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Incorporating Patient Goals, Patient-Centered Data, and Quality of Life
Information in Electronic Health RecordsApril 25, 2018
Patient Centered Outcomes ResearchEighth Annual Symposium
Mary K. Goldstein, MD
Professor of Medicine (Center for Primary Care & Outcomes Research)
Stanford University
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Disclosures and Disclaimers
This speaker has had research grant funding from several federal grant sources including NIH, HHS, and Department of Veterans Affairs.
There are no commercial disclosures.
Views expressed are those of the speaker and not necessarily those of funding agencies, Department of Veterans Affairs, or other affiliated organizations
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Growing Old is not for Sissies II: Portraits of Senior Athletes
Older adults also express their physicality
in many different ways… there are senior
athletes as shown here …
Clark, Etta. Growing Old is not for Sissies
II: Portraits of Senior Athletes.
Pomegranate Communications, 1995.
Image removed
After Ninety. Imogene Cunningham
From the beautiful book After Ninety by
Imogene Cunningham
(image removed)
Objectives for Session
At the end of this session, participants should be able to do
the following:
• To understand the challenges of applying clinical trial
evidence when patients have multiple chronic co-morbid
conditions
• To recognize that clinical detail can be incorporated into
clinical decision support systems so that
recommendations from these systems can be patient-
specific
• To describe a way to incorporate patient preferences, via
a patient portal, into decision analysis for a clinical
question
MCCs: Two-Thirds of Medicare beneficiaries with 2 or more chronic conditions; 14% with 6 or more.
Centers for Medicare and Medicaid Services. Chronic Conditions Among Medicare Beneficiaries, Chartbook. 2012th ed.
Baltimore, MD: 2012.
Multiple CoMorbidities
• Multiple co-occurring chronic conditions,
– Known as multiple comorbidities or multimorbidity
– See “Designing health care for the most common chronic condition: multimorbidity”. Tinetti ME, Fried TR, Boyd CM. JAMA 2012
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What happens when multiple single-disease CPGs are applied to a hypothetical 79 yowoman with 5 common chronic diseases (osteoporosis, osteoarthritis, DM, HTN, COPD). Only 1 CGP discussed relationship between life expectancy and time needed to treat to achieve benefit. 12 meds with 19 doses per day taken at 5 times a day, plus weekly med. Long list of patient tasks and physician tasks. Drug-disease interactions. Contradictory recommendations.
Co-prevalence of Chronic Diseases among Medicare Beneficiaries, 2012
Node = Prevalence
Link = Co-prevalence
HTN
DM
HL
OBSTR
ASM
OSP
FIB
AD
COPD
DEP
CKDHF
ARTH
IHD
Leung T et al see ref next slide
Frequency of disease-comorbidity pairs:
Number of CPGs for each condition (each node)
And number of disease-comorbidity pairs
(directed edge)
DM-IHD pair occurs 153 time in 43 DM CPGs;
IHD-DM pair occurs 323 times in 38 IHD CPGs
CPGs for concordant diseases mention each
other most often. Non-condordant diseases are
Mentioned least often. Alzheimer’s disease
And osteoporosis mentioned least
Prevalence of each chronic
disease among Medicare
beneficiaries and co-occurrence of
each condition with another
common chronic condition (edges)
Leung T Hawre J, Zulman DM, Domontier M, Owens
DK, Musen MA, Goldstein MK AMIA Jt Summits Transl
Sci Proc 2015
Automating Identification of MCCs in CPGs
Comorbidity Interrelatedness
Quality of Care for Patients with Multiple Chronic Conditions: the roleof comorbidity interrelatednessZulman DM, Asch SM, Martins SB, Kerr EA, Hoffman BB, Goldstein MK. J Gen Intern Med 29(3):529–37, 2014
Population
sampleStudy sample
Application by statistical inference
Applying Clinical Evidence for Individual Patient Care
StudyPopulation
sampleStudy sample
Application by statistical inference
Larger Group of Patients with the Condition
StudyPopulation
sampleStudy sample
Application by statistical inference
Distance Your Patient May Be from Studies that formthe Evidence Base
Patient 1
Patient 2 Patient 3
Guidelines for Patients with Multimorbidity: Separate Guidelines
Lipids
Hypertension
OtherDiabetes
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Guidelines for Patients with Multimorbidity: Linked Guidelines
Index Comorbidity:
Diabetes
Lipids
Hypertension Other
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Concordant versus discordant comorbid conditions
Lagu, et al. J Gen Intern Med 2008
Kerr, et al. J Gen Intern Med 2007
Making Recommendations for Clinical Care: Clinical Decision Support (CDS)
• People are different from each other
• For patient-centered care, we need to think about the extent to which clinical evidence applies to different individuals
• Patient-specific recommendations taking account of as many clinical characteristics of the patient as available– Including co-morbidities and multiple medications
– Informatics can assist with this…providing timely considerations but leaving final decision to shared decision-making between health professional and patient
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Encode clinical knowledge into computer-interpretable “knowledge base”
ATHENA-HTN Knowledge Base built with Protégé
– open-source Java tool for creation of customized knowledge-based applications
• Developed Stanford Biomedical Informatics Research (BMIR), Mark Musen, MD, PhD **
http://protege.stanford.edu/
- Knowledge representation expert Samson Tu, Senior Research Associate at Stanford, now emeritus
** Musen, M.A. The Protégé project: A look back and a look forward. AI Matters. Association
of Computing Machinery Specific Interest Group in Artificial Intelligence, 1(4), June 2015
Making Clinical Knowledge Computable
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Goldstein et al Proc AMIA Symp. 2000;300-4Shankar et al Medinfo. 2001;10:538-42
Goldstein et al Proc AMIA Symp. 2001;:214-8
Knowledge Acquisition Program: Protege
Protégé, developed at Stanford Biomedical Informatics
Research (BMIR)
• Free, open-source, java-based
• National resource for biomedical knowledge bases
• Supported by National Library of Medicine
• A core component of the National Center for Biomedical Ontology
(NCBO)
• Strong community of developers and users
• Academic, government, and corporate
The Protégé resource is supported by grant GM10331601 from the
National Institute of General Medical Sciences of the United States
National Institutes of Health (NIH)
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Started with hypertension
▪ Designed as a model with plan for extension to other clinical domains
▪ Built ATHENA-Hypertension (HTN)
▪ VA collaboration with Stanford University
Athena in Greek mythology is a symbol of good counsel, prudent restraint, and practical insight
Assessment and Treatment of Hypertension: Evidence-based Automation - Clinical Decision Support:
ATHENA-CDS
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Goldstein MK, Coleman RW, Tu SW, et al. Translating research into
practice. JAMIA 2004 Sep-Oct;11(5):368-76
Goldstein MK. Current Hypertension Reports. 2008.
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Use of Concept Model: Defining Guideline-Specific Concepts
ATHENA-CDS Architecture
Electronic Medical RecordSystem Patient Data
EHR
ProtegeHTN Guideline
Knowledge Base
GuidelineInterpreter
TreatmentRecommendation
SQL Server: Relational database
Data Mediator
Dashboard
Guideline Interpreter processes detailed clinical characteristics about individual patients with encoded/computable clinical knowledge from evidence-based sources to generate patient-specific recommendations, displayed in electronic health record, for consideration by health professional at point of care with patient.
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Clinical Decision Support for Primary Care Teams
PCMH/PACTPanel of Patients
VISN 21 Data WarehousePerformance on Quality Indicators
V21 Dashboard monitored by PACT nursing or pharmacystaff
CDS generates recommendations with nurse orPharmacist who manage many issues directly
Items requiring PCP input discussed with PCP when needed
Detailed Knowledge Base
• We have developed a highly detailed knowledge base for hypertension– primarily from evidence-based guidelines– More than 1,000 frames of knowledge– Extensive detail about other comorbid conditions, lab
values, and medications
• This type of system could be built for genomic or other types of clinical information
• It is customized to patients from the clinical perspective…– But what is still missing are the patients’ goals and
preferences
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Addressing the Challenges: Incorporate Patient Preferences
“Guiding Principles for Care of Older Adults with Multimorbidity: An Approach for Clinicians”
• Patient preferences• Interpreting the evidence
• Prognosis
• Clinical feasibility and optimizing therapies and care plans
– American Geriatrics Society Multimorbidity Project
Journal of the American Geriatrics Society (JAGS) 60:E1-E-25, 2012
Patient Preferences Expressed as
Utilities for Health Outcome States
• There are many forms of expression for
patient preferences
• A quantitative form: patient valuations of
health outcome states
– Expressed as utilities, for example, standard
gamble utility or time-tradeoff utility
• Can be used as quality-weighting factors
for health outcome states in decision
analyses 27
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Example Patient Decision Aid Integrated
with Patient Portal to Electronic Health
Record
Health e-Decisions: a prototype system
Decision model (atrial fibrillation)
Utility elicitation (FLAIR2)
Designed for patient portal
Knowledge-Based Method for Building Patient
Decision-analytic Tools. Das AK, Ahmed BA, Garten
Y, Robin J, Goldstein MK. AMIA Annu Symp Proc
175-179, 2006
Challenge:
Designing technologies that work
for older adults
“Quality of Life Assessment Software for Computer-
Inexperienced Older Adults: multimedia utility elicitation for
activities of daily living” Goldstein MK, Miller DE, Davies S,
Garber AM. Proc AMIA Symp 2002:295-9
This was a bigger challenge in 2002 when
we conducted this study, when many fewer
people were computer-users than now, but
special design considerations to meet the
needs of some older adults are still
appropriate
Quality of life assessment software for computer-inexperienced older adults: multimedia utility
elicitation for activities of daily living.
M. K. Goldstein, D. E. Miller, S. Davies, A. M. Garber
Proc AMIA Symp. 2002: : 295–299.
Simple Counts of ADL Dependencies Do Not Adequately Reflect Older Adults' Preferences
toward States of Functional Impairment
Tamara Sims, TH. Holmes, DM Bravata, AM Garber, LM Nelson, MK Goldstein
J Clin Epidemiol. J Clin Epidemiol. 2008 December; 61(12): 1261–1270.
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FLAIR Project
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Health eDecisions: Integrating Patient Preference using
FLAIR Preference Assessment into Decision Aid
Option for patient to engage in preference assessment through
patient portal then incorporate patient’s preferences into decision
model and show patient which choice has highest expected
value, given his/her preferences
Das et al Proc AMIA, 2006 Sims TL et al AMIA 2005
Many Meanings of “Goals”
• Self-Management goals
– “SMART” goals
• specific, measurable, achievable, results-focused,
and time- bound
– Often use in behavioral medicine approaches
• Disease-specific goals
– for example, target A1c for diabetics
• Goals of care; Life goals; Advance care
planning and goals
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Example Patient Goals in an Intensive Program for High Risk Older Adults
• Intensive Management Patient Aligned Care Team (ImPACT)
• Intake goals for 113 high-need patients
• Categorized as Medical, Behavioral, Social• Hsu, KY et al. Evaluation of Patients’ Goals and Goal
Progress in a Veterans Affairs Intensive Outpatient Care Program. Presented at SGIM April 2018
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Example Goals by Category: ImPACT Program
• Medical– “improve hip and back pain” “maintain my health
and wellbeing”
• Behavioral– “walk 2x daily” “manage anxiety related to
retirement and cardiac conditions”
• Social– “get driver’s license and car” “restart dating”
“increase social activities”
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Future Electronic Health Record• There are many data elements not currently in electronic
health records that will need to be added as we go
forward
– Genomics data, patient entered data, quantified self data
– Different forms of patient goals and preferences
• Just as vital signs and lab values change over time and
are recorded multiple times, whenever they are
assessed
– Goals and preferences may also change and need to be
recorded with time-stamps
• Electronic health record of the future
– Accommodate the new data types
– Interfaces for multiple forms of clinical decision support
– Patient-facing
– Connect to large datasets to predict outcomes based on similar
patients35
Thanks to Collaborators and
Operational Partners
ATHENA-CDS team over the years
– Samson Tu, Mark Musen, Brian Hoffman, Bob
Coleman, and others as cited on papers
– Knowledge experts
– Collaborators at clinical sites including
Eugene Oddone, Hayden Bosworth, Clayton
Curtis
– Health care system operational partners
– …and many others who contributed to this
large team effort36
Mary K. Goldstein, MDProfessor of Medicine (Center for Primary Care & Outcomes Research), Stanford University
and
Chief, Medical Service,
VA Palo Alto Health Care System, Palo Alto, California
Contents of this presentation are views of the speaker and not necessarily those of the Department of Veterans Affairs or other affiliated organizations or funding agencies
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