Decision Support Systems in Medicine - Research Paper Example

? Decision Support Systems in Medicine Submitted to, Submitted By, of the Submitted on, [December 6th, The advent of Information technology decades ago has led to the transformation of the human society as a whole. Technological advancements have had both their positive and negative aspects on the entire human society as a whole. May it be the discipline of education, sports, leisure, entertainment, research or health, the introduction of technology in their integral processes has completely altered the dimensions of the domain concerned. The effect of information technology on the medium of health has been extremely substantial. Medicine is the fact of life, which is indispensible. It exists until even a single living being exists in this world. Technological changes in present day world have changed the medical technology upside down. Medical procedures that used to take hours in completion now get within no time at all. Technology has enabled the creation of such sophisticated weapons that by virtue of them medical treatments have become very less painful and timely. The patients suffer very less and the healer gets satisfied as well, at the exemplary means by which he gets to perform his medical healing procedures. This study is aimed at highlighting a few such facts that have assisted in the betterment of medical treatments by virtue of improvements in technology. It also summarizes how the said affects have been formalized along with assessing how this all has been of help to the physicians and medical practitioners as well. Before we proceed further in our report let us first analyze the core concepts of the basis of technology assisting medicine. INFORMATION SYSTEMS Computer software that takes raw data as input, process information and output useful information in line with the usability requirements of the stakeholders is referred to as an information system. Information systems are often termed as a means of acting as an intermediary between the business processes and computer systems. Specific information systems are often designed for specific information processing. For example a payroll system may be embedded in a business environment that would take care of all the pay generation requirements of the business. This computerized system would ease the timely calculation of the organization’s payroll and with very little chance of any errors in those calculations. Information Systems can be viewed as work systems where all activities are centered towards digital processing of data provided to them. An information system constitutes of: A Computing device, A storage device in order to store data being processed and also the outputs. Previous data bank Instructions and procedures. INFORAMTION SYSTEMS TYPES Categorically there may be several types of information systems. This categorization is with respect to the difference in the organizational levels that exist. The types may be narrated as follows: Transaction Processing Systems- example: Payroll systems, Order Processing System. Office Automation Systems- example: Word Processing Systems. Knowledge Work Systems- example: Engineering Workstation. Decision Support Systems- Clinical Support Systems. Management Information Systems – Sales Management Systems. Executive Support Systems – High Level Managerial Forecasting Systems MEDICAL INFORMATICS The infusion of Information Technology in the field of health and medicine has led to the rebirth of the said field. Technological advancements that have affected the field of health have affected the human society as a whole directly and have mostly proved to be beneficial rather than hazardous. Formally speaking, the definition of Medical Informatics, as quoted at openclinical.com, is stated as follows:  Medical Informatics is the "scientific field that deals with biomedical information, data, and knowledge - their storage, retrieval, and optimal use for problem solving and decision making. It accordingly touches on all basic and applied fields in biomedical science and is closely tied to modern information technologies, notably in the areas of computing and communication (medical computer science)"  [Stanford Medical Informatics]. Thus, in the light of the above-mentioned definition, Medical Informatics can very well be termed as the handling of data with respect to health and medicine with the assistance of computers and computer related systems. Medical Information Management, or simply put, the managing of all information related to health and medicine has become an integral part of the health care profession in the modern times. The present state of Health and Medicine cannot be executed without the involvement of information systems that are computer based. Medical Informatics can very well be termed as a field of information processing and health related tasks that involve the following processing: Medical cognitive tasks. This may involve the assessment of severity of a disease by analyzing the prior medical record history of a patient. Health Information Processing, The integration of healthcare related data that may possess different technological backgrounds at their respective ends. Communication tasks related to health. This may involve the coordination of physicians, patient and nursing staff within a healthcare facility. It may also refer to the integration of health related data present all over a certain boundary or area. Decision making tasks that are involved in healthcare such as the selection of prescription drugs upon the input of symptoms of patient’s state. Education and research related to Health such as the innovation in pharmacology and also in the methods of evaluating diseases and performing surgeries. Technological aspects that support health. These may involve tests that assess a person’s health. Technological advancements have made it possible that medical tests be conducted as such advanced levels that the health of an individual may be evaluated. Maintenance of Health Data. This data is in a vast range. Its variations extend from small clinics to large hospital databases. The health services in an organization and those related to the manufacturing of medical technologically advanced devices are all included in the category of health related data. The above mentioned details of properties of Health information systems clearly state that this all can very well be term into a statement that goes: All this is possible only with the integration and Interoperability of processes that make up a health information system. As mentioned at the Himms(2011) website, “Integration is the arrangement of an organization’s information systems in way that allows them to communicate efficiently and effectively and brings together related parts into a single system. Interoperability is the ability of health information systems to work together within and across organizational boundaries in order to advance the effective delivery of healthcare for individuals and communities.” (Himms, 2011) Deficiencies in either interoperability or integrity may lead to inefficient use of both the medical and technology based advancements. These may involve: Chances of occurrence of medical errors, Increased financial expenditures and Inability of awareness and alerts regarding the possible spreading of a viral or epidemic disease. Decision Support Systems are one of the types of information systems that assist in medicine. They can be termed as information systems that assist in decision making. Improved decision making can be achieved by information systems that are flexible, interactive and adaptable. Organizations use Decision Support Systems in order to make High level decisions. HISTORY OF DSS IN MEDICINE Computers have assisted man ever since their inception. The middle of the twentieth century was the time when the first research combining computers and medicine was published. Then there was no stopping at all. The last decade of the twentieth century witnessed the formal making of decision support systems in medicine usually referred to as clinical decision support systems. The decision Support Systems in Medicine may either be knowledge based or may not be knowledge based. PURPOSE OF DSS IN MEDICINE Clinicians are assisted at the hospitals and their clinics by incorporating modern Clinical Decision Support Systems. These systems assist modern physicians by determining analysis and determining diagnosis from the patient data that is provided at the backend of each of these systems. The earlier Clinical Decision Support Systems were designed to literally make decisions for the physicians. The present systems, however, are being designed such that the physician makes use of his knowledge and also of that provided from the Clinical Decision Support System assisting him. The resultant is a much better analysis of the patient’s data than either of the two could have made on their own. The Medical Decision Support systems literally provide a number of outputs for the physician to choose from with respect to the real time situation of the patient itself and the prior personal knowledge and experience that the physician himself has, something that cannot be put into words or embedded into computer systems at least with the current prevalent limited boundaries of information and computational technology. TYPES OF MEDICAL DECISION SUPPORT SYSTEMS The types of decision support systems may be divided into two types. These are: Knowledge Based Medical Decision Support Systems and Non- Knowledge Based Decision Support Systems. One of the examples in which Clinical Decision Support Systems are used is Diagnostic Decision Support Systems or DDSS. Diagnostic Decision Support Systems give output of a set of diagnosis. It is then up to the physician to decide which diagnosis is relevant to the situation and which is not. Another type of Clinical Decision Support Systems are timing based. The diagnoses provided by the systems are with respect to the timing of the patient’s state. Like, it gives separate output before the diagnosis, another type of output during the diagnosis and another type of output after the diagnosis. The output before the diagnosis assists the physician in preparing for the diagnosis and treatment of the patient. Physician’s preliminary diagnosis can be endorsed or revised by taking the output of this kind of a system during the diagnosis phase. The last type of output from the clinical decision support system enables data mining devising connections between the medical history of the patients and the current medical research in order to propose future health related strategies and prospects of the patient. Knowledge Based Clinical Decision Support Systems The three main parts of knowledge based Clinical Decision Support System are: the knowledge data bank it possesses, the intelligent inference mechanism it possesses and its communication mechanism. The knowledge base is mainly composed of a set of if- statements that provide alternates in the form: if ‘y’ is true THEN ‘x’ would be ‘u’ otherwise it would be ‘z’. Practically if the decision support systems would be for the determination of drug interactions then one of the rules in the knowledge base might be that: “If drug A is being taken then drug B should be completed with it. ELSE do not complement with any other drug” etcetera. These knowledge bases may be edited by advanced system users who would enter up-to-date drug information in them. The communications parts of the system permit interaction with the users and also enable data updating and entry. Non- Knowledge Based Clinical Decision Support Systems. Artificial intelligence, in the form of machine learning, is embedded into knowledge based systems that are the base line of clinical decision Support Systems. With the assistance and embedding of machine learning the software is expected to find patterns in the data input for a particular situation and also to learn from previously made decisions and their resultant outputs. Non- Knowledge Based Clinical Decision Support Systems are of two types: Artificial Neural Networks and Genetic Algorithms. Neural Networks Artificial Neural Networks are composed of nodes and the interconnections among them. These connections are weighted. The weights are used to assess the extent of associations between the diagnosis made and the symptoms in the light of the given data. Some readability and accountability reasons inhibit the use of this type of systems. Genetic Algorithms Direct selection mechanism s used when implementing Genetic Algorithms. This ends up in achieving optimal results in Clinical Decision Support Systems. The solutions once provided by the algorithm are refined and run through he algorithm again. This process is continued till the proper solution is obtained. Genetic Algorithms derive their knowledge from the patient data that they are provided with. Non-Knowledge based systems are usually discouraged with respect to the knowledge based systems because they cover lesser and narrower list of systems as compared to the latter. PRACTICAL PROOFS OF ADVANCEMENTS IN HEALTH INFORMATICS Recent advancements in Health informatics pertaining specifically to the implementation of information technology in the present day hospitals is ideally referred to as a means of reduction of distress for the patient as well as the practitioner. Generally, it is reflected as a means of reducing errors that may have occurred otherwise. These errors could be on the part of the health staff or the physician both. Unfortunately, advancements in health informatics have, at times also been a cause of inconvenience rather than convenience due to imperfection. One such practical instance is narrated below along with description of how, on the other hand health informatics has positively affected the present times. ADVANCEMENTS IN MEDICAL INFORMATICS Recent advancements in Health informatics pertaining specifically to the implementation of information technology in the present day hospitals is ideally referred to as a means of reduction of distress for the patient as well as the practitioner. The MEDICAL informatics based systems’ introduction into hospitals has been reflected majorly as a means of reducing errors that may have occurred otherwise. These errors could be on the part of the health staff or the physician both. USES OF INFORMATION SYSTEMS IN MEDICINE The uses of information systems with respect to medicine are innumerable. They can hardly be accounted for in a few statements. Shortly it may be said that information systems perform the following roles in medicine: Storage and retrieval of Information pertaining to patient Maintenance of Medical History of patient for any requirement at runtime. Decision making, Medical Error reduction Following are reviews of few papers that highlight implemented information systems in them. REDUCTION OF MEDICAL ERRORS Health Informatics’ induction into hospitals has become a source of reduction in the errors that sometimes used to occur in healthcare related tasks. David W Bates (2000) discussed the reduction of such errors in one of his works titled: “Using information technology to reduce rates of medication errors in hospitals”. David’s study focused on the facts that the manual method of administration of drugs has at times been erroneous. Since these errors have been incidental they were often overlooked. The study reveals that about 28% to 56% of the medical errors caused were preventable. The harsh fact that was highlighted was that despite the use of medical technology in hospitals, the safety of the procedure of administering drugs to patients had not been highlighted on a large scale. Thus, very little data is available regarding the safety of giving of drugs. The paper under discussion reveals that the ‘decision support systems’ that have specifically been designed for physicians as well as the ‘physician’s order entry system’ have proven to be causes of reduction of errors in drug administration. Information technology has assisted medicine by promoting the use of robots that perform the task of filling in prescriptions, bar code drug readers, computerized molding of the medical administration record into computerized form enabling its embedding into different medical systems. The study highlighted that the use of bar code readers for entering patient recognition number that was located at the waistband of every patient as well as for the entry of drug information reduced the percentage of medical errors by about 50% to 70% in progressive years. Similarly, Unit dosage systems were incorporated into healthcare facilities that administered the providing of medicines from a hospital’s pharmacy in a ‘ready to administer’ form. This meant that the medicines were just in the right potency and in the right proportion at the right time in line with the patient information entered into the hospital’s interactive medical system by the medical practitioner. The results showed that this practice reduced the rate of errors by 80 %. Some Other practically implemented health informatics advanced systems that were embedded into institutes like the DRMC institute USA were, online drug inquiry system, laboratory and clinical integrating systems, systems that generated alerts for the nursing staff if the medication being administered to a patient was being done earlier than on its due time. The results of the implementation proved to be a sigh of relief and most importantly reduced medical errors immensely. Other technologically advanced systems that were embedded into the DRMC institute were online drug inquiry system, laboratory, and the system alerts that tell the nurse if the medication being administered to a patient is being done earlier than its due time. OPEN SOURCE MEDICAL INFORMATICS: The open source regime is not new to the world. Generally, the term open source refers to non-proprietor based software that is available to all the programmers/coders of the world in terms of source code and for free. The notion of open source health informatics can ideally be considered as a fresh provision for the health sector to process health related data. Bika’s Lab Information and Management System is a very good example of Open Source Health Information Systems. The current challenges that are faced by the health sector are of two main types. Firstly, the health professionals are not well versed with the usage health information systems and the data they gather goes in vain without suitable processing. Secondly, the provision of suitable and correct software is very difficult due to the hefty amounts charged against the license fee and maintenance. In these circumstances, switching to the open source platform is one of the solutions. However, there are certain issues and obstructions that are needed to be taken care of. With the merits of open source health informatics, the demerits and difficulties in its adaption are there too. Decision Support Systems are also widely being built on the scale of Open Source nowadays. The future generations are set to witness the same with much precision and accuracy. CURRENT SITUATION: Currently the dominance of software giants like Microsoft can easily be witnessed in the field of medical informatics. Under Microsoft Amalga, leading software and hardware companies have collaborated to improve the field of health informatics. Few of them are DELL, Cognizant, Encore Health Resources, Health Unity and Medseek etc. CONCLUSIVELY, it can be summarized that the integration of MEDICAL information systems, EMBEDDING OF DECISION SUPPORT SYSTEMS and their ability of interoperability are domains of immense importance and are an integral part of the health and medical issues of present day. With the continued and progressing assistance of Clinical Decision Support Systems to Physicians, the field of medicine is proposed to be succeeding even more in providing efficient, timely and error free assistance to patients. REFERENCES Sublett P., (November 2002). Technology’s impact on reducing medication errors. Health management technology. Retrieved from www.healthmgttech.com Anderson G. J., Jay, J. S., Anderson M., Hunt J. T., (May-June 2003). Evaluating the impact of information technology on medication errors: a simulation. 10(3). Retrieved from http://www.ncbi.nlm.nih.gov/pmc/articles/PMC342060/ Bates, W. B., (March 18 2000). Using information technology to reduce rates of medication errors in hospitals. BMJ, 320(7237), Retrieved from http://www.bmj.com/content/320/7237/788.full Raths D., (2011). The Promise of Health IT to Reduce Adverse Drug Events . Healthcare Informatics. Retrieved from http://www.healthcare-informatics.com/ME2/dirmod.asp?sid=&nm=&type=Publishing&mod=Publications%3A%3AArticle&mid=8F3A7027421841978F18BE895F87F791&tier=4&id=5BC9C737E7DF4582A88FB81D892C6261 Open Clinical., (2011). Health Informatics . Health Informatics. Retrieved from http://www.openclinical.org/healthinformatics.html HIMSS. (2011). Integration and Interoperability. HIMSS. Retrieved from http://www.himss.org/ASP/topics_integration.asp Goossen, W., Goossen, A., Zel Van Der, M. (December 2010). Detailed Clinical models, A Review. Healthcare Informatics Research. Retrieved from http://pdf.medrang.co.kr/Hir/2010/016/Hir016-04-01.pdf Read More