knowledge management: the use of knowledge strategies to...

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COPYRIGHT © 2002 BY THE HEALTHCARE INFORMATION AND MANAGEMENT SYSTEMS SOCIETY. 1

2002 ANNUAL HIMSS CONFERENCE & EXHIBITION

Knowledge Management: The Use ofKnowledge Strategies to Transform Health Care

Session 152Session 152

Tonya Hongsermeier, MD, MBAVice President, Patient SafetyCerner CorporationKansas City, MO

Winnie Schmeling, PhD, RN, FAANSr. Vice President, Organizational

ImprovementTallahassee Memorial HealthcareTallahassee, FL

AUTHORS/PRESENTERS

INTRODUCTION

The American Productivity and Quality Center (http://www.apqc.org/km/) defines “knowledge manage-ment” as “the systematic process of identifying, capturing, and transferring information and knowledgepeople can use to create, compete and improve.”

At Tallahassee Memorial HealthCare (TMH), we have spent over 12 years creating a learning organ-ization. Tallahassee Memorial HealthCare (TMH), Inc., is a private, not-for-profit, corporation, oper-ating a diverse health system serving residents of northern Florida, southern Georgia and southeasternAlabama. We believe that health care is a “know-how” driven business, and that our knowledge andhow fast we can learn are both the “only sustainable source of competitive advantage” (Senge, 1990),and the only way to achieve excellence in patient care. We define excellence as optimal clinical out-comes for our patients and outstanding service for our customers, while using our scarce resourceswisely in a satisfying practice environment. The metrics we use to measure our success are clinicaloutcomes, customer satisfaction, cost, productivity and employee satisfaction—a balanced scorecard.Our model for learning in a learning organization is very concrete and defines the ultimate goal oflearning as improving organizational results. (Schmeling, 1996)

Knowledge management, on the other hand, has been an abstract, ethereal concept, so we have cre-ated our own working definition—“the systematic process of making sure everyone knows what thebest of us knows.” The learning organization culture, coupled with our IT strategy, means we can allknow the best of what is known, not just in our organization, but anywhere. There is simply too muchto be known. We must leverage our learning with tools to embed knowledge in our structures andprocesses, making it available and accessible at the precise point needed to support excellent clinicaldecisions.

In our journey toward becoming a learning organization, we have found that capturing and communi-cating best practice knowledge and patient data in a structured form is an essential core competencyfor health care delivery organizations. Discrete information and knowledge form the building blocksfor process improvement and measurement. The absence of health information systems (HIS) aloneis a frequent barrier to total quality management (TQM) programs. In addition, most existing systemsare unable to drive prospective decision support services (DSS). Both TQM and DSS greatly benefitfrom structured documentation. Without this approach, it is difficult, if not impossible, to deliver andmeasure care in a consistent fashion. And without clear and complete documentation of this sort, out-comes cannot be accurately measured and compared.

In delivering health care at the operational level, practitioners typically:

1. Review information from patients and from available records;

2. Assess the likelihood of probable health states including diseases;

3. Plan the relevant diagnostic or therapeutic measures; and

4. Document the salience of these issues.

2002 HIMSS Proceedings: Educational Sessions Session 152 /

Exhibit A: A Model for Learning in a Learning Organization

There are distinct challenges embodied in each step above, and there are emerging opportunities tomeet these challenges. The challenges include delineating appropriate measures of health improve-ment (including those of HEDIS, NCQA, JCAHO), and facilitating efficient and reliable performanceof each step, sometimes referred to as workflow. In addition, codification of the constituent parts ofthe assessment and plan is necessary to measure results and identify opportunity for real-time processimprovement. To improve health at the defined population level, the results of the operationalprocesses need to be examined for efficacy.

The use of structured encounter flow technology is invaluable for facilitating ‘best practices” work-flow while simultaneously collecting codified data. Using proven techniques, health care organiza-tions can manage and learn from codified data and communicate it back at a high level to clinicalanalysts, managers and planners. At both strategic and operational levels, reusable structuredencounter pathways can be built to guide best practices and enable collection and linking of codifieddata to facilitate effective use of information in clinical process reengineering.

OVERVIEW OF KNOWLEDGE MANAGEMENT APPROACH

Statistical analysis, continuous feedback, and open communication are operational values throughoutTMH. For ten years, care management and process improvement has been integrated into the TMHorganization to identify practices and variations that potentially impact the quality, efficiency and costof patient care as well as the patient perception of their care. In recent years, TMH has embarked onan ambitious, enterprise-wide clinical information systems project that will effectively automate allthe processes for delivering and measuring care.

Below, we will describe three primary approaches to collection, analysis and application of derivedhealth care knowledge: management, process and data.

ManagementFrom the executive perspective, attention to data and processes is insufficient to bring about institu-tional change. Change management requires attention to skills, interests, historical behavioral pat-terns, as well as incentive structures. Providing advanced technological solutions alone is never aneffective approach.

Process There are two aspects to the process approach: 1) facilitating the operational aspects of caring foreach patient; 2) facilitating learning from the aggregated experience of caring for many patients.

The process approach is an essential component to survival in a competitive health care marketplace.Collecting codified data and using a special guideline called a “Community Guideline” will helpillustrate the benefit of focusing on process to improve efficiency. In contrast to other guidelines, thecommunity guideline is machine-readable and executable. It presents the appropriate results andreports such as laboratory or physical therapy to the provider for review, without explicit action by theprovider. It presents opportunities to document the issues that are specifically pertinent for the givensituation, as well as to order interventions in a single, unified process.

Data Encounter documentation is done for a variety of purposes that drive the quality and quantity of the con-tent. Three major reasons for documentation are: 1) to remind the clinician of prior care to guide currentcare; 2) to communicate with other caregivers; and 3) to account for reimbursable care. The major goalsof process improvement and process efficiency are poorly served by these common, loosely structuredmethods. Structured encounter documentation is an emerging, alternative approach whereby the clini-cian is provided a knowledge-embedded template to use as the starting point of their documentation.These templates are the core components of multi-encounter, multi-disciplinary community guidelines.

MANAGEMENT

The most common failure of many health care transformation efforts is related to the failure of organiza-tions to develop empowered, accountable leaders or to the premature disengagement of leadership afterthe launching of TQM initiatives. It is essential that executive leaders build organizational structures thatclarify lines of accountability for clinical service excellence and efficiency and enhance communicationand effectiveness among leaders and direct care providers. Performance improvement initiatives in manyenterprises often have inadequate authority, sponsorship or methodology to succeed. Leadership isrequired to determine where centers of excellence would be developed and where standardized cross-sys-tem processes are preferable, and which elements of excellence in practice can be generalized.


TMH has invested significantly in defining the requisite infrastructure and leadership for creating alearning organization. Infrastructure includes clinical information systems, activity-based costingsystems, financial incentives, diversity and systems for tracking customer and staff satisfaction. Theleadership team includes closely aligned directors of Educational Services, Human Resources andPerformance Improvement. Exhibit B is a high level diagram illustrating how a requisite leadershipstructure might appear to drive transformation. The Performance Improvement Team is accountableto executive leadership. Of greater significance, the operational groups, including clinical automa-tion, are accountable to the Performance Improvement Team.

Existing administrative data sources are analyzed to determine where best to target performanceimprovement initiatives. This often results in several interdependent process vectors which must allbe accounted for to drive effective change:

1. Individual workflow: physicians, nurses, case managers, administrators, etc.

2. Venues: acute care, post-acute care, operating room, intensive care, emergency room, etc.

3. Service lines Acute Care—CABG, Total Hip Replacement, etcSafety—Medication Safety, Infection Control, Bed Sore Prevention, etcDisease Management—Asthma, Diabetes, Coronary Artery Disease, etc

Leadership must then analyze this data, prioritize Service Line and Venue initiatives, and developmulti-disciplinary team structures with aligned incentives to drive improvement. For example, aCongestive Heart Failure team will have representation from multiple venues and disciplines toenable improvements that account for workflow, venue, financial and disease outcomes. Exhibit Cshows how the different teams intersect and must function interdependently. Each team ideally con-tains of the following members:

• Sponsor:

• responsible for outcomes improvement i.e. Medication Safety

• Subject Matter Experts:

• multidisciplinary representative experts on all aspects of the processi.e. for Medication Safety—physicians, nurses, pharmacists, laboratory technicians

• Designated Design Decision Maker:

• empowered to make design decisions• respected opinion leader


Exhibit B: Organization Transformation Structure

• Design Facilitator:

• knowledge architects with software application and knowledge embedding expertise

• Application Specialists:

• receive design requirements and implement them in the software

PROCESS

After prioritization, staffing, and deployment of above teams, the teams are tasked to build the caredelivery and measurement frameworks for the targeted areas. Initially, through weekly meetings,teams review inventories of existing knowledge sources for selection, validation, and development ofthe ‘Community Guideline’, a multi-disciplinary, multi-venue, multi-encounter care managementguideline for the targeted area. Clinical knowledge sources are derived from internally developedpolicies and procedures, appropriate professional associations, and credible third-party clinicalguideline vendors. For each of the agreed upon targeted areas, design specifications and a rollout planare developed for all the relevant workflow and measurement applications as they relate to theembedding of content. In addition, team members meet with their constituencies to obtain feedbackand build consensus between meetings.

When implementing care management and decision support systems, the health care organizationoften begins in a paper-based state with technology supporting some ancillary and administrative sys-tems. Implementation of application functionality usually progresses in a sequential fashion that per-mits ever-increasing richness of decision support and structured data capture. Exhibit D, a stair-stepframework for knowledge management, is an illustration of how each type of automation has a corre-sponding opportunity to integrate knowledge and improve quality.


Exhibit C: Project Management Assist Teams to Manage Interdependencies

EXHIBIT D DEFINITIONS

MonitoringThe Monitoring stage is characterized by the implementation of a clinical data repository. This repos-itory can be enhanced with a rules engine that monitors incoming data and can deliver post-hoc noti-fications. An integrated clinical data repository makes it possible for caregivers to access data acrossdepartmental sources as well as across care settings. Access to continuum-of-care, longitudinalpatient data can reduce clinical errors that often occur during care setting ‘hand-offs’. Post-hoc alertscan be triggered by patient data from a variety of discrete data sources including lab, ADT, pharmacy,and radiology feeds. Rules can be stored in a knowledge repository to trigger notification to appro-priate personnel of significant incoming data. In essence, the rules engine can “push” high-valuepatient data to caregivers and advise them of the clinical significance of the data. Such alerts can bedesigned to improve processes such as infection control, preventive health, critical value notification,adverse drug effect early detection and high-risk patient identification.

InteractiveThe Interactive stage begins when automated orders and documentation functionality are added to therepository with the rules engine. Interactive decision support alerts can be implemented for the orderingphysician or pharmacy order-management system that critique the safety of proposed drug therapies refer-encing all available patient data. In addition to improving the medication use process, other clinical areascan be targeted. For example, to prevent pressure ulcers, a care plan can be designed such that documenta-tion of a low Braden Score automatically triggers a consult to the enterostomal nurse. As knowledge isembedded in these functional systems, measurement technologies can now begin to evaluate not only the“rate” of resource consumption, but also the rationale for comparison to the community guideline.

ProactiveThe Guiding stage begins when knowledge engines for DSS and multi-encounter pathway systems are ableto provide advice and guide care decisions before they are enacted rather than “react” to proposed interven-tions. For example, when a patient on a hypertension disease management protocol obtains a blood pressureduring a primary care visit, the blood pressure result is passed through the risk stratification engine. Thisengine can trigger a community guideline that offers the caregiver options, to continue the patient on currenttherapy or advance to more aggressive therapy. Alternatively, the pneumonia patient presenting to an emer-gency room may undergo a pneumonia severity index (PSI) assessment. The decision support system cancritique the PSI value, available culture results, patient allergies, current medications and current problemsand present the caregiver with a care setting recommendation and a list of proposed antibiotic regimens


Exhibit D: Stair-step Framework of Knowledge Management

ranked for safety and cost-effectiveness. The Guiding stage allows knowledge repositories to have optimumimpact on clinical and operating performance.

LearningThe Learning stage actually begins with the Monitoring stage, when the analytic data warehouse per-mits the integration of clinical and management data to perform enhanced clinical performanceanalysis. With progression through the Interactive and Proactive stages, performance analysis capa-bilities become richer with each level of increasing knowledge integration. Most organizations todayspend months trying to determine whether their process improvement initiatives have changed behav-ior or outcomes. Variance and outcomes data captured as a byproduct of automated processes canthen be used to refine the knowledge repository for real-time continuous learning and qualityimprovement. Cycle time from performance analysis to implementing process change is dramaticallyreduced because the automation tools for knowledge-driven care are comprehensive enough to enableproactive quality improvement.

DataDiscrete data capture is essential to drive the clinical decision support “knowledge engines” and per-formance measurement systems. In the above stair-step framework, it is evident that data capturedfrom laboratory and order management systems are more easily derived than data from documenta-tion. New tools are emerging to enable capture of discrete data from documentation. As these systemsare implemented, the challenge arises to ensure that organizations only expend effort to capture the‘structured data that matters’. Criteria for evaluating the value of capturing discrete data include:

• Care quality, safety and appropriateness

• Care performance measurement and clinical research

• Caregiver efficiency, professional satisfaction and workflow

• Patient flow, satisfaction and service

• Revenue and reimbursement

• Regulatory, accreditation, Leapfrog initiatives and/or HIPAA compliance

All of the processes for delivering and measuring care can be mapped to the requisite data requiredfor superior performance. With the above criteria in mind, the design and deployment of tools forstructured encounter flow can now be driven by the community guidelines developed by the processteams described above. The method for designing and deploying structured encounter flow documen-tation is an iterative one. First, as described above, the content sources are gathered and catalogued inan inventory. A “Knowledge Librarian” with nomenclature expertise and nomenclature tools is desig-nated to centralize the cataloguing of the existing paper forms, clinical guideline content and corre-sponding data elements. Exhibits E and F show example inventory grids with respect to workflow andservice line teams for the categorization of various existing paper forms across an enterprise.


Exhibit E: Knowledge Inventory for Nursing Workflow Ranked with Color Coding by Sequence ofClinical Automation Project Plan

For each service line and its associated community guideline, information technology allows theteam to iteratively ‘prune’ the encounter flow of redundant or duplicative steps leveraging the datathat is already in the system. Venue teams such as those for the emergency department, admissionsand registration can help ensure that cross-service line efficiencies can be realized to streamlinepatient flow and ensure internal consistency of approaches. Disease management teams such as thosefor diabetes and infection control are a resource to the other service lines to ensure commonapproaches, for example, to the diabetic patient undergoing total hip replacement.

THE TALLAHASSEE MEMORIAL HEALTH CARE EXPERIENCE

We have developed our community guidelines, interdisciplinary plans of care, for over 30 patientpopulations over the past six years. It is important to note that community guidelines are totally inte-grated into our organization-wide improvement structure and process, which we call InteractiveQuality Management (IQM). The Clinical Pathway Leadership Team is a chartered IQM team. Itincludes all service line leaders who meet regularly to assure learning across teams. Each team is alsochartered, and is accountable for results to the IQM Steering Committee. Our IQM structure includesfour levels of teams:

• Planning Boards who are responsible for improving results within departments (i.e. pharmacy,operating room, emergency department)

• Process Improvement Teams who are responsible for improving processes, which cross severaldepartments (i.e. admission, registration, scheduling)

• Service Line Teams who are responsible for improving results for patient populations which crossdepartments and processes and organization-wide (i.e. congestive heart failure)

• Function Teams who are accountable for improving results which cross all three of the foregoing,(i.e. pressure ulcer prevention, medication safety and infection control).

All teams use our interactive process and produce quarterly reports on their improvement results. Wehave compiled a spreadsheet that indicates how all pathways have performed over time. We also havesolid data to show how costs and length of stay have been impacted and moved over the course ofTMH’s five year effort. (Both of these studies in ROI will be highlighted visually during the presen-tation of this topic at HIMSS 2002.) All reports are entered into a database, which we call theWWOW database (Who’s Working on What). This was developed to be an organizational learningtool to speed learning. After many years, it has become our improvement knowledge repository.


Exhibit F: Knowledge Inventory Service Line View

THE LEARNING ORGANIZATION AND THE BALANCED SCORECARD

Effective analysis of health care information is a central component in formulating strategies forimprovement initiatives. These initiatives involve the analysis of episodes of care, quality measurecomparisons, resource utilization and investigation of temporal relationships between various factorsand outcomes.

We have adopted the IOM definition of quality (IOM, 2000) to include patient safety, evidence-basedbest practices and customization, i.e. taking into account the patient’s unique needs and preferences.We accomplish this through our care management process. Our community guideline structures andprocesses are designed, first and foremost, to achieve excellent outcomes for our patients, but also tobuild our knowledge base and make that knowledge available and accessible for clinical decision-making. Service line teams design the expected patient outcomes, and use the best available evidencefor achieving them. Variance from these outcomes is carefully tracked (presently a paper process).Every guideline has a measurement framework that includes metrics for effectiveness, efficiency andsatisfaction. In the early phases of our implementation, TMH has been faced with the common chal-lenge of pooling together data from a variety of sources including financial systems, administrativesystems, ancillary systems and paper chart abstraction. Our analysts enter the appropriate data intoour comparative database to generate the quarterly scorecard shown in Exhibit G, which benchmarksour outcomes against targets.


Exhibit G: 4Q200—Total Knee Replacement Clinical Pathway

Over time, more and more of this data is being aggregated in our management data repository thatintegrates clinical and financial data. As clinical assessment data is captured from online documenta-tion, the costs of paper chart abstraction decline. The community guideline scorecard includes a radargraph of key metrics for that population for that quarter, opportunities for improvement, and trenddata showing average length of stay and cost by category against benchmark. In the example providedfor Total Hip Replacement, the graph shows economic outcomes (cost and length of stay), patient sat-isfaction, and clinical outcomes (pain management and early ambulation). The service line teams usethese reports to drive continuous improvement. For example, in the Total Hip Replacement example,we are falling short of our targets for Pain Management. With the availability of structured encounterflow documentation tools, we can now develop on-line pain assessment forms that are embedded withTMH guidelines for pain management. When pain control is falling short of expected, alerts cannotify the appropriate physician to consider reassessment and more aggressive management.Compliance with recommended protocols for pain management can be measured as a byproduct ofcare because the data can be captured automatically. Further, pain control overall continues to fallshort of expectations despite compliance with the protocol, this is a signal for our team to revisit theprotocol and consider revision of the knowledge base.

We are continuing to build our knowledge base, and are now working to develop the IT infrastructurenecessary to embed this knowledge. Our definition of quality will determine our priorities for; first—patient safety; next, evidence-based best practices, finally, patients’unique needs and preferences.With on-line structured documentation, we will finally be able to do real-time care management. WithCPOE, we will finally be able to embed the knowledge necessary for effective clinical decision-making.

CONCLUSION

Knowledge-enabling provider workflow allows for opportunities to improve health care deliverythrough analyzing episodes of care, comparing clinical quality, predicting resource needs and exam-ining temporal relationships between interventions and outcomes.

Structured encounter documentation is emerging as an essential core competency for health caredelivery organizations. Without this approach, care cannot be delivered in a consistent fashion, norcan outcomes be fairly measured and compared.

The community guideline is focused on the total care management of the patient. It frequently includesa telecare/case manager’s process issues, a home health caregiver’s process issues, as well as thepatient’s self-case and self-assessment process issues. In this way, the multitude of care tasks can becentrally tracked and distributed to the most appropriate responsible parties. This is a vital, often omit-ted, aspect of TQM in patient care because these ‘hand-offs’ of tasks between primary care providers,specialty care providers, case managers, and the patient are frequently dropped because of inadequatecommunications. When these tasks require a change in behavior, there are additional factors conspir-ing against plans being carried out. The institution of community guidelines provides a safety net,reminding the patient and health care team of mutual expectations in a time appropriate fashion.

AUTHOR BIOGRAPHIES

Tonya Hongsermeier, M.D., M.B.A., is Vice President, Patient Safety for Cerner Corporation. Sincejoining the company in 1997, she has focused on client value and strengthening the organizationallearning capability of Cerner clients.

Winnie Schmeling, Ph.D., R.N., FAAN, is Senior Vice President for Organizational Improvement atTallahassee Memorial Healthcare. She is the author of “Facing Change in Health Care: LearningFaster in Tough Times.”

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