Data Mining For The Electronic Health Records

Healthcare has evolved over past decades and continues to remain an issue of concern for individuals everywhere. Effectively managing data is important to improving the performance in the health care system. Accumulating, evaluating, deciphering and acting on data for particular performance measures allow health professionals to identify shortcomings and make the necessary adjustment, and track the outcome.

In today’s society, the accuracy of health information, the availability of health records, and the professional resources in which one live are vital in decision making for health conditions. Meaningful Use (MU) is a program developed by CMS Medicare and Medicaid that awards, incentives in the health care industry in which the certified electronic health records (EHRs) are used to improve patient care (Practice Fusion, 2016). These incentives are for professionals that care for about 30% of their adult patient volume or 20% of their children’s volume for Medicare and Medicaid patients (CMS, 2016). In addition, adjusting from paper charts to electronic charts of patient’s information is beneficial for MU. Furthermore, the American

How data is captured varies from institution to institution. In order for data to be well understood, data should have a definition that is consistent and comprehensively understood by all users of the data. Standardization of how data is captured is critical to allow the production and export of data needed to support quality assessment, decision support, exchange of data for patients with multiple health care providers and public health surveillance. Patient safety and quality improvement are dependent upon embedded clinical guidelines that promote standardized, evidence-based practices. Unless we can achieve standardization with terminology, technologies, apps and devices, the goals of EHR implementation will not become a

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)

This Stage 1 started from 2011-2012, its objective dealt with data capture and sharing, these sheets are providing these services to assist professionals and hospitals understand the requirements of each objective and demonstrate meaningful use success. This stage also allows qualified providers to receive their payment after fulfilling nine core objectives and one public health objective. The second stage of the Meaningful Use is Stage 2 started in 2014; it dealt with the advanced clinical processes. This Stage introduces new aims and measures, as well as higher entries; it also required health care providers to prolong EHR capabilities to a greater portion of their patient populations. The last stage of the Meaningful Use is Stage 3, this Stage it still in a building phase. Its objective will be focusing on improving quality, safety, efficiency, and leading to improved outcomes. Even though the details of this program have not been finalized, Meaningful Use Stage 3 will work to make the program easier to understand. It will provide the professionals (EPs) and hospitals the ability to exchange and use information between electronic health records, and improve patient outcomes. Based on the current timeline, healthcare providers have the choice to begin Stage 3 Meaningful Use in 2017 but are not permitted to use it until

Studies have found that coded data collected with a sole focus on reimbursement can poorly affect the use of the data for other purposes. Coded data goes farther and does more than ever before, making it imperative that professionals stay up to date of many rapid changes. One of the biggest changes is the expansion of coding from its traditional role of translating narrative clinical text into diagnosis and procedure codes. Coded data are now used for purposes such as severity adjustment, quality of care assessment, patient safety evaluation, public health surveillance, and decision support process development. Coding must meet an emerging need to capture healthcare data in a standard format that has universal meaning and can be applied both at the individual and aggregate levels. With this expansion come additional new responsibilities, such as entry of health information into a database and the need to understand how the quality and accuracy of the data are

Combined with data analytics, aggregate-level EHR enable examination and development of effective medicines and therapies for chronic diseases (Kohli & Tan, 2016). There are several forces that are driving many of the changes to the EHR including health and safety concerns with the number of preventable deaths from medical errors , a changing society, the internet, with an increasing amount of mobile patients, as well as government response (Gartee, 2011) The response to the IOM report was swift and positive, within both the government and private sectors (Gartee, 2011). The government responded with establishing a position of the National Coordinator for Health Information Technology, under the U.S. Department of Health and Human Services (HHS) to “develop, maintain, and direct the implementation of a strategic plan to guide the nationwide implementation of interoperable health information technology in both the public and private health care sectors that will reduce medical errors, improve quality, and produce greater value for health care expenditures (Gartee,

Nevertheless, with the date of implementation already expired, larger facilities gain an advantage, whereas smaller practices lag behind (Conn, 2015). That is to say, ICD-10 doesn’t purposely affect health care facilities negatively. Wall (2016) further identifies the impact of ICD-10 with regards on data capturing. It is important to note that data capturing is further improved to capture undetectable data not thought possible. Data can include the under dosing of drugs, which further clarifies two classifications (e.g. specific drugs underutilized, and reason for drug misuse), which play a significant role with the Centers for Medicaid and Medicare Services data collection, hospital resource utilization, and potential future reimbursement.

The government instigated the change, technology designed an application to manage and control the data, now it’s left to the healthcare industry to configure a business strategy for using data analytics to improve the outcomes related to the patient, secure population data and the improve the business of healthcare performance. What do all three have in common – survival?

Anthem with its state of the art Health Care Analytics engine and access to wide range of comprehensive information including complete claims history is able to provide doctors meaningful actionable information like high

Bowles, Potashnik, Ratcliffe, Rosenberg, Shih, Topaz, Homes, and Naylor (2013) intended to explain solutions, implications and difficulties related to semantic harmonization, while performing research utilizing electronic health record data from four hospitals. The method utilized was unidentified data from variables collected from about 1200 nursing admission assessments and documentation of patients throughout their admission in the hospital (Bowles et al., 2013). Findings from the study consisted of challenges with working with electronic health records from three different sites. The sites were found to have various versions of the electronic health record, different customization policies, and user interface features varied (Bowles et al., 2013). The conclusion of the study was through awareness of the outcomes of customization, differences in user interface and documentation policies, barriers may be prevented (Bowles et al., 2013).

Once data is collected it can be used by numerous health care providers and decision makers to monitor the health and needs of individuals and populations, as well as contribute to the analysis of the health system. Users including hospitals, health care practitioners, government, professional associations, researchers, media, students, and the general public. Having the correct and up-to-date coded data is critical, not only for the delivery of high-quality clinical care, but also for continuing health care, maintaining health care at an optimum level, for clinical and health service research, and planning and management of

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 .

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.

ABSTRACT The clinical data repository (CDR) is a frequently updated relational data warehouse that provides users with direct access to detailed, flexible, and rapid retrospective views of clinical, administrative, and financial patient data for the University of Virginia Health System. This article presents a case study of the CDR, detailing its five-year history and focusing on the unique role of data warehousing in an academic medical center. Specifically, the CDR must support multiple missions, including research and education, in addition to