This document summarizes a presentation on using patient care data from medical devices to assist in managing acutely ill patients. The presentation discusses: - How data from devices like monitors can provide insights into a patient's condition - Issues like "alarm fatigue" from the high volume of alerts that can cause clinicians to miss important alarms - Using metrics and thresholds to balance detecting true issues while minimizing false alarms - Examples of how tracking trends in vital signs over time through tools like Kalman filtering could help predict problems like difficulties weaning a patient from a ventilator The goal is to turn real-time device data into "actionable information" that helps clinicians without overburdening them with false alarms and non-essential

1. Use of patient care data to assist in
management of acutely ill patients
John R. Zaleski, Ph.D., CPHIMS

2. Abstract
The HITECH Act and ACA strive to establish measurements of
process improvement and use of evidence-based medicine to
promote improved patient care management.
In this presentation, an example of the use of bedside data is
employed to show how early understanding of patient state
evolution can assist in providing actionable information of
impending issues.
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 2

3. Medical Devices
(OR & ICU)
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 3

4. 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

5. Source: Edward H. Shortliffe,
“Medical Thinking: What Should
We Do?,” Conference on Medical
Thinking. University College of
london. June23rd, 2006.
5
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 5

6. 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/

7. 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

8. 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

9. 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)
Alternative
Hypothesis
Null Hypothesis

10. 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

11. 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)

12. 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)

13. Type I & Type II Error
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 13
Reality
True False
Measuredor
Perceived
True Correct Type I – False Positive
False Type II – False Negative Correct
Image source:
http://effectsizefaq.files.wordpress.com/2010/05/t
ype-i-and-type-ii-errors.jpg

14. Top 20 Most Expensive Inpatient Conditions
Tuesday, March 17, 2015 (c) 2015 -- John R. Zaleski, Ph.D., CPHIMS 14
Top 20 Most Expensive Inpatient
Conditions
Becker’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 billion
2. Osteoarthritis — $14.8 billion
3. Complication of device, implant or graft — $12.9 billion
4. Liveborn (general childbirth) — $12.4 billion
5. Heart attack — $11.5 billion
6. Spondylosis, intervertebral disc disorders, other back problems — $11.2 billion
7. Pneumonia (except caused by tuberculosis and STDs) — $10.6 billion
8. Congestive heart failure — $10.5 billion
9. Coronary atherosclerosis — $10.4 billion
10. Adult respiratory failure — $8.7 billion
11. Acute cerebrovascular disease — $8.4 billion
12. Cardiac dysrhythmias — $7.6 billion
13. Complications of surgical procedures or medical care — $6.9 billion
14. Chronic obstructive pulmonary disease and bronchiectasis — $5.7 billion
15. Rehab care, fitting of prostheses and adjustment of devices — $5.5 billion
16. Diabetes mellitus with complications — $5.4 billion
17. Biliary tract disease — $5.1 billion
18. Hip fractures — $4.9 billion
19. Mood disorders — $4.8 billion
20. Acute and unspecified renal failure — $4.7 billion

15. 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
MeasuredorPerceived
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

16. 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)

17. 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

18. 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

19. 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

20. Thank you!
John R. Zaleski, Ph.D.
C: 484-319-7345
E: jzaleski@nuvon.com
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

21. 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