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Overview of Hospital Information Systems

บุญชัย กิจสนาโยธิน M.D., Ph.D.(Health Informatics)

นวนรรน ธีระอัมพรพันธุ M.D., Ph.D.(Health Informatics)

PHARMACY INFORMATICS EDUCATIONAL SERIES

โครงการอบรมการพัฒนาและจัดการระบบขอมูลเภสัชกรรมในโรงพยาบาล

: การเตรียมขอมูลยาเพ่ือการเบิกจาย

วันที่ 18 มิถุนายน 2557

คณะเภสัชศาสตร จุฬาลงกรณมหาวิทยาลัย

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Outline

• Healthcare & Information • Why We Need ICT in Healthcare • Health IT • Hospital Information Systems • Health Information Exchange • Q&A

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What Clinicians Want?

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)

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High Quality Care

• 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.

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Information is Everywhere in Healthcare

Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.

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Why We Need ICT in Healthcare?

#1: Because information is everywhere in healthcare

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

Landmark IOM Reports

(IOM, 2003)

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Patient Safety

• 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

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IOM Reports Summary

• 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

10 Image 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

11 Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University

To Err is Human 2: Memory

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To Err is Human 3: Cognition

• Cognitive Errors - Example: Decoy Pricing

The Economist Purchase Options • Economist.com subscription $59 • Print subscription $125 • Print & web subscription $125

Ariely (2008)

16 0 84

The Economist Purchase Options • Economist.com subscription $59 • Print & web subscription $125

68 32

# of People

# of People

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Cognitive Biases in Healthcare

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

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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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Use of information and communications technology (ICT) in health & healthcare

settings Source: The Health Resources and Services Administration, Department of

Health and Human Service, USA

Slide adapted from: Boonchai Kijsanayotin

Health IT

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Health Information Technology

Goal

Value-Add

Tools

Health IT: What’s in a Word?

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• Patient’s Health • Population’s Health •Organization’s Health

(Quality, Reputation & Finance)

“Health” in “Health IT”

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Hospital Information System (HIS) Computerized Provider Order Entry (CPOE)

Electronic Health

Records (EHRs)

Picture Archiving and Communication System

(PACS)

Various Forms of Health IT

Screenshot Images from Faculty of Medicine Ramathibodi Hospital, Mahidol University

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mHealth

Biosurveillance

Telemedicine & Telehealth

Images from Apple Inc., Geekzone.co.nz, Google, HealthVault.com and American Telecare, Inc.

Personal Health Records (PHRs) and Patient Portals

Still Many Other Forms of Health IT

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• Guideline adherence • Better documentation • Practitioner decision making or

process of care • Medication safety • Patient surveillance & monitoring • Patient education/reminder

Values of Health IT

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• Master Patient Index (MPI) • Admit-Discharge-Transfer (ADT) • Electronic Health Records (EHRs) • Computerized Physician Order Entry (CPOE) • Clinical Decision Support Systems (CDS) • Picture Archiving and Communication System

(PACS) • Nursing applications • Enterprise Resource Planning (ERP) - Finance,

Materials Management, Human Resources

Enterprise-wide Hospital IT

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• Pharmacy applications

• Laboratory Information System (LIS)

• Radiology Information System (RIS)

• Specialized applications (ER, OR, LR, Anesthesia, Critical Care, Dietary Services, Blood Bank)

• Incident management & reporting system

Departmental IT in Hospitals

Hospital Information Systems EHR

EMR

PHR

HIS - EHR – EMR – PHR relationship

EHR= Electronic Health Record EMR= Electronic Medical Record PHR= Personal Health Record

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Computerized Provider Order Entry (CPOE)

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Values

• No handwriting!!! • Structured data entry: Completeness, clarity,

fewer mistakes (?) • No transcription errors! • Streamlines workflow, increases efficiency

Computerized Provider Order Entry (CPOE)

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Ordering Transcription Dispensing Administration

CPOE Automatic Medication Dispensing

Electronic Medication

Administration Records (e-MAR)

Barcoded Medication

Administration

Barcoded Medication Dispensing

Stages of Medication Process

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• The real place where most of the values of health IT can be achieved – Expert systems

• Based on artificial intelligence, machine learning, rules, or statistics

• Examples: differential diagnoses, treatment options (Shortliffe, 1976)

Clinical Decision Support Systems (CDS)

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– Alerts & reminders • Based on specified logical conditions • Examples:

– Drug-allergy checks – Drug-drug interaction checks – Reminders for preventive services – Clinical practice guideline integration

Clinical Decision Support Systems (CDS)

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Example of “Reminders”

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• Reference information or evidence-based knowledge sources – Drug reference databases – Textbooks & journals – Online literature (e.g. PubMed) – Tools that help users easily access

references (e.g. Infobuttons)

More CDS Examples

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Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html

Infobuttons

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• Pre-defined documents – Order sets, personalized “favorites” – Templates for clinical notes – Checklists – Forms

• Can be either computer-based or paper-based

Other CDS Examples

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Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm

Order Sets

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• Simple UI designed to help clinical decision making – Abnormal lab highlights – Graphs/visualizations for lab results – Filters & sorting functions

Other CDS Examples

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Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html

Abnormal Lab Highlights

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External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

Working Memory

CLINICIAN

Elson, Faughnan & Connelly (1997)

Clinical Decision Making

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Abnormal lab highlights

Clinical Decision Making

External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

Working Memory

CLINICIAN

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Clinical Decision Making

External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

Working Memory

CLINICIAN Drug-Allergy

Checks

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External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

Working Memory

CLINICIAN

Elson, Faughnan & Connelly (1997)

Clinical Decision Making

Drug-Drug Interaction

Checks

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External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

Working Memory

CLINICIAN

Elson, Faughnan & Connelly (1997)

Clinical Decision Making

Clinical Practice Guideline

Reminders

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External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

Working Memory

CLINICIAN

Elson, Faughnan & Connelly (1997)

Clinical Decision Making

Diagnostic/Treatment Expert Systems

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• CDSS as a replacement or supplement of clinicians? – The demise of the “Greek Oracle” model (Miller & Masarie, 1990)

The “Greek Oracle” Model

The “Fundamental Theorem” Model

Friedman (2009)

Wrong Assumption

Correct Assumption

Proper Roles of CDS

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Some risks • Alert fatigue

Unintended Consequences of Health IT

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Workarounds

สถานการณ HIS/EHR 2553-54 (N=902) (นพ.นวนรรน ธีระอัมพรพันธุ 2554)

Product/Vendor Frequency (%) HOSxP 449 (50.17%) Self-developed or outsourced 142 (15.87%) Hospital OS 64 (7.15%) SSB 32 (3.58%) Mit-Net 22 (2.46%) MRecord 21 (2.35%) H.I.M. Professional 20 (2.23%) MedTrak/TrakCare 19 (2.12%) HoMC 18 (2.01%) No hospital information system used 14 (1.56%)

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Hospital A Hospital B

Clinic C

Government

Lab Patient at Home

Health Information Exchange (HIE)

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Outline

Healthcare & Information Why We Need ICT in Healthcare Health IT Hospital Information Systems Health Information Exchange • Q&A