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.
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Designing Machine Learning Driven Clinical Decision Support Tools
- 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 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 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 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 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 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 F I N D I N G S - Decision Landscape Overview - Barriers for DSTs Coming to Effect
- 9. 9 Findings
- 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 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 “ ” 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 “ ” 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 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 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 I M P L I C AT I O N S
- 17. 17 Barriers for Decision-supportTool Adoption Implications • Attitudinal Barriers • Need Barriers • Informational Mismatch • Environmental Barriers
- 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 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 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 Thanks to Contact Author: Qian Yang qyang1@cs.cmu.edu Full Paper Available Here