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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

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Benefits and risks of ehrs

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]


  • 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

American medical group association
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

Amga values
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”

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. John’s Clinic (MO)

  • University of Utah Hospitals & Clinics

  • American Medical Group Association (ex officio)

Driving forces for development of health it
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

Three eras of it in health care




Efficacyof Care




Patient Financial SystemsDepartmental Clinical Systems

Institute of Medicine (IOM) reports

Three Eras of IT in Health Care




Process IntegrationWorkflow Transformation

Data Integration: Patient-Centric View

Clinical Decision Support – CPOE








Technology Infusionfrom Other Industries

Implications for skill development?

...for culture?

Goals for hospital point of care systems

Potential Quantitative Benefits

Recruitmentand Retention

Goals for (Hospital) Point-of-Care Systems

  • Important “twists” in ambulatory care

  • Longitudinal perspective—prevention

  • Fee-for-service payment—documentation

  • 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.

Reasons to be cautious
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

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


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

Electronic Medical Record

128-Slice CT Scanner, orRobotic Surgery System


Rate of absorption of stimulus funding
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

Hypothetical 79-year-old woman with

chronic obstructive pulmonary disease,

type 2 diabetes mellitus,


osteoarthritis, and


all of moderate severity.

12 separate medications

19 doses per day

05 separate dosing times/day

$4,877 medication cost/year (generics)

  • 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 10–15 years and cost $10–300 million

    • Cannot keep pace with development of new diagnostic and therapeutic modalities


No safety net for medication administration

No errors intercepted!

48% of errors intercepted

37% of errors intercepted

23% of errors intercepted

No “Safety Net” for Medication Administration

Errors Resulting in Preventable and Potential Adverse Drug Events









Bates et al., JAMA 1995;274:29-34

Medication management cycle

  • right patient

  • right drug

  • right dose

  • right route of administration

  • right time

Medication Management Cycle

  • Provide advice to prescriber:

  • Protocols/algorithms

  • Check allergies, labs, diet

  • Drug–drug interactions

  • Drug–disease (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



  • order information to pharmacy

  • copy of order in chart (until full EMR)

  • copy of order onto Kardex



Patient Monitoring

Medication Administration Record (MAR)

Quality Control

Symbol PPT 2740ruggedized, pen/touch input PDA

w/ laser barcode reader and WiFi

Critical success factors for clinical systems
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

Business Process Reengineering

Product Purchase

Cultural Initiative



“Big Bang?”

Estimated roi for full ambulatory ehr
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





Analytical systems are essential for integration and transformation.



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

  • 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



Deploy improved practice

Develop improved practice

External Data






Concept or reality?


New approach to quality management
“New” Approach to Quality Management

Traditional Quality Assurance

“Bad Apples”


Hypothetical distribution of patients treated, showing

how often various levels of quality are attained.

Level of Quality


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).


Level of Quality

Los for kidney transplant





Hospital A


Hospital B





LOS for Kidney Transplant



Hosp A, B

Hosp A





Percent of Cases



















Length of Stay (LOS)

1991 UHC data

Differences in rates of hospital admission
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”

Children s hospital of pittsburgh
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.

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.



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….”

N Engl J Med 359:50–60,

July 3, 2008

Prospects for the future
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