Why Do (Many) Health IT Projects Fail?

1. Why Do (Many) Health IT Projects Fail? Georgetown University July 12, 2007 John K. Cuddeback, MD, PhD Chief Medical Informatics Officer Anceta • AMGA’s Collaborative Data Warehouse American Medical Group Association jcuddeback@anceta.com

2. 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

3. The American Medical Group Association The Association Representing Large Multi-specialty Medical Groupsand Integrated Delivery Systems Founded in 1949 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

4. Director Andrew S. Warner, MD Chairman, Finan/Ops Comm.,Lahey Clinic 2007 AMGA Board Members ChairAllen D. Kemp, MD CEO/Chair, Dean Health Systems, Inc. Chair Elect Ronald H. Kirkland, MD President and Chair of Board, The Jackson Clinic, PA Director Albert W. Fisk, MD Medical Director, The Everett Clinic Director Scott Hayworth, MD President & CEOMount Kisco Medical Group President and Chief Executive OfficerDonald W. Fisher PhD, CAE President and CEO, AMGA Treasurer C. Edward Brown Chief Executive Officer, The Iowa Clinic Director Karen Kennedy, BA, MPH CEO, Medical Clinic of North Texas Director Robert E. Nesse, MD Pres/CEO, Franciscan Skemp HC, Mayo Secretary David L. Bronson, MD Chair BofD, Cleveland Clinic Fdn Director Susan Schooley, MD Chair Dept Family Med, Henry Ford MG Member At-Large Francis A. Marzoni, Jr, MD Exec Dir, Palo Alto Med Fdn Immediate Past Chair Francis J. Crosson, MD Exec. Dir., The Permanente Federation Director Nicholas Wolter, MD CEO, Billings Clinic Director Michael W. Bukosky, FACMPE EVP/CAO, Carle Clinic Assoc, PC Director Robin L. Lloyd MPA Exec. Dir. for Ambulatory Svcs, Univ. of Utah Hospital and Clinics Director Bruce H. Hamory, MD Exec. VP/CMO, Geisinger Health System

5. AMGA Values • Physician leadership in medicine • Integrated, patient-centered, multi-specialty medical group model of patient care delivery • Continuous improvement of patient care systems 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 • Strong tradition of “learning from the best” in collaborative studies • Systems thinking—care coordination, team-based care

6. 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

7. Operational Efficiency Efficacyof Care Patient Safety CQI/TQM Patient Financial SystemsDepartmental Clinical Systems 1980 1990 2000 2010 2020 TODAY Institute of Medicine (IOM) report Four Eras of IT in Health Care ANALYTICS CONTINUOUS IMPROVEMENT Process IntegrationWorkflow Transformation Data Integration: Patient-Centric View Clinical Decision Support – CPOE Technology Infusionfrom Other Industries

8. Implications for skill development?

9. 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”

10. Hypothetical 79-year-old woman with • osteoporosis, • osteoarthritis, • type 2 diabetes mellitus, • hypertension, and • chronic obstructive pulmonary disease, all of moderate severity. 12 separate medications 19 doses per day 05 separate dosing times/day $4,877 medication cost/year (generics) 9

11. 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

12. Complement, rather than • replace, 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 11

13. 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

14. We tend to underestimate thelong-term impact of technology,but we invariably overestimatethe pace of adoption. — Bill Gates “It will take a national investment, leadership from both the public and private sectors, and an increasedfocus on government research.” — Lynn M. Etheredge, PhD 13

15. 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 Health Affairs 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 14

16. Data Data Data ImprovedPractice Analytical systems are essential for integration and transformation. POINT-OF-CARESYSTEMS ANALYTICALSYSTEMS 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 CONCURRENT RETROSPECTIVE Deploy improved practice Develop improved practice External Data TRANSACTION SYSTEMS CLINICAL DATA REPOSITORY DATA WAREHOUSES InformationInformation Knowledge Concept or reality? 15

17. New Approach to Quality Management Traditional Quality Assurance “Bad Apples” Frequency Hypothetical distribution of patients treated, showing how often various levels of quality are attained. Level of Quality MinimumStandard Continuous Quality Improvement For these distributions, better quality is on the right- hand side. CQI both raises the overall level of quality and reduces variation from case to case (indicated by a narrower distribution). Frequency Level of Quality

18. 30% 25% 20% 7 Hospital A 18 Hospital B 15% 10% 5% 0% LOS for Kidney Transplant 15% All UHC Hosp A, B Hosp A Median 12 All UHC 10% Percent of Cases 5% 0% 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75+ Length of Stay (LOS) 1991 UHC data

19. 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

20. 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 Ordering “Transcribing” • order information to pharmacy • copy of order in chart (until full EMR) • copy of order onto Kardex Dispensing Administering Patient Monitoring Medication Administration Record (MAR) Quality Control Symbol PPT 2740ruggedized, pen/touch input PDA w/ laser barcode reader and WiFi

21. 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 Administration 26% Ordering 49% Dispensing 14% Transcription 11% Bates et al., JAMA 1995;274:29-34

22. 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

23. 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

24. 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

25. Business Process Reengineering Product Purchase Cultural Initiative 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 Incremental or “Big Bang?”

26. 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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28. See excerpt, next slide.

29. 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. NEW SLIDE

30. 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.

31. Traditional Linear View of a Health IT Project We must allow for a certain degree of “experimentation and mutual learning.” • 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.” Consultant 1 Vendor 1 SelectedVendor Product Consultant 1 “Plan” Vendor 1 Implement Use Consultant 1 Vendor 1 This is the hardest part. It deservesthe most time and attention. NEW SLIDE

32. 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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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