Resources that Support Information Management Systems in Healthcare - Assignment Example

Resources that support information management systems in healthcare Medical informatics or Medical information launched in the United s in 1950 with the arrival of computers and microchip. Earlier it was known as different names such as medical computing, computer medicine, medical computer science, medical electronic data processing, medical information processing, medical software engineering, medical information science and medical computer technology. Health care information is a part of information science, computer science and healthcare. It deals with the resources, devices, and methods required optimizing the acquisition, storage, retrieval, and use of information in health and biomedicine (Avery, Ellen, Mary & Richardson, Douglas, 2009). There are so many devices included in this information system. The devices such as clinical guide lines, formal medical terminologies, and information and communication systems and computers are playing a vital role in healthcare informatics. These tools are applied to different medical areas like nursing, clinical care, dentistry, public health, pharmacy and bio-medical research. There are several aspects consist of information management system in healthcare. They are; 1. Designs for electronic medical records and other health information management systems applied for medical billing, scheduling and to do research. 2. Standards and integration profiles to aid the exchange of information between information management systems in healthcare. 3. Controlled medical vocabularies like SNOMED, MEDCIN, LOINC, Open GALEN and UMLS are used to permit a standard and accurate exchange data between systems and providers. 4. use of mobile device (also known as hand-held) or portable tools to aid providers with data entry or medical decision making support system 5. Earlier, computation for medicine was used in the dental projects in the 1950’s in the United States measurement standards laboratory called National Bureau of Laboratory by Robert Ledley. In the mid of 1950, information management system in healthcare was developed the devices such MYCIN and INTERNIST-1. The United States National Library of Medicine started to use MEDLINE (Medical Literature Analysis and Retrieval System Online) and MEDLARS (Medical Literature Analysis and Retrieval System) in 1965. At the same time the founder of medical information technology, Inc Antonino Neil Pappalardo, Curtis Marble, and Robert Greens developed the system called Massachusetts General Hospital Utility Multi-Programming System (MUMPS) at Massachusetts General Hospital in Boston. It was used as most common programming language for clinical applications in between 1970 and 1980. The operating system Massachusetts General Hospital Utility Multi-Programming System (MUMPS) was used to support MUMPS language specifications. Since 2004, this operating system being part of the VA (Veterans Affairs) hospital system. This is the biggest enterprise-wide health information system which consists of electronic medical record identified as Vista Veterans Health Information Systems and Technology Architecture. The user interface called CPRS (Computerized Patient Record System) permits healthcare provider s to assess and revise the patient’s electronic medical record at any of the Veterans Affairs system. The members of European Union are ready to share their experience and practices to build up a European Health Area because which improved the quality of healthcare as well as the development of new industrial sector. The European Institute for Health Records playing vital role in the European Union Strategy that involved in the promotion of electronic health record systems.\ The publicly funded health care system in England namely National Health Service contracted out to many sellers for a National Medical Informatics System. The NHS National Program for IT (NPFIT) that split the nation into five provinces and is to be unified by a central electronic medical record system called “the spine.” In 2006, 60% of residents in England and Wales have more or less extensive clinical records and their prescriptions generated on 4000 installations of one system (EMIS) written in M (MUMPS as was). The other 40% predominantly have records stored on assorted SQL or file-based systems (Health Informatics Technician, 2008). Scotland also followed the similar approach but it was more advanced than the England in several ways. Scotland has the system called GPASS, (General Practice Administration System for Scotland) that is owned by the State and administered and developed by National Health Service Scotland. The GPASS system has been provided to all General practitioners in Scotland but it has developed badly. There are several types of system using decision support system in clinical practice in the United Kingdom. Except Medline and healthcare literature databases, decision support systems have connected with health care information systems for a long period of time. Generally it seems to be support the retrospective analysis of financial and administrative data. In recent years, sophisticated data mining approaches also have been planned for the similar type of retrospective analysis. Different studies have identified Clinical decision support system (CDSS) as computer systems that enable that clinician in decision making about individual patients at the point in time that these decisions are made. To prevention of medical errors computer-based physician order entry (CPOE) systems attached with Clinical decision support system (CDSS). The Clinical decision support system (CDSS) has the capability to shift the way drug has been taught and practiced. Generally, nowadays Clinicians relied on computers because laboratory data management software, pharmacy information management systems, applications for tracking patient location, mechanical ventilators, and oxygen saturation measurement devices are all part of the computer system. Therefore, computer system is identified as the key element of healthcare system. The devices and system can capture, transform, display or analyze data for use in clinical decision making (DiCenso, Alba, Guyatt, Gordon & Ciliska, Donna, 2009 P. 320). In Clinical decision support systems (CDSS) a patient s entire data entered in to the computer and are coordinated to programs in a computerized database and resulting in the generation of patient-specific assessments or recommendations for clinicians (DiCenso, Alba, Guyatt, Gordon & Ciliska, Donna, 2009 P. 320). Clinical decision support systems (CDSS) must follow three important challenges. They are: CDSS should be basis on the right type of information for the decision, should take into account natural differences in patients and hence work with the individual person profiles and data and should offer personalized advice. Alerting, reminding, Critiquing, interpreting, predicting, diagnosing, assisting, suggesting, and facilitating are the main functions of Clinical decision support systems (CDSS) Many functions such as alerting, reminding, and critiquing are basis on simple rules which disclose the computer system what to do when a certain incident takes place. Alerting system produces the continuous signal or data or message in way of alertness which helps clinicians to take an immediate action. Reminder system helps clinicians to complete the task before an event takes place. An outpatient clinic reminder program may produce a list of necessitate vaccinations by every patient on the daily based program. Administrative, managing clinical complexity and details, cost control and decision supports are the four key functions of electronic clinical decision support system. There are several number of decision support system developed in the world. Few of them mentioning below: AAPHelp: de Dombals system for acute abdominal pain (1972): this was an early attempt to execute automated reasoning under uncertainty. This system was developed at Leeds University. de Dombal’s System was designed to support the diagnosis of severe abdominal pain and the decision making of the system was based on the naïve Bayesian approach. INTERNIST I (1974): it was considered as one of the first clinical decision support system. It was designed for supporting complex diagnosis of complex problems in general internal medicine. INTERNIST uses patient observations to derive a list of coherent disease conditions. In 1980’s it was considered that most important product due to its medical knowledge support. MYCIN (1976): this was designed to diagnose and suggest treatment for some blood disease. Later MYCIN was expanded to tackle other infections. It was implemented in the 1970’s by Ted Shortliffe at Stanford University at California, United States. The professor of biomedicine, Mark Musen commented on Mycin that “the first convincing demonstration of the power of the rule based approach in the development of robust clinical decision-support systems” (Open Clinical, 2009). CASNET/Glaucoma: developed in the 1960’s for the diagnosis and treatment of diseases. CASNET is the most significant expert application for the diagnosis and treatment of glaucoma. (Open clinical, 2009). PIP (the Present Illness Program): this system was developed by MIT and Tufts-New England Medical Center. PIP was gathering data and produced hypotheses about treatment procedures for who is suffering from renal diseases. ABEL (Acid-Base and ELectrolyte program) is an expert system developed at the Laboratory for Computer Science, MIT. ONCOCIN: a rule based medical expert system developed for the treatment of cancer patient. ONCOIN was one of the first Decision Support System which ventured to making decisions and sequencing actions overtime with the use of customized flowchart language. The main advantages of using electronic decision support system in clinical practice divided into three wide categories. They are: Improved patient safety: using electronic decision support system in clinical practice reducing prescription and medicinal errors and unfavorable events and enhanced medicine and test ordering Improved quality of care: clinicians will get enough time for patient care by the increment of clinicians as well as the application of clinical pathways and guidelines. Improved efficiency in healthcare delivery: using electronic decision support system in clinical practice reduces expenses according to faster order processing. It also reduces the test duplication and unfavorable events. Automatic provision of applicable, tailored advice, expertise and recommendations sourced from up-to-date, best practice acquaintance, decrease differences in the quality of care, instant feedback to patients, maintain and improve constancy of care, support medical education and training, help overcome troubles of ineffective coding of data, supply clinical information anytime it is needed and can provide audit trail and support research are the informal list of potential benefits. There are many factors determines the use of decision support systems (CDSS) in clinical practice. They are price, attitude of target users, degree of user recognition prior to and after installation, easiness of use , accessibility of maintenance and support, interoperability, legal and moral issues, user interface, technique, approach of presentation of advise, patients attitudes to use, participation of local users for the period of development phase, and ease of integration within organizational context and routine workflow-degree to which it entails are design of clinical processes (Open clinical, 2009). Reference List: Avery, Ellen, Mary & Richardson, Douglas (2009) History and Epidemiology. [Online] LcWebLink.Info. Available from: http://www.lcweblink.info/lcwmain.asp?p=Services&sm=MedicalInfo&l=e) DiCenso, Alba, Guyatt, Gordon & Ciliska, Donna, (2009) Evidence based nursing: a guide to clinical practice, Elsevier Health Sciences. P.320 Health Informatics Technician (2008) [Online] Advancing Women (TM) Available from: http://www.advancingwomen.com/career_rapid_growth_high_pay/health_informatics_technician.html Open clinical (2009) Potential benefits and drawbacks of the use of CDSSs; Factors which may help determine the successful use of CDSSs in clinical practice. [Online] Open clinical. Available from: http://www.openclinical.org/dssSuccessFactors.html Open Clinical (2009) [Online] Open Clinical. Available from: http://www.openclinical.org/dss.html#musen1999 Read More