5.0 - abstract 1083 zaleski shs - publish

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)


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


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


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


“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).


Type I & II Error


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


pdf-1(x) pdf-2(x)

Type I & II Error


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


pdf-1(x) pdf-2(x)

Type I & II Error


Operating Point

Type I error, a (false alarms)

Type I & II Error


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


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


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


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


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


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)


Number of consecutive signal counts > 105 (minimal filtering ~ process noise = 1.0)


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.


Thank you!John R. Zaleski, Ph.D.C: 484-319-7345

E: [email protected]

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

http://www.nuvon.com/

http://www.medicinfotech.com/

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

5.0 - abstract 1083 zaleski shs - publish

Documents