess PHR data types and functionalities through a review of the literature to inform the health care informatics community, and to provide recommendations for PHR design, research, and practice. Methods: We conducted a review of the literature to assess PHR data types and functionalities. We searched PubMed, Embase, and MEDLINE databases from 1966 to 2015 for studies of PHRs, resulting in 1822 articles, from which we selected a total of 106 articles for a detailed review of PHR data content. Results: We present several key findings related to the scope and functionalities in PHR systems. We also present a functional taxonomy and chronological analysis of PHR data types and functionalities, to improve understanding and provide insights for future directions. Functional taxonomy analysis of the extracted data revealed the presence of new PHR data sources such as tracking devices and data types such as time-series data. Chronological data analysis showed an evolution of PHR system functionalities over time, from simple data access to data modification and, more recently, automated assessment, prediction, and recommendation. Conclusions: Efforts are needed to improve (1) PHR data quality through patient-centered user interface design and standardized patient-generated data guidelines, (2) data integrity through consolidation of various types and sources, (3) PHR functionality through application of new data analytics methods, and (4) metrics to evaluate clinical outcomes associated with automated PHR system use, and costs associated with PHR data storage and analytics