Healthcare Business Intelligence: A Case Study of Universiti Utara Malaysia Health Center (PKU)

Organizations, private or public, feel increasing pressures, forcing them to respond quickly to changing conditions and be innovative in the way they operate. Such activities require organizations to be agile and make frequent and strategic, tactical, and operational decisions. Making such decision may require considerable amounts of timely and relevant data, information, and knowledge. Every semester that UUM admits new students, they do subject them to medical screening which sometimes includes the staffs and returning students. However, the results of the medical test from the laboratory technologists and the doctors, such as patient diagnosis, treatment and medical prescription are currently kept in the PKU data repository for record purposes without being further explored for their managerial activities. Therefore, this research applied Business Intelligence (BI) method for exploring the PKU database repository. The data warehouse was built for the activities in PKU and a prototype was developed at the end, while the system is evaluated by the prospective users of the system at PKU. The result of this research (PKUBI) helps the PKU management by simplifying the technique needed for managerial decision making and forecasting future activities that would help the PKU. Also, the PKUBI is also useful to know the medical statistics of the patients in UUM and the drugs that need to be frequently ordered for

Evaluation of SOVAT: An OLAP-GIS decision support system for community health assessment data analysis

Background. Data analysis in community health assessment (CHA) involves the collection, integration, and analysis of large numerical and spatial data sets in order to identify health priorities. Geographic Information Systems (GIS) enable for management and analysis using spatial data, but have limitations in performing analysis of numerical data because of its traditional database architecture. On-Line Analytical Processing (OLAP) is a multidimensional datawarehouse designed to facilitate querying of large numerical data. Coupling the spatial capabilities of GIS with the numerical analysis of OLAP, might enhance CHA data analysis. OLAP-GIS systems have been developed by university researchers and corporations, yet their potential for CHA data analysis is not well understood. To evaluate the potential of an OLAP-GIS decision support system for CHA problem solving, we compared OLAP-GIS to the standard information technology (IT) currently used by many public health professionals. Methods. SOVAT, an OLAP-GIS decision support system developed at the University of Pittsburgh, was compared against current IT for data analysis for CHA. For this study, current IT was considered the combined use of SPSS and GIS ("SPSS-GIS"). Graduate students, researchers, and faculty in the health sciences at the University of Pittsburgh were recruited. Each round consisted of: an instructional video of the system being evaluated, two practice tasks, five assessment tasks, and one post-study questionnaire. Objective and subjective measurement included: task completion time, success in answering the tasks, and system satisfaction. Results. Thirteen individuals participated. Inferential statistics were analyzed using linear mixed model analysis. SOVAT was statistically significant (α = .01) from SPSS-GIS for satisfaction and time (p < .002). Descriptive results indicated that participants had greater success in answering the tasks when using SOVAT as compared to SPSS-GIS. Conclusion. Using SOVAT, tasks were completed more efficiently, with a higher rate of success, and with greater satisfaction, than the combined use of SPSS and GIS. The results from this study indicate a potential for OLAP-GIS decision support systems as a valuable tool for CHA data analysis. © 2008 Scotch et al; licensee BioMed Central Ltd

Implementierung und Verifikation eines Report-Browsers für Multidimensionale Datenbanken im Klinikumfeld

Die großen Datenmengen der klinischen Routine stellen für die medizinische Forschung ein großes Potenzial dar. So lassen sich zum Beispiel doppelte Erhebungen vermeiden oder Studienteilnehmer schneller finden. Sollen diese Daten genutzt werden, bedarf es geeigneter Werkzeuge und Prozesse. Im Rahmen des RWH Projektes der Medizinischen Uniklinik Heidelberg und dem GECKO Institut der Hochschule Heilbronn soll in dieser Arbeit ein Abfragewerkzeug für multidimensionale Datenbanken erstellt und verifiziert werden. Den Schwerpunkt der Arbeit bildet die Wahl einer geeigneten Softwarearchitektur. Im Anschluss an eine Anforderungsanalyse wird das Abfragewerkzeug mit Hilfe von Java Technologien, wie dem Google Web Toolkit und dem Open Java API for OLAP, erstellt. Die Anforderungen werden mit zwei Anwendungsszenarien verifiziert. Der RWH Report-Browser konnte mit der festgelegten Architektur implementiert werden. Zum Erstellen von MDX Anfragen an das DataWarehouse wurde ein Anfragegenerator implementiert. Die Verifikation zeigt, dass der Report-Browser als Plattform für den Zugriff auf klinische Routinedaten geeignet ist. Eine gute Testbarkeit der Architektur konnte nachgewiesen werden

The Use of Olap Reporting Technology to Improve Patient Care Services Decision Making Within the University Health Center

The purpose of this paper is to demonstrate that it is feasible for the student health center to leverage existing clinical data in a data warehouse by using OLAP reporting in order to improve patient care and health care services decision making. Historically, University health care centers have relied mainly on operational data sources for critical health care decision making. These sources of data do not contain enough information to allow health officials to recognize trends or predict how future changes in health care services might vastly improve overall heath care. Four proof of concept artifacts are constructed through design science research methodology, and a feasibility study is presented to build the case for the adoption of OLAP reporting technology. The study concludes that it is feasible to implement an OLAP reporting infrastructure at the student health center if physicians, clinical staff, and administration clearly define the need for the new technology, develop proper data extraction, loading, and transformation strategy, and adequately provide project management and data warehouse design towards the implementation of the solution

Modellierung, Entwicklung und Nutzung eines Data Warehouse für medizinische Communication Centers

Das heutige Gesundheitssystem wird von den vielen Akteuren, den komplexen Beziehungen, den anspruchsvollen Patienten sowie dem veränderten Gesundheitsbewusstsein geprägt. Medizinische Communication Centers können als zentraler Kontaktpunkt zwischen Gesundheitssystem und Bevölkerung dienen. Durch die verschiedenen angebotenen medizinischen Dienstleistungen und die permanente Erreichbarkeit werden medizinische Communication Centers zu wichtigen Institutionen für eine bevölkerungsorientierte Versorgung. Für die ganzheitliche Bertreuung der Patienten/Versicherten müssen die Daten der Patienten/Versicherten an einer zentralen Stelle gespeichert werden. Data Warehouse Systeme ermöglichen die integrierte Speicherung der Daten und deren Auswertung. Bei einem Kontakt mit dem medizinischen Communication Center sind die Patienten-/Versichertendaten aus den früheren Kontakten bekannt und die Patienten/Versicherten können passend angesprochen und beraten werden. Die vorliegende Arbeit beschreibt den Einsatz von Data Warehouse Systemen für medizinische Communication Centers. Dabei wird der gesamte Prozess der Data Warehouse Entwicklung – Erhebung der Anforderungen an das Data Warehouse, Modellierung und Implementierung des Data Warehouse, Applikationen zur Auswertung der Data Warehouse Daten – betrachtet. Das entwickelte Data Warehouse unterstützt die Kommunikation mit den Patienten/Versicherten sowie die Qualität und Effizienz der angebotenen Dienstleistungen und Prozessen im medizinischen Communication Center. Mittels des entwickelten Data Warehouse Modells und der Auswertungsapplikationen können Daten bezüglich Beschwerden und bezogener Dienstleistungen, schweizweit und zeitbezogen, einfach und in Abhängigkeit von unterschiedlichen Analysekriterien visualisiert werden. Weiterhin können verschiedene Mitarbeiter bezogene Kennzahlen berechnet und das Reporting für die Vertragspartner zur Verfügung gestellt werden. Data Warehouse Systeme sind im Gesundheitsbereich, im Vergleich zu anderen Bereichen, weniger stark verbreitet. Das in der Arbeit beschriebene Data Warehouse zeigt das Potential und die Vorteile des Einsatzes solcher Systeme in medizinische Communication Centers und somit auch im Gesundheitswesen

Strategic alignment in data warehouses : two case studies

This research investigates the role of strategic alignment in the success of data warehouse implementation. Data warehouse technology is inherently complex, requires significant capital investment and development time. Many organizations fail to realize the full benefits from it. While failure to realize benefits has been attributed to numerous causes, ranging from technical to organizational reasons, the underlying strategic alignment issues have not been studied. This research confirms, through two case studies, that the successful adoption of the data warehouse depends on its alignment to the business plans and strategy. The research found that the factors that are critical to the alignment of data warehouses to business strategy and plans are (a) joint responsibility between data warehouse and business managers, (b) alignment between data warehouse plan and business plan, (c) business user satisfaction, (d) flexibility in data warehouse planning and (e) technical integration of the data warehouse. In the case studies, the impact of strategic alignment was visible both at implementation and use levels. The key findings from the case studies are that a) Senior management commitment and involvement are necessary for the initiation of the data warehouse project. The awareness and involvement of data warehouse managers in corporate strategies and a high level of joint responsibility between business and data warehouse managers is critical to strategic alignment and successful adoption of the data warehouse. b) Communication of the strategic direction between the business and data warehouse managers is important for the strategic alignment of the data warehouse. Significant knowledge sharing among the stakeholders and frequent communication between the data warehouse managers and users facilitates better understanding of the data warehouse and its successful adoption. c) User participation in the data warehouse project, perceived usefulness of the data warehouse, ease of use and data quality (accuracy, consistency, reliability and timelines) were significant factors in strategic alignment of the data warehouse. d) Technology selection based on its ability to address business and user requirements, and the skills and response of the data warehousing team led to better alignment of the data warehouse to business plans and strategies. e) The flexibility to respond to changes in business needs and flexibility in data warehouse planning is critical to strategic alignment and successful adoption of the data warehouse. Alignment is seen as a process requiring continuous adaptation and coordination of plans and goals. This research provides a pathway for facilitating successful adoption of data warehouse. The model developed in this research allows data warehouse professionals to ensure that their project when implemented, achieve the strategic goals and business objectives of the organization