HEALS: Health Examination Automatic Logic System

HEALS: Health Examination Automatic Logic System

Kuan-Liang Kuo, MD *#

Chiou-Shann Fuh, PhD *

*Family Medicine Department, Taipei City Hospital, Taipei, Taiwan

#Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

Download

• Related files on:

http://www.csie.ntu.edu.tw/~d93009/AMIA2006/

Introduction

Health Examinations

• Health examinations play important roles in preventive medicine.

• The clinical condition of an individual

continuously changes and needs a series of observations and follow-up.

• In the past, in order to meet such requirement,

clinical workers have to fetch health examination data from scattered sources, such as medical charts or general-purpose hospital information system.

Health Examinations

• The lack of a health examination information system meant that the

availability, efficiency, and quality of

further health care management would

degrade.

Asymmetry between Rural and Urban Medical Settings

• Scale

• Equipment

• Quality

• Information technology

HEALS: History

• A health care system developed in Taipei City Hospital since 1996 (original Taipei Municipal Jen Ai Hospital)

• Dedicated for health examinations

HEALS: Overview

• Fully web-based application

• Services provided via ubiquitous Internet accessing

• Enormous applications of the database

• Integrate healthcare domain knowledge to provide sophisticated user-defined functions and interfaces

• Built-in decision-support system for automatic report generation including medical data

interpretation, automatic summary, and suggestion

HEALS: Overview

• Provides a user-friendly, intelligent, fully- functional application experience to clinical workers in a distributed way

• Rural clinics can seamlessly share the services provided by HEALS via web-browsing

• Customers can also easily derive integrated

healthcare information from HEALS under certain security authentication

HEALS: Overview

• Web-based:

– PHP – Java

• Database:

– SQL compliant

PHP: Personal Home Page

SQL: Structured Query Language

HEALS: Integrated into HIS

HIS: Health Information System DB: Data Base

JDBC: Java Data Base Connectivity

HEALS: Integrated into HIS

Screen Shots

HEALS: Portal Page

HEALS : Results Input

HEALS: Automatic Summary

HEALS: Report Preview

HEALS: Maintenance of Phrases and

Inference Information

HEALS: Maintenance of Inference

Information

HEALS: Maintenance of Inference

Information

Implementation of CDSS in HEALS

CDSS: Clinical Decision Support System

Implementation of CDSS in HEALS:

Inference Engine

Database

Field Name Value Field1 Text Field2 Numeric Field3 Symbolic

Rule Name Boolean Value

Rule1 TRUE

Rule2 FALSE

Rule3 TRUE

Rule Rule1 Cond1 Rule Rule2 Cond2 Rule Rule3 Cond3

Rule Name Action Caption Rule1 Action1 Caption1 Rule3 Action3 Caption3 False

True

Update

Caption Action Caption G R S D L

Rule-Codes Table Rule Action Table

Inference Engine

Rules

Ignore Case Data

Effective Inference Result

Presentation Rule Value Table

Implementation of CDSS in HEALS:

Rules Syntaxes

limitdef <name> <condition>

rangedef <name> <condition>

clausedef <name> <condition>

ruledef <name> <condition>

Implementation of CDSS in HEALS:

Rule Example

Syntax:

limitdef <name> <condition>

Example:

limitdef HBSAG_pos HBSAG == "+"

Implementation of CDSS in HEALS:

Rule Example

Syntax:

rangedef <name> <condition>

Example:

rangedef HB_mildLow HB [10 12) Description:

HB>=10 and HB<12 (HB: hemoglobin)

Implementation of CDSS in HEALS:

Rule Example

Syntax:

clausedef <name> <condition>

Example:

clausedef RFT_H CR_H || BUN_H Description:

CR_H: pre-defined rule with the condition of high Creatinine

BUN_H : pre-defined rule with the condition of high BUN

Implementation of CDSS in HEALS:

Rule Example

Syntax:

ruledef <name> <condition>

Example:

ruledef HBV_nnn HBSAG_neg && HBSAB_neg

&& HBCAB_neg && !AGE_50 Description:

HBSAG_neg: pre-defined rule with the condition of negative HBsAg.

Implementation of CDSS in HEALS:

Inference Results

Triggered action:

Section code; Suggestion Codes Example:

1;D20,G24R2,T82 Description:

1: Division of Family Medicine

D20: To be under observation. Please visit the Outpatient Clinic of

$$ for a follow-up diagnosis and treatment, in the event of doubts or discomfort.

G24R2: group 24, rank 2 T82: r/o Thalassemia

Implementation of CDSS in HEALS:

Effective Inference Results

……..

Action 1 Action 2

•Classification

•Translation

•Format

Human-Readable Results

Summary and

Conclusion

Advantages of HEALS

• Provide services beyond the territory boundary between rural and urban medical settings

• Improve the quality of health examination information flow.

• Improve the efficiency of health examination information flow.

• Reduce the mundane daily work of clinical workers

• Provide education for junior doctors

• Eliminate common misses in health reports

Common Misses in Health Reports

• Fail to correctly detect problems from multiple examination results

• Fail to give proper suggestions for further medical management or life style

modification

• Fail to write a well-edited report

• Fail to meet the deadline for sending reports

Common Misses in Health Reports:

Fail to correctly detect problems from multiple examination results

• Anemia:

– RBC (red blood cell count) – Hb (hemoglobin)

– MCV (mean corpuscle volume)

Common Misses in Health Reports:

Fail to correctly detect problems from multiple examination results

• Hepatitis B:

– HBsAg (hepatitis B surface antigen)

– Anti-HBs (hepatitis B surface antibody) – Anti-HBc (hepatitis B core antibody) – HBeAg (hepatitis B e antigen)

– Liver enzymes

– Abdominal sonography

Common Misses in Health Reports:

Fail to give proper suggestions for further medical management or life style

modification

• To give a succinct and complete suggestion

at a time is a difficult job.

Common Misses in Health Reports:

Fail to write a well-edited report

• Different quality by different doctors

• Different quality by a doctor at different time

Common Misses in Health Reports:

Fail to meet the deadline for sending reports

• Regular workload of a doctor in the health examination center

– 20 reports/day

• Per report’s conclusion time requirement:

– With CDSS: in seconds

– Without CDSS: 10 minutes to half an hour

CDSS: Clinical Decision Support System

Possible Disadvantages of CDSS

• Changing relation between patient and the physician

• Limiting professionals’ possibilities for independent problem solving

• Legal implications - with whom does the

onus of responsibility lie?

Evaluation Results

• HEALS has served in Taipei City Hospital for more than 40000 cases.

• Time of editing a report markedly decreased from 20 minutes to 5 minutes per case

• The ratio of customer complaints about the

health reports was decreased to nearly zero

Perspective of HEALS

• To be one of the references of health examination information systems

• Future improvement of the CDSS in HEALS

– Analyze the database to extract stochastic or

domain-specific methods to improve healthcare quality and efficacy

References

• S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2^nd Ed., Prentice Hall, New York, 2002.

• E. Bernstern, “Decision Support and Computers in

Education,” HI5300: Introduction to Health Informatics, School of Health Information Sciences and Department of Internal Medicine, University of Texas – Houston, 2004.

• HL7 Working Group, “Standards in Clinical Decision Support: Using Arden Syntax,”

http://cslxinfmtcs.csmc.edu/hl7/arden/, 2003.

• P. Caleb-Solly, "Clinical Decision Support Systems,"

Seminar for the Health Informatics, 2001.

The Decision Support System Used in HEALS (Health Examination Automatic Logic System)

Chiou-Shann Fuh, PhD # Kuan-Liang Kuo, MD * #

*Family Medicine Department, Taipei City Hospital, Taipei, Taiwan

#Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan

Download

• Related files on:

http://www.csie.ntu.edu.tw/~d93009/AMIA2006/

Introduction

HEALS: Overview

• Fully web-based application

• Services provided via ubiquitous Internet accessing

• Enormous applications of the database

• Integrate healthcare domain knowledge to

provide sophisticated user-defined functions and interfaces

• Built-in decision-support system for automatic report generation including medical data

interpretation, automatic summary, and suggestion

HEALS: Overview

• Provides a user-friendly, intelligent, fully- functional application experience to

clinical workers in a distributed way

• Rural clinics can seamlessly share the services provided by HEALS via web- browsing

• Customers can also easily derive

integrated healthcare information from HEALS under certain security

authentication

Clinical Diagnosis

• Infer a disease state that is not directly observable

• Forms of a physician’s knowledge:

– Rule – Pattern

Knowledge-Based Agents

• Knowledge base

– The center component – A set of sentences (rules)

• Knowledge representation language

• Inference

– Reasoning engine

Define the Task Environment of an Intelligent Agent

• PEAS

– Performance measure – Environment

– Actuators – Sensors

Define the Task Environment - Performance Measure

• Minimizing error

• Minimizing operating time

• Maximizing quality

• Maximizing reports output

– Over 6000 reports per year now

Define the Task Environment - Environment

• Known examination items

– Physical examinations – Laboratory tests

– Others

• Known examination types

– Laboratory

• Numerical (glucose, liver function, …)

• Symbolic (hepatitis markers, …)

– Others

• Text (gastroenteroscopy, …)

Define the Task Environment - Actuators

• Output the diagnoses

– Possible diseases

• Output the suggestions

– Life style modification – Medical suggestion

Define the Task Environment - Sensors

• Examination results

– From database system

• Personal Health History

CDSS of HEALS

CDSS: Clinical Decision Support System

Algorithm

Reasoning Engine Exam.

Results

Rules

Diagnoses and Suggestion Codes

Codes-Text Tables

Reports

Other Information

Rule-Codes Table

Presentation Engine

Rule-Codes Table

Rule Name Description

Section

Code Suggestion codes MCVLHBN1

1 d20,602,g24r1,t84 MCVLHBN2

1 d20,G24R2,t82 MCVLHBL1

1 G24R2,t83 MCVLHBL2

1 d20,G24R2,t81

MCVNHBL1

1 t10,d50,40,403,g24r1

MCVNHBL2

1 t11,d50,40,403,g24r1

Code-Text Table:

Diagnoses

Code Diagnosis

81 r/o Thalassemia

82 r/o Thalassemia

83 Microcytic anemia

84 Low MCV

Code-Text Table: Suggestion

601 目前不需治療

601e No medical treatment required at the moment.

602 疑帶有地中海型貧血基因

602e It is suspected that you have a thalassemia gene.

603 因無明顯分類異常或伴隨其他血球變化，可觀察

603e

No marked classification abnormality is found

concurrently with any other pathological change in blood cells, thus no medical treatment is required at the

moment.

604 疑因過敏體質所致，

604e

No medical treatment is required, as the disorder is caused by an irritable body.

605 正值發育期間的小孩及青少年，其值常見偏高，

605e

The value is usually relatively high among kids and teenagers who grow rapidly.

BNF Grammar of Rules

declaration ::= limit_declaration

| range_declaration

| clause_declaration

| rule_declaration

BNF: Backus-Naur Form

Full text on:

http://www.csie.ntu.edu.tw/~d93009/AMIA2006/

BNF Grammar of Rules (cont.)

limit_declaration ::= limitdef limit_name limit

range_declaration ::= rangedef range_name range

clause_declaration ::=

clausedef clause_name clauses rule_declaration ::= ruledef

rule_name rules

Examples of Rules

limitdef UWBC2 UWBC > 20 rangedef UWBC1 UWBC (5 20]

clausedef UWBC6 UWBC2 || UWBC4 ruledef UWBCABNH UWBC1 &&

URBCN && !URBC4

Reasoning Engine

• Forward chaining reasoning

– Data-driven reasoning

• Procedural approach part of this implementation

– Initiation of variables used in rules

• Processing rules

• Generating result codes

Examples of Encoding Domain Knowledge into Rules

• Some Hepatitis B Markers

– Hepatitis B surface antigen (HBsAg)

• Outer surface coat

– Hepatitis B surface antibody (HBsAb)

• Antibody to HBsAg

– Hepatitis B core antibody (HBcAb)

• Antibody to inner nucleocapsid core

Interpretation of Hepatitis B Markers

limitdef HBSAG_pos HBSAG == "+“

** HBsAg is positive

limitdef HBSAG_neg HBSAG == "-“

** HBsAg is negative

limitdef HBSAB_pos HBSAB == "+“

** HBsAb is positive

limitdef HBSAB_neg HBSAB == "-“

** HBsAb is negative

Interpretation of Hepatitis B Markers

limitdef HBCAB_pos HBCAB == "+“

** HBcAb is positive

limitdef HBCAB_neg HBCAB == "-“

** HBcAb is negative

Interpretation of Hepatitis B Markers

limitdef OLD_HBSAG_pos OLD_HBSAG == "+“

** any of the previous HBsAg tests is positive limitdef OLD_HBSAG_neg OLD_HBSAG == "-“

** all of the previous HBsAg tests are negative limitdef OLD_HBSAB_pos OLD_HBSAB == "+“

** any of the previous HBsAb tests is positive limitdef OLD_HBSAB_neg OLD_HBSAB == "-“

** all of the previous HBsAb tests are negative

Interpretation of Hepatitis B Markers

limitdef OLD_HBCAB_pos OLD_HBCAB

== "+“

** any of the previous HBcAb tests is positive

limitdef OLD_HBCAB_neg OLD_HBCAB

== "-“

** all of the previous HBcAb tests are negative

Interpretation of Hepatitis B Markers

ruledef HBSAG_nil !HBSAG_pos && !HBSAG_neg

** If HBsAg is not tested

ruledef HBSAB_nil !HBSAB_pos && !HBSAB_neg

** If HBsAb is not tested

ruledef HBCAB_nil !HBCAB_pos && !HBCAB_neg

** If HBcAb is not tested

ruledef HBCAB_diff HBCAB_neg && OLD_HBCAB_pos

** If HBcAb is negative now and any of the previous HBcAb tests is positive

Interpretation of Hepatitis B Markers

ruledef HBV_nnn HBSAG_neg && HBSAB_neg &&

HBCAB_neg && !AGE_50

** If HBsAg is negative, HBsAb is negative, HBcAb is negative, and age is less than 50

Interpretation of Hepatitis B Markers

ruledef HBV_nnn HBSAG_neg && HBSAB_neg &&

HBCAB_neg && !AGE_50

HBsAg: negative; Anti-HBs: negative; Anti-HBc: negative

˙You have never got hepatitis B infection, thus it is recommended that you receive vaccine injections.

˙Please visit the Outpatient Clinic of Family Medicine Division for a follow-up diagnosis and treatment.

Interpretation of Hepatitis B Markers

ruledef HBV_pnp HBSAG_pos &&

HBSAB_neg && HBCAB_pos

** If HBsAg is positive, HBsAb is negative, and HBcAb is positive

Interpretation of Hepatitis B Markers

ruledef HBV_pnp HBSAG_pos && HBSAB_neg &&

HBCAB_pos

HBsAg: positive; Anti-HBs: negative; Anti-HBc: positive

˙Don't take medication without a

physician's instructions, so as to avoid augmenting the workload of liver.

˙Take a rest, as appropriate; avoid being physically and mentally exhausted.

˙You are a hepatitis B carrier. Please have a follow-up consultation in the Outpatient

Clinic every half a year.

Interpretation of Hepatitis B Markers

ruledef HBV_nn HBSAG_neg && HBSAB_neg

&& HBCAB_nil && !AGE_50

** If HBsAg is negative, HBsAb is negative, HBcAb is not tested, and age is less than 50

Interpretation of Hepatitis B Markers

ruledef HBV_nn HBSAG_neg && HBSAB_neg &&

HBCAB_nil && !AGE_50

HBsAg: negative; Anti-HBs: negative

˙Your antibody test is negative.

It is recommended that you go to the

OPD and pay for a test for core antibody.

If the subsequent test is negative again,

you may consider receiving vaccine injections.

˙Please visit the Outpatient Clinic of

Family Medicine Division for a follow-up diagnosis and treatment.

Interpretation of Hepatitis B Markers

ruledef HBV_nnp1 HBSAG_neg && HBSAB_neg

&& HBCAB_pos && OLD_HBSAG_pos

** If HBsAg is negative, HBsAb is negative, HBcAb is negative, and any of the previous HBsAb tests is positive

Interpretation of Hepatitis B Markers

ruledef HBV_nnp1 HBSAG_neg && HBSAB_neg &&

HBCAB_pos && OLD_HBSAG_pos

HBsAg: negative; Anti-HBs: negative; Anti-HBc: positive;

Previous HBsAg: positive

˙Do not take medication without physician's instructions, to avoid augmenting the workload of liver.

˙Take a rest, as appropriate; avoid being physically and mentally exhausted.

˙You are a hepatitis B carrier. Please have a

follow-up consultation in the Outpatient Clinic every half a year.

Examples of Encoding Domain Knowledge into Rules

• Anemia

– RBC (Red Blood Cell)

– MCV (Mean Corpuscle Volume) – Hb (Hemoglobin)

Anemia

rangedef MCV_Low78 MCV [78 80) rangedef MCV_Low MCV (0 80) rangedef HB_N HB [12 19]

rangedef HB_Low HB (0 12) rangedef HB_mildLow HB [10 12) rangedef HB_severeLow HB (0 10) limitdef HB_High HB > 19

limitdef RBC_High RBC > 7 limitdef RBC_mildHigh RBC > 5

Anemia

rangedef MCV_DIV_RBC_Low MCV_DIV_RBC (0 13)

** MCV/RBC > 0, and < 13

limitdef AGE_YOUNG AGE < 21

** age less than 21

ruledef MCVABNL21 AGE_YOUNG && MCV_Low78 &&

HB_N

** If age less than 21, MCV>=78 and <80, and Hb is normal

Anemia

ruledef MCVLHBN2 !MCVABNL21 && MCV_Low && HB_N

&& MCV_DIV_RBC_Low

** If MCVABNL21 is false, MCV<80, Hb is normal, and MCV/RBC<13 (r/o Thalassemia carrier)

Anemia

ruledef MCVLHBN2 !MCVABNL21 && MCV_Low && HB_N

&& MCV_DIV_RBC_Low

RBC: 6.00*10⁶/ul, Hb: 12.8g/dL, MCV: 63fl r/o Thalassemia carrier

˙To be under observation. Please visit the Outpatient Clinic of

Family Medicine Division

for a follow-up diagnosis and treatment, in the event of doubts or discomfort.

Anemia

ruledef MCVLHBL1 !MCVABNL21 && MCV_Low && HB_Low

&& !MCV_DIV_RBC_Low

** If MCVABNL21 is false, MCV<80, Hb<12, and MCV/RBC>=13 (microcytic anemia)

Anemia

ruledef MCVLHBL1 !MCVABNL21 && MCV_Low && HB_Low

&& !MCV_DIV_RBC_Low

RBC: 3.00*10⁶/ul, Hb: 8.0g/dL, MCV: 70fl Microcytic anemia

˙Please visit the Outpatient Clinic of Division of Family Medicine

for a follow-up diagnosis and treatment.

Anemia

ruledef MCVLHBL2 !MCVABNL21 && MCV_Low && HB_Low

&& MCV_DIV_RBC_Low

** If MCVABNL21 is false, MCV<80, Hb<12, and MCV/RBC<13 (Thalassemia)

Anemia

ruledef MCVLHBL2 !MCVABNL21 && MCV_Low && HB_Low &&

MCV_DIV_RBC_Low

RBC: 6.00*10⁶/ul, Hb: 10.0g/dL, MCV: 62fl r/o Thalassemia

˙Please visit the Outpatient Clinic of Division of Family Medicine

for a follow-up diagnosis and treatment.

Anemia

ruledef MCVNHBL2 !MCV_Low && HB_severeLow

** If MCV>=80 and Hb<10

Anemia

ruledef MCVNHBL2 !MCV_Low && HB_severeLow RBC: 3.00*10⁶/ul, Hb: 8.0g/dL, MCV: 82fl

Anemia

˙Have a balanced diet, taking in all sorts of nutrients, avoid partiality for a particular kind of food; take sufficient amount of vitamins.

˙Refrain from blood donation.

˙Please visit the Outpatient Clinic of Division of Family Medicine

for follow-up diagnoses and treatments.

Summary and

Conclusion

HEALS:

Advantages

• Provide services beyond the territory boundary between rural and urban medical settings

• Improve the quality of health examination information flow.

• Improve the efficiency of health examination information flow.

• Reduce the mundane daily work of clinical workers

• Provide education for junior doctors

• Eliminate common misses in health reports

CDSS of HEALS: Advantages

• A way of using clinical guidelines in medical practices.

• Simple and clear rule syntax

• Efficient reasoning algorithm

• To ensure the state-of-the-art of the

knowledge base, the rules can be readily

updated by domain experts easily

Evaluation Results

• HEALS has served in Taipei City Hospital for more than 40000 cases.

• Time of editing a report markedly decreased from 20 minutes to 5 minutes per case

• The ratio of customer complaints about the

health reports was decreased to nearly zero

HEALS: Next

• To be one of the references of health examination information systems

• Future improvement of the CDSS in HEALS

– Analyze the database to extract stochastic or

domain-specific methods to improve healthcare quality and efficacy

References

• S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 2^nd Ed., Prentice Hall, New York, 2002.

• C. Chizzali-Bonfadin, K. P. Adlassnig, M. Kreihsl,A. Hatvan,W.

Horak, "Knowledge-Based Interpretation of Serologic Tests for Hepatitis on the World Wide Web.” Clin. Perform. Qual. Health Care, vol. 5(2), pp. 61-3, 1997.

• E. Bernstern, “Decision Support and Computers in Education,”

HI5300: Introduction to Health Informatics, School of Health Information Sciences and Department of Internal Medicine, University of Texas – Houston, 2004.

• HL7 Working Group, “Standards in Clinical Decision Support:

Using Arden Syntax,” http://cslxinfmtcs.csmc.edu/hl7/arden/, 2003.

• P. Caleb-Solly, "Clinical Decision Support Systems," Seminar for the Health Informatics, 2001.