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Information Technology In Healthcare

5149 Words21 Pages

I. INTRODUCTION
Healthcare organizations today are capable of generating and collecting a large amounts of data. This increase in volume of data requires automatic way for these data to be extracted when needed. With the use of data mining techniques it is possible to extract interesting and useful knowledge and regularities. Knowledge acquired in this manner, can be used in appropriate area to improve work efficiency and enhance quality of decision making process. Above stated points that there is a great need for new generation of computer theories and tools to help people with extracting useful information from constantly growing volume of digital data [1]. Information technologies are being increasingly implemented in healthcare organizations …show more content…

Development and implementation of new information technologies that allow global networking, give modern medicine the epithet of “informatical medicine”. Information technologies increasingly provide the help in system approach of solving medical problems [16]. Disposition of the right information enables the preparation of accurate reports, for example, usage of hospital capacities, or number of occupied beds. At the same time it is easier to monitor treatment and to check the information exchange. Use of information technologies enables change of the healthcare system - how to improve public health, the healthcare of the system users, reduce costs, save time and …show more content…

This is different from standard data mining practice, which simply begins with a set of data without obvious hypothesis [19]. While the traditional data mining is focused on patterns and trends in data sets, data mining in healthcare is more focused on minority that is not in accordance with patterns and trends. The fact that standard data mining is more focused on describing and not explaining the patterns and trends, is the one thing that deepens the difference between standard and healthcare data mining. Healthcare needs these explanations since the small difference can stand between life and death of a patient.

Analytical techniques used in data mining, in most cases have long been known mathematical techniques and algorithms. Although data mining is a young technology, the process of data analysis is nothing new. The thing that linked these techniques and large databases is a cheaper storage space and processing power. Here are some of the techniques of data mining, which are successfully used in healthcare, such as artificial neural networks, decision trees, genetic algorithms and nearest neighbor

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We must filter and customize that downloaded data for the health conditions that we primarily try to improve. Once data is customized and filtered properly, it gives us “care gaps”. Those care gaps can be easily closed out by accessing patient’s EMR or by referral. This updated data then gets uploaded back to the healthcare insurance company data set for reporting purpose. Data analytics helps health profession close the care gaps and improv care coordination between

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Sources Sayles, B. N., (2013), Health Information Management Technology: An Applied Approach. Chicago: American Health information management Association.

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Since many health information infrastructure systems are relatively new, there is still variability in the implementation stages that different organizations have achieved. Additionally, most systems will have more than one capability that provides value, so the relationship between the system’s functionality and the resulting impact to patient care must be analyzed in order to determine the value it provides (Einstein, Juzwishin, Kushniruk, & Nahm, 2011). Value of health information infrastructures can be assessed in many different ways, including whether the technology allows the availability of useful information, how that information is utilized by staff and patients, and its impact on health outcomes. For information to be of value and influence medical decision making, it must be comprehensive, accessible, useful, and valid (Fitterer, Mettler, Rohner, & Winter, 2011).

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A healthcare chief information officer (CIO) is “an executive at a healthcare organization that oversees the operation of the information technology (IT) department and consults with other C-level personnel on technology-related needs and purchasing decisions” (Margaret, 2015). The emerging role of a CIO is versatile and quite challenging. Besides providing software and computer support, nowadays CIO’s role also involves providing strategic business leadership along with operational and tactical activities such as ensuring maintenance, safety and privacy of patient’s medical records. The CIO possess various responsibilities such as assessing current and future technological needs of the organization, managing day-to-day operations of the

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Computer-based algorithms provide patient-specific assistance. An early warning system that provides timely alerts designed to ensure that appropriate actions are initiated as soon as problems begin to develop. Four key applications have been developed to achieve these goals.

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This includes creating, managing and following patient data. The American Health Information Management Association (AHIMA) defines information governance as “an organization wide framework for managing information throughout its lifecycle and for supporting the organization’s strategy, operations, regulatory, legal, risk, and environmental requirements.” In today’s healthcare system, it is more important than ever to know and understand how healthcare information is created, transferred and used. Due to the development of systems such as electronic health records and clinical decision support systems it is important that health information maintains its reliability and validity throughout its

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Health Information Exchange (HIE) sounds like a lofty concept but it enables health care professionals and patients to securely share and access a patient’s vital medical information electronically. It is the ability to transmit healthcare information across organizations within a healthcare system such as a hospital, a community, state or region. state. In this paper, I will focus on the key concerns of healthcare leaders have about health information exchanges and whether it has helped healthcare in terms of delivery, quality of care and cost savings. Also, whether health information exchanges have become an essential part of the healthcare system and how close is the United States to the goal of nationwide implementation.

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Abstract The use of medical AI in the healthcare industry has been a topic of much discussion and excitement in recent years. However, there is a growing concern that the hasty adoption of these technologies may be premature, and that the potential risks and drawbacks outweigh the potential benefits. This paper argues that at this stage, it is advisable to avoid using medical AI in healthcare.

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The Veteran’s Health Administration (VHA) Algorithms are just as solid as the data they depend on. While algorithms for acute clinical issues (e.g., heart attack, septic stun) may not require a lot of information to predict risk, algorithms that use more prominent measures of clinical information have more accuracy precision and potential clinical applications. The Veteran 's Health Administration (VHA), the biggest health system in the United States, has gathered electronic information from its patients for more than three decades. Starting in 2006, the VHA fabricated a corporate data warehouse as an archive for patient-level information over its national locales.

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Dr. Song, Clinical Decision Support has been defined as a “process for enhancing health-related decisions and actions with pertinent, organized clinical knowledge and patient information to improve healthcare, as well as, healthcare delivery (Campbell & CPHIMS, 2013). Clinical Decision-supporting tools are utilized to manage and support patient care. Healthcare information systems and information-retrieval systems are tools that manage information. There are various programs that provide custom tailored assessments or advice based on sets of patient specific data (Musen, Middleton, & Greenes, 2014, p. 701). Decision tools may follow simple logics (such as algorithms), may be based on decision theory, cost benefit analysis, or may use numerical approaches only as an adjunct to symbolic problem solving (Musen, Middleton, & Greenes, 2014, p. 701).

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However, if no appropriate technique is developed to find great potential economic values from big healthcare data, these data might not only become meaningless but also requires a large amount of space to store and manage. Over the past two decades, the miraculous evolution of data mining technique has imposed a major impact on the revolution of human’s lifestyle by predicting behaviors and future trends on everything which can convert stored data into meaningful information. These techniques are well suitable for providing decision support in the healthcare setting. To speed up the diagnosis time and improve the diagnosis accuracy, a new system in healthcare industry should be workable to provide a much cheaper and faster way for diagnosis [1]. Clinical Decision Support System (CDSS), with various data mining techniques being applied to assist physicians in diagnosing patient diseases with similar symptoms, has received a great attention

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Or in simple terms it may be defined as the process for extracting or mining knowledge from large amounts of data. So the chapter 3 will provide a brief overview on the various datamining techniques. This is because there should be a clear understanding on the basic principles of the area of concern because any research is incomplete without the knowledge of the basic principles. So not only that the chapter gives the idea about data mining it also describes some of its techniques which leads to the ability to differentiate each one of them from other and allows one to explore the possibilities amongsts them. To do so the techniques studied in this chapter in brief are Association Rule, classification rule, Frequent Episodes and Deviation

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Population Health Management means proactive application of strategies and interventions to defined cohorts of individuals across the continuum of healthcare delivery in an effort to maintain and/or improve the health of the individuals within the cohort at the lowest necessary cost. With the new era of healthcare, a good population management system in place is critical to a health system. Organizations will grow to become dependent on data and analayis in order to carry out actions or what not within the said system. Furthermore, with the advent of information technology and a well-designed population health tools, it can create substantial impact to the patient through electronic medical records and healthcare information exhancge,;

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Giving an in depth overview of the different ways that technology is used in informatics would be useful as well. For example, one could implement the use of wearable bio sensors in order to monitor the vital statistics of a patient and record this information in the facility computers or a personal data entry device such as a laptop, tablet, etc. Monitoring patients in this way gives the healthcare professional a variety of statistics that they can measure over a period of time and because the patient is constantly being monitored the healthcare professional can look and find patterns in this information. Recording this information in the patient’s records and relaying this information to any other doctors and/or other medical staff that the patient may be transferred to helps the patient get consistent and efficient care without their new caretakers missing a step. This example gives us a good look at the information needed in an informatics course.

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The possibility of monitoring chronic illnesses leads to a more organized, structured and predictive way to approach patients, decreasing the need for treatment at general hospitals and cutting adverse drug events. Analyzing big data on healthcare, hospitals and clinics can predict and prevent crises, expand their preventive care offerings, optimizing the admissions and cutting the costs connected to them. Analysis of disease patterns and tracking of disease outbreaks and transmission are fundamental to improve public health surveillance, to have a faster response, to avoid predictable chronic situations reducing the need of hospitals admissions and to cut the costs related to waste of time and

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