Stephan Kudyba

Information Technology and Corporate Profitability: A Focus on Operating Efficiency

This work involves an empirical analysis, incorporating firm-level investment in information technology and financial statement information, which provides an accurate measure of operating revenue, in a profitability function over the period from 1995-1997. The results indicate that IT can enhance firm level profitability. Factors such as advanced computer processing, the proliferation of PCs to the consumer and business environment, the development of the Internet, and advanced software applications have significantly augmented previously existing information technology. This new IT has provided infrastructure for advanced information networks which facilitate the flow of value added information to decision makers and enable corporate enterprises to more easily operate in the new global ecomomy. As a result, larger companies can provide a variety of goods and services that more effectively meet consumer preferences in a more efficient, cost-effective manner.

The impact of information technology on US industry

Abstract This work analyzes firm-level investment in information technology and corresponding productivity through the use of a production function over the period from 1995 to 1997. The results are then compared to previous studies that utilized similar data and methodologies to compare productivity estimates over time. The analysis indicates that investment in IT enhances productivity over the period in question and has illustrated increasing returns over time. It cites such factors as advanced computer processing, software applications and Internet related technology, that facilitate communication of information, as potential drivers of productivity. This work then ranks US industries according to IT intensity and categorizes industry groups as either high, normal or low IT intensity. Once again, through the use of economic theory, we estimate productivity of investment in IT according to industry grouping and find that the higher IT intensive group experienced greater returns from investment in information technology.

Identifying factors that impact patient length of stay metrics for healthcare providers with advanced analytics

Managing patients’ length of stay is a critical task for healthcare organizations. In order to better manage the processes impacting this performance metric, providers can leverage data resources describing the network of activities that impact a patient’s stay with analytic methods. Interdependencies between departmental activities exist within the patient treatment process, where inefficiency in one element of the patient care network of activities can adversely affect process outcomes.This work utilizes the method of neural networks to analyze data describing inpatient cases that incorporate radiology process variables to determine their effect on patient length of stay excesses for a major NJ based healthcare provider. The results indicate that inefficiencies at the radiology level can adversely extend a patient’s length of stay beyond initial estimations. Proactive analysis of networks of activities in the patient treatment process can enhance organizational efficiencies of healthcare providers by enabling decision makers to better optimize resource allocations to increase throughput of activities.

Enhancing efficiency in the health care industry

This paper illustrates the use of advanced analytics to increase efficiency in the healthcare sector through cost reduction. The application of multivariate techniques on health population data depicted better accuracy in identifying patients at risk of developing a chronic illness (diabetes) than more conventional techniques. The model results enable healthcare providers to more effectively apply preventive treatment methods to the at-risk population to reduce the likelihood of individuals from experiencing a fully developed illness. An estimate of the cost savings in the form of preventing cases of fully developed diabetes through predictive modeling is included.

ENHANCING ORGANISATIONAL INFORMATION FLOW AND KNOWLEDGE CREATION IN RE-ENGINEERING SUPPLY CHAIN SYSTEMS: AN ANALYSIS OF THE U.S. AUTOMOTIVE PARTS AND SUPPLIES MODEL

The ongoing initiative of business process re-engineering in organisations has largely been attributed to innovations in information technologies that have enabled firms to increase productivity in their operations. The following paper addresses essential concepts in supply chain networks and describes the systems approach the U.S. automotive industry has implemented to augment their supply chain management initiatives. The focus of re-engineering the supply chain is enhanced data capture and analysis of activities in various segments of the chain which augments organisational information flow and knowledge generation resulting in communication of proactive decision making throughout the supply network to maintain operational efficiency.

Utilising neural network applications to enhance efficiency in the healthcare industry: predicting populations of future chronic illness

Advanced analytic and forecasting methodologies can enable organisations to more fully leverage the data resources available to them. In the healthcare industry, service providers can use data mining methods to enhance the decision-making process in optimising resource allocation by identifying the sources of future high-cost treatment in a given health plan population. The following paper includes a case study by Healthways Inc. that illustrates how predictive modelling techniques (e.g., neural networks) can help healthcare providers identify the sources of future high resource demand, enabling them to more effectively apply preemptive treatment to mitigate future high-cost treatment of fully developed cases of chronic illness.

Informatics Application Challenges for Managed Care Organizations: The Three Faces of Population Segmentation and a Proposed Classification System

Organizations across industry sectors continue to develop data resources and utilize analytic techniques to enhance efficiencies in their operations. One example of this is evident as Managed Care Organizations (MCOs) enhance their care and disease management initiatives through the utilization of population segmentation techniques. This article proposes a classification system for population segmentation techniques for care and disease management and provides an evaluation process for each. The three proposed operational areas for Managed Care Organizations are: 1) Risk Status: early identification of high-risk patients, 2) Treatment Status: compliance with treatment protocols, and 3) Health Status: severity of illness or episodes of care groupings, all of which require particular analytic methodologies to leverage data resources. By applying this classification system an MCO can improve its ability to clarify internal goals for population segmentation, more accurately apply existing analytic methodologies, and produce more appropriate solutions.