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For more than a decade, the focus of information technology has been on capturing and sharing data from a patient within an all-encompassing record (a.k.a. the electronic health record, EHR), to promote improved longitudinal oversight in the care of the patient. There are both those who agree and those who disagree as to whether this goal has been met, but it is certainly evolving. A key element to improved patient care has been the automated capture of data from durable medical devices that are the source of (mostly) objective data, from imagery to time-series histories of vital signs and spot-assessments of patients.

The capture and use of these data to support clinical workflows have been written about and thoroughly debated. Yet, the use of these data for clinical guidance has been the subject of various papers published in respected medical journals, but without a coherent focus on the general subject of the clinically actionable benefits of objective medical device data for clinical decision-making purposes. Hence, the uniqueness of this book is in providing a single point-of-capture for the targeted clinical benefits of medical device data--both electronic- health-record-based and real-time--for improved clinical decision-making at the point of care, and for the use of these data to address and assess specific types of clinical surveillance.

Clinical Surveillance: The Actionable Benefits of Objective Medical Device Data for Crucial Decision-Making

focuses on the use of objective, continuously collected medical device data for the purpose of identifying patient deterioration, with a primary focus on those data normally obtained from both the higher-acuity care settings in intensive care units and the lower-acuity settings of general care wards. It includes examples of conditions that demonstrate earlier signs of deterioration including systemic inflammatory response syndrome, opioid-induced respiratory depression, shock induced by systemic failure, and more. The book provides education on how to use these data, such as for clinical interventions, in order to identify examples of how to guide care using automated durable medical device data from higher- and lower-acuity care settings. The book also includes real-world examples of applications that are of high value to clinical end-users and health systems.

List of Figures
ix
List of Tables
xiii
Preface xv
Acronyms xix
1 Introduction to Clinical Surveillance
1(22)
1.1 Patient Safety and Clinical Surveillance
3(5)
1.2 The State Vector Analogy
8(4)
1.3 The Decompensating Patient
12(4)
1.4 Medical and Clinical Significance of Real-Time Data
16(6)
1.5
Chapter Summary
22(1)
2 Use of Patient Care Device Data for Clinical Surveillance
23(20)
2.1 Patient Care Device (PCD) Integration
23(4)
2.2 Patient Care Device Data Integration
27(6)
2.3 Future Vision of Data for Clinical Surveillance
33(1)
2.4 Semantic Data Alignment
34(4)
2.5 Combining Data from Multiple Sources for Clinical Surveillance
38(3)
2.6
Chapter Summary
41(2)
3 Alarms and Clinical Surveillance
43(32)
3.1 Introduction to Alarms
43(2)
3.2 False and Nuisance Alarms
45(3)
3.3 Paving the Way for More Intelligent Alarms
48(3)
3.4 Defining Actionable and Non-Actionable Alarms
51(6)
3.5 Methods for Alarm Signal Creation
57(3)
3.6 Alarm Signals Based on Limit Threshold Breaches
60(1)
3.7 Alarm Signals Based on Non-Self-Correcting Measurements
61(2)
3.8 Alarm Signals Based on Deviations from the Historical Trend
63(5)
3.9 Multi-Parameter Threshold Alarms: Alarm Signals Based on Two or More Findings
68(3)
3.10 Frequency-Based Alarms or Alarm Signals Based on Cyclic Parameter Behavior
71(1)
3.11
Chapter Summary
72(3)
4 Mathematical Techniques Applied to Clinical Surveillance
75(34)
4.1 Moving Average: Simple Time-Averaging to Achieve Signal Smoothing
75(6)
4.2 More Formalized Time-Series Filtering: The Kalman Filter
81(8)
4.3 Signal Periodicity and Filtering Noisy Signal Behavior
89(3)
4.4 Signal Frequency Assessments
92(6)
4.5 The Discrete Wavelet Transform
98(9)
4.6
Chapter Summary
107(2)
5 Clinical Workflows Supported by Patient Care Device Data
109(38)
5.1 Opioid-Induced Respiratory Depression (OIRD)
109(12)
5.2 Patient Monitoring in Higher Acuity Settings
121(7)
5.3 Mechanically Ventilated Patients
128(1)
5.4 Clinical Surveillance of the Mechanically Ventilated Patient
129(10)
5.5 Systemic Infection and Shock
139(3)
5.6 Intracranial Pressure Influences on Blood Pressure
142(3)
5.7
Chapter Summary
145(2)
Epilogue: Lessons Learned from Continuous Monitoring 147(4)
Bibliography 151(10)
Index 161
John Zaleski is Head of Clinical Informatics at Capsule Tech, Inc. John Zaleski brings 25+ years of experience in healthcare and medical device data principally as a researcher and, later, as a clinician, to improve the safety and care of patients and clinicians.