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Incorporating Emerging Technologies to Support Data Analysis Innovation and Impact
CHAD | October 29, 2018
Agenda
Technology to Support CareFocus on Data Dashboards
Technology Considerations
Revisiting Analytics Capability Assessment
Electronic Patient EngagementFocus on Diabetes
Technology to Support OperationsFocus on Data Validation
Revisiting Analytics Capability Assessment
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Num
ber
of R
espo
nden
ts in
this
Rang
e
Analytics Capability Assessment: TECHNOLOGY
Reactive Responsive Proactive Predictive
Technology Analytic Capability level for most is
between 3.25 and 5.75– or responsive.
Analytics Capability Assessment: TECHNOLOGY
Reactive Responsive Proactive Predictive
IT Tools and Support for Analytics: IT support for analytics includes support for reporting and data mining from existing systems and basic analytics support. Analysis tools are limited to spreadsheets and databases with limited functions for systematic reporting, advanced data analyses, and self-service analytics.
Integration: Specific reports combining data from different internal sources are available but only for limited sets of data and conducted on a project-by-project basis; some effort is made to identify, combine, and use important external data, but it is not reconciled or audited.
Self Service Analytics: Reports, typically monthly, provide actionable information for selected departments and reports may be generated at any time. Data and information to support the care team is limited.
Technology Considerations
Technology must link to Data Strategy.
example
EHRs and other Health IT can leave us feeling boxed in by their templates and structure, but there are many options. Before pursuing any of these options, be sure they tie directly back to your data strategy.
Data Driven Decision Making Relies Heavily on Effective use of Technology.
exampleTraining staff on a new process for identifying
patients for care management or at risk of developing diabetes will only be effective if IT systems align with the
process and support needed collection and
analysis.
Avoid looking at data governance or quality improvement as a ‘project’, instead focus on using data throughout all efforts.
Technology is Not a Magic Bullet.
exampleNot having reliable
data for UDS reporting or identifying care gaps
is often a function of unreliable workflows or data capture. Adding additional health IT is unlikely to solve this.
Without the people and processes in place, technology cannot make the difference that health centers may want.
Ongoing Data Validation of all Health IT systems is essential.
exampleIf we are using Health IT (registries, care gap reports, dashboards) to
monitor how our diabetes patients are
doing, then we need to regularly ensure that
information is accurate.
Formal data validation processes to ensure that processes are maintained and information is captured accurately.
CDS 5 Rights Framework
To improve targeted care processes/outcomes, get:the right information
current, evidence-based, actionable… [what]
to the right people clinicians and patients… [who]
in the right formats Registry reports, documentation
tools, data display, care plans… [how]
through the right channels EHR, patient portal, smartphones/ apps,
home monitoring, HIE … [where]
at the right times key decision/action points, prior to visits … [when]
Recommended as a QI best practice by CMS: bit.ly/cmscdstips© 2016 TMIT Consulting, LLC
Technology to Support Operations
Data Validation
• In-depth Data Validation will generally need to be done manually.
• Seeks to identify specific use cases to be addressed–opportunities for training or alignment, or correcting mapping.
Numerator issuesReport not finding evidence of compliance in chart
Denominator issuesReport including patients that should not be in the Universe: wrong timeframe, missing exclusions
Clinical issuesIndicated service not being provided or outcome not being achieved
Initial Strategies for Validation
Opportunities• Other Health IT Tools
– Compare EHR results to those from other health IT (i.e. Azara, i2i, etc.)
• Stratify data by site, insurance, other dimensions to identify drivers
• Review EHR report criteria compared to eCQM
Challenges• With many types of
issues, the underlying issue will cause the same issues in both, so the data will match, but the issue will persist
Manual Data validation is obviously labor intensive and relies on having access to certain data (such as is a chart compliant?). There are other options that can be worthwhile first steps.
Deeper Strategies
• Use Data Validation tools to structure a chart audit process.
• Review EHR results for the same period that is being validated, to identify discrepancies.Where should we select charts from for validation process?
How many charts should we review during data validation?
• From full universe for measure
• From non-compliant only• Equal samples of
compliant/ non-compliant
• Level of confidence• Level of detail• Level of effort• Aim for more than
1.5% of total universe
Conducting Chart Audit
Input during Chart Audit
Examining Results
Examining Results
EHRs Not Used to Full Advantage
• Only 4% providers use the full functionality of their EHR (DesRoches, 2008)
• 1/3 of providers polled plan to replace their current EHR (KLAS 2010)
Where can we turn?
Vendor• Support• Documentation• Sales presentation• Contract
FQHC community• PCA/HCCN• User group
(NACHC, etc.)• HITEQ
Other• Online
forums
Some Key Issues
• Support for your health center’s financial/billing model
• Support for UDS and other reporting requirements
• Data access/rights/ownership• Provisioning
Technology to Support Care
Two Tools Available
Available on HITEQCenter.org
What is a Dashboard?
• Dashboards can take many forms, including: – Visual reports of routine data (monthly,
quarterly) by site or across sites – Interactive visual displays that let the user
explore the data within a file – Interactive web-based visual displays that
simplify access across multiple sites (but come with their own cost and data sharing considerations)
– Generally, maintained by IT and data analysts, and are self-service (staff can access them on-demand)
Tell Data Stories
• Change over time – Annual or quarterly rates
• Comparison of actual to target or benchmark – Current rates compared to HP2020
• Part-to-whole – Provider or care team panel compared to overall
performance – Homeless patient outcomes compared to total
patient population
Routine Data at a Glance
Patients by Insurance
Comparisons and Part to Whole
How can a dashboard help you?
The best dashboards give health centers actionable information at their fingertips, and use great design practices to focus a user’s attention on the most important information on the page. If you’re embarking on the process of designing a new dashboard with existing data, a bit of advance planning can set you up for success in how your dashboard is used for decision making.
Design Framework
Included in the HITEQ GuideId
entif
ying
and
un
ders
tand
ing
the
user
s
Iden
tifyi
ng a
nd
clea
ning
the
data
Sele
ctin
g a
soft
war
e fo
r th
e da
shbo
ard
Des
igni
ng m
ock-
ups
and
crea
ting
a pr
otot
ype
Dev
elop
ing
the
dash
boar
d
Pitf
alls
to a
void
Identifying /Understanding Users
Understanding visual design preferences
Understanding the users’ computer skills
Understanding the users’ workflows
Understanding users’ key questions
Identifying + Cleaning the Data
Cleaning the data
Identifying the data
Cleaning the Data
• What form can your data take?
• How are you able to access it?
Selecting a Platform
Platform OptionsEx
cel
Pow
er B
I
Tabl
eau
Tabl
eau
Publ
ic
Goo
gle
Dat
a St
udio
DH
IS2
Have you tried or seen these? What other options are there?
Microsoft Power BI
Tableau Public
Google Data Studio
How Health Centers are Using These
• Dashboarding SDoH in CO• Monitoring health of homeless
patients in OR
Pitfalls to Avoid
1. Not investing time in understanding user.
2. Assuming data is in “good enough” shape to connect to a visual analytics platform.
3. Skipping the prototyping stage.
4. Lack of clarity in the roles/ responsibilities throughout the design, testing, and use phases.
5. Trying to do it all.
Electronic Patient Engagement
mHealth + Electronic Patient Engagement
• Can be a more reliable way to get in touch with some patients, such as those who do not have a stable address or phone number.
• Allows greater confidentiality for patients, avoiding the need for phone messages or undesired face to face contact.
• Patients may be more comfortable discussing sensitive issues via secure messaging.
Fogg Behavior Model
• Optimizing interactions with individual patients (including when they are not in the clinic) can assist in triangulating on the needed triggers to nudge and encourage patients
Behavior Change = MAP
• MOTIVATION: help patients understand the impact small changes in diabetes self-management can make.
• ABILITY: anticipate objections, educate accordingly, and deploy the right interventions at the right time.
• PROMPT: get patients enrolled in a “project” that supports them in every step.
EPE Adoption Framework
Conceptual Level Constructs Factors
Personal • Cultural• Financial• Education• Behavioral
• Significant differences to be expected depending on Socio-Economic Status
• Engagement and activation factors key to sustainability
Technical • Standards• Regulations• Precision
• U.S. HIE standards still primarily based within the clinical environment
• Precision of metrics and device ability to effectively report are in need of continued innovation
• Systems designs still not targeted to the underserved
Organizational • Workflow• Workforce• Reimbursement
• Few clinical workflows include procedures for incorporation of patient reported data
• Concerns by clinicians of responsibility to act (or not) on data provided to them
Policy • Security• Privacy• Quality• Prevention
• U.S. government is working hard to keep up in establishing policies that provide effective guidance toward patient portal adoption
• Need further work in finding the balance between protection and effective use
Adoption + Implementation Questions
• User Question: How can EPE tools support your patients in diabetes management? What barriers will they experience? – reminders/ appointment management / communication/ FAQs
• Technology Question: How well do certain EPE systems fit for the technology access and utilization patterns of your population?– email access / computers vs mobile / social networking
presence
mHealth + Electronic Patient Engagement
Best Practices1. Pilot test the EPE service (both functional
and logistical) before rolling it out2. Develop a robust promotion strategy3. Educate patients about the benefits4. Proactively help patients get started5. Plan for ongoing technical assistance6. Monitor usage # or % of patients using
7. Assess satisfaction8. Re-evaluate and Repeat
Health App Decision Tree
