Pediatric Informatics: Computer Applications in Child Health (Health ...

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11 Clinical Practice Guidelines: Supporting Decisions, Optimizing Care 153 and educated consumers have met with variable success 11,12 . Effective modalities include reminders 13 (when used sparingly), individual outreach (“academic detailing”), 14 interactive educational programs, interventions that focus directly on recognized barriers and combined strategies. 11.6.3 Lack of Standard Implementation Systems Although computerized systems can be effective in implementing guidelines in clinical practice, creating computer-mediated implementation systems has proven to be onerous and not uniformly successful. Challenges include: lack of explicit definitions, focus on errors of omission rather than errors of commission, not accounting for other factors (such as comorbid conditions, concurrent treatments, timing of interventions and follow-up). One suggestion has been for all guideline recommendations to be written in a simple if-then-else format with all parameters strictly defined using routinely collected clinical data. Nonetheless, this recommendation has not gained acceptance in the US. 15 Informaticians have long struggled with transforming knowledge contained in clinical practice guidelines into systems that reliably influence clinician behavior. Implementers currently create computer-based decision support systems from published guidelines by applying poorly specified, largely tacit knowledge acquisition processes to a wide variety of knowledge representations. This approach often results in inconsistent encoding of guideline knowledge and potential inaccuracy of the advice that is provided. In addition guideline recommendations regularly fail to address a topic comprehensively, leaving users to design their own solutions for situations that are not covered. 16 11.7 Approaches to Guideline Implementation 11.7.1 Knowledge Representation Recent publications comparing features of guideline implementation systems have focused on representing guideline knowledge. Projects that represent executable guideline knowledge (Asbru, EON, GLIF, GUIDE, PRODIGY, and PROforma) 17 vary in their intended scope, the way that decisions are applied and the ways that clinical goals are represented and utilized. In projects where authoring has been included (PRODIGY, ZYNX), it has been performed by dedicated multidisciplinary teams that fit their analyses of the medical literature into templates that facilitate implementation. In guideline implementation projects, knowledge acquisition can be identified as model-centric or document-centered. 18 In a model-centric approach, 19 a knowledge engineer reads and assimilates the guideline narrative, formulates an internalized conceptual model and converts the model to a fully operational (i.e., computable) representation (Table 11.1). Translation is implicit and mediated by the engineer
154 R.N. Shiffman Table 11.1 Choices for guideline knowledge representation A comparison of several prominent guideline-modeling approaches. Note that authoring of guidelines is rarely a focus and that several systems have not been implemented in health care settings Project (encoding) Authoring Implementations Arden (HL7) – a standardized format for encoding logic; encoding tools exist; there are difficulties encoding complex guideline logic17 EON (Stanford)/SAGE (SAGE Consortium); encoding with Protégé/SAGE workbench, which requires some expertise in knowledge engineering 18 GLARE (U. Piemonte Orientale, IT) 19 GASTON (Eindhoven, NL); encoding with Protege 20 GLIF (Intermed Collaboratory); encoding tools exist but inconsistent encoding has been demonstrated 21 GUIDE (Pavia); workflow-aware encoding using Petri nets27 HGML (UMDNJ) has an XML markup tool for encoding recommendation logic only28 PROforma (Cancer Research UK) uses JAVA-based, commercially available AREZZO tool for encoding29 ZYNX; MLMs 30 Recommendations are created following a meta-analysis by a dedicated authoring team using templates GEM (Yale University) GEM Cutter transforms published guideline into XML format 26 – Sharable Medical Logic Modules (MLMs); in use in several commercial EHR environments – EON, ATHENA, T-Helper systems based on Protege; SAGE implementations not yet evaluated in practice setting – Graphical guideline execution tool; In use in bladder ca, reflux esophagitis, CHF – DSS implemented in critical care, family practice, psychiatry, cardiology; limited interface with existing IT systems – GLEE Execution engine, GELLO Exp/Query l anguage; diabetes foot care guideline – In use for stroke, breast cancer, CHF – Applications are “under develop ment” but not currently deployed – Commercial product used in HIV (Retrogram), cancer care, (ARNO) renal anemia management (ORAMA) In use by two US national specialty societies in guideline authoring activities; GEM-Q, GEM-COGS, GEM- Arden tools available EKM-web-based rule sets and orders for advice on >100 topics for inpatient management; implementation advice available; commercial product Implementations in chronic asthma management, glaucoma diagnosis and management, smoking cessation counseling
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Health Informatics (formerly Comput
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Editors Christoph U. Lehmann George
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vi Series Preface changes to both t
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viii Foreword This text will prove
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x Preface financial investment and
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How to Use This book This book is t
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xvi Contents 9 Pediatric Care, Safe
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xviii Contents Part VII A Vision an
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xx Contributors Donna M. D’Alessa
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xxii Contributors Christoph U. Lehm
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xxiv Contributors Mark M. Simonian,
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4 G.R. Kim pharmacy, medical educat
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6 K.B. Johnson and G.R. Kim Deaths
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8 K.B. Johnson and G.R. Kim 2.4.1 I
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10 K.B. Johnson and G.R. Kim 2.4.3
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12 K.B. Johnson and G.R. Kim Educat
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14 K.B. Johnson and G.R. Kim 2.6 Co
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Chapter 3 Core Pediatric Data Kevin
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3 Core Pediatric Data 21 3.3 Pediat
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3 Core Pediatric Data 23 3.6 Pediat
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Chapter 4 Neonatal Care and Data De
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4 Neonatal Care and Data 27 Essenti
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4 Neonatal Care and Data 29 4.3 The
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4 Neonatal Care and Data 31 some ma
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4 Neonatal Care and Data 33 Measure
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4 Neonatal Care and Data 35 4.5.2.4
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4 Neonatal Care and Data 37 4.6.3 I
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4 Neonatal Care and Data 39 4.7.2.2
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4 Neonatal Care and Data 41 Availab
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44 D.M.N. Paperny Tasks related to
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46 D.M.N. Paperny Behavioral risk d
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48 D.M.N. Paperny for outside ancil
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50 D.M.N. Paperny Video reinforceme
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52 D.M.N. Paperny 3. Litzelman DK,
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Chapter 6 Children with Development
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6 Children with Developmental Disor
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Chapter 7 Pediatric Emergency and P
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7 Pediatric Emergency and Pediatric
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84 W.H. Drummond et al. A recovery
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86 W.H. Drummond et al. 8.2.2 Forma
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88 W.H. Drummond et al. 8.2.2.1 Sys
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90 W.H. Drummond et al. The multidi
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92 W.H. Drummond et al. noise overl
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94 W.H. Drummond et al. needs requi
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96 W.H. Drummond et al. Children’
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98 W.H. Drummond et al. After the P
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15 Pediatric Care Coordination: The
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Chapter 16 Prioritizing Pediatric I
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16 Prioritizing Pediatric Investmen
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Chapter 17 Aligning Pediatric Ambul
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17 Aligning Pediatric Ambulatory Ne
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258 G.R. Kim and C.U. Lehmann Elect
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260 G.R. Kim and C.U. Lehmann 18.2.
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262 G.R. Kim and C.U. Lehmann There
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264 G.R. Kim and C.U. Lehmann 13. C
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266 K.B. Johnson and C.G.M. Weigle
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280 C. Sable et al. computer for la
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282 C. Sable et al. Table 20.3 Tele
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284 C. Sable et al. without increas
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286 C. Sable et al. Fig. 20.1b Wash
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288 C. Sable et al. prior to case d
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290 C. Sable et al. presence of con
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292 C. Sable et al. 17. Sable C, Ro
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294 A.E. Zuckerman and G.R. Kim 21.
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296 A.E. Zuckerman and G.R. Kim �
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Chapter 22 Privacy Issues David M.N
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22 Privacy Issues 305 22.3 Pediatri
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22 Privacy Issues 307 patient must
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22 Privacy Issues 309 and receipt o
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Chapter 23 Electronic Mail in Pedia
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23 Electronic Mail in Pediatric Pra
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Chapter 24 Information Management b
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24 Information Management by Patien
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Chapter 25 Overview of Pediatric In
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25 Overview of Pediatric Inpatient
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Chapter 26 Prescribing/Ordering: Co
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26 Prescribing/Ordering: Computeriz
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346 S.H. Mitchell et al. 27.2.1 Med
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348 S.H. Mitchell et al. context of
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350 S.H. Mitchell et al. 27.3.3.1 C
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352 S.H. Mitchell et al. challenges
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354 S.H. Mitchell et al. work round
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Chapter 28 Medication Administratio
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28 Medication Administration and In
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Chapter 29 Understanding and Preven
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29 Understanding and Preventing Err
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386 D.C. Stockwell and A.D. Slonim
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400 J.H. Schneider a number of prob
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402 J.H. Schneider on governance, u
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404 J.H. Schneider 31.6.1 Data Rele
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406 J.H. Schneider Understanding op
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408 J.H. Schneider of this componen
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410 J.H. Schneider Table 31.1 (cont
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412 J.H. Schneider References 1. Am
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Chapter 32 Developing Pediatric Dat
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32 Developing Pediatric Data Standa
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430 G.R. Kim and S.T. Rosenbloom Co
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432 G.R. Kim and S.T. Rosenbloom No
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434 G.R. Kim and S.T. Rosenbloom pr
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436 G.R. Kim and S.T. Rosenbloom ab
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Chapter 34 Pediatric Research and I
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34 Pediatric Research and Informati
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Chapter 35 The Moving Picture of Pe
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35 The Moving Picture of Pediatric
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470 G.R. Kim and K.B. Johnson Cente
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472 G.R. Kim and K.B. Johnson 36.4
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Index A 2006 AAP recommendations on
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Index 477 psychological data, 45 se
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Index 479 High frequency flow inter
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Index 481 O Online Mendelian Inheri
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Index 483 Standards development org
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