3. Hospitalâs Roles
⢠Provider of Secondary & Tertiary Care
â Acute Care
â Chronic Care
â Emergency
⢠Facilitator of Primary Care
⢠Sometimes Teaching & Research
4. Levels of Hospitals
⢠Community Hospitals
⢠General/Provincial Hospitals
⢠Tertiary/Regional Hospitals
⢠University Medical Centers
⢠Specialty Hospitals
5. Types of Hospitals
⢠Public
⢠Private For-Profit
⢠Private Not-For-Profit
⢠Stand-Alone
⢠Part of Multi-Hospital System
⢠Teaching vs. Non-Teaching Hospitals
6. Why They Matter: The Importance of âContextâ
⢠$$$ (Purchasing Power)
⢠Bureaucracies & regulations
⢠Organizational cultures & management styles
⢠Level of organizational/workflow complexity
⢠Facilities & level of information needs
⢠Service volume, resources, priorities
⢠Internal IT capabilities & environments
7. IT Decision Making in Hospitals: Key Points
⢠Depends on local context
⢠IT is not alone -> Business-IT alignment/integration
⢠âKnow your organizationâ
⢠View IT as a tool for something else, not the
end goal by itself
⢠Focus on the real goals (what define âsuccessâ)
9. CLASS EXERCISE #3
Suggest 2-3 examples of âsuccessâ
of IT implementation in hospitals
for each of DeLone & McLeanâs
Model (1992)
10. Success of IT Implementation
System Quality
⢠System performance (response time, reliability)
⢠Accuracy, error rate
⢠Flexibility
⢠Ease of use
⢠Accessibility
11. Success of IT Implementation
Information Quality
⢠Accuracy
⢠Currency, timeliness
⢠Reliability
⢠Completeness
⢠Relevance
⢠Usefulness
12. Success of IT Implementation
Use
⢠Subjective (e.g. asks a user âHow often do you use the
system?â)
⢠Objective (e.g. number of orders done electronically)
User Satisfaction
⢠Satisfaction toward system/information
⢠Satisfaction toward use
13. Success of IT Implementation
Individual Impacts
⢠Efficiency/productivity of the user
⢠Quality of clinical operations/decision-making
Organizational Impacts
⢠Faster operations, cost & time savings
⢠Better quality of care, better aggregate outcomes
⢠Reputation, increased market share
⢠Increased service volume or patient retention
22. Master Patient Index (MPI)
⢠A hospitalâs list of all patients
⢠Functions
â Registration/identification of patients (HN/MRN)
â Captures/updates patient demographics
â Used in virtually all other hospital service applications
⢠Issues
â A large database
â Interface with other systems
â Duplicate resolutions
â Accuracy & currency of patient information
â Language issues
23. Admit-Discharge-Transfer (ADT)
⢠Functions
â Supports Admit, Discharge & Transfer of patients
(âpatient managementâ)
â Provides status/location of admitted patients
â Used in assessing bed occupancy
â Linked to billing, claims & reimbursements
⢠Issues
â Accuracy & currency of patient status/location
â Handling of exceptions (e.g. patient overflows, escaped
patients, home leaves, discharged but not yet departed,
missing discharge information)
â Input of important information (diagnoses, D/C summary)
â Links between OPD, IPD, ER & OR
24. EHRs & HIS
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)
25. EHRs
Commonly Accepted Definitions
⢠Electronic documentation of patient care by providers
⢠Provider has direct control of information in EHRs
⢠Synonymous with EMRs, EPRs, CPRs
⢠Sometimes defined as a patientâs longitudinal records over
several âepisodes of careâ & âencountersâ (visits)
26. EHR Systems
Are they just a system that allows electronic documentation of
clinical care?
Or do they have other values?
Diag-
nosis
History
& PE
Treat-
ments
...
27. Documented Benefits of Health IT
⢠Literature suggests improvement through
â Guideline adherence (Shiffman et al, 1999;Chaudhry et al, 2006)
â Better documentation (Shiffman et al, 1999)
â Practitioner decision making or process of care
(Balas et al, 1996;Kaushal et al, 2003;Garg et al, 2005)
â Medication safety
(Kaushal et al, 2003;Chaudhry et al, 2006;van Rosse et al, 2009)
â Patient surveillance & monitoring (Chaudhry et al, 2006)
â Patient education/reminder (Balas et al, 1996)
â Cost savings and better financial performance
(Parente & Dunbar, 2001;Chaudhry et al, 2006;Amarasingham et al, 2009;
Borzekowski, 2009)
28. Functions that Should Be Part of EHR Systems
⢠Computerized Medication Order Entry (IOM, 2003; Blumenthal et al, 2006)
⢠Computerized Laboratory Order Entry (IOM, 2003)
⢠Computerized Laboratory Results (IOM, 2003)
⢠Physician Notes (IOM, 2003)
⢠Patient Demographics (Blumenthal et al, 2006)
⢠Problem Lists (Blumenthal et al, 2006)
⢠Medication Lists (Blumenthal et al, 2006)
⢠Discharge Summaries (Blumenthal et al, 2006)
⢠Diagnostic Test Results (Blumenthal et al, 2006)
⢠Radiologic Reports (Blumenthal et al, 2006)
29. EHR Systems/HIS: Issues
⢠Functionality & workflow considerations
⢠Structure & format of data entry
â Free text vs structured data forms
â Usability
â Use of standards & vocabularies (e.g. ICD-10, SNOMED CT)
â Templates (e.g. standard narratives, order sets)
â Level of customization per hospital, specialty, location, group, clinician
â Reduced clinical value due to over-documentation (e.g. medico-legal, HA)
â Special documents (e.g. operative notes, anesthetic notes)
â Integration with paper systems (e.g. scanned MRs, legal documents)
⢠Reliability & contingency/business continuity planning
⢠Roll-out strategies & change management
⢠Interfaces
30. Computerized (Physician/Provider) Order Entry
Functions
⢠Physician directly enters
medication/lab/diagnostic/imaging orders
online
⢠Nurse & pharmacy process orders
accordingly
⢠Maybe considered part of an EHR/HIS
system
31. Values
⢠No handwriting!!!
⢠Structured data entry: Completeness, clarity,
fewer mistakes (?)
⢠No transcription errors!
⢠Streamlines workflow, increases efficiency
Computerized Provider Order Entry (CPOE)
32. Computerized (Physician/Provider) Order Entry
Issues
⢠âPhysician as a clerkâ frustration
⢠Usability -> Reduced physician productivity?
⢠Unclear value proposition for physician?
⢠Complexity of medication data structure
⢠Integration of medication, lab, diagnostic, imaging &other orders
⢠Roll-out strategies & change management
Washington Post (March 21, 2005)
âOne of the most important lessons learned to date is that the complexity
of human change management may be easily underestimatedâ
Langberg ML (2003) in âChallenges to implementing CPOE: a case study of a work in progress at Cedars-Sinaiâ
33. ⢠The real place where most of the
values of health IT can be achieved
â Expert systems
⢠Based on artificial intelligence,
machine learning, rules, or
statistics
⢠Examples: differential
diagnoses, treatment options(Shortliffe, 1976)
Clinical Decision Support Systems (CDS)
34. â Alerts & reminders
⢠Based on specified logical conditions
⢠Examples:
âDrug-allergy checks
âDrug-drug interaction checks
âReminders for preventive services
âClinical practice guideline integration
Clinical Decision Support Systems (CDS)
36. ⢠Reference information or evidence-
based knowledge sources
âDrug reference databases
âTextbooks & journals
âOnline literature (e.g. PubMed)
âTools that help users easily access
references (e.g. Infobuttons)
More CDS Examples
38. ⢠Pre-defined documents
âOrder sets, personalized âfavoritesâ
âTemplates for clinical notes
âChecklists
âForms
⢠Can be either computer-based or
paper-based
Other CDS Examples
40. ⢠Simple UI designed to help clinical
decision making
âAbnormal lab highlights
âGraphs/visualizations for lab results
âFilters & sorting functions
Other CDS Examples
42. External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
43. External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
Elson, Faughnan & Connelly (1997)
Clinical Decision Making
Abnormal lab
highlights
44. 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-Allergy
Checks
45. 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
46. 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
47. 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
50. ⢠CDSS as a replacement or supplement of
clinicians?
â The demise of the âGreek Oracleâ model (Miller & Masarie, 1990)
The âGreek Oracleâ Model
The âFundamental Theoremâ Model
Friedman (2009)
Wrong Assumption
Correct Assumption
Proper Roles of CDS
53. Clinical Decision Support Systems (CDSSs)
⢠The real place where most of the values of health IT can be
achieved
⢠A variety of forms and nature of CDSSs
â Expert systems
⢠Based on artificial intelligence, machine learning, rules, or statistics
⢠Examples: differential diagnoses, treatment options
â Alerts & reminders
⢠Based on specified logical conditions
⢠Examples: 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
â Evidence-based knowledge sources e.g. drug database, literature
â Simple UI designed to help clinical decision making
54. Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
Memory
CLINICIAN
From a teaching slide by Don Connelly, 2006
55. Clinical Decision Support Systems (CDSSs)
Issues
⢠Choosing the right CDSS strategies
⢠Expertise required for proper CDSS design & implementation
⢠Integration into the point of care with minimal productivity/
workflow impacts
⢠Everybody agreeing on the ârulesâ to be enforced
⢠Maintenance of the knowledge base
⢠Evaluation of effectiveness
56. âTen Commandmendsâ for Effective CDSSs
⢠Speed is Everything
⢠Anticipate Needs and Deliver in Real Time
⢠Fit into the Userâs 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)
57. Nursing Applications
Functions
⢠Documents nursing assessments, interventions & outcomes
⢠Facilitates charting & vital sign recording
⢠Utilizes standards in nursing informatics
⢠Populates and documents care-planning
⢠Risk/incident management
⢠etc.
Issues
⢠Minimizing workflow/productivity impacts
⢠Goal: Better documentation vs. better care?
⢠Evolving standards in nursing practice
⢠Change management
58. Pharmacy Applications
Functions
⢠Streamlines workflow from medication orders to dispensing and
billing
⢠Reduces medication errors, improves medication safety
⢠Improves inventory management
59. Stages of Medication Process
Ordering Transcription Dispensing Administration
CPOE
Automatic
Medication
Dispensing
Electronic
Medication
Administration
Records
(e-MAR)
Barcoded
Medication
Administration
Barcoded
Medication
Dispensing
60. Pharmacy Applications
Issues
⢠Who enters medication orders into electronic format at which
stage?
⢠Unintended consequences
⢠âPower shiftsâ
⢠Handling exceptions (e.g. countersigns, verbal orders,
emergencies, formulary replacements, drug shortages)
⢠Choosing the right technology for the hospital
⢠Goal: Workflow facilitation vs. medication safety?
61. Imaging Applications
Picture Archiving and Communication System (PACS)
⢠Captures, archives, and displays electronic images captured from
imaging modalities (DICOM format)
⢠Often refers to radiologic images but sometimes used in other
settings as well (e.g. cardiology, endoscopy, pathology,
ophthalmology)
⢠Values: reduces space, costs of films, loss of films, parallel
viewing, remote access, image processing & manipulation,
referrals
Radiology Information System (RIS) or Workflow Management
⢠Supports workflow of the radiology department, including patient
registration, appointments & scheduling, consultations, imaging
reports, etc.
62. Take-Away Messages
⢠Health IT in clinical settings comes in various forms
⢠Local contexts are important considerations
⢠Clinical IT is a very complex environment
⢠Health IT has much potential to improve quality & efficiency of care
⢠But it is also risky...
â Costs
â Change resistance
â Poor design
â Alert fatigue
â Workarounds and unintended consequences
â Use of wrong technology to fix the wrong process for the wrong goal
⢠We need to have an informaticianâs mind (not just
a technologistâs mind) to help us navigate through the complexities