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Prelims

Data Ethics and Digital Privacy in Learning Health Systems for Palliative Medicine

ISBN: 978-1-80262-310-9, eISBN: 978-1-80262-309-3

ISSN: 2050-2060

Publication date: 15 November 2023

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Citation

(2023), "Prelims", Miori, V.M., Miori, D.J., Burton, F. and Cardamone, C.G. (Ed.) Data Ethics and Digital Privacy in Learning Health Systems for Palliative Medicine (Studies in Media and Communications, Vol. 23), Emerald Publishing Limited, Leeds, pp. i-xiv. https://doi.org/10.1108/S2050-206020230000023012

Publisher

:

Emerald Publishing Limited

Copyright © 2024 Virginia M. Miori, Daniel J. Miori, Flavia Burton and Catherine G. Cardamone

Half Title Page

Data Ethics and Digital Privacy in Learning Health Systems for Palliative Medicine

Series Page

STUDIES IN MEDIA AND COMMUNICATIONS

Series Editors: Laura Robinson, Shelia R. Cotten and Jeremy Schulz

Volumes 8–10: Laura Robinson and Shelia R. Cotten

Volume 11 Onwards: Laura Robinson, Shelia R. Cotten and Jeremy Schulz

Recent Volumes:

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Volume 14: Social Movements and Media – Edited by Jennifer Earl and Deana A. Rohlinger
Volume 15: e-Health: Current Evidence, Promises, Perils and Future Directions – Edited by Timothy M. Hale, Wen-Ying Sylvia Chou and Shelia R. Cotten; Assistant Editor: Aneka Khilnani
Volume 16: Media and Power in International Contexts: Perspectives on Agency and Identity – Edited by Apryl Williams & Laura Robinson; Guest Editor: Ruth Tsuria; Associate Editor: Aneka Khilnani
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Editorial Board

  • Rebecca Adams

    University of North Carolina Greensboro

  • Ron Anderson

    University of Minnesota

  • Denise Anthony

    University of Michigan

  • Alejandro Artopoulos

    University of San Andrés

  • Jason Beech

    University of San Andrés

  • Grant Blank

    University of Oxford

  • Geoffrey C. Bowker

    University of California, Irvine

  • Casey Brienza

    Media Sociology Preconference

  • Jonathan Bright

    University of Oxford

  • Manuel Castells

    University of Southern California

  • Mary Chayko

    Rutgers University

  • Wenhong Chen

    University of Texas at Austin

  • Lynn Schofield

    Clark University of Denver

  • Jenny L. Davis

    Australian National University

  • Hopeton S. Dunn

    University of the West Indies

  • Jennifer Earl

    University of Arizona

  • Joshua Gamson

    University of San Francisco

  • Hernan Galperin

    University of Southern California

  • Blanca Gordo

    International Computer Science Institute

  • Tim Hale

    University of Illinois at Urbana-Champaign

  • David Halle

    University of California, Los Angeles

  • Caroline Haythornthwaite

    Syracuse University

  • Anne Holohan

    Trinity College

  • Heather Horst

    University of Sydney

  • Gabe Ignatow

    University of North Texas

  • Samantha Nogueira

    Joyce Saint Mary’s College of California

  • Vikki Katz

    Rutgers University

  • Nalini Kotamraju

    Salesforce

  • Antonio C. La Pastina

    Texas A&M University

  • Robert LaRose

    Michigan State University

  • Sayonara Leal

    University of Brasilia

  • Brian Loader

    University of York

  • Monica Martinez

    University of Sorocaba

  • Noah McClain

    Illinois Institute of Technology

  • Gustavo Mesch

    University of Haifa

  • Sonia Virgínia Moreira

    Rio de Janeiro State University

  • Gina Neff

    University of Oxford

  • Christena Nippert-Eng

    Indiana University

  • Hiroshi Ono

    Hitotsubashi University

  • C. J. Pascoe

    University of Oregon

  • Trevor Pinch

    Cornell University

  • Anabel Quan-Haase

    University of Western Ontario

  • Kelly Quinn

    University of Illinois at Chicago

  • Violaine Roussel

    University of Paris

  • Saskia Sassen

    Columbia University

  • Sara Schoonmaker

    University of Redlands

  • Markus S. Schulz

    International Sociological Association

  • Joseph D. Straubhaar

    University of Texas at Austin

  • Mike Stern

    Michigan State University

  • Simone Tosoni

    Catholic University of Milan

  • Zeynep Tufekci

    University of North Carolina, Chapel Hill

  • Eduardo Villanueva

    Pontifical Catholic University of Peru

  • Keith Warner

    Santa Clara University

  • Barry Wellman

    Ryerson University

  • Jim Witte

    George Mason University

  • Simeon Yates

    University of Liverpool

Title Page

Studies in Media and Communications - Volume 23

DATA ETHICS AND DIGITAL PRIVACY IN LEARNING HEALTH SYSTEMS FOR PALLIATIVE MEDICINE

Edited by

Virginia M. Miori

Saint Joseph's University, USA

Daniel J. Miori

Erie County Medical Center, USA

Flavia Burton

Saint Joseph's University, USA

And

Catherine G. Cardamone

Eigen X, USA

United Kingdom – North America – Japan – India – Malaysia – China

Copyright Page

Emerald Publishing Limited

Emerald Publishing, Floor 5, Northspring, 21-23 Wellington Street, Leeds LS1 4DL.

First edition 2024

Editorial Matter and Selection & 2024 Virginia M. Miori, Daniel J. Miori, Flavia Burton and Catherine G. Cardamone.

Individual chapters & 2024 The authors.

Published under exclusive licence by Emerald Publishing Limited.

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No part of this book may be reproduced, stored in a retrieval system, transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without either the prior written permission of the publisher or a licence permitting restricted copying issued in the UK by The Copyright Licensing Agency and in the USA by The Copyright Clearance Center. Any opinions expressed in the chapters are those of the authors. Whilst Emerald makes every effort to ensure the quality and accuracy of its content, Emerald makes no representation implied or otherwise, as to the chapters’ suitability and application and disclaims any warranties, express or implied, to their use.

British Library Cataloguing in Publication Data

A catalogue record for this book is available from the British Library

ISBN: 978-1-80262-310-9 (Print)

ISBN: 978-1-80262-309-3 (Online)

ISBN: 978-1-80262-311-6 (Epub)

ISSN: 2050-2060 (Series)

Contents

List of Figures and Tables ix
About the Authors xiii
Acknowledgments xiv
Chapter 1: Making the Case
Daniel J. Miori 1
Chapter 2: Privacy and Learning Health Systems
Daniel J. Miori 9
Chapter 3: Shaping the Continuum of Care Through Public Policy and Data
Thomas R. Martin 17
Chapter 4: Public Data Sources: Cleaning and Wrangling
Virginia M. Miori 27
Chapter 5: Public Data Sources: Sizing the Palliative Population
Virginia M. Miori 61
Chapter 6: Private Data Sources, Data Privacy and Data Simulations for Palliative LHS
Virginia M. Miori 79
Chapter 7: Synthea Descriptive Analysis
Virginia M. Miori 91
Chapter 8: Palliative LHS Analysis
Virginia M. Miori 111
Chapter 9: Data Repository Design for Public Data Analysis
Brian W. Segulin 125
Chapter 10: Palliative LHS Development and API to Ensure Data Privacy
Brian W. Segulin 137
Chapter 11: Learning Health Systems Ethics Review
Daniel J. Miori 151
Index 157

List of Figures and Tables

Figures

Fig. 5.1. Percentage of Population That Speaks English Less Than Very Well 65
Fig. 5.2. Percentage of Population at Levels of Educational Attainment 66
Fig. 5.3. Household Income as a Percentage of Poverty Level 67
Fig. 5.4. Percentage of Population by Health Insurance Status 68
Fig. 5.5. Percentage of Population by Employment Status 69
Fig. 5.6. General Health Status by Age Group 71
Fig. 5.7. General Health Status by Hypertension Status 73
Fig. 5.8. General Health by Heart Issue Status 73
Fig. 5.9. General Health by Cancer Status 74
Fig. 5.10. General Health by Kidney Disease Status 74
Fig. 5.11. General Health by Diabetes Status 75
Fig. 5.12. General Health by COPD Status 75
Fig. 5.13. General Health by Metabolic Syndrome Status 76
Fig. 5.14. Palliative Care Potential by Race and General Health Status 76
Fig. 7.1. Histogram of the Count of Comorbidities in Synthetic Patients 94
Fig. 7.2. Histogram of the Transformed Count of Comorbidities in Palliative Patients 95
Fig. 8.1. Kaplan-Meier Curves for Palliative Population Groups 1–3 119
Fig. 8.2. Hazard Rates for Palliative Patients in Groups 1–3 119
Fig. 9.1. Logical Representation of DP Table 126
Fig. 9.2. Cross-Reference Database Tables 128
Fig. 9.3. SQL Statement to Populate FIELD_NAMES Table 130
Fig. 9.4. SQL Statement to Populate FIELD_CROSS_REFERENCE 131
Fig. 9.5. Python Template for Building URL 132
Fig. 9.6. ACS_VALUES Table Integration into the Database 133
Fig. 9.7. SQL Query Generated to Store Values in Database 134
Fig. 9.8. ACS Data View Definition 134
Fig. 9.9. ACS Data Query 135
Fig. 10.1. LHS Logical Architecture 139
Fig. 10.2. Palliative Consultation Landing Page 146
Fig. 10.3. Palliative Status Display 147
Fig. 10.4. Kaplan-Meier Curve Request Page 147
Fig. 10.5. Generated Kaplan-Meier Curves 149

Tables

Table 3.1. Overview of Common Electronic Palliative Care Coordination Systems 22
Table 4.1. Overall Homeless Population/States with Population in Excess of 10,000 29
Table 4.2. BRFSS Core Sections 2019 and 2020 33
Table 4.3. BRFSS Optional Modules Utilized in 2019 34
Table 4.4. BRFSS Optional Modules Utilized in 2020. 34
Table 4.5. Chronic Conditions Identified/Calculated with BRFSS. 35
Table A4.1. Data Profile 02: DP02. 37
Table A4.2. Data Profile 03: DP03. 44
Table A4.3. Data Profile 04: DP04. 50
Table A4.4. Data Profile 05: DP05. 56
Table 5.1. Patient Characteristics to Determine Quality and Availability of Palliative Care. 64
Table 5.2. HHS Published Poverty Levels. 67
Table 5.3. General Health by Age Group as % of Race. 72
Table 5.4. Size of Potential Palliative Population by Race. 77
Table 6.1. EHR Primary Characteristics. 81
Table 6.2. Synthea Output Formats. 83
Table 6.3. Levels for Education, Employment, and Income Variables. 85
Table 6.4. Groupings for Race, Sex, and Age Group. 85
Table 6.5. Racial Groupings. 86
Table 6.6. Age Group Mapping. 86
Table 6.7. Sample CDFs for Employment Status. 87
Table 7.1. Comorbidities to Predict Need for Palliative Care. 92
Table 7.2. Demographic Breakdown of All Patients (30 and Over). 93
Table 7.3. Demographic Breakdown All Palliative Patients. 96
Table 7.4. Demographic Breakdown Deceased Palliative Patients. 96
Table 7.5. Palliative Population by Sex. 97
Table 7.6. Palliative Population by Race. 97
Table 7.7. Palliative Population by Age Group. 97
Table 7.8. Educational Attainment Completion Percentages by Year. 98
Table 7.9. Education Percentages for Entire Palliative Population. 98
Table 7.10. Employment Percentages for Entire Palliative Population. 99
Table 7.11. Insurance Status Percentages for Entire Palliative Population. 100
Table 7.12. Income Brackets for Entire Palliative Population. 101
Table 7.13. Income as Percent of Poverty Level for Entire Palliative Population. 101
Table 7.9a. Incomplete Secondary Education by Sex-Race-Age Group. 103
Table 7.9b. Secondary Education by Sex-Race-Age Group. 104
Table 7.9c. Post-Secondary Education by Sex-Race-Age Group. 104
Table 7.10a. Unemployed Status by Sex-Race-Age Group. 105
Table 7.10b. Underemployed Status by Sex-Race-Age Group. 106
Table 7.10c. Well-Employed/Retired Status by Sex-Race-Age Group. 106
Table 7.11a. Uninsured by Sex-Race-Age Group. 107
Table 7.11b. Underinsured by Sex-Race-Age Group. 108
Table 7.11c. Insured by Sex-Race-Age Group. 108
Table 7.13a. Income 0%–150% of Poverty Level by Sex-Race-Age Group. 109
Table 7.13b. Income 150%–250% of Poverty Level by Sex-Race-Age Group. 110
Table 7.13c. Income >250% of Poverty Level by Sex-Race-Age Group. 110
Table 8.1. Mean Income Assignment for Percent of Poverty Level Calculation. 113
Table 8.2. Group Characteristics for Palliative Care. 114
Table 8.3. Income Level and Insurance Status Cross-Tabulation. 115
Table 8.4. Chi-Square Results for Employment by Income. 116
Table 8.5. Chi-Square Results Summary. 116
Table 8.6. Mean and Median Output from the Kaplan-Meier Curve Generation. 117
Table 8.7. Pairwise Tests on Kaplan-Meier Curves. 118
Table 8.8. Education Levels. 122
Table 8.9. Translation Levels. 122
Table 8.10. Employment Levels. 122
Table 8.11. Income Ranges. 122
Table 8.12. Income Levels. 123
Table 8.13. Insurance Levels. 123
Table 8.14. Age Groups. 123
Table 9.1. DP 2 Meta Data Subset. 127
Table 9.2. DATA_PROFILE Database Table Schema. 128
Table 9.3. FIELD_GROUPS Database Table Schema. 129
Table 9.4. FIELD_NAMES Database Table Schema. 129
Table 9.5. FIELD_CROSS_REFERENCE Database Table Schema. 129
Table 9.6. DATA_PROFILE Records. 130
Table 9.7. Selected Fields DP Field Groups. 130
Table 9.8. Cross-Reference Label Example. 131
Table 9.9. US Census ACS Profile URL Template Placeholders. 132
Table 9.10. ACS_VALUES Database Table Schema. 133
Table 9.11. FIPS_CODES Database Table Schema. 133
Table 9.12. CSV File Contents. 134
Table 9.13. ACS Data Values Extracted from Database Using Unique Labels. 135
Table 10.1. API Routines. 140
Table 10.2. Insurance Status. 141
Table 10.3. Educational Attainment. 141
Table 10.4. Employment Status. 141
Table 10.5. Translation. 142
Table 10.6. Comorbidity List. 142
Table 10.7. Sample Size Calculations. 148

About the Authors

Thomas R. Martin began his journey in healthcare working at the Cleveland Clinic Foundation. He has served as Project Lead for numerous IT implementations and mobile app developments and is a former director with Healthcare Information and Management Systems Society (HIMSS), a global, cause-based, not-for-profit organization focused on better health through information technology (IT). He has lived and worked overseas in Australia, traveling extensively in Asia Pacific for business. His research seeks to explore the intersection of health information technology and public policy with a focus on health administration using both quantitative and qualitative methods. Dr Martin is the author of articles, book chapters, and thought leadership pieces on the role of Connected Health technologies, telehealth, and health information exchange to support coordinated care.

Daniel J. Miori, MS PA-C, is a physician assistant and author. He works on the Palliative and Supportive Care team and is on the ethics committee at Erie County Medical Center in Buffalo, NY, USA. He began work in palliative medicine and bioethics in 2008 and has been active in education as well as having authored material on ethics in both scientific and popular press. He remains ever optimistic for the courage and good in all of us and for our ability to inhabit the moments of clarity and truth which define us, in whatever form those moments take.

Virginia M. Miori, Ph.D., has over 21 years of teaching experience and over 14 years of industry experience in developing and implementing statistical and operations research models in the area of supply chain/logistics. She is active in research in the areas of healthcare informatics, healthcare operations, rehab center effectiveness, scheduling, simulation, supply chain, and predictive analytics. Dr Miori has published over 38 scholarly articles, coauthored one text book and is actively engaged in interdisciplinary research, applying data science to institutional and industry problems.

Brian W. Segulin is a software developer focusing on process automation. He has over 35 years of experience designing and developing software solutions for integration, scheduling, and adaptive modeling. He has done work in the process industries including metals, glass, oil and gas, paper, and food and beverage. He specializes in integrating legacy systems with state-of-the-art control solutions, focusing on data security.

Acknowledgments

We, the authors, humbly acknowledge the great time and energy our team put into creating this book. Brian W. Segulin’s help in coding and data preparation was indispensable as was Thomas R. Martin’s work and expertise in health information systems. We are also grateful to Kyle Chalmers, Catherine G. Cardamone, and Gabriel Gil Olavarrieta for their hard work. Most especially, however, we wish to thank Flavia Burton for her critical eye and tireless efforts. We know she will go on to do great things.

In addition to the work of those who contributed, we also wish to thank the families of all those who spent their free time bringing this volume to publishable form. You are our foundation.

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