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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