The common issues in medical services within the country are mostly associated with doctors' referral process, data transfer between health institutions, and portals for patients to access their medical information. Specific issues arise, such as sharing health Records across institutes or hospitals, problems with misuse of data once shared, no security, etc. The Electronic Health Record (EHR) Framework on Blockchain addresses those issues, resulting from a collaboration of all stakeholders involved. This paper explores the likelihood of representing medical records to make sure data privacy, data accessibility, and data interoperability for the healthcare-specific scenario. Data privacy refers to affording protection to ensure data is available when needed and not used, imparted, accessed, altered, or deleted while being stored or retrieved, or transmitted. Data accessibility is the ability to access the data regardless of natural or artificial accidents, hardware, or others. Improving the accessibility of health data in the healthcare sector while ensuring privacy has been identified as a necessary capability that involves every individual and organization. Traditionally, healthcare interoperability has centered on sharing data between business institutions, such as various hospital systems. The emphasis has lately been on patient-driven information sharing, where the exchange of medical information is patient-mediated and patient-driven. We propose implementing a large-scale information infrastructure to access Smart Contracts sponsored by EHRs as information mediators. The decentralized nature of blockchain technology will aid in making the EHR accessible over a broader network. Using Blockchain will help make far-reaching changes in the healthcare industry by providing immutable, authentic, and accessible medical records, privacy, and faster payments.

A Blockchain based Electronic Medical Health Records Framework using Smart Contracts

In this paper a unique approach towards decision making process and better quality care in Pati healthcare applications is developed by using the Machine Learning (ML) concepts as an alternative mode for identifying the characteristics and patient satisfaction (PS) in the proposed healthcare system. Extracting the information from raw data using some algorithmic approach is known as Data mining. In this a ML approach is used in determining the patient satisfaction in health care sector. Applied regression models to determine the patient satisfaction and also correlation methods is identified as an important attribute to be considered in determining the better quality of health care application models. The data set is taken based on the opinion on three types of data such as a) patient opinion towards hospital care b) nurse opinion towards workplace and c) Administrative aspects of healthcare. Our findings revealed high accuracy in Regression (88%), that helps in concluding by considering the administrative and workplace attributes related to patient satisfaction. The results are validated using traditional statistical methods like binomial correlation and linear regression.

Reliable Machine Learning Approach to Predict Patient Satisfaction for Optimal Decision Making and Quality Health Care

In the recent past, the globalization of economies paved a way to exponential growth of technological expansions in almost all verticals of business and therefore accumulation of huge data became inevitable. The big data is a new paradigm and it has evoked to play a critical role in the advancement of healthcare monitoring system. There is a prospective and subsidy in developing and implementing big data solutions within this terrain. By processing a huge amount of structured, semi-structured and unstructured data from various sources like medical records, pharmaceutical industry, clinical data etc., and assimilate it can offer a persuasive outcome. Envisagative computing is a method used in data analytics process that includes gathering and managing data composed by wide range of biomedical experiments, reviews by expert medical teams, clinical trials and therapeutic trades, then to envisage these data for new pattern and trend evolution, subsequently integrate it with the existing big data tools. Mosquito borne fevers are the prime example of how these data, are an innate aspect of result it generates. In this paper Envisagative computing for mosquito borne disease have been discussed.

Envisagation and Analysis of Mosquito Borne Fevers: A Health Monitoring System by Envisagative Computing Using Big Data Analytics

Government funded projects require the use of bidding via tenders, this process is tedious and leads to corruption as there is very less transparency. This paper proposes a solution using Blockchain as a Service for easy and transparent administration of public projects which allows various stakeholders to scrutinize the entire process.

BlockchainAs a Service (BaaS) Framework for Government Funded Projects e-Tendering Process Administration and Quality Assurance using Smart Contracts

IOT adoption is significantly increasing across different industries in the recent decade and the security is the biggest concern for the enterprise and industries to safe guard the data which is emanating out of IOT sensors, Drones and aggregators. In this paper we discuss how blockchain's smart contracts will help to manage the security challenges of IOT sensors and devices. We provide the literature review and approaches on how we build Agro's drone specific smart contracts with different ledger and platform in section II and III. We will elaborate the high-level approach with conceptual framework in section V. we will share the deployment considerations and issues in section VI. In section VII we will present our significance and in section VIII we will share our recommendations and conclusion

Reliable Administration Framework of Drones and IoT Sensors in Agriculture Farmstead using Blockchain and Smart Contracts

This paper evaluates the different dimensions of Big Data in various fields of applications with the Volume, Variety and frequency of generations of huge data. Also this paper focuses on the areas where large volume of data is being used for the growth and progress of the organizations, in which the data received currently, must ensure that it attains the uniqueness which will be taken by the different stakeholders of the organizations in their respective environments.

Investigations on big data features research challenges and applications

WAC (Web based life, Analysis and Cloud) s the data system that produces a seamless information spreading quicker that is bigger than a Big data. The huge amount of data derived from this humongous volume of information, so called Big data, demands the advancement of a proficient and viable choice with emotionally ground-breaking supportive network schemes in the region of healthcare. The pool of data could be investigated in a manner to assist the framework that more likely comprehend its partner requirements by gathering, mining conclusions on each subject of premium. Preciously administration might be able to acknowledge the crucial decision making process where the new investigations would be accounted for different research avenues. There is a tire need for review every break down and assess research outcomes of crosswise over writing that encouraged us to distinguish existing patterns, potential headings of the future work within the space. This article is going to deal with detailed review of existing research works proposed in the field of opinion mining for healthcare sector.

G. Sabarmathi

Papers

Investigations on big data features research challenges and applications

Reliable Machine Learning Approach to Predict Patient Satisfaction for Optimal Decision Making and Quality Health Care

Envisagation and Analysis of Mosquito Borne Fevers: A Health Monitoring System by Envisagative Computing Using Big Data Analytics

Big Data Analytics Framework for Opinion Mining of Patient Health Care Experience