Gpt buchman

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  • The BodySystem View creates a focus on patient centered care that is informed by real insight into the situation of each patient. Physician review/documentation and multi-disciplinary workflows are enhanced through standard views of each body system that result in simpler interpretation, decision-making and ordering. 
  • Part of the solution is technology. No one would use this raw data safely. Yet each of us uses the processed data to best advantage. It creates situational awareness, so we understand the data and can project the data into the future.

Transcript

  • 1. 00 Better Health, Better Care, Lower Cost : How telehealth and real-time analytics can help critical care achieve this triple aim. Emory Critical Care Center Tim Buchman 22 March 2014 Disclosures: None Relevant to the Presentation
  • 2. 11 Why monitor? :“Situation Awareness”
  • 3. 22 Mica Endsley’s Original Conception Human Factors 37: 32-64 (1995)
  • 4. 33 SA involves more than “more data”
  • 5. 44 SA “on the road”
  • 6. 55 • Present all information in readily interpretable form, much as a GPS receiver takes data from satellites and creates situational awareness to provide a map back to health Desiderata
  • 7. 66 Situation Awareness: Why does this “feel right”? 1. The perception of the data 2. The comprehension of its meaning 3. The projection of that understanding into the future in order to anticipate what might happen
  • 8. 77 This is NOT SA…
  • 9. 88 Because excess, uncorrelated data constitute distractions…
  • 10. 99 ICUs, Present Day Loss of situational awareness is easy and common
  • 11. 1010 Staff cannot absorb more data. Really. In today’s ICU, there is too much opportunity for error
  • 12. Do distractions matter in critical care? An experimental study Task: Alarm and vent checks Distraction:”I’m ready for handover!” Miss rate, 25%
  • 13. 1212 The Four V’s of Data Challenge National Security Medicine Need Volume “We are... swimming in sensors... and drowning in data" • Medical literature doubling every 19 years • Torrent of patient data •Management of large data •Transform data to information Velocity Decision timelines range from days to seconds Decision timelines range from days to seconds Rapid extraction and presentation Variety Range of data types: imagery, video, signals, seismic data, field reports, informants, news reports Physiology, lab tests, physician notes, interventions, patient history • Data association • Information representation • Data provenance Veracity Military operations, targeting, collateral damage, rules of engagement Diagnosis & treatment of patients, life & death decisions, side effects, complications, malpractice concerns High-confidence decisions: Costs of mistakes are high
  • 14. 1313 “In the moment”—what is the current physiologic status of my patient? “Flowing data”—What is the trajectory of my patient? Data (4Vs)-> Monitoring-> Situation Awareness
  • 15. 1414 Challenge Medicine Need Patterned Biology, and especially pathobiology , is not random. The state space is “lumpy”. Treatments are aimed at lumps. Not all patterns are evident to clinicians. Management of large data requires meaningful pattern detection. Personalized There are three time scales that influence personalization: •Inherited aspects (“forever”); •chronic aspects (acquired, “allostasis”); •acute aspects (immediate threats, “homeostasis”) Data often convolve all three time scales. Knowing the patient‟s set-points and dynamics around the set points matters. Predictive Prediction horizons related to the time scales, e.g. •Lifetime risk for cancer •Obesity risk related to environmental stress •Arrhythmia risk due to electrolyte disturbance All three horizons require not only situation awareness but also a mechanism of alerting when the risks change. By extension, risk-management implies ongoing “what-if” scenarios. The 3 P’s that Matter to Health Care
  • 16. 1977 (single dimension)
  • 17. 1977 (multidimension)
  • 18. 1717 Does this matter?
  • 19. Yes, it does. An example… Duration of hypotension before initiation of effective antimicrobial therapy = critical determinant of survival, so knowing a single parameter contributing to the state affects decision-making Kumar A, et al. Crit Care Med 2006;34:1589 State= “sepsis”
  • 20. 1986
  • 21. 1986 1969
  • 22. 2 Simulation Evaluation of an Enhanced Bedside Monitor Display for Patients With Sepsis. Giuliano, Karen; RN, PhD; Johannessen, Ann; RN, MSN; Hernandez, Cheri; RN, MSN; EdD, CCRN AACN Advanced Critical Care. 21(1):24-33, January/March 2010. DOI: 10.1097/NCI.0b013e3181bc8683
  • 23. Simulation Evaluation of an Enhanced Bedside Monitor Display for Patients With Sepsis. Giuliano, Karen; RN, PhD; Johannessen, Ann; RN, MSN; Hernandez, Cheri; RN, MSN; EdD, CCRN AACN Advanced Critical Care. 21(1):24-33, January/March 2010. DOI: 10.1097/NCI.0b013e3181bc8683
  • 24. 2424 Does this matter?
  • 25. One of our ICUs, 3 years ago
  • 26. 2626 “Patterned, Personalized, Predictive”
  • 27. 2727 ●Physiologic time series – Heart (EKG) – Vasculature (Blood Pressure) – Lungs (CO2) – Brain (EEG) – … Detecting patterns at the bedside Beat-to-beat heart rate heart n t heart n t 1 time, sec ECG II, mV
  • 28. Goldberger AL, et al. Proc. Nat. Acad. Sci. USA (2002) 99 Supp1:2466 Which is the healthy pattern?
  • 29. Heart Failure Goldberger AL, et al. Proc. Nat. Acad. Sci. USA (2002) 99 Supp1:2466 Which is the healthy pattern?
  • 30. Heart Failure Heart Failure Goldberger AL, et al. Proc. Nat. Acad. Sci. USA (2002) 99 Supp1:2466 Which is the healthy pattern?
  • 31. Heart Failure Heart Failure Atrial Fibrillation Goldberger AL, et al. Proc. Nat. Acad. Sci. USA (2002) 99 Supp1:2466 Which is the healthy pattern?
  • 32. Heart Failure Heart Failure Normal Atrial Fibrillation Goldberger AL, et al. Proc. Nat. Acad. Sci. USA (2002) 99 Supp1:2466 Which is the healthy pattern?
  • 33. 3333 ● Nonstationarity – Statistics change with time ● Nonlinearity – Components interact in unexpected ways ( “cross-talk” ) ● Multiscale Organization – Fluctuations/structures typically have fractal organization Patterns of health->Inferences about “not” health Healthy Dynamics
  • 34. 3434 What “not health” looks like Goldberger, Peng, Costa. Nature 1999; 399:461; Phys Rev Lett 2002; 89 : 068102 Healthy dynamics are poised between too much order and total randomness The breakdown “data patterns” are similar in various organ systems
  • 35. 3535 “Not health” : infection (sepsis) • Two similarly septic patients • First 24 hr of data shown • During the second 24 hr, the patient on the right developed multiple organ failure and died on day 12. Pontet J, et al, J. Critical Care (2003) 18:156
  • 36. 22% reduction in mortality! If data-driven prediction was a drug in this setting, that 22% reduction in mortality would make it a BLOCKBUSTER
  • 37. 3737 Reengineering Critical Care Patients and Conditions Population Specification Populations Care Path Development Fully Specified Care Processes and Protocols Current Care Workflow Modification Delegation, Algorithms Situation Awareness, Response Caregiver and Patient Activation Low Efficiency and Reliability High
  • 38. • Recognize physiologic decompensation as it occurs • Classify decompensation by actionable mechanism • Mitigate decompensation by reversal of cause and supportive treatment Situation Awareness, Response
  • 39. • Harvest data in motion • Real-time analytics • Intuitive display • Reliable interventions Situation Awareness, Response
  • 40. Center for Critical Care Data in Motion and Real-Time ICU Analytics
  • 41. Testing Novel Analytics
  • 42. Synchrography πRadians
  • 43. •Situation Awareness: Current State Philips eICU ECCC Coarse data Fine data
  • 44. Quasi-real-time display and analysis of physiologic data: architecture that we are currently using
  • 45. Center for Critical Care Architecture Example Filter ECG data RR Beat Detector SampEn COSEn LDS Database BedMasterEx Filter ICU Beds
  • 46. Center for Critical Care ECG with beat detection
  • 47. Analytics, etc. MIT-BIH: 12 beats q30 min for 24 hours 400 600 800 1000 1200 0 100 200 300 AF NSR CHF III and IV CHF I and II meanofthestandarddeviation mean RR interval
  • 48. Center for Critical Care Coefficient of sample entropy (COSEn) • An entropy metric optimized to detect atrial fibrillation in very short records. • It has ROC area 0.98 for detecting AF in 12-beat records. 0 20 40 60 80 100 -4 -3 -2 -1 0 AF male AF female NSR male NSR female COSEn Age (years) Lake and Moorman, Am J Physiol, 2011 Demazumder et al, Circulation 2013
  • 49. Center for Critical Care Real-time COSEn/AF Example
  • 50. 5151 Making the tools work: the eICU platform
  • 51. Better Health (outcomes that matter to patients and families) Better Care (High-reliability and evidence based) Lower Costs (Optimal configuration of people and materials) Right Care, Right Now, Every Time Execution Layer Strategy Workforce Operations Plan Ensuring that every test, drug, and procedure add value to care Event driven Intervention 1. Multiple event initiation triggers: such as requests from site (eLert button); admission/transfer event; detection of deterioration or collapse; advisory from another eICU staffer 1. Consistent (normative) behaviors 2. Verification that outcomes are achieved Processes Matter 1. Bundles are “DO-LISTS” 2. Standard list-driven responses to common care challenges in critical care 3. Responses are also “DO-LISTS” 4. eRN and eMD are PARTNERS in verifying adherence to standard bundles: DO-LISTS completed 5. eRN and eMD are PARTNERS supporting standard responses to common situations. DO-LISTS completed 6. eICU collaborates with ICU staff to verify desired results are driven by standard bundles and interventions 7. Scheduled e-rounding for initiation and adherence to “bundles” 8. Two-person e Staff confirmation of DO-LISTS completion 9. Remote support by eICU for bundle/response order sets. Value derives from what we do, making a difference 1. Debridement of drug lists 2. Elimination of unnecessary standing orders 3. Conversion to less expensive choice or route 4. Avoidance of complications (drug interactions) ECCC-eICU Driver Diagram Key Drivers Interventions
  • 52. 5353 ●A lot of technology, rivers of data, lots of expense → opportunities to create and deliver value ●„In the moment descriptions‟ of „where the patient is‟ would be very helpful (“situation awareness”) ●Predictive analytics to drive towards treatment goals would be very helpful ●Predictive analytics that fail (patients off the predicted trajectory) even more important Takehomes
  • 53. 5454 Questions?