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Health Informatics

Introduction Health informatics is the bridging of computer science, information and the health care field. This interdisciplinary field can be applied to a range of medical fields such as nursing, biomedicine, medicine and subspecialties such as immunology (immunoinformatics). Informatics not only has roles to play in day-to-day areas of immunology such as data storage/retrieval, decision support, standards and electronic health care records but also in research and education such as data mining and simulation systems (Coiera, 2002).

Informatics and more specifically, health informatics first started being used in in the late 1950s with the rise of computers (Ho, 2010). Technologies such as computers allowed practitioners and researches to sort, store and retrieve information like never before (Ho, 2010). As the advantages of informatics increased, so did technology which further disseminated health informatics into the healthcare industry. This essay will be discussing the six main areas of health informatics.

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These areas include telemedicine, decision support, data mining, electronic health records, standards and finally simulation systems (mhoff, Webb, & Goldschmidt, 2001). These areas of health informatics are more predominate in some areas of the health care industry than others. However, they all influence each area in some way. For example, decision support systems are used extensively by medical practitioners in day-to-day work. On the other hand, researches would rarely rely on decision support systems within their area of expertise.

In contrast, researches may use simulation systems and data mining more expensively than the medical practitioner. However, this is not to say that either profession is not influenced in some way by each area of health informatics. In this essay we will also be giving a more specific focus on the subspecialty of medicine: immunology and how health informatics has, currently and potentially will impact on this field of medicine. Telemedicine Telemedicine is the use of technologies, such as information systems and telecommunication systems, to allow the medical practitioner to deliver healthcare at a distant location (Coiera, 2002).

This type of field in health informatics allows peoples isolated in distant locations to receive and benefit from health care services. In addition, this field allows people who may not be distantly isolated but their region might be lacking in specialised medical personal, to have access to these specialists. In early forms of telemedicine, the telephone or similar audio devices was all that was available. This is restrictive to the practitioner and may frustrate the patient.

However, as technologies have advanced, so too has telecommunication (Ho, 2010). Physicians are now able to communicate with patients at great distance and with high definition visual equipment. In some unique and highly advanced areas of telemedicine, physicians are able to perform surgery on the patient through the use of technology located at different sites (Coiera, 2002). The use of these client-server technologies has turned traditional telemedicine from being relatively diagnostically based to being able to perform physical surgery.

Although the immunology field may not fully utilize all areas of this remarkable field, there is some subspecialties of telemedicine that can be used. For example, telepathology is a field of telemedicine that allows the practice of pathology at a distance. Patients suspected of immunological disorders may use this system to transport medical images to the pathologist for diagnostic purposes. In this way tissue samples may never physically leave the patients area but diagnostics needed by the immunologist from the pathologist can be made from distant locations. Decision Support

Immunoinformatics provide extensive information for decision support systems (DSS) which is needed by data bases and prediction tools for DSS (Coiera, 2002). A decision support system is designed to help an individual or organization in a decision making activity. In health informatics this system is extensively used by practitioners as a diagnostic tool (Bosworth, York, Kotansky, & Berman, 2011). Decision support systems are ideally interactive systems that allow the decision making physician to come to the conclusion based on a host of information pulled from data bases, personal knowledge, predesigned modals etc.

Decision support systems have many benefits such as; patient-time efficiency, speed up process of decision making, promotes learning and training, reveals new approaches in thought process, generates new evidence in support of a decision and encourages exploration and discovery of the decision maker (Bosworth, York, Kotansky, & Berman, 2011). Although these systems require end user expertise, correct inputs and appropriate modals, they also require vast and exstenive information. Immunoinformatics are used to compile vast amounts of data for the immunology field (De Groot A. Immunomics: discovering new targets for vaccines and therapeutics , 2006). This data includes genetic mapping, protein structures, cytometry data and many other data pools needed by immunologists to make correct decisions. Immunoinformatics face to challenge of compiling this enormous amounts of data in an organised and correct way. This data needs to be mapped into correct diagnostic modals for the physician to use in their decision support system (Barh, Misra, & Kumar, 2010). This data leads to new ways of hypothesis testing for immune responses using data bases and prediction tools (De Groot A.

S. , 2007). This is an example where decision support is used by physicians an researches of immunology alike. Data Mining Data mining is of critical importance in the field of immunoinformatics. Genome sequencing of humans and animals have led to the accumulation of huge quantities of data (De Groot, Ardito, McClaine, Moise, & Martin, 2009). The intersection of experimental immunology and computational approaches both require access to good data that has been collected and stored in a efficient and organized way with international access (Jalbout, 2008).

As a definition, data mining is the analysis step after data collection and databaseing has occurred. This is important interdisciplinary field of computer science as it allows the recognition of patterns in huge amounts of data. This is achieved through the use of statistics, artificial intelligence and database management. This analysis of data is integral to both research in the immunology field and also practicing immunological physicians (De Groot, Ardito, McClaine, Moise, & Martin, 2009). This is due to the fact that every day more genetic mapping of proteins, cells and cell processing are being fed into data bases.

This information can then be used to discover otherwise unconceivable processes of the immunological system. Once patterns and therefore patterns are discovered, the information can then be fed into simulation systems (discussed later) to discover new medications and treatments for immune diseases. These prediction tools are then able to be used to create vaccinations and immune modelling. It therefore can be concluded that data mining is a pivotal part of health informatics and extremely important for both research and practical applications of immunology.

Electronic Health Records The electronic health care record, also known as electronic patient record, is an important concept in the collection and storage or patient or even population health information (Coiera, 2002). This information is stored in a digital format on computer systems. Usually each physician (98%) stores their patient information on an electronic health record. The purpose of an electronic version of a health record is to achieve easy flow between patient-doctor visits, accurate and secure record keeping.

This allows doctors to make less mistakes between the many patients they see. In addition, these records can be interconnected between many health organizations to further streamline the patients’ health care. Many electronic health care record systems also have built in decision support and other supportive software to further assist the physician in patient care. As will be discussed later, standards are an important concept when dealing with electronic health care systems, especially when interconnected healthcare systems are being used.

Advantages of electronic health care systems include reduction in cost (no paper trail), improved quality of care (diagnostic errors are decreased), promotion of evidence based medicine (the extra data able to be stored on EHR are able to assist physician thought processes) and motility (the records can be accessed at distant locations or be transported more easily than manual records). However, the electronic health record also has disadvantages that must also be discussed. Privacy concerns are an important factor when considering health care records.

Questions that must be addressed include; who will have access to the electronic record, can the patient change this information if requested and whether this information can be used against the patient such as health insurance companies. Legal issues regarding electronic health care records are also an issue. For example, who is responsible for failure of electronic health care records that lead to the result of miss treatment of a patient? The software designers, or the physician keeping and utilizing the software (Ho, 2010). Standards Standards are an important aspect of health informatics.

There are many types of standards to consider. Firstly, the information technology standards that are linking between the user and the digitally stored data. Secondly, the networking standards that allow interconnected systems to communicate. Lastly, the standards needed for human interpretation. For example, different English speaking countries, and indeed non English speaking countries, may have different terminology when discussing a patients illness. Doctors between different countries, disciplines and information systems need to communicate effectively and accurately.

For this process to happen, standards need to be created that allow all parties involve being able to communicate without error. SNOMED is an example of medical terminology that allows physicians to communicate about medical treatments, procedures or medications without confusion. Simulation Systems Simulation systems are used extensively within the immunoinformatics field. Simulation systems are being designed for many different purposes such as education, training, diagnostic purposes and research using computer aided modelling. Education and training is an important aspect of the health care industry.

Firstly, simulation systems allow students to interact with computer based systems to enhance their education experience. Some concepts that may otherwise not be able to be instructed without the means of practical experience could be taught through the use of simulations. For example, in the field of immunology, blood banking procedures could be performed on simulated systems to reduce the error or confusion before the blood banking practical. This would enhance student learning considerably. In addition, difficult concepts such of those in immunology could be more easily explained through the use of computer games.

The University of Ballarat, for example, are developing a computer based game to assist student learning in this difficult field. Simulation systems are also an important diagnostic and research tool. They currently are being used to simulate immunological processes in both human and rat modals. This allows for vastly faster research without the need to wait for generation times between offspring. In addition, it may reveal failure of procedures before they are performed. Computer aided modelling are being used extensively in HIV simulations for example (De Groot, et al. 2005). These simulations give researches a greater depth to their research at the biochemical level. Conclusion In conclusion, health informatics is an important interdisciplinary field which has vastly changed the health care industry. The health care industry has seen vast improvements with the introduction of telemedicine, allowing for distant medical practice. Decision support is also rapidly becoming common practice in general practices and research areas. Decision support systems reduce error and speed up the process of physician decision making.

Data mining of information has lead to improved patient care and public health. Patterns discovered from large data pools are also being used in research to further develop the understanding of immunology. Electronic health records also play an important role in health informatics and immunology. Records reduce doctor mistakes, speed up medical processes and allow researches in the immunology field to identify pubic health issues. Standards are of vital importance now and even more so for the future as health informatics systems are further integrated.

Being able to communicate between system without error or confusion is of paramount importance in the medical field. Finally, simulation systems are rapidly advancing research with the use of computer aided modelling. In addition simulation systems are being used for educational purposes to help produce better educated students in the immunological field. Health informatics is a relatively new field but also is a fast growing field and will be the key to many health breakthroughs for both research and patient care in the future.


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