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Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions (2009)

Chapter: Appendix C: Observations, Consequences, and Opportunities: The Site Visits of the Committee

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Suggested Citation:"Appendix C: Observations, Consequences, and Opportunities: The Site Visits of the Committee." National Research Council. 2009. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press. doi: 10.17226/12572.
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Page 93
Suggested Citation:"Appendix C: Observations, Consequences, and Opportunities: The Site Visits of the Committee." National Research Council. 2009. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press. doi: 10.17226/12572.
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Page 94
Suggested Citation:"Appendix C: Observations, Consequences, and Opportunities: The Site Visits of the Committee." National Research Council. 2009. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press. doi: 10.17226/12572.
×
Page 95
Suggested Citation:"Appendix C: Observations, Consequences, and Opportunities: The Site Visits of the Committee." National Research Council. 2009. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press. doi: 10.17226/12572.
×
Page 96
Suggested Citation:"Appendix C: Observations, Consequences, and Opportunities: The Site Visits of the Committee." National Research Council. 2009. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press. doi: 10.17226/12572.
×
Page 97
Suggested Citation:"Appendix C: Observations, Consequences, and Opportunities: The Site Visits of the Committee." National Research Council. 2009. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press. doi: 10.17226/12572.
×
Page 98
Suggested Citation:"Appendix C: Observations, Consequences, and Opportunities: The Site Visits of the Committee." National Research Council. 2009. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press. doi: 10.17226/12572.
×
Page 99
Suggested Citation:"Appendix C: Observations, Consequences, and Opportunities: The Site Visits of the Committee." National Research Council. 2009. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press. doi: 10.17226/12572.
×
Page 100
Suggested Citation:"Appendix C: Observations, Consequences, and Opportunities: The Site Visits of the Committee." National Research Council. 2009. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press. doi: 10.17226/12572.
×
Page 101
Suggested Citation:"Appendix C: Observations, Consequences, and Opportunities: The Site Visits of the Committee." National Research Council. 2009. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press. doi: 10.17226/12572.
×
Page 102
Suggested Citation:"Appendix C: Observations, Consequences, and Opportunities: The Site Visits of the Committee." National Research Council. 2009. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press. doi: 10.17226/12572.
×
Page 103
Suggested Citation:"Appendix C: Observations, Consequences, and Opportunities: The Site Visits of the Committee." National Research Council. 2009. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions. Washington, DC: The National Academies Press. doi: 10.17226/12572.
×
Page 104

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Appendix C Observations, Consequences, and Opportunities: The Site Visits of the Committee Table C.1, which summarizes the committee’s observations from the site visits, is structured as follows. •  olumn 1—Observations (what committee members saw during C the site visits). Under each observation are listed one or more de- identified data points. The high-level observation is the abstraction for those data points. The committee grouped the observations into six categories: — ategory 1. The medical record itself—the display, the applica- C tion, the paper; in general, what the user interacts with directly. — ategory 2. The health care delivery process—the workflow, C what happens when, who does it, how decisions are made, how communication occurs. — ategory 3. Health care professionals—what they are like, how C they react to IT, and so on. — ategory 4. IT infrastructure and management—the underlying C computing substrate and how it is managed. — ategory 5. Data capture and flow—how data are gathered, C recorded, and passed among systems, records, and people. 93

94 COMPUTATIONAL TECHNOLOGY FOR EFFECTIVE HEALTH CARE — ategory 6. Change in a sociotechnical system—how to create envi- C ronments that facilitate large-scale change. •  olumn 2—Consequences (why the observations matter). For C each observation, the committee infers one or more consequences. That is, why do we care about the observation in question? How might it affect health care delivery? •  olumn 3—Opportunities for Action (what we can do about the C consequences). Every observation-consequence pair should pro- vide one or more opportunities for action. Solutions known today but not yet implemented are indicated by an “S” (for short-term) in Column 3; challenges for research, where solutions are not known today, are indicated by an “R” (for research) in Column 3. In Table C.1, the notation CxOy is used. Cx refers to Category x of the committee’s observations as grouped in the table (which lists six categories of observations), and Oy refers to a particular observation as numbered in the table (which includes a total of 25 observations).

APPENDIX C 95 TABLE C.1  Committee’s Observations from Its Site Visits Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Do Saw Matter About Ita Category 1. The Medical Record Itself 1 Patient records are • Synthesis depends • Techniques to fragmented on intra-team synthesize and • Computer-based conversation summarize information and paper records • Problem recognition about the patient in co-exist is left to chance and across systems • Computer records • Team members with drill-downs for are divided among waste time getting detail (S/R) task-specific information in the • Mechanisms to focus transaction- form they want to on a constellation of processing systems use related factors (S/R) • Users have to know • Single search box that where to look returns all appropriate • Individual information in the manually annotated appropriate format (R) work lists are the • Alerts to problems or norm trends for investigation (S/R) • “Virtual patient” displays leveraging biological and disease models to reduce multiple data inputs to intelligent summaries of key human systems (R) 2 Clinical user interfaces • Important • Design reflecting mimic their paper information and human and safety predecessors trends are easily factors (S) • The flow sheet is overlooked • Automatic capture and the predominant • Cognitive burden use of context (what, display construct of absorbing the who, when. . .) (S) • No standardization information detracts • Techniques to of location of from thinking represent and capture information or use about what the data at multiple levels of symbols and information means of abstraction (Care— color plan, order, charting; • Font size is data—raw signal, challenging concept derived from the signal; biology) (S/R) continued

96 COMPUTATIONAL TECHNOLOGY FOR EFFECTIVE HEALTH CARE TABLE C.1  Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Do Saw Matter About Ita Category 1. The Medical Record Itself (continued) 3 Systems are used most • Missed opportunity • See Category 5, often to document for decision or observation 19 (C5O19) what has been done, workflow support frequently hours after • Variable the fact completeness and accuracy • Redundant work 4 Support for evidence- • Lost opportunity • Peer-to-peer and social based medicine and to provide patient- networking techniques computer-based advice specific decision for development is rare support of guidelines and decision support content (S/R) • Mass customization techniques for practice guidelines (modules) (R) • Computable knowledge structures and models (R) Category 2. The Health Care Delivery Process 5 High complexity • Reactive care • Dynamically and coordination • Handoff errors computable models to requirements of care • Redundant care represent plan for care, • Within teams workflow, escalation, • Across teams and and so on (R) services within settings • Across settings

APPENDIX C 97 TABLE C.1  Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Do Saw Matter About Ita Category 2. The Health Care Delivery Process (continued) 6 Non-transparent • No clear thinking • Scripting languages for workflow about overall decision and workflow • Clinical roles and workflows, support content (S/R) responsibilities are process design, • Uniform provider ID not explicit and efficiency and (S) • Scheduling is handoff errors • Explicit team roles and negotiated and • Unpredictable escalation paths (S/R) manual escalation and • Capabilities for • Care processes response context-aware efficient steps and outcomes scheduling (S/R) are rarely documented in machine-readable manner 7 Work is frequently • See observations 5 • See observations 5 and interrupted with gaps and 6 (C2O5, C2O6) 6 (C2O5, C2O6) between steps and manual handoffs at seams of the process 8 Shift of care from • See observations 5 • See observations 5 and inpatient, to and 6 (C2O5, C2O6) 6 (C2O5, C2O6) outpatient, home, • Support for varying patients, families cultures and education (R) 9 Errors and near misses • Low voluntary • Instrumented process are frequent and use reporting that limits to track steps (S/R) of data to identify proactive use of • Automated patterns is rare near misses for surveillance for system correction potential problems (S/R) 10 Clinical research • Difficulty deciding • Computable models activities not well what to charge to of research plan, integrated into ongoing whom for research or workflow, researcher clinical care care roles, etc. (S/R) • Barriers to subject • Data exchange enrollment between care and • Duplication of research research systems (S/R) and care processes • De-identification • Limited learning from algorithms (S/R) routine practice continued

98 COMPUTATIONAL TECHNOLOGY FOR EFFECTIVE HEALTH CARE TABLE C.1  Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Do Saw Matter About Ita Category 3. Health Care Professionals 11 Clinical users choose • Time is money • See Category 5, speed over all else • Each second added observation 19 (C5O19) to the time to write each prescription in the United States adds 470 physician full-time equivalents 12 Clinical users do not • Inefficient • Design system have a consistent workflow modules for use in understanding of the • Incomplete or production (operation) purpose of a system or inaccurate data and simulation the functionality of the entry (training) (S) user interface • Misinterpretation of information • System work- arounds 13 Health professionals’ • Health • Educate health understanding of professionals do not professionals in how IT might help is know what to ask systems approaches limited for • Imbed informatics • Health experts in clinical professionals do teams (as is done with not know how to pharmacists) test whether an IT • Expand informatics intervention will training programs solve their problem in their setting

APPENDIX C 99 TABLE C.1  Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Saw Matter Do About Ita Category 4. IT Infrastructure and Management 14 Legacy systems are • Rigid workflow Architectures to permit predominant in an era of rapid holistic management • Each is handled change of patient information as a separate • Semantic meaning and decision support implementation of clinical content information across (set-up, profiles, is not explicit information systems management of • Data are not • Decouple infrastructure, decision support easily shared transaction processing, content, etc.) within or across data aggregation, and • Implementation organizations decision/workflow focuses on the • Clinical best support (S) technology, not on practice and • Wrap purchased enabling process decision support applications as Web and role changes content are not services (S) • Management of easily shared • Leverage ontology and change holds all document architectures units supported (S) by a system to the • Use open-source implementation techniques for rate of the slowest infrastructure layer (S) member • Develop utility • Data flow among approaches to “operating an organization’s system on demand” systems is very (mass virtualization) (S) limited 15 Centralization • Does not support a • See Category 2, of management dynamic learning observations 5 and 6 and reduction in health care system (C2O5, C2O6) the number of that can adapt • See observation 14 information systems to accommodate (C4O14) is the predominant local needs and method for capabilities standardization continued

100 COMPUTATIONAL TECHNOLOGY FOR EFFECTIVE HEALTH CARE TABLE C.1  Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Saw Matter Do About Ita Category 4. IT Infrastructure and Management (continued) 16 Implementation time • Requires • See observation 14 lines are long and investment far in (C4O14) course changes are advance of benefit expensive • Inconsistent with • Actual president’s goal implementation for electronic time lines for medical records by enterprise-wide 2014 functionality commonly exceed a decade • New systems are being implemented while the previous generations are still being rolled out 17 Security and • Neither is effective • Techniques to privacy compete authenticate a patient to with workflow his/her record (S/R) optimization • Techniques to loosely couple the individual and his/her identities (S/R) • Architectures that enable confidentiality by limiting access according to need to know while supporting transparency in authorization (S/R) 18 Response times • Work-arounds • Approaches that balance are variable (from • Redundant local caching of data subsecond to processes with timeliness of data minutes) and long • Flying blind (S/R) down-times occur (clinical systems down for >24 hours and equipment down for weeks)

APPENDIX C 101 TABLE C.1  Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Saw Matter Do About Ita Category 5. Data Capture and Flow 19 Data capture/data • More time spent • Redesign roles, entry are commonly entering data than process, and manual using data technology to capture • Variable data at the source as completeness and data are created (S/R) accuracy • Self-documenting • Loss of opportunity sensor-rich for decision and environments workflow support (multimedia) (S/R) • See Category 1, observation 2 20 User interfaces do not • Systems intended to • Design reflecting reflect human factors reduce error create human and safety and safety design new errors factors (S) • Improperly structured pull- down lists • Inconsistent use of location, symbol, and color 21 Biomedical devices are • Inefficient charting • Mechanism for poorly integrated in and intra-team positively identifying every location conflict relationship of device • Inaccurate charting to patient and to use (errors of omission (e.g., drip composition) and inappropriate (S) copying) • Handle a physician’s • Unsafe (5 rights drip order (order for errors) substance, titration parameter), the current setting (nurse response to order), and amount actually administered (charting) as three related but separate concepts (S) continued

102 COMPUTATIONAL TECHNOLOGY FOR EFFECTIVE HEALTH CARE TABLE C.1  Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Saw Matter Do About Ita Category 5. Data Capture and Flow (continued) 22 Implementation of • Defeats safety • Limit use to positive identification objective subprocesses where the technology is technology is adequate problematic for the workflow (S) • Gaps in the • Measure and chain of positive systematically identification eliminate work- • Work-arounds arounds (S) are common • Find better technology because of missing workflow matches or mismatched (S/R) information • Portable devices are task-specific (different device for lab specimen and medication administration) • Unit doses of medication are not manufactured with computer-readable tags 23 Semantic • Lack of • Interfaces that enable interoperability is interoperability entry of data in almost non-existent limits data and flexible ways, but that knowledge reuse guide the user into using common fields and terminologies in a non-obtrusive fashion (S/R) • Methods to reconcile multiple references to the same real-world entities (e.g., different ways of referring to penicillin) (S/R) • Mechanisms for mining data to discover emerging patterns in data (S/R)

APPENDIX C 103 TABLE C.1  Continued Observations—What Consequences—Why Opportunities for Committee Members the Observations Action—What We Can Saw Matter Do About Ita Category 6. Change in a Sociotechnical System 24 Most systems are • Inconsistent use • Focus on the desired partially or poorly and work-arounds outcomes instead of or incompletely increase error the technology (S/R) integrated into practice • Benefits are significantly less than anticipated • Reduced investment 25 Innovation requires • Limited innovation • Management that locally adaptable and standardization encourages initiation systems but of improvements by interoperability health professionals (S) and evidence-based • Technology and medicine require more processes that allow standardization local innovation and flexibility but foster collaboration and learning at a national scale (R) aR, solutions still to be discovered (research); S, solutions known today but not imple- mented (short term).

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Despite a strong commitment to delivering quality health care, persistent problems involving medical errors and ineffective treatment continue to plague the industry. Many of these problems are the consequence of poor information and technology (IT) capabilities, and most importantly, the lack cognitive IT support. Clinicians spend a great deal of time sifting through large amounts of raw data, when, ideally, IT systems would place raw data into context with current medical knowledge to provide clinicians with computer models that depict the health status of the patient.

Computational Technology for Effective Health Care advocates re-balancing the portfolio of investments in health care IT to place a greater emphasis on providing cognitive support for health care providers, patients, and family caregivers; observing proven principles for success in designing and implementing IT; and accelerating research related to health care in the computer and social sciences and in health/biomedical informatics.

Health care professionals, patient safety advocates, as well as IT specialists and engineers, will find this book a useful tool in preparation for crossing the health care IT chasm.

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