IT & Decision Support Systems in Hospital Supply Chains

IT & Decision Support Systems
in Hospital Supply Chains
EGIE 512 Hospital Logistics and Supply Chain Management
Nawanan Theera-Ampornpunt, M.D., Ph.D.
March 20, 2014
http://www.SlideShare.net/Nawanan

2
2003 M.D. (First-Class Honors) (Ramathibodi)
2009 M.S. in Health Informatics (U of MN)
2011 Ph.D. in Health Informatics (U of MN)
2012 Certified HL7 CDA Specialist
• Deputy Executive Director for Informatics (CIO/CMIO)
Chakri Naruebodindra Medical Institute
• Lecturer, Department of Community Medicine
Faculty of Medicine Ramathibodi Hospital
Mahidol University
nawanan.the@mahidol.ac.th
SlideShare.net/Nawanan
http://groups.google.com/group/ThaiHealthIT
Introduction

3
Outline
• Healthcare & Information
• Health Information Technology
• Clinical Decision Making
• Clinical Decision Support Systems
– Definitions
– Types & examples
• Issues Related to CDS Implementation
• Other Decision Support Systems
• Summary

4
Let’s take a look at
these pictures...

5
Image Source: Guardian.co.uk
Manufacturing

6
Image Source: http://www.oknation.net/blog/phuketpost/2013/10/19/entry-3
Banking

7
ER - Image Source: nj.com
Healthcare (on TV)

8
Healthcare
(at an undisclosed hospital)

9
• Life-or-Death
• Difficult to automate human decisions
– Nature of business
– Many & varied stakeholders
– Evolving standards of care
• Fragmented, poorly-coordinated systems
• Large, ever-growing & changing body of
knowledge
• High volume, low resources, little time
Why Healthcare Isn’t Like Any Others

10
• Large variations & contextual dependence
Input Process Output
Patient
Presentation
Decision-
Making
Biological
Responses
Why Healthcare Isn’t Like Any Others

11
Transfer
Banking
Value-Add
- Security
- Convenience
- Customer Service
Location A Location B
But...Are We That Different?

12
Assembling
Manufacturing
Raw
Materials
Finished
Goods
Value-Add
- Innovation
- Design
- QC

13
Patient Care
Health care
Sick Patient Well Patient
Value-Add
- Technology & medications
- Clinical knowledge & skills
- Quality of care; process improvement
- Information

14
Engineer’s Perspectives
• Logistics & Supply Chain
(Administrative)
• Focus on Processes
• Analytical, Systematic Mind
• Tracking & Improving
– Patient Flow
– Materials Flow (Drugs,
Documents, Equipments)
– Information Flow
• Main Objectives
– Efficiency
– Variability
– Traceability
Clinician’s Perspectives
• Patient Care (Clinical)
• Focus on Outcomes
• Specialized Clinical Mind
• Improving
– Patient Care Process
– Healthcare Delivery
• Main Objectives
– Quality
• Effectiveness
• Safety
• Timeliness
Engineers & Clinicians

15
Back to
something simple...

16
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?

17
• 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

18
Shortliffe EH. Biomedical informatics in the education of
physicians. JAMA. 2010 Sep 15;304(11):1227-8.
Information Is Everywhere in Healthcare

19
Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.
“Information” in Medicine

20
Why We Need ICT
in Healthcare?
#1: Because information is
everywhere in healthcare

21
(IOM, 2001)(IOM, 2000) (IOM, 2011)
Landmark IOM Reports

22
• 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

23
• 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
IOM Reports Summary

24
• Perception errors
Image Source: interaction-dynamics.com
To Err Is Human 1: Perception

25
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 2: Attention

26
Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University
To Err Is Human 3: 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
84
The Economist Purchase Options
• Economist.com subscription $59
• Print & web subscription $125
68
32
# of
People
# of
People
To Err Is Human 4: Cognition

28Klein 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”
Cognitive Biases in Healthcare

29
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.

30
Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them.
Acad Med. 2003 Aug;78(8):775-80.

31
• Medication Errors
–Drug Allergies
–Drug Interactions
• Ineffective or inappropriate treatment
• Redundant orders
• Failure to follow clinical practice guidelines
Common Errors

32
Why We Need ICT
in Healthcare?
#2: Because healthcare is
error-prone and technology
can help

33
Why We Need ICT
in Healthcare?
#3: Because access to
high-quality patient
information improves care

34
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

35
Health
Information
Technology
Goal
Value-Add
Tools
Health IT: What’s in a Word?

36
• Patient’s Health
• Population’s Health
• Organization’s Health
(Quality, Reputation & Finance)
“Health” in Health IT

37
Hospital Information System (HIS) Computerized Provider Order Entry (CPOE)
Electronic
Health
Records
(EHRs)
Picture Archiving and
Communication System
(PACS)
Screenshot Images from Faculty of Medicine Ramathibodi Hospital, Mahidol University
Various Forms of Health IT

38
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

39
• Guideline adherence
• Better documentation
• Practitioner decision making or
process of care
• Medication safety
• Patient surveillance & monitoring
• Patient education/reminder
Values of Health IT

40
• 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

42
• Business
Intelligence
• Data Mining/
Utilization
• MIS
• Research
Informatics
• E-learning
• CDSS
• HIE
• CPOE
• PACS
• EHRs
Enterprise
Resource
Planning
(ERP)
• Finance
• Materials
• HR
• ADT
• HIS
• LIS
• RIS
Strategic
Operational
ClinicalAdministrative
Position may vary based on local context
4 Ways IT Can Support Hospitals

43
The Challenge - Knowing What It Means
Electronic Medical
Records (EMRs)
Computer-Based
Patient Records
(CPRs)
Electronic Patient
Records (EPRs)
Electronic Health
Records (EHRs)
Personal Health
Records (PHRs)
Hospital
Information
System (HIS)
Clinical
Information
System (CIS)
EHRs & HIS

44
Computerized Physician 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 Physician Order Entry
(CPOE)

46
Ordering Transcription Dispensing Administration
CPOE
Automatic
Medication
Dispensing
Electronic
Medication
Administration
Records
(e-MAR)
Barcoded
Medication
Administration
Barcoded
Medication
Dispensing
Stages of Medication Process

49
Wisdom
Knowledge
Information
Data
Data-Information-Knowledge-
Wisdom (DIKW) Pyramid

50
Wisdom
Knowledge
Information
Data
Contextualization/
Interpretation
Processing/
Synthesis/
Organization
Judgment
Data-Information-Knowledge-
Wisdom (DIKW) Pyramid

51
Wisdom
Knowledge
Information
Data
Contextualization/
Interpretation
Processing/
Synthesis/
Organization
Judgment
100,000,000
I have 100,000,000
baht in my bank
account
I am rich!!!!!
I should buy a luxury car
(and a BIG house)!
Example

52
Example: Problem A
• Patient A has a blood pressure reading of
170/100 mmHg
• Data: 170/100
• Information: BP of Patient A = 170/100 mmHg
• Knowledge: Patient A has high blood pressure
• Wisdom (or Decision):
– Patient A needs to be investigated for cause of HT
– Patient A needs to be treated with anti-hypertensives
– Patient A needs to be referred to a cardiologist

53
Example: Problem B
• Patient B is allergic to penicillin. He was recently
prescribed amoxicillin for his sore throat.
• Data: Penicillin, amoxicillin, sore throat
• Information:
– Patient B has penicillin allergy
– Patient B was prescribed amoxicillin for his sore throat
• Knowledge:
– Patient B may have allergic reaction to his prescription
• Wisdom (or Decision):
– Patient B should not take amoxicillin!!!

54
Decision & Decision Making
• Decision
– “A choice that you make about something
after thinking about it : the result of deciding”
(Merriam-Webster Dictionary)
• Decision making
– “The cognitive process resulting in the
selection of a course of action among several
alternative scenarios.” (Wikipedia)

55
LET’S TAKE A LOOK AT
PATIENT CARE PROCESS

56
Patient Care
Image Sources: (Left) Faculty of Medicine Ramathibodi Hospital (Right) /en.wikipedia.org/wiki/File:Newborn_Examination_1967.jpg (Nevit Dilmen)

57
EXERCISE 1
Provide some examples of
“decisions” health care
providers make

58
Clinical Decisions
• Patient Care
– What patient history to ask?
– What physical examinations to do?
– What investigations to order?
• Lab tests
• Radiologic studies (X-rays, CTs, MRIs, etc.)
• Other special investigations (EKG, etc.)
– What diagnosis (or possible diagnosis) to
make?

59
Clinical Decisions
• Patient Care
– What treatment to order/perform?
• Medications
• Surgery/Procedures/Nursing Interventions
• Patient Education/Advice for Self-Care
• Admission
– How should patient be followed-up?
– With good or poor response to treatment, what
to do next?
– With new information, what to do next?

60
Clinical Decisions
• Management
– How to improve quality of care and clinical
operations?
– How to allocate limited budget & resources?
– What strategies should the hospital pursue &
what actions/projects should be done?

61
Clinical Decisions
• Public Health
– How to improve health of population?
– How to investigate/control/prevent disease
outbreak?
– How to allocate limited budget & resources?
– What areas of the country’s public health need
attention & what to do with it?

62
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making

63
IOM (2000)
“To Err Is Human”

64
ROLES OF
INFORMATION TECHNOLOGY
IN DECISION MAKING

65
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Clinical Decision Making

66
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Possible Human Errors
Possibility of
Human Errors

67
CLINICAL DECISION
SUPPORT SYSTEMS
(CDS)

68
• Clinical Decision Support (CDS) “is a
process for enhancing health-related
decisions and actions with pertinent,
organized clinical knowledge and patient
information to improve health and healthcare
delivery” (Including both computer-based &
non-computer-based CDS)
(Osheroff et al., 2012)
What Is A CDS?

69
• Computer-based clinical decision support
(CDS): “Use of the computer [ICT] to bring
relevant knowledge to bear on the health
care and well being of a patient.”
(Greenes, 2007)
What Is A CDS?

70
• The real place where most of the values
of health IT can be achieved
• There are a variety of forms and nature
of CDS
Clinical Decision Support
Systems (CDS)

71
• Expert systems
–Based on artificial
intelligence, machine
learning, rules, or
statistics
–Examples: differential
diagnoses, treatment
options
CDS Examples
Shortliffe (1976)

72
• Alerts & reminders
–Based on specified logical conditions
• Drug-allergy checks
• Drug-drug interaction checks
• Drug-lab interaction checks
• Drug-formulary checks
• Reminders for preventive services or certain actions
(e.g. smoking cessation)
• Clinical practice guideline integration (e.g. best
practices for chronic disease patients)
CDS Examples

74
• 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)
CDS Examples

75
Infobuttons
Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html

76
• Pre-defined documents
–Order sets, personalized “favorites”
–Templates for clinical notes
–Checklists
–Forms
• Can be either computer-based or
paper-based
CDS Examples

77
Order Sets
Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm

78
• Simple UI designed to help clinical
decision making
–Abnormal lab highlights
–Graphs/visualizations for lab results
–Filters & sorting functions
CDS Examples

79
Abnormal Lab Highlights
Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html

80
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
How CDS Supports
Decision Making
Abnormal lab
highlights

81
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
How CDS Supports
Decision Making
Order Sets

82
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
How CDS Supports
Decision Making
Drug-Allergy
Checks

83
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
How CDS Supports
Decision Making
Drug-Drug
Interaction
Checks

84
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
How CDS Supports
Decision Making
Drug-Drug
Interaction
Checks

85
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
How CDS Supports
Decision Making
Clinical Practice
Guideline
Alerts/Reminders

86
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
How CDS Supports
Decision Making
Integration of
Evidence-Based
Resources (e.g.
drug databases,
literature)

87
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
How CDS Supports
Decision Making
Diagnostic/Treatment
Expert Systems

88
User User Interface
Patient
Data
Inference Engine
Knowledge
BaseOther Data
• Rules & Parameters
• Statistical data
• Literature
• Etc.
• System states
• Epidemiological/
surveillance data
• Etc.
Example of CDS
Architecture
Other
Systems

89
ISSUES RELATED TO
CDS IMPLEMENTATION

90
• How will CDS be implemented in real life?
• Will it interfere with user workflow?
• Will it be used by users? If not, why?
• What user interface design is best?
• What are most common user complaints?
• Who is responsible if something bad
happens?
• How to balance reliance on machines &
humans
Human Factor Issues of CDS

91
IBM’s Watson
Image Source: socialmediab2b.com

92
Image Source: englishmoviez.com
Rise of the Machines?

93
Issues
• CDSS as a supplement or replacement of clinicians?
– The demise of the “Greek Oracle” model (Miller & Masarie, 1990)
The “Greek Oracle” Model
The “Fundamental Theorem”
Friedman (2009)
Wrong Assumption
Correct Assumption

94
• Features with improved clinical practice
(Kawamoto et al., 2005)
– Automatic provision of decision support as part of
clinician workflow
– Provision of recommendations rather than just
assessments
– Provision of decision support at the time and location of
decision making
– Computer based decision support
• Usability & impact on productivity

95
Issues
• Alert sensitivity & alert fatigue
Alert Fatigue

96
• Liabilities
– Clinicians as “learned intermediaries”
• Prohibition of certain transactions vs.
Professional autonomy
(see Strom et al., 2010)
Ethical-Legal Issues of CDS

98
• “Unanticipated and unwanted effect of
health IT implementation”
(www.ucguide.org)
• Resources
– www.ucguide.org
– Ash et al. (2004)
– Campbell et al. (2006)
– Koppel et al. (2005)
Unintended Consequences of
CDS & Health IT

99
Ash et al. (2004)
CDS & Health IT

100
• Errors in the process of entering and
retrieving information
– A human-computer interface that is not
suitable for a highly interruptive use context
– Causing cognitive overload by
overemphasizing structured and “complete”
information entry or retrieval
• Structure
• Fragmentation
• Overcompleteness
Ash et al. (2004)
CDS & Health IT

101
• Errors in communication & coordination
– Misrepresenting collective, interactive work as
a linear, clearcut, and predictable workflow
• Inflexibility
• Urgency
• Workarounds
• Transfers of patients
– Misrepresenting communication as information
transfer
• Loss of communication
• Loss of feedback
• Decision support overload
• Catching errors
Ash et al. (2004)
CDS & Health IT

102
• Which type of CDS should be chosen?
• What algorithms should be used?
• How to “represent” knowledge in the system?
• How to update/maintain knowledge base in
the system?
• How to standardize data/knowledge?
• How to implement CDS with good system
performance?
Technical Issues of CDS

103
• Choosing the right CDSS strategies
• Expertise required for proper CDSS design &
implementation
• Everybody agreeing on the “rules” to be enforced
• Evaluation of effectiveness
Other Issues

104
• Speed is Everything
• Anticipate Needs and Deliver in Real Time
• Fit into the User’s Workflow
• Little Things (like Usability) Can Make a Big Difference
• Recognize that Physicians Will Strongly Resist Stopping
• Changing Direction Is Easier than Stopping
• Simple Interventions Work Best
• Ask for Additional Information Only When You Really Need It
• Monitor Impact, Get Feedback, and Respond
• Manage and Maintain Your Knowledge-based Systems
Bates et al. (2003)
“Ten Commandments” for
Effective CDS

105
OTHER DECISION SUPPORT
SYSTEMS

106
• Provides information needed to manage
an organization (e.g. a hospital)
effectively and efficiently
• A broad category of information systems
– Administrative reports
– Enterprise resource planning (ERP)
– Supply Chain Management (SCM)
– Customer Relationship Management
(CRM)
– Project management tools
– Knowledge management tools
– Business intelligence (BI)
Management Information
Systems (MIS)

107
• Allows for
– Data analysis
– Correlation
– Trending
– Reporting of data across multiple sources
Health IT Workforce Curriculum
Version 2.0/Spring 2011
Business Intelligence (BI)

108
• Examples
– Clinical and Financial Analytics and Decision
Support
– Query and Reporting Tools
– Data Mining
– Online Scoreboards and Dashboards
Business Intelligence & Data Warehousing for Healthcare. Clinical Informatics Wiki. 2008.
Available from: http://www.informatics-
review.com/wiki/index.php/Business_Intelligence_&_Data_Warehousing_for_Healthcare
Health IT Workforce Curriculum
Version 2.0/Spring 2011

109
Image Source: http://www.hiso.or.th/dashboard/
Data Reporting Systems

110
Image Source: http://www.inetsoft.com/business/solutions/applying_business_intelligence_to_manufacturing/

111
Image Source: https://www.sas.com/technologies/bi/entbiserver/
Business Dashboards

112
• There are several decisions made in a clinical
patient care process
• Data leads to information, knowledge, and
ultimately, decision & actions
• Human clinicians are not perfect and can make
mistakes
• A clinical decision support systems (CDS) provides
support for clinical decision making (to prevent
mistakes & provide best patient care)
• A CDS can be computer-based or paper-based
Key Points

113
• CDS comes in various forms, designs, and
architecture
• There are many issues related to design,
implementation and use of CDS
– Technical Issues
– Human Factor Issues
– Ethical-Legal Issues
Key Points

114
• Current mindset: CDS should be used to help, not
replace, human providers
• Be attentive to workarounds, alert fatigues, and
other unintended consequences of CDS
– They can cause more danger to patients!!
– They may lead users to abandon using CDS (a failure)
• There are recommendations on how to best design
& implement CDS
• There are other administrative (non-clinical)
decision support systems as well
Key Points

115
Intelligent &
helpful
robots
Intelligent
humanistic robots
in a human world
Machines that
replace humans
for a “better” world
HAL 9000 Data David NS-5
Dangerous
killer machines
What Will The Future Be
for Health Care?

116
References
• Ash JS, Berg M, Coiera E. Some unintended consequences of information
technology in health care: the nature of patient care information system-related
errors. J Am Med Inform Assoc. 2004 Mar-Apr;11(2):104-12.
• Ariely D. Predictably irrational: the hidden forces that shape our decisions. New
York City (NY): HarperCollins; 2008. 304 p.
• Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, Spurr C, Khorasani R,
Tanasijevic M, Middleton B. Ten commandments for effective clinical decision
support: making the practice of evidence-based medicine a reality. J Am Med
Inform Assoc. 2003 Nov-Dec;10(6):523-30.
• Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of unintended
consequences related to computerized provider order entry. J Am Med Inform
Assoc. 2006 Sep-Oct;13(5):547-56.
• Elson RB, Faughnan JG, Connelly DP. An industrial process view of information
delivery to support clinical decision making: implications for systems design
and process measures. J Am Med Inform Assoc. 1997 Jul-Aug;4(4):266-78.
• Friedman CP. A "fundamental theorem" of biomedical informatics. J Am Med
Inform Assoc. 2009 Apr;16(2):169-170.

117
References
• Greenes RA. Clinical decision support: the road ahead. Oxford (UK): Elsevier;
2007. 581 p.
• Institute of Medicine, Committee on Quality of Health Care in America. To err is
human: building a safer health system. Kohn LT, Corrigan JM, Donaldson MS,
editors. Washington, DC: National Academy Press; 2000. 287 p.
• Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice
using clinical decision support systems: a systematic review of trials to
identify features critical to success. BMJ. 2005 Apr 2;330(7494):765.
• Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, et al. Role of
computerized physician order entry systems in facilitating medication errors.
JAMA. 2005 Mar 9;293(10):1197-1203.
• Miller RA, Masarie FE. The demise of the "Greek Oracle" model for medical
diagnostic systems. Methods Inf Med. 1990 Jan;29(1):1-2.
• Osheroff JA, Teich JM, Levick D, Saldana L, Velasco FT, Sittig DF, Rogers KM,
Jenders RA. Improving outcomes with clinical decision support: an
implementer’s guide. 2nd ed. Chicago (IL): Healthcare Information and
Management Systems Society; 2012. 323 p.

118
References
• Shortliffe EH. Computer-based medical consultations: MYCIN. New York (NY):
Elsevier; 1976. 264 p.
• Strom BL, Schinnar R, Aberra F, Bilker W, Hennessy S, Leonard CE, Pifer E.
Unintended effects of a computerized physician order entry nearly hard-stop
alert to prevent a drug interaction: a randomized controlled trial. Arch Intern
Med. 2010 Sep 27;170(17):1578-83.

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IT & Decision Support Systems in Hospital Supply Chains