rrocha - clinical decision support and knowledge ... · informaonexplosion?’ 0 100,000 200,000...

Clinical Decision Support and Knowledge Management

Roberto A. Rocha, MD, PhD Sr. Corporate Manager

Clinical Knowledge Management and Decision Support, Clinical Informa@cs Research and Development, Partners Healthcare System

Lecturer in Medicine Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School

Biomedical Informa/cs Course Marine Biological Laboratory in Woods Hole, MA

-‐ June, 2010 -‐

Objec:ves

•  Outline the main factors that jus@fy the need for computerized Clinical Decision Support (CDS) and Clinical Knowledge Management (CKM)

•  Describe the history and benefits of CDS systems

•  Describe the main components of a CDS system

•  Describe the different modali@es of CDS and their associated requirements –  Provide examples of CDS modali@es integrated with EHRs

•  Describe the CKM processes required to create, deploy, disseminate, and maintain CDS interven@ons

•  Describe the main components of a CKM system –  Provide examples of CKM tools

•  Outline challenges and opportuni@es related to CDS & CKM

Expected Results

•  As a result of par@cipa@ng in this ac@vity, learners will be able to: –  Explain uses and benefits of Clinical Decision Support (CDS) and Clinical Knowledge Management (CKM)

– Describe the main components of a CDS system

– Describe the different modali@es of CDS – Describe CKM processes and associated tools – Outline important challenges and opportuni@es related to CDS and CKM

Outline

1.   Background –  Mo@va@on –  History & Benefits

2.   Clinical Decision Support (CDS) –  CDS modali@es (examples) and standards

–  Components of a CDS system

3.   Clinical Knowledge Management (CKM) –  Mo@va@on for CKM

–  CKM Program: processes, people, and infrastructure

4.   Challenges and opportuni:es

Background

Mo@va@on

History of CDS Demonstrated benefits

Informa:on needs

•  Informa@on needs –  47 physicians (self-‐reported)

  269 ques@ons raised during 409 visits »  2 ques@ons for every 3 pa@ents seen

  Answers not pursued 70% of the @me

•  Frequent barriers –  Pursued answers only 55%

  Doubt that an answer existed – lack of usable informa@on

–  Sources: human (informa@on consulta@on) and/or textbook (63%), electronic resource (16%)   Unable to find answer in 28%

Covell DG, Uman GC, Manning PR. Informa@on needs in office prac@ce: are they being met? Ann

Intern Med. 1985 Oct;103(4):596-‐9.

Ely JW, Osheroff JA, Chambliss ML, Ebell MH, Rosenbaum ME. Answering physicians' clinical ques@ons: obstacles and poten@al solu@ons. J Am Med Inform Assoc. 2005 Mar-‐Apr;12(2):217-‐24.

Informa:on explosion?

0

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

MEDLINE®/PubMed® Baseline Yearly Cita:on Count Totals

Sta@s@cal Reports on MEDLINE®/PubMed® Baseline Data

Over 18 million cita@ons total 50% since 1991

Survival of Systema:c Reviews

Shojania KG, Sampson M, Ansari MT, Ji J, Douceoe S, Moher D. How quickly do systema@c reviews go out of date? A survival

analysis. Ann Intern Med. 2007 Aug 21;147(4):224-‐33.

Cri:cal diges:on of informa:on

•  “… the expanding of informa@on into dimensions greater than can be traversed rapidly and efficiently is raising needs for synop@c and cri@cal diges@on of needed informa@on. Such diges@on and synopsis is costly in intellectual effort that is not well rewarded academically or commercially.”

Huth EJ. The informa@on explosion. Bull N Y Acad Med. 1989 Jul-‐Aug;65(6):647-‐61.

Defini:ons (1)

• Medical/Clinical Decision Support System –  “a computer program that provides reminders, advice or interpreta@on specific to a given pa@ent at a par@cular @me”

–  “computer systems that provide the correct amount of relevant knowledge at the appropriate @me and context, ul@mately contribu@ng to improved clinical care and outcomes.”

Wyao JC. Decision Support Systems. J R Soc Med 2000; 93:629-‐33

Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. A roadmap for na@onal ac@on on clinical decision support. J Am Med Inform Assoc. 2007 Mar-‐Apr;14(2):141-‐5.

Defini:ons (2)

•  Medical Decision Analysis (decision-‐making) –  Assist with the uncertain nature of medical informa@on –  Fundamental concepts include probability, u@lity, and expected value decision making

–  Make clinicians beoer decision makers –  Pa@ents need to be directly involved (u@lity/value)

•  Typical ques@ons –  How should I interpret new diagnos@c informa@on?

–  How do I select the appropriate diagnos@c test? –  How do I choose among several risky treatments?

H.C. Sox, M.A. Blao, M.C. Higgins, K.I. Marton. Medical Decision Making. Buoerworth-‐Heinemann, Stoneham, MA, 1988.

Evolu:on: 1960s and 1970s

•  1960s –  Early explora@ons using digital computers, probabilis@c (sta@s@cal)

models (Bayes’ theorem), good diagnos@c accuracy, need for reliable and contextualized data sources;

–  Medical decision making methods, u@lity theory

•  1970s –  Symbolic and heuris@c reasoning methods, medical Ar@ficial

Intelligence (AI), need for knowledge engineering, expert systems

–  Inadequacy of expert judgment, enhance data collec@on (large databases), superior diagnos@c accuracy when compared to experts

–  Complexity of decision-‐analy@c models, teaching principles to physicians and health workers

Shortliffe EH. Medical Knowledge and Decision Making. Methods in InformaEon in Medicine. 1988 27, 209-‐218.

Evolu:on: 1980s and 1990s

•  1980s –  Combina@on of symbolic and probabilis@c models, theory of fuzzy

sets, importance of explana@ons, cri@quing instead of diagnosing (decision support)

–  Personal computers, rapid dissemina@on and faster processing, graphical user interfaces, knowledge authoring tools, knowledge acquisi@on, importance of proper evalua@on

•  1990s –  Demise of stand-‐along consulta@on model (expert system), integra@on

with data management systems (clinical informa@on systems),

–  Sosware cer@fica@on and legal liability, ownership and maintenance of knowledge bases, standard formats for knowledge encoding and exchange, clinical and IT governance (“poli@cal challenges”)

Shortliffe EH. Medical Knowledge and Decision Making. Methods in InformaEon in Medicine. 1988 27, 209-‐218.

Evolu:on of CDS

•  1960 -‐ 1985 –  Enthusiasm for CDS (and AI), research and new ideas

•  1985 -‐ 1998 –  Successful CDS implementa@ons, evalua@ons showing benefit, but limited dissemina@on

•  1998 -‐ –  Na@onal agendas (call to ac@on), safety and quality (errors, ADEs), roll out of

Electronic Health Records (EHRs), Computer-‐base Provider Order Entry (CPOE), Electronic Prescribing (eRx), and Personal Health Records (PHRs)

•  2005 -‐ –  Recognizing knowledge management as enabling CDS at scale

  Federal Health IT Strategic Plan (ONC): 2008-‐2012   Rector, 1986 (“Defaults, excepEons and ambiguity in a medical knowledge representaEon

system” Med Inform (Lond). Oct-‐Dec;11(4):295-‐306.)

•  2010 -‐ –  Government incen@ves to implement EHR systems with CDS

Shortliffe EH. Medical Knowledge and Decision Making. Methods in

InformaEon in Medicine. 1988 27, 209-‐218.

Regenstrief Medical Record System (RMRS)

Clement McDonald, Marc Overhage, William Tierney, Paul Biondich, et al.

Greenes RA, editor. Clinical decision support: the road ahead. Academic Press, 2006. Chapter 4: Regenstrief Medical Informa@cs

RMRS: Timeline (1)

•  1972: management of outpa@ent diabetes care

•  1976: results of the first randomized controlled studies –  300-‐400 rules; posi@ve effect of reminders (as paper reports); no “training

effect” – “non-‐perfectability of man” (NEJM)

•  Late 1970s: evolu@on to a hospital system; rela@onal database; language for rule (“CARE”)

•  1980: larger study (reminders + literature) –  410 protocols; confirming results; no interest in the suppor@ng literature (no

@me, already knew)

•  1984: results of a 2-‐year RCT – 130 providers, 14,000 pa@ents, +50,000 visits; +140,000 paper reminders generated –  1,490 rules; confirming results; greatest effect on preven@ve interven@ons;

only 40-‐50% responded to reminders (did not see, inappropriate reminders due largely to missing data)


RMRS: Timeline (2)

•  Mid 1980s: “Medical Gopher” – order entry; outpa@ent and inpa@ent –  CDS Lab test ordering: previous results, test cost, likelihood of posi@ve result –  CDS Medica@on ordering: charge for med, allergy warnings, drug-‐drug, and

drug-‐diagnosis interac@ons; corollary orders

•  1990s: Extensions to handle prac@ce guidelines –  Problems with guidelines: vague terminology, omit branch points, data is not

available, no considera@on for concurrent therapy and comorbidi@es

–  Physicians ignored most reminders about chronic disease management: intrusive, cost control emphasis, logic is too complex for discrete rules

•  2000s: Indianapolis Network for Pa@ent Care (INPC) –  Leveraging commitment to standards (HL7 and LOINC)

–  Ongoing studies at a larger scale, involving mul@ple ins@tu@ons


Brigham Integrated Compu:ng System (BICS)

David Bates, Jonathan Teich, Gilad Kuperman, John Glaser, et al.

Greenes RA, editor. Clinical decision support: the road ahead. Academic Press, 2006. Chapter 5: CDS at Brigham and Women’s Hospital

BICS: Timeline

•  1984: decision to develop clinical system; emphasis on decision support; complete hospital informa@on system (clinical, financial, and administra@ve func@ons) –  derived from systems developed at Beth Israel Hospital (Slack & Bleich

– MIIS system); implemented in MUMPS; independent since 1988

•  1989: outpa@ent electronic medical record system (Miniamb); free text notes (dicta@on)

•  1993: computerized physician order entry (CPOE – Glaser & Teich); embedded with real-‐@me decision support; front-‐end

•  1997: longitudinal medical record (LMR); now Web-‐based

Teich JM, Glaser JP et al. The Brigham integrated compu@ng system (BICS): advanced clinical systems in an academic hospital environment. Int J Med Inform. 1999 Jun;54(3):197-‐208.


BICS: Studies

•  1995: Adverse drug events (Leape et al. & Bates et al.) –  common (6.5/100 admissions); osen preventable (28%); osen serious (43%)

•  1997: Costs of ADEs (Bates et al.) –  $2,600 (2.2 days) per ADE or $5.6M/year at BWH; $4,700 (4.6 days) per

preventable ADE or $2.8M/year at BWH

•  ADEs major mo@va@on for CPOE –  Wrong dose, wrong choice, known allergy, wrong frequency, drug-‐drug

interac@on, etc.

–  CPOE reduced serious medica@on error rate by 55% (Bates et al. 1998) –  Overall medica@on error rate fell 83% with CPOE (Bates et al. 1999)

•  Display of lab test charges, redundant lab tests, corollary orders, radiology ordering, follow-‐up of abnormal results, among others

•  Many opportuni@es remain: what best to deliver and how to deliver it


Health Evalua:on through Logical Processing (HELP)

Homer Warner, T. Allan Pryor, Reed Gardner, R. Scoo Evans,

Peter Haug, et al.

Greenes RA, editor. Clinical decision support: the road ahead. Academic Press, 2006. Chapter 6: CDS at LDS Hospital

HELP: Timeline (1)

•  1966-‐72: design and implementa@on of the HELP system; designed from the outset as a clinical system –  clinical data stored in a common database; most data stored in a coded

format

–  knowledge base organized as “medical logic modules” (MLMs); ranging simple rules to complex logic using data from mul@ple sources

–  ability to data-‐ and @me-‐drive the knowledge base; all data is inspected by the decision engine

•  1976: pharmacy applica@on; adverse drug events: drug-‐drug, allergy, laboratory, disease, dose, diet, and interval; alerts displayed to pharmacists (not a CPOE) –  MDs changed therapy for 77% of the alerts (Hulse et al.)

•  1975: interpreta@on of blood gas results

•  1980s: bedside char@ng by nurses; installa@on of bedside computers


HELP: Timeline (2)

•  1985: infec@ous disease monitoring; hospital-‐acquired infec@ons, reportable diseases, an@bio@c-‐resistant pathogens, infec@ons in sterile body sites; hospital-‐wide surveillance

•  1990: therapeu@c an@bio@c monitor; appropriate an@bio@c based on culture and suscep@bility results

•  1989: preopera@ve an@bio@cs 2-‐hours prior to incision; surgery schedule –  improvement from 40% to 96%; decreased wound infec@on (Classen et al)

•  1989: iden@fica@on of high-‐risk for hospital-‐acquired infec@ons (diagnosis) •  1990: computerized laboratory aler@ng system; life-‐threatening

condi@ons; flashing yellow lights; paging nurses –  67% @me unaware of cri@cal result (Tate el al.)

•  1990: blood ordering applica@on


HELP: Timeline (3)

•  1990-‐1: ven@lator protocols; treatment of acute respiratory distress syndrome (ARDS); distributed to other hospitals –  survival with computer protocol was 67%, compared to 33% (East et al.)

•  1991: adverse drug event monitor; sen@nel events (lab results, serum drug levels, treatment of ADEs, physiologic signs) –  increased annual number of ADEs from 10 to over 500 (Classen & Evans et al.)

•  1992: dura@on of an@bio@c therapy; prophylac@c an@bio@cs longer that 48 hours; significant cost savings

•  1994: an@-‐infec@ve agent assistance; logis@c regression models using accumulated data; suggest proper an@bio@c prior to culture results –  reduced inappropriate an@-‐infec@ve use, excessive dosages, number of ADEs

caused by an@-‐infec@ve, reduced cost (Evans et al.)


CDS: addi:onal evidence

•  Systema@c review of 70 studies (RCTs), up to 2003 –  Evalua@ng the ability of CDS to improve clinical prac@ce –  Focus on 15 CDS features (derived from literature)

•  CDS improved prac@ce in 68% of trials –  Key features (independent predictors)

  CDS as part of clinician workflow   Recommenda@ons rather than just assessments

  CDS at the @me and loca@on of decision making

  CDS triggered by computerized data analysis

Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical prac@ce using clinical decision support systems: a systema@c review of trials to iden@fy features cri@cal to success. BMJ. 2005;330(7494):765

CDS: value & impact

•  Most profound impact of ambulatory CPOE arises with sophis@cated CDS

•  Advanced CPOE systems cost 5 @mes as much as basic CPOE, but were projected to generate 12 @mes greater financial return

•  Model projected reduc@on of more than 2 million adverse drug events (ADEs) annually with na@onwide implementa@on of ambulatory CPOE

•  Annual savings of approximately $44 billion from reduced medica@on, radiology, laboratory, and ADE-‐related expenses

Johnston D, Pan E, Middleton B, Walker J, Bates D. The Value of Computerized Provider Order Entry in Ambulatory SeSngs. Center for Informa@on Technology Leadership (CITL), 2003.

Clinical Decision Support (CDS)

CDS modali@es

Examples: content + process

CDS: modali:es

1.   Reference knowledge selec:on and retrieval –  e.g., infobuoons, crawlers

2.   Informa:on aggrega:on and presenta:on –  e.g., summaries, reports, dashboards

3.   Data entry assistance –  e.g., forcing func@ons, calcula@ons, evidence-‐based templates for

ordering and documenta@on

4.   Event monitors –  e.g., alerts, reminders, alarms

5.   Care workflow assistance –  e.g., protocols, care pathways, prac@ce guidelines

6.   Descrip:ve or predic:ve modeling –  e.g., diagnosis, prognosis, treatment planning, treatment outcomes

Knowledge Lifecycle

Generate

Acquire

Represent Deploy

Maintain

CDS Modali:es: example 1

Health Maintenance Reminders

Acquire

Reminders: overview

•  Extensive literature about reminders – Mul@ple studies indica@ng posi@ve results

•  Specific example: – Method that facilitates the authoring, discussion, review, and approval of reminders by prac@cing clinicians (expert panels)  Promotes ‘fingerprin@ng’ and collabora@on

 Virtual sessions are recorded (retrievable)

Reminders

Structure of rules

IF {Pa@ent in risk group} AND

{Pa@ent has triggering condi:on} AND

{NOT previously suppressed by user} AND {NOT suppressed by another reminder} AND {Ac:ve at specific prac@ce}

THEN {Reminder message and/or ac@ons}

Risk Group

State

Display

Trigger

Regier R, Gurjar R, Rocha RA. A clinical rule editor in an electronic medical record setting: development, design, and implementation. AMIA Annu Symp Proc. 2009 Nov 14;2009:537-41.

Narra:ve specifica:on

Risk Group

Triggers

Display

New & exis@ng reminders

Focused ques@ons with hyperlinks to suppor@ng evidence

Opinions and opportuni@es for

discussion

Logic of each Reminder is made available through the KM Portal

Example 1: Reminders

•  Emphasis on acquisi:on & review – Virtual collabora@on spaces – Overcome tradi@onal knowledge acquisi@on and elucida@on boolenecks

•  Stakeholder involvement during different lifecycle phases effec@ve adop@on and use – Direct involvement of domain experts (panels)

•  Knowledge content accessible and maintained with a detailed audit trail – Access to knowledge assets using open portal


Problem List Infobudons

Represent

Infobudons: overview

•  Extensive literature about infobuoons –  Successfully balloted HL7 dras standard

•  Specific example: – Method that facilitates the retrieval and naviga@on of common prac@ce guidelines by physicians at the point of care   “Infobuoons” linked to problems in an EHR

  Each infobuoon displays a list of common ques@ons that can be answered by the guideline

Poon SK, Rocha RA, De Fiol G. Rapid Answer Retrieval from Clinical Practice Guidelines at the Point of Care. 19th IEEE International Symposium on Computer-Based Medical Systems, 2006. pages 143-150

Problem List Infobuoons

‘Ques@on-‐driven’ Infobuoons


Infobudons: knowledge model


Infobudons: requirements

•  EHR with coded Problem List   Problem List concepts: terminology or classifica@on

  Problem List records: structured observa@ons

• Models – Ques@ons (previous slide)

 Ques@ons, ranking, classes, answer sources – Guideline metadata

  Indexed with same codes used by EHR Problem List

– Guideline ‘tagging’   Tagged with same codes used by Ques@ons

Example 2: Infobudons

•  Emphasis on Modeling & Representa@on –  Enhanced informa@on retrieval based on a ques@on-‐answer paradigm

–  Avoid knowledge modeling and data availability ‘roadblocks’ related to ac@onable/executable CDS

•  Leverage widespread availability of authorita@ve reference content (guidelines) –  Maximize the usefulness of these documents

•  Provides a ‘passive’ alterna@ve to CDS, without interfering with clinician workflow

•  Rela@vely simple to implement (infobuVon manager) –  Cumbersome ‘tagging’ of reference documents


CPOE Inpa@ent Order sets

Maintain

Order Sets: overview

•  Extensive literature about order sets –  Cri@cal for CPOE success, but cumbersome to create and maintain

•  Specific example: – Method that enables con@nuous refinement of order sets using u@liza@on tracking data   Interac@ons of prescribers with order sets are recorded  U@liza@on data is analyzed and presented back to order set authors

Hulse NC, Del Fiol G, Bradshaw RL, Roemer LK, Rocha RA. Towards an on-demand peer feedback system for a clinical knowledge base: a case study with order sets. J Biomed Inform. 2008 Feb;41(1):152-64.

Run-‐@me changes to order sets

Check or Uncheck

Select values

Add new orders

Enter values



Report u@liza@on using authoring tool and/or CPOE UI

Items from a pull-‐down menu (sequence)

Mutually exclusive orders (pre-‐selec@on)


Comprehensive sugges@ons to

improve order set

Example 3: Order sets

•  Emphasis on maintenance (CQI) –  Detailed u@liza@on tracking providing aggregated end-‐user feedback (constantly updated)

–  Refinements based on how knowledge is used

•  Reduce the cost and increase efficiency of knowledge maintenance –  Enable (passive) end-‐user involvement

–  Avoid poten@al liability due to lack of coverage or update •  Iden@fy opportuni@es for educa@on (interven@on)

–  Monitor quality, safety, and opera@ng business drivers –  S@ll requires oversight provided by expert panels


Disease Management: Care Pathway

Deploy

Care Pathway: overview

•  Extensive literature about guidelines/pathways –  Cri@cal for disease management, but very difficult to computerize (research prototypes)

•  Specific example: – Method that implements EHR “Smart Forms” to integrate mul@ple modali@es of CDS  Data visualiza@on, documenta@on, and interpreta@on  Ordering guidance and tracking

Schnipper JL, Linder JA, Palchuk MB, Einbinder JS, Li Q, Postilnik A, Middleton B. "Smart Forms" in an Electronic Medical Record: documentation-based clinical decision support to improve disease management. J Am Med Inform Assoc. 2008 Jul-Aug;15(4):513-23.

View: Data Display

Assessment, Orders, and Plan

Assessment and recommenda@ons generated from rules engine

Documenta:on

•  Lipids •  An@-‐platelet therapy •  Blood pressure •  Glucose control • Microalbuminuria •  Immuniza@ons •  Smoking • Weight •  Eye and foot examina@ons

Smart Forms (1)

Smart Forms (2)

Medica:on Orders

Lab Orders

Referrals

Handouts/Educa:on

Example 4: CDS-‐enabled workflow

•  Detailed structured and coded data •  Intui@ve authoring and maintenance of protocols and workflows (complex knowledge)

•  Able to merge overlapping protocols and workflows –  mul@ple diseases, various user roles, transi@ons of care, …

•  Able to rollback triggered ac@ons and revise context, including ‘pa@ent’ and ‘protocol’ states

•  Proper handling of errors and uncertainty affec@ng data and workflow defini@on –  Detect unexpected condi@ons and ‘fail gracefully’

•  Performance (online/real-‐@me execu@on)

CDS: implementa:on strategies

CDS modality Implementa:on Strategies 1. Reference

knowledge selec@on and retrieval

Reference 2. Informa@on

aggrega@on and presenta@on Ac:onable 3. Data entry

assistance

4. Event monitors

Executable 5. Care workflow

assistance

6. Descrip@ve or predic@ve modeling

Cost

Availability

Complexity

Maintaina

bility

Clinical Decision Support (CDS)

Components of a CDS system

CDS standards

Clinical Data!

•  Data are the ‘diamonds’ of medical informa@cs –  Computer systems come and go –  Data is forever

  Or at least it should be –  The data you have constrains what you can do with decision support   Be very conscious of these limits

–  Do not assume you can capture data that you don’t have

Slide from Clem McDonald, MD

Haug PJ, Rocha BH, Evans RS. Decision support in medicine: lessons from the HELP system. Int J Med

Inform. 2003 Mar;69(2-‐3):273-‐84.

Basic model

Knowledge Base

Inference Engine

Interface

User

Pa:ent database

Editor

CDS Rules Manager (‘Event Monitor’)

Rocha R.A,Bradshaw R.L., Hulse N.C., and Rocha B.H.S.C. The clinical knowledge management infrastructure of Intermountain Healthcare. In: Clinical Decision Support: The road ahead, RA. Greenes (ed.). Academic Press, Boston, 2007, pp. 469–502.

CDS: standard representa:on formats

CDS modality Standard Formats 1. Reference

knowledge selec@on and retrieval

Guideline Elements Model (GEM): ASTM Context-‐aware Info Retrieval (Infobuoon): HL7 (dras)

2. Informa@on aggrega@on and

presenta@on Clinical Document Architecture (CDA): HL7 Quality Measures (eMeasure): HL7 (dras)

Order sets: HL7 (in progress) 3. Data entry assistance

4. Event monitors Arden Syntax for Medical Logic Systems: HL7 GELLO -‐ A Common Expression Language: HL7

Decision Support Services: HL7 (dras) Virtual Medical Record: HL7 (in progress)

5. Care workflow assistance

6. Descrip@ve or predic@ve modeling

Clinical Knowledge Management (CKM)

Mo@va@on for CKM

CKM Program characteris@cs

Implica:ons of CDS strategy

Development of CDS content

CDS content available for EHR use

Standard CDS content formats

Implement EHRs with

CDS capabili@es

Knowledge Management

Acquisi:on Representa:on Dissemina:on Deployment

Desirable CDS Features CKM

  Knowledge is based on the best evidence available

  Knowledge covers problem in detail – allow sophis@cated problem solving, advice, explana@ons

  Knowledge can be readily updated by a clinician without unexpected effects

  Knowledge base provides links to related local and Internet material – lifelong learning

•  Most pa@ent data drawn from exis@ng sources – ease of use

  System (knowledge) performance is validated against suitable gold standard

  Demonstrated prac@ce or outcomes improvements in rigorous study

  Clinician always in control –  Receive advice, browse the

knowledge base, get help and explana@ons, try out ‘what-‐if’ scenarios, and obtain a cri@que of the pa@ent management plan

•  System is easy to access – for example via the Web

Modified from Wyatt JC. Decision Support Systems. J R Soc Med 2000;93:629-633

CKM: mo:va:on

•  Quan@ty of knowledge (explosion) –  Evolu@on towards stra@fied/personalized clinical prac@ce –  Complex decision making process demanding computerized support

•  Distributed care delivery processes (fragmented) –  Extensive knowledge is needed beyond organiza@onal boundaries –  Learning opportuni@es leading to op@mal care and stewardship

•  Global trends towards knowledge socializa@on –  Consumers (pa@ent) constantly seeking knowledge (empowerment)

–  Shared responsibility only possible with proper understanding •  Knowledge content maintainability (long-‐term)

–  Content diversity and quan@ty makes tradi@onal cura@on unrealis@c

Engineering vs. Deployment

Knowledge Engineering

Knowledge Deployment

Crea:on/Revision

Review/Approval

Configura:on/Tes:ng

Deployment/Valida:on

Evalua:on/Monitoring

CDS consumers

CDS developers

CKM: deployment models

Import

Configure

EHR with CDS

Update

Integrate

Configure EHR with CDS

Knowledge Content only

Knowledge Services + Content

Both require content localiza:on (configura:on)

CKM: concurrent lifecycles Generate

Acquire

Represent Deploy

Maintain

Rules

Generate

Acquire

Represent Deploy

Maintain

Order Sets

Generate

Acquire

Represent Deploy

Maintain

Dic:onaries

Generate

Acquire

Represent Deploy

Maintain

Protocols

Generate

Acquire

Represent Deploy

Maintain

Workflows Generate

Acquire

Represent Deploy

Maintain

Reports

Generate

Acquire

Represent Deploy

Maintain

Templates

Import

Configure

EHR with CDS

Update

Integrate

Configure EHR with CDS Dic:onaries Rules

Import

Configure

EHR with CDS

Update Monographs

Integrate

Configure EHR with CDS

Guidelines

Import

Configure

EHR with CDS

Update Templates

Strategic Goals @ Partners

•  Enable all knowledge content to be accessible, updatable, and maintained with an audit trail

•  Reduce the cost and increase efficiency of both design and implementa@on maintenance

•  Enable stakeholder involvement in the design process to support effec@ve adop@on and use

•  Ensure alignment with quality, safety, and opera@ng business drivers (HPM, Joint Commission, etc.)

•  Avoid poten@al liability of making incorrect or incomplete recommenda@ons due to lack of coverage or update

CKM: program components

Personnel

Domain Experts

Knowledge Engineers

Informa:on Modelers

Terminology Engineers

Framework

Lifecycle Processes

Governance Processes

Sonware Plaoorm

Assets Knowledge Repositories

Logical Data Templates

Concept Dic:onaries

Clinical Content Commidee Priori@zes and Sponsors Opera@onal Stewardship of Content

Safety

CAD/CHF, Diabetes, Heme-‐Onc, Asthma, ID/HIV, Nephrology,

Psych

Disease Areas

Adult, Geriatrics, Pediatrics,

Women’s Health

Primary Care

PCHI P&T

Pharmacotherapy

Quality Disease Management Trend Management

SME Groups

Medica:on Knowledge Commidee

BWH Precipio

Imaging Studies

MGH ROE

Knowledge Repositories

Knowledge Engineers

Tools

CKM: staffing challenges

•  Recrui@ng is a lengthy process –  Technology professionals with clinical training (exposure) –  Partnerships with local academic programs

•  Training is quite intense and takes @me (6-‐18 months) –  Processes, domains, mul@tude of systems, KM Framework

–  Informa@cs Principles: external courses and internal mentoring

•  Reten@on can be problema@c –  Uncommon skills (differen@a@on)

–  Compe@@on with similar organiza@ons (and EHR vendors)

•  Crea@on of specific job families (interrelated) –  Knowledge and Terminology Engineers

–  Clinical Subject Maoer Experts

–  Informa@cians

CKM: sonware plaoorm requirements

•  Enable consistent lifecycle and configura@on •  Proper handling of dependencies

–  Preserva@on of reference sources (with control) •  Enable collabora@ve authoring and localiza@on

–  Promote modularity and reuse

•  Extensible and “intelligent” –  Maintain assets with “meta-‐knowledge” –  Leveraging seman@c technologies

•  Built-‐in u@liza@on monitoring •  Built-‐in analy@cal capabili@es (CQI/Discovery)

CKM: sonware infrastructure

View Analyze

Concept Dic:onaries

Logical Data Templates

Knowledge Repositories

Lifecycle Management

Collabora/on Management

Edit Publish

Store Archive Import Map

Challenges & Opportuni:es

CDS and CKM

  User is right; data gaps -‐ override alerts and reminders

  Workflow

•  CDS can become overwhelming

  Con@nuous user input, collabora@on, and feedback

  Prospec@ve evalua:on studies

o  Importance of standards: interoperability

o  Speed is everything

  An@cipate user needs   Workflow o  Details are important

o  Stopping vs. changing direc@on

  Simple interven:ons

  Data is expensive   Prospec@ve evalua:on

studies   Knowledge must be

managed and maintained

  Data is cri@cal   Knowledge is a team

effort   Workflow

o  Proper tes:ng before

  Simple interven:ons

•  Evidence-‐based and matching local prac@ces

  Knowledge has to be periodically reviewed

•  Ease of use   Evalua:on is difficult

  Cost-‐effec:ve to implement and maintain

RMRS BICS HELP

CDS: important lessons compared

Greenes RA, editor. Clinical decision support: the road ahead. Academic Press, 2006. Chapters 4 (RMRS), 5 (BICS), and 6 (HELP)

Healthcare IT: Meaningful Use

2009 2011 2013 2015

HIT-Enabled Health Reform

Mea

ning

ful U

se C

riter

ia

HITECH Policies 2011 Meaningful

Use Criteria (Capture/share

data) 2013 Meaningful

Use Criteria (Advanced care processes with

decision support)

2015 Meaningful Use Criteria (Improved Outcomes)

Diagram from: Tang & Mostashari (chairs) et al., Meaningful Use Workgroup Presentation. HIT Policy Committee, June 16, 2009.

Quality improvement with EHR use

•  Study looked at the quality of care delivered in ambulatory prac@ces and dura@on of EHR use: –  Survey of physicians’ adop@on/use of EHR (Massachuseos)

  137 physicians using an EHR; average of 4.8 years –  Claims data reflec@ng quality of care as indicated by widely used quality measures   Healthcare Effec@veness Data & Informa@on Set (HEDIS): Breast cancer screening, HbA1c tes@ng, LDL screening, Well-‐child visits, …

•  No associa@on between dura@on of using an EHR and performance with respect to quality of care –  “Intensifying the use of key EHR features, such as clinical decision support, may be needed to realize quality improvement from EHRs”

Zhou et al. The relationship between Electronic Health Record Use and Quality of Care. J Am Med Inform Assoc. 2009;16:457-64.

CPOE with advanced CDS

Metzger J, Welebob E, Bates DW, Lipsitz S, Classen DC. Mixed results in the safety performance of computerized physician order entry. Health Aff (Millwood). 2010 Apr;29(4):655-63.

62 hospitals volunteered to assess

CDS applied to medica@on ordering – simulated test orders likely to cause serious harm entered in local CPOE (8 vendors)

CKM: implementa:on challenges (1)

•  Clinical governance and stewardship is poorly defined –  Clinical strategy does not drive KM ac@vi@es nor is informed by KM principles

–  Opportuni@es for strategic interven@ons not proac@vely iden@fied or planned •  Projects and resources defined in compe@@on with other ac@vi@es

–  IT efforts not aligned with clinical quality and safety ini@a@ves –  Inadequate defini@on and priori@za@on of KM strategic ac@vi@es –  Cost of not having ‘knowledge’ is not frequently considered

•  Deployment of knowledge assets is underes@mated and inconsistent –  Domain experts (clinicians) frequently unavailable; limited commitment

–  Processes for configuring and ve|ng knowledge are not explicitly defined

–  Lack of a systemic view promotes overlapping efforts (varia@on)

Modified from Tonya Hongsermeier, MD, MBA – Partners/CIRD

CKM: implementa:on challenges (2)

•  Sosware tools to create and maintain knowledge are inadequate –  Knowledge once deployed for use is not easily accessible (‘locked’) –  Tools frequently ignore content dependencies and lifecycle

requirements (subsequent updates)

•  Maintenance of knowledge assets is an aserthought –  Long-‐term commitment to content maintenance is underes@mated

–  Liability resul@ng from outdated or incorrect recommenda@ons not recognized

•  Analy@c data regarding impact on clinical processes and outcomes is generally not available

Modified from Tonya Hongsermeier, MD, MBA – Partners/CIRD

Personalized Medicine

•  Greatly expanded diagnos@c space –  1920s: 1 leukemia & 1 lymphoma

–  1940s: 3 leukemia & 2 lymphoma –  Today: 38 leukemia & 51 lymphoma (outdated?)

•  Greatly reduced (targeted) therapeu@c space –  “Blockbusters” (e.g., atorvasta@n, sildenafil) –  “Niche busters” (e.g., ima@nib – “magic bullet” to cure cancer) –  “Orphans” (e.g., imiglucerase – Gaucher’s disease)

Modified from Michael G. Kahn MD, PhD – University of Colorado

Test for gene:c differences

Knowledge Sharing

•  Clinical Decision Support Consor:um –  “Goal of the CDS Consor@um is to assess, define, demonstrate, and evaluate best prac@ces for knowledge management and clinical decision support in healthcare informa@on technology at scale – across mul@ple ambulatory care se|ngs and EHR technology pla}orms.”  hop://www.partners.org/cird/cdsc/

CDSC Portal

hdp://cdsportal.partners.org

Acknowledgements

Blackford Middleton Tonya Hongsermeier

Saverio Maviglia

Beatriz Rocha

CIRD/KM Team at Partners

Stanley Huff

David Burton

KB Team at Intermountain

Addi:onal readings

•  Book “Clinical decision support: the road ahead” –  Greenes RA, editor. Academic Press, 2006.

•  Paper “Ten commandments for effec@ve clinical decision support: making the prac@ce of evidence-‐based medicine a reality.” –  Bates DW, Kuperman GJ, Wang S, Gandhi T, Kioler A, Volk L, Spurr C, Khorasani R, Tanasijevic M,

Middleton B. J Am Med Inform Assoc. 2003 Nov-‐Dec;10(6):523-‐30. (PMID: 12925543)

•  Paper “A roadmap for na@onal ac@on on clinical decision support.” –  Osheroff JA, Teich JM, Middleton B, Steen EB, Wright A, Detmer DE. J Am Med Inform Assoc. 2006

Jul-‐Aug;13(4):369-‐71. (PMID: 17213487)

•  Paper “Just-‐in-‐@me delivery comes to knowledge management.” –  Davenport TH, Glaser J. Harv Bus Rev. 2002 Jul;80(7):107-‐11, 126. (PMID: 12140850)

•  Paper “Using commercial knowledge bases for clinical decision support: opportuni@es, hurdles, and recommenda@ons.” –  Kuperman GJ, Reichley RM, Bailey TC. J Am Med Inform Assoc. 2007 Mar-‐Apr;14(2):141-‐5. (PMID:

16622160)

•  Paper “Predic@ve data mining in clinical medicine: current issues and guidelines.” –  Bellazzi R, Zupan B. Int J Med Inform. 2008 Feb;77(2):81-‐97 (PMID: 17188928)

•  Web site “Open Clinical” (UK): hop://www.openclinical.org/

Thank you!

Roberto A. Rocha, MD, [email protected] !

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