SMALL BRAIN PROJECT. James Ryan DO Kevin Perdue MS

Transcription

1 SMALL BRAIN PROJECT James Ryan DO Kevin Perdue MS

2 take home message: multimedia recordings can improve patient engagement, and population health management, while reducing clinicians charting burden.

3 {busy family doctor, my wife manages our independent, solo provider, PCMH}

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6 what? a prototype EHR that has been in clinical use for the past 10 months. {350 patients, 3000 problems, 750 unique, encounters, patient generated changes} why not use off the shelf EHR? not modeled on clinical workflow, manages complexity poorly. designed for static, not dynamic data. {served on QI committee reviewed charts, managed patients at CMH, they made their own EHR} fundamentally poorly designed for clinical work: that s why CDS, and measure tracking is poorly implemented.

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8 main page: zoomed out artificial 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.

9 collaborative: patient and provider manage together. problem based: actional data,goal, todos, media files are problem associated. curated: any changes made by patient affect the authentication state of the problem.

10 when i m finished seeing a patient i should have 2 things: an encounter document, and an updated patient chart. what we have built attempts to allow the users to focus on managing complexity, and as a byproduct of doing so create an encounter document (objective recording of that encounter).

11 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.

12 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 and contributes to clinical science.w Wqa

13 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 population health.

14 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.

15 this is an example of an organized encounter: problems and tasks are reviewed completely.

16 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.

17 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.

18 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.

19 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!

20 engagement starts with giving. only after we give can we ask for the loop to be completed.

21 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.

22 what happens in a group practice? who i am? collaboratively answered question.

23 patient regularly adds notes to her problems (result of an agreement we made). her mental health counsellor has access to her chart.

24 patient regularly writes a summary of our encounter, which is part of the encounter document not shown here.

25 Mrs. E, 35 yo, multiple problems {biopsychosocial} including bipolar disorder, and pituitary agenesis, her daughter 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} population health? {machine learning data: more accurate and complete. what really happens when we do a med review, or depression screening?}

26 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, occasionally these are the first sign of something very significant. 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. negatives: prototype, lot s more work ahead to optimize and expand.

27 next steps: dartmouth project. integrating discrete reportable data: {problem based, and preventative} SMART on FHIR integration.

28 why i came here today: open source project, we want collaborators. care coordination with special needs high school. reportable data tool given by insurance company to small providers: free software in exchange for better data: star ratings :)