Oncology informatics : using health information technology to improve processes and outcomes in cancer

417 Pages·2016·26.41 MB·English

by  Ahern

ONCOLOGY INFORMATICS
ONCOLOGY
INFORMATICS
Using Health Information Technology
to Improve Processes and Outcomes
in Cancer
Edited by
Bradford W. Hesse, PhD
Health Communication and Informatics Research Branch,
Division of Cancer Control and Population Sciences,
National Cancer Institute, Rockville, MD, United States
David K. Ahern, PhD
Health Communication and Informatics Research Branch, Healthcare Delivery Research Program,
National Cancer Institute, Rockville, MD, United States; Program in Behavioral Informatics and eHealth,
Department of Psychiatry, Brigham & Women’s Hospital, Boston, MA, United States;
Harvard Medical School, Boston, MA, United States
Ellen Beckjord, PhD MPH
Population Health Program Design and Engagement Optimization, UPMC Health Plan,
Pittsburgh, PA, United States
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Dedication
This book is dedicated to two courageous women—Dr Jessie Gruman and Dr Abagail Prestin—whose work, which
they completed during their own cancer journeys, survives them and will inspire and empower all who take part
in the fight against cancer.
List of Contributors
Ekland Abdiwahab MPH Healthcare Delivery Research Lori L. DuBenske PhD Department of Psychiatry, School
Program, Division of Cancer Control and Population Science, of Medicine and Public Health, University of Wisconsin–
National Cancer Institute, Rockville, MD, United States Madison, Madison, WI, United States
Amy P. Abernethy MD, PhD Flatiron Health, New York, Gabriel Eichler PhD PatientsLikeMe, Inc., Cambridge, MA,
NY, United States United States
David K. Ahern PhD Health Communication and Euginia Eng PhD, MPH Gillings School of Global Public
Informatics Research Branch, Healthcare Delivery Research Health, University of North Carolina Chapel Hill, Chapel
Program, National Cancer Institute, Rockville, MD, United Hill, NC, United States
States; Program in Behavioral Informatics and eHealth, Michael C. Fiore MD, MPH, MBA Center for Tobacco
Department of Psychiatry, Brigham & Women’s Hospital, Research and Intervention, University of Wisconsin
Boston, MA, United States; Harvard Medical School, School of Medicine and Public Health, Madison, WI,
Boston, MA, United States United States
Neeraj K. Arora PhD Improving Healthcare Systems, Gilles Frydman BSc Smart Patients, Mountain View, CA,
Patient-Centered Outcomes Research Institute, United States; Association of Cancer Online Resources,
Washington, DC, United States Mountain View, CA, United States
Ellen Beckjord PhD MPH Population Health Program David H. Gustafson PhD Center for Health Enhancement
Design and Engagement Optimization, UPMC Health Systems Studies, University of Wisconsin–Madison,
Plan, Pittsburgh, PA, United States Madison, WI, United States
Janice F. Bell PhD, MPH, MN Betty Irene Moore School of Bradford W. Hesse PhD Health Communication and
Nursing, University of California Davis, Sacramento, CA, Informatics Research Branch, Division of Cancer Control
United States and Population Sciences, National Cancer Institute,
Timothy W. Bickmore PhD College of Computer and Rockville, MD, United States
Information Science, Northeastern University, Boston, MA, Bradford Hirsch MD, MBA Flatiron Health, New York,
United States NY, United States
Ilana M. Braun MD Dana-Farber Cancer Institute, Harvard Todd S. Horowitz PhD Behavioral Research Program,
Medical School, Boston, MA, United States Division of Cancer Control and Population Sciences,
Emil Chiauzzi PhD PatientsLikeMe, Inc., Cambridge, MA, National Cancer Institute, Rockville, MD, United States
United States Devlon Jackson PhD, MPH Health Communication and
Wen-ying Sylvia Chou PhD, MPH Health Communication Informatics Research Branch, Division of Cancer Control
and Informatics Research Branch, Division of Cancer and Population Science, National Cancer Institute,
Control and Population Science, National Cancer Institute, Rockville, MD, United States
Rockville, MD, United States Nora Jones MS The Partnership Project, Greensboro, NC,
CommunityRx University of Chicago Medicine, University United States
of Chicago, Chicago, IL, United States Warren Kibbe PhD Center for Biomedical Informatics
Mary E. Cooley PhD, RN, FAAN Phyllis F. Cantor Center, and Information Technology, National Cancer Institute,
Research in Nursing and Patient Care, Dana-Farber Cancer Rockville, MD, United States
Institute, Boston, MA, United States; College of Nursing Katherine K. Kim PhD, MPH, MBA Betty Irene Moore
and Exercise Health Science, University of Massachusetts- School of Nursing, University of California Davis,
Boston, Boston, MA, United States Sacramento, CA, United States
Gloria Coronado PhD Kaiser Permanente Center for William Klein PhD Behavioral Research Program, Division
Health Research, Portland, OR, United States of Cancer Control and Population Sciences, National
Samuel Cykert MD NC AHEC Program, UNC School of Cancer Institute, Rockville, MD, United States
Medicine, Chapel Hill, NC, United States Gary L. Kreps PhD Center for Health and Risk
Heather Dacus DO, MPH New York State Department of Communication, Department of Communication, George
Health, Albany, NY, United States Mason University, Fairfax, VA, United States
xi
xii List of Contributors
Alex H. Krist MD, MPH Department of Family Medicine Ruth Rechis PhD Programs & Strategy, LIVESTRONG
and Population Health, Virginia Commonwealth Foundation, Austin, TX, United States
University, Richmond, VA, United States Sarah C. Reed MSW, MPH Betty Irene Moore School of
Melissa Leypoldt RN Division of Public Health, Nebraska Nursing, University of California Davis, Sacramento, CA,
Department of Health and Human Services, Lincoln, NE, United States
United States Ronald A. Rensink PhD Departments of Computer
Deborah K. Mayer PhD School of Nursing, University of Science and Psychology, University of British Columbia,
North Carolina Lineberger Comprehensive Cancer Center, Vancouver, BC, Canada
Chapel Hill, NC, United States William T. Riley PhD Office of Behavioral and Social
Marc McKenzie BS, MBA Retired Computer Industry Sciences Research, National Institutes of Health, Bethesda,
Senior Executive, Survivor of Cancer and Stem-Cell MD, United States
Transplant, Windsor, CO, United States Jennifer Schaal MD The Partnership Project, Greensboro,
Robert S. Miller MD, FACP, FASCO CancerLinQ, NC, United States
American Society of Clinical Oncology, Alexandria, VA, Richard L. Schilsky MD, FACP, FASCO CancerLinQ,
United States American Society of Clinical Oncology, Alexandria, VA,
Glen D. Morgan PhD Tobacco Control Research Branch, United States
Behavioral Research Program, Division of Cancer Control Susan M. Scott MPH Surveillance Research Program,
and Population Sciences, National Cancer Institute, Division of Cancer Control and Population Sciences,
Rockville, MD, United States National Cancer Institute, Rockville, MD, United States
Donald E. Nease MD, MPH Department of Family Ben Shneiderman PhD Computer Science Department,
Medicine, University of Colorado, Denver, CO, United States Human-Computer Interaction Lab, UMIACS, University
April Oh PhD, MPH Health Communication and of Maryland, College Park, MD, United States
Informatics Research Branch, Division of Cancer Control Celette Skinner PhD Department of Clinical Sciences, UT
and Population Science, National Cancer Institute, Southwestern Medical Center, Dallas, TX, United States
Rockville, MD, United States
Stephen H. Taplin MD, MPH Healthcare Delivery
Eberechukwu Onukwugha MS, PhD Department of Research Program, Division of Cancer Control and
Pharmaceutical Health Services Research, School of Population Science, National Cancer Institute, Rockville,
Pharmacy, University of Maryland, Baltimore, MD, MD, United States
United States
G.J. van Londen MD, MS Division of Hematology-
Linda Overholser MD, MPH Department of General Oncology, Department of Medicine, University of
Internal Medicine, University of Colorado, Denver, CO, Pittsburgh, Pittsburgh, PA, United States
United States
Robin Whitney RN Betty Irene Moore School of Nursing,
Kevin Patrick MD, MS Department of Family Medicine University of California Davis, Sacramento, CA,
and Public Health, The Qualcomm Institute/Calit2, United States
University of California-San Diego, San Diego, CA,
Paul Wicks PhD PatientsLikeMe, Inc., Cambridge, MA,
United States
United States
Lynne T. Penberthy MD, MPH Surveillance Research
Deborah M. Winn PhD Office of the Director, Division of
Program, Division of Cancer Control and Population
Cancer Control and Population Sciences, National Cancer
Sciences, National Cancer Institute, Rockville, MD,
Institute, Rockville, MD, United States
United States
Roni Zeiger MD, MS Smart Patients, Mountain View, CA,
Susan K. Peterson PhD, MPH Department of Behavioral
United States; Santa Clara Valley Medical Center, Santa
Science, University of Texas MD Anderson Cancer Center,
Clara, CA, United States
Houston, TX, United States
Catherine Plaisant PhD Human-Computer Interaction
Lab, UMIACS, University of Maryland, College Park, MD,
United States
Foreword
The anatomy of a decision, decision making under A concurrent revolution is underway with the advent
stress and uncertainty by providers and patients, the of a truly patient-centered care model in the era of
visualization and interpretation of data to support clini- genomic medicine—based broadly on the subject mat-
cal reasoning, gleaning insight from big data, correlating ter of this book: applied oncology informatics. Clinical
with gene variants and the various “omics”—these are practice is on the cusp of an evolution from George
the overarching subjects of oncology informatics to be Engel’s biopsychosocial model [10] to one informed by
addressed in this text—Oncology Informatics. Many envi- personalized medicine [11–14]—characterized by deci-
sion a future state where health information technology sion making informed by patient personal history, fam-
(health IT) is supportive of much higher order clinical ily history, social/environmental factors, and clinical
reasoning than possible in today’s systems—where the data along with genomic data and patient preferences.
interaction between the computer, providers, and their To achieve this vision, clinical decision making will need
patients will be intuitive and integrated seamlessly in the to be patient-centered in new ways, bringing the best
clinical workflow, where increasing data enhances under- evidence at the genetic level to bear on many clinical
standing and insight, rather than confounding clinicians scenarios [15–18], and an understanding of patient pref-
and their patients. This future health IT will deemphasize erences for genetic information [19–22]. The relevance to
the technology itself, and restore the focus appropriately oncology practice and care is clear. Personalized genetic
on patients, evidence-based clinical goals, and outcomes. medicine [23] is expected to generate data that will out-
This evolution toward a more informed future state strip the information and knowledge processing capa-
of clinical decision making is perhaps most especially bilities of most practitioners, and many clinicians feel
relevant in oncology. Studies of health IT have dem- overwhelmed by this impending tsunami of additional
onstrated its ability to reduce medication errors [1], knowledge they must master [21,24,25]. One-on-one
improve patient safety [2], improve the quality of care genetic counseling will not be available to all: primary
[3], and lower costs [4–6]. Health IT may cause unin- care and specialist practitioners alike will need to man-
tended adverse consequences, however, and negatively age their patients with basic genomic test interpretation
impact clinical processes and outcomes [7]. The over- and guidance at the point of care [26–28].
arching critical challenge for the successful design and The practicing clinician may be supported with
effective use of health IT is to fundamentally reorient health information technologies, however, which enable
the current model of use [8]: from one where the user a personalized approach especially to clinical genetic
of health IT is obliged to support health IT—to labo- medicine [29–33]. Key to personalized medicine will be
riously enter, search, retrieve, and manually interpret tools to support “prospective” medicine [34,35]—health
patient data, to execute and manage transactions in the risk assessments, acquisition of a detailed family his-
system—to one where health IT supports the user. In tory, genomic information, and clinical decision support
the current model, the user must interact with a system (CDS) in an electronic medical record (EMR) [36,37]. CDS
that has a fundamentally different (machine) model of has been shown to impact physician behavior [38,39],
the patient’s data, organized for transactions and bill- diagnostic test ordering and other care processes [40,41],
ing, with little or no intrinsic model of the care process, the costs of care [6,42–45], and clinical outcomes [46–49].
and desired clinical goals. Rather, health IT should truly While there is great promise with health IT and CDS, it
support its users, informing the clinician’s and patient’s is not without potential peril: health IT poorly designed
mental models of disease, wellness, and care delivery, or implemented, or misused, may generate unintended
as well as their shared care goals and objectives and the consequences [50–52], and new types of medical error
processes they engage in together to pursue these goals [53].
and objectives [9]. Unless health IT systems are adapted Why is this important now? The goal of oncology
to provide such advanced patient-centered cognitive informatics may be fundamentally to bring genomic-, and
support capabilities, the effective and transformative patient preference-based, personalized medicine CDS to
use of health IT will remain a vision rather than a reality. any provider receiving genetic sequencing test results at
xiii
xiv Foreword
the point of care in EMRs, and eventually to the patient Assessment of patient preferences for genetic test-
via a personal health record. Whole genome sequencing ing has found that patients are more likely to wish to
will soon be available to almost any patient and their obtain incidental genetic results than expected [56–58],
physician, and with it will come enormous potential for but that concerns exist about quality of the informa-
return of incidental genetic findings [54]. The molecular tion provided, misunderstanding the significance of
laboratory will make choices of which incidental findings information disclosed, maintaining the confidentiality
to analyze and report to the physician, and the physician of genetic information [59,60], and restricting access to
in turn, will make choices as to which of these to return results by insurers [61]. Prior research suggests that four
to the patient. The choices of which reported variants to key themes exist among patients, with several impor-
return to each individual patient will require contextu- tant subthemes [61], including understanding source of
alization by the physician and preference setting by the information, experiences with conventional prescribing,
patient [55]. However, scalable solutions for dealing with pharmacogenomic-based testing issues (access, discom-
incidental information by both clinicians and patients fort, test reliability and validity, costs, etc.), and several
have not yet been addressed. As described in this text, issues surrounding targeted therapeutics (effectiveness,
the field of oncology informatics will leverage large-scale adverse effects, increase treatment options, prevention,
whole genome sequencing and is developing scalable preferences for tailored therapy, costs, quality of life,
CDS tools that can support the era of genomic medicine. access, and compliance or adherence). The multidi-
Oncology informatics will address four related ques- mensionality of the preference problem for genetic test
tions (originally conceived by PCORI) of particular rel- information [62] necessitates consideration of another
evance to the cancer patient: important dimension to the traditional doctor–patient
relationship [63] around clinical significance, and
“Given my personal characteristics, conditions,
patient-preferences: that of communicability [62]. The
and preferences, what should I expect will happen
communicability dimension addresses issues around
to me?”
health literacy, comprehensible message, appropriate-
Oncology informatics will provide the caregiver and
ness of message for participant, and clarity of message.
patient insight on the current knowledge base with
Even traditional notions of “clinical utility” may be
respect to gene sequencing and personalized medicine,
challenged in communicating about genetic test results
tailored to the patient’s own expressed preferences. If
[64–66]. Traditional notions of clinical utility may need
patients choose to know, oncology informatics can help
to be broadened to consider ethical, legal, and social
inform them of their own unique expected outcomes given
implications [67,68].
the best evidence to date.
While the scientific and applied foundations of
“What are my options and what are the benefits
oncology and informatics are advancing in the United
and harms of those options?”
States, we are in the midst of a profound sea change in
Oncology informatics will help the patient and
how clinical care is delivered, measured, and rewarded.
provider understand unique diagnostic and therapeutic
Health IT will both enable a better understanding and
options given the patient’s incidental genomic findings
assessment of clinical processes and outcomes, and
and preferences for return of genomic information.
should serve as the foundation with which clinicians
Patient’s will be able to make more informed choices
and health systems can accept more financial risk or
about knowing, or not knowing specific genetic test
stake in the value of the care being provided [69,70].
results, and make more informed decisions when sequence
This may in fact be the most important goal of oncology
data are known about specific treatment options.
informatics, and all of clinical medicine—to advance the
“What can I do to improve the outcomes that are
state-of-the-art such that we fundamentally transform
most important to me?”
care to achieve the Triple Aim of better patient experi-
Oncology informatics will help patients and providers
ence, improved population health, and reduced cost [71].
understand better the patient’s unique profile, and tailor
both care plans, and identify patient behaviors that
may mitigate risk, or improve health outcomes. Better
understanding of the options is the first step to patient
activation and engagement. Blackford Middleton MD, MPH, MSc,
“How can the health care system improve my FACP, FACMI, FHIMSS
chances of achieving the outcomes I prefer?” Chairman, American Medical Informatics
Oncology informatics helps the patient and provider, Association, Past-Chair, Healthcare Information
and care team, act in concert with respect to a shared Management and Systems Society, Past-Chair,
understanding of patient preferences for personalized Computer-based Patient Record Institute, Harvard
genomic information in designing and pursuing an T.H. Chan School of Public Health, Stanford Clinical
individually tailored care plan. Excellence Research Center (adjunct)
Foreword xv
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