A perspective on the proposal for European public sector accounting standards, in the context of accruals in UK government accounting

This paper offers a UK perspective on the proposal to develop European Public Sector Accounting Standards (EPSAS). It offers the fundamentals of the UK government’s system of budgeting and accounting, which is the responsibility of the UK Treasury, being one part of its responsibilities for the UK’s fiscal and monetary policies. In the light of this, the EPSAS proposal remains a puzzle and a peripheral one at that. The paper ponders on the forces underlying the EPSAS proposal and notes that for the government practitioner in an EU member state, rules emanating from the EU would naturally have a macro-level focus. Consequently, any potential advantages of an accrual accounting system at micro-level may not be fully appreciated.peer-reviewe

Health Literacy Redefined through Patient Engagement Framework

Today, Internet is competing with traditional Medicine. People go online to seek information for their wellbeing, look up treatment options or to find patients like them. Thus, patients' engagement in the healthcare system is becoming inevitable. A Pew recent study showed that 72% of U.S. adults have searched online for health information in the past year and 35% went online to diagnose a condition (Fox & Duggan, 2013). This has the potential to reshape patient-physician relationship and eventually the health care delivery at large. The government, recognizing the emerging role of patients, has launched an initiative that foster patient engagement, the "Patient Engagement Framework", where patients are invited to a new era of health care where they become partners instead of "patients." The Patient Engagement Framework (PEF) provides an excellent approach for healthcare providers to gradually engage patients using e-health resources and thus improve outcomes in terms of quality, safety and costs while meeting Meaningful Use criteria

Ontological Based CDSS in the Management of Breast Cancer

Digitized for IUPUI ScholarWorks inclusion in 2021.Accurate diagnosis is a key to ensure the most appropriate treatment. The diagnosis accuracy depends on physician's knowledge on the patient case. The ontological based CDSS is based on comprehending the diagnostic process and criteria to make accurate diagnosis and appropriate treatment plan

Healthcare Data Analytics for Parkinson’s Disease Patients: A Study of Hospital Cost and Utilization in the United States

Parkinson's Disease (PD), a prevalent problem, especially for the aged populations, is a progressive but non-fatal nervous system disorder. PD patients have special motor as well as non-motor symptoms over time. There are several limitations in the study of PD such as unavailability of data, proper diagnosis and treatment methods. These limitations significantly reduce the quality of PD patient life quality, either directly or indirectly. PD also imposes great financial burdens to PD patients and their family. This project aims to analyze the most common reasons for PD patient hospitalization, review complications that occur during inpatient stays, and measure the costs associated with PD patient characteristics. Using the HCUP NIS data, comprehensive data analysis has been performed. The results are customized visualized using Tableau and other software systems. The preliminary findings sheds light into how to improve the life quality of PD patients

Development and Evaluation of a Natural Language Conversational Bot for Identifying Appropriate Clinician Referral from Patient Narratives

Digitized for IUPUI ScholarWorks inclusion in 2021.Recent years have seen a significant increase in automated conversational agent chatbots. Conversational agents like chatbots for health may provide timely and cost-effective support in clinical care. Some studies show that chatbots could have an impact on patient engagement. Additionally, health systems are attempting to connect with patients over social networks, mainly where specialists are limited. By 2025, the Association of American Medical Colleges estimates that the United States will have a shortfall of 61,700-94,700 physicians and critical shortage in many specialties, delaying available appointments by months in many cases. Thus, we need innovative solutions that can manage the time of limited specialists appropriately. Recent research has demonstrated that deep learning methods are superior for natural language classification tasks compared to other machine learning methods. The primary objective of this study was to develop a telegram chatbot which reads patient narratives and acts as a conversational agent by redirecting the case to the appropriate specialist. Besides simply working on improving conversational capabilities of chatbots, we developed a novel method for referring the cases to specialists based on their responses to previous cases on a social network group. As far as we know, no other chatbot has the level of accuracy or referral system like our developed chatbot

A Predictive Modelling Approach in the Diagnosis of Parkinson's Disease Using Cerebrospinal Fluid Biomarkers

Digitized for IUPUI ScholarWorks inclusion in 2021.The research in Parkinson's disease {PD) using biomarkers has long been dominated by measuring dopamine metabolites or alpha-Synuclein in cerebrospinal fluid. However, these markers do not allow early detection or monitoring of disease progression. In the recent years, metabolic profiling of body fluids has become powerful and promising tools in identification of the novel biomarkers in the diagnosis of the disease. While not much research has been done using machine learning techniques and predictive modeling to predict the severity of Parkinson's disease. The purpose of this project is to apply a predictive modeling approach in the diagnosis of Parkinson's disease using Cerebrospinal Fluid Biomarkers. The dataset for this study was collected from the PPMI website which comprises of 360 - Parkinson's patient, 220 - Control and 20 - SWEDD (Scans without evidence for dopaminergic deficit). Various predictive models were developed in order to classify the disease based on its severity. The various machine learning algorithms used in this process are Decision tree, Random forest, Support Vector Machine {SVM), K- Nearest Neighbor (KNN), and Gradient boosting. Feature scaling and Mean normalization was applied to standardize the dataset. The above mentioned machine learning algorithms were applied on the Parkinson's Progression Markers Initiative (PPMI) data and accuracy for each algorithm was calculated. Out of all the models, Random forest and Gradient Boosting gave the best classification accuracy of 66.67%. In conclusion, the main factors that might have affected accuracy of the model are dataset size, missing data and number of features. To sum up, while the results show some predictive power, we conclude negative results and hence these models are not Clinically significant

An Expandable Drug Information Retrieval System for Oncology (EDIRS-FO)

Digitized for IUPUI ScholarWorks inclusion in 2021.Breast cancer was life threating a decade ago, however, now with the improvement in treatment the survival rate has increased considerably. Avoidable adverse effects accompany these treatments. The risk of acute and chronic adverse effects caused due to treatment greatly influence the quality of life in breast cancer survivors. An information system can reduce the avoidable ADR, thus, improving the decision making during patient encounters. It also makes the access to the information easier for the clinicians

IUPUI Center for Cancer Population Analytics and Patient-Centered Informatics

poster abstractAbstract: More than 30,000 Indiana residents are diagnosed with cancer each year. Cancer is the second leading cause of death in the state, claiming more than 12,000 lives annually. More than $1 billion was spent in Indiana on direct costs of treating the cancer population in 2003. Indirect costs to cancer patients and their families are also of great importance. Cancer care coordination has the potential to reduce costs and improve quality in cancer care delivery. Coordination may occur both among (1) multiple cancer care providers caring for populations of cancer patients, and (2) between providers and individual patients with cancer The IUPUI Center for Cancer Population Analytics and Patient-Centered Informatics was established in 2013. The center’s mission is to develop team science that combines innovative health information technologies with rigorous health services research methods in order to create knowledge that will have an impact upon the health and health care of patients and populations with cancer in the state of Indiana and the U.S. The center’s goals are (1) to build collaborative, multidisciplinary scientific teams to create national leaders in the state of Indiana in the fields of cancer health services research and informatics, and (2) to perform top-tier national cancer health services research and “big data” analytics to improve the quality, efficiency, coordination, and outcomes of cancer care The Center Cores: To build our research portfolio, we have the following 2 main cores of activity: I. Cancer Population Analytics Core: Data sources from multiple health care organizations throughout central Indiana are being joined together to answer important clinical/epidemiologic questions regarding the quality of cancer care, and design population-based, system interventions to improve the lives of Indiana cancer patients. Further support has been leveraged for this work, namely, the IU Cancer Center has provided a pilot grant to link the Indiana state cancer registry with data from the Regenstrief Institute’s Indiana Network for Patient Care in order to study the utilization of high-cost imaging among cancer survivors. Furthermore, support from a Regenstrief/Merck collaboration will facilitate assessment of the quality of the data linkage at the level of both the patient and cancer case. II. Cancer Patient-Centered Informatics Core: Multiple platforms are being leveraged to develop and test patient-centered technologies to enable individuals to track health care received and communicate with providers. Utilizing OpenMRS, a personal health record (PHR) module was created for colorectal cancer patients including treatment summary information, evidence-based decision support regarding surveillance, and online communication tools. Additional development is being focused upon updating the user interface, creating patient social networks, and providing tools to support patient well-being. Support has also been obtained from the Walther Cancer Foundation to collect information about patient symptoms and from the Regenstrief/Merck collaboration to collect patient-reported outcome measures. Finally, an NIH proposal has been developed for the SUrvivorship Care Plan-PERsonal Health Record Intervention Trial (SUPER-IT), a randomized controlled trial designed to test the effect of this new technology upon both the quality of care received and patient-centered outcomes

Barriers and Benefits Associated with Nurses Information Seeking Related to Patient Education Needs on Clinical Nursing Units

The purpose of this study was to answer the following two questions: What are clinical nurses’ rationales for their approaches to finding patient educational materials on the web? What are perceived barriers and benefits associated with the use of web-based information resources for patient education in the context of nursing clinical practice