The Digital Patient E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: Wiley Series in Modeling and Simulation
• Sprache: Englisch

Seitenzahl: 684

Veröffentlichungsjahr: 2015

Beschreibung

A modern guide to computational models and constructive simulation for personalized patient care using the Digital Patient The healthcare industry's emphasis is shifting from merely reacting to disease to preventing disease and promoting wellness. Addressing one of the more hopeful Big Data undertakings, The Digital Patient: Advancing Healthcare, Research, and Education presents a timely resource on the construction and deployment of the Digital Patient and its effects on healthcare, research, and education. The Digital Patient will not be constructed based solely on new information from all the "omics" fields; it also includes systems analysis, Big Data, and the various efforts to model the human physiome and represent it virtually. The Digital Patient will be realized through the purposeful collaboration of patients as well as scientific, clinical, and policy researchers. The Digital Patient: Advancing Healthcare, Research, and Education addresses the international research efforts that are leading to the development of the Digital Patient, the wealth of ongoing research in systems biology and multiscale simulation, and the imminent applications within the domain of personalized healthcare. Chapter coverage includes: * The visible human * The physiological human * The virtual human * Research in systems biology * Multi-scale modeling * Personalized medicine * Self-quantification * Visualization * Computational modeling * Interdisciplinary collaboration The Digital Patient: Advancing Healthcare, Research, and Education is a useful reference for simulation professionals such as clinicians, medical directors, managers, simulation technologists, faculty members, and educators involved in research and development in the life sciences, physical sciences, and engineering. The book is also an ideal supplement for graduate-level courses related to human modeling, simulation, and visualization.

Leseprobe

Table of Contents

COVER

TITLE PAGE

LIST OF CONTRIBUTORS

PREFACE

EDITORS’ NOTE

PART 1: THE VISION: THE DIGITAL PATIENT—IMPROVING RESEARCH, DEVELOPMENT, EDUCATION, AND HEALTHCARE PRACTICE

1 THE DIGITAL PATIENT

HEALTH, THE GOAL

PERSONALIZED MEDICINE

THE BEST OUTCOMES

THE EMERGENCE OF THE DIGITAL PATIENT

THE HUMAN PHYSIOME

ENABLING THE DIGITAL PATIENT

P4 MEDICINE

CONCLUSION

REFERENCES

2 REFLECTING ON DISCIPULUS AND REMAINING CHALLENGES

INTRODUCTION

A BRIEF CONTEXTUAL BACKGROUND AND A CALL FOR INTEGRATION: PERSONALIZED MEDICINE IS HOLISTIC

THE MANY VERSIONS OF THE DIGITAL PATIENT: ON THE ROAD TO MEDICAL AVATARS

DISCIPULUS: THE DIGITAL PATIENT TECHNOLOGICAL CHALLENGES AND MAIN CONCLUSIONS

THE REMAINING CHALLENGES AND BIG DATA

CONCLUSION

REFERENCES

3 ADVANCING THE DIGITAL PATIENT

INTRODUCTION

THE DIGITAL PATIENT: ITS EARLY START

ENGAGING THE DIGITAL PATIENT

CONCLUSION

4 THE SIGNIFICANCE OF MODELING AND VISUALIZATION

INTRODUCTION

MODELING A COMPLEX SYSTEM: HUMAN PHYSIOLOGY

MEDICAL MODELING, SIMULATION, AND VISUALIZATION

MODES AND TYPES OF VISUALIZATION

VISUALIZATION FOR PATIENT-SPECIFIC USEFULNESS

CONCLUSION

REFERENCES

PART 2: STATE OF THE ART: SYSTEMS BIOLOGY, THE PHYSIOME, AND PERSONALIZED HEALTH

5 THE VISIBLE HUMAN: A GRAPHICAL INTERFACE FOR HOLISTIC MODELING AND SIMULATION

INTRODUCTION

EDUCATION

MODELING

VIRTUAL REALITY TRAINERS AND SIMULATORS

CONCLUSION

REFERENCES

6 THE QUANTIFIABLE SELF: PETABYTE BY PETABYTE

INTRODUCTION

SMARR’S QUANTIFIED SELF

EXTENDING SMARR’S RESEARCH

THE QUANTIFIED SELF-VISION, SIMPLIFIED

CRITICISM

CONCLUSION

REFERENCES

7 SYSTEMS BIOLOGY AND HEALTH SYSTEMS COMPLEXITY: IMPLICATIONS FOR THE DIGITAL PATIENT

INTRODUCTION

SYSTEMS BIOLOGY

THE INSTITUTE FOR SYSTEMS BIOLOGY

THE COMPLEXITY INSTITUTE

THE POTENTIAL OF SYSTEMS BIOLOGY

CRITICISM

CONCLUSION

REFERENCES

8 PERSONALIZED COMPUTATIONAL MODELING FOR THE TREATMENT OF CARDIAC ARRHYTHMIAS

INTRODUCTION

BASICS OF CARDIAC ELECTROPHYSIOLOGY

CARDIAC MODELING ADVANCEMENTS

REGULATION OF INTRACELLULAR CALCIUM

FROM CELLS TO CABLES TO SHEETS TO TISSUE TO THE HEART

WHERE CAN WE GO FROM HERE? WHAT IS THE CARDIAC MODEL IN THE DIGITAL PATIENT?

REFERENCES

9 THE PHYSIOME PROJECT,

open

EHR ARCHETYPES, AND THE DIGITAL PATIENT

INTRODUCTION

MULTISCALE PHYSIOLOGICAL PROCESSES

PHYSIOME PROJECT STANDARDS, REPOSITORIES, AND TOOLS

ARCHETYPE SPECIALIZATION

ARCHETYPE DEFINITION LANGUAGE

LINKING ARCHETYPES TO EXTERNAL KNOWLEDGE SOURCES (TERMINOLOGY AND BIOMEDICAL ONTOLOGIES)

ARCHETYPE ANNOTATIONS

Open

EHR MODEL REPOSITORY AND GOVERNANCE

FAST HEALTHCARE INTEROPERABILITY RESOURCES

A DISEASE SCENARIO

SUMMARY AND CONCLUSIONS

REFERENCES

10 PHYSICS-BASED MODELING FOR THE PHYSIOME

INTRODUCTION

MODELING SCHEMES

FUTURE CHALLENGES

CONCLUSION

ACKNOWLEDGMENTS

REFERENCES

11 MODELING AND UNDERSTANDING THE HUMAN BODY WITH SwarmScript

INTRODUCTION

RELATED WORK

MULTIAGENT ORGANIZATION

DESIGNING INTERACTIVE AGENTS

SPEAKING SwarmScript

ANSWERING DEMAND: THE DESIGN OF SwarmScript

GRAPH-BASED RULE REPRESENTATION

THE SOURCE–ACTION–TARGET

SwarmScript INTO3D

A SwarmScript DIALOGUE

DISCUSSION

SUMMARY

REFERENCES

12 USING AVATARS AND AGENTS TO PROMOTE REAL-WORLD HEALTH BEHAVIOR CHANGES

INTRODUCTION

AVATARS AND AGENTS

USING AGENTS AND AVATARS TO PROMOTE HEALTH BEHAVIOR CHANGES

CONCLUSION

REFERENCES

13 VIRTUAL REALITY AND EATING, DIABETES, AND OBESITY

INTRODUCTION

VIRTUAL REALITY

CONCLUSION

REFERENCES

14 IMMERSIVE VIRTUAL REALITY TO MODEL PHYSICAL: SOCIAL INTERACTION AND SELF-REPRESENTATION

INTRODUCTION

THEORY FOR IMMERSIVE VIRTUAL LEARNING SPACES

CONCLUSION

REFERENCES

PART 3: CHALLENGES: ASSIMILATING THE COMPREHENSIVE DIGITAL PATIENT

15 A ROADMAP FOR BUILDING A DIGITAL PATIENT SYSTEM

INTRODUCTION

APPROACH

BUILDING THE DIGITAL PATIENT THROUGH INTEROPERABILITY

CONCLUSION

ACKNOWLEDGMENT

REFERENCES

16 MULTIDISCIPLINARY, INTERDISCIPLINARY, AND TRANSDISCIPLINARY RESEARCH: CONTEXTUALIZATION AND RELIABILITY OF THE COMPOSITE

INTRODUCTION

INTERDISCIPLINARITY AND INTERDISCIPLINARY RESEARCH

BASE OBJECT MODELS TO SUPPORT TRANSDISCIPLINARITY AND COMPOSABILITY

OPEN CHALLENGES ON RELIABILITY

SUMMARY AND CONCLUSION

REFERENCES

17 BAYES NET MODELING: THE MEANS TO CRAFT THE DIGITAL PATIENT

INTRODUCTION

OTHER INTERESTING APPLICATIONS

CONCLUSION

REFERENCES

PART 4: POTENTIAL IMPACT: ENGAGING THE DIGITAL PATIENT

18 VIRTUAL REALITY STANDARDIZED PATIENTS FOR CLINICAL TRAINING

INTRODUCTION

THE RATIONALE FOR VIRTUAL STANDARDIZED PATIENTS

CONVERSATIONAL VIRTUAL HUMAN AGENTS

USC EFFORTS TO CREATE VIRTUAL STANDARDIZED PATIENTS

CONCLUSION

REFERENCES

19 THE DIGITAL PATIENT: CHANGING THE PARADIGM OF HEALTHCARE AND IMPACTING MEDICAL RESEARCH AND EDUCATION

INTRODUCTION

OVERVIEW DIGITAL MEDICINE PROJECTS

PERSONALIZED PATIENT CARE CLINICAL USE

RECOMMENDED EDUCATION AND TRAINING FOR VPH PROJECT PARTICIPATION

FROM FLEXNER TO THE 2010 CARNEGIE REPORT

SUMMARY STATEMENTS

REFERENCES

20 THE DIGITAL PATIENT: A VISION FOR REVOLUTIONIZING THE ELECTRONIC MEDICAL RECORD AND FUTURE HEALTHCARE

INTRODUCTION

APPLICATIONS OF THE DIGITAL PATIENT AS THE EMR

DISCUSSION

CONCLUSION

REFERENCES

21 REALIZING THE DIGITAL PATIENT

INDEX

Modeling and Simulation

End User License Agreement

List of Tables

Chapter 09

TABLE 9.1 The Data and Modeling Standards in Use in the Physiome Project, and Examples of Repositories and Software Tools Supporting them

Chapter 10

TABLE 10.1 An Overview of FDA Requirements for Computer Modeling and Simulation (CM&S) Submissions for Device Approval

Chapter 15

TABLE 15.1 Total Number of SISO Documents per Year

TABLE 15.2 SISO Ranking of Concepts by Relevance Between 2000 and 2010

TABLE 15.3 Concept Relevance Ranked by Average in Percentages

Chapter 17

TABLE 17.1 Summary of the Five Applications Described in Detail in this Chapter

List of Illustrations

Chapter 01

FIGURE 1.1 System of systems and levels of informatics.

Chapter 02

FIGURE 2.1 Different “maturity levels” of the Digital Patient [1].

FIGURE 2.2 Different areas needed to achieve the “Digital Patient,” according to the DISCIPULUS vision [1].

Chapter 04

FIGURE 4.1 The virtual operating room at Old Dominion University [38].

FIGURE 4.2 Bubble graph of the wealth and health of nations over 200 years [39].

FIGURE 4.3 The wound debridement simulator with haptic feedback at Old Dominion University [40].

FIGURE 4.4 1683 map of West Africa by the cartographer Cloveris showing Sierra Leone (left) [41]. NASA GES DISC projected and visualized A-Train swath data along with A-train vertical profiles in Google Earth (right) [42].

FIGURE 4.5 Da Vinci’s sketches of muscles and skeleton (left) [43]. Volume rendering of a native thoracic CT scan (right) [44].

FIGURE 4.6 Surface model of a pelvis bone from a few X-ray images (left) [45]. Volume rendering of CT scan data (right) [46].

FIGURE 4.7 Interactive 3D flow visualization using particle systems [47].

FIGURE 4.8 Ultrasound tool trainer at the Virginia Modeling Analysis and Simulation Center [48].

FIGURE 4.9 Vector graph visualization of intracardiac blood flow in a young (a) and an old (b) healthy volunteer. The formation of diastolic vortex flow in the LA and LV is indicated by the dashed circles. Note the reduced diastolic inflow velocities (color coding) and less prominent vortex flow in the older volunteer. Ao, aorta; LA, left atrium; and RV, right ventricle [49].

FIGURE 4.10 Laparoscopic surgery port placement visualization module [50].

FIGURE 4.11 Apple’s health app [51].

Chapter 05

FIGURE 5.1 Visible Human Male images through the right femoral head are on the right. Corresponding images of a specimen sectioned and imaged at 50 µ resolution is on the left. The lower images are magnified views from the region of the foveal ligament and accentuate the nearly two orders of magnitude difference in image resolution. (The VH Male images were acquired with 330 µ pixels while their plane spacing was 1 mm.)

FIGURE 5.2 This sequence from left to right demonstrates the alimentary system three-dimensionally extending both anteriorly and posteriorly from a coronal plane through the esophagus. The left and right images are rotated 40° off an orthogonal view of the coronal plane. The center images are rotated 80° off an orthogonal view of the coronal plane.

FIGURE 5.3 This screen capture from a VH Dissector Sectra Visualization Table illustrates real-time oblique slicing through the full resolution Visible Human Database. The multitouch interface provides collaborative interaction as students explore and reveal 3D anatomy and its extension from the 2D transverse plane (horizontal plane outline through the distal femurs), 2D coronal plane (vertical plane outline posterior to the femoral shafts), and 2D oblique plane (plane outline through the left hip and just superior to the right knee).

FIGURE 5.4 These images demonstrate an innovative interactive and engaging use of the Visible Human in the VH Dissector by Debra Patten at Durham University, Durham, UK. The projection adds photorealistic internal anatomy on the skin surface of the students. These projections might serve as a template for body painting or marking.

FIGURE 5.5 The screen capture on the left is a simulation of ultrasound reflection with the transducer contacting the surface just enough to visualize the blood vessels. The two circular dark areas are arteries, while the elongated shape is a vein. The second image shows the same area with the transducer pressed with greater force. The arteries are slightly deformed, while the vein is substantially collapsed.

FIGURE 5.6 Flexion of the VH Male neck (left) and further neck flexion accompanied by more extensive spinal flexion (right) is required for optimal patient positioning for needle access to the spinal canal. Some modification to the soft tissues overlying the skeletal components has been applied.

FIGURE 5.7 The rubber bust and skeletal system are built to the geometry of the VH Male. The vertical position of the tip of the “real” needle attached to a “real” syringe (just toward the left of the keyboard) determines which transverse cross section is displayed on the monitor. The syringe and needle are also displayed (updated in real time) on the right-hand side of the monitor. Rotation of the bust rotates the 3D rendering on the monitor. As the user penetrates deep to the skin with the tip of the needle (the entire length of the needle if the syringe and needle are held in a horizontal plane at the correct location of the needle is displayed in the cross section. The needle position can be frozen at any time and the 3D rendering dissected to reveal the entire needle in the 3D VH rendering. An optional display utilizes the orientation of the syringe and needle to control the display of an oblique cross section in place of the transverse cross section. The full length of the needle is always in the oblique cross section.

FIGURE 5.8 This virtual reality, partial task trainer is similar to the augmented reality version in Figure 5.7. This trainer includes all the features of the augmented reality version with the addition of ultrasound image guidance training. Haptic response for needle penetration is provided by 3D Systems Omnis.

Chapter 07

FIGURE 7.1 System of systems and levels of informatics.

FIGURE 7.2 NTU health systems complexity program.

Chapter 08

FIGURE 8.1 Hodgkin–Huxley computational model of a neuron. (Left) The circuit diagram represents the electrical properties of the squid giant axon: The cellular membrane is represented by a capacitive element (

C

m

), and the sodium (Na), potassium (K), and leak (L) ionic currents are represented by resistors with conductances

g

Na

,

g

K

, and

g

L

, respectively, each in series with a voltage source,

E

Na

,

E

K

, and

E

L

, respectively, which represent the ionic current reversal potential (voltage at which the net current is zero). (Right) A representative simulation reproduces the repetitive firing of the neuron, that is, action potentials, in response to an applied stimulus.

FIGURE 8.2 Luo–Rudy 1991 model of the ventricular myocyte. The myocyte model is stimulated by a brief applied current pulse that elicits an action potential, every 500 ms. The voltage trace is characteristic of the cardiac action potential, with the rapid upstroke, plateau phase, and gradual repolarization. A calcium transient with slow upstroke and recovery follows each action potential. The model contains six currents: sodium (Na), slow inward (typically associated with calcium), three potassium (K) currents, and a background (b) or leak current. The units for voltage, calcium, and ionic currents are millivolts, micromolar, and microamperes/cm

2

, respectively.

FIGURE 8.3 Stochastic gating of an ion channel. A simulation of the stochastic gating of a three-state ion channel, with a kinetic scheme given by C

1

  C

2

  O

3

, where C

1

and C

2

are two closed states and O

3

is an open state. The simulation shows that the duration of and time between channel openings and closings are random. In the more detailed Markov chain models of ion channel gating, more states may be present, such as inactivated or multiple closed or open states. The transitions between states may depend on voltage, ion concentrations (i.e., calcium), or concentration of a particular ligand or pharmacological agent.

FIGURE 8.4 Calcium signaling in the cardiac myocyte. Illustration of calcium signaling in the cardiac myocyte shows the invagination of the sarcolemma (SL), or cell membrane, known as a t-tubule, and the intracellular store of calcium, the sarcoplasmic reticulum (SR). Voltage-gating calcium channels on the sarcolemma trigger the release of calcium from the SR through channels called “ryanodine receptors.” The large efflux of calcium triggers contraction of the myocyte, after which calcium is returned to the SR via ATP-dependent pumps on the SR membrane.

FIGURE 8.5 Reconstruction of anatomical geometry. A. MRI scan of the infarct heart. B. Calculation of myocyte orientation from DTMRI data. C. Segmentation of imaging data into healthy myocardium, periinfarct or gray zone (GZ), and scar tissue. D. Separation of atria from ventricles. E. Finite element mesh. F. Three-dimensional representation of the heart. G. Representation of the DTMRI-reconstructed myocyte orientation. H. Action potentials from the healthy and GZ myocytes.

Chapter 09

FIGURE 9.1 The physiology circuitboard, showing (left) the major anatomical components of the body and (right) an expanded view of the vascular cardiac system. The terminology is from the Foundation Model of Anatomy (FMA) and all 50,000 anatomical terms of the FMA can be accessed by click/zooming into any region.

FIGURE 9.2 Overlaying a FieldML model of flow in the cardiovascular system (figure on right) with tissue components of interest in the physiology circuitboard (figure on left). Annotating components of the vascular model with the FMA terms ensures that the 3D computational FieldML model can be projected onto the circuitboard to link with a variety of CellML models operating in different tissue regions. See Ref. [30] for further details.

FIGURE 9.3 Mock-up of an ApiNATOMY circuitboard display, showing anatomical layout of a tiled depiction of body regions, edge-based illustration of advective conduits, and a cylindrical pFTU. The histology component of this workflow generates tissue parcellations from 3D histology images, known as primary functional tissue units (pFTUs) [35, 1]—these images are annotated with terms from the FMA and CT. We apply the modeling component of the workflow to link these tissue units to models of long-range fluid flow over the circuit board.

FIGURE 9.4 Different layers of standardization for health information.

FIGURE 9.5 Schematic representation of the

open

EHR multilevel modeling approach.

FIGURE 9.6 The openEHR CKM blood pressure measurement archetype mindmap.

FIGURE 9.7 Archetype Definition Language sections and their notations (ODIN, OCL and FOPL).

FIGURE 9.8 Illustrative example of the interaction between ApiNATOMY, PMR web services, and the GMS. ApiNATOMY is able to execute queries using the PMR metadata repository (arrow 1, in bold). In the example shown, ApiNATOMY is querying for a given FMA term (for the renal proximal tubule) and a specific paper identified via a PubMed ID. PMR responds to the query providing all matching PMR exposures (arrow 2), from which the ApiNATOMY user selects the appropriate PMR workspace (identified by the URL shown in the diagram). From the selected workspace, the ApiNATOMY user selects a specific CellML model (or the tool infers the required CellML model from information obtained from the exposure definition in PMR) and the GMS is instructed to load that CellML model (arrow 3). Upon receiving this instruction, the GMS will request the model from PMR [12] and instantiate that model into an internal executable form. ApiNATOMY is able to sample spatial fields to extract temporal snapshots for a specific spatial location. Using services provided by the GMS, ApiNATOMY is able to select a particular variable in the instantiated CellML model and instruct the GMS to use the temporal snapshot to define that variable. This service requires the transfer of the temporal snapshot from ApiNATOMY to the GMS using a standard JavaScript array encoded as a JSON string. Once a particular simulation is fully defined, ApiNATOMY instructs the GMS to execute the simulation, over the time interval specified by the central timing module. Following the execution of the simulation, ApiNATOMY requests the simulated variable transient(s) for the desired model variables and presents the results to the user. Once again, this data is transferred as JavaScript arrays encoded in the JSON format.

FIGURE 9.9 Problem/diagnosis archetype from

open

EHR CKM with example SNOMED CT bindings.

Chapter 10

FIGURE 10.1 The hierarchy of model types and levels. The hierarchy of models stretches from the most reductionist intracellular model, developed from data obtained in tightly controlled conditions, through the classical multiscale integration of cellular processes into tissue and organ process, up to the top-down models of macroscopic interactions between organ systems. The digital patient will require full integration through all levels.

FIGURE 10.2 Population modeling of blood pressure. Thomas’s group used one-at-a-time methodology to perform a sensitivity analysis on an extended form of Guyton’s model [4]. Calculating mean pressure from diastolic and systolic pressure, almost 2/3 of sampled individuals were hypertensive.

FIGURE 10.3 Simulation (black lines) and experimental responses to baroreflex stimulation. Experimental response to a baroreflex stimulation device in dogs was compared to a human simulation (HumMod) to explore the mechanisms behind chronic blood pressure reduction. The studies identified factors that correlated with positive response.

FIGURE 10.4 A. The pharmacological response to three compounds as measured in a bench experiment. B. The effect of Shiener’s method of effect compartment modeling on normalizing the response to allow interpretation of the pharmacodynamic properties of the compounds relative to one another.

FIGURE 10.5 Model-based drug development (MBDD) at Pfizer. MBDD can be applied at all stages of drug development, and it impacts the success rates of trials at every stage after the simulation work was done. The dotted lines show the expected success rate assuming no change from 2004 levels [98].

Chapter 11

FIGURE 11.1 One of a set of graph rewriting rules that describe the proliferation behavior of a cell.

FIGURE 11.2 Screenshots from the implementation of the Source-Action-Target representation of SwarmScript. (a) Several operators can be selected by means of the enclosing rubber band and wrapped into (b) a high-level operator.

FIGURE 11.3 Instances of (a) query, (b) action, and (c) circuit operators. The spherical shapes allow for a consistent view in 3D space, the attached cones convey the flow of information between operators. The depictions include label windows that the user can create clicking the respective UI elements. Labels of input connectors contain a text field that presents the currently received input and allows the user to assign a constant value.

FIGURE 11.4 (a) Dragging across an output connector creates a new edge, (b) whose head is navigated by the user, (c) to be dropped onto an input connector of another operator. (d) The new connection has established a properly phrased behavioral rule. Therefore, the action operator now receives and processes input information as seen in the input connector’s label window.

FIGURE 11.5 Visualizations of the biological agents that drive the SwarmScript INTO3D simulation of the secondary human immune response to Influenza A. (a) Epithelial cells. (b) Influenza A virus. (c) Dendritic cell. (d) B cell. (e) T cell. (f) Killer T cell. (g) Antibody. (h) Macrophage.

FIGURE 11.6 The secondary immune-response simulation initially hosting 25 tissue cells at different time steps

t

. The progression shows the initial infection, the reaction of the macrophages, the differentiation and recruitment of lymphocytes, the viral spread, the production of antibodies, and the eventual recovery of healthy tissue. Please note that the behavioral logic described earlier is algorithmically and visually translated into temporary relationships and interactions (edges) among the agents. At any point of the simulation, the user can dive into and reconfigure the agents’ behaviors.

FIGURE 11.7 Gray spheres indicate the embedded SwarmScript INTO3D agents and their logic. (a) The lung inside the human body and (b) containing the infected tissue.

FIGURE 11.8 Combined visualization and behavioral logic of the biological agents that drive the presented SwarmScript INTO3D simulation of the human immune system. (a) Epithelial cells. (b) Influenza A virus. (c) Dendritic cell. (d) B cell. (e) T cell. (f) Killer T cell. (g) Antibody. (h) Macrophage. In (f), a nested operator of one of the agents is magnified (conic overlay).

FIGURE 11.9 The headsup display for navigating the modeling and simulation phases with SwarmScript INTO3D (top), for selecting and deploying previously stored operators in the current scene (center), and to configure the currently selected operator (right).

Chapter 13

FIGURE 13.1 Threshold model of social influence in digital environments at high levels of self-relevance.

Chapter 15

FIGURE 15.1 Concept map of SISO between 2000 and 2010.

FIGURE 15.2 Percent concept relevance per year.

FIGURE 15.3 Main focus area for developing and sustaining a digital patient.

Chapter 16

FIGURE 16.1 Multidisciplinarity, interdisciplinarity, and transdisciplinarity.

FIGURE 16.2 Domains of information exchange in ISO/IEC 11179 [6].

FIGURE 16.3 NIEM core, NIEM domains, and future domains [9].

FIGURE 16.4 NIEM-based information exchange contextualization.

FIGURE 16.5 BOM example.

FIGURE 16.6 Levels of LCIM.

Chapter 17

FIGURE 17.1 Bayesian network example [1].

FIGURE 17.2 Dynamic Bayesian networks for insulin regulation.

FIGURE 17.3 Steps in development of the Bayesian network model.

FIGURE 17.4 The Bayesian network structure is modeled with uniform probabilities. Inset shows the day 1 and day 2 nodes for a single syndrome after learning.

Chapter 18

FIGURE 18.1 “Justin.”

FIGURE 18.2 “Justina.”

FIGURE 18.3 (a) Sgt. Castillo military virtual standardized patient and (b) in use with trainee using wall projection.

FIGURE 18.4 MILES virtual patient.

FIGURE 18.5 USC standard patient “select-a-chat” structured virtual human encounter authoring tool.

FIGURE 18.6 A structured virtual human encounter depicting a vaccine-resistant parent (USC standard patient).

FIGURE 18.7 Virtual Child Witness—a structured encounter.

FIGURE 18.8 NLRA-style VSPs permit learners to ask questions in a natural manner through speech or typed input (USC standard patient).

FIGURE 18.9 VSP interview mind-map.

Chapter 21

FIGURE 21.1 Different areas needed to achieve the Digital Patient.

FIGURE 21.2 A sampling of data sources for the Digital Patient.

FIGURE 21.3 Different layers of standardization for health information (from Chapter 9).

Guide

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THE DIGITAL PATIENT

Advancing Healthcare, Research, and Education

Edited by

C. DONALD COMBS

Eastern Virginia Medical School

JOHN A. SOKOLOWSKI

Old Dominion University

CATHERINE M. BANKS

Old Dominion University

Copyright © 2016 by John Wiley & Sons, Inc. All rights reserved

Published by John Wiley & Sons, Inc., Hoboken, New JerseyPublished simultaneously in Canada

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Library of Congress Cataloging-in-Publication Data:

The digital patient : advancing healthcare, research, and education / edited by C. Donald Combs, John A. Sokolowski, Catherine M. Banks.  p. ; cm. Includes bibliographical references and index.

 ISBN 978-1-118-95275-7 (paperback)I. Combs, C. Donald, editor. II. Sokolowski, John A., 1953– , editor. III. Banks, Catherine M., 1960– , editor.[DNLM: 1. Patient Care Management. 2. Individualized Medicine–methods. 3. Patient-Specific Modeling. W 84.7]RA971.6362.10285–dc23

      2015029789

Cover image courtesy of Hector M. Garcia

To Pam, Cole, and Ford—C. Donald Combs

Marsha, Amy, and Whitney—John A. Sokolowski

The two boys in my life—Catherine M. Banks

LIST OF CONTRIBUTORS

Sun Joo (Grace) Ahn, PhDDepartment of Advertising and Public RelationsGrady College of Journalism and Mass CommunicationUniversity of GeorgiaAthens, GA, USA

Mona Alimohammadi, PhDDepartment of Mechanical EngineeringUniversity College LondonLondon, UK

Koray Atalag, PhD, MDBioengineering InstituteUniversity of AucklandAuckland, New Zealand

Catherine M. Banks, PhDVirginia Modeling, Analysis and Simulation CenterOld Dominion UniversitySuffolk, VA, USA

Scarlett R. Barham, MPHSchool of Health ProfessionsEastern Virginia Medical SchoolNorfolk, VA, USA

Eric B. Bauman, PhD, RNClinical Playground LLCMadison, WI, USA

Jim Blascovich, PhDDepartment of Psychological and Brain SciencesUniversity of CaliforniaSanta Barbara, CA, USA

C. Donald Combs, PhDVice President and Dean School of Health ProfessionsEastern Virginia Medical SchoolNorfolk, VA, USA

Jessica E. Cornick, BADepartment of Psychological and Brain SciencesUniversity of CaliforniaSanta Barbara, CA, USA

Bernard de Bono, PhD, MDBioengineering InstituteUniversity of AucklandAuckland, New Zealand

Saikou Y. Diallo, PhDVirginia Modeling, Analysis and Simulation CenterOld Dominion UniversitySuffolk, VA, USA

Vanessa Díaz-Zuccarini, PhDDepartment of Mechanical EngineeringUniversity College LondonLondon, UK

Barry C. Ezell, PhDVirginia Modeling, Analysis and Simulation CenterOld Dominion UniversitySuffolk, VA, USA

Hector M. Garcia, PhDVirginia Modeling, Analysis and Simulation CenterOld Dominion UniversitySuffolk, VA, USA

Jörg Hähner, PhDOrganic Computing InstituteUniversity of Augsburg Augsburg, Germany

Robert L. Hester, PhDDepartment of Physiology and BiophysicsUniversity of Mississippi Jackson, MS, USA

Peter J. Hunter, PhDBioengineering InstituteUniversity of AucklandAuckland, New Zealand

Christian Jacob, PhDDepartment of Biochemistry and Molecular Biology and Department of Computer ScienceUniversity of CalgaryCalgary, Alberta, Canada

Christopher J. Lynch, MSVirginia Modeling, Analysis and Simulation CenterOld Dominion UniversitySuffolk, VA, USA

David P. Nickerson, PhDBioengineering InstituteUniversity of AucklandAuckland, New Zealand

V. Andrea Parodi, PhD, RNVirginia Modeling, Analysis and Simulation CenterOld Dominion UniversitySuffolk, VA, USA

César Pichardo-Almarza, PhDDepartment of Mechanical EngineeringUniversity College LondonLondon, UK

William A. Pruett, PhDDepartment of Physiology and BiophysicsUniversity of Mississippi Jackson, MS, USA

Albert Rizzo, PhDInstitute for Creative TechnologiesUniversity of Southern CaliforniaLos Angeles, CA, USA

Richard M. Satava, MDDepartment of SurgeryUniversity of WashingtonSeattle, WA, USA

Stefan Schellmoser, M Sc Organic Computing InstituteUniversity of Augsburg Augsburg, Germany

Peter M. A. Sloot, PhDDepartment of Computational ScienceUniversity of Amsterdam Amsterdam, the Netherlands

John A. Sokolowski, PhDVirginia Modeling, Analysis and Simulation CenterOld Dominion UniversitySuffolk, VA, USAVictor M. Spitzer, PhDCenter for Human SimulationUniversity of Colorado-DenverAurora, CO, USA

Thomas Talbot, MDInstitute for Creative TechnologiesUniversity of Southern CaliforniaLos Angeles, CA, USA

Joseph A. Tatman, PhDInnovative Decisions, Inc.Vienna, VA, USA

Andreas Tolk, PhDSimulation Engineering DepartmentThe MITRE CorporationHampton, VA, USA

Sebastian von Mammen, PhDOrganic Computing InstituteUniversity of Augsburg Augsburg, Germany

Seth H. Weinberg, PhDVirginia Modeling, Analysis and Simulation CenterOld Dominion UniversitySuffolk, VA, USA

PREFACE

Understanding in detail and with certainty what is going on within one’s own body has been an elusive quest. Partial glimpses and general understanding are the best we have been able to do with the data we have at our disposal and with the limitations of population-normed theories of what the data mean for diagnosis and treatment of individuals. In the not-too-distant future, however, that will change as the Digital Patient platform is developed. The capacity to measure one’s personal physiological and social metrics, compare those metrics with the metrics of millions of other humans, personalize needed therapeutic interventions, and measure the resulting changes will realize the vision of personalized medicine. Incorporating all of this rich data in simulations will have significant impacts on medical research, education, and healthcare systems around the world, as more interventions are simulated and assessed prior to their use in therapy.