Transcript Slides - Open Online Courses

Benefits (and Risks) of EHRs
Georgetown University
April 2, 2009
John K. Cuddeback, MD, PhD
Chief Medical Informatics Officer
Anceta • AMGA’s Collaborative Data Warehouse
American Medical Group Association
[email protected]
Agenda
 Background on AMGA
 Multi-specialty medical group model of health care delivery: “systems thinking” in a fragmented industry
 History of IT in healthcare
 Driving forces
 Four “eras”
 Goals for point-of-care systems
 Reasons to be cautious about economic stimulus
 “Inferential gap” in medicine
 Other opportunities and ROI studies
 Complementary tools: Point-of-Care Systems and Retrospective Analytics
 Substantial variation in practice
 Unintended consequences of IT in healthcare
 Recent research on adoption and effectiveness of EHRs
 Discussion: Policy implications
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American Medical Group Association
AMGA improves health care for patients
by supporting multispecialty medical groups
and other organized systems of care.
Founded in 1949
 340 medical groups
 95,000 physicians
 Delivering health care to more than 95 million patients each year, in 47 states
 Average group size is 286 physicians, with 20 sites
 Median group size is 110 physicians, with 9 sites
 Approximately one-third of members own one or more hospitals
2008 data
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AMGA Values
 Physician leadership
 Fully integrated, efficient, patient-centered, care
 Team work across specialties
 Continuous improvement of patient care systems
 Systems thinking
 Total coordinated care through the use of:
 “Learning from the best”
 Interoperable electronic health records
 Dedicated care managers or care coordinators
 Evidence-based care guidelines
 Systematic monitoring of quality and efficiency
 Transparency and accountability for clinical care outcomes
at the group level
2009 AMGA Board Members
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Carilion Clinic (VA)
Carle Clinic Association (IL)
Cleveland Clinic
Franciscan Skemp Healthcare / Mayo Health System
Geisinger Health System (PA)
Henry Ford Health System (MI)
Intermountain Healthcare
The Iowa Clinic
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The Jackson Clinic (TN)
Lahey Clinic (MA)
Mount Kisco Medical Group (NY)
Northwest Physicians Network (WA)
The Permanente Federation
St. John’s Clinic (MO)
University of Utah Hospitals & Clinics
American Medical Group Association (ex officio)
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Driving Forces for Development of Health IT
 Parallels trends seen in other industries
 Automate administrative functions (billing, financial management)
 Automate core business processes  access to information, greater consistency
 Transform core business processes  dramatic gains in quality and efficiency
 Pre-2000 emphasis in health care systems
 Administrative—patient management (registration, bed control) and patient billing
 Systems for clinical departments—laboratory, radiology, pharmacy, operating room, ED
 Current emphasis, pre-stimulus package
 It’s not about technology, or even information—it’s about leveraging “I” as well as “T” to transform care
 Automate risk-prone processes—barcode medication administration
 Integrate data and systems around the patient, not hospital departments
 And beyond the bedside—across the continuum of care
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Integrate across institutions—health information exchange (HIE), regional health information organization (RHIO)
Involve the patient and family—personal health record (PHR)
 Care coordination—Patient-Centered Medical Home (PCMH)
 Comparative effectiveness research
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Use real-world data to determine which treatments are most (cost-)effective
 Economic stimulus package—American Recovery and Reinvestment Act
 $19 billion for Health IT—combination of grants and loans for purchase, incentives for “meaningful use”
 $1.1 billion for comparative effectiveness research
 Promotion of standards and certification, expand privacy protections, “extension” program
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O
Three Eras of IT in Health Care
ANALYSIS 
COLLABORATION 
CONTINUOUS IMPROVEMENT
Process Integration
Workflow Transformation
Efficacy
of Care
Patient
Safety
Data Integration: Patient-Centric View
Clinical Decision Support – CPOE
Operational
Efficiency
CQI / TQM
Patient Financial Systems
Departmental Clinical Systems
1980
1990
2000
2010
2020
TODAY
Technology Infusion
from Other Industries
Institute of Medicine (IOM) reports
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Goals for (Hospital) Point-of-Care Systems
 Enhance patient safety
Important “twists” in ambulatory care
 Longitudinal perspective—prevention
 Reduce unwarranted variation in practice
 Fee-for-service payment—documentation
 Smart resource utilization  better outcomes
at lower cost
 Improve productivity and convenience for clinicians
 Physician loyalty  volume
 Recruitment and retention for nurses and other clinicians
 Competitive position of GME programs
Potential Quantitative Benefits
Safety
 Increase operational efficiency (workflow)
 Eliminate rework and delay
 Credibility for resource utilization efforts
Efficiency
Convenience
Recruitment
and Retention
Patient safety may be the main reason
to adopt point-of-care systems,
but safety is only one of many benefits.
Variation
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Reasons to be Cautious
 Technology—EHR is far more than an electronic “record”
 Point-of-care—decision support, decision execution (workflow management/monitoring), team interaction
 Semantic interoperability—messaging standards, coding/content (information in “computable” form)
 Use of data for improvement—analytical tools and skills, leading change
 Workflow redesign
 Never “designed” in the first place
 Hospital ≠ Ambulatory
 Payment incentives
 System developers have focused on documentation and coding  tangible ROI (“pay-for-verbosity”)
 Fee-for-service encourages services: if costs are to be controlled, the payment mechanism must change
 Culture of collaboration
 Trust
 Systems thinking
 Data-driven QI
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Electronic Medical Record
≠
128-Slice CT Scanner, or
Robotic Surgery System
 New way of performing current functions
 Completely new capability
 “Soft” benefits: Quality, Safety, Efficiency
 Direct reimbursement for new service
 Little incremental revenue
 Fundamental organizational change
 Also drives volume
 “Appliance”
 Impacts everyone—change management,
workflow redesign, device ergonomics
 Few users, many beneficiaries
 Requires culture, leadership commitment
 Risk is limited in scope
 Sells itself
 Perceived as high-risk
 Relatively immature technology
 Mature technology
 Still significant R&D on basic components
 Development investment  new product
 Complex, expensive implementation
 Plug it in
 Organizational knowledge management
 Embedded algorithms
 Benefits have many dependencies
...but are likely to be sustained
 Benefits easily realized
...but may be short-lived
 High-stakes career move
 Reliable win on “traditional” criteria
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Rate of “Absorption” of Stimulus Funding
 Informatics training—AMIA 10×10 initiative
 Both practical skills (project management, workflow redesign) and theoretical work (knowledge representation)
 Pace of cultural change
 Organizational structures and governance
 Clarify roles and expectations, build trust—generational effects
 Alignment of incentives (payment)
 Realistic expectations
 Care coordination in an “open” system—many moving parts to the “medical home”
 Many complex issues—are “alerts” and provider responses part of the legal medical record?
 Current products and standards are still maturing—limited adoption, limited measurable impact
 Stimulus includes $20 billion for health IT and comparative effectiveness
 Entire US health IT industry was $26 billion in 2007
 Stimulus funding is a great deal, but it is also not enough to cover full implementation
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Hypothetical 79-year-old woman with
 chronic obstructive pulmonary disease,
 type 2 diabetes mellitus,
 hypertension,
 osteoarthritis, and
 osteoporosis,
all of moderate severity.
12 separate medications
19 doses per day
05 separate dosing times/day
$ 4,877 medication cost/year (generics)
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 Alerts and reminders
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“Evidence-based” care guidelines
 Documentation standards
 Potentially even more powerful: customized
care protocols
 Randomized controlled trials (RCTs) are regarded as
the “gold standard”
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Questions are narrow by design, relying on
randomization to neutralize potentially confounding
effects, in order to obtain “definitive” answers
 RCTs typically involve younger patient populations, with
single diagnoses, over brief study periods
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Are the conclusions applicable to older patients
and those with multiple diseases?
 RCTs are expensive and time-consuming
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Typical drug trial may take 10–15 years and cost
$10–300 million
Cannot keep pace with development of new
diagnostic and therapeutic modalities
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No “Safety Net” for Medication Administration
Errors Resulting in Preventable and Potential Adverse Drug Events
No errors intercepted !
Administration
26%
Ordering
49%
Dispensing
14%
48% of errors intercepted
37% of errors intercepted
Transcription
11%
23% of errors intercepted
Bates et al., JAMA 1995;274:29-34
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Medication Management Cycle
Provide advice to prescriber:
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Protocols/algorithms
Check allergies, labs, diet
Drug–drug interactions
Drug–disease (w/ problem list
or working diagnosis)
 Antibiotic sensitivity data
Ordering
“Transcribing”
 order information to pharmacy
 copy of order in chart (until full EMR)
 copy of order onto Kardex
 Complete, “formatted” orders
 Formulary, drug database
(vs. reliance on memory)
 Generic/ trade names
 Typical doses
 PO meds if on regular diet
Dispensing
Administering
Impose (friendly) constraints:
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right patient
right drug
right dose
right route of administration
right time
Patient Monitoring
Medication Administration Record (MAR)
Quality Control
Symbol PPT 2740
ruggedized, pen/touch input PDA
w/ laser barcode reader and WiFi
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Critical Success Factors for Clinical Systems
 Clinical and operations leadership (#1)
 Strategic commitment—beyond the “IT project” mentality
 Clinical and operational improvement initiative that leverages information technology, not a technology initiative
 Focus on realizing clinical and operational benefit, rather than vendor selection
Product Purchase
Business Process
Reengineering
Cultural Initiative
 Knowledge management—clinical “content”
 Outcomes data—analytical skills
 Understand process–outcome relationships
 Process redesign skills
Incremental
or
 Technical support—availability/reliability
“Big Bang?”
 User support, device ergonomics
 Tracking ROI  on-going reinvestment
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Estimated ROI for Full Ambulatory EHR
 Estimated cost savings
 Save $28,000 per “average” provider per year
 Revenue enhancement
 Eliminate more than $10 in rejected claims per outpatient visit
 Address drug, procedure and coding issues through advanced clinical decision support
 Productivity Gains
 Neutral effect on provider time with improved staff productivity
2004 study by Center for IT Leadership
Partners Healthcare, Boston, MA
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Even Greater Potential ROI from “Interoperability”
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External Data
TRANSACTION SYSTEMS
CLINICAL DATA REPOSITORY
Data
Data
Data
DATA WAREHOUSES
Information
Information
Knowledge
Analytical systems are essential for
Concept
reality?
integration andor
transformation.
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POINT-OF-CARE
SYSTEMS
ANALYTICAL
SYSTEMS
Patient Level
Population Level
Administrative systems (scheduling, ADT)
Clinical observations, assessment, plan
Orders—tied to protocols, w/ decision support
Tests, results, documentation of care (eMAR)
Capture outcomes, key process variables
Error/near-miss reporting
CONCURRENT
Deploy improved practice
Improved
Practice
 Analytical models, risk adjustment
 Ad hoc query tools—exploratory analysis,
hypothesis generation/testing
 Comparative data, “best” practices
 Support for quality improvement teams
 Practice profile reports for clinicians
RETROSPECTIVE
Develop improved practice
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“New” Approach to Quality Management
Frequency
Traditional Quality Assurance
“Bad Apples”
Hypothetical distribution of patients treated, showing
how often various levels of quality are attained.
Minimum
Standard
Level of Quality
Frequency
Continuous Quality Improvement
For these distributions, better quality is on the righthand side. CQI both raises the overall level of quality
and reduces variation from case to case (indicated
by a narrower distribution).
Level of Quality
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LOS for Kidney Transplant
15%
30%
All UHC
Hosp A
A, B
Median
25%
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All UHC
Percent of Cases
10%
Hospital A
7
Hospital B
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20%
15%
5%
10%
5%
0%
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
0%
75+
Length of Stay (LOS)
1991 UHC data
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Differences in Rates of Hospital Admission
“Small-area analysis”
Wennberg JE, Series Ed. The Quality of Medical Care in the United States:
A Report on the Medicare Program. The Dartmouth Atlas of Health Care 1999.
AHA Press, 1999. pp. 74-75.
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Children’s Hospital of Pittsburgh
“The usual ‘chain of events’ that occurred when a patient was admitted through our transport system was altered after CPOE
implementation. Before implementation of CPOE, after radio contact with the transport team, the ICU fellow was allowed to order
critical medications/drips, which then were prepared by the bedside ICU nurse in anticipation of patient arrival. When needed, the
ICU fellow could also make arrangements for the patient to receive an emergent diagnostic imaging study before coming into the
ICU. A full set of admission orders could be written and ready before patient arrival. After CPOE implementation, order entry was
not allowed until after the patient had physically arrived to the hospital and been fully registered into the system, leading to
potential delays in new therapies and diagnostic testing (this policy later was rectified). The physical process of entering
stabilization orders often required an average of ten ‘clicks’ on the computer mouse per order, which translated to ~1 to 2 minutes
per single order as compared with a few seconds previously needed to place the same order by written form. Because the vast
majority of computer terminals were linked to the hospital computer system via wireless signal, communication bandwidth was
often exceeded during peak operational periods, which created additional delays between each click on the computer mouse.
Sometimes the computer screen seemed ‘frozen.’
“This initial time burden seemed to change the organization of bedside care. Before CPOE implementation, physicians and nurses
converged at the patient’s bedside to stabilize the patient. After CPOE implementation, while 1 physician continued to direct
medical management, a second physician was often needed solely to enter orders into the computer during the first 15 minutes to
1 hour if a patient arrived in extremis. Downstream from order entry, bedside nurses were no longer allowed to grab critical
medications from a satellite medication dispenser located in the ICU because as part of CPOE implementation, all medications,
including vasoactive agents and antibiotics, became centrally located within the pharmacy department. The priority to fill a
medication order was assigned by the pharmacy department’s algorithm. Furthermore, because pharmacy could not process
medication orders until they had been activated, ICU nurses also spent significant amounts of time at a separate computer
terminal and away from the bedside. When the pharmacist accessed the patient CPOE to process an order, the physician and the
nurse were ‘locked out,’ further delaying additional order entry.” (pp. 1508–1509)
Yong Y. Han et al. Unexpected Increased Mortality After Implementation of a Commercially Sold Computerized Physician
Order Entry System. Pediatrics 2005; 116: 1506–1512.
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Computer Technology and Clinical Work
Robert L. Wears, MD, MS, and Marc Berg, MA, MD, PhD
JAMA, March 9, 2005 — Vol. 293, No. 10, pp. 1261-1263
 Rather than framing the problem as “not developing the systems right,” these failures demonstrate “not developing the
right systems” due to widespread but misleading theories about both technology and clinical work.
 The misleading theory about technology is that technical problems require technical solutions; i.e., a narrowly technical
view of the important issues involved that leads to a focus on optimizing the technology. In contrast, a more useful
approach views the clinical workplace as a complex system in which technologies, people, and organizational routines
dynamically interact....
 …There is quite a large mismatch between the implicit theories embedded in these computer systems and the real
world of clinical work. Clinical work, especially in hospitals, is fundamentally interpretative, interruptive, multitasking,
collaborative, distributed, opportunistic, and reactive. In contrast, CPOE systems and decision support systems are
based on a different model of work: one that is objective, rationalized, linear, normative, localized (in the clinician’s
mind), solitary, and single-minded. Such models tend to reflect the implicit theories of managers and designers, not of
frontline workers.
 Introduction of computerized tools into health care should not be viewed as a problem in technology but rather a
problem in organizational change, in particular, one of guiding organizational change by a process of experimentation
and mutual learning rather than one of planning, command, and control….
 This implies that any IT acquisition or implementation trajectory should, first and foremost, be an organizational change
trajectory.
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25
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January 9, 2009
IT-related activities of health professionals observed by the committee in these institutions were rarely well integrated into clinical practice.
Health care IT was rarely used to provide clinicians with evidence-based decision support and feedback; to support data-driven process
improvement; or to link clinical care and research. Health care IT rarely provided an integrative view of patient data. Care providers spent a
great deal of time in electronically documenting what they did for patients, but these providers often said that they were entering the
information to comply with regulations or to defend against lawsuits, rather than because they expected someone to use it to improve clinical
care. Health care IT implementation time lines were often measured in decades, and most systems were poorly or incompletely integrated
into practice.
“Although the use of health care IT is an integral element of health care in the 21st century, the current focus of the health care IT efforts that
the committee observed is not sufficient to drive the kind of change in health care that is truly needed. The nation faces a health care IT
chasm that is analogous to the quality chasm highlighted by the IOM over the past decade….”
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N Engl J Med 359:50–60,
July 3, 2008
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Prospects for the Future
 Growing public expectations—safety and quality are no longer taken for granted
 Providers face increasing pressures on cost, as well as quality
 We’ve done all the easy stuff—unit cost, straightforward utilization management
 We’re forced to address the higher level issues—workflow, process integration, over-use, access to care
 Growing willingness to learn from real-world experience—data warehouses, analytics
 We are beginning to see realistic incentives: pay-for-performance programs (P4P)
 Incent improved care enabled by IT, not HIT adoption per se
 Still need more fundamental payment reform
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EHR designs have responded to payment pressures: volume (piecework orientation), “pay-for-verbosity”
Align economic benefits with investment
 Still too optimistic about “interoperable IT” as a solution for a fragmented care system
 Gaining a critical mass of health care workers who demand, rather than reject, technology
 Learning to distinguish clinical content and systems thinking from techno-gadgetry
 Recognizing the possibility of making things worse (negative unintended consequences)
and learning how to avoid doing so
We tend to underestimate the long-term impact of technology,
but we invariably overestimate the pace of adoption.
— Bill Gates
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