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Use of patient care data to assist in management of acutely ill patients
John R. Zaleski, Ph.D., CPHIMS
Abstract
The HITECH Act and ACA strive to establish measurements ofprocess improvement and use of evidence-based medicine topromote improved patient care management.
In this presentation, an example of the use of bedside data isemployed to show how early understanding of patient stateevolution can assist in providing actionable information ofimpending issues.
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 2
Medical Devices(OR & ICU)
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 3
Medical Device Data (high acuity, such as intensive care, surgery, emergency)
• Real-time or near real-time
• Time series
• Multivariate
• Varying data collection frequencies
• Varying and often non-standard methods for collecting (that is, not homogeneous)
• Objective, for the most part
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 4
Source: Edward H. Shortliffe,
“Medical Thinking: What Should
We Do?,” Conference on Medical
Thinking. University College of
london. June23rd, 2006.
5Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 5
Medical Device Alarms in High Acuity Settings
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 6
Source: http://www.ucsf.edu/sites/default/files/styles/600w/public/fields/field_insert_file/news/Alarm-Fatigue-UCSF-Nursing.jpg?itok=Jr_IvM6U
Source: http://www.wltx.com/story/news/health/2014/02/11/1669870/
Medical Device Alarms & Alarm Fatigue
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 7
“Hospital staff are exposed to an average of 350 alarms
per bed per day, based on a sample from an intensive
care unit at the Johns Hopkins Hospital in Baltimore.”
Source: Ilene MacDonald, “Hospitals rank alarm fatigue as top patient safety concern”, Fierce
Healthcare. January 22, 2014.
“The alarms can lead to ‘noise fatigue,’ and doctors and
nurses sometimes inadvertently ignore the sounds when there's
a real patient emergency, possibly resulting in treatment delays
that endanger patients…[one] government database lists
more than 500 deaths potentially linked with hospital alarms in
recent years.”
Source: http://www.wltx.com/story/news/health/2014/02/11/1669870/
HR < 40 bpm
RR < 8 breaths/min
etCO2 > 50 mmHg
Actionable Information vs Noise
• Problem with attenuating alarm data:• Achieving balance between communicating the
essential, patient-safety specific information that will provide proper notification to clinical staff,
• While minimizing excess, spurious and non-emergent events that are not indicative of a threat to patient safety.
• In the absence of contextual information• Err on the side of excess because the risk of missing an
emergent alarm or notification carries with it the potential for high cost (e.g.: patient harm or death).
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 8
Type I & II Error
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 9
0.00000
0.01000
0.02000
0.03000
0.04000
0.05000
0.06000
0.07000
0.08000
0.09000
0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00
Two Distributions of Sample Populations
pdf-1(x) pdf-2(x)
AlternativeHypothesis
Null Hypothesis
0.00000
0.01000
0.02000
0.03000
0.04000
0.05000
0.06000
0.07000
0.08000
0.09000
0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00
Two Distributions of Sample Populations
pdf-1(x) pdf-2(x)
Type I & II Error
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 10
Operating Point
0.00000
0.01000
0.02000
0.03000
0.04000
0.05000
0.06000
0.07000
0.08000
0.09000
0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00
Two Distributions of Sample Populations
pdf-1(x) pdf-2(x)
Type I & II Error
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 11
Operating Point
Type I error, a (false alarms)
Type I & II Error
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 12
0.00000
0.01000
0.02000
0.03000
0.04000
0.05000
0.06000
0.07000
0.08000
0.09000
0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00
Two Distributions of Sample Populations
pdf-1(x) pdf-2(x)
Type II error, b (false negatives)
Operating Point
Type I error, a (false alarms)
Type I & Type II Error
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 13
Reality
True False
Me
asu
red
or
Pe
rce
ive
d True Correct Type I – False Positive
False Type II – False Negative Correct
Image source: http://effectsizefaq.files.wordpress.com/2010/05/type-i-and-type-ii-errors.jpg
Top 20 Most Expensive Inpatient Conditions
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 14
Top 20 Most Expensive Inpatient ConditionsBecker’s Hospital Review | Bob Herman | October 09, 2013 http://www.beckershospitalreview.com/racs-/-icd-9-/-icd-10/top-20-most-expensive-inpatient-conditions.html
1. Septicemia (except in labor) — $20.3 billion2. Osteoarthritis — $14.8 billion3. Complication of device, implant or graft — $12.9 billion4. Liveborn (general childbirth) — $12.4 billion5. Heart attack — $11.5 billion6. Spondylosis, intervertebral disc disorders, other back problems — $11.2 billion7. Pneumonia (except caused by tuberculosis and STDs) — $10.6 billion8. Congestive heart failure — $10.5 billion9. Coronary atherosclerosis — $10.4 billion10. Adult respiratory failure — $8.7 billion11. Acute cerebrovascular disease — $8.4 billion12. Cardiac dysrhythmias — $7.6 billion13. Complications of surgical procedures or medical care — $6.9 billion14. Chronic obstructive pulmonary disease and bronchiectasis — $5.7 billion15. Rehab care, fitting of prostheses and adjustment of devices — $5.5 billion16. Diabetes mellitus with complications — $5.4 billion17. Biliary tract disease — $5.1 billion18. Hip fractures — $4.9 billion19. Mood disorders — $4.8 billion20. Acute and unspecified renal failure — $4.7 billion
Type I & Type II Error: Sepsis
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 15
Reality
True (Disease Evident)
False (No Disease Evident)
Total
Me
asu
red
or
Pe
rce
ive
d True(Disease Evident)
242 1294 1536
False(No Disease
Evident)48 940 988
Total 290 2234 2524
Use of Shock Index (SI), SI = HR / NBPs >= 0.7, to predict likelihood of sepsis [1], as defined criteria to predict the primary outcome of hyperlactatemia (serum lactate >= 4.0 mmol/L) as a surrogate for disease severity.
[1] Berger, T; Green, J; Shapiro, N; “Shock Index Recognition of Sepsis in the Emergency Department: Pilot Study”http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3628475#!po=2.50000
𝑃𝑃𝑉 =𝑇𝑃
𝑇𝑃 + 𝐹𝑃
𝑁𝑃𝑉 =𝑇𝑁
𝑇𝑁 + 𝐹𝑁
𝑆𝑒𝑛𝑠 =𝑇𝑃
𝑇𝑃 + 𝐹𝑁
𝑆𝑝𝑒𝑐 =𝑇𝑁
𝑇𝑁 + 𝐹𝑃
𝑃𝑃𝑉 = 0.16
𝑁𝑃𝑉 = 0.95
𝑆𝑒𝑛𝑠 = 0.83
𝑆𝑝𝑒𝑐 = 0.42
Discrete Rapid-Shallow Breathing Measurements Over Time
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 16
RSBI = RR / TV > 105:• Weaning: very common
occurrence in ICUs• Reduction in support from
mechanical ventilation• Regaining spontaneous
respiratory function extremely critical
• Metric of patient viability to wean from mechanical ventilation
• Yang & Tobin [1] & Meade etal.[2] suggested a ratio of 105 as a predictor of failure.
0.00
20.00
40.00
60.00
80.00
100.00
120.00
140.00
160.00
0 50 100 150 200 250 300 350 400 450 500
Rapid Shallow Breathing Index (breaths/min/liter)
zk (measurements) Xk (Estimate)
[1] Yang & Tobin: “A prospective study of indexes predicting the outcome of trials of weaning from mechanical ventilation”, NEJM vol 324 No 21 May 23, 1991[2] Meade etal.: “Predicting success in weaning from mechanical ventilation”. CHEST 2001; 120:400s-424s
Optimal least-squares filter (Kalman)
Number of consecutive signal counts > 105 (moderate filtering ~ process noise = 0.1)
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 17
Number of consecutive signal counts > 105 (minimal filtering ~ process noise = 1.0)
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 18
Summary
• Data collected at bedside provides rich source of information for clinical decision making
• Balance between sensitivity to real clinical events & reduction of Type I error
• Selection of thresholds control of licensed clinicians • Technology cannot make these decisions as technical
algorithms would need to take into account full context of patient and training of clinical staff.
• Maybe someday this will be possible (if desirable), but it is certainly not the case today.
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 19
Thank you!John R. Zaleski, Ph.D.C: 484-319-7345
W: http://www.nuvon.com
Blog: http://www.medicinfotech.com
Book III:
Published by HIMSS Media
Title:
communicating with medical devices
integrating patient care data with health information systems in the hospital
Anticipated availability: April 2015
3/17/2015 (c) 2014 Copyright John R. Zaleski 20
Day 1 Theme: Driving Value in Healthcare through Leadership and Education
Key Learning: What waste do you see on a day to day basis that makes you wacky?
#SHS2015 #SHSwackywaste