What Is 4.1 Medical Decision Support

Decision support systems can drastically improve the part of the diagnostic error. With the CDS tools, the ability to process and analyze large data sets and evidence-based rules from medical science has improved significantly. CDS systems assist physicians and nurses in diagnosing and provide the right treatment plan for the

Crockett, D., & Eliason, B. (2016). What is Data Mining in Healthcare?. Insights: Health Catalyst.

The article, “How Predictive Analytics Are Transforming Health Care,” has a big vision for health care researchers and practitioners as well as reducing the hospital readmissions. These predictive analytics have a positive impact for the health care systems and have a potential to transform the health care system towards the information technology. These also help to advance the practitioners’ awareness on decisions making towards data sources. As the article mentioned, the predictive analytics will help to predict critical illnesses and try to find the solutions as quickly as possible. At the same time, large hospitals and most health care organizations have already implemented the electronic health records and accessed their patient’s data

Huge amounts of digitized patient data with the growing in electronic health records (EHRs), more and more facilities is gathering. On by nurses, who previously recorded patient info in paper based formats much of the obligation for patient data input has been taken? Computerized patient data also enhances quality of the entire healthcare system although accurate documentation is essential for patient care. Data mining to bring out previously unknown patterns from vast data stores and then apply this info to build predictive frameworks healthcare providers can apply. Fraud detection, and maintenance scheduling since the 1990s, business concerns have used data mining for activities such as credit rack uping. Value in data mining now,

In recent years, there has been a growing interest in the application of clinical decision-support systems (CDSS) in the prevention and control of ischemic heart disease (IHD); this is due to an increasing recognition that stronger CDSS are crucial to achieve a high quality care with attendance lower morbidity and mortality.1 CDSS is defined as “computer-based information systems designed to assist health care providers in implementing clinical guidelines at the point of care. CDSS use patient data to provide tailored patient assessments and evidence-based treatment recommendations for healthcare providers to consider1”. Patient information is entered manually or automatically through an electronic health record (EHR) system2,3.

The Why of applying predictive analytics in healthcare: If predictive analytics is applied extensively to the rapidly growing healthcare industry, limitless advantages can be realized. Some of the advantages are: 1) Improved real-time decisions about treatment and support, consumer commitment 2) Effortless revenue management with focus on global as well as local markets 3) Standardized clinical processes, guidelines and protocols greatly improving operational efficiency 4) Reduction in fraud claims, security threats greatly helping insurance companies 5) Mining for unknown variables that determine quality such as “hidden” re-admission factors or finding out

Many hospitals are maintaining their patient’s database online like the records related to tests suggested, their results and the prescriptions suggested. This generates huge data which could be in any form like text, numbers as well as images and videos. In fact, all this data is important in making clinical decisions. In order to handle such a large data efficiently, usage of multistage classifier has become necessary. In existing systems, all the features are tested at a time by the classifier in order to detect whether patient is suffering from that particular disease or not. The entire testing consumes lot of time if the system is testing all attributes of a patient who is not actually suffering from the disease. So in such cases if we test the attributes step by step with few attributes in each step then we will be able to arrive at conclusion in primary stage itself leading to more efficient use of time as well as money. Simultaneously patient is also relieved from unnecessary stress as well as fatigue. This system optimizes resources very efficiently. Also it aims to identify the problem in the very preliminary stage and suggest reliable solution to problems increasing life of

This decision support tool is simply a yes and no question and answer decision tool. In other words, the doctor will ask several yes and no questions to make a final decision, which means any variables can lead a doctor to a final decision. This tool utilizes a faster and quicker logical method to come up with a decision that needs to be made under time pressure and time constraint. The downside to this tool, however, is the dependency of the questions being asked. For example, an experience doctor might take a shorter time to ask the right question that will bring the doctor to a quick decision; while an inexperience doctor might have to ask few more or a lot more questions to lead them to a final decision. As a decision analyst, ideally all questions asked should be consistent and uniformed in order to bring a finding to a solid conclusion, however in the medical field, yes and no answer can generate a different set of following questions depending on the judgement of the doctor. For example, a chest pain patient will rate their level of pain differently based on their pain tolerance, if a doctor were to ask “will you rate your pain level is at a 10?” – a patient with high tolerance towards pain will say no, while another patient with a low tolerance towards pain will say yes, these two patients will each lead their doctor to another

Predictive modeling is another form of data analytics that is focused on forecasting the future medical costs. This model utilizes patients’ medical information in an effort to evaluate health risks and forecast the future of medical utilization (Ingenix, 2006). A large variety of predictive modeling procedures are available, which are created to assign a specific risk level or score to the patients (Asparouhov, 2012). Risk scores are controlled by the risk markers and are given to each patient in a specific population (Ingenix, 2006). In using any past diagnoses and other information gathered, the predictive models will calculate the individual patient costs that can then be used by healthcare providers and the insurers. Therefore, certain

Internist: It is designed with an intention to support diagnosis. It links diseases with symptoms by making use of tree-structured database.

The Era has begun where a tremendous amount of data and silos of information are being generated. The websites, blogs, the electronic health record are all jam-packed with information. Posts from Facebook, tweets from twitters, interactive websites are a rich source of information. If one can imagine, information has as much as widespread as the universe. However, what happens to the heap of information generated? Do we use the information to convert into knowledge and apply it to our daily life? Are they helping physicians, patients and other clinical staff in their decision making? The integration of all available information has resulted in Clinical Decision Support System (CDSS). This system is invented to help the physicians and other clinical staff in decision making process. But the question again is – are they comfortable using CDSS. What if the users could write decision rule which would be their own personal experience. If they could write their own rule will it increase their acceptability? The article answers the questions raised and provides a brief description of a CDSS developed by the Regenstrief Institute, which is contributing to an altogether different face of CDSS. This, if successfully launched can result in better acceptability among users, which can solve other user related or initiated difficulties.

Hosseinzadeh et al. In [17] this work analyzed predictability of hospital readmission without targeting any specific disease. In this analysis considered a dataset ,covers demographic, clinical procedure-related and diagnostic-related features along with drug information of patients predict the readmission within 30 days (short-term readmission prediction) above 65 years of age. Therefore, it’s contained comprehensive results of feature reduction methods and the performance of the prediction models is modest. Consequently, this studied did not consider cost analysis and grouped features importance mining and these studied not targets towards specific disease prefer like diabetes.

In healthcare organization data mining plays the most leading role in the research area. Data mining plays a vital role in various fields of technology. In healthcare industry we gather more information regarding patients, diseases, hospital resource, electronic patient’s records, diagnosis methods, etc., by using health care in data mining it is easy to classify or group the patients having the same disease so that it helps to treat them effectively. In this paper I have reviewed about data mining application in health care and data mining challenges in health care.

Data mining as it has been established is simply a way of getting hidden knowledge or information from data, the technique(s) employed search for concurrency, relationships, and outliers in this data that they present as knowledge [4]. This knowledge can then be used in different applications. Prediction is one of the ways that the hidden knowledge gotten from the data can be used. The main aim is to use a large number of past values to consider probable future [9]. Heart disease is the common term for a variety of diseases, conditions, and disorders that affect the heart and the blood vessels [6]. Symptoms of heart disease vary depending on the type of heart disease. Congenital heart disease suggests to a problem with the heart 's structure and function due to abnormal heart development before birth. Congestive heart failure is when the heart does not pump adequate blood to the other organs in the body [8]. Coronary heart disease or in its medical term Ischemic heart disease is the most frequent type of heart problem [24]. Coronary heart disease is a term that refers to damage to the heart that happens because its blood supply is decreased; it leads to fatty deposits build up on the linings of the blood vessels that provide the heart muscles with blood, resulting in heart failure. Heart disease prediction using data mining is one of the most interesting and challenging tasks. Shortage of specialists and high wrongly diagnosed

The database must also be able to, “accept, store, and manage molecular physiology data” (Moore, 2007). In doing so, selecting the correct database is essential, and many companies choose to create their own private database. However, the creation and use of public databases are on the rise. After all the genetic information has been inputted into a database, it needs to be analyzed. Data analysis consists of data mining and machine learning methods, through the use of high-powered computers. Data analysis is also used for identifying any bimolecular patterns in the observed outcome (Moore, 2007). In addition, data mining is then used to manage and study the genetic information. After all of the analysis and studying, the interpretation of the information is what is most important. The researchers need to be able to understand and comprehend what the data information signifies. From there, bioinformatics can then be used for other various resources. For instance, bioinformatics can be valuable in delivering therapeutics (Bioinformatics, 2000).