A Basic Introduction to Neural Networks, Research Paper Example

Decision Support System

DSS 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 DSS contribute to clinical guidelines. Precisely, DSS 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 ).

Advantages and Disadvantages

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 DSS 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.” (A basic introduction to neural networks, n.d). 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 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).

Integration in an Organization

In a typical healthcare DSS, 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).

Conclusion

I have chosen this technology because DSS 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 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

A basic introduction to neural networks Retrieved 6/20/2011, 2011, from http://pages.cs.wisc.edu/~bolo/shipyard/neural/local.html

Clinical Decision Support Systems Retrieved 6/20/2011, 2011, from http://www.cwru.edu/med/epidbio/mphp439/Clinical_Decision.htm

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.

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

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