Healthcare Analytics Leadership: Clinical & Business Intelligence Plan Development

Future healthcare leaders require expert knowledge and practical capabilities in the evaluation, selection, application and ongoing oversight of the best types of analytics to create continuous learning healthcare systems. These systems may result in continuously improving the demonstrable quality, safety and efficiency of healthcare organizations. Data is an asset for organizations. However, many companies do not know how to establish analytical road maps for future action. Population Health Intelligence describes a new discipline whose role is to collect, organize, harmonize, analyze, disseminate and act upon the data available to clinicians, health system leaders, the pharmaceutical and biotechnology industry, and healthcare payers. This webinar on Analytics Leadership will demonstrate how to create and implement Clinical & Business Intelligence Plans that transform data into actionable organizational insights. Agenda Introduction Healthcare Analytics Leadership: Clinical & Business Intelligence Plan Development Population Health Intelligence Presentation: 53:3

Analysis of Business Intelligence Applications in Healthcare Organizations

In today’s healthcare (HC) market there are lots of pressures on HC organizations (Os). Besides, many challenges including; demographic changes and the need to manage vastly increasing data volumes in HC, have motivated these organizations to adopt business intelligence (BI) solutions. Through a systematic review of the literature, this study establishes the patterns of BI adoption in the HC domain by examining the nature of BI solutions in use, expected outcomes from BI use, specific types of BI capabilities deployed, and aspects of HCOs directly impacted. Findings from our study provide a foundation for future research agenda on BI in Healthcare. We conclude by highlighting the shortcomings of current BI practice in the HC domain in the context of the emerging value-based (VB) HC delivery model and the need for research in this direction

Healthcare Data Analytics on the Cloud

Meaningful analysis of voluminous health information has always been a challenge in most healthcare organizations. Accurate and timely information required by the management to lead a healthcare organization through the challenges found in the industry can be obtained using business intelligence (BI) or business analytics tools. However, these require large capital investments to implement and support the large volumes of data that needs to be analyzed to identify trends. They also require enormous processing power which places pressure on the business resources in addition to the dynamic changes in the digital technology. This paper evaluates the various nuances of business analytics of healthcare hosted on the cloud computing environment. The paper explores BI being offered as Software as a Service (SaaS) solution towards offering meaningful use of information for improving functions in healthcare enterprise. It also attempts to identify the challenges that healthcare enterprises face when making use of a BI SaaS solution

Implementation Issues of Enterprise data Warehousing and Business Intelligence in the Healthcare Industry

The healthcare industry is following the lead of other industries and finding value in enterprise data warehousing (EDW) and business intelligence (BI) tools. Healthcare organizations are leveraging these tools to provide a plethora of benefits realized through enhanced business operations and performance. The EDW combines data from multiple source systems across an enterprise, and BI tools extract the data in meaningful ways to enable managers to make the best informed decisions. As with all management information systems, there are technical issues to be considered that impact the design, build, implementation, and support of the system. These benefits and challenges are explored, as well as special considerations necessary for the healthcare industry compared to other industries utilizing data warehousing and business intelligence. This paper investigates these critical issues and provides suggestions to harness the implementation of EDW and BI in the healthcare industry

Towards a Business Intelligence Maturity Model for Healthcare

Healthcare is a very complex, knowledge-driven industry. Electronic health record implementations have created massive amounts of clinical and financial data. The accumulation of data is outpacing the ability of organizations to leverage the data for improving financial and clinical efficiencies and quality of care. It is believed that careful and attentive use of business intelligence (BI) in healthcare can transform data into knowledge that can improve patient outcomes and operational efficiency. BI maturity models are a way of identifying strengths and weaknesses of the information maturity of a business. This paper presents a comprehensive review of existing BI maturity models to determine their adequacy for use in healthcare. The review identifies gaps in existing BI maturity models and presents requirements for a healthcare-specific maturity model. The results of this study will be used to develop a BI maturity model that addresses the complex characteristics and needs of healthcare organizations

Towards a Business Intelligence Maturity Model for Healthcare

Healthcare is a very complex, knowledge-driven industry. Electronic health record implementations have created massive amounts of clinical and financial data. The accumulation of data is outpacing the ability of organizations to leverage the data for improving financial and clinical efficiencies and quality of care. It is believed that careful and attentive use of business intelligence (BI) in healthcare can transform data into knowledge that can improve patient outcomes and operational efficiency. BI maturity models are a way of identifying strengths and weaknesses of the information maturity of a business. This paper presents a comprehensive review of existing BI maturity models to determine their adequacy for use in healthcare. The review identifies gaps in existing BI maturity models and presents requirements for a healthcare-specific maturity model. The results of this study will be used to develop a BI maturity model that addresses the complex characteristics and needs of healthcare organizations

An Adaptive Neuro-Fuzzy System with Semi-Supervised Learning as an Approach to Improving Data Classification: An Illustration of Bad Debt Recovery in Healthcare

Business analytics has become an increasingly important priority for organizations today as they strive to achieve greater competitiveness. As organizations adopt business practices that rely on complex, large-scale data, new challenges also emerge. A common situation in business analytics is concerned with appropriate and adequate methods for dealing with unlabeled data in classification. This study examines the effectiveness of a semi-supervised learning approach to classify unlabeled data to improve classification accuracy rates. The context for our study is healthcare. The healthcare costs in the U.S. have risen at an alarming rate over the last two decades. One of the causes for the rising costs could be attributed to medical bad debt, i.e., debt that is not recovered by healthcare institutions. A major obstacle to debt classification, hence better debt recovery, is the presence of unlabeled cases, a situation not uncommon in many other business contexts. There is surprisingly very little research that explores the performance of computational intelligence and soft computing methods in improving bad debt recovery in the healthcare industry. Using a real data set from a healthcare organization, we address this important research gap by examining the performance of an adaptive neuro-fuzzy inference system (ANFIS) with semi-supervised learning (SSL) in improving debt recovery rate. In particular, this study explores the role of ANFIS in conjunction with SSL in classifying unknown cases (those that were not pursued for debt collection) as either a good case (recoverable) or a bad case (unrecoverable). Healthcare institutions can then pursue these potentially good cases and improve their debt recovery rates. Test results show that ANFIS with SSL is a viable method. Our models generated better classification accuracy rates than those in prior studies. These results and their analysis show the potential of ANFIS with SSL models in classifying unknown cases, which are a potential source of revenue recovery for health care organizations. The significance of this research extends to all types of organizations that face an increasingly urgent need to adopt reliable practices for business analytics

Towards a Framework for Realizing Healthcare Management Benefits Through the Integration of Patient\u27s Information

Business Intelligence (BI) applications, including customer relationship management systems, decision support systems, analytical processing systems, and data mining systems, have captured the attention of practitioners and researchers for the last few years. Health care organizations, which are data driven and in which quality and integration of data is of paramount importance, have adopted BI applications to help and assist healthcare managers in improving the quality of the information input to the decision process. Based on preliminary data collection results, it is found that high quality data is essential to successful BI performance and that technological support for data acquisition, analysis and deployment are not widespread. Yet, business organizations are not investing in improving data quality and data integration. In this paper the authors propose a framework for evaluating the quality and integration of patient’s data for BI applications in healthcare organizations. In doing so, a range of potential benefits is highlighted. Even though this framework is in an early stage of development, it intends to present existing solutions for evaluating the above issues. The authors conclude that further research needs to be carried out to refine this framework, through model testing and case studies evaluation

Cloud Computing with Intelligent Agents to Support Service Oriented System Control and Management

the past few years, Cloud computing has becoming one of the revolutionary technologies in ICT which grows in both popularity and importance, both in industry and in academic domain. More and more private companies, government organizations and institutions are convinced and happy to promote Cloud to improve both connectivity and instant social ability. For IT services and solutions for business, Cloud-based platform promises to offer better business intelligence and productive experience by using unified communications, consistent collaborated data and service management. It is well believed that Cloud Computing will also bring a revolution in the healthcare IT sector along with other ICT business. To exploit Cloud computing productivity potential, this paper focuses on adopting Cloud computing technologies with agent-based solutions to support service oriented system control and management. The on-going research and practice demonstrates an application to the management of community care provision, which shows transforming to Software-as-a-Service (Saas) with the combination of a private healthcare cloud and integrated agents can improve business efficiency by providing flexible services scheduling, smarter health care services control and management

An Intelligent Risk Detection Framework Using Business Intelligence Tools to Improve Decision Efficiency in Healthcare Contexts

Leading healthcare organizations are recognizing the need to incorporate the power of a decision efficiency approach drivenby intelligent solutions. The primary drivers for this include the time pressures faced by healthcare professionals coupledwith the need to process voluminous and growing amounts of disparate data and information in shorter and shorter timeframes and yet make accurate and suitable treatment decisions which have a critical impact on successful healthcareoutcomes. This research contends that such a context is appropriate for the application of real time intelligent risk detectiondecision support systems using Business Intelligence (BI) technologies. The following thus proposes such a model in thecontext of the case of Congenital Heart Disease (CHD), an area which requires complex high risk decisions which need to bemade expeditiously and accurately in order to ensure successful healthcare outcomes