Measures for Electronic Health Records - Recommended Social and Behavioral Domains

Measures for Electronic Health Records - Recommended Social and Behavioral Domains

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Description: In this medical presentation show the measurement of health record with digital device. The Institute of Medicine’s Committee on Recommended Social and Behavioral Domains and Measures for Electronic Health Records will identify domains and measures that capture the social determinants of health to inform the development of recommendations for Stage 3 meaningful use of electronic health records (EHRs). Lessons Learned from IOM Recommendations for capturing Race, Ethnicity, and Language Data What could be Improved?People who needed the guidance did not get the guidance.

There are many data elements which are most undefined and / or not in a structured form (e.g., fluency), Patients, who must provide most of the data, may be reluctant to share it(e.g., household income, risky behaviors, etc.), Some data elements require data capture tools that may be under copyright, and require license fees (e.g., SF36) , Providers must understand the rationale for data collection to implement a quality data collection process, Some of the data elements are collected and / or used in other domains, requiring consensus on data standardization requirements, Data exchange standards, such as for transitions of care (Consolidated CDA), e.g., social history, behavioral health history, etc. Quality Measurement, such as OASIS and more, Health risk assessments.

 
Author: Charlene Underwood MBA FHIMSS (Fellow) | Visits: 1897 | Page Views: 1972
Domain:  Medicine Category: Practice Mngmnt Subcategory: Digital Health 
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Contents:
Recommended Social and Behavioral
Domains and Measures for Electronic
Health Records - February 6, 2014
Institute of Medicine
Charlene Underwood, MBA, FHIMSS
Senior Expert, Siemens Healthcare
Restricted © Siemens AG 2013 All rights reserved.

Answers for life.

Recommended Social and Behavioral Domains and
Measures for Electronic Health Records

Charter
Research Sources
Lessons Learned: Race, Ethnicity, and Language Data
Applying Lessons Learned: Social and Behavioral Domains
Conclusions
Recommendations
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Page 2

2013-06-02

Institute of Medicine
Recommended Social and Behavioral Domains and
Measures for Electronic Health Records
The Institute of Medicine’s Committee on Recommended Social and Behavioral
Domains and Measures for Electronic Health Records will identify domains and
measures that capture the social determinants of health to inform the
development of recommendations for Stage 3 meaningful use of electronic
health records (EHRs).
Phase 1
Identify specific domains to be considered by the Office of the National
Coordinator.
• Specify criteria that should be used in deciding which domains should be included,
• Identify core social and behavioral domains to be included in all EHRs, and
• Identify any domains that should be included for specific populations or settings defined
by age, socioeconomic status, race/ethnicity, disease or other characteristics.

A brief Phase 1 report will be produced and submitted to the committee's
sponsors.
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Institute of Medicine
Recommended Social and Behavioral Domains and
Measures for Electronic Health Records
Phase 2
The committee will consider the following questions:
• What specific measures under each domain specified in Phase 1 should be
included in EHRs? The committee will examine both data elements and
mechanisms for data collection.
• What are the obstacles to adding these measures to the EHR and how can
these obstacles be overcome?
• What are the possibilities for linking EHRs to public health departments, social
service agencies, or other relevant non-healthcare organizations? Case
studies will be considered, including how privacy issues have been
addressed.
A final report that includes the Phase 1 report and addresses the Phase 2
questions will be the final product.

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Research Sources: Small, but representative sample
Thank you for contributing time and knowledge
Providers:
Albany Medical Center:
• Patricia Hale, MD, PhD, FHIMSS,
CMIO
Ellis Medicine:
• James W. Connolly, President,
Chief Executive Officer
Sparrow Health System:
• Michael H. Zaroukian, MD, PhD,
FACP, FHIMSS, VP & CMIO

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Page 5

Vendors:
Epic:
• Howard Bregman, MD, MS
• Sasha TerMaat
McKesson:
• Ginny Meadows, RN
NextGen:
• Sarah Corley, MD
Siemens:
• Sue Lundquist, RN
• Karen Nielsen, MBA, MPA/HSA
• Elizabeth Schofield, RN
• James Walker, MD

Lessons Learned from IOM Recommendations for
capturing Race, Ethnicity, and Language Data
What Worked?
• Capability was required and providers understood the relevancy of the data to
clinical practice.
• Data standards enabled vendors to implement IOM recommendations and
guidance provided enough clarity to guide vendors on implementation.
• Hierarchical approach to capturing ethnicity enabled vendors to give
providers tools necessary to tailor granular ethnicity to their populations.
• Requiring providers to meet a threshold and providing tools to report
compliance with data capture enabled them to improve data quality.

Ulmer C, McFadden B, Nerenz DR, Institute of Medicine (U.S.). Subcommittee on Standardized Collection of Race/Ethnicity Data for Healthcare Quality Improvement Board on
Health Care Services. Race, ethnicity, and language data : standardization for health care quality improvement. Washington, D.C.: National Academies Press; 2009
Gottlieb L, Sandel M, Adler NE. Collecting and applying data on social determinants of health in health care settings. JAMA internal medicine. Jun 10 2013;173(11):1017-1020.
Wilson G, Hasnain-Wynia R, Hauser D, Calman N. Implementing institute of medicine recommendations on collection of patient race, ethnicity, and language data in a community
health center. Journal of health care for the poor and underserved. May 2013;24(2):875-884.

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Lessons Learned from IOM Recommendations for
capturing Race, Ethnicity, and Language Data
What could be Improved?
Implementation guidance:
• People who needed the guidance did not get the guidance.



Patients initially uncomfortable. Vendors devoted resources to create material to
help customers overcome resistance.
Neither race and ethnicity are “black and white.” Vendors educated providers on
appropriate responses using OMB and IOM guidance.

Adopting consensus standards:
• Some IOM recommendations were not implemented and / or not
communicated.


A national scheme to rollup granular ethnicity to applicable OMB and Hispanic
categories was not provided. Each vendor figured out rollup on their own.
The findings from pilot projects to confirm the best way to elicit accurate and reliable
data for these fields, if done, were not widely communicated.
Other HIT tracking, such as whether the data was self reported and / or observer
reported, was not implemented.






Other federal and state programs did not implement the data elements
consistently, which created provider rework and burden when reporting.
Implementation Guidance on Data Collection Standards for Race, Ethnicity, Sex, Primary Language, and Disability Status. U.S. Department of Health and Human Services . October
2011. Available at: http://aspe.hhs.gov/datacncl/standards/ACA/4302/index.shtml

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Kaplan RM. Collecting Information on Behavioral and Social Factors in the EHR. Presented at: Recommended Social and Behavioral Domains and Measures for Electronic Health
Records Meeting; September 24-25, 2013;The National Academies; Washington, DC. http://www.iom.edu/~/media/Files/Activity%20Files/PublicHealth/SocialDeterminantsEHR/Kaplan_Bob.pptx Accessed January 25, 2014

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Applying Learnings to Social and Behavioral
Domains and Measures
• There are many data elements.
• Most are undefined and / or not in a structured form (e.g., fluency).
• Patients, who must provide most of the data, may be reluctant to share it
(e.g., household income, risky behaviors, etc.).
• Some data elements require data capture tools that may be under copyright
and require license fees (e.g., SF36).
• Providers must understand the rationale for data collection to implement a
quality data collection process.
• Some of the data elements are collected and / or used in other domains,
requiring consensus on data standardization requirements.
• Data exchange standards, such as for transitions of care (Consolidated
CDA), e.g., social history, behavioral health history, etc.
• Quality Measurement, such as OASIS, PROMIS and more
• Health risk assessments
• Etc.
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CMS Framework for Measurement

Clinical Quality
of Care
• Care type
(preventive, acute,
post-acute, chronic)
• Conditions
• Subpopulations

Person- and
Caregiver- Centered
Experience and
Outcomes
• Patient experience
• Caregiver experience
• Preference- and goaloriented care

Care Coordination
• Patient and family
activation
• Infrastructure and
processes for care
coordination
• Impact of care
coordination

Population/
Community Health
• Health Behaviors
• Access
• Physical and Social
environment
• Health Status

Function
Efficiency and
Cost Reduction
Safety






All-cause harm
HACs
HAIs
Unnecessary care
Medication safety

• Cost
• Efficiency
• Appropriateness

• Measures should
be patientcentered and
outcome-oriented
whenever possible
• Measure concepts
in each of the six
domains that are
common across
providers and
settings can form
a core set of
measures

Thinking Ahead in Post Acute Care Data Element Standardization, Stella Mandl, RN
Technical Advisor, Division of Chronic and Post Acute Care, Center for Clinical Standards
and Quality, Center for Medicare & Medicaid Services, Stella.mandl@cms.hhs.gov

10

PROMIS (tools to measure health outcomes from a
patient perspective)

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PROMIS Adult Self-Reported Health Domain Frameworks . The Patient–Reported Outcome Measurement Information System (PROMIS®), National Institutes of Health. Bethesda, MD.
http://www.nihpromis.org/Measures/domainframework1.aspx?AspxAutoDetectCookieSupport=1#ability Accessed January 25, 2014.
AG 2014 All rights reserved.

Conclusions











To understand how the data should be defined, the objective for collecting the data
must be defined first. Consider using research that identifies the most impactful data
elements.
Better educate the clinician and public on reasons and importance for data collection
and methods to collect.
Consider the burden of data collection in a fee-for-service environment; adding to strain
in primary care, while taking time from patient care.
Climate in general makes patients less trustful of how data might be used.
Must demonstrate usefulness of data collection to care and / or to community
Starting small, using three, allows you to build in data quality and integrity. Reinforced in
literature which states that this number allows focus and improvement.
Using the meaningful use program for mandating data collection or certification is
premature until consensus standards are defined.
Alternatively, establishing such standards for data elements makes it easier for vendors
to implement them to meet user needs when such data is desired.
Governance to assure uniformity in adoption and harmonization with other domains,
such as measures, is a must.

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Recommendations

Standard
Framework

Standardized
Terminology

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Standardized
Validated Patient
Data Gathering
Instruments

Data
Quality

Recommended Requirements
Standard Framework
• Explains the rationale for why the data is being collected and its clinical relevance; including evidence base.
• Ideally provides standardized data elements needed by all care team members that can be acted upon
regardless of site of care and are useful for other purposes, i.e., care transitions, measures, payment, .etc.
• Provides the opportunity for patient generated data collection.

Standard Terminology

• Establish standardized concept for the data element and value sets.
• Must be evidenced-based, actionable, and measureable.
• Must align and be harmonized with other domains, such as quality measurement, transitions of care data
exchange standards, public health, and non-healthcare domains.
• Enable cost effective data capture and / or acquisition.

Standard Data Collection Instruments

• Provide standardized instruments for data capture; however, recognize today there are multiple formats for
such data collection tools, i.e., health risk assessments.
• Address costs of licensing tools and / or make them free.

Data Quality
• Provide validated ways to ask the questions and advice about who asks them.
• Allow for capturing data from the patient but account for when patients decline to respond and / or
provide inaccurate information.
• Build in ways to improve data quality and integrity: start small , be judicious, and provide time and tools
to improve upon data quality.
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Closing Recommendation …
HHS must complete the work to:
• establish consensus on what data elements should be collected and define
usable standards to collect them before there is any mandate to build new
collection tools or to collect the elements.
and, when done, then rollout the program fully, including
• educating stakeholders (the clinician and public) about why data is being
collected and how it should be collected.

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