Making decisions based on instinct alone has never been sufficient. This is certainly true in healthcare. Clinical practitioners require data to make their medical diagnosis, treatment recommendation, and prognosis. A richer set of near-real-time information can greatly help physicians determine the best course of action for their patients, discover new treatment options, and potentially save lives. The Analysis of all the data gathered from healthcare sector or simply the healthcare analytics leads to improvements in quality of the healthcare programs and the ability to create new ones effectively. The possibilities to improve outcomes and contain costs from the analysing big data in healthcare is massive and something that needs to be considered. According to a 2011 report by the McKinsey Global Institute on big data, “If US healthcare were to use big data creatively and effectively to drive efficiency and quality, the sector could create more than $300 billion in value every year. Two-thirds of that would be in the form of reducing US healthcare expenditure by about 8 percent. “Clinical operations and R & D are two large areas where $165 billion and $108 billion is actually spent on unnecessary expenditures and is completely …show more content…
waste". McKinsey believes big data could help reduce waste and inefficiency in the following three important areas: Clinical operations: Using Comparative research to determine more clinically important and cost-effective ways to diagnose and treat patients. Clinical decision support systems to enhance the efficiency and quality of operations. Research & development: Predictive modelling equips the healthcare sector to lower the overhead and produce a system that is more targeted towards research and development of drugs and devices. Usage of statistical tools and algorithms to enhance clinical trial design and patient recruitment to better the treatments to patients, reduces trial failures and accelerates the innovations of new treatments to market . Analysing clinical trials and patient records to discover adverse effects before products are made available in the open market. Personalized medicine that is emerging from the analysis of large datasets ultimately help match the appropriate medicine or treatment to be provided to the right patient at the right time. Public health: Analysis of data on disease patterns and tracking the outbreaks aides in preventing major outbreaks in the future. This also enables a swift response if in case there is similar pattern emerging in the future. Studying the data related to the transmission of many diseases helps avoid spreading of the epidemic. The data collected aides in faster development targeted vaccines with a high rate of accuracy. e.g., choosing the annual influenza strains. The drugs are more accurately targeted and development of such drugs is faster now than compared to the past with the help of big data analytics. The prospect of synthesizing data into information that can be used to recognise needs, provide effective services. Data helps predict and prevent crises especially for the benefit of the public. In addition to the above, big data analytics in healthcare can contribute to Evidence-based medicine: • Combining and analysing a variety of structured and unstructured data from EMR’s or EHR’s, clinical data, financial and operational data, and genomic data to match treatments with outcomes, predict readmissions and diseases to provide more efficient and timely care. Genomic analytics • Data can used for Gene sequencing to improve efficiency and curb costs successfully to make genomic analysis a part of the regular medical care decision process and the growing patient medical record • Efficient and more effective gene sequencing and involving the genomic analysis as a part of the decision-making process for the regular medical care and patient medical record enables to cut down costs. Pre-adjudication fraud analysis: • Frauds can be reduced by rapid analysis of large amount of claim requests. Device/remote monitoring: • Capturing and analysing real-time large volumes of data from in-hospital and in-home devices, in huge quantities improves safety monitoring and adverse event prediction effectively. This is very much the current trend in the ICU’s. Patient profile analytics • Applying advanced healthcare analytics such as segmentation and predictive modelling to patient profiles or patient data to identify individuals who would benefit from preventive care, proactive care or lifestyle changes and avoid readmissions. For example: The patients at risk of developing a specific disease would benefit from preventive care. (Example: Diabetes) Impact of Big Data on Healthcare: Healthcare stands to benefit a lot from the big data revolution and the developments in data management. However, data alone cannot help us, it is how the accumulated data is analysed that matters in a field such as healthcare. Here we look at how analytics in big data is helping the healthcare sector to improve the quality of care provided to the patients. The following are some of the key highlights of the impact big data has on improving the quality of healthcare • Recognising patients that pay high costs can help determine which patients are most likely to spend more on healthcare and separate people who would benefit be providing interventions and how to provide effective health care plans based on their needs. • Predictive analytics and modelling enables us to predict the rate of readmissions and enable care coordination after the patient has been discharged. • Integrating triage practices into the workflow of clinical operations can help manage information related to beds, staff and patient inbound and outbound transfers • Using healthcare analytics in ICU’s to evaluate multiple streams of data from various patient monitors helps predicting whether a patient's condition is likely to worsen. • Intense studies on data patterns of patients for prescription drug use and vital sign changes, can help prevent adverse drug reactions and infections. • Data from multisite disease registries and clinical networks will help manage patients with chronic conditions that span more than one organ system. Finding and targeting the right people The audience to which the healthcare serves consists of multiple groups of people who are in various stages of health, from completely healthy to being sick or the intermediate.
It is important to identify the people who need care and use big data analytics to provide care and help at the earliest. The absolute need of the hour is to identify who is at risk of diseases or conditions like coronary artery disease and who could benefit from extra screenings, programs that are focussed on weight management or smoking termination programs. The efforts to provide care to such people begins by the analysis of multiple sources of data, from claims data to data that is personally provided by the patient at the time of initial health
assessment. The health risk valuation data can provide an immediate overview of potential healthcare needs among people. Without the data, everyone would have to wait and observe for various symptoms to determine who requires care coordination immediately. Additionally, we can make use of healthcare analytics to understand what might motivate people and how to change this assumed behaviour of the people. Taking a closer look at evaluation rates among people in different geographical locations can help identify barriers to screening. We can also determine the best way to encourage specific sets of people to complete recommended evaluations, thereby recording their healthcare related data. When large poulations are involved, it is paramount to collect data to know who can potentially benefit from intervention by analysing the data to improve health and lower costs. Delivering the Right Intervention Big data is helping to identify people who are at risk and with the help of analytics effective care can be accorded. The next important measure is to ensure that the utmost possible intervention is provided for each person specifically and that it’s provided when needed. This is the most important aspect of big data and predictive analyticsanalytics in healthcare where technological enhancements combined with appropriate analytics are driving major changes in a positive way. For example, large amounts of real-time information are now available from wireless monitoring devices that postoperative members and those with chronic diseases are wearing at home and in their daily lives. This enables The ability to implement the right intervention measures will improve as people begin to fathom their own risks, keep a track of their health and share important health related information with their care providers, physicians or hospitals. This also aides for a coordinated approach across settings and providers, physicians and the hospitals to have the same healthcare information. With more access to data and analytical methods used, we can identify high risk members easily and efficiently and recommend more preventive actions and enable real time monitoring with the help of data. Adjusting programs and closing the loop As more information on health and disease and the patterns of care is available, more useful insights will allow for healthcare programs to be more quickly adjusted. Studies of care management and wellness programs have been largely positive. As per one recent study which tested the hypothesis that care management program changed the likelihood of having appropriate medication dispensed, the needed tests performed and increased medication adherence. As per the study, 7.3 percent fewer members would have had a prescription for asthma controller medication in the absence of the program. The same study looked at pneumococcal vaccination and statin medication metrics, finding that in year 4, without the care management intervention, 16.6 percent fewer members would have had a prescription for statin medication. Improved healthcare analytics paves ways to improved healthcare programs and the ability to create new effective ones that are focussed towards each individual. The potential to improve Quality and contain costs from the analysing big data is huge. It has been reported that preventive actions identified after careful analysis of data can reduce the total cost of care by a vast amount. Also, readmissions can be avoided through the prevention of medical episodes, early identification of the most suitable treatment and avoidance of interim chronic care.
Unfortunately, the quality of health care in America is flawed. Information technology (IT) offers the potential to address the industry’s most pressing dilemmas: care fragmentation, medical errors, and rising costs. The leading example of this is the electronic health record (EHR). An EHR, as explained by HealthIT.gov (n.d.), is a digital version of a patient’s paper chart. It includes, but is not limited to, medical history, diagnoses, medications, and treatment plans. The EHR, then, serves as a resource that aids clinicians in decision-making by providing comprehensive patient information.
Healthcare is one of the most dynamic industries in our great nation. To truly understand just how dynamic the industry is, one needs to understand that healthcare in and of itself is a living, breathing industry that is ever changing and conforming to meet the ideals set forth from a broad group of stakeholders. When one looks at the evolution that healthcare has undergone in the past 165 years, the picture of the true dynamics of this industry is painted. One must take this evolutional history into account when looking at the next ten years in our industry. When looking at these evolutional processes, one can see that the systems have changed as our country and its people have required it to (Williams & Torrens, 2008). When looking at how this industry will change or evolve over the next decade, one can ascertain that it will be by the demands of those involved that change will come.
Tan & Payton (2010) describe the electronic health record (EHR), which dates back to the 1950s. These computer-based patient records have evolved into complex systems with many capabilities. They were designed to provide healthcare professionals with a comprehensive picture of a patient’s health status at any time and are meant to automate and streamline the workflow of the healthcare professional (Tan & Payton,
McGonigle and Mastrian (2013) defines data mining as a process of utilizing software to sort through data so as to discover patterns and ascertain or establish relationships. They also state this process may help to discover or uncover previously unidentified relationships among the data in a database. Data mining is very important to healthcare organizations. It can help in ways such as to determine treatment effectiveness, identify problems, decrease costs for the organization, and can even detect possible fraudulent activity. Not only is data mining used in healthcare, but it is also used in other businesses as well. Although data mining is a great asset to healthcare, an informatics nurse has to be very careful due to the lack of a standardized
For starters, HCOs must build a foundation that fosters on patient engagement since this is a key success factor in coordinated care. Patients are educated to understand their condition and the steps they should take to help manage it by using an evidence-based care plan (Sawardekar, 2015). This value-based HIT platform follows patients across services provided, the sites where care was given, and the time for the full episode of care, including hospitalization, outpatient visits, testing, physical therapy, and other interventions (Porter & Lee, 2013). With this in mind, the data collected are then aggregated around patients in a given population. Additionally, the terminologies and data fields related to diagnoses, lab values, treatments,
Evidenced-based clinical practice approaches health care decision making by using the best relevant evidence available from systematic research with the incorporation of the provider’s clinical expertise and the patient’s values and expectations to decide on the most suitable treatment option (Cochrane Collaboration, 2014). However, it is impossible for an individual provider (physician, nurse practitioner, or registered nurse) to be aware of the all of the latest research findings since present knowledge becomes outdated in a short period. Health information technology (HIT) allows health care providers to make the best possible decisions utilizing clinical decision support (CDS), health information and data, results management, and public health management. However, providers use knowledge, understanding, and wisdom when making clinical decision in addition to HIT technology.
Various healthcare datasets play different roles in the healthcare sector. With the world working towards organization of information, efficiency and prudent information storage, the data sets are developed to suit the functions that they are designed for. In the healthcare sector, the major data sets are the HEDIS, OASIS and UHDDS. All these data sets work together to ensure that there is an efficient healthcare system that serves all citizens. This paper seeks to analyze each of these data sets in regards to their function, applicability and value added to the healthcare system.
Master’s in health informatics degree programs will offer advanced electives for students, whose careers will financially benefit from specialized knowledge. A class on medical vocabularies teach health care terminologies and classification systems. Knowing this will help graduates better understand and effectively manage health care data. A course on health care data analysis will provide integrated overviews of data analysis and
Fortunately, the Affordable Care Act of 2014 encompassed the integration of health information system (HIS). That is, the health data assimilated and analyzed framed to identify what determines the price, quantity, and expenditures in health care. Closely linked is health insurance, thus, HIS technology monitors, track and, reports third-party discrepancies according to transactions between health care organizations and the
You may ask what big data analytics is. Well according to SAS, the leading company in business analytics software and services describes big data analytics as “the process of examining big data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.” As the goal of many companies which is to seek insights into the massive amount of structured, unstructured, and binary data at their disposal to improve business decisions and outcomes, it is evident why big data analytics is a big deal. “Big data differs from traditional data gathering due to that it captures, manages, and processes the data with low-latency. It also one or more of the listed characteristics: high volume, high velocity, or high variety. Big data comes from sensors, devices, video/audio, networks, log files, web, and social media which much of it is generated in real time and in a very large scale.”(IBM) In other words, companies moving towards big data analytics are able to see faster results but it continues to reach exceptional levels moving faster than the average person can maintain.
Big data refers to large datasets that are challenging to store, search, share, visualize, and analyze and so the Testing. Testing of Big Data is one of the toughest since there is a lack of knowledge on what to test and how much to test.Traditional DW testing approach is inadequate due to Technology Changes, Infrastructure (DB/ETL on Cloud) and Big Data.
To better understand the roles needed to enhance the public health infrastructure; one must first know the purpose of a health informatician. An informatician is a person who studies or work in the field of informatics. According to the American Medical Informatics Association Inc., “Public Health Informatics is the application of informatics in areas of public health, including surveillance, prevention, preparedness, and health promotion. Public health informatics and the related population informatics, work on information and technology issues from the perspective of groups of individuals” (2016). In order to build a solid infrastructure
Artificial intelligence has expanded drastically over the last few years. In healthcare it is especially important since it can assist with patient care and treatment. Artificial intelligence is exactly as it sounds; it is a machine, like a computer, that goes beyond those parallels taking a step further by thinking and predicting what its user would do next. In their paper, Advances in artificial intelligence research in health, Khanna, Sattar and Hansen describe artificial intelligence and its capabilities as “focused on traits of reasoning, knowledge representation, planning, learning, communication, perception and social intelligence, AI has been widely applied to augment the state of the art in Health Informatics” (Khanna, Sattar and Hansen,
Big data will then be defined as large collections of complex data which can either be structured or unstructured. Big data is difficult to notate and process due to its size and raw nature. The nature of this data makes it important for analyses of information or business functions and it creates value. According to Manyika, Chui et al. (2011: 1), “Big data is not defined by its capacity in terms of terabytes but it’s assumed that as technology progresses, the size of datasets that are considered as big data will increase”.
Technology has revolutionized the medical field, bringing it into the future and saving lives around the world. Better communications from doctors, online medical health records and new life saving technology has increased how quickly a doctor diagnoses patients and the communicative relationship between doctors and patients. Despite others claiming that using technology is dangerous to patient privacy, special has actually made medical technology very secure and safe. It has also increased communication between doctors around the world, helping with difficult diagnoses and finding cures together. Technology within medical practices has enhanced the field due to quicker diagnoses with new software programs, which has helped reduce the spread of rare diseases, as well as given patients and doctors quick access to records and generally has improved the care hospitals can give, as well as its efficiency.