w3c standards applicable to health cared93009/mydoc/slide/... · –failure to give proper...

輔仁大學醫學資訊與創新應用學士學位學程

醫療標準及術語

W3C standards applicable to Health Care

臺北市立聯合醫院仁愛院區家庭醫學科郭冠良

Kuan-Liang Kuo, M.D., Ph.D.

2019-06-11

1

HEALS: A Health Examination System

Integrated with CDSS

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Contents

• Introduction• Design Objectives• A Rule-based Clinical Decision Model to Support

Interpretation of Multiple Data in Health Examinations

• Implementation• Execution of the CDSS• Evaluation and Result• Discussion• Conclusion

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Introduction

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Health Examination

• Health examinations play an important role in preventive medicine.

• Evidence-based clinical preventive services have been demonstrated to be important elements in healthcare.

• The health examinations are designed as "packages" for the convenience and needs of people.

• Consumer-driven healthcare

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Health Examination

• Health examinations obtain relatively complete health information simultaneously

– Early diagnosis of diseases

– Self-health management

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Clinical Decision Support System

• CDSS is a computer program can help clinicians make better decisions

• Provide reminders, advice, or interpretation specific to a given patient at a particular time

• Potential benefits for healthcare• Improve quality

• Reduce cost

• Improve workflow

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Health Examinations and the Need of Clinical Decision

Support• A health examination package

– Multiple examination items

– Mostly normal or insignificantly abnormal

– Checking and interpreting the examination results

– Need a comprehensive health report

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Support• Pitfalls of continuous interpretation of

numerous health examination data by human

– Labor-intensive

– Tedious and monotonous

– Complex and error-prone

– Human-fatigue

– Clinicians tend to simplify the procedures

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Support• Health reports generated by different

clinicians manually versus computer assistance

– Maintain consistent quality

– Satisfy most customers

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Ontology

• In the context of knowledge sharing, ontology is defined as a specification of a conceptualization.

• A description of the concepts and relationships

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The Importance of Ontology

• Constructing a domain model, or ontology, is an important step in the development of a knowledge-based system.

• Advantages• Share and reuse of knowledge • Better engineering of knowledge-based system with

respect to acquisition, verification, and maintenance

• With the help of ontology, the knowledge is not only human-readable but also machine-readable

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The Importance of Ontology

• Each knowledge base is an extension of some domain ontology, where the ontology provides a roadmap for the class of the concepts that will comprise the knowledge base.

• In another words, ontology provides the framework for the domain knowledge base.

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The Health Examination Ontology

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The Hierarchy of The Health Examination

Ontology

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Clinical Guidelines

• Clinical practice guidelines • Improve health-care quality

• Reducing inappropriate variations in clinical practice

• Control medical costs

• Problem• Health care organizations typically pay more

attention to guideline development than to guideline implementation for routine use in clinical settings, evidently hoping that clinicians will simply familiarize themselves with written guidelines and then apply them appropriately during the care of patients.

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Clinical Guidelines

• Solution

– Guideline-based CDSS

• Studies have shown that CDSSs can improve clinician performance and patient outcomes.

• To seamlessly apply computer and guidelineto clinical practice

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Clinical Knowledge Representation Models

• Critical issue for CDSS development• Knowledge representation models

• Arden Syntax, PROforma, GLIF (GuideLine Interchange Format), GASTON, etc.

• Primitives are categorized by the task types they intend to represent• A decision is a logical path based on predefined rules. • An action is a clinical task that is indicated by a decision.

• But, there has been no standardized way to represent the clinical knowledge for every circumstance.

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HEALS(Health Examination Automatic Logic

System)

• A web-based health examination information system

• Integrated with CDSS (Clinical Decision Support System)

• Runs well at Taipei City Hospital

• Proposed in 2006 AMIA Spring Congress

• Papers published in 2009

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Design Objectives

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Design Objectives of HEALS

1. Reduce the mundane daily tasks of clinical workers

2. Improve the quality of health examination

3. Improve the efficiency of health examination

4. Provide education for novices

5. Eliminate common mistakes in health reports

6. Provide services beyond the territory boundary

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1. Reduce the Mundane Daily Tasks of Clinical Workers

• Relieve clinicians from repetitious and noncreative daily tasks

• Allow experts to focus on irreplaceable and innovative affairs.

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2. Improve the Quality of Health Examination

• Establish a standardized information flow model for health examinations

• Eliminates probable human errors and improves the quality of health examinations

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3. Improve the Efficiency of Health Examination

• Important elements of the health examination workflow– Iterative data collection

– Filtering

– Transformation

– Merging

– Interpretation

– Presentation

• Take advantage of information technologies to ameliorate such tedious operations

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4. Provide Education for Novice Clinical Workers

• Provide a computerized implementation of clinical guidelines, expert consensus, and local experiences

• The output context of CDSS is composed of diagnoses, medical recommendations, and lifestyle recommendations for clinical disorders.

• The output of CDSS has been verified and confirmed by clinical experts during the development course.

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5. Eliminate Common Mistakes in Health Reports

• Four common mistakes in processing health reports

– Failure to correctly detect problems from multiple examination results

– Failure to give proper suggestions for further medical management or life style modification

– Failure to write a well edited report

– Failure to meet the deadline for sending reports

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6. Provide Services Beyond the Territory Boundary

• Indistinguishable user experiences for both remote and local users

• Access in the same way and obtain the same services regardless of their locations

• Health experts can process health reports and miscellaneous operations in different medical settings or at home.

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A Rule-based Clinical Decision Model to Support

Interpretation of Multiple Data in Health Examinations

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Architecture of HEALS

• Four major parts of HEALS

– User interface

– CDSS

– Report presentation subsystem

– Data application subsystem

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• 1. User interface

– Provide optimal facilities for data entry, report generation, and information query

• 2. CDSS

– The crucial component of HEALS

– Two major units

• The functional unit: artificial intelligence

• The informational unit: knowledge and data

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• 3. The report presentation subsystem – Provide final reports with high flexibility and

quality

– User-defined presentation templates based on HTML

– Satisfy various requirements of print formats

• 4. The data application subsystem– Data analysis, evaluation, and health

management

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HEALS CDSS

• Follow the philosophy of fitting the best compromise between ideal and practice

• The functional unit of HEALS CDSS– Reasoning engine

– Blackboard control architecture

– Connection component

• The informational unit of HEALS CDSS– Data source

– Knowledge base

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HEALS CDSS

• Two major units of HEALS CDSS

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The information flow of the clinical decision support system in HEALS

HEALS CDSS Architecture

• 1. Reasoning engine• 2. Blackboard control architecture• 3. Connection component• 4. Data source • 5. Knowledge base

– Rule base– Decision rules

– Content base– Diagnosis terms– Recommendation contents

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1. Reasoning Engine

• 1. Data source: health examination result data

• 2. Execution of the decision rules

• 3. OutputObject {

Discode (D); // diagnosis code

Sugcode (S); // suggestion code

Seccode (S’); // section code

Group (G); // group code

Rank (R); // rank code

}15:27 37

Structure of the Output Object: Five Attributes

• Five attributes are defined in the object

– Discode: the code of a clinical disorder

– Sugcode: a set of codes of recommendations associated with the Discode

– Seccode: the code of medical department

– Group: objects with the same Group code are clinically related

– Rank: the object’s priority in the group

• Always couples with Group15:27 38

2. Blackboard Control Architecture

• The blackboard control architecture comprises three components:

– A central blackboard or a global database

– A set of independent knowledge sources

– A scheduling mechanism

• Each event of reasoning monitors the state of the blackboard

• Condition

• Increment to the solution on the blackboard15:27 39

• Group• Each reasoning output object is aggregated into

corresponding group by its Group attribute.

• Rank• Rank indicates the priority of each reasoning

output object in the group.

• Discode, Seccode, and Sugcode• While an object is put into a certain group, its

Discode, Seccode, and Sugcode are accumulated to their corresponding set in the group for further processing after the reasoning procedure completed.

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• Code-style outputs

• Representation engine

• Final human readable text

– Mapping to • Diagnosis terms

• Recommendation contents

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The example output of automated reasoning15:27 42

3. Connection Component

• Controls the input and output of the CDSS

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• Isolated as a swappable module

– Flexibility and reusability

– Advantage of system maintenance

• Information systems of medical settings are mostly built on diverse platforms

– Hardware, operating system, and software

– The distributed environment makes the inter-system connectivity highly specified.

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• Our real world– HIS: HP, UNIX, COBOL

– LIS: x86, Windows, VB

– Health Examination System: x86, Linux, PHP

• These systems– Built in different time points

– For different tasks

– Different software lifecycles

– Tremendous costHIS: Hospital Information System

LIS: Laboratory Information System

CDSS: Clinical Decision Support System

VB: Visual Basic

PHP: Personal Home Page

COBOL: COmmon Business-Oriented Language

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4. Data Source

• Clinical data of health examination– Physical examination and

examination with text report• User key-in

– Hospital Information System• Administration information

– Laboratory Information System

• Laboratory machines

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5. Knowledge Base• The knowledge base is the

intelligence-embedded component of the CDSS.

• Domain-specific knowledge – Decision rules

– Diagnosis terms

– Clinical recommendation contents

– Definitions of the relations among above contents

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Decision Rules

• We propose a novel syntax for effectively and efficiently representing the decision rules.

• The philosophy of simplicity and clarity

• Four types of rule commands

– limitdef

– rangedef

– clausedef

– ruledef15:27 48

Rule Syntax: Four Types of Rule Command (1)

• Either limitdef or rangedef command declares atomic rules, which represent the constraints of variable values

limitdef B1 VAR1 <logical operator> VALUE1

rangedef B2 VAR2 <value range>

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Rule Syntax: Four Types of Rule Command (2)

• Commands clausedef and ruledef are used in declaring the composition of propositional logicsclausedef B3 <disjunctions of literals>

ruledef B4 <conjunctions of literals>

• Theorem – Every sentence of propositional logic is

logically equivalent to a conjunction of disjunctions of literals

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Diagnosis Terms

• Medical terms thesauri

• Clinical Interface Terminology Tool

– A systematic collection of health care related phrases that support clinicians' entry

– A part of user interface

– Embeds clinical decision support

ti: one of the terms of a phrase entity

A: actionn: number of terms of a phrase entityE: clinicians’ entry

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Recommendation Contents

• Medical recommendations

– Further medical intervention requirements to the patients

• Lifestyle recommendations

– Lifestyle modification suggestions, such as ceasing unhealthy habits, eating a balanced diet, or getting adequate exercise

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Modular Design

• Reasoning engine and knowledge base

• Advantages for:

– Programmers

– Medical experts

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Implementation

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Implementation of HEALS

• Platform independent

• Open-source development tools and technologies

– Apache web server

– PHP

– Java

– MySQL

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HEALS

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• 自動匯入檢驗結果

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自動判讀範例一

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• 相關健檢結果

自動判讀範例一

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• 自動判讀、群組化、並產生建議與摘要

自動判讀範例二

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• 相關健檢結果

自動判讀範例二

• 自動判讀、群組化、並產生建議與摘要

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健檢資料庫應用

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健檢預約系統

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Chapter 5Execution of the CDSS

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The UML Activity

Flowchart of the CDSS

UML: Unified Modeling Language

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Hepatitis B

• In Taiwan, hepatitis B infection is an important medical and public health issues.

• Before early 1980, one in five persons who were infected by the hepatitis B virus (HBV) would become chronic carriers.

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Hepatitis B Related Tests

• Surface antigen (HBsAg)

• Surface antibody (Anti-HBs)

• Core antibody (Anti-HBc)

• Liver enzymes

• Previous hepatitis B tests results

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Four Major Possible Conditions

Related with Hepatitis B• HBsAg is positive.

• HBsAg is negative and anti-HBs is positive.

• HBsAg is negative; anti-HBs is negative; and anti-HBc is positive.

• HBsAg is negative; anti-HBs is negative; and anti-HBc is negative or unknown.

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Condition 115:27 81

Example of Rules

• limitdef HBSAG_pos HBSAG == "+“

• limitdef HBSAG_neg HBSAG == “-“

• limitdef HBSAB_pos HBSAB == "+"

• limitdef HBSAB_neg HBSAB == "-"

• limitdef HBCAB_pos HBCAB == "+"

• limitdef HBCAB_neg HBCAB == "-"

• ruledef HBSAG_nil !HBSAG_pos && !HBSAG_neg

• ruledef HBSAB_nil !HBSAB_pos && !HBSAB_neg

• ruledef HBCAB_nil !HBCAB_pos && !HBCAB_neg

• limitdef AST_high GOT > 39

• limitdef ALT_high GPT > 42

• clausedef LFT_ABN1 AST_high || ALT_high

• limitdef AFP_high AFP > 13.4

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Example of Rules

• ruledef HBV_LFT_AFP HBSAG_pos && LFT_ABN1 && AFP_high

• ruledef HBV_LFT HBSAG_pos && LFT_ABN1 && !AFP_high

• ruledef HBV_AFP HBSAG_pos && !LFT_ABN1 && AFP_high

• ruledef HBSAG_pos_only HBSAG_pos && !LFT_ABN1 && !AFP_high

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Example of Rules

• HBV_AFP

•

• B型肝炎表面抗原陽性併甲型胎兒蛋白過高(HBsAg:+, elevated

AFP)

• ˙請勿亂服藥物以免增加肝臟負擔

• ˙宜適度休息，避免身心過勞

• ˙請避免喝酒

• ˙為B型肝炎帶原者，請每半年定期至家醫科門診追蹤

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Example of Rules

• HBV_LFT

•

• B型肝炎表面抗原陽性併肝功能異常(HBsAg:+, abnormal LFT)





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Example of Rules

• HBV_LFT_AFP

•

• B型肝炎表面抗原陽性併肝功能異常及甲型胎兒蛋白過高(HBsAg:+,

abnormal LFT, elevated AFP)





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Example of Rules

• limitdef PETYPE_HEP PETYPE == "hep"

• ruledef HBV_HEP HBSAG_pos && PETYPE_HEP

• clausedef HBV_pos_finished HBV_LFT_AFP || HBV_LFT || HBV_AFP ||

HBV_HEP

• ruledef HBV_pnn !HBV_pos_finished && HBSAG_pos_only && HBSAB_neg &&

HBCAB_neg

• ruledef HBV_pnp !HBV_pos_finished && HBSAG_pos_only && HBSAB_neg &&

HBCAB_pos

• ruledef HBV_ppn !HBV_pos_finished && HBSAG_pos_only && HBSAB_pos &&

HBCAB_neg

• ruledef HBV_ppp !HBV_pos_finished && HBSAG_pos_only && HBSAB_pos &&

HBCAB_pos

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Example of Rules

• HBV_pnn

• B型肝炎表面抗原陽性,表面抗體陰性,核心抗體陰性(HBsAg:+,

Anti-HBs:-, Anti-HBc:-)





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Example of Rules

• ruledef OLD_HBV_immunity OLD_HBSAG_neg && OLD_HBSAB_pos

• limitdef AGE_50 AGE >= 50

• ruledef HBV_nnn HBSAG_neg && HBSAB_neg && HBCAB_neg

&& !OLD_HBV_immunity && !AGE_50

• ruledef HBV_nnn2 HBSAG_neg && HBSAB_neg && HBCAB_neg &&

OLD_HBV_immunity

• HBV_n; HBsAg:-; 1;t131

• HBV_nn; HBsAg:-,Anti-HBs:-; 1;704,t120

• HBV_nnn; HBsAg:-,Anti-HBs:-,Anti-HBc:-; 1;702,t110

• HBV_nnn2; HBsAg:-,Anti-HBs:-,Anti-HBc:-,但以前有Anti-HBs; 1;709,t139

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The example result15:27 90

Evaluation for HEALS

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Design of Evaluation

1. Evaluation of Quality

2. Evaluation of Effectiveness

3. Evaluation of System Performance

4. Evaluation of Efficiency

5. Evaluation of User Satisfaction

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1. Evaluation of Quality

• Result

– Very high quality interpretation

– Automated inference output is nearly error-free

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1. Evaluation of Quality--- Method

• Evaluate the error in automated inference output– The output includes the diagnoses and

recommendations auto-generated by HEALS CDSS– Errors are reported by users (physicians, nurses, or

assistants)

• Four classes of errors in automated inference– 1. Major system error– 2. Minor system error– 3. User-induced error– 4. Improvement comment

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• 1. Major system error

– Unable to inference for abnormal health examination results

– Wrong inference for abnormal health examination results

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• 2. Minor system error

– Wrong grouping for abnormal health examination results

– Insufficient recommendation for abnormal health examination results

– Inaccurate recommendation for abnormal health examination results

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• 3. User-induced error

– Input missing

– Wrong input data type

– Wrong input data

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• 4. Improvement comment

– Improvement for input

– Grouping for abnormal health examination results

– Improvement of the recommendation for abnormal health examination results

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1. Evaluation of Quality--- Result

• Among 14773 cases during six months of health examination operation

– Automated inference output is nearly error-free

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Total

Major system error 0

Minor system error 0

User-induced error 44

Improvement comment 10

Total case number of health examination

14773

2. Evaluation of Effectiveness

• We focus on studying if HEALS CDSS can assist physicians in interpretation.

• Result

– Significantly improve novices' interpretation

– Can assist and educate novices in interpretation

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2. Evaluation of Effectiveness--- Method

• We designed a test with questions which asked common clinical problems originating from our health examination database and within the knowledge domain of HEALS CDSS.

• Gold standard– These answers were judged by experienced

physicians with more than 15 years of experience in health examinations.

– HEALS’s automated inference outputs are identical with the gold standard answers.

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• Three correctness-levels for the answers– Completely correct

– Partially correct

– Least correct

• This division is designed to reduce the experimental bias due to inevitable medical uncertainty. However, it is difficult to make an absolute, correct medical decision with limited clinical information.

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• Example 1– HBsAg: negative, Anti-HBs: negative, Anti-HBc

total: positive

– Please make a diagnosis.• a. Indicate immunity to HBV

• b. Indicate chronic HBV infection

• c. Indicate acute HBV infection

• A. a B. b C. c D. a or b

• E. a or c F. b or c G. a or b or c

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Complete: G

Partial: D,E,F

Least: A,B,C


• Example 2– Follow Question 1. What related information

you may need for a more definite diagnosis?• a. Previous test result of HBsAg

• b. Previous test result of Anti-HBs

• c. Previous test result of Anti-HBc

• d. Previous test result of liver enzymes

• A. a B. a + b

• C. a + b + c D. a + b + c + d

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Complete: B

Partial: A

Least: C, D

2. Evaluation of Effectiveness--- Result

• Eighteen percent of the answers of the participants qualified as “least correct”.

• That is, novices would get at least 18

percent improvement in solving these test cases under the assistance of HEALS CDSS.

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Question No. 1 2 3 4 5 6 7 8 9 10 11 Total

Complete (C) 1 7 2 8 1 4 5 6 5 3 5 47

Partial (P) 6 1 2 0 7 0 2 0 3 3 1 25

Least (L) 1 0 4 0 0 4 1 2 0 2 2 16

Ratio of C+P 87.5% 100.0% 50.0% 100.0% 100.0% 50.0% 87.5% 75.0% 100.0% 75.0% 75.0%

Ratio of L 12.5% 0.0% 50.0% 0.0% 0.0% 50.0% 12.5% 25.0% 0.0% 25.0% 25.0% 18.0%

• Result

– High speed interpretation and fast automated summary generation ( < 2 sec per case)

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--- Method• System performance measurement

includes only the time used solely for automated summary generation, which completely avoids the influence of human.

• Performance of HEALS CDSS automatic summary text generation is measured during daily operation.

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• The consistency of less than two seconds fulfils our expectation.

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--- Result

4. Evaluation of Efficiency

• Result

– High efficiency health report throughput

– Cost down in human resource ( est. 2349 hours saved in 9 months)

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4. Evaluation of Efficiency--- Method

• The user operating time required to complete a health report

• The measured time starts with the data entry and ends at report printing.

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4. Evaluation of Efficiency --- Result

• High efficiency health report throughput– In 81.5% cases, take less than 5 minutes to complete

a health examination report per case– Contrastively, a experienced physician needs 20

minutes averagely to complete a health examination report manually

• Data were collected during the operation of HEALS among five health examination institutes of Taipei City Hospital in 2011

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Seconds H1 H2 H3 H4 H5 TOTAL

60-180 72.0% 84.9% 61.6% 76.7% 57.2% 67.9%

180-300 81.3% 92.7% 76.6% 85.0% 77.5% 81.5%


• Efficiency: Time to complete a health report

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• Improvement of efficiency and cost down in human resource

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Case number (2011-01 ~ 2011-09) 30555

Average seconds per case with HEALS 323

Total operating hours with HEALS 2744

Estimated hours needed without

HEALS5093

Estimated hours saved by HEALS 2349


• Result

– High user satisfaction with HEALS (93.16%)

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5. Evaluation of User Satisfaction--- Method

• Doll and Torkzadeh’s end-user computing satisfaction instrument

– Twelve questions with a five-point scale

• An additional question about the impact of clinical data interpretation was appended

– “Does the automated reasoning positively influence your interpretation on health examination results?”

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--- Result• User Satisfaction: 93.16% got 4 points

and above

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Point C1 C2 C3 C4 A1 A2 F1 F2 E1 E2 T1 T2 I1 Total Total%

5 0 0 4 3 3 4 3 3 0 2 3 0 4 29 24.79%

4 9 9 5 6 6 5 6 6 9 7 3 4 5 80 68.38%

3 0 0 0 0 0 0 0 0 0 0 3 5 0 8 6.84%

2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00%

1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0.00%

Discussions

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Six Dimensions of Success in Information Systems

• Six dimensions of success (DeLone and McLean, Meijden et al.)• System quality

• Information quality

• Use

• User satisfaction

• Individual impact

• Organizational impact.

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Principle of Evaluation for CDSS

• Comparing the CDSS output result with experts’ opinions

– Evaluation of quality

– Evaluation of efficiency

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Four Classes of Common Errors in Health Reports

• 1. Failure to correctly detect problems from multiple examination results

• 2. Failure to give proper recommendations for further medical management or life style modification

• 3. Failure to write a well-edited report

• 4. Failure to meet the deadline for delivering reports

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1. Correctly Detect Problems from Multiple Examination

Results• Humans do make errors. The chance of

an error increases in proportion to the complexity of the examination.

• Any error in interpreting health examination data risks causing mistakes in health care and, thus, patient safety.

• Two kinds of mistakes

– false negatives and false positives15:27 121

2. Give Proper Recommendations

• Different physicians may give different recommendation for the same clinical condition.

• Also, a physician may even give different recommendations for the same clinical condition at different times.

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3. Write a Well-edited Report

• Clinical conditions and recommendations should be translated into comprehensive text which can be understood by patient to prevent misunderstanding of the reports

• To provide assistance to alleviate the language gap between health examination customers and physicians, the knowledge base of HEALS CDSS contains plenary content that can be assembled to a comprehensive text after the automated inference.

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4. Meet the Deadline for Delivering Reports

• The promptness of health reports is also a crucial issue for customer satisfaction.

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Conclusion

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Main Contributions

• Construct a novel knowledge-based system to fundamentally improve the workflow of health examinations

– A novel rule syntax is proposed to construct the knowledge base of CDSS.

• A multi-facet method to evaluate a CDS embedded system

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Main Contributions

• A successful health examination information system --- HEALS

– Quality

• Very high quality interpretation

• Automated inference output is nearly error-free

– Effectiveness

• Significantly improve novices' interpretation.

• Can assist and educate novices in interpretation

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Main Contributions

• A successful health examination information system --- HEALS (cont.)– System Performance

• High speed interpretation and fast automated summary generation ( < 2 sec per case)

– Efficiency• High efficiency health report throughput• Cost down in human resource ( est. 2349 hours saved

in 9 months)

– User Satisfaction• High user satisfaction with HEALS (93.16%)

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Thanks for your attention

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