Leverage Healthcare Data Assets with Predictive Analytics

2017 ◽  
pp. 823-837
Author(s):  
Nilmini Wickramasinghe ◽  
Hoda Moghimi ◽  
Jonathan L. Schaffer
Keyword(s):  
Predictive Analytics ◽  
Healthcare Sector ◽  
Hospital Data ◽  
Healthcare Organizations ◽  
Data Set ◽  
Private Hospitals ◽  
Healthcare Data ◽  
Disparate Data ◽  
Tools And Techniques ◽  
Decision Efficiency

Multi-spectral data residing in disparate data bases represents a critical raw asset for today's healthcare organizations (). However, in order to gain maximum value from such data, it is essential to apply prudent technology solutions and tailored analytic techniques. The following chapter proposes how the application of bespoke predictive analytic tools and techniques can be designed and then applied to a hospital data warehouse, called the Hospital Casemix Protocol (HCP) Extended data set, in order to improve decision efficiency in the private healthcare sector in Australia. The main objective of this chapter is to present the developed conceptual model to demonstrate inputs, outputs, components, principles and services of predictive analytics for private hospitals.

Leverage Healthcare Data Assets with Predictive Analytics

2016 ◽  
pp. 237-251
Author(s):  
Nilmini Wickramasinghe ◽  
Hoda Moghimi ◽  
Jonathan L. Schaffer
Keyword(s):  
Predictive Analytics ◽  
Healthcare Sector ◽  
Hospital Data ◽  
Healthcare Organizations ◽  
Data Set ◽  
Private Hospitals ◽  
Healthcare Data ◽  
Disparate Data ◽  
Tools And Techniques ◽  
Decision Efficiency

Multi-spectral data residing in disparate data bases represents a critical raw asset for today's healthcare organizations (). However, in order to gain maximum value from such data, it is essential to apply prudent technology solutions and tailored analytic techniques. The following chapter proposes how the application of bespoke predictive analytic tools and techniques can be designed and then applied to a hospital data warehouse, called the Hospital Casemix Protocol (HCP) Extended data set, in order to improve decision efficiency in the private healthcare sector in Australia. The main objective of this chapter is to present the developed conceptual model to demonstrate inputs, outputs, components, principles and services of predictive analytics for private hospitals.

Survey of Cloud Computing in Healthcare Sector

2017 ◽  
Vol 7 (7) ◽  
pp. 337
Author(s):  
S. Karthiga Devi ◽  
B. Arputhamary
Keyword(s):  
Cloud Computing ◽  
Healthcare Providers ◽  
High Volume ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Cloud Infrastructure ◽  
Healthcare Organizations ◽  
Privacy And Security ◽  
Healthcare Data ◽  
Number Of Patients

Today the volume of healthcare data generated increased rapidly because of the number of patients in each hospital increasing.  These data are most important for decision making and delivering the best care for patients. Healthcare providers are now faced with collecting, managing, storing and securing huge amounts of sensitive protected health information. As a result, an increasing number of healthcare organizations are turning to cloud based services. Cloud computing offers a viable, secure alternative to premise based healthcare solutions. The infrastructure of Cloud is characterized by a high volume storage and a high throughput. The privacy and security are the two most important concerns in cloud-based healthcare services. Healthcare organization should have electronic medical records in order to use the cloud infrastructure. This paper surveys the challenges of cloud in healthcare and benefits of cloud techniques in health care industries.

Big Data and Analytics

2020 ◽  
pp. 47-65
Author(s):  
Sheik Abdullah A. ◽  
Priyadharshini P.
Keyword(s):  
Social Media ◽  
Big Data ◽  
Predictive Analytics ◽  
Data Warehousing ◽  
Big Data Analytics ◽  
Structured Data ◽  
Social Media Analytics ◽  
Data Set ◽  
Data Volume ◽  
Tools And Techniques

The term Big Data corresponds to a large dataset which is available in different forms of occurrence. In recent years, most of the organizations generate vast amounts of data in different forms which makes the context of volume, variety, velocity, and veracity. Big Data on the volume aspect is based on data set maintenance. The data volume goes to processing usual a database but cannot be handled by a traditional database. Big Data is stored among structured, unstructured, and semi-structured data. Big Data is used for programming, data warehousing, computational frameworks, quantitative aptitude and statistics, and business knowledge. Upon considering the analytics in the Big Data sector, predictive analytics and social media analytics are widely used for determining the pattern or trend which is about to happen. This chapter mainly deals with the tools and techniques that corresponds to big data analytics of various applications.

Predictive Analytics to Support Clinical Trials Get Healthier

2018 ◽  
pp. 222-239
Author(s):  
Ankit Lodha ◽  
Anvita Karara
Keyword(s):  
Clinical Trials ◽  
Predictive Analytics ◽  
Big Data Analytics ◽  
Sources Of Information ◽  
Healthcare Organizations ◽  
Data Set ◽  
Individual Trial ◽  
Utilization Data ◽  
The Individual ◽  
Clear Idea

The concept of clinical big data analytics is simply the joining of two or more previously disparate sources of information, structured in such a way that insights are prescribed from examination of the new expanded data set. The combination with Internet of Things (IoT), can provide multivariate data, if healthcare organizations build the infrastructure to accept it. Many providers are able to integrate financial and utilization data to create a portrait of organizational operations, but these sources do not give a clear idea of what patients do on their own time. Embracing the centrality of the IoT would relinquish the idea that provider is the only pillar around which healthcare revolves. This chapter provides deeper insights into the four major challenges: costly protocol amendments, increasing protocol complexity and investigator site burden. It also provides recommendations for streamlining clinical trials by following a two dimension approach-optimization at a program level (clinical development plan) as well as at the individual trial candidate level.

scholarly journals Integrated Prediction System for Chronic Disease Diagnosis to Ensure Better Healthcare

2020 ◽  
pp. 25-39
Author(s):  
Geetha Poornima K. ◽  
Krishna Prasad K.
Keyword(s):  
Health Care ◽  
Chronic Disease ◽  
Chronic Diseases ◽  
Predictive Analytics ◽  
Disease Diagnosis ◽  
Exercise Stress ◽  
Healthcare Sector ◽  
Human Beings ◽  
Healthcare Data ◽  
Using Data

Technology innovation has made life easy for human beings. Technology is being used everywhere. This also extends to the healthcare sector. The healthcare sector produces a large amount of data each minute. Because of privacy issues, much of the data generated is not used and is not publicly accessible. Healthcare data comes from diverse sources hence it will be always varied in nature. Keeping track of such data has become much easier these days. Predictive analysis in healthcare is an emerging technology that identifies the person with poor health where the risks of developing chronic conditions are more likely and provide better solutions in the field of healthcare. Statistical methods and algorithms can be used to predict the disease before the actual symptoms are revealed in humans. By using data analytics algorithms one can easily predict chronic diseases such as obesity, high/low Blood Pressure, diabetes, asthma, cardiopulmonary disorders. Because of an unhealthy diet, lack of proper exercise, stress, consumption of tobacco, alcohol, etc. chronic diseases are most common these days. If the symptoms of chronic diseases are detected in the early stages, there will be less risk of hospitalization by cost-effectively maintaining better health. Big data analysis and health care can be mixed to produce accurate results. The application of predictive analytics in healthcare is highlighted in this paper. It provides a broader analysis in the prevention of different chronic diseases by using predictive analytics. The paper also includes various issues that arise when handling health care data. For each chronic disease, diverse models, techniques, and algorithms are used for predicting and analyzing. The paper comprises a conceptual model that integrates the prediction of most common chronic diseases

Exploration of Healthcare Using Data Mining Techniques

2018 ◽  
pp. 243-259 ◽  
Author(s):  
Anindita Desarkar ◽  
Ajanta Das
Keyword(s):  
Data Mining ◽  
Patient Care ◽  
Knowledge Discovery ◽  
Healthcare Cost ◽  
Healthcare Sector ◽  
Huge Amount ◽  
Data Set ◽  
Data Mining Techniques ◽  
Using Data ◽  
Tools And Techniques

Huge amount of data is generated from Healthcare transactions where data are complex, voluminous and heterogeneous in nature. This large dataset can be used as an ideal store which can be analyzed for knowledge discovery as well as various future predictions. So, Data mining is becoming increasingly popular as it offers set of innovative tools and techniques to handle this kind of data set whereas traditional methods have limitations for that. In summary, providing the better patient care and reduction in healthcare cost are two major goals of application of data mining in healthcare. Initially, this chapter explores on the various types of eHealth data and its characteristics. Subsequently it explores various domains in healthcare sector and shows how data mining plays a major role in those domains. Finally, it describes few common data mining techniques and their applications in eHealth domain.

Description of multimorbidity clusters of admitted patients in medical departments of a general hospital

2021 ◽  
pp. postgradmedj-2020-139361
Author(s):  
María Matesanz-Fernández ◽  
Teresa Seoane-Pillado ◽  
Iria Iñiguez-Vázquez ◽  
Roi Suárez-Gil ◽  
Sonia Pértega-Díaz ◽  
...  
Keyword(s):  
General Hospital ◽  
Multiple Correspondence Analysis ◽  
Hypertensive Heart Disease ◽  
Chronic Kidney Failure ◽  
Hospital Environment ◽  
Heart Valve Disease ◽  
Malignant Neoplasms ◽  
Hospital Data ◽  
Data Set ◽  
And Gender

ObjectiveWe aim to identify patterns of disease clusters among inpatients of a general hospital and to describe the characteristics and evolution of each group.MethodsWe used two data sets from the CMBD (Conjunto mínimo básico de datos - Minimum Basic Hospital Data Set (MBDS)) of the Lucus Augusti Hospital (Spain), hospitalisations and patients, realising a retrospective cohort study among the 74 220 patients discharged from the Medic Area between 01 January 2000 and 31 December 2015. We created multimorbidity clusters using multiple correspondence analysis.ResultsWe identified five clusters for both gender and age. Cluster 1: alcoholic liver disease, alcoholic dependency syndrome, lung and digestive tract malignant neoplasms (age under 50 years). Cluster 2: large intestine, prostate, breast and other malignant neoplasms, lymphoma and myeloma (age over 70, mostly males). Cluster 3: malnutrition, Parkinson disease and other mobility disorders, dementia and other mental health conditions (age over 80 years and mostly women). Cluster 4: atrial fibrillation/flutter, cardiac failure, chronic kidney failure and heart valve disease (age between 70–80 and mostly women). Cluster 5: hypertension/hypertensive heart disease, type 2 diabetes mellitus, ischaemic cardiomyopathy, dyslipidaemia, obesity and sleep apnea, including mostly men (age range 60–80). We assessed significant differences among the clusters when gender, age, number of chronic pathologies, number of rehospitalisations and mortality during the hospitalisation were assessed (p<0001 in all cases).ConclusionsWe identify for the first time in a hospital environment five clusters of disease combinations among the inpatients. These clusters contain several high-incidence diseases related to both age and gender that express their own evolution and clinical characteristics over time.

scholarly journals Healthcare-related impact of gout in hospitalized patients in Spain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Diego Benavent ◽  
Diana Peiteado ◽  
María Ángeles Martinez-Huedo ◽  
María Hernandez-Hurtado ◽  
Alejandro Balsa ◽  
...  
Keyword(s):  
Heart Failure ◽  
Kidney Disease ◽  
Mortality Rates ◽  
Progressive Increase ◽  
Cerebrovascular Diseases ◽  
Hospitalized Patients ◽  
Data Set ◽  
Private Hospitals ◽  
Risk Of Mortality ◽  
General Mortality

AbstractTo analyze the epidemiology, clinical features and costs of hospitalized patients with gout during the last decade in Spain. Retrospective observational study based on data from the Minimum Basic Data Set (MBDS) from the Spanish National Health Service database. Patients ≥ 18 years with any gout diagnosis at discharge who had been admitted to public or private hospitals between 2005 and 2015 were included. Patients were divided in two periods: p1 (2005–2010) and p2 (2011–2015) to compare the number of hospitalizations, mean costs and mortality rates. Data from 192,037 patients with gout was analyzed. There was an increase in the number of hospitalized patients with gout (p < 0.001). The more frequent comorbidities were diabetes (27.6% of patients), kidney disease (26.6%) and heart failure (19.3%). Liver disease (OR 2.61), dementia (OR 2.13), cerebrovascular diseases (OR 1.57), heart failure (OR 1.41), and kidney disease (OR 1.34) were associated with a higher mortality risk. Women had a lower risk of mortality than men (OR 0.85). General mortality rates in these hospitalized patients progressively increased over the years (p < 0.001). In addition, costs gradually rose, presenting a significant increase in p2 even after adjusting for inflation (p = 0.001). A progressive increase in hospitalizations, mortality rates and cost in hospitalized patients with gout was observed. This harmful trend in a preventable illness highlights the need for change and the search for new healthcare strategies.

scholarly journals Effect of Work–Family Conflict, Psychological Job Demand, and Job Control on the Health Status of Nurses

2021 ◽  
Vol 18 (7) ◽  
pp. 3540
Author(s):  
Li-Chung Pien ◽  
Wan‐Ju Cheng ◽  
Kuei-Ru Chou ◽  
Li-Chiu Lin
Keyword(s):  
Health Status ◽  
Educational Level ◽  
Work Experience ◽  
Work Conditions ◽  
Job Control ◽  
Healthcare Sector ◽  
Hospital Data ◽  
Self Rated Health ◽  
Work Shifts ◽  
Work Family

Work–family conflicts (WFCs) are common in the healthcare sector and pose significant health risks to healthcare workers. This study examined the effect of WFCs on the health status and nurses’ leaving intentions in Taiwan. A self-administered questionnaire was used to survey 200 female nurses’ experiences of WFC from a regional hospital. Data on psychosocial work conditions, including work shifts, job control, psychological job demands, and workplace justice, were collected. Health conditions were measured using the Beck Depression Inventory-II and self-rated health. Leaving intentions were measured using a self-developed questionnaire. The participants’ average work experience was 6.79 (Standard Deviation (SD) = 5.26) years, their highest educational level was university, and work shifts were mostly night and rotating shifts. Approximately 75.5% of nurses perceived high levels of WFCs. Leaving intentions were correlated with WFCs (r = 0.350, p < 0.01) and psychological work demands (r = 0.377, p < 0.01). After adjusting for age, educational level, and work characteristics, high levels of WFCs were associated with poor self-rated health, and depression, but not associated with high leaving intentions. Nurses’ experiences of high levels of WFCs greatly affected their health status.

scholarly journals Investigation of seabed fishing impacts on benthic structure using multi-beam sonar, sidescan sonar, and video

2007 ◽  
Vol 64 (5) ◽  
pp. 1053-1065 ◽  
Author(s):  
Mashkoor A. Malik ◽  
Larry A. Mayer
Keyword(s):  
Gulf Of Maine ◽  
Fishing Ground ◽  
Sidescan Sonar ◽  
Data Sets ◽  
Frequency Filtering ◽  
Data Set ◽  
Detection And Identification ◽  
Spatial Frequency Filtering ◽  
Disparate Data ◽  
Fishing Impacts

Abstract Malik, M. A., and Mayer, L. A. 2007. Investigation of seabed fishing impacts on benthic structure using multi-beam sonar, sidescan sonar, and video. – ICES Journal of Marine Science, 64: 1053–1065. Long, linear furrows of lengths up to several kilometres were observed during a recent high-resolution, multi-beam bathymetry survey of Jeffreys Ledge, a prominent fishing ground in the Gulf of Maine located about 50 km from Portsmouth, NH, USA. These features, which have a relief of only a few centimetres, are presumed to be caused either directly by dredging gear used in the area for scallop and clam fisheries, or indirectly through the dragging of boulders by bottom gear. Extraction of these features with very small vertical expression from a noisy data set, including several instrumental artefacts, presented a number of challenges. To enhance the detection and identification of the features, data artefacts were identified and removed selectively using spatial frequency filtering. Verification of the presence of the features was carried out with repeated multi-beam bathymetry surveys and sidescan sonar surveys. Seabed marks that were clearly detected on multi-beam and sidescan sonar records were not discernible on a subsequent video survey. The inability to see the seabed marks with video may be related to their age. The fact that with time, the textural contrasts discernible by video imagery are lost has important ramifications for the appropriateness of methodologies for quantifying gear impact. The results imply that detailed investigations of seabed impact are best done with a suite of survey tools (multi-beam bathymetry, sidescan sonar, and video) and software to integrate the disparate data sets geographically.

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