Data Mining In Healthcare

Establishing standards for vocabularies promote data quality within health information systems. Scholarly works support this principle within the Health Information Management profession. The following articles support data quality, standardization, and interoperability as critical components of health information management and exchange. The goal of true exchange will include data sharing of critical information across the care continuum, often across disparate systems.

Worldwide use of computer technology in medicine began in the early 1950s with the rise of the computers. In 1949, Gustav Wagner established the first professional organization for informatics in Germany. Medical informatics research units began to appear during the 1970s in Poland and in the U.S. Since then the development of high-quality health informatics research, education and infrastructure has been a goal of the U.S. and the European Union. (NYU graduate training program, 2010) Changes in the healthcare environment produced fundamental shifts in the delivery of healthcare. The altering landscape of healthcare is creating a huge demand for health data analytics. The growth and maturity of healthcare informatics over the past decade has been a prime catalyst in positioning the healthcare industry for the changes posed by reform measures. By understanding the process of analytics, clinical informatics specialists say healthcare providers have the insight necessary to make the process adjustments in the future.(Riskin, 2013)

The American Recovery and Reinvestment Act was created in 2009 to try and help multiple systems in the failing economy. The main goal of this Act is to promote economic recovery, spur technological advances in health and to stabilize state and local budget. The clinical business intelligence improves the patient care process, this data analytics provide the quality data which is timelier, and it also helps in billing and supports clinical

Coding consists of ICD-9-CM, ICD-10-CM, CPT and HCPCS codes. The codes help classify and document the information for the healthcare system in the United States. The ICD-9-CM stands for the International Classification of Diseases, Clinical Modifications. October 1st 2014 ICD-10-CM came out to update the class with more illnesses, conditions, and injuries of any patient that used the medical services. The ICD-9-CM is generally classified as numerical and alphanumerical codes with codes describing the illness and injuries. The CPT which is Current Procedural Terminology and the HCPCS which is the Healthcare Common Procedure Coding System are used in various medical settings. The CPT is published by the AMA yearly. CPT

Accurate data mining works wonders for medical billing agencies to improve their level of service and collection. With new and improved innovations being incorporated in medical billing software, unique reports can be executed that help in providing specific information which then assists in decision making and implementing improvements.

The preferred format for clinical health information is Health Level Seven (HL7) Clinical Document Architecture (CDA) for both Personal Health Record (PHR) and Electronic Medical Record (EMR). It is XML-based format identifies the encoding, configuration, and semantics of a clinical file. Blue Button+ Direct implementation ensure ease of transmission of medical records to a third party by the health care provider or patient. Direct uses SMTP, S/MIME, and X.509 licenses to maintain security, data reliability, privacy and verification of sender and receiver. Additionally, it meets the requirements for Meaningful Use (MU) Stage 2 of View, Download, and Transmit (VDT) that is required for certified EMR/EHR (Graham-Jones & Panchadsaram, 2013).

Referring to a study conducted by IBM, over $30 billion are spent in US each year on unnecessary hospital admissions. Clinical and behaviour data can be used to identify patients at high-risk and lead healthcare providers to develop strategies to care for patients before it is too late. The prediction of patient’s problems can reducing the number of unnecessary hospitalizations and the related costs.

Health care facilities, physicians, health care personnel and most importantly patients will definitely benefit from the data mining health care information. This paper will discuss different ways data mining health care information will be beneficial to health care facilities, health care personnel and patients alike and also the risk of data mining health care data.

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

Data mining for healthcare is useful in evaluating the effectiveness of medical treatments and it is interdisciplinary field of study that has its roots in databases statistics machine learning and data visualization. Diabetic disease refers to the heart disease that develops in persons with diabetes. The term diabetes is a chronic disease that occurs either when the pancreas does not produce enough insulin. The cardiovascular disease is class of diseases that involves the heart or blood vessels Even though many data mining classification techniques exist for the prediction of heart disease there is insufficient data for the prediction of heart diseases in a diabetic individual. The main objective focus on this research is to find an optimal

It is not only information about the patient that the infectious image delivers, but how the disease may be spreading, the speed of the spread, and any concentrated areas of infection within the body. This kind of knowledge greatly informs the approach within ICU to treatment, depth and breadth of knowledge about

In my opinion with technology developing constantly data mining holds great potential for the healthcare industry to enable health systems to systematically use data and analytics to identify inefficiencies and best practices that improve care and reduce costs. It has been proven by some experts that the opportunities to improve care and reduce costs concurrently could apply to as much as 30% of overall healthcare spending. This could be a win/win overall. But due to the complexity of healthcare and a slower rate of technology adoption, our industry lags behind these others in implementing effective data mining and analytic strategies so in conclusion data mining will continually be a great asset to the health care industries .

Abstract - The healthcare industry collects large amounts of Healthcare data, but unfortunately not all the data are mined which is required for discovering hidden patterns and effective decision making. We propose efficient genetic algorithm with the back propagation technique approach for heart disease predic-tion. This paper has analyzed prediction systems for Heart dis-ease using more number of input attributes. The System uses medical terms such as Gender, blood pressure, cholesterol like13 attributes to predict the likelihood of patient getting a Heart dis-ease.

While the study is twenty years old and the results unavailable, it highlights difficulties in informatics research that are still present today. The authors noted difficulty in obtaining data from EHRs, even if from the same systems, as institutions implement them differently. Data mining methodologies were utilized to find patterns that could formulate prediction about ARDS risk factors. Data mining is “a method in computer science that is used to discovered patterns and trends within large data sets,” (McBride and Tietze, 2016). The data mining technique utilized in this study was association rule learning which identifies relationships between variables and is necessary for predictive modeling. “Our limited human information processing capacities cannot detect the patterns and trends in massive volumes of data, but data mining software uses a variety of mathematical techniques to sift through mountains of both qualitative and quantitative data to find the repeated themes and matched variables that often escape our human brains,” (Goodwin et al, 1997). Therefore, utilizing informatics strategies, such as data mining, allows large datasets to be process in ways that will allow for development of prediction tools, such as patients at high risk for developing ARDs.

It is important to understand that using electronic health system helps physicians to provide a more accurate diagnosis which helps to reduce medical errors and incorrect diagnosis which make patients very happy knowing that physicians have their best interest at heart (Kudyba, 2010). In electronic health system, information is structured and well organized in a manner that helps to eliminate the time spent searching for information. Moreover, patients are very happy since electronic health system helps to provide privacy and security of patients’ information and data so as to eliminate the problem of leaving patients’ information unattended on papers so that unauthorized personnel can see and