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

jcuddeback@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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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

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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)

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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 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

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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

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Implications for

skill development ?

...for culture?

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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

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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

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

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≠ 128-Slice CT Scanner, or

Robotic Surgery System128-Slice CT Scanner, or

Robotic Surgery System

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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 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)

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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

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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

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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

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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

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?”

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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 LeadershipPartners Healthcare, Boston, MA

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Even Greater Potential ROI from “Interoperability”

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

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Concept or reality?

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“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).

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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

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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”

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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 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.

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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

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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 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