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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