Real Time Clinical Decision Support System

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Real Time Clinical Decision Support System

• ???

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Challenge of Modern Clinical Medicine

• Dilemma
• Patients increase
• Clinical Time decrease
• Quality of Health Care debase
• Requirement
• Patient comfort
• Clinical Time efficiency
• Quality of Health Care improvement
• Ethics
• Patient Right and Privacy

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Strategy

• RTCDSS Real Time Clinical Decision Support
  System
• QOL Quality of Life
• Infometrix Information Psychometrics? Online
  Measurement for QOL
• CDW Clinical Data Warehouse
• OLAP Online Analytical Process
• CDSS Clinical Decision Support System
• Internet Web Services and Real-Time Technology
• Foundation
• EBM Evidence-based Medicine
• HISES Hospital Information System Expert
  System

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Scope

• Operating Interface
• Accessible design of human-computer interface
• Patient- and Clinician-oriented interface
• Patient-to-Clinician (P2C) communication
• Flexible and Expandable modules
• Web-based auto data transportation
• Proposed Functionality
• Traceable clinical markers for chronic diseases
• Instantaneous Patient Clinical Record (PCR)
• Reliable Patient Reported Outcome (PRO)
• Quantitative medical informatics
• Analytical online diagram

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INTRODUCTION
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Clinical Treatment

• Routine treatment procedure
• Physicians take much time to study patients
  clinical records (PCRs) prior to explain abstruse
  clinical markers to patients in clinics.
• For chronic and traceable diseases, they need to
  refer patients quality of life (QOL) and their
  patient-reported outcomes (PROs) for prescribing
  the proper therapies.
• Modern hospital information systems
• Information technology (IT) and Web-based
  facility have become the major backbone of the
  modern hospital information systems (HIS)
• Traceable clinical markers for the chronic
  diseases
• Clinical decision support system (CDSS) with
  patient- and clinician-oriented interface as well
  as patient-to-clinician (P2C) communication.

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• Major cancer therapy
• Ability Find and destroy tumors
• Disability Bring numbers of side effect
• It implies that QOL is deeply impacted by
  uncertainty and after-effect due to treatment of
  oncology clinic.
• Traps of clinical and health care
• Incorrect judgments when patients embarrass on
  answering private questions or hiding actual
  conditions
• Inconvincible diagnosis by computer-based CDSS
  (or HIS) on clinician performance and patient
  outcome
• HIS difficulty and possible solution
• Function-oriented system uneasy to create a
  universal system for varied clinical requirements
• Flexible platform available to build up
  expandable components for specified clinical
  purpose by customized rules

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Quality of Life

• Assessment of Quality of Life
• EORTC provides QOL questionnaires to highlight
  physicians awareness of patients status and
  greatly facilitate physician-patient
  communication
• Infometrics combines information and
  psychometrics technology for measurement,
  statistical modeling, informatics and practice,
  in palliative care with computerized procedure by
  interactive assessment system
• Concept of instant QOLPROPCR
• Measurement and management of PROs Infometrics
  technique can assist clinicians to more precisely
  recognize actual response of patients and improve
  the quality of care with instant process and real
  time outcomes statistics.
• Graphical diagrams clinicians thus can convince
  patients by presenting PRO instantly with other
  PCR.

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Needs of Clinical Practice

• Problems to improve quality of clinical
  treatments
• Clinicians may take several hours, or even a
  couple of days, to review PCRs but only have a
  few minutes to explain their opinions to patients
• Patients typically find difficulty to understand
  their condition since clinicians may only explain
  the disease adequately using written descriptions
• The CDSS is computerized, but it may not have
  online capability in many clinics
• Real-time analysis is not supported by many
  commercial computational tools
• Need clinical data tracking ? real-time decision
  making
• The flexible Web-based CDSS with online
  evidence-based medicine (EBM) progress is a
  growing trend in advanced clinical care

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Clinical Decision Support

• Enhanced CDSS
• Analytical tools assist clinicians in
  estimating the relative pretreatment parameters
  and for tracking the proper diagnostic guidelines
  on visualized interfaces
• Clinician-oriented interface improve accuracy
  and efficiency of decision support
• RTCDSS support interactive diagrammed interface
  with real-time online analysis to efficiently
  evaluate instant informatics and to make clinical
  decisions
• Expandability and Feasibility follow the model
  to take chronic diseases with traceable markers
• Clinical Guideline
• Work with medical evidences and recommend
  appropriate treatments by graphical interface
• Allow users to traverse the algorithm by
  flowcharts in an interactive fashion due to
  Web-interfaced process

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• Clinical Benefits
• Rapid knowledge acquisitions, shareable guideline
  models, and robust information systems while
  evaluating its impacts on outcomes
• The electronic guidelines improve decision
  quality and physician-patients interaction
  significantly
• Encountered Obstacles
• Management of workflow integration would be the
  most difficult tasks ex, integrating a new
  RTCDSS with the PROs, PCRs, CDSS, and interactive
  guidelines into the legacy HIS.
• The framework for interactive clinical guidelines
  should consider readiness of clinicians for
  practice, barriers to change as experienced by
  clinicians, and the target level of interventions

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DEVELOPMENT OF RTCDSS INFRASTRCTURE

• Model-view-controller model
• Object relation mapping model
• Clinical data warehouse model
• Web services model
• Online analytical process model
• Asynchronous JavaScript XML-HttpRequest model

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Model-view-controller model

• Design patterns in software engineering
• First made by introducing 23 patterns related to
  creational, structural and behavioral models
• For software design to progress recurrent
  elements (Gamma et al., 1994).
• The model-view-controller (MVC) pattern
• Hybrids strategy, observer, and composite
  patterns
• Divides system responsibilities into the model,
  the view, and the controller
• Software framework with MVC paradigm
• Open-source framework such as Strut, Spring,
  Hibernate for development.
• Use polling for its input control to solve the
  problems on consuming computation resources when
  the user is not interacting with the interface
  and avoid unnecessary performance loss.

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• MVC design patterns
• Model maintains program data and logic
• View provides a visual presentation of the
  model
• Controller processes user input and makes
  modifications to the model
• Modelized architecture
• Expandable and reusable components of the
  Web-based platform
• Efficient and flexible collaboration for (a)
  instantaneous disease evaluation, (b) risk
  analysis, and (c) treatment guidance
• Web assessment for acquiring PROs from online
  infometrics system and adapting PCRs with the
  legacy HIS in hospital.

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Web-based MVC Modelized Architecture
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Web-based architecture with model, view,
controller components
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4-Tier MVC-based RTCDSS for Clinics
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• Components of MVC-based RTCDSS for Clinics
• Four-Tier Components
• Presentation interactive guideline, real-time
  diagram ? Browser Windows
• Management privilege administration,
  informatics management ? Application Server
• Database data filtering ? clinical data
  warehouse
• Analysis data analysis tools adapting ? SAS,
  SPSS, MATLAB
• Three-Level Users
• Patients / Healthcare Staff ? Input data
• Clinicians / Decision Maker ? Decide data
• Engineers / Administrator ? Analyze data

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• Models disease evaluation, risk analysis,
  treatment guidance, and data processing
• First 3 models for clinical data computation and
  last one for IT modules
• Disease evaluation retrieve clinical variables,
  calculate pretreatment parameters, and evaluate
  PROs and PCRs.
• Risk analysis analyze clinical variables and
  parameters, identify risk indicators and
  criteria, and so on.
• Guidance criteria enables the generation of
  evidence-based diagrams, online guidance and
  decision support.
• Data processing supports IT-related modules such
  as clinical data conversion, database connection,
  and graphical display.

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• Views OLAP portal, EBM informatics, management
  interface, analysis view
• Presentation patient- and clinician-oriented
  interfaces direct OLAP portal and EBM
  informatics.
• Management management interface provides
  security administration.
• Analysis and database analysis view displays all
  clinical data.
• Controllers Data flow transformation, data
  input validation, privilege control, role
  identification, heterogeneous data transaction
• Presentation data flow transformation and
  data input validation control online inquiries
• Management privilege control and role
  identification secure system maintenance
• Analysis and Database heterogeneous data
  transaction coordinate clinical data

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Object Relation Mapping

• ORM a programming technique that converts data
  between incompatible type systems in relational
  databases and object-oriented programming
  languages.
• session interface conducts lightweight
  instances in safe as the necessary data are
  requested on the web tier all the time
• session factory share many application objects
  and cache scripted database transaction and other
  mapping metadata for converting data.
• configuration interface configures the location
  of mapping documents and specific properties for
  data retrieval
• transaction interface keep applications
  portable between different execution
  environments.
• query interface performs instances to control
  data queries against the database
• criteria interface executes object-oriented
  criteria queries.

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ORM Data Flow
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Clinical Data Warehouse

• Data warehouse
• an integrated, subject-oriented, time-variant and
  non-volatile database
• provides support for decision making
• builds up an integral database for historical
  data repository with lack of systematic
  arrangement
• allows complex queries and analyses on the
  information without slowing down the operating
  system
• unified by the extract-transform-load (ETL)
  procedure into database through extraction,
  consolidation, filtering, transformation,
  cleansing, conversion and aggregation
• Clinic application
• integrate practical PROs and PCRs with a standard
  procedure from different hospital databases into
  the knowledge bank for advanced analysis

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ETL process
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Web Services

• Web Services (WS)
• an interface for describing a collection of
  operations that are network accessible through
  standardized XML messaging
• W3C definition a software system designed to
  support interoperable machine to machine
  interaction over a network.
• Standards
• WSDL (Web service definition language) -
  translate metadata
• SOAP (simple object access protocol) - transport
  data
• UDDI (universal description, discovery, and
  integration) - search information

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Parsing flow for web service data with XML schema
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Online Analytical Process

• OLAP Online Analytical Process
• Keep complex query behind data mining for
  knowledge bank
• Leave simple data transaction through dynamic
  views in data warehouse
• OLAP in RTCDSS
• Cross over the web server and database
• Lead online computation within the RTCDSS
• Manage and analyze infometrix and clinical data
• PCR queries integrated with heterogeneous
  databases are primarily progressed while
  accessing the database server
• Risk evaluations embedded within online session
  logs are efficiently retrieved as connecting the
  web server

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Framework withWS,MVC,ORMbeyondOLAP
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AJAX

• Asynchronous JavaScript XML-HttpRequest
• The AJAX technique is widely applied for online
  interactive interface to grab instant information
  and to avoid lag in transportation of
  client-server data
• Store transient data (e.g. images) at client
  sites to reduce redundant data query with
  database sites and enhance interactive patient-
  and clinician-oriented interface.
• Process numerous data queries between database
  and web server
• If the client site keeps sessions at online
  status, the browser is calling JavaScriptTM and
  restoring data.
• Once the session needs reconnection or updating,
  the client-server communication is activating.
• Adjust data interaction performance as adopting
  light-weight data like QOL questionnaires, risk
  evaluations or guideline indexes.
• The method doesnt need to request database all
  the time but load into browsers temporary
  container at client site.

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AJAX data transportation
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Three stage RTCDSS with CDW Integration
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DESIGN OF PATIENT AND CLINICIAN ORIENTED
INTERFACES

• Patient-oriented interface
• Clinician-oriented interface
• Integration design

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Interface and Infrastructure

• Two types of interfaces
• Patient-oriented and clinician-oriented
  interfaces
• Process PRO with clinical infometrics and
  analyzing PCRs upon the EBM.
• Five-layer infrastructure
• Acquisition acquire patient-reported outcomes
• Presentation present online clinical diagraph
• Management manage clinical information
• Analysis analyze patients clinical records
• Database coalesce diverse clinical databases
• Practice CIPC project in CMUH

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Patient-oriented Interface

• Infometrics for Quality of Life
• Infometrics Information Psychometrics
• Quality of Life (QOL)
• WHO individuals perceptions of their position
  in life in the context of the culture and value
  systems in which they live, and in relation to
  their goals, expectations, standards, and
  concerns.
• EORTC QOL assessments in cancer clinical trials
  to provide a more accurate evaluation of the
  well-being of individuals or groups of patients
  and of the benefits and side-effects that may
  result from medical intervention.
• EORTC C30 30 questionnaires for cancer
• EORTC PR25 25 questionnaires for prostate
  cancer

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EORTC QOL Assessment

• C30 30-item cancer-speci?c questionnaire that
  has often been used for patients with head and
  neck cancer
• 5 functional scales (physical, role, cognitive,
  emotional, and social) with 9 multi-items
• 3 symptom scales (fatigue, pain, and nausea and
  vomiting)
• 1 global health QOL scale and 6 single items
• PR25 25-item questionnaire for use among
  patients with localized and metastatic prostate
  cancer
• Urinary symptoms (9 items)
• Bowel symptoms (4 items)
• Treatment-related symptoms (6 items)
• Sexual functioning (6 items)

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Clinical Implementation for Prostate Cancer

• CIPC Clinical Infometrix for Prostate Cancer
• CIPC Scope
• QOL is an important healthcare index but patients
  probably conceal the truth because of private
  manners.
• The traditional paper-based QOL assessment
  usually causes reading difficulty for patients
  because of improper font size and print space.
• Most of prostate caner patients are seniors who
  initially might not know how to click
  mouse-button or scroll the browser to navigate
  the computer.
• The infometrics module of CIPC system is designed
  for patient orientation through sufficient
  accessibility and accompanies QLQ with instant
  PRO analysis and evaluation.

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• CIPC for patients
• The fonts of questionnaires are enlarged for
  elderly patients who have poor eyesight.
• the selection buttons are displayed on a touch
  screen for patients who are not familiar with
  using computer mouse.
• The Web-page design is simplified by one-touch
  action per question before the users are well
  trained.
• A multimedia function played with head phones is
  optionally provided for low education level
  patients who could read questions with limited
  literacy.
• CIPC procedure
• Patients are arranged privately in a consulting
  room to complete the questionnaires while waiting
  for the clinicians.
• Clinicians could immediately evaluate the real
  time reports with online analysis according to
  automatic computation and statistical models.

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Clinic progress implemented with clinical
infometrics system
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Waiting for Clinic
Clinic Time
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• CIPC mechanism
• Clinicians and researchers can immediately access
  infometrix data after patients completed the
  questionnaires.
• The clinician can make cross compare overall
  treatment information with instant expert
  opinions for advanced communicate with patients.
• CIPC network infrastructure
• Network of CIPC needs to link hospital and campus
  networks, but under hospitals information
  security policy, to collaborate tiers of
  database, analysis, management, presentation, and
  acquisition for clinical and research workflows.
• The infrastructure bridges both networks of
  clinics and campus through the firewall to
  routinely backup clinical data and maintain the
  CIPC system.

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Network infrastructure of the CIPC system with
RTCDSS components
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• Five-tier infrastructure within CIPC network
• Database tier supports clinical and infometrix
  data warehouse
• Analysis tier assists analysts analyzing data and
  feeds back statistical results as resource of the
  knowledge bank
• Management tier is the control center for
  administrating data flow throughout the entire
  system
• Presentation tier presents real-time functions
  for online decision support and interactive
  guideline on a friendly interface for P2C
  communication
• Acquisition tier becomes the data collector to
  execute online QOL assessment with accessibility
  interface.

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Clinician-oriented interface

• Scope
• Evaluate pretreatment parameters for clinical
  evidences
• Guide clinicians to concurrently collect and
  analyze specific clinical markers with instant
  diagrams in CIPC for prostate cancer patients.
• The CIPC system is proposed in urology clinic for
  reflecting relationship between QOL and
  pretreatment parameters such as PSA, clinical
  classification stage, and Gleason score, etc.
• Function
• Open source frameworks
• Graphical diagrams for PROs and PCRs
• Combine PCRs and biomarkers from diverse database
  through networks
• Guidelines for decision support

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• Prostate cancer treatment
• Suspicion of prostate cancer resulting in
  prostatic biopsy is most often raised by
  abnormalities found on digital rectal examination
  (DRE).
• PSA has evolved for the detection, staging, and
  monitoring of men diagnosed with prostate cancer
  since its discovery in 1979.
• The four-stage TNM system indicates how far the
  cancer has spread for defining prognosis and
  selecting therapies the size of the tumor (T),
  the number of involved lymph nodes (N), and the
  presence of any other metastases (M)
• The Gleason grade is based on a low-magnification
  microscopic description of the architecture of
  the cancer and is the most commonly used
  classification scheme for the histological
  grading of prostate.

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• PSA Level
• PSA Density density of prostate-specific
  antigen
• Most prostate cancer arises as clinically
  nonpalpable disease with PSA between 2.5 and 10
  ng/mL
• PSAV PSA Velocity
• Linear regression of logarithm for PSA records
• PSADT PSA Doubling Time
• The relationship of two arbitrary PSAs measured
  at the time T1 and T2 with respect to the
  doubling time TD is formulated when ln(2P1) is
  estimated.

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• TNM stage
• primary tumor (T)
• T1 stage presents tumor, but not detectable
  clinically or with imaging
• T2, the tumor can be palpated on examination, but
  has not spread outside the prostate
• T3, the tumor has spread through the prostatic
  capsule
• T4, the tumor has invaded other nearby
  structures.
• regional lymph nodes (N)
• N0, there has been no spread to the regional
  lymph nodes
• N1, there has been spread
• distance metastasis (M)
• M0, there is no distant metastasis
• M1, distant metastasis is found

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• Gleason score
• The predominant pattern that occupies the largest
  area of the specimen is given a grade between 1
  and 5.
• then added to the grade assigned to the second
  most dominant pattern
• Gleason sum can be arranged between 2 and 10.
• This system describes tumors as "well",
  "moderately, and "poorly" differentiated based
  on Gleason score of 2-4, 5-6, and 7-10,
  respectively.

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• Kaplan Meier survival estimation
• which is known as the product limit estimator,
  estimates the survival function from life-time
  data
• Let S(t) be the probability that an item from a
  given group of size N will have a lifetime
  exceeding t.
• ni is the number at risk just prior to time ti,
  and di, the number of deaths at time ti, where i
  1, 2, , N.
• ti is equal or less than ti1
• the intervals between each time typically will
  not be uniform.
• When there is no censoring, ni is the number of
  survivors just prior to time ti.
• With censoring, ni is the number of survivors
  less the number of losses.
• It is only those surviving cases that are still
  being observed that are at risk of an observed
  death.

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• Cox Proportional Hazard Model
• h0(t) is the baseline hazard involving t but not
  Xs
• X denotes a collection of p explanatory variables
  X1, X2, , Xp
• the model is nonparametric because h0(t) is
  unspecified.
• For PSA variables correlation in prostate cancer
  treatment, these variables may include age, race,
  initial PSA, PSAV, PSAD, clinical stage,
  treatment, and so on.

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K-M Survival vs. CoxPH Model Curves for the same
data set
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• Instant analytical diagram
• PSA-related information
• such as PSAD, PSAV and PSADT can be calculated
  and perform real time online analytical diagrams
• Partin table
• include primary clinical stage, serum PSA level,
  and Gleason score to determine the probability of
  having a final pathologic stage based on logistic
  regression analyses for all 3 variables combined
• predict cancer's pathologic stage after the
  prostate gland has been surgically removed and
  examined by a pathologist
• (1) organconfined disease
• (2) established capsular penetration
• (3) seminal vesicle involvement
• (4) lymph node involvement.

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PSA-related information on clinician-oriented
interface
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Partin table
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• Risk evaluation criteria
• constructed on the basis of large numbers of
  patients who have undergone radical prostatectomy
  to aid in the precise prediction of pathologic
  stage by using multiple clinical parameters as
  accurate predictors of both cancer extent and
  long-term outcomes after treatment of the primary
  tumor.

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Integration Design

• Platform
• Based on RTCDSS design, the CIPC system is built
  with open source framework of JavaTM technique
  while Apache TomcatTM and OracleTM are selected
  as Web and database servers
• The system components with flexible functionality
  and keep-in-simple-stupid (KISS) interface are
  designed to enhance the human computer
  interaction.
• The system involves database, analysis,
  management, presentation, and acquisition layers
  on the modelized architecture

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Five Layer CIPC RTCDSS
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Entity Relationship Diagram (ERD) of CIPC
58

• The database layer
• the foundation of the system for building
  clinical data warehouse.
• The ERD denote correlation between QOL domains
  and treatment effects due to two sets of fact
  tables for infometrix and clinical records
• Answer_Full and Answer_Domain tables store
  and transform assessment data to QOL domain score
• Patient_Info and Prostate_Cancer tables
  retrieve data from PCR.
• Answer_Index is an index table to bridge
  Answer and Question tables, which request and
  arrange assessment data, and derive cube
  dimensions of PSA, treatment, clinical stages and
  Gleason scores.

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• The analysis layer
• assists researchers analyzing data and feeds back
  statistical results as resource of knowledge
  bank.
• several types of data file formats converted from
  database were generated to satisfy different
  progresses supported by analysis tools.
• incorporates database and application servers
  with remote computation or offline data mining
  and feed expert opinions back to knowledge bank
  of RTCDSS.
• The management layer
• plays the role of control center for managing
  data flow within the entire system and share
  functionality with privilege roles of health care
  people, clinicians and researchers.
• enhance capability of data access functions for
  health care people and researchers to plot online
  charts, identify single sign-on, process data
  conversion, administrate user privilege, and
  acquire QOL assessment.

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• The presentation layer
• the interface of real time decision support for
  communication of patients and clinicians.
• present instantaneous statistical diagrams by
  referencing expert opinions from knowledge bank
  for clinicians.
• Perform real time QOL evaluation with respect to
  other patients, initial PSA, Gleason score,
  treatment stage, etc.
• Clinicians are able to indicate treatment indexes
  online through graphical interface for decision
  support.
• The acquisition layer
• becomes the data receiver of the system to
  execute QLQ online behind accessibility interface
  design.
• the touched screen, the sizeable large fonts, and
  the audio media with ear phones are functioned
  for accessibilities of patients who fell
  uncomfortable for traditional paper works or
  private issues.

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PRACTICE OF PATIENT-TO-CLINICIAN STRATEGY

• Interactive guideline
• P2C communication
• Advantage and difficulty
• Practice discussions

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Interactive guideline

• Statistics of QOL domains
• All QOL domains reflect the functions and
  symptoms of a patient in physical and mental
  conditions during the treatment cycle.
• Clinicians can realize statistic results of
  patients disease conditions at the beginning of
  clinic.
• It helps the patient describe their real health
  condition to avoid ambiguous conversation.
• Disease evaluation of the PSA level
• The real-time diagram shows the disease
  information of the patients PSA level throughout
  different treatments.
• the system retrieved the patients PCRs with
  related pretreatment parameters and disease
  history.
• The baseline of PSA is completely plotted during
  different treatment cycles.

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Patient-Oriented Interface
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Statistics of QOL Domains
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Real time online chart of patients QOL history
66
Graphical QOL diagram with clinical marker - QOL
vs. PSA baseline
67

• Risk guidance with the Partin table
• the clinician can easily find and input
  pretreatment parameters such as PSA, Gleason
  score, and a clinical stage to determine the risk
  percentage by using the Partin table module.
• Ex the clinician input T1c, 32.4, and 5-6 for
  TNM stage, PSA value and Gleason grade,
  respectively the system estimated risk
  percentage in high risk group that shows more
  than 50 for 5 years of post-therapy PSA failure
  as well as 30 and 29 for 5-year and 10-year PSA
  failure-free survival, respectively.
• Interactive guidelines for treatment reference
• Clinicians can interact with the guideline thus
  the phase of treatment procedure based on the
  criteria are carried out for decision making as
  PCRs are input.
• Ex if the PSA was 25.5ng/ml, the clinical stage
  was T3, Gleason score was 8, life expectancy was
  more than 5 years, with symptomatic therapy as
  bone scan.

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Online recurrence risk evaluation and Partin table
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Interactive Guideline of CIPC system
70
Interactive guideline of CIPC system (with AJAX)
71

• Special diagnostic chart for automated
  prescription
• The special diagnostic chart lists primary
  clinical markers for selection to prevent typing
  error by clinicians as input PCRs.
• the clinician can select required item to
  concurrently produce unified statement of
  automated prescription as inputting data into the
  database for related HIS.

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P2C communication

• In the past years
• prostate cancer patients were tracked by
  handwriting QOL assessments during cure period
  but lacks of automatic progress
• patients used to complete paper works through
  conversation with health care people because of
  physical or mental suffering
• doctors took many efforts to explain PROs at
  clinic time by printing out PCRs for advices
• Expected communication to consolidate
  relationship
• increase interaction with patients
• reduce official burden of health care people
• encrypt data prior to database to avoid being
  falsified and stolen
• simple operating process and user-friendly
  interface

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Patient-Computer interface for P2C communication
74

• P2P Functions
• Cross comparison
• the real time online chart supports the clinician
  and patient to take an overview for discussing
  the variation of QOL compared with the mean value
  of other patients.
• It enhanced the patient with confidence to
  interact with doctors for advanced treatment as
  recognizing QOL history and clinical markers.
• Overall evaluation
• PSA history is compared with QOL domains to study
  heal condition after accepting treatment

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• Real time informetrix categories for patients
• The best practice of RTCDSS is for patients whose
  disease can be chronically and periodically
  tracked by clinical markers.
• The CIPC design provides real time infometrix to
  display PSA values with QOL domains including
  categories of physical, role, cognitive,
  emotional, and social functions besides that of
  fatigue, pain, nausea and vomiting symptoms.
• It reflects urinary, bowel, treatment-related
  symptoms and male-related sexual functioning.
• Patients can recognize personal health condition
  immediately with respect to others through the
  instant graphical chart in the clinic.

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Real time online QOL vs. Other patients
77

• System implementation and approvement
• Online informatics for clinicians
• assist in the treatment of chronic diseases that
  can be periodically tracked
• online informatics displays PSA-related data to
  provide categories of diagnostic information
• Clinicians can identify patients health
  conditions directly with respect to treatments
  through the instant diagrams.
• save hours, even several days, of analysis by
  providing instant computation of the relevant
  parameters
• Improvement in clinician-patients relationships
• clinicians predicted potential disease risk
• clinicians offer proper suggestions, treatments,
  and tracking conditions

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• Enhancement in P2C communication
• enhance clinicians awareness of their patients
• Clinicians discover reliable predictive
  information for prostate cancer patients through
  real time statistics and computation
• manual mistakes can be eliminated by the
  automatic transportation procedure to ensure data
  quality

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Advantage and Difficulty

• Advantage
• enhance clinical care for patients, support
  optimal treatment options for clinicians, and
  increase efficiency in clinics.
• (a) ensuring quality of clinical care
• (b) providing the clinicians real-time online
  clinical informatics
• (c) enhancing P2C communication
• (d) improving clinician-patients relationships.
• Difficulty
• Hospital management and security policy limited
  the CIPC system only work partially for proposed
  clinic beyond hospital network.
• As adapting the system to the legacy HIS, noises
  of diverse systems are always counted for
  integration.
• The structural reformation on original HIS should
  be avoidant but through gradual data immigration
  under administration rules.

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• Strategy quasi real time procedure
• CIPC system was installed in the server at the
  urology clinics in which an individual patient
  used to make appointment on a specific day of
  week. In compliance with hospital management for
  safety policy, the power of clinic room was
  turned off after clinics and the CIPC server must
  be shutdown.
• To avoid conflicting with the HIS but ensure data
  transformation can synchronize between
  inconsistent systems, a process scheduling module
  was employed for data importation between the HIS
  and CIPC system. The module was embedded in both
  systems to retrieve required data from the backup
  log of CIPC system at a specified time point and
  transform data into the HIS database.
• As a result, clinicians could obtain the last
  patient outcomes if their data were imported into
  the HIS just before clinic time.

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• Quasi real time procedure
• Presents three data-update jobs in a cycle as
  considering two scheduling points before and
  after the clinics.
• At the first point in the first clinic, the
  required data are replicated into the backup log.
  In the cycle, clinicians activate the start
  button to load PCRs and clinical markers from the
  log into CIPC database and then, the new
  outcomes will be input during clinic time.
• After the clinic, clinicians can update complete
  remarks of PCR to CIPC database and all data will
  be restored to the backup log concurrently.
• At the second point in the last clinic, the log
  will be retrieved in schedule and be transformed
  into the HIS.
• Once the CIPC server is activated, the scheduling
  will be automatically started.
• In the study, the process scheduling was executed
  twice per week by urological clinics.

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Quasi real time procedure
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Practice discussions

• Benefits
• (1) clinicians can explain health conditions
  clearly to patients by visualized clinical
  variables and pretreatment parameters
• (2) patients are more easily convinced by
  evidence-based diagrams before accepting the risk
  evaluation of treatments and the treatment
  quality can be confirmed
• (3) the design presents real-time disease and
  risk evaluation while the interactive guidelines
  with treatment suggestions offer the clinician
  efficient online tools for instant decision
  making
• (4) the proposed framework is constructed upon
  the Web-based MVC architecture that consists of
  reusable models, making it flexible and adaptable
  with many hospital information systems

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• Discussions
• Clinicians learned more reliable information
  regarding patients private QOL. The efficient
  diagnosis and communication certainly encourages
  the advanced study.
• With RTCDSS for predicting treatment assistance,
  diverse functionalities can be expectant for
  advance clinical decision, context-specific
  access, automatic risk assessment, personal
  digital assistant screens, as well as
  practitioner performance and cost-effectiveness
  on patient outcomes
• It can also incorporate with advanced prediction
  models such as nomograms, which may help patients
  and their treating physicians make informed
  decisions based on the probability of a
  pathologic stage, the patients risk tolerance,
  and the values they place on the various
  potential outcomes

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CONCLUSIONS
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Conclusion Remarks

• This study reveals clinical and infometrics
  progress with information technology to establish
  fundamentals of the RTCDSS.
• Methodologies include MVC architecture, Web
  services, online analytical process, clinical
  data warehouse, object relation mapping, and AJAX
  while the practical CIPC system is implemented
  for approval.
• The infrastructure integrates five layers to
  establish expandable models with flexibility for
  providing accessible functions in clinic
  applications of prostate cancer.

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• Heterogeneous database systems distributed in
  hospital, clinic and campus networks were
  integrated for an expert bank with remote data
  backup and disaster recovery.
• A patient- and clinician-oriented interface is
  considered as a major subject to assist P2C
  communication.
• In advance, the patient outcome is available to
  offer instant statistical charts for decision
  making as well as improved communication and
  relationship between clinicians and patients.
• Furthermore, the RTCDSS enables interactive
  guideline for knowledge feedback, facilitate
  decision-making, and to improve quality of care.

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Acknowledgment

• The author sincerely appreciates Prof. Hsi-Chin
  Wu, Chih-Hung Chang, Tsai-Chung Li, Wen-Miin
  Liang, and Jong-Yi Wang for their encouragements
  and consultants.
• The author also thanks IT-engineer Yu-Yuan Chou
  and Statistician Yi-Chun Yeh as well as
  Biostatistics Center of China Medical University
  for their help in statistical analysis and
  informatics support.
• This study was granted by National Science
  Council and China Medical University with
  projects no. NSC100-2625-M-039-001, CMU96-153,
  CMU96-228, and CMU97-321.

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APPENDIXCIPC PHOTOS
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CIPC Clinics QOL Assessment
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Patient-oriented Interface
93
CIPC Server Room
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Clinic Time
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CIPC Demo