Data-Driven Decision Support Systems - Assignment Example

Data Driven Decision Support Systems College: Data-Driven Decision Support Systems Decision support systems (DSS) are critical decision making tools that are gaining popularity in the world of global business. The DSS are computer based systems that help information storage, processing and presentation which contribute to efficiency in the business management, operation and planning. In the globalized world, organizations are facing challenges in handling big data in making swift decisions and evaluating their performance throughout the year. The aspects of business are crucial as the volume of data continues to increase and the need for quick decision making arises within the dynamic business environment of the 21st century. The clinical field is one of the areas that the management has to handle a lot of information from their daily records, patient responses or even from the social media platforms. Notably, a number of health firms have deployed data driven DSS to streamline their decision making process, providing an opportunity to impact changes within the organizational level. It is on this rationale that DSS becomes recommendable for any clinics that intend to remain efficient in the big data era. Management Problems within Health Institutions In the era of big data, organizations have been faced by a number of challenges while making their management decisions. Notably, organizations use data to inform their decision making process as one way of ensuring accuracy and reliability in business development. However, clinical organizations are experiencing a challenge in handling large loads of data that are now available for decision making. A number of health facilities have to collect data from all its branches and use this data to monitor their business performance and make future decision for their operation. All data including information from patient records, customer feedbacks and other departments are crucial while launching an organization’s strategic plan. In the presence of big data, it storage, processing, analysis and presentation are present challenges for the management (Turban, Sharda, Delen & Efraim, 2007). Secondly, big data requires a lot of time to process while business decisions are time sensitive in a dynamic business environment. Therefore, managers need important efficient mechanisms of acting on available information. According to a study by MIT, many corporations have either already adopted a data-centric model for decision making, or indicated that implementation of such a model was a top priority (Turban, Sharda, Delen & Efraim, 2007). Top performing businesses are five times more likely to use data-driven analytics than lower performing businesses, which has created a significant amount of pressure to utilize these analytics as a means to stay competitive. The use of data to make decisions is an idea that is not associated with business efficiency. Organizations depend on data to analyse their profits, assess their expenses and analyse their development trends. They are able to construct graphical solution to business strategy through use of data procession software. The success of organizations that use data to guide their decision making process is prove for reliability of this approach. However many business leaders admit that they have more data than they can use effectively, and it is becoming increasingly more difficult to digest the large amounts of data (Turban, Sharda, Delen & Efraim, 2007). One of the aspects of the big data concept that have raised issues is the availability of more than enough data, making it a challenge for organizations to process it and base their strategic decisions on this large volume of information. While it is necessary to use as much data as possible, it is a big challenge for the organization to act on all the data. Processing such volumes of data may require quite a long time, while the management decisions are required to respond promptly to issues affecting their relationship with the customers. As a result the concept of data filtering is an important issue that expound on essential data can be screed and used for making prompt decisions. For data to be effective, it must be analysed in a way that is meaningful and directly associated to a business objective. IBM is one of the popular data-driven DSS vendors in the world that provides business intelligence to organizations. From a personal experience, an IBM data driven DSS would be the solution to data processing for with a clinical business environment. The ability of the system to work on multi-format data, process big data and provide clear tabulation is unique and provides a quick way of processing information and generating business intelligence (Turban, Sharda, Delen & Efraim, 2007). The unique features of this system will help managers within the health services to overcome the challenges of business monitoring, evaluation and decision making. Data-Driven DSS as Clinical Decision Support Tool In the era of technological advancement and a time of healthy information technology proliferation, a number of health agencies have integrated technology within their operations. Mayo Clinic is one of the health service providers in Minnesota that have used the Data driven DSS as a tool for performance evaluation and in drawing their strategic plans. The organization adopted this technology after experiencing challenges in data handling and the slow process of manual data processing within the institution. Mayo Clinic is a general medical facility with a 1, 132 bed capacity, and a surgery facility that handles over 62, 000 cases per year. In the year 2014, the organization handled over 50, 000 inpatient cases and over 21, 000 outpatient surgeries (Berner, 2007). It was ranked among the best health organizations of the year in terms of quality and quantity of service. The ability of the organization to handle a large number of patient cases can be attributed to it’s the use of an efficient business decision making tool. Fortunately, this system is flexible and can be used by hospitality, health as well as government agencies. In Mayo Clinic, the clinical support system has become the back bone for decision making within the organization. The management team are the custodians of the system and are responsible for spearheading the data collection, storage, and processing and presentation procedures. The computer based system presents a user-interface for the managers in which they have privileges to access information stores within the system. However, the management works hand in hand with other departments especially in the data collection process. Each department head collects raw data from the clinical officers regarding treatment, patient characteristics, customer information and any other relevant information. This information is then keyed within the computer and it is stored within a secure online platform. The management can access this information and design the processing formulas. Besides, other members of staff can access this information on their portals (Berner, 2007). However, the managers have the privilege to conduct analysis depending on the need, whether evaluation of decision making whenever there is need. One of the advantages of the system is that it helps in improving the quality of service in Mayo clinic. The management can confirm patient history before administering further services. For instance, when a patient attends the facility and finds a new doctor, the new service provider can enter into the system and retrieve information from the network. This will help them to treat the patient depending on their service, which is crucial for quality service provision (Gaynor, Seltzer, Moulton & Freedman, 2005). Secondly, the system has helped the organization to conduct personal evaluation at any time of the year, which helps managers to streamline their strategic objectives. Since they know when they run out of their management path, they can implement corrective strategies to achieve the pre-set goals. Besides, the system has helped the managers to make decisions in the swiftest manner. The modern health environment is constantly changing and the management should match this speed if they have to survive in the long-term (Berner, 2007). The ability of managers to use this system to act on big data in the shortest time possible helps to make accurate decisions with the shortest time possible. This explains the reason behind the success of Mayo clinic in serving a large population while still maintaining quality service. However, the system has its share of challenges especially in the implementation stage. One of the challenges that come with implementation is the high cost of purchasing the system and training the employees on its use. The initial cost is a burden to the organizations financial returns, which is the reason why majority of organization have procrastinated to implement the program. Secondly, the data collection procedure presented a challenge to Mayo Clinic management as they sought to work hand in hand with other departments. Since the department felt this as additional work load, they had a negative attitude towards the project (Gaynor, Seltzer, Moulton & Freedman, 2005). Apart from this, it took a number of months before the department head could master how to report accurately and to key in information in the right query tables. From a critical analysis, these challenges are inevitable for any organization due to the technical complexity of the system. The best way to get rid of the challenges is to prepare early and to find solutions as first as possible. Another major challenge that comes along with the DSS is the privacy and security issues that arise within organizations. The privilege of data access puts data for retrieval free from clinical officers. Therefore, it becomes a challenge to ascertain the privacy of the patient information, which at times may lead to legal issues. While has not happened in Mayo Clinic, it is an issue that may arise within an organization. Secondly, since the system operates within the online platform, security issues have become more critical at the current height of cybercrime. Cyber criminals are technologists who specialize in authorized access of information from systems without the consent of the owner. Since health service providers have the mandate to secure the patient’s data, it becomes crucial to adopt strict security measures within the system (Gaynor, Seltzer, Moulton & Freedman, 2005). Fortunately, the DSS vendor, IBM, work hand in hand with their clients to ensure that they achieve standard system security. There are a number of lessons that an organization may learn from the challenges that Mayo Clinic has faced in its DSS implementation plan. To begin with, it is crucial for clinical agencies to prepare for the introduction of the system by mobilising resources and gathering the staff support. This will ensure that the funding process is easy and that the organization expenses do not lead a collapse of the organization (Gaynor, Seltzer, Moulton & Freedman, 2005). Training the employees would be handy in creating awareness and helping the employees to understand the system and its value for the organization. This will solicit the right attitudes and help a cooperative approach to the implementation process. When the employees support the management decisions, the implementation process becomes much easier as all stakeholders work as a team. Also, training will help the employees to learn easily and to understand the nature of data required and how it should be recorded with respect to accuracy and format. Resultantly, the management will find it easy to work with employees who are familiar with the system. Overcoming the challenges of implementation is crucial in ensuring successful implementation of data driven clinical decision support system. DSS Product Use The features and capabilities of the Data driven DSS makes it preferable for clinical decision support. The system has ad hoc data filtering and retrieval that allows users to search for information from the system. For instance, typing the name of a patient will provide all the information registered under their name. Another important feature is the alert and trigger that often provide notifications on sent e-mails and new data entries. Next, the system has data management terminal that allows managers to format data or even manipulate it. The display unit ensures that the users can display scatter diagrams, bars and pie charts that are crucial for information presentation. Important to note is that the system provides for data summarization which allows managers to create tables or cross tabulations or even compute arithmetic computations (Power, 2008).. Lastly, users have the benefit of using the system hand with Excel, through integration allowing them to do further analysis. A Data-driven DSS accepts data from both internal and external sources. This implies that managers can collect information from the organization as well as from other sources such as the internet that feel may be important in management decision making. The broad range of data allows managers to achieve efficiency as they can acquire information from both internal and external market environment, hence helping them to model plans that work for the organization considering the environment (Gaynor, Seltzer, Moulton & Freedman, 2005). Potential sources of information include the patient daily records, the internet platforms, and customer and employee feedback. For instance, an organization that intends to evaluate its public rating may set online questionnaires that customers can fill (Power, 2008). This way, this external data can be used to fine tune management decision at a time when services are specially designed to suit customer preferences. Another crucial factor in data collection is that Data driven DSS accepts data in all formats and has a feature to integrate data with different formats. This makes it easy for managers to use input data into the system. Once the data is keyed into the system, the information is stored within an online storage platform to ensure that the data is available for future use. The data management terminal helps the users to process available information depending on the function that they intend it for. For instance, if an organization intends to evaluate their future projection, they can use graphical interface to predict their profits in the next 10 years. The display interface is the presentation panel that will plot the graph and allow the management to see it on the screen of their computers (Gaynor, Seltzer, Moulton & Freedman, 2005). The fact that this information is stored within the system ensures that such computations are available in time of need and the managers can access it at any time (Power, 2008). The system forms the core part of the organizational network and can communicate with other computers within system. For instance, computer users can record data on an excel spread sheet and send this information to the system vial an e-mail. This intercommunication with other systems binds the organization together and helps the system to collect as much information as possible from other systems within the institution. In conclusion, a data-driven decision support system is a distinct solution to the problem posed by increase in volume and complexity of organization data within health institutions. Organizations such as Mayo Clinic have achieved efficiency through use of such systems as a strategic planning tool. The systems unique features allows an organization to gather, stores, process and present large volumes of data in the most convenient way. This helps organization to make decisions grounded on accurate and authentic data. However, implementing the system has its load of challenges. Issues of uncooperativeness, training cost and system complexity may slow the process of implementation. Preparing well will ensure that managers have a smooth process of implementation. On this basis, it would be recommendable for the organization to adopt a data driven decision support system to overcome data analysis challenges that have hindered organizational a decision making in the era of big data. References Berner, E. S. (2007). Clinical Decision Support Systems (pp. 3-22). New York: Springer Science Business Media, LLC. Gaynor, M., Seltzer, M., Moulton, S., & Freedman, J. (2005). A dynamic, data-driven, decision support system for emergency medical services. In Computational Science–ICCS 2005 (pp. 703-711). Springer Berlin Heidelberg. Labrinidis, A., & Jagadish, H. V. (2012). Challenges and opportunities with big data. Proceedings of the VLDB Endowment, 5(12), 2032-2033. Power, D. J. (2008). Understanding data-driven decision support systems. Information Systems Management, 25(2), 149-154. Turban, E., Sharda, R., Delen, D., & Efraim, T. (2007). Decision support and business intelligence systems. Pearson Education India. Read More