Expert System: Decision Support System Description - Report Example

? Full Paper Expert System Decision Support System Decision support systems abbreviated as DSS, were invented in 1970s. The primary function of a DSS is to facilitate management with informed decisions. However, the systems are not capable to take decisions themselves, it provides adequate information to the managers to react and take decisions based on that information. A typical DSS may “collect, store, process, and provide access to information to support managerial decision making. Decision support facilitate unstructured and one-off decision making, as the standard reporting capabilities of MIS s were perceived to be more suitable for routine day-to-day decisions. Data on an organization's external operating environment, as well as internal operational information, is included and an interactive interface allows managers to retrieve and manipulate data. Modeling techniques are used to examine the results of alternative courses of action” (Decision Support System. 2007). Later on, DSS contributed to several domains, one of them is the healthcare sector. The Armed Forces Longitudinal Technology Application (AHLTA) is an internationally recognized medical and dental information management system that provides a safe and secure online accessibility to the military health systems (MHS) reports of beneficiary. Medical clinicians, who are deployed in various medical treatment facilities worldwide, access the system (DHIMS, 2011). They are provisioned with complete and appropriate health data in order to take informed decisions regardless of location and time. Moreover, AHLTA also facilitates central storage of electronic health records (EHR) data used to retrieve patients overall history of injuries and diseases (, JMIS). Expert Systems provide aid to the organization at the strategic level, as it takes input from internal and external aggregated data. Likewise, processing is carried out via graphics, simulations and outputs are transmitted via projectors and response to different queries. Expert systems are accessible to senior management that strategically oversees improvements in organizational processes to achieve business goals. AHLTA Functionality The information flow and sharing of information on various levels of the AHLTA system are illustrated below (How data travels through AHLTA, n.d): The flow of information does not cover all aspects of the system and only prioritize on nontechnical aspects. As there are four primary components of the system including: Client Workstations: The workstations that will be used by the clinical staff Local Cache Database: The local cache database stores all the retrieved information, in order to provide information much faster as compared to a normal request of patient information from the system. Clinical Data Repository: The clinical data repository hosts the primary database and includes all the patient and clinical data connected to all the military treatment facilities worldwide. CHCS: Composite Health Care System is a legacy system that is performing the task of maintaining information and information retrieval related to results, appointments and vice versa. Moreover, the AHLTA system operates on two modes i.e. the normal mode and the Failover mode. The normal mode provides provision of all the available information related to patient’s history. The CHCS retrieves and demonstrates demographics, appointment information and results from the clinical data repository that displays all the information to the computer of the client (, How Data Travels through AHLTA, n.d). The fail over mode also operates on the same four components excluding the clinical database repository. In this scenario, the clinical team can have access to document encounters related to patients. Nevertheless, they cannot access the complete record of the patients and historical records i.e. they cannot access CDR. If CDR is not accessible, CHCS sends all the information related to patient appointments, demographics along with all the relevant information to local database cache (LDC) (How data travels through AHLTA, n.d). Furthermore, the fail over mode supports: Scheduling of appointment information Last four encounters of scheduling patients Information related to patient and family history for patients that are scheduled Inventory information related to stores User Configuration The client server architecture of an AHLTA system were illustrated by “S. Ward Casscells III, the assistant secretary of defense for health affairs, unveiled a long-term plan to switch from AHLTA's client-server architecture with a centralized databases to a Web-based, service-oriented architecture and a network of distributed databases” (Microsoft Word – medrecords, n.d.) Advanced Nursing Practice Expert System provides assistance to the clinical staff in order to make an informed decision. However, there are several systems, which may facilitate the staff, by providing information related various reminders to protect diseases, patient diagnosis, medicine dosage and prescription. A review that was conducted recently concluded that Expert System contribute to clinical guidelines. Precisely, Expert System benefit to the clinical system in terms of awareness in clinical guidelines, advancing disease examination and inspection, resulting in eliminating medication errors (INFORMATION TECHNOLOGY AND THE CLINICAL NURSE SPECIALIST: Decision support and the clinical nurse specialist, n.d ). Agency Utilization of DSS A web based clinical system is implemented for facilitating depression care management. The DCM is considered as the foundation of a chronic care model that is also known as collaborative care. The system provides engagement of parent in care, endorsing self-management, reviewing symptoms and providing treatment responses (Fortney et al., 2010). In the domain of agile manufacturing, where industrial sector has contributed due to agility, are rising giants in the competitive global market. Due to the current situation, where the industrial sector has embrace agility, the research was conducted to adopt an agile model. However, in order to incorporate the model, quantification is required for the current level of an organization’s agility level. Likewise, after the implementation of DESSAC (Decision Support System for Quantifying Agile Criteria), the time consuming complex task of quantifying organization’s agility has become efficient and robust (Vinodh et al., 2008). Moreover, maintenance is a time consuming process for any organization, despite of any sort of activity. Previously, the DSS were prioritized to capitalize on the productivity of a single machine. However, in a real world scenario, there is more than one machine, to achieve the overall productivity. An on line plant-level maintenance decision support system (PMDSS) was developed to augment the overall performance of the production department. The system minimized the unplanned downtime reduction as well as augmented the machines to produce limited maintenance and production assets (Li, Ambani, & Ni, 2009). Currently, the Expert Systems are integrated with artificial neural networks. The ANN is defined as “a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.” (Filo, Lotan 2010). Due to the integration of ANN in the clinical based DSS, the results were significantly better as the system was able to do multi tasking i.e. can process multiple instructions related to demographic information, patient registration information, patient medical history, invent prescriptions and diagnose patients. Moreover, the state of the art systems are able to calculate the likeliness of a potential disease that most likely may affect the patient. Likewise, this system also facilitates best clinical practices for the nursing staff (CLINICAL DECISION SUPPORT SYSTEMS, n.d). The features of the system allow the patient and the physician to select the relevant options and access to appropriate information. For instance, a decision is taken to give a dose of three months to the patient. The system will schedule and organize the vital actions that need to be performed along with clinical care factors (CLINICAL DECISION SUPPORT SYSTEMS, n.d). There are certain challenges while operating Clinical DSS, as it is accessed by physicians, hospital staff, system administrators and system experts. However, system experts need contribution of the physicians while improving the system functionality. Physicians are involved in the design stage, implementation stage and system improvement stage. Moreover, the decisions recommended by the DSS and graphical representations with strategic views of Expert systems affect the traditional medical practice. As systems are advancing, increasing negative factors for physicians of their natural behavior towards patients. Furthermore, there is a significant negative impact if one of the systems does not comply with the requirements (CLINICAL DECISION SUPPORT SYSTEMS, n.d). In order to overcome the above-mentioned issues, physicians give first priority to their experience and decisions. As the DSS only provides relevant information that may be used to heal or cure a disease for a patient, there are certain cases where physicians only assist themselves by the DSS and do not take final decisions based on information provided by these systems. Moreover, physicians may discard the suggestions and instruction of these systems on a certain period, as they are more confident to cure the patience by their experience and expertise (CLINICAL DECISION SUPPORT SYSTEMS, n.d). DSS/ ES Product Use In a typical healthcare DSS/ES, information is processes through various stages. The first stage is the data capturing stage where the entire patient’s information is entered in the system. The contents related to patient information are clinical and financial outcomes, procedural compliance and reasons that are conflicted. Moreover, after the diagnostics of the patient, the diagnostic data, demographics and health history along with the provider data is stored in the DSS. All the relevant information of the patient is stored in the data and information repository along with relevant documentation. The information facilitates the system to demonstrate experiences, lessons learned, heuristics and tactic knowledge to the physician. In the next stage, the system provides information to share and discuss the treatment and recommendation, hypothesis and assumptions and suggestions of research about the disease (Wellness - evidence-based HealthCare decision support system, n.d). However, the system process research and analysis via information services and communication via people. Moreover, the system then links the data and integrates it with possible associations that can be formed. The information in the system can be discussed and challenged to extract predictions and assumptions. After the decision has been made, the system then develop clinical models related to patient treatment and diagnostic processes. The finalized decision is validated by analyzing the evidences and recommendations. The validation process involves the guidelines of clinical safety measures for the physician as well as for the patients. After passing through validation, the system provides evidence based clinical practice guidelines for the physicians. Now the system validates the information once more by incorporating knowledge base of health and sciences. Related references and instruction materials scrutinize these knowledge bases. The last process involves intervention of an event based decision support tool that includes computerized guidelines and clinical pathways. In addition, these tools also provide a feedback loop to the first process, which involves stage of entering patient data in the system (Wellness - evidence-based HealthCare decision support system, n.d). Services provided by E-health Social Networking Sites E-health plays an important role in finding patients that are suffering from the same health issues so that they can communicate with each other and can discuss or share their views related to their treatment, symptoms and conditions. As a result, with the help of other patients experience and views many users can receive information and can make their own decisions about their own health care. PatientsLikeMe is the most popular website that provides its clients to examine their medical condition, medicine routines and symptoms. Following are the four major kinds of services that are offered to the clients by the E-health social network sites Clinical trial access Emotional support and information sharing Quantified self-tacking Q&A with a professional physician. CureTogether, PatientsLikeMe and MedHelp are the social networking sites that help to resolve a number of medical problems suffering of the patients. For instance, the famous social networking site ‘PatientsLikeMe’ can only resolve for about 16 health related issues and helps to support the patients emotionally. Emotional support and information sharing Healthcare social networking sites provide emotional support and information sharing service as, it is considered the basic service provided to the patients. For example, a healthcare website named CureTogether patients can share their personal information and can track their own medical condition namelessly while highlighting the main cause i.e. an emotional support. Conclusion DSS/ES has contributed effectively for improving the decision making process. Whether it is healthcare management, industrial sector or artificial neural networks (ANN), research and study has shown that a DSS/ES can efficiently enhance the overall performance of a physician or an organization. Moreover, with the aid of Armed Forces Longitudinal Technology Application (AHLTA), physicians can treat patients anywhere and anytime. The mobility factor is essential, as it extracts and demonstrates the patient’s history from the system, in a military health facility. The provision of these information services within a remote location is extremely beneficial for the patients to be cured in no time. In future, more systems are required to provide mobility to the clinical staff, in order to cure patients in rural areas, where resources are available but proper clinical practices are insufficient. References FILO, O. and LOTAN, N., 2010. Information Processing by Biochemical Systems: Neural Network-Type Configurations. Wiley. CLINICAL DECISION SUPPORT SYSTEMS Retrieved 6/20/2011, 2011, from http://www.cwru.edu/med/epidbio/mphp439/Clinical_Decision.htm Decision Support System. 2007. Bloomsbury Business Library - Business & Management Dictionary, , pp. 2287-2287. , JMIS . Available: http://www.health.mil/MHSCIO/programs_products/jmis.aspx [1/19/2013, 2013]. Fortney, J. C., Pyne, J. M., Steven, C. A., Williams, J. S., Hedrick, R. G., Lunsford, A. K., . . . Smith, J. L. (2010). A web-based clinical decision support system for depression care management. American Journal of Managed Care, 16(11), 849-854. , How Data Travels through AHLTA . Available: http://www.navyehr.org/index.php/tips-tricks-top-menu/tips-tricks-how-data-travels-through-ahlta [1/19/2013, 2013]. INFORMATION TECHNOLOGY AND THE CLINICAL NURSE SPECIALIST: Decision support and the clinical nurse specialist Retrieved 6/20/2011, 2011, from http://www.nursingcenter.com/library/JournalArticle.asp?Article_ID=834917 Li, L., Ambani, S., & Ni, J. (2009). Plant-level maintenance decision support system for throughput improvement. International Journal of Production Research, 47(24), 7047-7061. doi:10.1080/00207540802375705 Microsoft Word - medrecords. (n.d.). Retrieved from http://www.defensestandard.com/DS%20Edit%20V7/medrecords.pdf Vinodh, S., Sundararaj, G., Devadasan, S. R., Maharaja, R., Rajanayagam, D., & Goyal, S. K. (2008). DESSAC: A decision support system for quantifying and analysing agility. International Journal of Production Research, 46(23), 6759-6780. doi:10.1080/00207540802230439 Wellness - evidence-based HealthCare decision support system Retrieved 6/20/2011, 2011, from http://wellness.wikispaces.com/Evidence-based+HealthCare+Decision+Support+System Read More