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VA HSR&D Salt LakeInformaticsDecisionEnhancementAndSurveillance Center
Human Factors in Prescription Medication
Management
Jonathan R. Nebeker MS MDVA Salt Lake City GRECC
Acknowledgements Charlene R. Weir,
PhD Frank Drews, PhD Molly Leecaster, PhD Rand Rupper, MPH
MD Kenneth Boockvar,
MD Brittany Mallin, MS
MPH
AHRQ R18 HS017186 VA Salt Lake City
GRECC VA Salt Lake City
IDEAS Center
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Overview
The Electronic Health Record context Current Future How theory gets us to future
Theoretical Framework Study design Preliminary Findings
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Current CPRS VistA
Emphasis on access
Information siloed in tabs
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Future CPRS VistA
Emphasis on control
Information integrated
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Goal: EHR of future
Decision Support v. Sense Making
Computerized decision support is typically normative and targets the right decision.
The CPRS of the future will emphasize an information-rich environment that targets sense making to support higher quality decisions in the highly variable context of patient care.
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Progress
The Electronic Health Record context Theoretical Framework
(The pathway to the future) Joint Cognitive Systems or
Cognitive Systems Engineering Contextual Control Model
Study Design Preliminary Findings
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Towards the Future
Apply Cognitive Systems Engineering
Human Factors in this talk Not about usability About the human-computer system
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Joint Cognitive Systems
Erik Hollnagel and David Woods System of artifact(s) + human(s) that
accomplishes work. Not what do human and computer do best
Control is a measure of the work’s quality.
Examples of JCS: Scissors Fighter jets Combat robots
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Contextual Control Model (CoCoM)
Performance in context Different types of behaviors predict
better outcomes Functional not structural approach Not about information processing models:
Memory, programs, etc.
Used in engineered systems ABS at Saab Nuclear Power Plants
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CoCoM Main Concepts
Competencies: possible actions in context
Constructs: assumptions about situation
Control modes: characteristics of performance that govern quality of performance
Feed forward and feedback: anticipatory versus reactive control
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Control Cycle in Healthcare
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What is going on.
Control Modes
Scrambled Lack of purposeful activity
Opportunistic Addressing salient characteristics
Tactical Following procedure, limited scope
Strategic Broader scope and higher-level goals
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Control Characteristics
Goal Complexity (Number and Interaction)
Perceived Time Pressure Evaluation of Outcome Selection of Action
Expertise Motivation Familiarity
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Progress
The Electronic Health Record context Joint Cognitive Systems
Contextual Control Model Study Design Preliminary Findings
Control characteristics
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Study Goals
Immediate Aim Translate CoCoM to medication
management for chronic diseases Explore associations between control
characteristics and surrogate outcomes
Next Aim Establish validity of adapted CoCoM
control characteristics as predictor of higher quality outcomes through simulation
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Study Design
Subjects: 40-50 physicians, mid-levels, residents, nurses, pharmacists in 5 outpatient clinics/4 states. Focus on HTN
Think-aloud protocol + Interview Saturation coding for control
characteristics
Content analysis Multi-dimensional scaling
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Preliminary Findings
Semi-Qualitative Stories of control modes
Scrambled Opportunistic Tactical Strategic
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Scrambled Mode
Type: Trial and error performance Case of the new intern and forgetful
patient. Low information quality and
availability+
Low experience
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Opportunistic Mode
Type: Reaction to salient characteristics Have not seen yet for HTN
Reaction to SBP only Pain syndromes even among experienced
Poor construct of problem Low information quality Vague goals: difficult to resolve competition Vague evaluation of outcome: not
mentioned, then OK.
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Tactical Type: Following procedure Dominant mode for HTN Use of protocol Focus on procedure* (forget clinical goal) Minimal consideration of interacting goals Low use of feed-forward control Problem with information quality-clinical
inertia Less common in highly experienced MDs
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Strategic
Type: Broad consideration of context Almost exclusively with experienced MDs Awareness of protocols but deviation to
accomplish conflicting patient goals Familiarity with past therapy a key factor Feed forward strategies account for
physiologic and organizational factors Still, incomplete use of explicit control
limits
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Conclusions
CoCoM reveals interesting characteristics of system performance.
High-mode characteristics have face validity for predicting better outcomes.
Implications for software design: Need to support efficient, rich
reconstruction of mental model of patient Need to highlight interaction of goals and
therapies Need to increase time horizon including feed
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