disease surveillance & registries

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นพ.นวนรรน ธีระอัมพรพันธ์ุภาควิชาเวชศาสตร์ชุมชน คณะแพทยศาสตร์ รพ.รามาธิบดี 27 เม.ย. 2559

โอกาสพัฒนาระบบเฝ้าระวังโรคและ Disease Registry

www.SlideShare.net/Nawanan

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2003 Doctor of Medicine (1st-Class Honors) Ramathibodi

2009 M.S. (Health Informatics) University of Minnesota

2011 Ph.D. (Health Informatics) University of Minnesota

ต ำแหน่งปัจจุบัน• อาจารย์ ภาควิชาเวชศาสตร์ชุมชน

คณะแพทยศาสตร์โรงพยาบาลรามาธิบดีContacts

E-mail [email protected]/NawananFacebook: Informatics RoundLine ID: NawananT

A Brief Introduction

mailto:[email protected]

http://www.slideshare.net/nawanan

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• Data Problems in Disease Control• Science of Data & Information• Health IT in Clinical & Public Health Settings• Data Collection & Use• Thailand’s “eHealth” Situation• Creating Health Information Systems of

Tomorrow

Outline

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คุณเคยเจอปัญหาเหล่านี้บ้างหรือไม่?

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• ผู้บริหารขอรายงานข้อมูลกะทนัหัน• ผู้ใช้งานบ่น ภาระงานเยอะ บันทึกซ้้าซ้อน• Data-rich, information-poor (& wisdom-none)• ข้อมูลจาก 2 แหล่ง ไม่ตรงกัน• 2 โปรแกรมคุยกันไม่รู้เรื่อง• คนถือข้อมูล “หวง” ข้อมูล ไม่ยอมแชร์• ข้อมูลที่แชร์ไปให้หน่วยงานอื่น “หลุด”

คุณเคยเจอปัญหาเหล่านี้บ้างหรือไม่?

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https://www.blognone.com/node/79473

ข้อมูลส่วนบุคคล “หลุด”

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• ผู้เกี่ยวข้องเข้าใจ “ศาสตร”์ ของข้อมูลสารสนเทศ

• หลายปัญหามีทางออก แต่ต้องหันหน้าเข้าหากัน– ผู้บริหาร ผู้ใช้ข้อมูล ผู้ถือข้อมูล นักไอที และ

นักวิชาการ

ปัญหาเหล่านี้จะหมดไป ถ้า...

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• ขาด “การออกแบบ” ระบบข้อมูลที่ดี• ขาดความเข้าใจเรื่อง “ข้อมูล” และ “ระบบ

ข้อมูล/ระบบสารสนเทศ”• ผู้บริหารมองเรื่องนี้เป็นเรื่อง “เทคนิค”• User มองเรื่องนี้เป็นเรื่อง “ไอที” แทนที่จะมอง

เป็นเรื่อง “ข้อมูล” (ในงานของตัวเอง)• ขาด “การจัดการ” ข้อมูลสารสนเทศที่ดี

สาเหตุบางส่วนของปัญหาเหล่านี้

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“ศาสตร์” ของข้อมูลสารสนเทศ

(Science of Data & Information)

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• French: informatique = the science and technology of information processing using computers (Greenes & Shortliffe, 1990)

• “[T]he discipline focused on the acquisition, storage, and use of information in a specific setting or domain” (Hersh, 2009)

• “[T]he science of information”(Bernstam et al, 2010)

What is “Informatics”

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• “Ancient” term

• Being retired

• Future use discouraged by experts

• Only retained in titles of professional organizations

Main Problems

• Medical = Doctor? (e.g. not nursing?)

• Medical informatics vs. Clinical informatics

Medical Informatics

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• Biomedical informatics• Health informatics• Biomedical and Health informaticsA Few Subtleties• Health informatics suggests the goal is “health”• Health informatics vs. Public health informatics• Health informatics includes Bioinformatics?• No clear winner between

Biomedical informatics vs. Health informatics

Better Terms

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• Medical computing/computers in medicine?• ‘[R]eferring to biomedical informatics as

“computers in medicine” is like defining cardiology as “stethoscopes in medicine”.’ (Bernstam et al, 2010)

• “[T]he field concerned with the cognitive, information processing, and communication tasks of medical practice, education, and research, including the information science and technology to support these tasks” (Greenes & Shortliffe, 1990)

But What Is M/B/H Informatics Anyway?

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• “[T]he field that is concerned with the optimal use of information, often aided by the use of technology, to improve individual health, health care, public health, and biomedical research” (Hersh, 2009)

• “[T]he application of the science of informationas data plus meaning to problems of biomedical interest” (Bernstam et al, 2010)

More Definitions of M/B/H Informatics

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• Focuses more on information, not technology

• Task-oriented view:

Collection Processing

Storage

Utilization

Communication

/Dissemination/

Presentation

Summary of M/B/H Informatics

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• Health service delivery (health care)– Medical, dental, nursing, pharmacy, etc.– IT management in health care organizations

• Public health– Policy & administration, epidemiology, environmental

health, health services research, etc.• Individual patient/consumer’s health• Education of health professionals• Biomedical research (clinical trials, public health

research, research in biomedical sciences)

Areas Under M/B/H Informatics

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Data-Information-Knowledge-Wisdom

(DIKW) Pyramid

So What Is Information?

Wisdom

Knowledge

Information

Data

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Data-Information-Knowledge-Wisdom (DIKW) Pyramid

Contextualization/

Interpretation

Processing/

Synthesis/

Organization

JudgmentWisdom

Knowledge

Information

Data100,000,000

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Contextualization/

Interpretation

Processing/

Synthesis/

Organization

JudgmentWisdom

Knowledge

Information

Data100,000,000

I have 100,000,000

baht in my bank

account

I am rich!!!!!

I should buy a BMW

(and a BIG house)!

Example

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Class Exercise #1

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• Patient A is allergic to penicillin. He was recently prescribed amoxicillin for his sore throat.

• What are data, information, knowledge, and wisdom?

Class Exercise #1: Case A

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• Hospital B received complaints from many patients about poor service of providers in the OPD.

• What kind of data & information do you need, and for what knowledge & wisdom?

Class Exercise #1: Case B

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http://www.nationtv.tv/main/content/social/378498782/

Class Exercise #1: Case C

• As a disease control officer responsible for rabies, what data & information do you need? Why? What knowledge & wisdom do you wish to have?

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• What are data, information, knowledge, and wisdom?


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• Data: Penicillin, amoxicillin, sore throat

• Information:– Patient A has penicillin allergy

– Patient A was prescribed amoxicillin for his sore throat

• Knowledge:– Patient A may have allergic reaction to his prescription

• Wisdom:– Patient A should not take amoxicillin!!!


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• Hospital B received complaints from many patients about poor service of providers in the OPD.

• What kind of data & information do you need, and for what knowledge & wisdom?

Class Exercise #1: Case B

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http://www.nationtv.tv/main/content/social/378498782/

Class Exercise #1: Case C

• As a disease control officer responsible for rabies, what data & information do you need? Why? What knowledge & wisdom do you wish to have?

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• “[T]he field that is concerned with the optimal use of information, often aided by the use of technology, to improve individual health, health care, public health, and biomedical research” (Hersh, 2009)

• “[T]he application of the science of informationas data plus meaning to problems of biomedical interest” (Bernstam et al, 2010)

Back to the Definitions

Informatics focuses on “I”, not “T”

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(Shortliffe, 2002)

Informatics as a Field

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(Hersh, 2009)


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Biomedical/Health

Informatics

Computer & Information

Science

Engineering

Cognitive & Decision Science

Social Sciences

(Psychology, Sociology, Linguistics,

Law & Ethics)

Statistics & Research Methods

Medical Sciences &

Public Health

Management

Library Science,

Information Retrieval,

KM

And More!

Informatics and Other Fields

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(Theera-Ampornpunt, 2015)


tmi.or.th/jtmi/

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Health IT in Clinical & Public Health Settings

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To treat & to care for their patients to their best abilities,given limited time & resources

Image Source: http://en.wikipedia.org/wiki/File:Newborn_Examination_1967.jpg (Nevit Dilmen)

What Clinicians Want

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• Safe

• Timely

• Effective

• Patient-Centered

• Efficient

• Equitable

Institute of Medicine, Committee on Quality of Health Care in America. Crossing the quality

chasm: a new health system for the 21st century. Washington, DC: National Academy

Press; 2001. 337 p.

High Quality Care

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Information is Everywhere in Healthcare

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Shortliffe EH. Biomedical informatics in the education of physicians. JAMA.

2010 Sep 15;304(11):1227-8.

“Information” in Medicine

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WHO (2009)

Components of Health Systems

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WHO (2009)

WHO Health Systems Framework

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(IOM, 2001)(IOM, 2000) (IOM, 2011)

Landmark Institute of Medicine Reports

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• To Err is Human (IOM, 2000) reported that:

– 44,000 to 98,000 people die in U.S. hospitals each year as a result of preventable medical mistakes

– Mistakes cost U.S. hospitals $17 billion to $29 billion yearly

– Individual errors are not the main problem

– Faulty systems, processes, and other conditions lead to preventable errors

Health IT Workforce Curriculum Version

3.0/Spring 2012 Introduction to Healthcare and Public Health in the US: Regulating Healthcare - Lecture d

Patient Safety

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• Humans are not perfect and are bound to make errors

• Highlight problems in U.S. health care system that systematically contributes to medical errors and poor quality

• Recommends reform

• Health IT plays a role in improving patient safety

Institute of Medicine Reports Summary

43Image Source: (Left) http://docwhisperer.wordpress.com/2007/05/31/sleepy-heads/

(Right) http://graphics8.nytimes.com/images/2008/12/05/health/chen_600.jpg

To Err is Human 1: Attention

44Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital

To Err is Human 2: Memory

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• Cognitive Errors - Example: Decoy Pricing

The Economist Purchase Options

• Economist.com subscription $59

• Print subscription $125

• Print & web subscription $125

Ariely (2008)

16

0

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The Economist Purchase Options

• Economist.com subscription $59

• Print & web subscription $125

68

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# of

People

# of

People

To Err is Human 3: Cognition

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• It already happens....(Mamede et al., 2010; Croskerry, 2003; Klein, 2005; Croskerry, 2013)

What If This Happens in Healthcare?

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Mamede S, van Gog T, van den Berge K, Rikers RM, van Saase JL, van Guldener C,

Schmidt HG. Effect of availability bias and reflective reasoning on diagnostic accuracy

among internal medicine residents. JAMA. 2010 Sep 15;304(11):1198-203.

Cognitive Biases in Healthcare

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Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them.

Acad Med. 2003 Aug;78(8):775-80.


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Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr

2;330(7494):781-3.

“Everyone makes mistakes. But our reliance on cognitive processes prone to bias makes treatment

errors more likely than we think”


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• Medication Errors

– Drug Allergies

– Drug Interactions

• Ineffective or inappropriate treatment

• Redundant orders

• Failure to follow clinical practice guidelines

Common Errors in Clinical Practice

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Why We Need ICT

in Healthcare?

#1: Because information is

everywhere in healthcare

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Why We Need ICT

in Healthcare?

#2: Because healthcare is

error-prone and technology

can help

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Why We Need ICT

in Healthcare?

#3: Because access to

high-quality patient

information improves care

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Why We Need ICT

in Healthcare?

#4: Because healthcare at all

levels is fragmented &

in need of process improvement

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Health

Information

Technology

Goal

Value-Add

Tools

Health IT: What’s in a Word?

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Hospital A Hospital B

Clinic C

Government

Lab Patient at Home

Health Information Exchange

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• Health IT for Consumers/Patients– Personal Health Records

– Telemedicine

– Web Sites for Patient Education & Engagement

– mHealth & Social Media

Images from HealthVault.com, American Telecare, Inc. & WHO

Health IT Beyond Hospitals & Clinics

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• eHealth & mHealth– mHealth in disaster management: #ThaiFlood

• Data reporting to government agencies– Claims & reimbursements– Diseases– Utilization statistics– Quality measures– Such as MoPH’s Health Data Centers (HDC) reports

• Biosurveillance / Disease Surveillance (active vs. passive; case reporting vs. predictive)

• Epidemiologic & health services research

Other IT for Public Health

59Source: Google.org/FluTrends

Innovative IT for Public Health: Google Flu Trends

60Source: www.biophics.org

Thailand’s Disease Surveillance

61Image Source: https://en.wikipedia.org/wiki/United_Nations_Security_Council_Resolution_1540

Credits: Wikimedia Commons, User:Andux, User;Vardion, and Simon Eugster

Global Threats: Bioterrorism

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• eHealth & mHealth– mHealth in disaster management: #ThaiFlood

• Data reporting to government agencies– Claims & reimbursements– Diseases– Utilization statistics– Quality measures– Such as MoPH’s Health Data Centers (HDC) reports

• Biosurveillance / Disease Surveillance (active vs. passive; case reporting vs. predictive)

• Epidemiologic & health services research

Other IT for Public Health

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IT for Public Health

Shortliffe EH, Cimino JJ, editors. Biomedical Informatics: Computer Applications in Health Care and Biomedicine. 3rd ed. New York: Springer; 2006. 1037 p.

http://www.amazon.com/Biomedical-Informatics-Computer-Applications-Biomedicine/dp/0387289860/


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IT for Public Health

Shortliffe EH, Cimino JJ, editors. Biomedical Informatics: Computer Applications in Health Care and Biomedicine. 3rd ed. New York: Springer; 2006. 1037 p.


Disease Surveillance & Registries

Consumer Health IT, Websites, mHealth, Social Media

Data Reporting for

Planning & Evaluation


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• Key Issues– Interdisciplinary Communications– Organizational and Collaborative Issues– Funding and Sustainability– System Design

Example: Immunization Registries

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Data Collection & Use

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Quantitative Data/Information/Knowledge

Qualitative Data/Information/Knowledge

Types of Data/Information/Knowledge

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Use of data for the purpose originally intended when collecting data, such as Use of hospital’s patient data for treatment &

patient care

Use of disease surveillance data for outbreak monitoring, investigation & control

Use of research data for analysis & reporting

Primary Use of Data

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Use of data for other purposes other than primary use, such as

Use of patient care data for education & research

Use of hospital’s patient data for reimbursements, quality improvement, or other management activities

Use of patient care data for public health activities (disease surveillance, evaluation, policy-making, regulation/auditing, etc.)

Use of research data collected in one study for other studies

Secondary Use of Data

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• Operational Information System– Clinical: Hospital Information Systems, Electronic Medical

Records, Health Information Exchange (HIE), etc.

– Public Health: Disease Surveillance, Disease Registries, HIE

• Management Information Systems– Clinical: Hospital’s Data Warehouse & Reporting Systems

– Public Health: Disease Surveillance, Disease Registries, Data Warehouse & Reporting Systems & Business Intelligence

Types of Health Information Systems

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Inefficiencies: Costs, time & effort associated with primary data collection

Data Quality: Duplicated & inconsistent data

Burden on data informants/custodians

Primary Use of Data: Issues

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Privacy: Is sharing/disclosure/reuse of data originally collected for other purposes allowed/appropriate/legal? (“หวงข้อมูล”, “ละเมิดสิทธิ”)

Changes in context -> unaware changes in meaning -> incorrect interpretation of data

Obtaining data & integrating data from multiple sources

Data standardization

Secondary Use of Data: Issues

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Acquisition & Processing

• Receive data from manual data entry or from other systems

• Perform data display to users or other processing functions as programmed

Storage

• Store data in operational databases

• Perform other data processing as needed (if any)

Data Warehouse

• Extract, Transform, Load (ETL) of data from operational databases into analytics databases in the data warehouse (DW)

Utilization

• Data analysis & mining

• Data presentation & reporting

• Data sharing/disclosure for secondary uses

Processes for Data Use & Reuse

74Image Source: http://www.hiso.or.th/dashboard/

Data Reporting Systems

75Image Source: http://www.inetsoft.com/business/solutions/applying_business_intelligence_to_manufacturing/

Business Intelligence

76Image Source: https://www.sas.com/technologies/bi/entbiserver/

Business Dashboards

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Practical Guides on Data Management &

Reporting

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What?

Where?

From Whom?

How?

Action!

Now What?

Guides for Data Collection & Use

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Step 1: Identify data elements needed for a specific project (“What?”)What are data, information, knowledge & wisdom we

want from the project?

What is the scope (geographic location, timeframe, population, disease, clinical setting, etc.)?


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Step 2: Identify potential sources for needed data (“Where?”)

Data quality: completeness, accuracy, validity & reliability

Data quality closer to the source generally has better quality

Collecting primary data is more expensive

Two copies of data will often lead to inconsistencies

Are these sources manual or electronic data? What are pros & cons for each source?

Sometimes, paper records can’t be avoided (don’t aim for paperless, but rather less paper)


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Validity & Reliability

Image Source: https://commons.wikimedia.org/wiki/File:Reliability_and_validity.svg (© Nevit Dilmen)

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Systematic Errors (Biases) Selection Bias

Information Bias / Misclassification

Confounding

Threats to Validity

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Random Errors Sampling Errors

Random measurement errors

Threats to Reliability

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Step 3: Determine if primary or secondary use of data is more feasible & appropriate (“From Whom?”) Check if there are legal, privacy, policy & technical concerns

for obtaining data for secondary uses

Management/administrative data should ideally be by-product of well-designed operational/clinical data

Be aware of context/meaning when data were collected (loss of context and potential misinterpretations are easy if attention is not paid to context)


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Step 4: Plan how to collect data (“How?”) Primary Data: Plan procedures & resources needed for data collection

Secondary Data: Discuss with data custodian & make arrangements for obtaining data (if necessary, go back to Steps 2-3)

Use standard data format if possible, especially if integrating data from multiple sources

If multiple sources are available on same data, choose best one

Document the data collection process, technical procedures, and relevant metadata (data about data)


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Step 5: Collect data & perform data analysis/use (“Action!”) Data validation, cleaning, preparation & transformation

Executive data collection as planned

Revisit previous steps if facing challenges in data collection

Perform analysis/use & verify/validate results

Interpret data/information to yield knowledge

What’s Next: Convert knowledge into wisdom & actions


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Step 6: Maintenance (“Now What?”) Pay attention to security, privacy, and disclosure for data in custody

Data custodian is legally responsible for unauthorized access, disclosure, and use

Once project is over and data no longer need to be maintained, destroy them securely

For long-term or ongoing data uses, consider implementing DW, BI, or other appropriate tools Expertise & technical knowledge required


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Step 6 (Continued): Periodically check for following changes to determine if changes/updates

are necessary Business processes IT environment Data structure Collection processes Context & meaning Data needs Etc.

Evaluation & continuing improvements


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Thailand’s eHealth Situation

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WHO ITU. National eHealth strategy toolkit. Geneva, Switzerland: 2012.

Components of eHealth (WHO-ITU)

91

Kijsanayotin B, Kasitipradith N, Pannarunothai S. eHealth in Thailand:

the current status. Stud Health Technol Inform. 2010;160(Pt 1):376-80.

Thailand’s eHealth: 2010

92

Str

ate

gy &

In

ve

stm

en

t

Standards & Interoperability

Infrastructure

Services, Applications

Software

Leadership & governance

Le

gis

latio

n, p

olic

y &

co

mp

lian

ce

Wo

rkfo

rce

Thailand’s eHealth: Unbalanced Development

Slide courtesy of Dr. Boonchai Kijsanayotin

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eHealth Applications

Enabling Policies and Strategies

Foundation Policies and Strategies

Thailand’s eHealth: Shaky Foundations

Slide courtesy of Dr. Boonchai Kijsanayotin

94

ความส้าคัญล้าดับต้นของการพัฒนาระบบข้อมูลข่าวสารสุขภาพ1. มีหน่วยงานระดับประเทศเพื่อวางนโยบาย และก้ากับดูแลการพัฒนา

ระบบข้อมูลข่าวสารสุขภาพของประเทศ2. วางรากฐานด้านมาตรฐานข้อมูลสุขภาพ เพื่อบูรณาการระบบข้อมูล

และเกิดการใช้ และการแลกเปลี่ยนข้อมูลอย่างกว้างขวาง3. มีกฎหมายและระเบียบเกี่ยวกับระบบข้อมูลสุขภาพโดยเฉพาะ4. มีการผลิต และพัฒนาบุคลากรด้านระบบข้อมูลสุขภาพอย่างเป็น

ระบบและเพียงพอ

ข้อเสนอการพัฒนา

Kijsanayotin B, Kasitipradith N, Pannarunothai S. eHealth in Thailand:

the current status. Stud Health Technol Inform. 2010;160(Pt 1):376-80.

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eHealth Governance

96

Creating Health Information Systems of Tomorrow

98


https://en.wikipedia.org/wiki/Winchester_Mystery_House

• A mansion in San Jose, California which was once the personal residence of Sarah Winchester, the widow of gun magnate William Wirt Winchester.

• It is privately owned and serves as a tourist attraction.• Renowned for its size & its architectural curiosities.• Construction commenced in 1884

99



• Carpenters were hired and worked on the house day and night until it became a 7-story mansion.

• Home contains numerous oddities such as doors and stairs that go nowhere, windows overlooking other rooms and stairs with odd-sized risers.

• No architect was used. Building was added on in a haphazard fashion.

• There was a lack of any master building plan.

100

Architecture


• มีอะไรใน IT ขององค์กร (หรือของระบบสุขภาพ) ที่คล้ายคลึงกับ Winchester Mystery House หรือไม่?

• เราจะป้องกันปัญหานี้ได้อย่างไร

101

Enterprise Architecture

Federation of EA Professional Organizations, Common Perspectives on Enterprise Architecture, Architecture and Governance Magazine,

Issue 9-4, November 2013 (2013). Retrieved on November 19, 2013. via https://en.wikipedia.org/wiki/Enterprise_architecture

• สถาปัตยกรรมองค์กร (EA) is "a well-defined practice for conducting enterprise analysis, design, planning, and implementation, using a holistic approach at all times, for the successful development and execution of strategy. Enterprise architecture applies architecture principles and practices to guide organizations through the business, information, process, and technology changes necessary to execute their strategies..."

102

• ผู้บริหารขอรายงานข้อมูลกะทนัหัน• ผู้ใช้งานบ่น ภาระงานเยอะ บันทึกซ้้าซ้อน• Data-rich, information-poor (& wisdom-none)• ข้อมูลจาก 2 แหล่ง ไม่ตรงกัน• 2 โปรแกรมคุยกันไม่รู้เรื่อง• คนถือข้อมูล “หวง” ข้อมูล ไม่ยอมแชร์• ข้อมูลที่แชร์ไปให้หน่วยงานอื่น “หลุด”

Enterprise Architecture จะแก้ปัญหาเหล่านี้

103

WHO ITU. National eHealth strategy toolkit. Geneva, Switzerland: 2012.

Components of eHealth (WHO-ITU)

104

Message for LDC Participants

• Focus on the I, not the T, in the word “IT”• Use data to produce value-add (information, knowledge,

wisdom & action!)• There is a science (and experts) of information in health• Various IT applications in clinical & public Health settings• Answers are in the WHO-ITU eHealth model• DDC needs to work with others to “reform” Thailand’s

eHealth