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Health IT in Hospital Settings
1. Health IT in Hospital Settings
Nawanan Theera-Ampornpunt MD MSNawanan Theera Ampornpunt, MD, MS
Healthcare CIO Program, Ramathibodi Hospital Administration School
Dec. 3, 2010 SlideShare.net/Nawanan
Except whereÂ
citing other works
2. Health Care SystemHealth Care System
Home
Hospital
Emergency
Responders
N i H /HospitalResponders
Nursing Home/
Long-Term Care
Facility
Ministry ofy
Public Health The Payers
Clinic/
Physicianâs Office Community
Lab
Pharmacy
Health Center (PCU)Lab
3. Hospitalâs RolesHospital s Roles
⢠Provider of Secondary & Tertiary CareProvider of Secondary & Tertiary Care
â Acute Care
â Chronic Care
â EmergencyEmergency
⢠Facilitator of Primary Care
⢠Sometimes Teaching & Research
4. Levels of HospitalsLevels of Hospitals
⢠Community HospitalsCommunity Hospitals
⢠General/Provincial Hospitals
⢠Tertiary/Regional Hospitals
U i i M di l C⢠University Medical Centers
⢠Specialty HospitalsSpecialty Hospitals
5. Types of HospitalsTypes of Hospitals
⢠PublicPublic
⢠Private For-Profit
⢠Private Not-For-Profit
⢠Stand-AloneStand Alone
⢠Part of Multi-Hospital System
6. Why They Matter: The Importance of âContextâWhy They Matter: The Importance of Context
⢠$$$ (Purchasing Power)$$$ (Purchasing Power)
⢠Bureaucracies & regulations
⢠Organizational cultures & management styles
L l f i i l/ kfl l i⢠Level of organizational/workflow complexity
⢠Facilities & level of information needsFacilities & level of information needs
⢠Service volume, resources, priorities
⢠Internal IT capabilities & environments
7. IT Decision Making in Hospitals: Key PointsIT Decision Making in Hospitals: Key Points
⢠Depends on local contextDepends on local context
⢠IT is not alone -> Business-IT alignment/integration
⢠âKnow your organizationâ
Vi IT l f hi l h⢠View IT as a tool for something else, not the
end goal by itself
⢠Focus on the real goals (what define âsuccessâ)
8. Success of IT ImplementationSuccess of IT Implementation
DeLone & McLean (1992)DeLone & McLean (1992)
9. CLASS EXERCISE #3CLASS EXERCISE #3
Suggest 2-3 examples of âsuccessâSuggest 2 3 examples of success
of IT implementation in hospitals
f h f D L & M L âfor each of DeLone & McLeanâs
Model (1992)( )
10. Success of IT ImplementationSuccess of IT Implementation
System QualitySystem Quality
⢠System performance (response time, reliability)
⢠Accuracy, error rate
Fl ibili⢠Flexibility
⢠Ease of useEase of use
⢠Accessibility
11. Success of IT ImplementationSuccess of IT Implementation
Information QualityInformation Quality
⢠Accuracy
⢠Currency, timeliness
R li bili⢠Reliability
⢠CompletenessCompleteness
⢠Relevance
⢠Usefulness
12. Success of IT ImplementationSuccess of IT Implementation
UseUse
⢠Subjective (e.g. asks a user âHow often do you use the
system?â)system? )
⢠Objective (e.g. number of orders done electronically)
User Satisfaction
S i f i d /i f i⢠Satisfaction toward system/information
⢠Satisfaction toward useSatisfaction toward use
13. Success of IT ImplementationSuccess of IT Implementation
Individual ImpactsIndividual Impacts
⢠Efficiency/productivity of the user
⢠Quality of clinical operations/decision-making
Organizational ImpactsOrganizational Impacts
⢠Faster operations, cost & time savings
⢠Better quality of care, better aggregate outcomes
⢠Reputation increased market shareReputation, increased market share
⢠Increased service volume or patient retention
14. NOW WHAT ARE SOMENOW, WHAT ARE SOME
IMPORTANT HOSPITAL IT?
15. Examples of Hospital ITExamples of Hospital IT
Enterprise-widete p se de
⢠Infrastructural IT (e.g. hardware, OS, network, web, e-mail)
Office Automation⢠Office Automation
⢠MPI, ADT
⢠EHRs/EMRs/HIS/CIS
⢠CPOE & CDSSs
⢠Nursing applications
⢠Billing Claims & Reimbursements⢠Billing, Claims & Reimbursements
⢠MIS, ERP, CRM, DW, BI
16. Examples of Hospital ITExamples of Hospital IT
Departmental ApplicationsDepartmental Applications
⢠Pharmacy applications
⢠LIS, PACS, RIS
⢠Specialized applications (ER OR LR AnesthesiaSpecialized applications (ER, OR, LR, Anesthesia,
Critical Care, Dietary Services, Blood Bank)
I id t t & ti t⢠Incident management & reporting system
⢠E-Learning
⢠Clinical research informatics
17. 4 Quadrants of Hospital IT
⢠Business CDSS
Modified from
Strategic
Business
Intelligence
⢠MIS
⢠Data Mining/
Utilization
⢠CDSS
⢠HIE
⢠CPOE
⢠PACS
⢠Medication
Theera-
Ampornpunt, 2010
Utilization
⢠Customer-
Relationship
Management
⢠Medication
Safety Apps
⢠EHRs
Cli i lAd i i t ti
⢠Enterprise
Resource
Planning
⢠HIS
⢠RIS
LIS
ClinicalAdministrative
Planning
(Finance,
Materials, HR)
⢠Data Warehouse
⢠Office
⢠LIS
⢠ADT
⢠MPI
⢠Most
d t t l⢠Office departmental
systems
OperationalPosition may vary based on local context OperationalPosition may vary based on local context
19. Infrastructural ITInfrastructural IT
⢠HW/SW Acquisition installation & maintenanceHW/SW Acquisition, installation & maintenance
⢠System
administrationadministration
⢠Network
d i i t tiadministration
⢠Security
20. Infrastructural ITInfrastructural IT
Issues
⢠Expertise
⢠Insourcing vs. Outsourcing
⢠Policy & Process Controls
⢠Best Practices in Design & Management
⢠Documentation!!!
⢠Risks
â Confidentiality/Integrity
â Outages
R d d C tâ Redundancy vs. Cost
â Configuration complexities & patch management
â Compatibility & Technology ChoicesCompatibility & Technology Choices
22. Master Patient Index (MPI)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 applicationsy p pp
⢠Issues
â A large databaseA large database
â Interface with other systems
â Duplicate resolutionsDuplicate resolutions
â Accuracy & currency of patient information
â Language issuesâ Language issues
23. Admit-Discharge-Transfer (ADT)Admit Discharge Transfer (ADT)
⢠Functions
S t Ad it Di h & T f f ti tâ Supports Admit, Discharge & Transfer of patients
(âpatient managementâ)
â Provides status/location of admitted patientsProvides status/location of admitted patients
â Used in assessing bed occupancy
â Linked to billing, claims & reimbursementsg,
⢠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)missing discharge information)
â Input of important information (diagnoses, D/C summary)
Links between OPD IPD ER & ORâ Links between OPD, IPD, ER & OR
24. EHRs & HISEHRs & HIS
The Challenge - Knowing What It Means
Electronic Health
Records (EHRs)
Hospital
Electronic Medical
Records (EMRs)
Hospital
Information
System (HIS)
Records (EMRs)
Electronic Patient
C t B d
Records (EPRs)
Personal Health
Clinical
Information
System (CIS)Computer-Based
Patient Records
(CPRs)
Records (PHRs)
System (CIS)
25. EHRsEHRs
Commonly Accepted Definitions
El t i d t ti f ti t b id⢠Electronic documentation of patient care by providers
⢠Provider has direct control of information in EHRs
⢠Synonymous with EMRs EPRs CPRs⢠Synonymous with EMRs, EPRs, CPRs
⢠Sometimes defined as a patientâs longitudinal records over
several âepisodes of careâ & âencountersâ (visits)p ( )
26. EHR SystemsEHR Systems
Are they just a system that allows electronic documentation of
clinical care?clinical care?
DiHi TDiag-
nosis
History
& PE
Treat-
ments
...
Or do they have other values?
27. Documented Benefits of Health ITDocumented 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)
Medication safetyâ Medication safety
(Kaushal et al, 2003;Chaudhry et al, 2006;van Rosse et al, 2009)
â Patient surveillance & monitoring (Chaudhry et al, 2006)g ( y )
â Patient education/reminder (Balas et al, 1996)
â Cost savings and better financial performanceg p
(Parente & Dunbar, 2001;Chaudhry et al, 2006;Amarasingham et al, 2009;
Borzekowski, 2009)
28. Functions that Should Be Part of EHR SystemsFunctions 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)Medication Lists (Blumenthal et al, 2006)
⢠Discharge Summaries (Blumenthal et al, 2006)
⢠Diagnostic Test Results (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 entryStructure & 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)
Special documents (e g operative notes anesthetic notes)â Special documents (e.g. operative notes, anesthetic notes)
â Integration with paper systems (e.g. scanned MRs, legal documents)
⢠Reliability & contingency/business continuity planningReliability & contingency/business continuity planning
⢠Roll-out strategies & change management
⢠InterfacesInterfaces
30. Computerized (Physician/Provider) Order Entry
Functions
Ph i i di tl t di ti /l b/di ti /i i⢠Physician directly enters medication/lab/diagnostic/imaging
orders online
⢠Nurse & pharmacy process orders accordinglyNurse & pharmacy process orders accordingly
⢠Maybe considered part of an EHR/HIS system
Values
⢠No handwriting!!!
⢠Structured data entry (completeness, clarity, fewer mistakes)
⢠No transcription!
⢠Entry point for CDSSs
⢠Streamlines workflow, increases efficiency
31. Computerized (Physician/Provider) Order Entry
Issues
âPh i i l kâ f t ti⢠âPhysician as a clerkâ frustration
⢠Usability -> Reduced physician productivity?
⢠Unclear value proposition for physician?⢠Unclear value proposition for physician?
⢠Complexity of medication data structure
⢠Integration of medication lab diagnostic imaging &other ordersIntegration 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
f h h b il d i dâof 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
32. Clinical Decision Support Systems (CDSSs)
⢠The real place where most of the values of health IT can be
achievedachieved
⢠A variety of forms and nature of CDSSs
â Expert systemsp y
⢠Based on artificial intelligence, machine learning, rules, or statistics
⢠Examples: differential diagnoses, treatment options
Alerts & remindersâ 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
33. Clinical Decision Support Systems (CDSSs)
PATIENT
P tiPerception
Attention
CLINICIAN
External MemoryLong Term Memory
Attention
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Working
Memory
Knowledge DataKnowledge Data
InferenceInference
DECISIONFrom a teaching slide by Don Connelly 2006 DECISIONFrom a teaching slide by Don Connelly, 2006
34. Clinical Decision Support Systems (CDSSs)
PATIENT
P tiPerception
Attention
CLINICIAN
Abnormal lab
highlights
External MemoryLong Term Memory
Attention highlights
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Working
Memory
Knowledge DataKnowledge Data
InferenceInference
DECISIONDECISION
35. Clinical Decision Support Systems (CDSSs)
PATIENT
P tiPerception
Attention
CLINICIAN
Order Sets
External MemoryLong Term Memory
Attention
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Working
Memory
Knowledge DataKnowledge Data
InferenceInference
DECISIONDECISION
36. Clinical Decision Support Systems (CDSSs)
PATIENT
P tiPerception
Attention
CLINICIAN
Drug-Allergy
Checks
External MemoryLong Term Memory
Attention Checks
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Working
Memory
Knowledge DataKnowledge Data
InferenceInference
DECISIONDECISION
37. Clinical Decision Support Systems (CDSSs)
PATIENT
P tiPerception
Attention
CLINICIAN
Drug-Drug
Interaction
Checks
External MemoryLong Term Memory
Attention
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Working
Memory
Knowledge DataKnowledge Data
InferenceInference
DECISIONDECISION
38. Clinical Decision Support Systems (CDSSs)
PATIENT
P tiPerception
Attention
CLINICIAN
Clinical
Practice
Guideline
External MemoryLong Term Memory
Attention
Reminders
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Working
Memory
Knowledge DataKnowledge Data
InferenceInference
DECISIONDECISION
39. Clinical Decision Support Systems (CDSSs)
PATIENT
P ti
Integration of
Evidence-Based
Resources (e gPerception
Attention
CLINICIAN
Resources (e.g.
drug databases,
literature)
External MemoryLong Term Memory
Attention
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Working
Memory
Knowledge DataKnowledge Data
InferenceInference
DECISIONDECISION
40. Clinical Decision Support Systems (CDSSs)
PATIENT
P tiPerception
Attention
CLINICIAN
External MemoryLong Term Memory
Attention
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Working
Memory
Knowledge DataKnowledge Data
Inference Diagnostic/TreatmentInference
DECISION
Diagnostic/Treatment
Expert Systems
DECISION
41. Clinical Decision Support Systems (CDSSs)
Issues
CDSS l t l t f li i i ?⢠CDSS as a supplement or replacement of clinicians?
â The demise of the âGreek Oracleâ model (Miller & Masarie, 1990)
The âGreek Oracleâ Model
The âFundamental Theoremâ
(Friedman, 2009)
42. Clinical Decision Support Systems (CDSSs)
Issues
F t ith i d li i l ti (K t t l 2005)⢠Features with improved clinical practice (Kawamoto et al., 2005)
â Automatic provision of decision support as part of clinician workflow
â Provision of recommendations rather than just assessmentsj
â Provision of decision support at the time and location of decision making
â Computer based decision support
⢠Usability & impact on productivity
43. Clinical Decision Support Systems (CDSSs)
Issues
Al t iti it & l t f ti⢠Alert sensitivity & alert fatigue
44. Clinical Decision Support Systems (CDSSs)
Issues
Ethi l l l i⢠Ethical-legal issues
â Liabilities: Clinicians as âlearned intermediariesâ
â Prohibition of certain transactions vs. Professional autonomyy
(see Strom et al., 2010)
⢠Unintended Consequences (e.g. workarounds)
See Koppel et al (2005) Campbell et al (2006) & Harrison et al (2007)â See Koppel et al. (2005), Campbell et al. (2006) & Harrison et al. (2007)
45. Clinical Decision Support Systems (CDSSs)
Issues
Ch i th i ht CDSS t t i⢠Choosing the right CDSS strategies
⢠Expertise required for proper CDSS design & implementation
⢠Integration into the point of care with minimal productivity/⢠Integration into the point of care with minimal productivity/
workflow impacts
⢠Everybody agreeing on the ârulesâ to be enforcedy y g g
⢠Maintenance of the knowledge base
⢠Evaluation of effectiveness
46. Nursing Applications
Functions
D t i t i t ti & t⢠Documents nursing assessments, interventions & outcomes
⢠Facilitates charting & vital sign recording
⢠Utilizes standards in nursing informatics⢠Utilizes standards in nursing informatics
⢠Populates and documents care-planning
⢠Risk/incident managementRisk/incident management
⢠etc.
Issues
⢠Minimizing workflow/productivity impacts
⢠Goal: Better documentation vs. better care?
⢠Evolving standards in nursing practice
⢠Change management
47. Pharmacy Applications
Functions
St li kfl f di ti d t di i d⢠Streamlines workflow from medication orders to dispensing and
billing
⢠Reduces medication errors improves medication safetyReduces medication errors, improves medication safety
⢠Improves inventory management
48. Stages of Medication Process
Ordering Transcription Dispensing Administration
CPOE
Automatic
Medication
Dispensing
Electronic
Medication
AdministrationDispensing Administration
Records
(e-MAR)
Barcoded
Barcoded
Medication
Administration
Barcoded
Medication
Dispensing
d s a o
49. Pharmacy Applications
Issues
Wh t di ti d i t l t i f t t hi h⢠Who enters medication orders into electronic format at which
stage?
⢠Unintended consequencesUnintended consequences
⢠âPower shiftsâ
⢠Handling exceptions (e.g. countersigns, verbal orders,g p ( g g , ,
emergencies, formulary replacements, drug shortages)
⢠Choosing the right technology for the hospital
⢠Goal: Workflow facilitation vs. medication safety?
50. Imaging Applications
Picture Archiving and Communication System (PACS)
C t hi d di l l t i i t d f⢠Captures, archives, and displays electronic images captured from
imaging modalities (DICOM format)
⢠Often refers to radiologic images but sometimes used in otherOften 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,
referralsreferrals
Radiology Information System (RIS) or Workflow Management
⢠Supports workflow of the radiology department, including patientSupports workflow of the radiology department, including patient
registration, appointments & scheduling, consultations, imaging
reports, etc.
51. Take-Away Messages
⢠Health IT in hospitals comes in various forms
L l t t i t t id ti⢠Local contexts are important considerations
⢠Hospital IT is a very complex environment
⢠Health IT has much potential to improve quality & efficiency of care⢠Health IT has much potential to improve quality & efficiency of care
⢠But it is also risky...
â Costs
â Change resistance
â Poor design
Alert fatigueâ 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