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Book part
Publication date: 30 September 2020

Shivinder Nijjer, Kumar Saurabh and Sahil Raj

The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness…

Abstract

The healthcare sector in India is witnessing phenomenal growth, such that by the year 2022, it will be a market worth trillions of INR. Increase in income levels, awareness regarding personal health, the occurrence of lifestyle diseases, better insurance policies, low-cost healthcare services, and the emergence of newer technologies like telemedicine are driving this sector to new heights. Abundant quantities of healthcare data are being accumulated each day, which is difficult to analyze using traditional statistical and analytical tools, calling for the application of Big Data Analytics in the healthcare sector. Through provision of evidence-based decision-making and actions across healthcare networks, Big Data Analytics equips the sector with the ability to analyze a wide variety of data. Big Data Analytics includes both predictive and descriptive analytics. At present, about half of the healthcare organizations have adopted an analytical approach to decision-making, while a quarter of these firms are experienced in its application. This implies the lack of understanding prevalent in healthcare sector toward the value and the managerial, economic, and strategic impact of Big Data Analytics. In this context, this chapter on “Predictive Analytics in Healthcare” discusses sources, areas of application, possible future areas, advantages and limitations of the application of predictive Big Data Analytics in healthcare.

Details

Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

Keywords

Content available
Book part
Publication date: 30 September 2020

Abstract

Details

Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

Article
Publication date: 1 March 2006

Michael A. Cucciare and William O'Donohue

Risk‐adjustment is designed to predict healthcare costs to align capitated payments with an individual's expected healthcare costs. This can have the consequence of reducing…

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Abstract

Purpose

Risk‐adjustment is designed to predict healthcare costs to align capitated payments with an individual's expected healthcare costs. This can have the consequence of reducing overpayments and incentives to under treat or reject high cost individuals. This paper seeks to review recent studies presenting risk‐adjustment models.

Design/methodology/approach

This paper presents a brief discussion of two commonly reported statistics used for evaluating the accuracy of risk adjustment models and concludes with recommendations for increasing the predictive accuracy and usefulness of risk‐adjustment models in the context of predicting future healthcare costs.

Findings

Over the last decade, many advances in risk‐adjustment methodology have been made. There has been a focus on the part of researchers to transition away from including only demographic data in their risk‐adjustment models to incorporating patient data that are more predictive of healthcare costs. This transition has resulted in more accurate risk‐adjustment models and models that can better identify high cost patients with chronic medical conditions.

Originality/value

The paper shows that the transition has resulted in more accurate risk‐adjustment models and models that can better identify high cost patients with chronic medical conditions.

Details

Journal of Health Organization and Management, vol. 20 no. 2
Type: Research Article
ISSN: 1477-7266

Keywords

Article
Publication date: 5 November 2021

Manimuthu Arunmozhi, Jinil Persis, V. Raja Sreedharan, Ayon Chakraborty, Tarik Zouadi and Hanane Khamlichi

As COVID-19 outbreak has created a global crisis, treating patients with minimum resources and traditional methods has become a hectic task. In this technological era, the rapid…

Abstract

Purpose

As COVID-19 outbreak has created a global crisis, treating patients with minimum resources and traditional methods has become a hectic task. In this technological era, the rapid growth of coronavirus has affected the countries in lightspeed manner. Therefore, the present study proposes a model to analyse the resource allocation for the COVID-19 pandemic from a pluralistic perspective.

Design/methodology/approach

The present study has combined data analytics with the K-mean clustering and probability queueing theory (PQT) and analysed the evolution of COVID-19 all over the world from the data obtained from public repositories. By using K-mean clustering, partitioning of patients’ records along with their status of hospitalization can be mapped and clustered. After K-mean analysis, cluster functions are trained and modelled along with eigen vectors and eigen functions.

Findings

After successful iterative training, the model is programmed using R functions and given as input to Bayesian filter for predictive model analysis. Through the proposed model, disposal rate; PPE (personal protective equipment) utilization and recycle rate for different countries were calculated.

Research limitations/implications

Using probabilistic queueing theory and clustering, the study was able to predict the resource allocation for patients. Also, the study has tried to model the failure quotient ratio upon unsuccessful delivery rate in crisis condition.

Practical implications

The study has gathered epidemiological and clinical data from various government websites and research laboratories. Using these data, the study has identified the COVID-19 impact in various countries. Further, effective decision-making for resource allocation in pluralistic setting has being evaluated for the practitioner's reference.

Originality/value

Further, the proposed model is a two-stage approach for vulnerability mapping in a pandemic situation in a healthcare setting for resource allocation and utilization.

Details

International Journal of Quality & Reliability Management, vol. 39 no. 9
Type: Research Article
ISSN: 0265-671X

Keywords

Article
Publication date: 8 May 2017

Yichuan Wang and Terry Anthony Byrd

Drawing on the resource-based theory and dynamic capability view, this paper aims to examine the mechanisms by which business analytics (BA) capabilities (i.e. the effective use…

4954

Abstract

Purpose

Drawing on the resource-based theory and dynamic capability view, this paper aims to examine the mechanisms by which business analytics (BA) capabilities (i.e. the effective use of data aggregation, analytics and data interpretation tools) in healthcare units indirectly influence decision-making effectiveness through the mediating role of knowledge absorptive capacity.

Design/methodology/approach

Using a survey method, this study collected data from the hospitals in Taiwan. Of the 155 responses received, three were incomplete, giving a 35.84 per cent response rate with 152 valid data points. Structural equation modeling was used to test the hypotheses.

Findings

This study conceptualizes, operationalizes and measures the BA capability as a multi-dimensional construct that is formed by capturing the functionalities of BA systems in health care, leading to the conclusion that healthcare units are likely to obtain valuable knowledge through using the data analysis and interpretation tools effectively. The effective use of data analysis and interpretation tools in healthcare units indirectly influence decision-making effectiveness, an impact that is mediated by absorptive capacity.

Originality/value

This study adds values to the literature by conceptualizing BA capabilities in healthcare and demonstrating how knowledge absorption matters when implementing BA to the decision-making process. The mediating role of absorptive capacity not only provides a mechanism by which BA can contribute to decision-making practices but also offers a new solution to the puzzle of the IT productivity paradox in healthcare settings.

Details

Journal of Knowledge Management, vol. 21 no. 3
Type: Research Article
ISSN: 1367-3270

Keywords

Article
Publication date: 24 March 2022

Mahmoud El Samad, Sam El Nemar, Georgia Sakka and Hani El-Chaarani

The purpose of this paper is to propose a new conceptual framework for big data analytics (BDA) in the healthcare sector for the European Mediterranean region. The objective of…

Abstract

Purpose

The purpose of this paper is to propose a new conceptual framework for big data analytics (BDA) in the healthcare sector for the European Mediterranean region. The objective of this new conceptual framework is to improve the health conditions in a dynamic region characterized by the appearance of new diseases.

Design/methodology/approach

This study presents a new conceptual framework that could be employed in the European Mediterranean healthcare sector. Practically, this study can enhance medical services, taking smart decisions based on accurate data for healthcare and, finally, reducing the medical treatment costs, thanks to data quality control.

Findings

This research proposes a new conceptual framework for BDA in the healthcare sector that could be integrated in the European Mediterranean region. This framework introduces the big data quality (BDQ) module to filter and clean data that are provided from different European data sources. The BDQ module acts in a loop mode where bad data are redirected to their data source (e.g. European Centre for Disease Prevention and Control, university hospitals) to be corrected to improve the overall data quality in the proposed framework. Finally, clean data are directed to the BDA to take quick efficient decisions involving all the concerned stakeholders.

Practical implications

This study proposes a new conceptual framework for executives in the healthcare sector to improve the decision-making process, decrease operational costs, enhance management performance and save human lives.

Originality/value

This study focused on big data management and BDQ in the European Mediterranean healthcare sector as a broadly considered fundamental condition for the quality of medical services and conditions.

Details

EuroMed Journal of Business, vol. 17 no. 3
Type: Research Article
ISSN: 1450-2194

Keywords

Book part
Publication date: 30 September 2020

K. Kalaiselvi and A. Thirumurthi Raja

Big Data is one of the most promising area where it can be applied to make a change is health care. Healthcare analytics have the potential to reduce the treatment costs, forecast…

Abstract

Big Data is one of the most promising area where it can be applied to make a change is health care. Healthcare analytics have the potential to reduce the treatment costs, forecast outbreaks of epidemics, avoid preventable diseases, and improve the quality of life. In general, the lifetime of human is increasing along world population, which poses new experiments to today’s treatment delivery methods. Health professionals are skillful of gathering enormous volumes of data and look for best approaches to use these numbers. Big data analytics has helped the healthcare area by providing personalized medicine and prescriptive analytics, medical risk interference and predictive analytics, computerized external and internal reporting of patient data, homogeneous medical terms and patient registries, and fragmented point solutions. The data generated level within healthcare systems is significant. This includes electronic health record data, imaging data, patient-generated data, etc. While widespread information in health care is now mostly electronic and fits under the big data as most is unstructured and difficult to use. The use of big data in health care has raised substantial ethical challenges ranging from risks for specific rights, privacy and autonomy, to transparency and trust.

Details

Big Data Analytics and Intelligence: A Perspective for Health Care
Type: Book
ISBN: 978-1-83909-099-8

Keywords

Article
Publication date: 19 November 2018

Claudia Affonso Silva Araujo, Peter Wanke and Marina Martins Siqueira

The purpose of this paper is to estimate the performance of Brazilian hospitals’ services and to examine contextual variables in the socioeconomic, demographic and institutional…

Abstract

Purpose

The purpose of this paper is to estimate the performance of Brazilian hospitals’ services and to examine contextual variables in the socioeconomic, demographic and institutional domains as predictors of the performance levels attained.

Design/methodology/approach

The paper applied a two-stage approach of the technique for order preference by similarity to the ideal solution (TOPSIS) in public hospitals in 92 Rio de Janeiro municipalities, covering the 2008–2013 period. First, TOPSIS is used to estimate the relative performance of hospitals in each municipality. Next, TOPSIS results are combined with neural networks in an effort to originate a performance model with predictive ability. Data refer to hospitals’ outpatient and inpatient services, based on frequent indicators adopted by the healthcare literature.

Findings

Despite a slight performance increase over the period, substantial room for improvement is observed. The most important performance predictors were related to the demographic and socioeconomic status (area in square feet and GDP per capita) and to the juridical nature and type of ownership of the healthcare facilities (number of federal and private hospitals).

Practical implications

The results provide managerial insights regarding the performance of public hospitals and opportunities for better resource allocation in the healthcare sector. The paper also considers the impact of external socioeconomic, demographic and institutional factors on hospitals’ performance, indicating the importance of integrative public health policies.

Originality/value

This study displays an innovative context for applying the two-stage TOPSIS technique, with similar efforts not having been identified in the healthcare literature.

Details

International Journal of Productivity and Performance Management, vol. 67 no. 9
Type: Research Article
ISSN: 1741-0401

Keywords

Article
Publication date: 29 June 2023

Sapna Tyagi

The relevance of analytics to the healthcare supply chain is increasing with emerging trends and technologies. This study examines how analytics are used in the healthcare supply…

Abstract

Purpose

The relevance of analytics to the healthcare supply chain is increasing with emerging trends and technologies. This study examines how analytics are used in the healthcare supply chain in the “new normal” environment.

Design/methodology/approach

A systematic literature review was conducted by extracting research articles related to analytics in the healthcare supply chain from Scopus. The author used a hybrid review approach that combines bibliometric analysis with a theories, contexts, characteristics, and methodology (TCCM) framework-based review to identify various themes of analytics in the healthcare supply chain.

Findings

The hybrid review strategy yielded results that focus on prevalent theories, contexts, characteristics, and methodologies in the field of healthcare supply chain analytics. Future research should explore the resulting antecedents, decision-making processes and outcomes (ADO) framework, which integrates technological, economic, and societal concerns and outcomes. Future research agendas could also seek to apply theoretical perspectives in the field of analytics in the healthcare supply chain.

Originality/value

The result of a review of selected studies adds to the current body of work and contributes to the growth of research in the field of analytics in the healthcare supply chain. It also provides new directions to healthcare supply chain managers and academic scholars.

Details

Benchmarking: An International Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1463-5771

Keywords

Article
Publication date: 6 May 2021

Rajesh Kumar Singh, Saurabh Agrawal, Abhishek Sahu and Yigit Kazancoglu

The proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of…

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Abstract

Purpose

The proposed article is aimed at exploring the opportunities, challenges and possible outcomes of incorporating big data analytics (BDA) into health-care sector. The purpose of this study is to find the research gaps in the literature and to investigate the scope of incorporating new strategies in the health-care sector for increasing the efficiency of the system.

Design/methodology/approach

Fora state-of-the-art literature review, a systematic literature review has been carried out to find out research gaps in the field of healthcare using big data (BD) applications. A detailed research methodology including material collection, descriptive analysis and categorization is utilized to carry out the literature review.

Findings

BD analysis is rapidly being adopted in health-care sector for utilizing precious information available in terms of BD. However, it puts forth certain challenges that need to be focused upon. The article identifies and explains the challenges thoroughly.

Research limitations/implications

The proposed study will provide useful guidance to the health-care sector professionals for managing health-care system. It will help academicians and physicians for evaluating, improving and benchmarking the health-care strategies through BDA in the health-care sector. One of the limitations of the study is that it is based on literature review and more in-depth studies may be carried out for the generalization of results.

Originality/value

There are certain effective tools available in the market today that are currently being used by both small and large businesses and corporations. One of them is BD, which may be very useful for health-care sector. A comprehensive literature review is carried out for research papers published between 1974 and 2021.

Details

The TQM Journal, vol. 35 no. 1
Type: Research Article
ISSN: 1754-2731

Keywords

1 – 10 of over 5000