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
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
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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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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.
- Prelims
- Chapter 1: Making the Case
- Chapter 2: Privacy and Learning Health Systems
- Chapter 3: Shaping the Continuum of Care Through Public Policy and Data
- Chapter 4: Public Data Sources: Cleaning and Wrangling
- Chapter 5: Public Data Sources: Sizing the Palliative Population
- Chapter 6: Private Data Sources, Data Privacy and Data Simulations for Palliative LHS
- Chapter 7: Synthea Descriptive Analysis
- Chapter 8: Palliative LHS Analysis
- Chapter 9: Data Repository Design for Public Data Analysis
- Chapter 10: Palliative LHS Development and API to Ensure Data Privacy
- Chapter 11: Learning Health Systems Ethics Review
- Index