Upload
nawanan-theera-ampornpunt
View
365
Download
2
Embed Size (px)
Citation preview
Hospital Decision Support
PHBS 644 Research Design & Evaluation in Health Informatics
Nawanan Theera-Ampornpunt, M.D., Ph.D.February 13, 2015http://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 MedicineFaculty of Medicine Ramathibodi HospitalMahidol University
[email protected]/Nawananhttp://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...
5Image Source: Guardian.co.uk
Manufacturing
6Image 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
Input Process Output
Transfer
Banking
Value-Add- Security- Convenience- Customer Service
Location A Location B
But...Are We That Different?
12
Input Process Output
Assembling
Manufacturing
Raw Materials
Finished Goods
Value-Add- Innovation- Design- QC
But...Are We That Different?
13
Input Process Output
Patient Care
Health care
Sick Patient Well Patient
Value-Add- Technology & medications- Clinical knowledge & skills- Quality of care; process improvement- Information
But...Are We That Different?
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
19Shortliffe 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 errorsHealth 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
25Image 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
27
• Cognitive Errors - Example: Decoy Pricing
The Economist Purchase Options
• Economist.com subscription $59• Print subscription $125• Print & web subscription $125
Ariely (2008)
16084
The Economist Purchase Options
• Economist.com subscription $59• Print & web subscription $125
6832
# 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.
Cognitive Biases in Healthcare
30
Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad Med. 2003 Aug;78(8):775-80.
Cognitive Biases in Healthcare
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
HealthInformation 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
41
• 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)
45
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
47
CLINICAL DECISION MAKING
48
WHAT IS A DECISION?
49
Wisdom
Knowledge
Information
Data
Data-Information-Knowledge-Wisdom (DIKW) Pyramid
50
Wisdom
Knowledge
Information
DataContextualization/
Interpretation
Processing/Synthesis/
Organization
Judgment
Data-Information-Knowledge-Wisdom (DIKW) Pyramid
51
Wisdom
Knowledge
Information
DataContextualization/
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 1Provide 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
WorkingMemory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
63IOM (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
WorkingMemory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
66
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIAN
Elson, Faughnan & Connelly (1997)
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
73
Example of “Reminders”
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
WorkingMemory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports Decision Making
Abnormal lab highlights
81
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports Decision Making
Order Sets
82
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports Decision Making
Drug-Allergy Checks
83
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports Decision Making
Drug-Drug Interaction
Checks
84
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports Decision Making
Clinical Practice Guideline
Alerts/Reminders
85
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports Decision Making
Integration of Evidence-Based Resources (e.g. drug databases,
literature)
86
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIAN
Elson, Faughnan & Connelly (1997)
How CDS Supports Decision Making
Diagnostic/Treatment Expert Systems
87
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
88
ISSUES RELATED TO CDS IMPLEMENTATION
89
• 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
90
IBM’s Watson
Image Source: socialmediab2b.com
91
Image Source: englishmoviez.com
Rise of the Machines?
92
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)
Human Factor Issues of CDS
Wrong Assumption
Correct Assumption
93
• 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
Human Factor Issues of CDS
94
Issues• Alert sensitivity & alert fatigue
Alert Fatigue
95
• Liabilities– Clinicians as “learned intermediaries”
• Prohibition of certain transactions vs. Professional autonomy (see Strom et al., 2010)
Ethical-Legal Issues of CDS
96
Workarounds
97
• “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
98
Ash et al. (2004)
Unintended Consequences of CDS & Health IT
99
• 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)
Unintended Consequences of CDS & Health IT
100
• 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)
Unintended Consequences of CDS & Health IT
101
• 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
102
• 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
103
• 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
104
OTHER DECISION SUPPORT SYSTEMS
105
• 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)
106
• Allows for – Data analysis– Correlation– Trending– Reporting of data across multiple sources
Health IT Workforce Curriculum Version 2.0/Spring 2011
Business Intelligence (BI)
107
• 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
Business Intelligence (BI)
108
Image Source: http://www.hiso.or.th/dashboard/
Data Reporting Systems
109Image Source: http://www.inetsoft.com/business/solutions/applying_business_intelligence_to_manufacturing/
Business Intelligence (BI)
110Image Source: https://www.sas.com/technologies/bi/entbiserver/
Business Dashboards
111
• 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
112
• 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
113
• 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
114
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?
115
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.
116
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.
117
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.