A Data Warehouse in an E-Health System

A Data Warehouse in an E-Health System P. DI BITONTO, F. DI TRIA, T. ROSELLI, V. ROSSANO, F. TANGORRA Dipartimento di Informatica Università degli Studi di Bari Aldo Moro via Orabona 4, 70125 Bari ITALY francescoditria@di.uniba.it, {teresa.roselli, veronica.rossano,filippo.tangorra}@uniba.it Abstract: - Nowadays, data analysis is getting more and more important also in health environments, because medical teams need to obtain statistical information when monitoring patients conditions. This is especially true in e-health systems, where patients are de-hospitalized and followed through remote controls. In this context, the implementation of a data warehouse, which automatically collects daily monitoring data stored in medical records and, then, provides historical information about patients, supports medical teams when performing data analyses devoted both to discover hidden data correlations and to optimize health business processes. In this paper, we present the data warehouse developed for UBI-CARE, a project whose aim is to support medical teams in the remote management of patients affected by peritoneal dialysis and chronic heart failure. Key-Words: - Data warehouse, OLAP, reports, medical analyses 1 Introduction Data analysis is a complex activity that uses software technologies for storing large volumes of historical and heterogeneous data in a central repository the data warehouse (DW) and for developing reports that offer a multidimensional view of data. The aim is to produce new information for managers and to support business decision making. For this reason, DWs are the most significant component of information systems, since they support the improvement of business processes. This requires the design and the implementation of a database optimized for multidimensional analyses, a feeding plan that regularly updates the database, and a set of software tools that support analytical processing [1]. In the last years, DWs have become important in new systems for which an easy access to information is important for decision making [2]. Then, also E-Health environments started to adopt DWs in order to extract information from a central and integrated repository [3]. In this context, typical objectives regarding the E-Health systems are collecting clinical data, monitoring the patients health, and making decisions in critical situations. The UBI-CARE (UBIquitous knowledgeoriented HealthCARE) system is a project that aims to realize an health-care network that favours the dehospitalization of patients suffering from peritoneal dialysis and chronic heart failure [4, 5]. In this system, medical teams are mainly interested in utilizing a centralized source of information accessible across different platforms in order to quickly analyze problems and get satisfactory solutions. For example, an abnormal weight variation may need a change of therapy or an immediate hospitalization. In the system, data are inserted using different data entry modalities: (a) automatic acquisition using medical devices; (b) manual acquisition of parameters monitored daily by patients, caregivers, doctors, and paramedics. These monitoring data are first stored in clinical folders and, then, collected in the data warehouse. The analytical processing about monitoring data allows doctors and caregivers to be continuously informed about the current and the past health conditions of a patient. This paper presents the medical DW realized to support the decisional activities of medical teams that aim at monitoring the state of health of patients who have been de-hospitalized. The paper is organized as follows. Section 2 presents the system architecture, including the design process and the feeding plan. Section 3 shows examples of reports deployed through a web-based application. Section 4 contains related work. Section 5 concludes the paper with our remarks. 2 System architecture A data warehouse in E-Health systems consists of applications and technologies that help medical teams to have a wide knowledge about the health conditions of patients who have been dehospitalized and are managed through remote ISBN: 978-960-474-358-2 87

control systems. So, in the UBI-CARE project the data warehouse is designed to provide a valid tool that satisfies the following needs: a unique system of analysis and reporting for the staff of the hospital; a system that supplies in real time information through a web-based environment. Figure 1 shows the architecture of the Medical Data Warehouse structured on the typical multi-level layout. Cardiology Medical Records Adaptor Medical Specialist Data Service Gateway Extract. Transf. Loading tool Medical Data Warehouse Cardio Territorial Specialist Feeding process Nephro Nurse Nephrology Medical Records Adaptor Data Source General Practitioner Decisional System Fig. 1. Data warehouse architecture in UBI-CARE. The patients monitoring data are first extracted from the Gateway (DSG) and, next, collected into the DW, using software tools that executes the so-called ETL (Extraction, Transformation, and Load) process, which also refreshes the DW with new and recent data. Then, Decisional Systems are used by decision makers to access the DW in order to perform analyses and develop reports. The architecture described is widely accepted by business companies and it is known as two-layer architecture (Sources level and DW level). However, there exist two variations to this architecture: in the one, the DW is virtual and it is represented by a middleware (one-layer architecture); in the other, the ETL process does not feed directly the DW but a global and reconciled database that, in turn, feeds the DW (three-layer architecture). In general, it is possible to distinguish two main areas: (1) the back-end area, composed of the Sources level, the Refresh level, and the DW level, usually managed by an OLAP (On Line Analytical Processing) Server; and (2) the front-end area, composed of systems and applications supporting decision making [6]. 2.1 Data source The DSG is a web service acting as a virtual database that integrates medical records stored in a proprietary format. This component ensures the extensibility of the system, for it is based on adaptors able to connect to physical folders containing medical records and to convert data in a standardized way. Moreover, it ensures the decoupling between the data source and the target system, that is the DW. The communication is based on the REST protocol and the web service returns a JSON document containing the clinical data about patient s history, surgeries, laboratory results, and monitoring data. These monitoring data are the most important for statistical analyses about the current historical state of health of patients. 2.2 Feeding process A DW must be populated by a periodically-executed feeding process that physically moves data from sources into the DW, according to an established plan. This process, essentially based on data integration, includes an important sub-process, whose aim is to clean data. In fact, the decision making process must be supported by reliable analyses, and it is strongly determined by the quality of available data. For these reasons, this activity must be able to ensure the elimination of errors that may lead to wrong choices, as these data will be used to provide information and knowledge for decision making. In fact, the main aim of a data warehouse system consists of performing analytical queries and developing reports, in order to produce information and knowledge that will be used for improving the operational processes in medical activities. The execution of ETL must be repeated at regular intervals of time and the right interval must be addressed on the basis of the refresh necessity. In UBI-CARE, the feeding process required a mapping among the data present in the JSON document retrieved from the DSG and the attributes of the data warehouse. Data are daily updated in order to have refreshed information and to make ISBN: 978-960-474-358-2 88

timely decisions about the patient s state. The feeding strategy relies on a batch process and is based on an incremental approach that extracts from clinical folders and loads into the DW only new data detected by examining the insertion date. 2.3 Medical data warehouse There are two main methodologies to design a data warehouse: requirement-driven and data-driven. The requirement-driven approach starts from the requirements analysis [7], while the data-driven approach is essentially based on a reengineering of data sources [8]. Hybrid methodologies are also adopted to design big data warehouse, especially when social networks are used as data sources [9]. However, in despite of the chosen design approach, the data warehouse must be optimized to allow analytical processing of data and must support multidimensional views of data. To this end, the data warehouse conceptual design is based on a multidimensional model which adopts the metaphor of the cube. According to this metaphor, a fact related to an event can be represented as a cube. A fact can be measured by numeric attributes, that provide quantitative information. These numeric attributes are the so-called measures. So, each cell of the cube stores a single numeric value, pointed out by a set of dimensions, representing points of view or coordinates of analysis. On turn, a dimension is composed of levels of analysis that define how the data can be aggregated. The levels that are in one-to-many relationship among themselves form a hierarchy, that represents an aggregation path for analytical processing. In the medical data warehouse, we performed a data-driven design aiming at producing a multidimensional view of the monitoring data stored in the clinical records. So, we created two cubes related to the different areas. The first cube is Cardio and allows to analyze data about the chronic heart failure. The second is Nephro, which allows to analyze data about peritoneal dialysis. They share the same dimensions and some measures, which are retrieved from different medical records. Of course, each cube is equipped with additional and specific measures. The first dimension is Patient and is used to store data about patients in an anonymous way. The track is ensured by an identifier and only the personnel of the health care network is able to associate a patient with his/her own identifier. The second dimension is Time and allows to execute analyses per day, week, month, and year. The third dimension is Location and allows to execute analyses per city and region. Figure 2 shows the logical schema obtained from the cubes realized during the conceptual design. Patient PK patient_id age sex Cardio PK,FK1 patient_id PK,FK2 date PK,FK3 city_id weight minpressure maxpressure NYHA heartrate Location PK city_id city_name region Nephro PK,FK1 patient_id PK,FK2 date PK,FK3 city_id diuresis edemasup edemainf weight minpressure maxpressure hearthrate Fig. 2. Medical Data warehouse. Time PK date week month year 2.4 Decisional system The system used for analyses is represented by phpmyolap [10], an open source web application written in PHP and using MySQL as a relational Database Management System, since this actually represents a valid solution for data warehousing environments [11]. This system provides several storage engines. We chose MyISAM, since this is a high performance engine. In fact, it is not transaction-oriented and it does not implement foreign key constraints. Indeed, in data warehousing systems, data consistency is more important than referential integrity [12, 1]. The software tool phpmyolap adopts the Mondrian XML schema format [13] to store the data warehouse s metadata that can be browsed through a tree-based visualization. On the basis of the Query-By-Example approach, users can create a report without using the MDX language [14]. This application uses a native OLAP engine which supports traditional operators such as roll-up, drilldown, pivoting and generates SQL statements to be executed on MySQL. This engine does not rely on Java-based OLAP engine acting as a middleware and requiring further web server as Tomcat. This makes phpmyolap independent and portable for Apache-MySQL-PHP systems. ISBN: 978-960-474-358-2 89

2.4.1 Decision makers The health care network in UBICARE is structured according to the hub-and-spoke distribution paradigm [15], which is a system of connections arranged like a chariot wheel, in which all traffic moves along spokes connected to the hub at the center. The model is becoming popular for hospital networks, where a single specialized center (Hub) is specialized for the treatment of a specific disease and focused on health care, supported by a network of services (Spokes) to transport the patients to reach the minimum severity levels required to take advantage of the specific treatment. The ultimate goal is to make the spoke centers able to follow the patient and to manage the low and medium critical situations, sending the patient to the hub center only in the case of serious problems. The network considers several figures, that are devoted to data analysis as users of the system. The leading expert of the disease is the Medical Specialist who belongs to the hub. S/he is interested in using the data warehouse in order to detect abnormal and out-of-range values which require an immediate hospitalization. The Territorial Specialist is a doctor who usually belongs to the spoke centres and is responsible of the follow-up of the patients dislocated in the territory. Usually, this is the first figure able to provide support in case of problems. The General Practitioner has access to the standard guidelines about diagnostic and therapeutic protocols in order to support patients even in case of unexpected events. This doctor is able to detect arising problems and to provide support by changing therapy. In this way, the patient will refer to the specialists only if necessary. The Nurses, or professional health workers, who assist the patient during hospitalization and at home. They are interested in learning both treatment protocols, monitoring and managing medical devices. This figure takes care of secondary symptoms. 3 Data analysis The decision makers are interested in creating interactive reports by navigating through the schema on the basis of the multidimensional model. So, the starting point is a tree-based representation of each cube. In Figure 3, we show the multidimensional elements of the Cardio cube of the Medical Data Warehouse. Such elements are retrieved from the XML file storing the data warehouse s metadata. For Cardio cube, its measure, along with the main statistical functions, and its dimensions are reported. Leaf nodes of the tree are attributes that can be selected and included in the report. Fig. 3. Tree-based representation of Cardio cube. The example is executed by a nurse and is devoted to analyze the average of weight of patients per week and month. The result of this query is shown in Figure 4 which gives information about the state of health of all the patients. In order to focus the attention on only one patient, a slice-anddice operation is necessary to establish some coordinates of analysis and to reduce the cardinality of the dataset. The report states that the patient (whose identifier is 1) has gained a weight reduction during the first month of the 2012. This patient is a overweight person who was asked to lose or maintain weight in order to avoid risk of heart attack. In this case, the nurse can verify the correct trend of the cure, without involving the participation of the specialists. If the nurse had found an abnormal increase of weight, then the nurse would have alerted the territorial specialists for a quick assistance or the medical specialist, in case of copresence of critical factors, such as high pressure. ISBN: 978-960-474-358-2 90

As a further and a deeper analysis, the nurse can perform a drill-down operation in order to disaggregate data and visualize the weight per day. The inverse of the drill-down is the roll-up operation, which allows to go up in the hierarchical levels. These OLAP operators are shown in Figure 5. analysis of data, by modifying a report, posting comments and opening discussion on the results. Expand/collapse Figure 6. Pivoting OLAP operator. Fig. 4. Average of the weight per patient, week, and month. Figure 5. Drill-down/Roll-up OLAP operator. An example of application of pivoting operator is illustrated in Figure 6, which is a report that shows the mean pressure per year and city. The report is devoted to check if there is a correlation between the territory and some health problems, as high pressure. The example shows that in Foggia city patients present a higher mean pressure than other cities of the same region. At last, users can create charts, save the reports or export them in traditional formats such as pdf or csv. However, since UBI-CARE is a system based on the social network paradigm, we added typical social features, such as sending a link to the report via email or sharing the report with members of the community. So, users can perform a collaborative 4 Related work In [16], the authors propose the term education data warehouse to describe a health system devoted to support the creation of individualized learning paths and to allow analyses about the career of physicians over time. The data warehouse is mainly used to collect and integrate data coming from different training programs and from national electronic databases. Such an integration is encouraged by emerging standards for health professions, since these standards are lowering the barriers among medical institutional organizations [17]. The trend of adopting data warehouses for health systems in confirmed in [18], where the design experience in the University of Michigan Health System is reported. Here, the data warehouse is obtained through the integration of clinical and financial data, in order to understand the financial implications of clinical decisions in the care of patients. The underlying assumption is that clinicians may take better decisions when they know the costs of a particular practice and can identify alternative practices. A similar case is that of the University of Virginia Health System, where the data warehouse is used to provide clinicians and researchers with direct, rapid access to retrospective clinical and administrative patients data [19]. In addition, they use the data warehouse also for educational and research aims, as it serves to face informatics issues such as data capture and to perform exploratory analyses of healthcare problems. ISBN: 978-960-474-358-2 91

5 Conclusion The paper summarizes the experience in designing a medical data warehouse modelled for an e-health system. The use of the data warehouse is a valid support for medical teams composed of several figures involved according to different levels of responsibility in the patients remote management. Each user is allowed to access the analytical layer of the system and to execute queries in order to obtain statistical information about the trend of the state of health of the patients. In this way, the dehospitalized patients are referred to spoke centres in case of need of assistance and are referred to hub centres only when they need to be hospitalized. Future work addresses the realization of a data warehouse containing synthetic data to be used for training purpose in order to provide a simulated environment for students of Medicine. Acknowledgment This work was supported in part by the Project UBICARE (UBIquitous knowledge-oriented HealthCARE) - EU-FESR P.O. Puglia Region 2007-2013 Grant in Support of Regional Partnerships for Innovation - Investing in your future (UE-FESR P.O. Regione Puglia 2007-2013 Asse I Linea 1.2 - Azione 1.2.4 - Bando Aiuti a Sostegno dei Partenariati Regionali per l Innovazione - Investiamo nel vostro futuro). References: [1] M. Golfarelli and S. Rizzi, Data Warehouse Design: Modern Principles and Methodologies, McGraw-Hill/Osborne Media, 2009. [2] F. Di Tria, E. Lefons and F. Tangorra, Research Data Mart in an Academic System, Proc. 2012 Spring World Congress on Engineering and Technology, vol. II, Xi an, China, 27-30 May, 2012, IEEE Computer Society Press, pp.18-22. [3] E. F. Ewen, C. E. Medsker, and L. E. Dusterhoft, Data warehousing in an integrated health system: building the business case, Proceedings of the 1st ACM international workshop on Data warehousing and OLAP, 1998, pp. 47 53. [4] F. Berni, N. Corriero, E. Pesare, V. Rossano, T. Roselli. a Knowledge Management Service For E-Health. International Proceedings of the 6th International Conference of Education, Research and Innovation, 2013. [5] P. Di Bitonto, F. Di Tria, T. Roselli, V. Rossano, and F. Berni, Distance Education and Social Learning in e-health, International Journal of Information and Education Technology vol. 4, no. 1, 2014, pp. 71-75. [6] R. Kimball and M. Ross, The Data warehouse toolkit 2 nd edition, New York: John Wiley & Sons, 2002. [7] J. N. Mazón, J. Trujillo, M. Serrano, and M. Piattini, Designing Data Warehouses: from Business Requirement Analysis to Multidimensional Modeling, In: Cox, K., Dubois, E., Pigneur, Y., Bleistein, S.J., Verner, J., Davis, A.M., and Wieringa, R. (Eds.), Requirements Engineering for Business Need and IT Alignment, University of New South, Wales Press, 2005. [8] C. dell Aquila, F. Di Tria, E. Lefons, and F. Tangorra, Dimensional Fact Model Extension via Predicate Calculus, The 24th International Symposium on Computer and Information Sciences, ISCIS 2009, 14-16 September 2009, North Cyprus, IEEE Press, pp. 211-217. [9] F. Di Tria, E. Lefons and F. Tangorra, Big Data Warehouse Automatic Design Methodology. In W. Hu, & N. Kaabouch (Eds.), Big Data Management, Technologies, and Applications (pp. 115-149). Hershey, PA: doi:10.4018/978-1- 4666-4699-5.ch006. [10] phpmyolap, http://sourceforge.net/projects/phpmyolap. [11] D. Feinberg and M. A. Beyer, Magic Quadrant for Data Warehouse DBMS Servers, Gartner, 2011. [12] Wayne W. Eckerson, Data Quality and the Bottom Line, The Data Warehousing Institute, 2002. [13] http://mondrian.pentaho.com/documentation/sch ema.php [14] MDX Solutions: With Microsoft SQL Server Analysis Services, George Spofford. [15] T. Warren Lee, E.F. Renaud, and O. Hills, Emergency Psychiatry: An Emergency Treatment Hub-and-Spoke Model for Psychiatric Emergency Services, Psychiatric Services, vol. 54, n. 12, December 2003. [16] M. Triola, M. Pusic. The Education Data Warehouse: A Transformative Tool For Health Education Research. Journal of Graduate Medical Education, March 2012, pp. 113-115. [17] MedBiquitous Consortium. http://www.medbiq.org.. [18] J.G. Dewitt and P.M. Hampton, Development of a data warehouse at an academic health system: knowing a place for the first time. Acad Med. 2005, vol. 80, no. 11, pp. 1019-25. [19] J.S. Einbinder, K.W. Scully, R.D. Pates, J.R. Schubart, and R.E. Reynolds, Case study: a data warehouse for an academic medical center, 2001, vol. 15, no. 2, pp. 165-75. ISBN: 978-960-474-358-2 92