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Benefits (and Risks) of EHRs
Georgetown UniversityApril 2, 2009
John K. Cuddeback, MD, PhDChief Medical Informatics Officer
Anceta • AMGA’s Collaborative Data WarehouseAmerican Medical Group Association
2
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
3
AMGA improves health care for patientsby supporting multispecialty medical groups
and other organized systems of care.
Founded in 1949
American Medical Group Association
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
4
2009 AMGA Board Members2009 AMGA Board Members
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
Systems thinking
“Learning from the best”
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. John’s Clinic (MO) University of Utah Hospitals & Clinics American Medical Group Association (ex officio)
5
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 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 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
6
Patient Financial SystemsDepartmental Clinical Systems
1980 1990 2000 20202010
OperationalEfficiency
Three Eras of IT in Health Care
CQI / TQM
Efficacyof Care
PatientSafety
Process IntegrationWorkflow Transformation
Data Integration: Patient-Centric ViewClinical Decision Support – CPOE
TODAY
ANALYSIS COLLABORATION
CONTINUOUS IMPROVEMENT
Institute of Medicine (IOM) reports
Technology Infusionfrom Other Industries
Four
2010
8
Goals for (Hospital) Point-of-Care Systems
Enhance patient safety
Reduce unwarranted variation in practice 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
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.
Patient safety may be the main reason
to adopt point-of-care systems,
but safety is only one of many benefits.
Safety
Convenience
Variation
Efficiency
Potential Quantitative Benefits
Recruitmentand Retention
Important “twists” in ambulatory care
Longitudinal perspective—prevention
Fee-for-service payment—documentation
Important “twists” in ambulatory care
Longitudinal perspective—prevention
Fee-for-service payment—documentation
9
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
Electronic Medical RecordElectronic Medical Record
New way of performing current functions
“Soft” benefits: Quality, Safety, Efficiency Little incremental revenue
Fundamental organizational change Impacts everyone—change management,
workflow redesign, device ergonomics
Requires culture, leadership commitment
Perceived as high-risk
Relatively immature technology Still significant R&D 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
10
≠ 128-Slice CT Scanner, or
Robotic Surgery System128-Slice CT Scanner, or
Robotic Surgery System
11
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
12
Hypothetical 79-year-old woman with chronic obstructive pulmonary disease, type 2 diabetes mellitus, hypertension, osteoarthritis, and osteoporosis,all of moderate severity.
12 separate medications19 doses per day05 separate dosing times/day
$ 4,877 medication cost/year (generics)
Hypothetical 79-year-old woman with chronic obstructive pulmonary disease, type 2 diabetes mellitus, hypertension, osteoarthritis, and osteoporosis,all of moderate severity.
12 separate medications19 doses per day05 separate dosing times/day
$ 4,877 medication cost/year (generics)
1313
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 10–15 years and cost
$10–300 million Cannot keep pace with development of new
diagnostic and therapeutic modalities
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 10–15 years and cost
$10–300 million Cannot keep pace with development of new
diagnostic and therapeutic modalities
Alerts and reminders “Evidence-based” care guidelines
Documentation standards
Potentially even more powerful: customized care protocols
Alerts and reminders “Evidence-based” care guidelines
Documentation standards
Potentially even more powerful: customized care protocols
14
No “Safety Net” for Medication Administration
Errors Resulting in Preventable and Potential Adverse Drug Events
Ordering49%
Transcription11%
Dispensing14%
Administration26%
48% of errors intercepted
No errors intercepted !
23% of errors intercepted
37% of errors intercepted
Bates et al., JAMA 1995;274:29-34
15
Medication Management Cycle
Symbol PPT 2740ruggedized, pen/touch input PDA w/ laser barcode reader and WiFi
“Transcribing”“Transcribing”
DispensingDispensing
AdministeringAdministering
Patient MonitoringPatient Monitoring
Quality ControlQuality Control
right patient right drug right dose right route of administration right time
order information to pharmacy copy of order in chart (until full EMR) copy of order onto Kardex
order information to pharmacy copy of order in chart (until full EMR) copy of order onto Kardex
Provide advice to prescriber: Protocols/algorithms Check allergies, labs, diet Drug–drug interactions Drug–disease (w/ problem list
or 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
Provide advice to prescriber: Protocols/algorithms Check allergies, labs, diet Drug–drug interactions Drug–disease (w/ problem list
or 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
Medication Administration Record (MAR) Medication Administration Record (MAR)
OrderingOrdering
16
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
Knowledge management—clinical “content”
Outcomes data—analytical skills Understand process–outcome relationships
Process redesign skills
Technical support—availability/reliability
User support, device ergonomics
Tracking ROI on-going reinvestment
Product PurchaseBusiness Process
ReengineeringCultural Initiative
Incrementalor
“Big Bang?”
17
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 LeadershipPartners Healthcare, Boston, MA
20Develop improved practiceDeploy improved practiceRETROSPECTIVECONCURRENT
InformationInformation Knowledge
DataDataData
ANALYTICALSYSTEMS
Population Level
Analytical systems are essential for integration and transformation.
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
POINT- OF - CARESYSTEMS
Patient 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
External Data
DATA WAREHOUSESTRANSACTION SYSTEMS
CLINICAL DATA REPOSITORY
ImprovedPractice
20
Concept or reality?
21
“New” Approach to Quality Management
“Bad Apples”
MinimumStandard
Traditional Quality Assurance
Level of Quality
Level of Quality
Fre
quen
cyF
requ
ency
Continuous Quality Improvement
Hypothetical distribution of patients treated, showinghow often various levels of quality are attained.
For these distributions, better quality is on the right-hand side. CQI both raises the overall level of qualityand reduces variation from case to case (indicatedby a narrower distribution).
22
Hosp A, B
LOS for Kidney Transplant
0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75+
All UHC 12
Hospital A 7
Hospital B 18
Median
0%
5%
10%
15%
Length of Stay (LOS)
Per
cen
t o
f C
ases
1991 UHC data
All UHC
0%
5%
10%
15%
20%
25%
30%
Hosp A
23
Differences in Rates of Hospital AdmissionWennberg 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”
24
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.
25
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 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.
28
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….”
January 9, 2009
32
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 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