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Knowledge Management Challenges in the Healthcare Delivery Market

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Presentation on theme: "Knowledge Management Challenges in the Healthcare Delivery Market"— Presentation transcript:

1 Knowledge Management Challenges in the Healthcare Delivery Market
Tonya Hongsermeier, MD, MBA Corporate Manager, Clinical Decision Support and Knowledge Management, Clinical Informatics Research & Development Partners HeatlhCare System, Inc.

2 Agenda About Partners Healthcare Knowledge Management and Informatics
Knowledge Application Knowledge Discovery Knowledge Asset Management Challenges in Healthcare Delivery Weak Organizational Alignment Weak Investment in Asset Management Implications for Clinical R&D Implications for Personalized Medicine

3 Partners HealthCare Massachusetts General Hospital, Brigham and Women’s Hospital and several other hospitals in the network Licensed Beds Births ,478 Admissions ,991 Patient Days ,321 Average LOS Total Outpatient Visits 2,324,073

4 Partners Information Systems
Much published on innovative use of informatics in healthcare (Bates, Teich, Glaser, Kuperman, Barnett, Chueh, and many others) 800 applications 520 active projects 680 employees based in 19 locations FY02 operating budget of $92.3M FY02 capital budget of $47M These are relatively generous numbers as a percentage of operating expenses

5 Some Current Clinical Knowledge Assets Developed at Partners
Medication Data Dictionary and DDIs Inpatient alerts and interactive order rules Gerios and Nephros for proactive filtering of drug doses for elderly and/or renal insufficient Radiology Ordering decision support Preventive health reminders Outpatient lab result decision support Outpatient documentation templates Piloting outpatient drug-lab, drug-disease interactive reminders

6 Current State Challenges Typical of Many Academic Healthcare Delivery Organizations
7 homegrown and 2 commercial CPOE systems, plan to evolve to “next generation CPOE” in next 5 years Limited implementation of structured (encoded) clinical documentation Proprietary approaches to knowledge encoding Not re-usable or sharable Much updating/maintenance is bottlenecked by resource constraints Research datawarehouse in place, but struggle to expand in face of fragmented clinical systems environment

7 Typical Committee and Project Structures Related to Medication Safety Illustrate Organizational Alignment Problem Information Technology Projects Committees and Departments Physician Order Entry Team Clinical Data Repository Team Pharmacy System Team Clinical Documentation Team Electronic Medication Adminstration Team Pharmacy and Therapeutics Patient Safety Quality or Performance Improvement Policies and Procedures Formulary Infection Control Health care systems that succeed at organizational alignment are committed to measuring patient safety and have established clear goals, incentives, and accountability for achieving safety. Building such a culture is challenging in the context of today’s medicolegal environment and the legacy of fragmented approaches to care processes. In the medication use process, for example, it is sometimes unclear who is accountable for a safe outcome because each step of medication ordering, dispensing, and administration is managed separately by the physician, pharmacy, and nursing staff respectively. Further, it is not uncommon that systems for physician order entry, pharmacy dispensing, and medication administration are purchased and implemented somewhat independently of one another. The table above is a sampling of typical committees and project teams relevant to the systems and clinical knowledge that impact the medication use process . It is evident that as project teams implement their respective systems, they can be highly challenged to manage all the inherent interdependencies of achieving both workflow simplification for their respective customers and a safe medication use process. Many healthcare organizations are unprepared for the organizational investment necessary to continuously re-engineer their processes through clinical automation. Consider the topic of adverse drug event surveillance and the specific example of declining renal function in a patient on a nephrotoxic drug. It is sometimes the case that such surveillance may be performed by expert systems in more than one of the applications (Clinical Data Repository, Pharmacy System, Physician Order Entry System). Regardless of the potential number of inferencing mechanisms, if there is fragmented rather than interdisciplinary design approach, each application team will be challenged to determine whom and when to notify and how to escalate notification.

8 Medication Use Process Organization
Medication Safety Steering Committee Chief Medical Officer, Chief Nursing Officer, Chief Information Officer, Chief Quality Officer Interdisciplinary Medication Use Process Advisory Team Physicians, Nurses, Pharmacists, Clinical Systems Architects Information Technology Projects Committees and Departments Physician Order Entry Team Clinical Data Repository Team Pharmacy System Team Clinical Documentation Team Electronic Medication Administration Team Pharmacy and Therapeutics Patient Safety Quality or Performance Improvement Policies and Procedures Formulary Infection Control Thus, it is important to have an organizational superstructure (in this instance, an interdisciplinary medication use process advisory team), which is empowered to analyze and make design decisions that account for workflow, patient safety and technical feasibility. This advisory team needs to be empowered and sponsored by a steering committee of clinical and information systems leaders who set the strategy and priorities for medication safety. Resources must be dedicated to both the consensus development, design, and final knowledge encoding steps in order to leverage information technology investments in a timely fashion.

9 Knowledge Management: The Core Processes
Application Knowledge Asset Management Knowledge Discovery Infrastructure for knowledge management, particularly the knowledge that is tightly embedded in clinical applications, is a quality imperative. Winnie Schmeling, RN, PhD is Senior Vice President of Organizational Improvement at Tallahassee Memorial Healthcare. This site is among 12 sites that were selected by the Robert Woods Johnson Foundation for the Pursuing Perfection grant program ( She defines knowledge management as a “systematic process for making sure that everybody knows what the best of us knows” to deliver excellent care. Knowledge Management in practice is comprised of three key processes. These include knowledge application, knowledge discovery and knowledge asset management. They are organized in a circle to emphasize that the knowledge management process is one of continuous learning and knowledge dissemination

10 A Continuum of Clinical Decision Support and Knowledge Discovery*
Surveillance Interactive Proactive Learning Reference Knowledge Linking Event Monitoring Safety Net Anticipation Understanding and Predicting Performance Monitoring patient data with passive decision support Intercepting incorrect clinical decisions Making the right decisions the easiest decisions Predictive Modeling Case-based Reasoning Learning Knowledge Repository Healthcare delivery is inherently complex and knowledge-dependent. To serve the needs of any clinical counter, relevant patient-centered knowledge must be accessible to the person supplying care at the “right time” in the workflow. Such knowledge can be delivered proactively before decisions are made, interactively as decisions are made, asynchronously such as via a pager, or passively as reference information that can be queried online. As biomedical research changes the standard of care at a rapid pace, the knowledge base required for safe care is growing exponentially. Knowledge Application is the science of leveraging knowledge at the right places in clinical workflow to support enhanced care-giver effectiveness, work-satisfaction, patient satisfaction and overall care quality. This view of Knowledge Application is broader and inclusive of the traditional definition of clinical decision support. Knowledge can be delivered through clinical applications in the form of templates for documentation, order sets for protocol-driven care management, dashboards and reports for feedback, and as alerts, reminders or suggestions. . In a high-performance learning organization, process and outcomes data is captured as a byproduct of workflow and rapidly analyzed to determine new models for process improvement. In essence, robust knowledge application evolves into and merges with knowledge discovery whereby the knowledge repository “learns” from the clinical outcomes in the patient database. *modified from the First Consulting Group Model of Clinical Decision Support

11 Medication Decision Support Categories at Partners
REFERENCE INFORMATION Drug-information knowledge linking via info button adjacent to drug name Partners handbook provides access to numerous drug information databases Planned drug-information knowledge linking via info button in electronic medication administration record in FY 04 SURVEILLANCE AND MONITORING Drug-induced abnormal lab result notification of physician Drug-induced abnormal lab result notification of pharmacist Renal function decline in patient on renally excreted drug notification of physician and pharmacist INTERACTIVE DECISION SUPPORT FOR PHYSICIAN AND PHARMACIST: Drug-allergy checking Drug-drug interaction checking Drug-food interaction checking Drug-herb interaction checking Drug-disease interaction checking INTERACTIVE DECISION SUPPORT FOR PHYSICIAN ORDER ENTRY ONLY: Drug-lab interaction checking Consequent order recommendations Relevant lab display Indication-required orders Height, weight, allergy update required notification Dose calculation tools Intravenous to oral conversion recommendation on renewal of intravenous order when patient receiving other oral medications Formulary substitution alerts Antibiotic restriction alerts PROACTIVE DECISION SUPPORT Gerios for elderly patient medication dosing Nephros for dosing in renal insufficiency Preventive health reminders Problem-linked order sets At Partners Healthcare Systems, a recent inventory of all medication-related decision support resulted in the above categorization. These are highly schematic representations. The taxonomy of a knowledge repository is highly informed not only by accepted standards for best practice, but by the local attributes of the healthcare organization, the features and functions of the applications that are used, and the clinical goals of the organization.

12 Reference information site at Partners used by clinicians across the system

13 Laboratory Notification with consequent order recommendations

14 Alternate Procedures, Redirects, Drug-Allergy, Drug-Drug, Drug-Lab etc.

15 Gerios: Dose-filters for age Nephros: Dose-filters for renal function
At Partners, there are some examples of anticipatory decision support for safety in knowledge bases called Gerios for advising clinicians about appropriate drug dosing in elderly patients and Nephros for advising about drug dosing in patients with renal dysfunction. These types of prescribing errors are among the most common and costly (Bates, Yuen). As the clinician proposes antibiotic therapy, Gerios and Nephros surface dosing options in the order selection screen that are personalized to the patient taking into account his/her age and laboratory test results; highlighting the safest drugs and doses. The screen shot below of an order for a medication, temazapm illustrates that how information is presented by the Gerios system. This approach has been very well received by Brigham and Women’s clinicians because it is perceived as a non-task interfering design approach that makes the right care decisions the easiest care decisions. Yuen EJ, et al, “Sedative-hypnotic use and increased hospital stay and costs in older people”. J Am Geriatr Soc Nov; Vol. 44(11): Bates DW, Spell N, Cullen DJ et al. The costs of adverse drug events in hospitalized patients. Adverse Drug Events Prevention Study Group [see comments]. JAMA 1997; 277(4): Inappropriately sedated elderly inpatients on average incur $5600 excess costs over expected for severity of illness

16 These reminders in our ambulatory health system remind physicians to perform preventive health interventions. Preventive Reminders

17 Problem-level anticipatory decision support
Today, order sets and documentation templates are static which means that clinician must change them to personalize them to patient We plan to use inferencing to dynamically generate problem-driven order sets and documentation templates that account for multiple co-morbidities Must be able to incorporate future onslaught of gene diagnostic and prognostic data

18 Clinical Standardization
Knowledge Application must anticipate these dimensions of the clinical encounter Clinical Standardization Standards of Practice, Role/Venue Requirements Billing/Regulatory Requirements The vendor community will be required to push development in the direction of anticipatory modes of decision support particularly in light of clinician concerns about speed and usability of information systems. This is illustrated by the recent events at Cedars-Sinai Medical Center in Los Angeles. The computerized physician order entry system was turned off in early 2003 after hundreds of physicians complained it slowed down the process of filling their orders (7).Further, with the expected arrival of gene-diagnostics as additional data to be considered in therapeutic decision making, exclusive reliance on the safety-net mode of decision support will become increasingly untenable. User Personalization End-user workflow preferences Learning and User-defined Improvisation Patient Preferences

19 Notice in this screen shot that the expert system is accounting for numerous types of data, some of it inferred from other data, in order to make a patient assessment and formulate a treatment recommendation. Poly-hierarchical inferencing with actionable advice – surveillance, interactive, or proactive mode

20 Recommendation accounts for patient immune status and renal function
This is an example from clinical decision support company called Theradoc

21 A Continuum of Clinical Decision Support and Knowledge Discovery
Surveillance Interactive Proactive Learning Reference Knowledge Linking Event Monitoring Safety Net Anticipation Understanding and Predicting Performance Monitoring patient data with passive decision support Intercepting incorrect clinical decisions Making the right decisions the easiest decisions Predictive Modeling Case-based Reasoning Learning Knowledge Repository Knowledge Discovery is the clinical research process of analyzing data for the purpose of understanding performance, reporting, predicting, and harvesting new knowledge on how to improve. With each successive stage of decision support in Figure 3, healthcare performance becomes increasingly transparent. As processes for care-giver communication, results access, ordering, documentation, task completion, and patient monitoring become supported by expert systems, more data is collected about clinical rationale, process and outcomes. In a well designed analytic data repository, this data can be mined to identify new knowledge that can, in turn, be disseminated through knowledge-enriched clinical applications. Rapid-cycle learning is the goal of knowledge management. *modified from the First Consulting Group Model of Clinical Decision Support

22 Current Initiatives Quality data warehouse with Clinician Dashboards
Early identification of patients at risk for case management Longer term knowledge discovery goals to use performance data to enhance knowledge repository Need to evolve towards non-human dependent modes of knowledge acquisition Currently, most hospitals have poor access to clinical data for measurement and rely, instead, on billing and administrative data. The architecture of a typical transaction-oriented Clinical Data Repository (CDR) is not optimized to support measurement. Further, the data that must be aggregated to enable deep analytics is often located in many databases across an integrated delivery system. In the absence of an analytic data repository, those engaged in the process of research and performance measurement must bear the cost and time delays of chart abstraction land data acquisition to aggregate clinical data, which consequently slows the translation of such insights into quality improvement.

23 Knowledge Asset Management Infrastructure:
Analysis of clinical performance data to understand where knowledge deficits are to support performance goals Authoring and support of virtual, asynchronous collaborative authoring by knowledge editors and leaders of research, safety and quality improvement initiatives (reference knowledge specs for encoding Knowledge acquisition from commercial/etc knowledge bases Validation and audit trail maintenance (meta-knowledge) Inventory (knowledge librarian) Publishing and Sharing Reference information and knowledge model Knowledge Asset Management is one of the more challenging aspects of knowledge management. In non-healthcare industries, the focus of asset management is typically on free-text document management. In healthcare, we must also focus on knowledge encoded in workflow applications to support clinical care, administrative functions, and clinical research. This is a list of processes that must be supporeted by that asset management system. These include…

24 What are the challenges today:
Healthcare delivery organizations purchase systems but don’t invest in knowledge asset management, they install plumbing Vendors sell knowledge editors, not knowledge management support infrastructure There is no repository of “best clinical IT practices” at a national level, few among the vendors No knowledge encoding and representation standards to facilitate knowledge sharing Robust knowledge asset management systems must support the collaborative authoring, encoding, inventory, versioning, updating, sharing, and dissemination of encoded knowledge. As discussed above, many delivery systems adopting an EHR (whether single clinic, or large integrated delivery network) often have poorly developed or absent organizational structures in place for consensus development on how to convert reference knowledge into the specifications for encoding alerts, reminders, order sets, documentation forms and templates. The proprietary knowledge editors of most vendor systems support knowledge encoding but rarely the inventory in a repository or any of the collaborative processes required for agreeing upon how knowledge should be encoded. Commercial content management systems and groupware have emerged in recent years that support virtual, asynchronous collaborative authoring of documents whereby subject matter expert opinion can be elicited and shared through simple and list-serve interfaces. These capabilities will need to be merged with the capabilities of traditional commercial knowledge-editors to create a knowledge asset management system that supports end-to-end knowledge engineering.

25 Partners-Wide Knowledge Management Model
KNOWLEDGE ASSET MANAGEMENT Signature Initiatives and Sub-Committees set Enterprise-wide Strategy, Clinical Standards and Performance Measures Performance Feedback to Leaders, SMEs, Committees, and End-users Subject Matter Expert (SME) Panels Advise on Entity, Venue, Role, Specialty, Primary Care, Disease Management, and Safety related requirements for application function and knowledge bases Data Warehouse Decision Support Design Teams direct the design of cross-functional knowledge to be encoded Partners Genetics Computing Platform PERFORMANCE and OUTCOMES (KNOWLEDGE DISCOVERY) Applications for Virtual Collaborative Knowledge Authoring and Maintenance This is a schematic drawing of a knowledge engineering and management factory for Partners Healthcare. Key Features are: Signature Initiatives determine Partners-Wide clinical standards of care and performance measure benchmarks (ie. HgA1C maintained between 7 & 8) Design Teams representing Inpatient Care, Ambulatory, and Medication Decision Support work on the design of Partners-wide knowledge bases (ie if HgA1C>8, increase insulin). Focus on the 20% of Content that drives 80% of business performance. Remaining content is authored by end-users. Subject Matter Expert Panels (SMEs) representing Entity, Venue, Specialty Care, Primary Care, Safety, and Disease Management provide advice on optimal approach from a function and knowledge perspective. (ie Diabetes Council advises on HgA1C rules). Also direct subspecialization of LMR. Design Teams direct Common Services knowledge editors to build knowledge base. (ie rule is updated in LMR reminders) Clinical Portfolio Assembly supports Inter-application coordination to address legacy application challenges (ie EPO guideline must be re-encoded 7+ times) Knowledge is served up through workflow applications in the form of data, order sets, rules, alerts, dashboards, documentation templates, and the like Activity data is collected by the Quality Data Warehouse for feedback and reporting back to end-users (dashboards), SI leaders, design teams. Knowledge Repository Clinical Workflow Applications and Services Knowledge Building Blocks Information Model Common Services Knowledge Editors DECISION SUPPORT (APPLIED KNOWLEDGE)

26 Knowledge Asset Management:
Translating Goals into a Knowledge Repository Taxonomy Care Applications (Results, Observations, Orders, Tasks/Proc/Mar,Messaging, CDS, Measurement) and Knowledge Bases Goal Framework: Safety, Quality, Efficiency, Research Data/Knowledge Seeking Assessment Dx/Rx Decision Making Order Fulfillment, Communication and Coordination Billing Reporting Transfer/ Handoff CORE CARE PROCESS AUTOMATION TAXONOMY Medical Management, Research, and Reporting Clinical Knowledge for Personalized Medicine Taxonomy Reference Information Model Role and Venue Domain Taxonomy When a healthcare organization begins an EHR implementation, they must inventory and organize their enterprise-wide knowledge assets into a human-readable knowledge repository. Such an inventory might include existing paper order forms, documentation forms, policies, procedures, as well as any data on design of legacy systems. They must then define a taxonomy for their knowledge repository informed by their core processes, knowledge domains, venues, and roles. These become the basis for defining the requisite care applications, knowledge bases, and reference information model to support the goals. It is important to emphasize that knowledge-based sources constitute a resource not only for physician order entry, but for all processes (documentation, task completion, medication administration, etc) and for any role (physician, nurse, pharmacist) in the healthcare delivery system. Requirements Care Applications and Knowledge Bases

27 MEDICATION USE PROCESS: Acetaminophen in a 2.5 Kg Premature Infant
The medication use process illustrates the importance of a multi-dimensional approach to clinical systems design. It requires the integration of differing information needs and workflows to achieve a safe order fulfillment process. In the following acetaminophen order for a 2.5 Kg premature infant, notice how the information needs differ by role.

28 Sample High-level Example Taxonomy for Knowledge Assets

29 Center for Clinical Knowledge Engineering
Welcome to the National Knowledge Engineering Repository Go Content search HEDIS Advanced Search Filters (press Ctrl to select more than one): Clinical Discipline: Surgical Informatics Mode Cardiothoracic Surgery Interactive Rules Search File Hierarchy Interventional Cardiology Orthopedics Etc. Surveillance and Notifications Documentation Templates Etc. Knowledge Asset Management Toolkit Link to references, survey instruments, diagrams, descriptions, process flow diagrams, etc on Partners and VA approaches to asset management Clinical Discipline: Non-Surgical Age Cardiovascular Adult Submit Content to Editor Endocrinology Gastroenterology Etc. Pediatrics Neonate Etc. About Us Clinical Discipline: Safety Role Nosocomial Infection Control Nurse Medication Safety Decubitus Ulcer Prevention Etc. Physician Case Manager Etc. Clinical Discipline: Disease Management Venue Diabetes Mellitus CCU Congestive Heart Failure Multiple Sclerosis Etc. Ambulatory Care Emergency Department Etc.

30 Knowledge Specifications
For Encoded Knowledge Vs Meta-knowledge about The knowledge

31 Future State KM Model Workflow Applications Portal
Collaborative Knowledge Authoring Tools Workflow Applications Meta-Knowledge Repository Knowledge-based Services Knowledge Repositories Information Model

32 Barriers to Success at the Intersection of Clinical Informatics and KM
Leadership inadequately committed Products inadequate to support processes Business case intangible Fear of exposure (technology increases transparency) Few roadmaps to success are proven in the healthcare arena

33 Market Drivers will Propel Progress
Aging population: computer literate and population growth will outstrip service capacity, informatics must support self-management Business community will aid transition from commodity to value based purchasing by employers and consumers, they know that the current inflation rate of the commodity is untenable Leapfrog and Government are beginning to purchase quality Genomics: personalized medicine will require technologies for personalization, these same technologies will enable more user-friendly safety solutions

34 Where are we?

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