Benefits (and Risks) of EHRs

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Benefits (and Risks) of EHRs

• Georgetown University
• April 2, 2009
• John K. Cuddeback, MD, PhD
• Chief Medical Informatics Officer
• Anceta AMGAs Collaborative Data Warehouse
• American Medical Group Association
• jcuddeback_at_anceta.com

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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 patientsby
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
• Total coordinated care through the use of
• 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

• 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

• The Jackson Clinic (TN)
• Lahey Clinic (MA)
• Mount Kisco Medical Group (NY)
• Northwest Physicians Network (WA)
• The Permanente Federation
• St. Johns 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
• Administrativepatient management (registration,
  bed control) and patient billing
• Systems for clinical departmentslaboratory,
  radiology, pharmacy, operating room, ED
• Current emphasis, pre-stimulus package
• Its not about technology, or even
  informationits about leveraging I as well as
  T to transform care
• Automate risk-prone processesbarcode medication
  administration
• Integrate data and systems around the patient,
  not hospital departments
• And beyond the bedsideacross the continuum of
  care
• Integrate across institutionshealth information
  exchange (HIE), regional health information
  organization (RHIO)
• Involve the patient and familypersonal health
  record (PHR)
• Care coordinationPatient-Centered Medical Home
  (PCMH)
• Comparative effectiveness research
• Use real-world data to determine which treatments
  are most (cost-) effective
• Economic stimulus packageAmerican Recovery and
  Reinvestment Act
• 19 billion for Health ITcombination of grants
  and loans for purchase, incentives for
  meaningful use

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Three Eras of IT in Health Care
O
ANALYSIS ? COLLABORATION ?CONTINUOUS IMPROVEMENT
Four
Process IntegrationWorkflow Transformation
Data Integration Patient-Centric View Clinical
Decision Support CPOE
2010
Technology Infusionfrom Other Industries
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Implications for skill development ?
...for culture?
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Goals for (Hospital) Point-of-Care Systems

• Important twists in ambulatory care
• Longitudinal perspectiveprevention
• Fee-for-service paymentdocumentation

• Enhance patient safety
• Reduce unwarranted variation in practice
• Smart resource utilization ? better outcomesat
  lower cost
• Improve productivity and convenience for
  clinicians
• Physician loyalty ? volume
• Recruitment and retention for nurses and other
  clinicians
• Competitive position of GME programs
• Increase operational efficiency (workflow)
• Eliminate rework and delay
• Credibility for resource utilization efforts

Patient safety may be the main reason to adopt
point-of-care systems, but safety is only one of
many benefits.
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Reasons to be Cautious

• TechnologyEHR is far more than an electronic
  record
• Point-of-caredecision support, decision
  execution (workflow management/monitoring), team
  interaction
• Semantic interoperabilitymessaging standards,
  coding/content (information in computable form)
• Use of data for improvementanalytical 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, orRobotic Surgery System
?

• New way of performing current functions
• Soft benefits Quality, Safety, Efficiency
• Little incremental revenue
• Fundamental organizational change
• Impacts everyonechange management, workflow
  redesign, device ergonomics
• Requires culture, leadership commitment
• Perceived as high-risk
• Relatively immature technology
• Still significant RD on basic components
• Complex, expensive implementation
• Organizational knowledge management
• Benefits have many dependencies
• ...but are likely to be sustained
• High-stakes career move

• Completely new capability
• Direct reimbursement for new service
• Also drives volume
• Appliance
• Few users, many beneficiaries
• Sells itself
• Risk is limited in scope
• Mature technology
• Development investment ? new product
• Plug it in
• Embedded algorithms
• Benefits easily realized
• ...but may be short-lived
• Reliable win on traditional criteria

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Rate of Absorption of Stimulus Funding

• Informatics trainingAMIA 1010 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
  trustgenerational effects
• Alignment of incentives (payment)
• Realistic expectations
• Care coordination in an open systemmany moving
  parts to the medical home
• Many complex issuesare alerts and provider
  responses part of the legal medical record?
• Current products and standards are still
  maturinglimited 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
• Evidence-based care guidelines
• Documentation standards
• Potentially even more powerful customized care
  protocols

• Randomized controlled trials (RCTs) are regarded
  as the gold standard
• 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
• Are the conclusions applicable to older
  patientsand those with multiple diseases?
• RCTs are expensive and time-consuming
• Typical drug trial may take 1015 years and cost
  10300 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
Administration
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Ordering
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Dispensing
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Transcription
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Bates et al., JAMA 199527429-34
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Medication Management Cycle

• Provide advice to prescriber
• Protocols/algorithms
• Check allergies, labs, diet
• Drugdrug interactions
• Drugdisease (w/ problem listor working
  diagnosis)
• Antibiotic sensitivity data
• Impose (friendly) constraints
• Complete, formatted orders
• Formulary, drug database(vs. reliance on memory)
• Generic/ trade names
• Typical doses
• PO meds if on regular diet

Ordering
Transcribing

• order information to pharmacy
• copy of order in chart (until full EMR)
• copy of order onto Kardex

Dispensing
Administering
Patient Monitoring
Medication Administration Record (MAR)
Quality Control
Symbol PPT 2740ruggedized, 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 commitmentbeyond 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
• Knowledge managementclinical content
• Outcomes dataanalytical skills
• Understand processoutcome relationships
• Process redesign skills
• Technical supportavailability/reliability
• User support, device ergonomics
• Tracking ROI ? on-going reinvestment

Incremental or Big Bang?
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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 WAREHOUSES
InformationInformation
Knowledge
Concept or reality?
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New Approach to Quality Management
Traditional Quality Assurance
Bad Apples
Frequency
Hypothetical distribution of patients treated,
showing how often various levels of quality are
attained.
Level of Quality
MinimumStandard
Continuous Quality Improvement
For these distributions, better quality is on the
right- hand side. CQI both raises the overall
level of quality and reduces variation from case
to case (indicated by a narrower distribution).
Frequency
Level of Quality
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LOS for Kidney Transplant
15
Hosp A, B
All UHC
Hosp A
Median
12
All UHC
10
Percent of Cases
5
0
0
5
10
15
20
25
30
35
40
45
50
55
60
65
70
75
Length of Stay (LOS)
1991 UHC data
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Differences in Rates of Hospital Admission
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.
Small-area analysis
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Childrens 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 patients 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 departments 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. 15081509) Yong
Y. Han et al. Unexpected Increased Mortality
After Implementation of a Commercially Sold
Computerized Physician Order Entry System.
Pediatrics 2005 116 15061512.
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Computer Technology and Clinical WorkRobert L.
Wears, MD, MS, and Marc Berg, MA, MD, PhDJAMA,
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 clinicians 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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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 3595060, July 3, 2008
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Prospects for the Future

• Growing public expectationssafety and quality
  are no longer taken for granted
• Providers face increasing pressures on cost, as
  well as quality
• Weve done all the easy stuffunit cost,
  straightforward utilization management
• Were forced to address the higher level
  issuesworkflow, process integration, over-use,
  access to care
• Growing willingness to learn from real-world
  experiencedata 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
• 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