The oncology service has many advances in technology that enables improved patient care in this facility. To improve the quality of the facilities, different systems are coming up each and every day. The technologists in this branch of medicine concerned with developing an effective device to treat cancer keep on changing the working systems in these facilities. The working systems, therefore, are the sources of clinical data. These systems once they are modified to improve on a certain procedure, the data may disappear thus forcing the facilities to start a new client register. Concerning business intelligence (BI) whereby the raw data is converted into meaningful data that can be analyzed to make several decisions and planning (Turban, 2015), the change of systems has a direct impact on business planning and strategies. The raw data stored in the systems as well has so many implications on budgeting and cost estimation. Most of the oncology services are very expensive to purchase and therefore cost estimation has to rely on workload and other variables collected from the clinical data. Regarding the business intelligence tools, a clear and flexible data source must be accessible to analyze and plan on business analytics like predictions, demand and business suitability (Turban, 2015).
The strategy to manage clinical data regarding business intelligence is creating a clinical data warehouse. In a comparative analysis of data in excel sheets stored by the system vs. data warehouse; the oncology facility requires a plan on adapting data warehouse. The Excel database is just a clinical data without flexibility for analytics while the data warehouse is a system that can be used in reporting, analysis, and patient follow-up in the clinical setup. Also, the excel data has a multistep process in generating facility report as compared to the data warehouse. Another problem associated with Excel data is the patient follow-up from other departments. To have a flexible patient follow-up, the warehouse data has the capability of generating data from other sources which is used to evaluate cost and quality issues. The oncology facility, therefore, has to rely on data warehouse where information concerning the patient in the facility is accessible.
In the strategy to create a clinical data warehouse, the proposal plan has several things to be considered for its success. First, the facility needs a team that will plan the budget especially the IT department. From the team step, we get to selection of team leader that will give the directions. The final step is to analyze the impact of this project to the facility. In this planning proposal, the clinical data warehouse in the oncology facility has to rely much on electronic health record (EHR) data. To start, the EHR system has to be installed in the oncology facility where clinical data will be generated across all departments. The EHR system, in this case, will be the background step of transition from Excels data to the clinical data warehouse in the oncology facility. After installation of EHR, the entire database across the facility will be integrated together. Another important system to be laid down in this plan is the laboratory information management system (LIMS). LIMS software can manage multiple aspects of oncology informatics. The flexibility construction of LIMS in collaboration with EHR will collect data across the facility sources thus giving easy access to a data warehouse. After installation of the above software in the oncology facility, the data warehouse system will be linked them to store information necessary business analytics.
The expected probable outcome of this project in the oncology facility is that improved patient care will be achieved. These improvements will be achieved after generating business analytics from the data warehouse. The business analytics to be obtained in the clinical data warehouse in the oncology department includes reports, cost per patient and patient satisfaction score (Madsen, 2012). For quality improvement, the satisfaction score generated from the data warehouse report will be used to evaluate performance. Another outcome of this project is the patient follow up. Patient follow-up in the oncology facility is paramount to determine the progress of treatment. Regarding business intelligence, interpretation of this vast data will be easier for planning and prediction of the business operations (Madsen, 2012). Another expected outcome is easy decision making from the analytics that will be used to locate new business opportunities. From the data warehouse analytics, the key expected outcome is predictive analytics. These predictions are useful in determining the direction of the business.
From the clinical data warehouse, easy strategic business planning will be an expected outcome. In the strategic business, the goals and priorities of the organization are easily derived from data analysis. For instance, the competitive market generates external data that need be compared to the internal information in the oncology facility. By using the data warehouse system in the facility, the internal data can be compared with the external data to generate a competition conclusion or performance difference (Madsen, 2012). Another probable outcome is the ability to assess the sustainability of services offered in the oncology facility. The clinical data warehouse in the oncology, therefore, will have positive expectations in the outcomes after installation.
Madsen, L. (2012). Healthcare business intelligence: A guide to empowering successful data
reporting and analytics. Hoboken, New Jersey: John Wiley & Sons, IncTurban, E. (2015). Business intelligence and analytics: Systems for decision support.
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