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Grounded Hues Group

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Castor Panfilov
Castor Panfilov

Management of Healthcare Organizations: An Introduction - Peter C. Olden - Google Books

Similar to EHR, an electronic medical record (EMR) stores the standard medical and clinical data gathered from the patients. EHRs, EMRs, personal health record (PHR), medical practice management software (MPM), and many other healthcare data components collectively have the potential to improve the quality, service efficiency, and costs of healthcare along with the reduction of medical errors. The big data in healthcare includes the healthcare payer-provider data (such as EMRs, pharmacy prescription, and insurance records) along with the genomics-driven experiments (such as genotyping, gene expression data) and other data acquired from the smart web of internet of things (IoT) (Fig. 1). The adoption of EHRs was slow at the beginning of the 21st century however it has grown substantially after 2009 [7, 8]. The management and usage of such healthcare data has been increasingly dependent on information technology. The development and usage of wellness monitoring devices and related software that can generate alerts and share the health related data of a patient with the respective health care providers has gained momentum, especially in establishing a real-time biomedical and health monitoring system. These devices are generating a huge amount of data that can be analyzed to provide real-time clinical or medical care [9]. The use of big data from healthcare shows promise for improving health outcomes and controlling costs.

Management Of Healthcare Organizations.pdf


Workflow of Big data Analytics. Data warehouses store massive amounts of data generated from various sources. This data is processed using analytic pipelines to obtain smarter and affordable healthcare options

In fact, IoT is another big player implemented in a number of other industries including healthcare. Until recently, the objects of common use such as cars, watches, refrigerators and health-monitoring devices, did not usually produce or handle data and lacked internet connectivity. However, furnishing such objects with computer chips and sensors that enable data collection and transmission over internet has opened new avenues. The device technologies such as Radio Frequency IDentification (RFID) tags and readers, and Near Field Communication (NFC) devices, that can not only gather information but interact physically, are being increasingly used as the information and communication systems [3]. This enables objects with RFID or NFC to communicate and function as a web of smart things. The analysis of data collected from these chips or sensors may reveal critical information that might be beneficial in improving lifestyle, establishing measures for energy conservation, improving transportation, and healthcare. In fact, IoT has become a rising movement in the field of healthcare. IoT devices create a continuous stream of data while monitoring the health of people (or patients) which makes these devices a major contributor to big data in healthcare. Such resources can interconnect various devices to provide a reliable, effective and smart healthcare service to the elderly and patients with a chronic illness [12].

The analysis of data from IoT would require an updated operating software because of its specific nature along with advanced hardware and software applications. We would need to manage data inflow from IoT instruments in real-time and analyze it by the minute. Associates in the healthcare system are trying to trim down the cost and ameliorate the quality of care by applying advanced analytics to both internally and externally generated data.

Big data is the huge amounts of a variety of data generated at a rapid rate. The data gathered from various sources is mostly required for optimizing consumer services rather than consumer consumption. This is also true for big data from the biomedical research and healthcare. The major challenge with big data is how to handle this large volume of information. To make it available for scientific community, the data is required to be stored in a file format that is easily accessible and readable for an efficient analysis. In the context of healthcare data, another major challenge is the implementation of high-end computing tools, protocols and high-end hardware in the clinical setting. Experts from diverse backgrounds including biology, information technology, statistics, and mathematics are required to work together to achieve this goal. The data collected using the sensors can be made available on a storage cloud with pre-installed software tools developed by analytic tool developers. These tools would have data mining and ML functions developed by AI experts to convert the information stored as data into knowledge. Upon implementation, it would enhance the efficiency of acquiring, storing, analyzing, and visualization of big data from healthcare. The main task is to annotate, integrate, and present this complex data in an appropriate manner for a better understanding. In absence of such relevant information, the (healthcare) data remains quite cloudy and may not lead the biomedical researchers any further. Finally, visualization tools developed by computer graphics designers can efficiently display this newly gained knowledge.

Heterogeneity of data is another challenge in big data analysis. The huge size and highly heterogeneous nature of big data in healthcare renders it relatively less informative using the conventional technologies. The most common platforms for operating the software framework that assists big data analysis are high power computing clusters accessed via grid computing infrastructures. Cloud computing is such a system that has virtualized storage technologies and provides reliable services. It offers high reliability, scalability and autonomy along with ubiquitous access, dynamic resource discovery and composability. Such platforms can act as a receiver of data from the ubiquitous sensors, as a computer to analyze and interpret the data, as well as providing the user with easy to understand web-based visualization. In IoT, the big data processing and analytics can be performed closer to data source using the services of mobile edge computing cloudlets and fog computing. Advanced algorithms are required to implement ML and AI approaches for big data analysis on computing clusters. A programming language suitable for working on big data (e.g. Python, R or other languages) could be used to write such algorithms or software. Therefore, a good knowledge of biology and IT is required to handle the big data from biomedical research. Such a combination of both the trades usually fits for bioinformaticians. The most common among various platforms used for working with big data include Hadoop and Apache Spark. We briefly introduce these platforms below.

In healthcare, patient data contains recorded signals for instance, electrocardiogram (ECG), images, and videos. Healthcare providers have barely managed to convert such healthcare data into EHRs. Efforts are underway to digitize patient-histories from pre-EHR era notes and supplement the standardization process by turning static images into machine-readable text. For example, optical character recognition (OCR) software is one such approach that can recognize handwriting as well as computer fonts and push digitization. Such unstructured and structured healthcare datasets have untapped wealth of information that can be harnessed using advanced AI programs to draw critical actionable insights in the context of patient care. In fact, AI has emerged as the method of choice for big data applications in medicine. This smart system has quickly found its niche in decision making process for the diagnosis of diseases. Healthcare professionals analyze such data for targeted abnormalities using appropriate ML approaches. ML can filter out structured information from such raw data.

AI has also been used to provide predictive capabilities to healthcare big data. For example, ML algorithms can convert the diagnostic system of medical images into automated decision-making. Though it is apparent that healthcare professionals may not be replaced by machines in the near future, yet AI can definitely assist physicians to make better clinical decisions or even replace human judgment in certain functional areas of healthcare.

A number of software tools have been developed based on functionalities such as generic, registration, segmentation, visualization, reconstruction, simulation and diffusion to perform medical image analysis in order to dig out the hidden information. For example, Visualization Toolkit is a freely available software which allows powerful processing and analysis of 3D images from medical tests [23], while SPM can process and analyze 5 different types of brain images (e.g. MRI, fMRI, PET, CT-Scan and EEG) [24]. Other software like GIMIAS, Elastix, and MITK support all types of images. Various other widely used tools and their features in this domain are listed in Table 1. Such bioinformatics-based big data analysis may extract greater insights and value from imaging data to boost and support precision medicine projects, clinical decision support tools, and other modes of healthcare. For example, we can also use it to monitor new targeted-treatments for cancer.

In order to analyze the diversified medical data, healthcare domain, describes analytics in four categories: descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics refers for describing the current medical situations and commenting on that whereas diagnostic analysis explains reasons and factors behind occurrence of certain events, for example, choosing treatment option for a patient based on clustering and decision trees. Predictive analytics focuses on predictive ability of the future outcomes by determining trends and probabilities. These methods are mainly built up of machine leaning techniques and are helpful in the context of understanding complications that a patient can develop. Prescriptive analytics is to perform analysis to propose an action towards optimal decision making. For example, decision of avoiding a given treatment to the patient based on observed side effects and predicted complications. In order to improve performance of the current medical systems integration of big data into healthcare analytics can be a major factor; however, sophisticated strategies need to be developed. An architecture of best practices of different analytics in healthcare domain is required for integrating big data technologies to improve the outcomes. However, there are many challenges associated with the implementation of such strategies.


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