Emergency Department Management: Data Analytics for Improving Productivity and Patient Experience

The onset of big data, typically defined by its volume, velocity, and variety, is transforming the healthcare industry. This research utilizes data corresponding to over 23 million emergency department (ED) visits between January 2010 and December 2017 which were treated by physicians and advanced practice providers from a large national emergency physician group. This group has provided ED services to health systems for several years, and each essay aims to address operational challenges faced by this group’s management team. The first essay focuses on physician performance. We question how to evaluate performance across multiple sites and work to understand the relationships between patient flow, patient complexity, and patient experience. Specifically, an evaluation system to assess physician performance across multiple facilities is proposed, the relationship between productivity and patient experience scores is explored, and the drivers of patient flow and complexity are simultaneously identified. The second essay explores the relationship between physician performance and malpractice claims as we investigate whether physicians’ practice patterns change after they are named in a malpractice lawsuit. Overall, the results of this analysis indicate that the likelihood of being named in a malpractice claim is largely a function of how long a physician has practiced. Furthermore, physician practice patterns remain consistent after a physician is sued, but patient experience scores increase among sued physicians after the lawsuit is filed. Such insights are beneficial for management as they address the issue of medical malpractice claims. The final essay takes a closer look at the relationship between advanced practice providers (APPs) and physicians. Can EDs better utilize APPs to reduce waiting times and improve patient flow? A systematic data-driven approach which incorporates descriptive, predictive, and prescriptive analyses is employed to provide recommendations for ED provider staffing practices

How 5G wireless (and concomitant technologies) will revolutionize healthcare?

The need to have equitable access to quality healthcare is enshrined in the United Nations (UN) Sustainable Development Goals (SDGs), which defines the developmental agenda of the UN for the next 15 years. In particular, the third SDG focuses on the need to “ensure healthy lives and promote well-being for all at all ages”. In this paper, we build the case that 5G wireless technology, along with concomitant emerging technologies (such as IoT, big data, artificial intelligence and machine learning), will transform global healthcare systems in the near future. Our optimism around 5G-enabled healthcare stems from a confluence of significant technical pushes that are already at play: apart from the availability of high-throughput low-latency wireless connectivity, other significant factors include the democratization of computing through cloud computing; the democratization of Artificial Intelligence (AI) and cognitive computing (e.g., IBM Watson); and the commoditization of data through crowdsourcing and digital exhaust. These technologies together can finally crack a dysfunctional healthcare system that has largely been impervious to technological innovations. We highlight the persistent deficiencies of the current healthcare system and then demonstrate how the 5G-enabled healthcare revolution can fix these deficiencies. We also highlight open technical research challenges, and potential pitfalls, that may hinder the development of such a 5G-enabled health revolution

Addendum to Informatics for Health 2017: Advancing both science and practice

This article presents presentation and poster abstracts that were mistakenly omitted from the original publication

An investigation of analytics and business intelligence applications in improving healthcare organization performance: a mixed methods research

The healthcare ecosystem in the US is currently undergoing series of refinement and reformation due to the need to (i) improve quality of care and (ii) reduce cost. To achieve their key objective, healthcare organizations (HCOs) currently face a fundamental challenge: how to best use or optimize limited resources while providing better care and services to patients? The answer to this question might lie within HCO’s massive data and the ability to identify and apply appropriate analytics and business intelligence (A&BI) techniques and technologies to discern and extract relevant information and knowledge from that data. However, despite the increasing interest in the implementation and utilization of A&BI techniques and technologies by various organizations to improve operational efficiencies and financial performance, HCOs still lag behind other sectors in the adoption and use of A&BI capabilities. Motivated by the “data rich but information poor” syndrome currently facing HCOs, this dissertation applies a mixed method research–case study (interpretivist) and survey (positivist) – to investigate how healthcare organizations can leverage A&BI techniques and technologies to improve their overall performance. In achieving this objective, I illustrate an exemplar of how A&BI techniques and technologies can effectively be applied by specifically answering this high-level research question (RQ): How can A&BI techniques, methods, and technologies be developed and leveraged to improve performance in healthcare organizations? This high-level RQ has been broken down into four sub-questions that will be answered in two different studies in this dissertation. In the first study, I investigate what combination of A&BI techniques and technologies HCOs are currently applying to create value. This study was conducted by using content/literature analysis and case study methods in a large healthcare organization. The second study builds on the first study to investigate, using both interview and survey data, how A&BI capabilities can be developed, cultivated and nurtured as a core competency or capability that significantly helps improve healthcare organizations’ overall performance (such as cost reduction, quick access to providers and treatment, effective diagnostics, etc.). I found very novel and interesting results in both studies that not only address the research questions, but also provide significant theoretical and practical contributions. Major contributions of study 1 include: revising and remodeling of an outdated healthcare value chain (HCVC) framework that is more realistic and applicable to current care delivery practices in the healthcare industry and mapping of A&BI capabilities to the different domains of the revised HCVC framework. Study 2 provides theoretical contribution to the existing literature by conceptualizing and empirically validating A&BI capability as a third-order multi-dimension construct and its significant influence on performance

Examining the Impact of Information Technology on Healthcare Performance: A Theory of Swift and Even Flow (TSEF) Perspective

The impact of information technologies on manufacturing operations and performance is well established. However, scant research has been devoted to examining information technology (IT) investment among hospitals and how it influences patient care and financial performance. Using the lens of the Theory of Swift Even Flow (TSEF), we present an operations management-based perspective on the effect of IT in streamlining hospital operations. Specifically, we examined the role of IT on patient flow and its consequences for improved hospital efficiency and performance. Analysis of data from 567 U.S. hospitals shows that IT is associated with swift and even patient flow, which in turn is associated with improved revenues. Interestingly, we find that the improvement in financial performance is not at the expense of quality because we find similar effects of IT and patient flow in improvements in the quality of patient care. Further, we observed differential effects of swift flow and even flow on various measures of hospital performance. Although swift flow affects financial performance, even flow primarily affects quality performance. Taken together, they have a mutually reinforcing overall impact on hospital performance. The implications of these findings for hospital decision makers are that patient flow is an important mediating variable that is affected by IT and can significantly affect the quality of patient care and financial performance

An Integrated Framework for Staffing and Shift Scheduling in Hospitals

Over the years, one of the main concerns confronting hospital management is optimising the staffing and scheduling decisions. Consequences of inappropriate staffing can adversely impact on hospital performance, patient experience and staff satisfaction alike. A comprehensive review of literature (more than 1300 journal articles) is presented in a new taxonomy of three dimensions; problem contextualisation, solution approach, evaluation perspective and uncertainty. Utilising Operations Research methods, solutions can provide a positive contribution in underpinning staffing and scheduling decisions. However, there are still opportunities to integrate decision levels; incorporate practitioners view in solution architectures; consider staff behaviour impact, and offer comprehensive applied frameworks. Practitioners’ perspectives have been collated using an extensive exploratory study in Irish hospitals. A preliminary questionnaire has indicated the need of effective staffing and scheduling decisions before semi-structured interviews have taken place with twenty-five managers (fourteen Directors and eleven head nurses) across eleven major acute Irish hospitals (about 50% of healthcare service deliverers). Thematic analysis has produced five key themes; demand for care, staffing and scheduling issues, organisational aspects, management concern, and technology-enabled. In addition to other factors that can contribute to the problem such as coordination, environment complexity, understaffing, variability and lack of decision support. A multi-method approach including data analytics, modelling and simulation, machine learning, and optimisation has been employed in order to deliver adequate staffing and shift scheduling framework. A comprehensive portfolio of critical factors regarding patients, staff and hospitals are included in the decision. The framework was piloted in the Emergency Department of one of the leading and busiest university hospitals in Dublin (Tallaght Hospital). Solutions resulted from the framework (i.e. new shifts, staff workload balance, increased demands) have showed significant improvement in all key performance measures (e.g. patient waiting time, staff utilisation). Management team of the hospital endorsed the solution framework and are currently discussing enablers to implement the recommendation

A Chronic Obstructive Pulmonary Disease Pilot Using Risk Stratification to Improve Resource Allocation and Reduce Readmissions

Background: Chronic Obstructive Pulmonary Disease (COPD) impacts 250 million people, is associated with high hospital readmission rates, and costs over $50 billion annually. Purpose: Apply risk stratification identifying higher risk patients to prioritize complex, time-consuming interventions and resources. Methods: Patients hospitalized with COPD were risk stratified using PEARL. Moderate-high risk patients were referred to specialty nurse practitioners, who used real-time interventions and motivational interviewing during intense weekly visits over 30 days targeting self-management, patient-specific risks, and resources. Results: No patients were readmitted or died during the pilot using risk stratification with patient-specific tertiary preventive care to communicate resource allocation. Impact: This process provided recommendations for expansion throughout the healthcare facility, other chronic health conditions, budgets and policy for value-based care, and further research

Value of Health Information Sharing in Reducing Healthcare Waste: An analysis of duplicate testing across hospitals

Recent healthcare reform has focused on reducing excessive waste in the US healthcare system, with duplicate testing being one of the main culprits. We explore the factors associated with duplication of radiology tests when information sharing across healthcare providers is fragmented, and patients switch from one hospital to another. We hypothesize that patients’ switching behavior across hospitals is associated with a higher levels of duplicate testing, and argue that implementation of intra- and inter-hospital information sharing technologies will help to reduce duplicate tests. We utilize a panel data set consisting of 39,600 patient visits across outpatient clinics of 68 hospitals from 2005 to 2012. Our results indicate that hospital switching is associated with greater duplicate testing and usage of inter-hospital information systems is associated with lower duplication. Our results support the need for implementation of health information exchanges as a potential solution to reduce the incidence of duplicate tests