Why Do (Many) Health IT Projects Fail?

Georgetown UniversityJuly 12, 2007

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 health care—why the future will be different from the past New emphasis on process integration and workflow transformation

Evolving understanding of the interplay of culture, workflow, information, and technology

Age of Analytics—BI (business intelligence) comes to healthcare

“Rapid learning” from real-world experience—bridging the “inferential gap” Data warehousing and analytics—a key part of an organizational knowledge management process, enabling

delivery of useful information (appropriate for the patient) at the point of care

Data-driven approaches to quality and performance improvement

Example: bar-code medication administration What does it realistically take to implement this obviously beneficial technology?

What can we learn from “failures” about how organizations should approach IT projects?

Unintended consequences of IT in healthcare

Hope for the future—it really will be different from the past!

REVISED SLIDE

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The Association Representing Large Multi-specialty Medical Groups

and Integrated Delivery Systems

Founded in 1949

The American Medical Group Association

Mission Statement

AMGA advocates for the multi-specialty medical group model of health care delivery

and for the patients served by medical groups, through innovation and information sharing,

benchmarking, leadership development and continuous striving to improve patient care.

Adopted September 18, 2004

Mission Statement

AMGA advocates for the multi-specialty medical group model of health care delivery

and for the patients served by medical groups, through innovation and information sharing,

benchmarking, leadership development and continuous striving to improve patient care.

Adopted September 18, 2004

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2007 AMGA Board Members

Chair Elect

Ronald H. Kirkland, MD President and Chair of Board,

The Jackson Clinic, PA

Secretary David L. Bronson, MD

Chair BofD, Cleveland Clinic Fdn

Immediate Past ChairFrancis J. Crosson, MD

Exec. Dir., The Permanente Federation

Treasurer C. Edward Brown

Chief Executive Officer, The Iowa Clinic

Member At-Large Francis A.

Marzoni, Jr, MD Exec Dir, Palo Alto Med Fdn

President and Chief Executive Officer Donald W. Fisher

PhD, CAE President and CEO, AMGA

DirectorScott Hayworth, MD

President & CEOMount Kisco Medical Group

DirectorAlbert W. Fisk, MD

Medical Director, The Everett Clinic

DirectorSusan Schooley, MD

Chair Dept Family Med, Henry Ford MG

DirectorRobert E. Nesse, MD

Pres/CEO, Franciscan Skemp HC, Mayo

DirectorMichael W. Bukosky, FACMPE

EVP/CAO, Carle Clinic Assoc, PC

DirectorKaren Kennedy, BA, MPH

CEO, Medical Clinic of North Texas

DirectorNicholas Wolter, MDCEO, Billings Clinic

Chair Allen D. Kemp, MD

CEO/Chair, Dean Health Systems, Inc.

DirectorRobin L. Lloyd MPA

Exec. Dir. for Ambulatory Svcs, Univ. of Utah Hospital and Clinics

DirectorBruce H. Hamory, MD

Exec. VP/CMO, Geisinger Health System

DirectorAndrew S. Warner, MD

Chairman, Finan/Ops Comm.,Lahey Clinic

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AMGA Represents...

Approximately 300 medical groups

Approximately 85,000 physicians

Delivering health care to more than 50 million patients in 43 states

Average group size is approximately 280 MDs, median 105 MDs

Average of 16 satellite facilities per group

AMGA Values

AMGA Values

Physician leadership in medicine

Integrated, patient-centered, multi-specialty medical group model of patient care delivery

Continuous improvement of patient care systems

Strong tradition of “learning from the best” in collaborative studies

Systems thinking—care coordination, team-based care

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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

Current emphasis Integrate data and systems around the patient, not hospital departments

It’s not about technology, or even information—it’s about leveraging “I” and “T” to transform care

It goes beyond automating the medical record—workflow, team interaction

And beyond the bedside—continuum of care: integrate across institutions (HIE), involve the patient (PHR)

Key challenges in clinical IT projects Computer hardware and communication mechanisms suitable for clinical settings

Portable devices, wireless networking, integration with telephony/paging/messaging Support for workflow, as well as clinical decision making

Measures and “models” (in the engineering sense) for patient care processes Defining roles, data needs, and real-time collaboration processes for clinicians Limited evidence for “evidence-based medicine”— recent focus on supplementing EBM via “rapid learning”

Culture, norms, expectations, and IT skills in health care Provider organizations—most IT staff have limited experience with quality measurement, PI, and process design Vendors—most healthcare organizations have not been demanding purchasers, have not rewarded innovation

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OperationalEfficiency

Four Eras of IT in Health Care

CQI / TQM

Efficacyof Care

PatientSafety

Patient Financial SystemsDepartmental Clinical Systems

Process IntegrationWorkflow Transformation

Data Integration: Patient-Centric ViewClinical Decision Support – CPOE

1980 1990 2000 2010 2020TODAY

ANALYTICS CONTINUOUS IMPROVEMENT

Institute of Medicine (IOM) report

Technology Infusionfrom Other Industries

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Implications for

skill development ?

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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”

Hypothetical 79-year-old woman with osteoporosis, osteoarthritis, type 2 diabetes mellitus, hypertension, and chronic obstructive pulmonary disease, all of moderate severity.

12 separate medications19 doses per day05 separate dosing times/day

$ 4,877 medication cost/year (generics)

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Need for “Rapid Learning” — Lynn M. Etheredge, PhD

New products and technologies are being developed at an increasingly rapid pace Uncertainties for physicians and patients

What are the impacts on quality and cost?

Major “inferential gaps” in the evidence base for clinical care 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

Typically use younger patient populations, with single diagnoses, over brief study periods Are the conclusions applicable to older patients who have 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

But…serious limitations to traditional real-world data sources Data standards have focused primarily on administrative processes—insurance claims

Aggregate databases have focused primarily on hospital care Medicare claims for hospital and professional fees (managed separately) All-payer hospital discharge abstract databases collected by various states

Limited “outcomes” data: cost/charges, length of stay, in-hospital mortality, 30-day mortality Elaborate risk adjustment models, to give “credit” to hospitals that treat sicker patients

Clinical data have been recorded primarily on paper

1212

Complement, rather thanreplace, RCTs

understand contributorsto increasing healthcarecosts—are we gettingvalue (better outcomes)?

geographic variation

variation in patientcompliance

differences in effects oftherapies across diversepatient populations...

patients with multiplecomorbid conditions, and...

older patients

customize guidelines forindividuals—what do these recommendationsmean to me?

“We are crossing the threshold of a major shift in the intellectual history of medicine.” — David M. Eddy, MD

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Converging Trends Enable Rapid Learning

Growth of “business intelligence” in other industries—migrating to healthcare Recognition that data are an important by-product of business operations data warehouses

Information is considered a strategic corporate asset

Tools and techniques for exploratory analysis Rapid hypothesis generation/testing is a key element of rapid-cycle quality improvement

Expansion of electronic data resources in health care Clinical systems in hospitals: CPOE, clinical documentation, eMAR (medication administration record)

New-generation operational systems: patient/resource scheduling, staff scheduling

Ambulatory EMRs—gaining momentum, with variable levels of adoption

Conceptual acceptance of P4P and transparency of quality/performance measures Debate continues on the meaning of “performance”

Few true outcomes, but consensus is emerging on process measures and intermediate outcomes

CMS: “Value-based purchasing” for Medicare

Recognition of the value of care coordination Cognitive activity by primary care physicians and care teams within medical groups

Promotes patient/family engagement, shared decision making—personal health records

Exchange of patient data enables “interoperability” among healthcare providers Regional Health Information Organizations (RHIOs), Health Information Exchanges (HIEs)

Need for better and more efficient surveillance of new drugs and devices

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“It will take a national investment, leadership from both the public and private sectors, and an increasedfocus on government research.”

— Lynn M. Etheredge, PhD

We tend to underestimate thelong-term impact of technology,but we invariably overestimatethe pace of adoption.

— Bill Gates

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Health Affairs briefing(January 26, 2007):

John Iglehart, Health Affairs

Carolyn Clancy, AHRQ

John Lumpkin, Robert WoodJohnson Foundation

Lynn Etheredge, GeorgeWashington University

David Eddy, Archimedes

Paul Wallace, KaiserPermanente

Joel Kupersmith, VeteransHealth Administration

Additional authors in HealthAffairs special edition:

Jonathan Perlin, HCA

Peter Neumann, Tufts-NewEngland Medical Center

Greg Pawlson, NCQA

Richard Platt, Harvard PilgrimHealth Care

Walter Stewart, Ron Paulus, et al., Geisinger Health System

Jean Slutsky, AHRQ

Louise Liang, Kaiser Permanente

Paul Ginsburg, Center forStudying Health SystemChange

Arnold Milstein, PBGH andLeapfrog Group co-founder

David Brailer, first nationalcoordinator for health IT

16Develop 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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New Issues/Dilemmas for the Age of Analytics

Ensuring constructive use (for performance improvement) of potentially sensitive data Culture alert!

Health data are complex, mirroring complex and highly variable care processes Business intelligence (BI) tools were developed in industries with simpler, more separable processes

New skills required—exploratory analysis, coupled with deep knowledge of clinical processes Team-based quality improvement initiatives

Quality professionals must ensure effective integration of all elements—need significant knowledge base

Shift from “report” mindset to exploratory analysis—interactive use of data for rapid hypothesis generation

Patient confidentiality—key issue for data warehouse initiatives HIE, RHIO, and NHIN initiatives are optimized to provide information for the care of an individual patient

Patient identification is an essential requirement, to meet this need Data are typically stored in a “federated” (distributed) model

Ideally, analytical data warehouses should contain only de-identified data—HIPAA specs Source institutions should be able to re-identify patients and providers, as part of a QI process or for clinical trials Aggregated data, but must retaining full detail to facilitate drill-down (slice ‘n dice)

Defining the “legal” medical record (for subpoenas) Specific alerts/reminders—and clinicians’ responses—should be regarded as protected peer review data

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Medication Management Cycle

Symbol PPT 2740ruggedized, pen/touch input PDA w/ laser barcode reader and WiFi

“Transcribing”

Dispensing

Administering

Patient Monitoring

Quality 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

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)

Ordering

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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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BCMA—What’s Necessary to Make It Work Well?

BCMA = barcode medication administration (using barcodes for verification)

Application software CPOE (computerized physician/provider/prescriber order entry)

Credible protocols—order sets, care plans Reminders (patient is due for…) and alerts (potential serious drug-drug interaction), presented judiciously to avoid

“alert fatigue” Ability to over-ride when patient needs are different, ability for the physician to document his/her rationale and for

the “knowledge management” team to analyze over-rides as pointers to possible improvements in the protocols May need to print a copy of orders, so they’re readily available on the unit (and to ensure that the paper chart is

complete, if the organization does yet have a fully electronic medical record)

Good user interface for BCMA on laptop and/or PDA-based barcode devices User confusion here could wipe out any patient safety benefits

eMAR (electronic medication administration record) Documentation created by BCMA system when meds are administered Ensure that MAR information is available to everyone—immediately included in electronic documentation system

and/or on paper, if not everyone on the care team (including pharmacy) has electronic access

Technical infrastructure Wireless network that’s reliable and available wherever medications are administered (including radiology

department and surgical suites)

Potential for different solution for scheduled medication rounds (e.g., laptop on med cart with Bluetooth barcode reader) vs. initial doses and p.r.n. (as needed) orders (e.g., wireless PDA with barcode reader)

Charged batteries (need hardware and operational protocols that are simple, efficient, and reliable)

Fast and reliable user authentication (log-in and log-out) on all devices

NEW SLIDE

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BCMA—What’s Necessary to Make It Work Well? (continued)

Other system/processes Patient identification—barcoded ID bracelets

Mechanism to replace damaged ID bands that discourages use of “replacement” bracelets as a work-around

User (nurse, pharmacist, physician) identification Barcodes on ID badges Temporary ID for when clinicians lose or forget their badge

Medication labeling Unit-dose oral solids (relatively easy, although surprisingly not facilitated by manufacturers) Vials, ampoules, pre-filled syringes (harder, if only due to awkward shapes) IV piggy-backs (different processes required for custom vs. “stock” piggy-backs)

Medication delivery process Getting meds to the patient care units

– for routine orders vs. emergency orders– for scheduled doses vs. initial doses and p.r.n. orders

Dispensing cabinet (Pyxis, Omnicell) design, policies, and procedures– interfaced to pharmacy system

Over-ride for emergencies (lesson from Children’s Hospital of Pittsburgh) Well-meaning efforts to use a new CPOE system to rigidly enforce a linear process designed for acute care but

inappropriate for complex, critically ill patients, such as transfers or direct admits to the ICU

Famous BCMA work-around Veterans Health Administration has been a leader in deploying BCMA, as part of its VistA EMR

Before there was a convenient, PDA-based solution for p.r.n. orders, some nurses would “reprint” patient ID bands and scan them at the nurse’s station, then walk to the patient’s room (hopefully the correct one) and administer the medication, to avoid having to take an entire med cart (with barcode reader wired to a laptop attached to the cart) for every dose

NEW SLIDE

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What Do We Mean by “Failure” of an IT Project?

Never finishes installation—project abandoned

Installed but rejected by users and removed CPOE system at Cedars-Sinai (see Ceci Connolly, Cedars-Sinai Doctors Cling to Pen and Paper, Washington Post, March 21,

2005, page A01)

In place, being used, but with work-arounds that defeat critical objectives Early work-arounds of the VA’s barcode medication administration system (since corrected)

In place, but causing errors or patient care problems Children’s Hospital of Pittsburgh (since corrected)

In place, but causing reductions in clinician productivity that are not more than recouped elsewhere in the care process

Additional time to write electronic notes during a patient office visit may be recouped (in terms of time or in terms of quality of care), e.g., when a physician who is covering call for his/her group has remote access to the system in the middle of the night and is able to get a clear, current summary on a patient of another physician and is thus able to make a better decision

In place and being used as intended, but took much longer to implement and cost much more than originally budgeted

Delays and cost increases may be a good sign (and entirely justified) if they indicate a recognition, albeit belatedly, of the need for significant process redesign work as part of the implementation

NEW SLIDE

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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?”

Incrementalor

“Big Bang?”

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Other Challenges for IT in Health Care

Practicalities “Form factor” for devices in clinical settings—wireless tablet PCs, PDAs, VoIP phones (Vocera)

Battery life is still a challenge (along with protocols/devices for battery charging)

Logic/mechanisms for communication with clinicians—dynamic worklists

User authentication—passwords, proximity cards, USB keys, etc.

Getting barcodes (or RFID) on medications—unit doses, ampoules and syringes, IVs, and piggybacks

User adaptation—teach basic mouse skills vs. “grew up with Windows”

Advanced IT is becoming a factor in recruiting higher productivity, less frustration, lower stress

Organizational culture issues Mechanisms for decision making—broad involvement, meaningful buy-in, executive leadership

IT is becoming an important issue for health care executives and board members But it is often still viewed as a “business issue” rather than an intrinsic part of clinical process design

Stories of many expensive failures make many healthcare executives skittish about IT

Sense of “ownership” for information (knowledge), management of information as a strategic resource

Linking the “technology” and “quality” worlds with each other—and with clinical practice Traditional “cottage industry” and discipline-specific training silo thinking Need to create forums for shared learning about IT and process redesign

Lack of process engineering skills and perspective within health care organizations

Clinical perspective within the IS organization

Demands of “legacy” systems Cost, distraction

Anchor people to traditional roles—inhibits change in culture and attitudes

REVISED SLIDE

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See excerpt, next slide.

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Children’s Hospital of Pittsburgh

NEW SLIDE

“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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Traditional Linear View of a Health IT Project

Linear thinking is not limited to the implicit models of workflow embedded in the design of many clinical systems.

Organizations often have an unrealistically simplistic view of health IT projects as a whole, thinking that the most difficult decisions are at the front end of the process—selecting the right consultant, drafting the right plan, and selecting the right vendor product. After elaborate processes for each of these steps, the organization may be tired of the project and impatient for progress on implementation.

Part of the goal of the planning process is to achieve “buy-in” and to develop consensus. Yet many planning processes fail to address the cultural dimensions of issues like workflow transformation, clinical change management (including surrendering individual autonomy in favor of a more team-based approach to care and ensuring patient safety), organizational knowledge management, and use of the resulting data for analysis and improvement. Awareness of these issues is growing, but slowly.

Organizations’ “strategic” plans for projects like this are often look suspiciously similar to the consultant’s boilerplate. They seldom address executives’ fears of failure, except in terms of broad notions of “managing project risk,” and cultural issues like changes in clinical workflow. Goals are often quite general, e.g., to improve patient safety, without any attempt to quantify the current areas of greatest risk.

Many organizations have succeeded with vendor products that have been at the center of failures in other organizations. While some vendors and products may be a better match for some organizations than for others, success is much more than simply “making the right choice.” Major application systems typically remain in place for 10–15 years, so the ongoing relationship with a vendor may be more important than a detailed analysis of current product functionality. At the technical level, a more relevant question is how well a vendor’s overall system design or architecture supports key business and clinical processes that are critical to the organization, e.g., a hospital lab that does commercial work for local physician practices needs additional functionality organized around the “client” (physician office) as well as the patient.

Implementation is often seen simply as a matter of good project management discipline—maintaining focus and avoiding “scope creep.” Those skills and processes are important, but some flexibility is essential.

Consider the conclusions of Wears and Berg in their JAMA editorial from March 2005 (previous slide). We must balance the “command and control” approach with allowing for—even promoting—”experimentation and mutual learning.”

NEW SLIDE

Consultant 1

Consultant 1

Consultant 1

“Plan”

Vendor 1

Vendor 1

Vendor 1

SelectedVendor Product

Implement Use

We must allow for a certain degree of “experimentation and mutual learning.”

This is the hardest part. It deservesthe most time and attention.

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Important “Design” Issues for Health IT Projects

Interfacing/integration with other systems Inpatient pharmacy must be tightly integrated with CPOE and clinical documentation (eMAR)

Departmental systems like lab and radiology can make do with an orders/results interface, but it’s important for clinicians in those departments to have access to the patient’s full clinical record

Clinical departments with specialized needs may require expanded functionality (or separate systems): Oncology—complex chemotherapy protocols, linked to patient scheduling/rescheduling functions, linked to chemo compounding

and chemo administration; must be integrated with institutional pharmacy and EMR systems (patients receiving chemo are likely to come to the ER, so their records must be available to ER physicians)

Obstetrics—specialized real-time documentation and decision support Ophthalmology—highly graphical documentation style may require different user interface Critical care—very data-intensive environment, requiring highly specialized display formats Rehab—coordination/scheduling and documentation requirements for highly team-oriented model of care

Document imaging, if only to make paper that is received from outside the organization electronically accessible

Ambulatory e-Prescribing should be embedded in an EMR (integrated with med list function) and requires two-way external interfaces with retail pharmacies and pharmacy benefit managers for full functionality

Ensure that key information in the EMR (e.g., orders) is also available on paper, during the transition

Project design and planning must encompass all the roles and issues on slide 24 (process design, knowledge management, etc.), including decisions about…

Sequence of implementing the major functions, e.g., CPOE before clinical documentation or vice-versa

Institutional vs. departmental vs. individual order sets and note templates

Use of standardized terminology vs. free text

Nature and frequency of alerts and reminders (balance safety warnings vs. risk of “alert fatigue”)

Focus on supporting clinical workflow and decision making, not just creating an electronic record

NEW SLIDE

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10

34

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 (P4P)

Focus on “interoperability” will drive adoption—deliver greater value to individual users

Finally, we have suitable point-of-care technologies that are reliable and (more) affordable Wireless networking is a key enabler, but battery life remains a challenge

Gaining a critical mass of health care workers who demand, rather than reject, technology

Learning to separate systems thinking (process design) from techno-gadgetry

Recognizing the possibility of making things worse (negative unintended consequences) and learning how to avoid doing so

Have a little patience…

We tend to underestimate the long-term impact of technology,

but we invariably overestimate the pace of adoption.

— Bill Gates

REVISED SLIDE