Ambulatory EMR Adoption in the USA: A Longitudinal Study

Based on a longitudinal national survey, this study examines the adoption of electronic medical records (EMR) by clinics in the USA between 2004 and 2014. A trend analysis suggests that government incentive, technological breakthrough and patient-centered care push the diffusion forward. The interaction among policy, technology and practice is likely to affect the decision-making of practitioners regarding EMR adoption. This study identifies clinic-, patient- and visit-related variables from the survey, and uses them to predict EMR adoption intention and usage in each year. The explanatory power of different variables changed over time in different ways, revealing how policy, technology, and practice influence EMR adoption together. The findings yield implications for the strategies and best practices of health IT diffusion

NETIMIS: Dynamic Simulation of Health Economics Outcomes Using Big Data

Many healthcare organizations are now making good use of electronic health record (EHR) systems to record clinical information about their patients and the details of their healthcare. Electronic data in EHRs is generated by people engaged in complex processes within complex environments, and their human input, albeit shaped by computer systems, is compromised by many human factors. These data are potentially valuable to health economists and outcomes researchers but are sufficiently large and complex enough to be considered part of the new frontier of ‘big data’. This paper describes emerging methods that draw together data mining, process modelling, activity-based costing and dynamic simulation models. Our research infrastructure includes safe links to Leeds hospital’s EHRs with 3 million secondary and tertiary care patients. We created a multidisciplinary team of health economists, clinical specialists, and data and computer scientists, and developed a dynamic simulation tool called NETIMIS (Network Tools for Intervention Modelling with Intelligent Simulation; http://www.netimis.com) suitable for visualization of both human-designed and data-mined processes which can then be used for ‘what-if’ analysis by stakeholders interested in costing, designing and evaluating healthcare interventions. We present two examples of model development to illustrate how dynamic simulation can be informed by big data from an EHR. We found the tool provided a focal point for multidisciplinary team work to help them iteratively and collaboratively ‘deep dive’ into big data

Exploring the Determinants of PAS, EDMS, and PACS Adoption in European Hospitals

Using data available in two microdatabases - the e-Business Watch survey 2006 and the European Hospital Survey (2012-2013) – the study reported in this paper explores the determinants that lead to the adoption of three of the most commonly used Health Information Systems (HIS) in European Hospitals: Patient Administration Systems (PAS), Electronic Documents Management Systems (EDMS), and Picture Archiving and Communication Systems (PACS). For statistical analysis and modeling purposes, the original variables in the two surveys were transformed into binary variables. In order to explore the determinants of system adoption, Probit models were built taking into consideration the following explanatory variables or predictors: public ownership; hospital size; and human resources allocated to Research and Development. It has been found that being a public hospital, particularly in recent years, has a negative impact on HIS adoption. Hospital size is one of the main positive predictors of HIS adoption. The impact of human resources allocated to R&D is also a determinant of HIS adoption, but less so in recent years

Exploring Strategies for Successful Implementation of Electronic Health Records

Adoption of electronic health records (EHR) systems in nonfederal acute care hospitals has increased, with adoption rates across the United States reaching as high as 94%. Of the 330 plus acute care hospital EHR implementations in Texas, only 31% have completed attestation to Stage 2 of the meaningful use (MU) criteria. The purpose of this multiple case study was to explore strategies that hospital chief information officers (CIOs) used for the successful implementation of EHR. The target population consists of 3 hospitals CIOs from a multi-county region in North Central Texas who successfully implemented EHRs meeting Stage 2 MU criteria. The conceptual framework, for this research, was the technology acceptance model theory. The data were collected through semistructured interviews, member checking, review of the literature on the topic, and publicly available documents on the respective hospital websites. Using methodological triangulation of the data, 4 themes emerged from data analysis: EHR implementation strategies, overcoming resistance to technology acceptance, strategic alignment, and patient wellbeing. Participants identified implementation teams and informatics teams as a primary strategy for obtaining user engagement, ownership, and establishing a culture of acceptance to the technological changes. The application of the findings may contribute to social change by identifying the strategies hospital CIOs used for successful implementation of EHRs. Successful EHR implementation might provide positive social change by improving the quality of patient care, patient safety, security of personal health information, lowering health care cost, and improvements in the overall health of the general population

Innovative Workforce Plan: Recently Graduated Nurses as Super Users for EHR Implementation in a Multi-Hospital Organization

A regional health system’s decision to rapidly implement a new electronic health record (EHR) in order to meet Stage 2 meaningful use requirements led to a need for innovative cost-containment strategies. Tapping the local pool of unemployed newly graduated nurses as half the required super user workforce leveraged the technology skills of novice nurses registered nurses as trainers of experienced nurses in five hospitals. The novel workforce migrated from hospital to hospital, thereby reducing the number of experienced nurses reassigned to super user duties in each hospital. This strategy also reduced the amount of contract labor required to backfill nurse super users’ clinical shifts. The innovative model reduced labor costs associated with super user staffing by 31.8%, while positioning the organization for successful attestation to Stage 2 meaningful use objectives. Employment of the recently graduated nurses as RN Residents upon completion of the EHR implementation enabled the organization to augment its clinical workforce with expert users of its EHR, and to rapidly achieve Stage 2 meaningful use compliance

Master of Science

thesisLack of information is a serious concern for clinicians. Information resources can address this problem, leading to improvements in decision making and patient outcomes. Genomics is an information-rich domain where searching for information can be complex. For example, most physicians agree that pharmacogenomics can be used to improve the quality of care, and there is evidence that many patients harbor actionable pharmacogenomic variation. However, surveys have shown that physicians feel their knowledge of pharmacogenomics to be inadequate. This represents an information need. A natural approach to meet this need is to provide context-aware access to the precise information needed. The Health Level 7 Context-Aware Knowledge Retrieval Standard, a.k.a the Infobutton, offers a modality to deliver context-aware knowledge into electronic health record (EHR) systems. OpenInfobutton is a reference implementation of this standard that offers an open-source instantiation. In this thesis, we aimed to provide insight into pharmacogenomics information needs and an automated mechanism for addressing these needs. Such work can aid the design of tools that support clinical decisions in genomics

Two Essays on Analytical Capabilities: Antecedents and Consequences

Although organizations are rapidly embracing business analytics (BA) to enhance organizational performance, only a small proportion have managed to build analytical capabilities. While BA continues to draw attention from academics and practitioners, theoretical understanding of antecedents and consequences of analytical capabilities remain limited and lack a systematic view. In order to address the research gap, the two essays investigate: (a) the impact of organization’s core information processing mechanisms and its impact on analytical capabilities, (b) the sequential approach to integration of IT-enabled business processes and its impact on analytical capabilities, and (c) network position and its impact on analytical capabilities. Drawing upon the Information Processing Theory (IPT), the first essay investigates the relationship between organization’s core information processing mechanisms–i.e., electronic health record (EHRs), clinical information standards (CIS), and collaborative information exchange (CIE)–and its impact on analytical capabilities. We use data from two sources (HIMSS Analytics 2013 and AHA IT Survey 2013) to test the theorized relationships in the healthcare context empirically. Using the competitive progression theory, the second essay investigates whether organizations sequential approach to the integration of IT-enabled business processes is associated with increased analytical capabilities. We use data from three sources (HIMSS Analytics 2013, AHA IT Survey 2013, and CMS 2014) to test if sequential integration of EHRs –i.e., reflecting the unique organizational path of integration–has a significant impact on hospital’s analytical capability. Together the two essays advance our understanding of the factors that underlie enabling of firm’s analytical capabilities. We discuss in detail the theoretical and practical implications of the findings and the opportunities for future research

Doctor of Philosophy

dissertationThe widespread use of genomic information to improve clinical care has long been a goal of clinicians, researchers, and policy-makers. With the completion of the Human Genome Project over a decade ago, the feasibility of attaining this goal on a widespread basis is becoming a greater reality. In fact, new genome sequencing technologies are bringing the cost of obtaining a patient's genomic information within reach of the general population. While this is an exciting prospect to health care, many barriers still remain to effectively use genomic information in a clinically meaningful way. These barriers, if not overcome, will limit the ability of genomic information to provide a significant impact on health care. Nevertheless, clinical decision support (CDS), which entails the provision of patient-specific knowledge to clinicians at appropriate times to enhance health care, offers a feasible solution. As such, this body of work represents an effort to develop a functional CDS solution capable of leveraging whole genome sequence information on a widespread basis. Many considerations were made in the design of the CDS solution in order to overcome the complexities of genomic information while aligning with common health information technology approaches and standards. This work represents an important advancement in the capabilities of integrating actionable genomic information within the clinical workflow using health informatics approaches