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and aims: Millions of Americans are discharged from hospitals to home health every year and about third of them return to hospitals. A significant number of rehospitalizations (up to 60%) happen within the first two weeks of services. Early targeted allocation of services for patients who need them the most, have the potential to decrease readmissi...
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Context 1
... shown in Figure 2, the boundaries of the DIKW framework components are not strict; rather, they are interrelated and there is a "constant flux" between the framework parts. Simply put, data are used to generate information and knowledge while the derived new knowledge coupled with wisdom, might trigger assessment of new data elements (Matney et al., 2011). ...
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Background:
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Objectives
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Citations
... The identification of innovative research contributions for the provision of effective and efficient health care systems is an important and challenging task [39][40][41][42][43][44][45][46][47][48]. Various studies are conducted in bioinformatics to improve the prioritization process and provide a solution for the scalability problems in health care services [38,49]. ...
Deep Learning (DL) algorithms enabled computational models consist of multiple processing layers that represent data with multiple levels of abstraction. In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer, abdomen, cardiac, and retina. Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification, prediction, evaluation.). A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study. A coherent taxonomy of research landscape from the literature has also been mapped, and the major aspects of this emerging field have been discussed and analyzed. A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area.
... Healthcare service concerns in telemedicine This section introduces an overview of healthcare service concerns in telemedicine. Further healthcare services are apparently required, particularly services supplied from outside of hospitals [170]. Chronic diseases (e.g., cardiovascular diseases, cancer and diabetes) are critical matters for healthcare services, given that these diseases embody one of the first causes of mortality, morbidity and disability [171]. ...
Remotely monitoring a patient’s condition is a serious issue and must be addressed. Remote health monitoring systems (RHMS) in telemedicine refers to resources, strategies, methods and installations that enable doctors or other medical professionals to work remotely to consult, diagnose and treat patients. The goal of RHMS is to provide timely medical services at remote areas through telecommunication technologies. Through major advancements in technology, particularly in wireless networking, cloud computing and data storage, RHMS is becoming a feasible aspect of modern medicine. RHMS for the prioritisation of patients with multiple chronic diseases (MCDs) plays an important role in sustainably providing high-quality healthcare services. Further investigations are required to highlight the limitations of the prioritisation of patients with MCDs over a telemedicine environment. This study introduces a comprehensive and inclusive review on the prioritisation of patients with MCDs in telemedicine applications. Furthermore, it presents the challenges and open issues regarding patient prioritisation in telemedicine. The findings of this study are as follows: (1) The limitations and problems of existing patients’ prioritisation with MCDs are presented and emphasised. (2) Based on the analysis of the academic literature, an accurate solution for remote prioritisation in a large scale of patients with MCDs was not presented. (3) There is an essential need to produce a new multiple-criteria decision-making theory to address the current problems in the prioritisation of patients with MCDs.
... Our team developed an innovative decision support tool called PREVENT to assist administrative nurses in determining which patients should be prioritized for the first homecare nursing visit (Topaz, 2014). Tools similar to PREVENT have been developed internationally. ...
... Each of the risk factors was assigned a specific score based on the logistic regression weights from the original study (Topaz, 2014). For example, if a wound was present (e.g., pressure ulcer, vascular ulcer), the patient received a score of 15 points. ...
... The scores for the factors were summed, generating a cumulative score ranging between 0 and 50, with any score >26 suggesting high priority for the first homecare visit. The cut-off of 26 points was established based on the PREVENT development study regression model (Topaz, 2014). ...
Patient admission to homecare is a complex process. Medicare policy requires that all patients receive a first home visit within 48 hr after the referral is received at the homecare agency. For unstable or high risk patients, waiting 48 hr to be seen by homecare nurses may not be safe. In this pilot study we tested an innovative clinical decision support tool (called PREVENT), designed to identify patients who may need to be prioritized for early homecare visits. The study was conducted in 2016 at a large homecare agency in the Northeastern US with 176 patients admitted to homecare from the hospital. In the control phase (n = 90 patients), we calculated the PREVENT priority score (indicative of high or medium/low first nursing visit priority) but did not share the score with the homecare intake nurses who influence visit scheduling. In the experimental phase, the PREVENT score was shared with the homecare intake nurses (n = 86 patients). During the experimental phase, high‐risk patients received their first homecare nursing visit about one‐half a day sooner than in the control phase (1.8 days vs. 2.2 days). Rehospitalizations from homecare decreased by 9.4% between the control (21.1%) and experimental phases (11.7%). This pilot study of patient prioritization showed promising results: high priority patients received their first homecare visit sooner and overall rehospitalization rates were lower.
... The number of healthcare services, especially those provided outside of hospitals, is projected to increase [249]. Chronic diseases (e.g. ...
... It also identifies how research can respond innovatively and contribute towards efficient and effective healthcare service provision systems [8, 9, 157-161, 176, 275, 276, 279]. Additional healthcare services, especially those provided outside of hospitals, are clearly needed [249]. Studies have been conducted to improve patient prioritisation for healthcare services in telemedicine and solve the scalability problem [225]. ...
The growing worldwide population has increased the need for technologies, computerised software algorithms and smart devices that can monitor and assist patients anytime and anywhere and thus enable them to lead independent lives. The real-time remote monitoring of patients is an important issue in telemedicine. In the provision of healthcare services, patient prioritisation poses a significant challenge because of the complex decision-making process it involves when patients are considered ‘big data’. To our knowledge, no study has highlighted the link between ‘big data’ characteristics and real-time remote healthcare monitoring in the patient prioritisation process, as well as the inherent challenges involved. Thus, we present comprehensive insights into the elements of big data characteristics according to the six ‘Vs’: volume, velocity, variety, veracity, value and variability. Each of these elements is presented and connected to a related part in the study of the connection between patient prioritisation and real-time remote healthcare monitoring systems. Then, we determine the weak points and recommend solutions as potential future work. This study makes the following contributions. (1) The link between big data characteristics and real-time remote healthcare monitoring in the patient prioritisation process is described. (2) The open issues and challenges for big data used in the patient prioritisation process are emphasised. (3) As a recommended solution, decision making using multiple criteria, such as vital signs and chief complaints, is utilised to prioritise the big data of patients with chronic diseases on the basis of the most urgent cases.
