This document summarizes a presentation about a prototype primary care EHR system called Small Brain Project. The system aims to increase patient engagement and capture more objective clinical data while reducing clinician documentation burden. It allows patients and providers to jointly manage health problems, goals, and tasks. Encounters are recorded through multimedia and problems are updated based on patient inputs. The system was tested with 375 patients and showed benefits like improved safety, efficiency and patient trust. Next steps discussed were integrating with other systems through FHIR and Dartmouth projects to advance the open source project.

1. A Next Generation EHR for
Primary Care: How we learned to
stop worrying and love Health IT
(SMALL BRAIN PROJECT)
Kevin Perdue MS
James Ryan DO
Invited Presentation by University of Michigan Medical School
Department of Learning Health Sciences
September 9, 2015

2. take home message:
multimedia recordings can increase patient
engagement and capture more objective
clinical data, while reducing clinician’s
charting burden.
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3. {family doctor, independent, solo practice, PCMH in Ludington}
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5. 5

6. Health informaticist, technical assistance
provider for state and federal health IT projects,
based in Michigan’s regional extension center.
Helping clinicians and administrators use and
learn from currently available EHR technology,
design improved workflows, meet performance
goals.
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7. 7

8. what? a prototype clinical module that has been in use for the
past year.
{375 patients, 3200 problems, 825 unique, 1100+ encounters,
1050+ patient generated changes}
why not use off the shelf EHR? not modeled on clinical
workflow, manage complexity poorly. designed for static, not
dynamic data.
fundamentally poorly designed for clinical work: that’s why
CDS, and measure tracking is poorly implemented.

9. 9

10. main page:
zoomed out, slightly distorted
view. web based, accessible to
patients and providers.
problem oriented:
snomed (bio-psycho-social phenomena).
not restricted to snomed, and can
handle non-western problems,
and also gives patients the
capacity to name their concerns.
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11. collaborative: patient and
provider manage together.
problem based: actionable
data. goals, todos, media files
are problem associated.
curated: any changes made
by patient affect the
“authentication state” of the
problem.
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12. when i’m finished seeing a patient i should have 2 things: an encounter document
(objective recording of that encounter), and an updated patient chart. what we
have built attempts to allow the users to focus on managing clinical complexity,
and as a byproduct of doing so create the encounter documentation.
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13. encounter document
both patient and provider can create and
access encounters.
events from the encounter are
bookmarked for quick access.
can use audio or video files.
no need to generate SOAP note.
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14. if we look at the actions of clinicians and patients as a series of discrete steps in a
workflow model, then the value of each step can be measured by how well it supports
patient engagement, data capture, and its potential contribution to clinical science.14

15. examples: two cases that demonstrate our
prototype’s workflow.
question: how does a current generation EHR
compare to our system, in terms of patient
engagement, and objective data capture?
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16. Mr. E {85 yo man. lives alone. 10+ active
problems including moderate fall risk, early
dementia. his daughter lives out of state and
wants to help}
current EHR? manage medications, stores
documents and clinical data.
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17. this is an example of an organized
encounter:
problems and tasks are reviewed
completely.
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18. patient’s daughter listens to the
encounters.
coordinates task completion: labs and
other studies, medication changes etc.
they talk on the phone regularly and
she helps coordinate his calendar.
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19. patient’s daughter adds
information to problems.
eg: he can’t afford hearing
aids, and too proud to tell me.
he’s been lonely. minimizing
his depression levels.
helping me understand this
individual. more accurate
assessment of his current
state, and needs.
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20. supervised machine learning is the task of inferring a function from labeled training data: our
encounter notes are labeled training data: physical events are captured by sensors. ontological
events are coupled to the recording as a byproduct of managing complexity. 20

21. Mrs. E: {35 yo, multiple biopsychosocial
problems including bipolar disorder, and pituitary
agenesis, her mother died yesterday and she
comes in today with chest pain}
YIKES!
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22. engagement starts with giving. only after we give can we ask for the loop to be completed.
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23. Mrs. E: {35 yo, multiple biopsychosocial problems including bipolar disorder, and
pituitary agenesis, her mother died yesterday and she comes in with chest pain}
current EHR? static data, and documents:
correspondence from specialists (varying
quality), labs, study reports.
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24. what happens in a group practice?
who i am: collaboratively answered question.
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25. patient regularly adds
notes to her problems
(result of an agreement
we made, and made
possible through system
design).
her mental health
counsellor has access to
her chart.
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26. patient regularly writes a summary of
our encounter, which is part of the
encounter document not shown here.
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27. Mrs. E, 35 yo, multiple problems {biopsychosocial} including bipolar disorder, and pituitary agenesis, her
mother died yesterday and she says she has chest pain.
patient engagement? {who i am. encounter and
problem; review, and summary. shared goal and
task management}
objective data? immediate value and potential
future value. {machine learning data: more
accurate and complete. what really happens
when we do a med review, or depression
screening?} 27

28. what did we learn from this in our clinic?
● safety: helps me not miss small blips: labs that start to trend up, incidental
findings on imaging.
● patients like being able to listen, and often they find it helpful.
● many people don’t care to engage: but the more ways we open to them,
the more likely they are to do so.
● efficiency: charting on complex encounters is simplified.
● engagement: i don’t worry about missing details while with a patient, it’s
recorded, can access later, and that let’s me relax a little more and be with
this person.
● trust: nothing to hide: only one patient with poorly controlled schizophrenia
asked not to be recorded. mostly people are actually excited.
● negative: prototype lots more work ahead to optimize and expand.
● negative: fails to annotate encounters that are extremely emotional/
chaotic, or physical. 28

29. {375 patients, 3200 problems, 825 unique, 1100+ encounters, 1050+ patient generated changes}
- uncommon data, discovery and learning underway
- dynamic control state of problems
- shared record keeping, visible provenance and authorizations
What are the possibilities for visualizing and learning from
these rare types of clinical data?

30. next steps:
● dartmouth project.
● integrating discrete reportable data:
{problem based, and preventative}
● SMART on FHIR integration. Make it into a
pluggable app.
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31. why we came here today:
open source project, we want collaborators. 31

32. questions:
● would anyone here use this system? why or why
not?
● what should we focus on doing next?
● are there any other models of encounter
documentation to consider?
● FHIR: thoughts on our proposed plan? start by aligning
our system with FHIR API resources, required access scopes for
authorization, test query and retrieval from SMART servers and
EHR sandboxes, then expose certain data from our system.
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