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Qian Yang, John Zimmerman, Aaron Steinfeld
Lisa Carey, James F. Antaki
INVESTIGATING THE HEART PUMP
IMPLANT DECISION PROCE...
2
The“HeartPump”
LVAD (leftventricular assist device),
implantable mechanical heart pump.
Adifficultend-of-lifedecision
•  H...
3
Many available decision-support tools (DSTs)
Clinicaldecisionsupporttools(DSTs):
Computersystemsthatusemedicalrecordsto
...
4
Most fail in clinical practice
Number1reasonforfailureisthelackofHCIconsiderations
HCI literatures provide no design pat...
5
Motivation
Emerging needs for HCI research
HCI research can help address these barriers
by integrating the richness of c...
6
Investigate clinician decision-making in context
Scope
Clinician decision-making in advanced heart failure services of
i...
7
F I N D I N G S
-  Decision Landscape Overview
-  Barriers for DSTs Coming to Effect
8
DecisionLandscape
Cardiologists
Surgeons
Implant
Physicians
(13interviews)
NursePractitioners
Fellows&Interns
PhysicianA...
9
Findings
10
Implantphysicians:
Decision is easy
Findings
Oral Medications
Other
Mechanical Support
HeartPump
“	
  
”	
  
Implant
De...
11
Implantphysicians
Expressed no need for DSTs
Findings
For most cases, there arewell-established precedence.
For grey ca...
12
“	
  
”	
  
Three paths of patient journey
Home
Clinic/
Local
Hospital
Implant
Hospital
1)Theconsolidatedpath
(Cardiolo...
13
“	
  
”	
  
Three paths of patient journey
Home
Clinic/
Local
Hospital
Implant
Hospital
2)emergencyroompath
(Cardiologi...
14
Implant Window
“	
  
”	
  
Three paths of patient journey
Home
Clinic/
Local
Hospital
Implant
Hospital
3)latereferralpa...
15
Decision breakdowns do not happen
whenfactoringpatientconditiontoimplantdecision
Instead, breakdowns happenwhen
•  Upst...
16
I M P L I C AT I O N S
17
Barriers for Decision-supportTool Adoption
Implications
•  Attitudinal Barriers
•  Need Barriers
•  Informational Misma...
18
1) Embracing the Richness of Clinical Context
incl.physicalandsocialcontexts
Implications for DST design
•  Need to min...
19
Implant Window
2) Decision Process as a Design Material
Animplantdecisioniscomposedofastringofsmallerdecisions.
Medicat...
20
3) Blending Human and Machine Intelligence
Implications for DST design
•  Support clinicians’ decisions, rather than ma...
21
Thanks to
Contact Author:
Qian Yang qyang1@cs.cmu.edu
Full Paper Available Here
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Designing Machine Learning Driven Clinical Decision Support Tools

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CHI'16 Paper Presented by Qian Yang from Carnegie Mellon University. The presentation describes a field study investigating how to design better machine-learning-driven systems in support of better LVAD (left-ventricular assist device, the "heart pump") implant decision.

Published in: Technology
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Designing Machine Learning Driven Clinical Decision Support Tools

  1. 1. Qian Yang, John Zimmerman, Aaron Steinfeld Lisa Carey, James F. Antaki INVESTIGATING THE HEART PUMP IMPLANT DECISION PROCESS OPPORTUNITIES FOR DECISION SUPPORT TOOLS TO HELP CHI’16 Best Paper Honorable Mention
  2. 2. 2 The“HeartPump” LVAD (leftventricular assist device), implantable mechanical heart pump. Adifficultend-of-lifedecision •  High-risk surgery and recovery •  Lifestyle change •  Critical implantwindow The “Heart Pump” as DestinationTherapy Source: www.mayoclinic.org Background
  3. 3. 3 Many available decision-support tools (DSTs) Clinicaldecisionsupporttools(DSTs): Computersystemsthatusemedicalrecordsto improvehealthcaredecision-making,efficiency, patientsatisfactionandcompliance. FunctionsDiagnosis generator, treatment recommender, or prognosis predictor. Outputsalerts, decisions, recommendations, predictions, or considerations. Example:Adecision-at-handsoftwareusing predictivedatamining(Bellazzi&Zupan,2008) Background
  4. 4. 4 Most fail in clinical practice Number1reasonforfailureisthelackofHCIconsiderations HCI literatures provide no design patterns or guidance. Identifiedbarriers Poor integration with clinical workflow Not designed for collective nature of clinical work No addedvalue perceived by clinicians … MissingfromLiterature Clinician workflow teamwork needs … (Yang etal.2015) Background
  5. 5. 5 Motivation Emerging needs for HCI research HCI research can help address these barriers by integrating the richness of context and redefining the role of DST technology in clinical practice. (Yangetal.,2015)
  6. 6. 6 Investigate clinician decision-making in context Scope Clinician decision-making in advanced heart failure services of implant hospitals, hospitals that provide heart pump implantation. DataCollection •  14-day field observation in 2 hospitals. •  24 one-hour semi-structured interviews in 3 hospitals. DataAnalysis Affinity diagram, service blueprint. 1. 2.Identify opportunities for DSTs to help Goals of Our Field Study Methods
  7. 7. 7 F I N D I N G S -  Decision Landscape Overview -  Barriers for DSTs Coming to Effect
  8. 8. 8 DecisionLandscape Cardiologists Surgeons Implant Physicians (13interviews) NursePractitioners Fellows&Interns PhysicianAssistants RegisteredNursesMid-levels (11interviews) VADCoordinators FinanceCoordinator SocialWorkers Pharmacists Nutritionists MultidisciplinaryConsults Implant Meeting Outpatient Clinic Inpatient WardRounds External Consults
  9. 9. 9 Findings
  10. 10. 10 Implantphysicians: Decision is easy Findings Oral Medications Other Mechanical Support HeartPump “   ”   Implant Decision We didn't knowwhat else to do. Then that's the time that he gets admitted for evaluation of LVAD. (NursePractitioner,site3) Intravenous Medications
  11. 11. 11 Implantphysicians Expressed no need for DSTs Findings For most cases, there arewell-established precedence. For grey cases, physicians don’t think extra data are helpful. Physicians do not use decision support tools. They consult colleagues for decision support. I can tell you who are really on the fringes. But there is no data can guide this decision.“   ”  (Cardiologist,site1)
  12. 12. 12 “   ”   Three paths of patient journey Home Clinic/ Local Hospital Implant Hospital 1)Theconsolidatedpath (Cardiologist,site3) He is a patient I’ve had 9 months to get know him, to do test on, to follow… It’s hard to saywhat else Iwill need. I had a lot of time to think through things. Oral Medications Other Mechanical Support Heart Pump Implant DecisionIntravenous Medications Implant Window Findings
  13. 13. 13 “   ”   Three paths of patient journey Home Clinic/ Local Hospital Implant Hospital 2)emergencyroompath (Cardiologist,site1) We've got patients that come in here who are on breathing tubes, and their families say go ahead. And they wake up on a mechanical pump. Heart Pump Implant Decision Implant Window Findings
  14. 14. 14 Implant Window “   ”   Three paths of patient journey Home Clinic/ Local Hospital Implant Hospital 3)latereferralpath The patient came hereveryvery sick. Hewas progressing in the community. Didn’t get referred here. Heart Pump Implant Decision (Cardiologist,site2) Findings
  15. 15. 15 Decision breakdowns do not happen whenfactoringpatientconditiontoimplantdecision Instead, breakdowns happenwhen •  Upstream physicians missed implantwindow •  Upstream physicians delayed implant consideration •  Implant team has difficulties clarifying patients’ social and/or medical conditions Findings
  16. 16. 16 I M P L I C AT I O N S
  17. 17. 17 Barriers for Decision-supportTool Adoption Implications •  Attitudinal Barriers •  Need Barriers •  Informational Mismatch •  Environmental Barriers
  18. 18. 18 1) Embracing the Richness of Clinical Context incl.physicalandsocialcontexts Implications for DST design •  Need to minimize input of data due to clinicians’ frequent hand washing and lack of time spent in front of a computer; •  Have to make an effort to reach and convince the decision-makers •  i.e. through mid-levels or weekly meetings
  19. 19. 19 Implant Window 2) Decision Process as a Design Material Animplantdecisioniscomposedofastringofsmallerdecisions. Medication Escalation DestinationTherapy Medication Escalation Treatment Escalation ClinicVisit FrequencyAdjustments Hospitalization Implant Decision Medication Adjustment Clinic/ Local Hospital Implant Hospital Implications for DST design
  20. 20. 20 3) Blending Human and Machine Intelligence Implications for DST design •  Support clinicians’ decisions, rather than make decisions for them; •  Explore potentials for AI in clarifying and monitoring patient condition as well as managing care escalations
  21. 21. 21 Thanks to Contact Author: Qian Yang qyang1@cs.cmu.edu Full Paper Available Here

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