scholarly journals Big data governance of personal health information and challenges to contextual integrity

2018 ◽  
Vol 35 (1) ◽  
pp. 36-51 ◽  
Author(s):  
Jenifer Sunrise Winter ◽  
Elizabeth Davidson
Keyword(s):  
Big Data ◽  
Health Information ◽  
Personal Health Information ◽  
Personal Health ◽  
Data Governance ◽  
Contextual Integrity

scholarly journals Linking First Nations data to administrative health data within Manitoba

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Venkata Shravan Ramayanam ◽  
Leona Star
Keyword(s):  
First Nations ◽  
Health Information ◽  
Personal Health Information ◽  
Personal Health ◽  
Data Governance ◽  
Information Research ◽  
University Of Manitoba ◽  
Manitoba Health ◽  
First Time ◽  
Administrative Datasets

IntroductionFirst Nation peoples (FNs) were unable to track their own health care trends due to limitations in datasets. The key linked file enables FNs to identify themselves within administrative datasets and work with Crown governments to bring equity in all services and departments to support FNs understanding of wellness. Objectives and ApproachFirst Nations Health and Social Secretariat of Manitoba (FNHSSM) was established by 2013 resolution of Assembly of Manitoba Chiefs (AMC) and incorporated in 2014. FNHSSM leads and supports research according to FNs criteria approved by the Chiefs in Assembly. Information Sharing Agreements (ISA) have been developed with federal and provincial governments to mandate the processes for data linkage. The ISA allows Indian Status Register (ISR) data of Department of Indigenous Services Canada (DISC) to be transferred to FNHSSM to provide oversight, and link to Provincial Personal Health Information Numbers (PHINs) to create the de-identified, scrambled, and encrypted Key Linked file. ResultsPrevious linkages were done in early 2000s with FNs approval and oversight. The 2018 linkage is the first time that ISAs have been formally developed. ISA-1 is between FNHSSM and Manitoba Health Seniors and Active Living (MHSAL) to create Key Linked file. ISA-2 is between FNHSSM, MHSAL and Manitoba Centre for Health Policy (MCHP) at University of Manitoba, to create the FNs Research File. This research file can only be accessed with application to and approval by the MFNs Health Information Research Governance Committee. This key linked file allows FNHSSM to prepare community health profiles specifically and only for each FN, to respect FNs Data Governance under Chief and Council. A regional report on Manitoba FNs will be created for all MFNs, FNHSSM and MHSAL. Conclusion/ImplicationsLinking datasets helps to strengthen FNs data governance in re-building nations, recognizing FNs inherent right to self-determination. Linking files help to provide meaningful data to advocate for FNs rights and access to the resources and social determinants of health needed to achieve equity in Manitoba.

Governance of artificial intelligence and personal health information

2019 ◽  
Vol 21 (3) ◽  
pp. 280-290 ◽  
Author(s):  
Jenifer Sunrise Winter ◽  
Elizabeth Davidson
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Health Information ◽  
Health Data ◽  
Personal Health Information ◽  
System Level ◽  
Personal Health ◽  
Data Governance ◽  
Privacy Regulation ◽  
Content Type

Purpose This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning innovations in health care. Risks to privacy and justice/equity are discussed, along with potential solutions. Design/methodology/approach This conceptual paper highlights the scale and scope of PHI data consumed by deep learning algorithms and their opacity as novel challenges to health data governance. Findings This paper argues that these characteristics of machine learning will overwhelm existing data governance approaches such as privacy regulation and informed consent. Enhanced governance techniques and tools will be required to help preserve the autonomy and rights of individuals to control their PHI. Debate among all stakeholders and informed critique of how, and for whom, PHI-fueled health AI are developed and deployed are needed to channel these innovations in societally beneficial directions. Social implications Health data may be used to address pressing societal concerns, such as operational and system-level improvement, and innovations such as personalized medicine. This paper informs work seeking to harness these resources for societal good amidst many competing value claims and substantial risks for privacy and security. Originality/value This is the first paper focusing on health data governance in relation to AI/machine learning.

A Study on Personal Health Information De-identification Status for Big Data

2016 ◽  
Author(s):  
Young-Chul Chung ◽  
Ya-Ri Lee ◽  
Jung-Sook Kim ◽  
Ho-Kyun Park
Keyword(s):  
Big Data ◽  
Health Information ◽  
Personal Health Information ◽  
Personal Health

scholarly journals De-Identified Personal Health Care System Using Hadoop

2015 ◽  
Vol 5 (6) ◽  
pp. 1492 ◽  
Author(s):  
Dasari Madhavi ◽  
B.V. Ramana
Keyword(s):  
Health Care ◽  
Big Data ◽  
Health Information ◽  
Health Care System ◽  
Demographic Data ◽  
Data File ◽  
Personal Health Information ◽  
Map Reduce ◽  
Personal Health ◽  
Care System

Hadoop technology plays a vital role in improving the quality of healthcare by delivering right information to right people at right time and reduces its cost and time. Most properly health care functions like admission, discharge, and transfer patient data maintained in Computer based Patient Records (CPR), Personal Health Information (PHI), and Electronic Health Records (EHR). The use of medical Big Data is increasingly popular in health care services and clinical research. The biggest challenges in health care centers are the huge amount of data flows into the systems daily. Crunching this Big Data and de-identifying it in a traditional data mining tools had problems. Therefore to provide solution to the de-identifying personal health information, Map Reduce application uses jar files which contain a combination of MR code and PIG queries. This application also uses advanced mechanism of using UDF (User Data File) which is used to protect the health care dataset. De-identified personal health care system is using Map Reduce, Pig Queries which are needed to be executed on the health care dataset. The application input dataset that contains the information of patients and de-identifies their personal health care.  De-identification using Hadoop is also suitable for social and demographic data.

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